System

Abstract

A system according to an embodiment is directed to providing future proposed investment stock and risk information based on individual needs.SOLUTION: A system according to an embodiment includes a needs collection unit, a historical information analysis unit, a future prediction information analysis unit, and a provision unit. The needs collection unit collects personal needs. The historical information analysis unit analyzes the historical information based on the needs collected by the needs collection unit. The future prediction information analysis unit analyzes future prediction information based on the information analyzed by the historical information analysis unit. The providing unit provides the proposed investment stocks and the risk information based on the information analyzed by the future prediction information analyzing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide appropriate information on potential future investment stocks and risks based on individual needs.

[0005] The system according to the embodiment aims to provide prospective investment stocks and risk information based on individual needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a needs collection unit, a historical information analysis unit, a future forecast information analysis unit, and a providing unit. The needs collection unit collects individual needs. The historical information analysis unit analyzes historical information based on the needs collected by the needs collection unit. The future forecast information analysis unit analyzes future forecast information based on the information analyzed by the historical information analysis unit. The providing unit provides investment candidate stocks and risk information based on the information analyzed by the future forecast information analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide prospective investment stocks and risk information based on individual needs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An investment proposal system according to an embodiment of the present invention is a system that proposes investment candidates that meet the diverse needs of individuals. This system receives the individual's needs as input information, utilizes historical information and publicly available future forecast information, such as investor and venture capital lists, and provides investment candidate stocks and risk information that meet the individual's needs and are expected to generate future capital gains. This allows the investment proposal system to provide investment candidate stocks and risk information that meet the individual's diverse needs.

[0029] The investment proposal system according to the embodiment includes a needs collection unit, a historical information analysis unit, a future forecast information analysis unit, and a providing unit. The needs collection unit collects individual needs. For example, an individual can input a specific need, such as "a company that has experienced fraudulent activity and whose stock price has plummeted, but whose stock price is expected to rise significantly in the future." The historical information analysis unit analyzes historical information based on the collected needs. For example, it analyzes past stock price trends, the history of fraudulent activity, and the company's financial status. The future forecast information analysis unit analyzes future forecast information based on the information analyzed by the historical information analysis unit. For example, it analyzes investor reports and venture capital forecast data. The providing unit provides investment candidate stocks and risk information based on the information analyzed by the future forecast information analysis unit. For example, it provides specific information, such as "This company has experienced fraud in the past, but has now improved and its stock price is expected to rise in the future." This allows the investment proposal system according to the embodiment to provide investment candidate stocks and risk information based on individual needs.

[0030] The needs gathering unit can analyze a user's past investment history and behavioral patterns to automatically extract latent needs. The needs gathering unit, for example, analyzes a user's past investment history to extract specific investment patterns and preferences. For example, for a user who has previously preferred to purchase high-risk, high-return stocks, the unit will suggest stocks with a similar risk profile. The needs gathering unit also analyzes a user's behavioral patterns to automatically extract latent needs. For example, it analyzes investment frequency and investment timing to understand the user's investment tendencies. In this way, it is possible to analyze a user's past investment history and behavioral patterns to automatically extract latent needs.

[0031] The needs gathering unit can analyze the user's social media activity and gather investment-related interests in real time. The needs gathering unit, for example, analyzes the user's social media posts and extracts investment-related keywords and hashtags. For example, for a user who posts frequently about a particular company or industry, stocks related to that company or industry are suggested. The needs gathering unit also monitors the user's social media activity in real time and gathers investment-related interests. For example, if the user frequently uses keywords such as "investment" or "stocks," investment candidates based on those interests are suggested. In this way, the user's social media activity can be analyzed and investment-related interests can be gathered in real time.

[0032] The historical information analysis unit analyzes not only stock price data, but also news articles and social media posts, making it possible to provide comprehensive historical information. For example, the historical information analysis unit analyzes past stock price data and company news articles to provide historical information on potential investment stocks. For example, it analyzes company financial reports and earnings announcements. The historical information analysis unit also analyzes social media posts to evaluate a company's reputation and market sentiment. For example, it determines that a company with many positive posts has a good reputation. This makes it possible to provide comprehensive historical information by analyzing not only past stock price data, but also news articles and social media posts.

