system

Abstract

The system according to the embodiment aims to efficiently determine the best times to buy and sell stocks and propose an optimal investment portfolio. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects the latest company information or stock movements. The analysis unit analyzes the data collected by the collection unit and determines the best time to buy or sell stocks. The proposal unit proposes a portfolio of which stocks to buy or sell, and how much of each, based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background 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, collecting and analyzing information to determine when to buy or sell stocks was time-consuming, making it difficult to make efficient investment decisions.

[0005] The system according to the embodiment aims to efficiently determine the best times to buy and sell stocks and propose an optimal investment portfolio. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects the latest company information or stock movements. The analysis unit analyzes the data collected by the collection unit and determines the best time to buy or sell stocks. The proposal unit proposes a portfolio of which stocks to buy or sell and how much to sell based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently determine the best time to buy or sell stocks and propose an optimal investment portfolio. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A stock trading support system according to an embodiment of the present invention collects the latest company information and stock trends, analyzes them using AI, and proposes optimal solutions for determining which stocks to buy or sell, in what quantities, and when. This stock trading support system collects and analyzes the latest company information and stock trends to propose optimal portfolios, enabling investors to trade stocks efficiently. For example, a data collection unit collects the latest company information and stock trends. This information includes news articles, financial reports, and stock price data. For example, the system collects the latest financial reports and stock price trends of a specific company. Next, an analysis unit analyzes the collected data. The analysis unit uses AI to analyze the data and predict stock trends. For example, the analysis unit uses a machine learning algorithm to find patterns in past data and predict future stock prices. Furthermore, a portfolio proposal unit proposes an optimal portfolio of which stocks to buy or sell, in what quantities, based on the analysis results. The portfolio proposal unit proposes an optimal investment strategy taking into account risk and return. For example, if a specific company's stock price is on an upward trend, the system proposes the best time to buy those stocks. Conversely, if the stock price is on a downward trend, the system proposes the best time to sell those stocks. This system enables investors to trade stocks efficiently and maximize their profits. This allows the stock trading support system to enable investors to trade stocks efficiently and maximize profits.

[0029] A stock trading support system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects the latest company information or stock movements. The collection unit collects information such as news articles, financial reports, and stock price data. The collection unit can collect, for example, the latest financial reports and stock price trends of a specific company. The analysis unit analyzes the data collected by the collection unit and determines the best time to buy or sell stocks. The analysis unit, for example, uses a machine learning algorithm to find patterns in past data and predict future stock prices. The analysis unit can predict stock price trends using, for example, regression analysis or a neural network. The proposal unit proposes a portfolio of which stocks to buy or sell and how much to sell based on the analysis results obtained by the analysis unit. The proposal unit, for example, proposes an optimal investment strategy taking into account risk and return. For example, if the stock price of a specific company is on an upward trend, the proposal unit proposes the best time to buy those stocks, and conversely, if the stock price is on a downward trend, the proposal unit proposes the best time to sell those stocks. This allows the stock trading support system according to an embodiment to enable investors to trade stocks efficiently and maximize profits.

[0030] The collection unit can collect information on news articles, financial reports, and stock price data. The collection unit can collect news articles such as economic news and industry news, for example. The collection unit can collect financial reports such as quarterly reports and annual reports, for example. The collection unit can collect stock price data such as real-time data and historical data, for example. This allows the collection unit to collect data from various information sources, enabling more accurate analysis. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input news articles, financial reports, and stock price data into a generation AI, causing the generation AI to collect information.

[0031] The analysis unit can use a machine learning algorithm to find patterns in past data and predict future stock prices. The analysis unit can use, for example, regression analysis to find patterns in past data and predict future stock prices. The analysis unit can also use, for example, a neural network to find patterns in past data and predict future stock prices. The analysis unit can also use, for example, a clustering algorithm to find patterns in past data and predict future stock prices. This improves the accuracy of future stock price predictions by using a machine learning algorithm. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past data into a generation AI and have the generation AI analyze patterns and predict future stock prices.

[0032] The proposal unit can propose an investment strategy taking into account risk and return. For example, the proposal unit can evaluate risk using a risk index and propose an investment strategy that maximizes return. For example, the proposal unit can also evaluate risk taking into account volatility and propose an investment strategy that maximizes return. For example, the proposal unit can evaluate return taking into account the investment return rate and propose an investment strategy that minimizes risk. In this way, an optimal investment strategy can be proposed for an investor by taking risk and return into account. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input risk and return data into a generation AI and have the generation AI propose an optimal investment strategy.

[0033] The proposal unit can suggest the timing to buy a specific company's stock when its stock price is rising. For example, the proposal unit can determine that a stock price is rising based on the rate of stock price increase over a certain period of time and suggest the timing to buy the stock. For example, the proposal unit can also determine that a stock price is rising based on an increase in trading volume and suggest the timing to buy the stock. For example, the proposal unit can determine that a stock price is rising based on stock price trend analysis and suggest the timing to buy the stock. This can maximize investor profits by suggesting appropriate buying timing when stock prices are on an upward trend. Some or all of the above-described processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input stock price data into a generation AI and have the generation AI suggest buying timings.

[0034] The proposal unit can suggest the timing to sell a specific company's stock when its stock price is falling. For example, the proposal unit can determine that a stock price is falling based on the rate of decline in the stock price over a certain period of time and suggest the timing to sell the stock. For example, the proposal unit can also determine that a stock price is falling based on a decrease in trading volume and suggest the timing to sell the stock. For example, the proposal unit can determine that a stock price is falling based on a stock price trend analysis and suggest the timing to sell the stock. This makes it possible to minimize investor losses by suggesting appropriate selling times when stock prices are on a downward trend. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input stock price data into a generation AI and have the generation AI suggest selling times.

[0035] The collection unit can filter information by focusing on a specific industry or region. For example, the collection unit filters information by focusing on a specific industry, such as the IT industry or the manufacturing industry. The collection unit can also filter information by focusing on a specific region, such as Asia or Europe. The collection unit can also collect news articles and financial reports related to a specific industry or region. This allows highly relevant information to be collected by focusing on a specific industry or region. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input information related to a specific industry or region into a generation AI and have the generation AI filter the information.

[0036] The collection unit may add a function for instantly capturing real-time market events or breaking news. The collection unit may instantly capture real-time market events, such as the release of economic indicators or important corporate announcements. The collection unit may instantly capture breaking news, for example, using breaking news alerts or news feeds. The collection unit may also collect, for example, important breaking news that affects stock prices in real time. The collection unit may also collect, for example, market events (e.g., corporate earnings announcements) in real time. The collection unit may also collect, for example, news related to political events or regulatory changes in real time. This allows for rapid response by capturing real-time information. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit may input real-time market events or breaking news into the generation AI, causing the generation AI to capture the information.

[0037] The collection unit can analyze social media trends and user posts to collect related information. The collection unit can analyze social media trends, for example, by analyzing hashtags and the frequency of posts, and collect related information. The collection unit can analyze user posts, for example, by analyzing the content of comments and the sentiment of posts, and collect related information. The collection unit can also collect information about companies that are trending on social media, for example. The collection unit can also analyze investment interests from user posts and collect related information. The collection unit can also analyze social media trends and collect information that influences investments, for example. In this way, the latest information can be collected by analyzing social media trends. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to analyze trends and collect information.

[0038] The collection unit can prioritize collecting highly relevant information based on the user's past investment history. The collection unit prioritizes collecting highly relevant information based on past investment history, such as trading history and investment patterns. The collection unit can prioritize collecting the latest information on companies in which the user has invested in the past. The collection unit can also collect relevant information based on the user's investment style (e.g., short-term investment, long-term investment), for example. The collection unit can also collect new investment opportunities that may be of interest to the user from the user's past investment history. This makes it possible to collect highly relevant information by taking the user's past investment history into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past investment history data into the generation AI and cause the generation AI to collect relevant information.

[0039] The analysis unit can improve the analysis accuracy by combining different machine learning algorithms. The analysis unit can improve the analysis accuracy by, for example, combining a regression analysis based on past data with a neural network based on real-time data. The analysis unit can also improve the analysis accuracy by, for example, combining a clustering algorithm and a classification algorithm. The analysis unit can also improve the analysis accuracy by, for example, combining a reinforcement learning algorithm and a supervised learning algorithm. In this way, the analysis accuracy is improved by combining different machine learning algorithms. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input different machine learning algorithms into the generation AI and cause the generation AI to improve the analysis accuracy.

[0040] The analysis unit can predict trends by comparing past market data with current market data. For example, the analysis unit can predict long-term trends by comparing market data from the past 10 years with current market data. For example, the analysis unit can predict short-term trends by comparing market data from the past year with current market data. For example, the analysis unit can predict seasonal trends by comparing past market data with current market data. This improves the accuracy of trend predictions by comparing past and current market data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past and current market data into a generation AI and have the generation AI perform trend predictions.

[0041] The analysis unit can integrate data sources and perform analysis. For example, the analysis unit can integrate economic indicator data and corporate financial data and perform analysis. For example, the analysis unit can also integrate stock price data and news article data and perform analysis. For example, the analysis unit can also integrate social media data and market data and perform analysis. In this way, by integrating different data sources, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different data sources into a generation AI and have the generation AI perform data integration and analysis.

[0042] The analysis unit can customize the analysis method based on the user's investment style. For example, the analysis unit can provide an analysis method that emphasizes short-term trends to a user who prefers short-term investment. For example, the analysis unit can also provide an analysis method that emphasizes long-term trends to a user who prefers long-term investment. For example, the analysis unit can also provide an analysis method that takes into account a good balance of risk and return to a user who prefers a balanced investment style. This makes it possible to propose a more appropriate investment strategy by providing an analysis method that suits the user's investment style. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's investment style data into the generation AI and have the generation AI customize the analysis method.