[0033] The historical information analysis unit can simultaneously analyze trends on different time scales when analyzing historical data, and provide analysis results from multiple perspectives. For example, the historical information analysis unit can simultaneously analyze short-term, medium-term, and long-term stock price data to understand trends on each time scale. For example, it can compare short-term volatility with long-term growth trends. The historical information analysis unit can also simultaneously analyze trends on different time scales and provide analysis results from multiple perspectives. For example, it can propose investment candidates that take into account short-term market trends and long-term economic growth. This makes it possible to simultaneously analyze trends on different time scales and provide analysis results from multiple perspectives.

[0034] The future prediction information analysis unit uses the generation AI to evaluate the reliability of future prediction information and can select and utilize only highly reliable information. The future prediction information analysis unit, for example, uses the generation AI to develop an algorithm to evaluate the reliability of future prediction information. For example, it calculates a reliability score based on past prediction accuracy and utilizes only information with a high score. The future prediction information analysis unit also selects and utilizes only highly reliable information. For example, it prioritizes the use of data from information sources with a high reliability evaluation score. This makes it possible to evaluate the reliability of future prediction information and select and utilize only highly reliable information.

[0035] The future forecast information analysis unit can set different scenarios in analyzing future forecast information and provide forecast results based on each scenario. The future forecast information analysis unit can set, for example, optimistic, neutral, and pessimistic scenarios and provide future forecast information based on each scenario. For example, it can present forecasts for both cases where economic growth continues and where it stagnates. The future forecast information analysis unit also provides forecast results based on different scenarios. For example, in an optimistic scenario, it can suggest stocks that are expected to have high returns, and in a pessimistic scenario, it can suggest stocks with low risk. This makes it possible to provide forecast results based on different scenarios.

[0036] The future forecast information analysis unit integrates future forecast information with different data sources to make more multifaceted predictions. For example, the future forecast information analysis unit integrates future forecast information with patent data to make predictions that take technological trends into account. For example, it evaluates the impact of new technologies on the market. The future forecast information analysis unit also integrates future forecast information with market research reports to make predictions that take economic trends into account. For example, it predicts market trends if economic growth continues. This allows for integration with different data sources to make more multifaceted predictions.

[0037] The future prediction information analysis unit can customize future prediction information according to the user's investment style and provide it individually. The future prediction information analysis unit customizes future prediction information according to, for example, the user's investment style. For example, the future prediction information analysis unit suggests stocks that are expected to have stable growth to a risk-averse user. The future prediction information analysis unit also suggests stocks that are expected to have high returns to a risk-seeking user. In this way, future prediction information can be customized according to the user's investment style and provided individually.

[0038] The needs gathering unit can provide a wider variety of input methods when gathering user needs using voice input and image analysis. The needs gathering unit, for example, allows the user to input their investment needs by voice, which is then converted into text using voice recognition technology. For example, the user may voice input, "I would like to invest in promising technology companies." The needs gathering unit also uses image analysis to gather investment needs by having the user upload images. For example, by uploading logos or product images of companies in which the user is interested, the system will suggest investment candidates related to those companies. This allows a wider variety of input methods to be provided when gathering user needs using voice input and image analysis.

[0039] The needs gathering unit can provide customized question sets for users of different age groups and occupations to gather their needs in more detail. The needs gathering unit, for example, provides customized question sets according to the user's age group to gather their investment needs. For example, for younger users, many questions related to risk tolerance are included. The needs gathering unit also provides customized question sets according to the user's occupation to gather their investment needs. For example, for freelance users, questions related to income fluctuations are included. This makes it possible to provide customized question sets for users of different age groups and occupations to gather their needs in more detail.

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

[0041] The needs gathering unit can also gather investment needs based on the user's health condition and lifestyle. For example, it can suggest healthcare-related stocks to a health-conscious user. It can also suggest stocks of eco-friendly companies or companies that focus on sustainability based on the user's lifestyle. This makes it possible to provide more personalized investment candidates based on the user's health condition and lifestyle.

[0042] The needs gathering unit can also gather investment needs based on the user's hobbies and interests. For example, it can suggest stocks of sports-related companies to a user who likes sports. It can also suggest stocks of companies with the latest technology to a user who is interested in technology. This makes it possible to provide more personalized investment suggestions based on the user's hobbies and interests.

[0043] The needs gathering unit can also utilize the user's geographical location information to propose investment candidates specialized for the region. For example, it can propose stocks related to companies in the region where the user lives or industries expected to grow in that region. It can also provide investment candidates that take into account the region's economic situation and market trends based on the geographical location information. This makes it possible to provide investment candidates specialized for the region by utilizing the user's geographical location information.