[0043] The proposal unit can propose different investment strategies based on the risk tolerance. For example, the proposal unit can evaluate the risk tolerance using a risk assessment and propose different investment strategies based on the risk tolerance. For example, the proposal unit can also evaluate the risk tolerance using a risk profile and propose different investment strategies based on the risk tolerance. For example, the proposal unit can propose a safe investment strategy to a user with a low risk tolerance. For example, the proposal unit can propose a balanced investment strategy to a user with a medium risk tolerance. For example, the proposal unit can propose a high-risk, high-return investment strategy to a user with a high risk tolerance. In this way, by proposing an investment strategy based on the risk tolerance, it is possible to provide an optimal investment strategy to the user. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input risk tolerance data to a generation AI and have the generation AI execute a proposed investment strategy.

[0044] The proposal unit can improve the accuracy of proposals by referring to past proposal history. For example, the proposal unit analyzes past proposal history and preferentially proposes successful investment strategies. For example, the proposal unit can identify a user's preferred investment strategy from the past proposal history and make a proposal based on that. For example, the proposal unit can also build a feedback loop to improve the accuracy of proposals based on the past proposal history. In this way, the accuracy of proposals is improved by referring to the past proposal history. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input past proposal history data into a generation AI and cause the generation AI to improve the accuracy of proposals.

[0045] The suggestion unit can suggest region-specific investment opportunities based on the user's geographical location information. The suggestion unit can collect the user's geographical location information using, for example, GPS data or regional economic indicators, and suggest region-specific investment opportunities based on the collected information. For example, if the user is in Asia, the suggestion unit can suggest investment opportunities for that region. For example, if the user is in North America, the suggestion unit can suggest investment opportunities for that region. For example, if the user is in Europe, the suggestion unit can suggest investment opportunities for that region. In this way, region-specific investment opportunities can be suggested by taking the user's geographical location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into a generation AI, causing the generation AI to suggest region-specific investment opportunities.

[0046] The suggestion unit can analyze the user's social media activity to suggest related investment opportunities. The suggestion unit can, for example, analyze the frequency of social media postings and the reactions of followers to suggest related investment opportunities. The suggestion unit can, for example, suggest investment opportunities for companies in which the user has shown interest on social media. The suggestion unit can, for example, suggest new investment opportunities that the user may be interested in based on the user's social media activity. The suggestion unit can, for example, analyze social media trends and suggest related investment opportunities. In this way, related investment opportunities can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input social media data into a generation AI and have the generation AI suggest investment opportunities.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The collection unit can prioritize collecting highly relevant information based on the user's past investment history. For example, the collection unit can prioritize collecting highly relevant information based on past investment history such as trading history and investment patterns. For example, the collection unit can prioritize collecting the latest information on companies in which the user has invested in the past. For example, the collection unit can also collect relevant information based on the user's investment style (e.g., short-term investment, long-term investment). For example, the collection unit can also collect new investment opportunities that may be of interest to the user from the user's past investment history. This makes it possible to collect highly relevant information by taking the user's past investment history into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past investment history data into the generation AI and cause the generation AI to collect relevant information.

[0049] The analysis unit can improve the analysis accuracy by combining different machine learning algorithms. For example, the analysis accuracy can be improved by combining a regression analysis based on past data with a neural network based on real-time data. The analysis unit can also improve the analysis accuracy by combining a clustering algorithm and a classification algorithm, for example. The analysis unit can also improve the analysis accuracy by combining a reinforcement learning algorithm and a supervised learning algorithm, for example. In this way, the analysis accuracy is improved by combining different machine learning algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different machine learning algorithms into the generation AI and have the generation AI improve the analysis accuracy.

[0050] The suggestion unit can suggest region-specific investment opportunities based on the user's geographic location information. For example, the suggestion unit can collect the user's geographic location information using GPS data, regional economic indicators, etc., and suggest region-specific investment opportunities based on the collected information. For example, if the user is in Asia, the suggestion unit can suggest investment opportunities for that region. For example, if the user is in North America, the suggestion unit can suggest investment opportunities for that region. For example, if the user is in Europe, the suggestion unit can suggest investment opportunities for that region. In this way, region-specific investment opportunities can be suggested by taking the user's geographic location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's geographic location information into a generation AI, causing the generation AI to suggest region-specific investment opportunities.

[0051] The proposal unit can improve the accuracy of proposals by referring to past proposal history. For example, the proposal unit can analyze past proposal history and prioritize successful investment strategies. The proposal unit can also identify a user's preferred investment strategy from the past proposal history and make a proposal based on that. The proposal unit can also build a feedback loop to improve the accuracy of proposals based on the past proposal history. This improves the accuracy of proposals by referring to the past proposal history. Some or all of the above-described processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input past proposal history data into a generation AI and cause the generation AI to improve the accuracy of proposals.

[0052] The proposal unit can propose different investment strategies based on the risk tolerance. For example, the proposal unit can evaluate the risk tolerance using a risk assessment and propose different investment strategies based on the risk tolerance. The proposal unit can also evaluate the risk tolerance using a risk profile and propose different investment strategies based on the risk tolerance. For example, the proposal unit can propose a safe investment strategy to a user with a low risk tolerance. For example, the proposal unit can propose a balanced investment strategy to a user with a medium risk tolerance. For example, the proposal unit can propose a high-risk, high-return investment strategy to a user with a high risk tolerance. In this way, by proposing an investment strategy based on the risk tolerance, it is possible to provide an optimal investment strategy to the user. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input risk tolerance data to a generation AI and have the generation AI execute a proposed investment strategy.

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

[0054] Step 1: The collection unit collects the latest company information or stock movements. The collection unit collects information such as news articles, financial reports, stock price data, etc. The collection unit can collect the latest financial reports and stock price movements of a specific company. Step 2: The analysis unit analyzes the data collected by the collection unit and determines when to buy or sell stocks. The analysis unit uses machine learning algorithms to find patterns in past data and predict future stock prices. The analysis unit can predict stock price trends using regression analysis and neural networks. Step 3: The proposal department proposes a portfolio of which stocks to buy or sell, and how much, based on the analysis results obtained by the analysis department. The proposal department proposes an optimal investment strategy taking into account risk and return. If the stock price of a particular company is on an upward trend, the proposal department will suggest the timing to buy that stock, and conversely, if the stock price is on a downward trend, the proposal department will suggest the timing to sell that stock.

[0055] (Example 2) A stock trading support system according to an embodiment of the present invention collects the latest company information and stock trends, analyzes them using AI, and proposes optimal solutions for determining which stocks to buy or sell, in what quantities, and when. This stock trading support system collects and analyzes the latest company information and stock trends to propose optimal portfolios, enabling investors to trade stocks efficiently. For example, a data collection unit collects the latest company information and stock trends. This information includes news articles, financial reports, and stock price data. For example, the system collects the latest financial reports and stock price trends of a specific company. Next, an analysis unit analyzes the collected data. The analysis unit uses AI to analyze the data and predict stock trends. For example, the analysis unit uses a machine learning algorithm to find patterns in past data and predict future stock prices. Furthermore, a portfolio proposal unit proposes an optimal portfolio of which stocks to buy or sell, in what quantities, based on the analysis results. The portfolio proposal unit proposes an optimal investment strategy taking into account risk and return. For example, if a specific company's stock price is on an upward trend, the system proposes the best time to buy those stocks. Conversely, if the stock price is on a downward trend, the system proposes the best time to sell those stocks. This system enables investors to trade stocks efficiently and maximize their profits. This allows the stock trading support system to enable investors to trade stocks efficiently and maximize profits.

[0056] A stock trading support system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects the latest company information or stock movements. The collection unit collects information such as news articles, financial reports, and stock price data. The collection unit can collect, for example, the latest financial reports and stock price trends of a specific company. The analysis unit analyzes the data collected by the collection unit and determines the best time to buy or sell stocks. The analysis unit, for example, uses a machine learning algorithm to find patterns in past data and predict future stock prices. The analysis unit can predict stock price trends using, for example, regression analysis or a neural network. The proposal unit proposes a portfolio of which stocks to buy or sell and how much to sell based on the analysis results obtained by the analysis unit. The proposal unit, for example, proposes an optimal investment strategy taking into account risk and return. For example, if the stock price of a specific company is on an upward trend, the proposal unit proposes the best time to buy those stocks, and conversely, if the stock price is on a downward trend, the proposal unit proposes the best time to sell those stocks. This allows the stock trading support system according to an embodiment to enable investors to trade stocks efficiently and maximize profits.

[0057] The collection unit can collect information on news articles, financial reports, and stock price data. The collection unit can collect news articles such as economic news and industry news, for example. The collection unit can collect financial reports such as quarterly reports and annual reports, for example. The collection unit can collect stock price data such as real-time data and historical data, for example. This allows the collection unit to collect data from various information sources, enabling more accurate analysis. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input news articles, financial reports, and stock price data into a generation AI, causing the generation AI to collect information.

[0058] The analysis unit can use a machine learning algorithm to find patterns in past data and predict future stock prices. The analysis unit can use, for example, regression analysis to find patterns in past data and predict future stock prices. The analysis unit can also use, for example, a neural network to find patterns in past data and predict future stock prices. The analysis unit can also use, for example, a clustering algorithm to find patterns in past data and predict future stock prices. This improves the accuracy of future stock price predictions by using a machine learning algorithm. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past data into a generation AI and have the generation AI analyze patterns and predict future stock prices.

[0059] The proposal unit can propose an investment strategy taking into account risk and return. For example, the proposal unit can evaluate risk using a risk index and propose an investment strategy that maximizes return. For example, the proposal unit can also evaluate risk taking into account volatility and propose an investment strategy that maximizes return. For example, the proposal unit can evaluate return taking into account the investment return rate and propose an investment strategy that minimizes risk. In this way, an optimal investment strategy can be proposed for an investor by taking risk and return into account. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input risk and return data into a generation AI and have the generation AI propose an optimal investment strategy.