[0044] The Historical Information Analysis Department analyzes not only past market data, but also weather data and natural disaster history to assess the risk of potential investments. For example, it assesses the risk of companies in areas where natural disasters occur frequently. It can also predict the performance of agricultural companies based on weather data. This allows it to analyze not only past market data, but also weather data and natural disaster history, providing a more comprehensive risk assessment.

[0045] The Future Forecast Information Analysis Department can collect the opinions of experts from different industries and make future forecasts from multiple perspectives. For example, it can combine the opinions of technology industry experts and economists. It can also provide more diversified future forecasts based on the opinions of experts from different industries. This allows it to collect the opinions of experts from different industries and make future forecasts from multiple perspectives.

[0046] The future forecast information analysis unit can customize and individually provide future forecast information according to the user's investment style. For example, the future forecast information analysis unit can suggest stocks that are expected to have stable growth to a risk-averse user. The future forecast information analysis unit can also suggest stocks that are expected to have high returns to a risk-seeking user. This allows future forecast information to be customized and individually provided according to the user's investment style.

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

[0048] Step 1: The needs gathering unit gathers the needs of individuals. For example, an individual can input a specific need such as "a company that has experienced fraud and whose stock price has plummeted, but whose stock price is expected to rise significantly in the future." Step 2: The Historical Information Analysis Department analyzes historical information based on the collected needs, such as past stock price trends, the history of fraudulent incidents, and the financial status of companies. Step 3: The future forecast information analysis unit analyzes future forecast information based on the information analyzed by the historical information analysis unit, for example, by analyzing investor reports and venture capital forecast data. Step 4: The information provider provides investment candidate stocks and risk information based on the information analyzed by the future forecast information analysis unit. For example, it provides specific information such as, "This company had a history of fraudulent activity, but has now improved, and its stock price is expected to rise in the future."

[0049] (Example 2) An investment proposal system according to an embodiment of the present invention is a system that proposes investment candidates that meet the diverse needs of individuals. This system receives the individual's needs as input information, utilizes historical information and publicly available future forecast information, such as investor and venture capital lists, and provides investment candidate stocks and risk information that meet the individual's needs and are expected to generate future capital gains. This allows the investment proposal system to provide investment candidate stocks and risk information that meet the individual's diverse needs.

[0050] The investment proposal system according to the embodiment includes a needs collection unit, a historical information analysis unit, a future forecast information analysis unit, and a providing unit. The needs collection unit collects individual needs. For example, an individual can input a specific need, such as "a company that has experienced fraudulent activity and whose stock price has plummeted, but whose stock price is expected to rise significantly in the future." The historical information analysis unit analyzes historical information based on the collected needs. For example, it analyzes past stock price trends, the history of fraudulent activity, and the company's financial status. The future forecast information analysis unit analyzes future forecast information based on the information analyzed by the historical information analysis unit. For example, it analyzes investor reports and venture capital forecast data. The providing unit provides investment candidate stocks and risk information based on the information analyzed by the future forecast information analysis unit. For example, it provides specific information, such as "This company has experienced fraud in the past, but has now improved and its stock price is expected to rise in the future." This allows the investment proposal system according to the embodiment to provide investment candidate stocks and risk information based on individual needs.

[0051] The needs gathering unit can analyze a user's past investment history and behavioral patterns to automatically extract latent needs. The needs gathering unit, for example, analyzes a user's past investment history to extract specific investment patterns and preferences. For example, for a user who has previously preferred to purchase high-risk, high-return stocks, the unit will suggest stocks with a similar risk profile. The needs gathering unit also analyzes a user's behavioral patterns to automatically extract latent needs. For example, it analyzes investment frequency and investment timing to understand the user's investment tendencies. In this way, it is possible to analyze a user's past investment history and behavioral patterns to automatically extract latent needs.

[0052] The needs gathering unit can analyze the user's social media activity and gather investment-related interests in real time. The needs gathering unit, for example, analyzes the user's social media posts and extracts investment-related keywords and hashtags. For example, for a user who posts frequently about a particular company or industry, stocks related to that company or industry are suggested. The needs gathering unit also monitors the user's social media activity in real time and gathers investment-related interests. For example, if the user frequently uses keywords such as "investment" or "stocks," investment candidates based on those interests are suggested. In this way, the user's social media activity can be analyzed and investment-related interests can be gathered in real time.