[0060] The proposal unit can suggest the timing to buy a specific company's stock when its stock price is rising. For example, the proposal unit can determine that a stock price is rising based on the rate of stock price increase over a certain period of time and suggest the timing to buy the stock. For example, the proposal unit can also determine that a stock price is rising based on an increase in trading volume and suggest the timing to buy the stock. For example, the proposal unit can determine that a stock price is rising based on stock price trend analysis and suggest the timing to buy the stock. This can maximize investor profits by suggesting appropriate buying timing when stock prices are on an upward trend. Some or all of the above-described processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input stock price data into a generation AI and have the generation AI suggest buying timings.

[0061] The proposal unit can suggest the timing to sell a specific company's stock when its stock price is falling. For example, the proposal unit can determine that a stock price is falling based on the rate of decline in the stock price over a certain period of time and suggest the timing to sell the stock. For example, the proposal unit can also determine that a stock price is falling based on a decrease in trading volume and suggest the timing to sell the stock. For example, the proposal unit can determine that a stock price is falling based on a stock price trend analysis and suggest the timing to sell the stock. This makes it possible to minimize investor losses by suggesting appropriate selling times when stock prices are on a downward trend. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input stock price data into a generation AI and have the generation AI suggest selling times.

[0062] The collection unit can estimate a user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit can, for example, estimate a user's emotions using an emotion analysis algorithm and determine the priority of information to be collected based on the estimated emotions. The collection unit can also, for example, estimate a user's emotions based on survey results and determine the priority of information to be collected based on the estimated emotions. The collection unit can, for example, analyze a user's facial expressions or voice data to estimate emotions and determine the priority of information to be collected based on the estimated emotions. This allows more appropriate information to be collected by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input user emotion data into a generation AI and have the generation AI determine the priority of information.

[0063] The collection unit can filter information by focusing on a specific industry or region. For example, the collection unit filters information by focusing on a specific industry, such as the IT industry or the manufacturing industry. The collection unit can also filter information by focusing on a specific region, such as Asia or Europe. The collection unit can also collect news articles and financial reports related to a specific industry or region. This allows highly relevant information to be collected by focusing on a specific industry or region. Some or all of the above-described processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input information related to a specific industry or region into a generation AI and have the generation AI filter the information.

[0064] The collection unit may add a function for instantly capturing real-time market events or breaking news. The collection unit may instantly capture real-time market events, such as the release of economic indicators or important corporate announcements. The collection unit may instantly capture breaking news, for example, using breaking news alerts or news feeds. The collection unit may also collect, for example, important breaking news that affects stock prices in real time. The collection unit may also collect, for example, market events (e.g., corporate earnings announcements) in real time. The collection unit may also collect, for example, news related to political events or regulatory changes in real time. This allows for rapid response by capturing real-time information. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit may input real-time market events or breaking news into the generation AI, causing the generation AI to capture the information.

[0065] The collection unit can estimate a user's emotions and adjust the type of information to be collected based on the estimated user emotions. The collection unit can, for example, estimate a user's emotions using an emotion analysis algorithm and adjust the type of information to be collected based on the estimated emotions. The collection unit can also, for example, estimate a user's emotions based on survey results and adjust the type of information to be collected based on the estimated emotions. The collection unit can, for example, analyze a user's facial expressions or voice data to estimate emotions and adjust the type of information to be collected based on the estimated emotions. This allows more appropriate information to be collected by adjusting the type of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input user emotion data into the generation AI and cause the generation AI to adjust the type of information.

[0066] The collection unit can analyze social media trends and user posts to collect related information. The collection unit can analyze social media trends, for example, by analyzing hashtags and the frequency of posts, and collect related information. The collection unit can analyze user posts, for example, by analyzing the content of comments and the sentiment of posts, and collect related information. The collection unit can also collect information about companies that are trending on social media, for example. The collection unit can also analyze investment interests from user posts and collect related information. The collection unit can also analyze social media trends and collect information that influences investments, for example. In this way, the latest information can be collected by analyzing social media trends. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input social media data into a generation AI and cause the generation AI to analyze trends and collect information.

[0067] The collection unit can prioritize collecting highly relevant information based on the user's past investment history. The collection unit prioritizes collecting highly relevant information based on past investment history, such as trading history and investment patterns. The collection unit can prioritize collecting the latest information on companies in which the user has invested in the past. The collection unit can also collect relevant information based on the user's investment style (e.g., short-term investment, long-term investment), for example. The collection unit can also collect new investment opportunities that may be of interest to the user from the user's past investment history. This makes it possible to collect highly relevant information by taking the user's past investment history into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past investment history data into the generation AI and cause the generation AI to collect relevant information.

[0068] The analysis unit can estimate the user's emotion and adjust the parameters of the analysis algorithm based on the estimated user's emotion. The analysis unit, for example, uses an emotion analysis algorithm to estimate the user's emotion and adjust the parameters of the analysis algorithm based on the estimated emotion. The analysis unit can also estimate the user's emotion based on survey results and adjust the parameters of the analysis algorithm based on the estimated emotion. The analysis unit can also estimate the user's emotion by analyzing the user's facial expressions or voice data, for example, and adjust the parameters of the analysis algorithm based on the estimated emotion. This allows for more appropriate analysis results to be obtained by adjusting the parameters of the analysis algorithm according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the parameters of the analysis algorithm.