[0053] The needs collection unit can use the emotion estimation function to analyze emotions regarding needs input by the user and make suggestions to elicit positive emotions. The needs collection unit, for example, performs emotion analysis on needs input by the user and generates suggestions to elicit positive emotions. For example, if the user is feeling anxious, it suggests investment candidates with low risk. The needs collection unit also uses the emotion estimation function to monitor the user's emotions in real time and make appropriate suggestions. For example, if the user is excited, it suggests investment candidates with high risk but high return. In this way, it is possible to analyze the user's emotions and make suggestions to elicit positive emotions.

[0054] The historical information analysis unit analyzes not only stock price data, but also news articles and social media posts, making it possible to provide comprehensive historical information. For example, the historical information analysis unit analyzes past stock price data and company news articles to provide historical information on potential investment stocks. For example, it analyzes company financial reports and earnings announcements. The historical information analysis unit also analyzes social media posts to evaluate a company's reputation and market sentiment. For example, it determines that a company with many positive posts has a good reputation. This makes it possible to provide comprehensive historical information by analyzing not only past stock price data, but also news articles and social media posts.

[0055] The historical information analysis unit can simultaneously analyze trends on different time scales when analyzing historical data, and provide analysis results from multiple perspectives. For example, the historical information analysis unit can simultaneously analyze short-term, medium-term, and long-term stock price data to understand trends on each time scale. For example, it can compare short-term volatility with long-term growth trends. The historical information analysis unit can also simultaneously analyze trends on different time scales and provide analysis results from multiple perspectives. For example, it can propose investment candidates that take into account short-term market trends and long-term economic growth. This makes it possible to simultaneously analyze trends on different time scales and provide analysis results from multiple perspectives.

[0056] The historical information analysis unit uses the emotion estimation function to analyze emotions in news articles and social media posts, and can evaluate corporate reputation and market sentiment. The historical information analysis unit, for example, performs emotion analysis on past news articles to evaluate corporate reputation. For example, it determines that a company with many positive articles has a good reputation. The historical information analysis unit also performs emotion analysis on social media posts to evaluate market sentiment. For example, if there are many positive posts from investors, it determines that the company's stock price is likely to rise. This makes it possible to analyze emotions in past news articles and social media posts and evaluate corporate reputation and market sentiment.

[0057] The future prediction information analysis unit uses the generation AI to evaluate the reliability of future prediction information and can select and utilize only highly reliable information. The future prediction information analysis unit, for example, uses the generation AI to develop an algorithm to evaluate the reliability of future prediction information. For example, it calculates a reliability score based on past prediction accuracy and utilizes only information with a high score. The future prediction information analysis unit also selects and utilizes only highly reliable information. For example, it prioritizes the use of data from information sources with a high reliability evaluation score. This makes it possible to evaluate the reliability of future prediction information and select and utilize only highly reliable information.

[0058] The future forecast information analysis unit can set different scenarios in analyzing future forecast information and provide forecast results based on each scenario. The future forecast information analysis unit can set, for example, optimistic, neutral, and pessimistic scenarios and provide future forecast information based on each scenario. For example, it can present forecasts for both cases where economic growth continues and where it stagnates. The future forecast information analysis unit also provides forecast results based on different scenarios. For example, in an optimistic scenario, it can suggest stocks that are expected to have high returns, and in a pessimistic scenario, it can suggest stocks with low risk. This makes it possible to provide forecast results based on different scenarios.

[0059] The future prediction information analysis unit can use the emotion estimation function to analyze market emotions regarding future prediction information and provide emotionally positive predictions preferentially. The future prediction information analysis unit, for example, analyzes market emotions regarding future prediction information using the emotion estimation function and provides predictions with positive emotions preferentially. For example, it prioritizes information with many optimistic predictions. The future prediction information analysis unit also provides emotionally positive predictions preferentially. For example, it prioritizes highly profitable predictions and low-risk predictions. This makes it possible to analyze market emotions regarding future prediction information and provide emotionally positive predictions preferentially.

[0060] The future forecast information analysis unit integrates future forecast information with different data sources to make more multifaceted predictions. For example, the future forecast information analysis unit integrates future forecast information with patent data to make predictions that take technological trends into account. For example, it evaluates the impact of new technologies on the market. The future forecast information analysis unit also integrates future forecast information with market research reports to make predictions that take economic trends into account. For example, it predicts market trends if economic growth continues. This allows for integration with different data sources to make more multifaceted predictions.