[0069] The analysis unit can improve the analysis accuracy by combining different machine learning algorithms. The analysis unit can improve the analysis accuracy by, for example, combining a regression analysis based on past data with a neural network based on real-time data. The analysis unit can also improve the analysis accuracy by, for example, combining a clustering algorithm and a classification algorithm. The analysis unit can also improve the analysis accuracy by, for example, combining a reinforcement learning algorithm and a supervised learning algorithm. In this way, the analysis accuracy is improved by combining different machine learning algorithms. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input different machine learning algorithms into the generation AI and cause the generation AI to improve the analysis accuracy.

[0070] The analysis unit can predict trends by comparing past market data with current market data. For example, the analysis unit can predict long-term trends by comparing market data from the past 10 years with current market data. For example, the analysis unit can predict short-term trends by comparing market data from the past year with current market data. For example, the analysis unit can predict seasonal trends by comparing past market data with current market data. This improves the accuracy of trend predictions by comparing past and current market data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past and current market data into a generation AI and have the generation AI perform trend predictions.

[0071] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. The analysis unit can estimate the user's emotion using, for example, an emotion analysis algorithm and adjust the display method of the analysis results based on the estimated emotion. The analysis unit can also estimate the user's emotion based on, for example, questionnaire results and adjust the display method of the analysis results based on the estimated emotion. The analysis unit can also estimate the user's emotion by analyzing, for example, the user's facial expression or voice data and adjust the display method of the analysis results based on the estimated emotion. This allows for adjusting the display method according to the user's emotion, thereby providing analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0072] The analysis unit can integrate data sources and perform analysis. For example, the analysis unit can integrate economic indicator data and corporate financial data and perform analysis. For example, the analysis unit can also integrate stock price data and news article data and perform analysis. For example, the analysis unit can also integrate social media data and market data and perform analysis. In this way, by integrating different data sources, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different data sources into a generation AI and have the generation AI perform data integration and analysis.

[0073] The analysis unit can customize the analysis method based on the user's investment style. For example, the analysis unit can provide an analysis method that emphasizes short-term trends to a user who prefers short-term investment. For example, the analysis unit can also provide an analysis method that emphasizes long-term trends to a user who prefers long-term investment. For example, the analysis unit can also provide an analysis method that takes into account a good balance of risk and return to a user who prefers a balanced investment style. This makes it possible to propose a more appropriate investment strategy by providing an analysis method that suits the user's investment style. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's investment style data into the generation AI and have the generation AI customize the analysis method.

[0074] The suggestion unit can estimate the user's emotions and adjust the proposal content based on the estimated user emotions. The suggestion unit can estimate the user's emotions using, for example, an emotion analysis algorithm and adjust the proposal content based on the estimated emotions. The suggestion unit can also estimate the user's emotions based on, for example, survey results and adjust the proposal content based on the estimated emotions. The suggestion unit can also estimate the user's emotions by analyzing, for example, the user's facial expressions or voice data and adjust the proposal content based on the estimated emotions. This allows the proposal content to be adjusted according to the user's emotions, thereby proposing a more appropriate investment strategy. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the proposal content.

[0075] The proposal unit can propose different investment strategies based on the risk tolerance. For example, the proposal unit can evaluate the risk tolerance using a risk assessment and propose different investment strategies based on the risk tolerance. For example, the proposal unit can also evaluate the risk tolerance using a risk profile and propose different investment strategies based on the risk tolerance. For example, the proposal unit can propose a safe investment strategy to a user with a low risk tolerance. For example, the proposal unit can propose a balanced investment strategy to a user with a medium risk tolerance. For example, the proposal unit can propose a high-risk, high-return investment strategy to a user with a high risk tolerance. In this way, by proposing an investment strategy based on the risk tolerance, it is possible to provide an optimal investment strategy to the user. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input risk tolerance data to a generation AI and have the generation AI execute a proposed investment strategy.

[0076] The proposal unit can improve the accuracy of proposals by referring to past proposal history. For example, the proposal unit analyzes past proposal history and preferentially proposes successful investment strategies. For example, the proposal unit can identify a user's preferred investment strategy from the past proposal history and make a proposal based on that. For example, the proposal unit can also build a feedback loop to improve the accuracy of proposals based on the past proposal history. In this way, the accuracy of proposals is improved by referring to the past proposal history. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input past proposal history data into a generation AI and cause the generation AI to improve the accuracy of proposals.

[0077] The suggestion unit can estimate a user's emotions and prioritize proposals based on the estimated user emotions. The suggestion unit can estimate a user's emotions using, for example, an emotion analysis algorithm and prioritize proposals based on the estimated emotions. The suggestion unit can also estimate a user's emotions based on survey results and prioritize proposals based on the estimated emotions. The suggestion unit can also estimate a user's emotions by analyzing a user's facial expressions or voice data and prioritize proposals based on the estimated emotions. This allows for proposal prioritization based on the user's emotions, thereby proposing a more appropriate investment strategy. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input user emotion data into the generation AI and have the generation AI determine the priority of proposals.

[0078] The suggestion unit can suggest region-specific investment opportunities based on the user's geographical location information. The suggestion unit can collect the user's geographical location information using, for example, GPS data or regional economic indicators, and suggest region-specific investment opportunities based on the collected information. For example, if the user is in Asia, the suggestion unit can suggest investment opportunities for that region. For example, if the user is in North America, the suggestion unit can suggest investment opportunities for that region. For example, if the user is in Europe, the suggestion unit can suggest investment opportunities for that region. In this way, region-specific investment opportunities can be suggested by taking the user's geographical location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into a generation AI, causing the generation AI to suggest region-specific investment opportunities.

[0079] The suggestion unit can analyze the user's social media activity to suggest related investment opportunities. The suggestion unit can, for example, analyze the frequency of social media postings and the reactions of followers to suggest related investment opportunities. The suggestion unit can, for example, suggest investment opportunities for companies in which the user has shown interest on social media. The suggestion unit can, for example, suggest new investment opportunities that the user may be interested in based on the user's social media activity. The suggestion unit can, for example, analyze social media trends and suggest related investment opportunities. In this way, related investment opportunities can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input social media data into a generation AI and have the generation AI suggest investment opportunities. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect news articles, financial reports, and stock price data using the control unit 46A of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to predict stock trends. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal portfolio based on the analysis results. The proposal unit is also realized, for example, by the control unit 46A of the smart device 14, and presents an investment strategy to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and proposal unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect news articles, financial reports, and stock price data using the control unit 46A of the smart glasses 214. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI to predict stock trends. The proposal unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal portfolio based on the analysis results. The proposal unit, for example, is also realized by the control unit 46A of the smart glasses 214 and presents investment strategies to the user. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect news articles, financial reports, and stock price data using the control unit 46A of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to predict stock trends. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal portfolio based on the analysis results. The proposal unit is also realized, for example, by the control unit 46A of the headset type terminal 314, and presents investment strategies to the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect news articles, financial reports, and stock price data using the control unit 46A of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using AI to predict stock trends. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal portfolio based on the analysis results. The proposal unit is also realized, for example, by the control unit 46A of the robot 414, and presents investment strategies to the user.

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

[0081] The collection unit can prioritize collecting highly relevant information based on the user's past investment history. For example, the collection unit can prioritize collecting highly relevant information based on past investment history such as trading history and investment patterns. For example, the collection unit can prioritize collecting the latest information on companies in which the user has invested in the past. For example, the collection unit can also collect relevant information based on the user's investment style (e.g., short-term investment, long-term investment). For example, the collection unit can also collect new investment opportunities that may be of interest to the user from the user's past investment history. This makes it possible to collect highly relevant information by taking the user's past investment history into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past investment history data into the generation AI and cause the generation AI to collect relevant information.

[0082] The analysis unit can improve the analysis accuracy by combining different machine learning algorithms. For example, the analysis accuracy can be improved by combining a regression analysis based on past data with a neural network based on real-time data. The analysis unit can also improve the analysis accuracy by combining a clustering algorithm and a classification algorithm, for example. The analysis unit can also improve the analysis accuracy by combining a reinforcement learning algorithm and a supervised learning algorithm, for example. In this way, the analysis accuracy is improved by combining different machine learning algorithms. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input different machine learning algorithms into the generation AI and have the generation AI improve the analysis accuracy.

[0083] The suggestion unit can suggest region-specific investment opportunities based on the user's geographic location information. For example, the suggestion unit can collect the user's geographic location information using GPS data, regional economic indicators, etc., and suggest region-specific investment opportunities based on the collected information. For example, if the user is in Asia, the suggestion unit can suggest investment opportunities for that region. For example, if the user is in North America, the suggestion unit can suggest investment opportunities for that region. For example, if the user is in Europe, the suggestion unit can suggest investment opportunities for that region. In this way, region-specific investment opportunities can be suggested by taking the user's geographic location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's geographic location information into a generation AI, causing the generation AI to suggest region-specific investment opportunities.

[0084] The proposal unit can improve the accuracy of proposals by referring to past proposal history. For example, the proposal unit can analyze past proposal history and prioritize successful investment strategies. The proposal unit can also identify a user's preferred investment strategy from the past proposal history and make a proposal based on that. The proposal unit can also build a feedback loop to improve the accuracy of proposals based on the past proposal history. This improves the accuracy of proposals by referring to the past proposal history. Some or all of the above-described processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input past proposal history data into a generation AI and cause the generation AI to improve the accuracy of proposals.

[0085] The proposal unit can propose different investment strategies based on the risk tolerance. For example, the proposal unit can evaluate the risk tolerance using a risk assessment and propose different investment strategies based on the risk tolerance. The proposal unit can also evaluate the risk tolerance using a risk profile and propose different investment strategies based on the risk tolerance. For example, the proposal unit can propose a safe investment strategy to a user with a low risk tolerance. For example, the proposal unit can propose a balanced investment strategy to a user with a medium risk tolerance. For example, the proposal unit can propose a high-risk, high-return investment strategy to a user with a high risk tolerance. In this way, by proposing an investment strategy based on the risk tolerance, it is possible to provide an optimal investment strategy to the user. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input risk tolerance data to a generation AI and have the generation AI execute a proposed investment strategy.

[0086] The collection unit can estimate a user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, the collection unit can estimate a user's emotions using an emotion analysis algorithm and determine the priority of information to be collected based on the estimated emotions. The collection unit can also estimate a user's emotions based on survey results and determine the priority of information to be collected based on the estimated emotions. The collection unit can also estimate a user's emotions by analyzing a user's facial expressions or voice data, and determine the priority of information to be collected based on the estimated emotions. This allows more appropriate information to be collected by determining the priority of information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit can be performed using an AI, for example, or without an AI. For example, the collection unit can input user emotion data into a generation AI and have the generation AI determine the priority of information.

[0087] The analysis unit can estimate the user's emotion and adjust the parameters of the analysis algorithm based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion using an emotion analysis algorithm and adjust the parameters of the analysis algorithm based on the estimated emotion. The analysis unit can also estimate the user's emotion based on survey results and adjust the parameters of the analysis algorithm based on the estimated emotion. The analysis unit can also estimate the user's emotion by analyzing the user's facial expressions or voice data and adjust the parameters of the analysis algorithm based on the estimated emotion. This allows for more appropriate analysis results to be obtained by adjusting the parameters of the analysis algorithm according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the parameters of the analysis algorithm.

[0088] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion using an emotion analysis algorithm and adjust the display method of the analysis results based on the estimated emotion. The analysis unit can also estimate the user's emotion based on survey results and adjust the display method of the analysis results based on the estimated emotion. The analysis unit can also estimate the user's emotion by analyzing the user's facial expressions or voice data, and adjust the display method of the analysis results based on the estimated emotion. This allows for adjusting the display method according to the user's emotion, thereby providing analysis results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0089] The suggestion unit can estimate a user's emotions and adjust the proposal content based on the estimated user emotions. For example, the suggestion unit can estimate a user's emotions using an emotion analysis algorithm and adjust the proposal content based on the estimated emotions. The suggestion unit can also estimate a user's emotions based on survey results and adjust the proposal content based on the estimated emotions. The suggestion unit can also estimate a user's emotions by analyzing a user's facial expressions or voice data, and adjust the proposal content based on the estimated emotions. This allows the proposal content to be adjusted according to the user's emotions, thereby proposing a more appropriate investment strategy. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input user emotion data into the generation AI and cause the generation AI to adjust the proposal content.

[0090] The suggestion unit can estimate a user's emotions and prioritize proposals based on the estimated user emotions. For example, the suggestion unit can estimate a user's emotions using an emotion analysis algorithm and prioritize proposals based on the estimated emotions. The suggestion unit can also estimate a user's emotions based on survey results and prioritize proposals based on the estimated emotions. The suggestion unit can also estimate a user's emotions by analyzing a user's facial expressions or voice data and prioritize proposals based on the estimated emotions. This allows for proposal prioritization based on the user's emotions, thereby proposing a more appropriate investment strategy. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input user emotion data into the generation AI and have the generation AI determine the priority of proposals.

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

[0092] Step 1: The collection unit collects the latest company information or stock movements. The collection unit collects information such as news articles, financial reports, stock price data, etc. The collection unit can collect the latest financial reports and stock price movements of a specific company. Step 2: The analysis unit analyzes the data collected by the collection unit and determines when to buy or sell stocks. The analysis unit uses machine learning algorithms to find patterns in past data and predict future stock prices. The analysis unit can predict stock price trends using regression analysis and neural networks. Step 3: The proposal department proposes a portfolio of which stocks to buy or sell, and how much, based on the analysis results obtained by the analysis department. The proposal department proposes an optimal investment strategy taking into account risk and return. If the stock price of a particular company is on an upward trend, the proposal department will suggest the timing to buy that stock, and conversely, if the stock price is on a downward trend, the proposal department will suggest the timing to sell that stock.

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

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

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

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

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

Claims

1. A collection department that collects the latest company information or stock movements; an analysis unit that analyzes the data collected by the collection unit and determines the best time to buy or sell stocks; a proposal unit that proposes a portfolio of which stocks to buy and sell and in what quantities based on the analysis results obtained by the analysis unit. A system characterized by:

2. The collecting unit Gather information on news articles, financial reports, and stock data 2. The system of claim 1.

3. The analysis unit Uses machine learning algorithms to find patterns in past data and predict future stock prices 2. The system of claim 1.

4. The proposal unit Propose investment strategies that take into account risk and return 2. The system of claim 1.

5. The proposal unit If a particular company's stock price is rising, we will suggest the best time to buy that stock.

2. The system of claim 1.

6. The proposal unit If a particular company's stock price is falling, we will suggest the best time to sell the stock.

2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Filter information to focus on a specific industry or region 2. The system of claim 1.

Images