[0061] The future prediction information analysis unit can customize future prediction information according to the user's investment style and provide it individually. The future prediction information analysis unit customizes future prediction information according to, for example, the user's investment style. For example, the future prediction information analysis unit suggests stocks that are expected to have stable growth to a risk-averse user. The future prediction information analysis unit also suggests stocks that are expected to have high returns to a risk-seeking user. In this way, future prediction information can be customized according to the user's investment style and provided individually.

[0062] The future prediction information analysis unit can use the emotion estimation function to monitor the user's emotional response to future prediction information in real time and dynamically adjust the prediction results. The future prediction information analysis unit, for example, uses the emotion estimation function to monitor the user's emotional response to future prediction information in real time. For example, it preferentially provides information to which the user has a positive response. The future prediction information analysis unit also dynamically adjusts the prediction results based on the user's emotional response. For example, if the user has a negative response, it suggests low-risk investment candidates. This makes it possible to monitor the user's emotional response to future prediction information in real time and dynamically adjust the prediction results.

[0063] The needs gathering unit can provide a wider variety of input methods when gathering user needs using voice input and image analysis. The needs gathering unit, for example, allows the user to input their investment needs by voice, which is then converted into text using voice recognition technology. For example, the user may voice input, "I would like to invest in promising technology companies." The needs gathering unit also uses image analysis to gather investment needs by having the user upload images. For example, by uploading logos or product images of companies in which the user is interested, the system will suggest investment candidates related to those companies. This allows a wider variety of input methods to be provided when gathering user needs using voice input and image analysis.

[0064] The needs gathering unit can provide customized question sets for users of different age groups and occupations to gather their needs in more detail. The needs gathering unit, for example, provides customized question sets according to the user's age group to gather their investment needs. For example, for younger users, many questions related to risk tolerance are included. The needs gathering unit also provides customized question sets according to the user's occupation to gather their investment needs. For example, for freelance users, questions related to income fluctuations are included. This makes it possible to provide customized question sets for users of different age groups and occupations to gather their needs in more detail.

[0065] The needs gathering unit can use the emotion estimation function to monitor the emotions of the user when entering input in real time and dynamically adjust the input content. For example, the needs gathering unit monitors the emotions of the user when entering input in real time, and if negative emotions are detected, dynamically adjusts the input content. For example, if the user is feeling anxious, the needs gathering unit suggests investment candidates with low risk. On the other hand, if positive emotions are detected, the needs gathering unit suggests investment candidates with high risk but high return. In this way, the emotions of the user when entering input can be monitored in real time and the input content can be dynamically adjusted.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The needs gathering unit can also gather investment needs based on the user's health condition and lifestyle. For example, it can suggest healthcare-related stocks to a health-conscious user. It can also suggest stocks of eco-friendly companies or companies that focus on sustainability based on the user's lifestyle. This makes it possible to provide more personalized investment candidates based on the user's health condition and lifestyle.

[0068] The needs gathering unit can also gather investment needs based on the user's hobbies and interests. For example, it can suggest stocks of sports-related companies to a user who likes sports. It can also suggest stocks of companies with the latest technology to a user who is interested in technology. This makes it possible to provide more personalized investment suggestions based on the user's hobbies and interests.

[0069] The needs gathering unit can also utilize the user's geographical location information to propose investment candidates specialized for the region. For example, it can propose stocks related to companies in the region where the user lives or industries expected to grow in that region. It can also provide investment candidates that take into account the region's economic situation and market trends based on the geographical location information. This makes it possible to provide investment candidates specialized for the region by utilizing the user's geographical location information.

[0070] The needs gathering unit can estimate the user's emotions and, based on the estimated emotions, suggest investment candidates that will give the user a sense of security. For example, if the user is feeling anxious, low-risk investment candidates can be suggested. On the other hand, if the user is excited, investment candidates that are high-risk but also offer high returns can be suggested. In this way, the user's emotions can be estimated and investment candidates that will give the user a sense of security can be provided.

[0071] The Historical Information Analysis Department analyzes not only past market data, but also weather data and natural disaster history to assess the risk of potential investments. For example, it assesses the risk of companies in areas where natural disasters occur frequently. It can also predict the performance of agricultural companies based on weather data. This allows it to analyze not only past market data, but also weather data and natural disaster history, providing a more comprehensive risk assessment.

[0072] The historical information analysis unit uses the emotion estimation function to analyze investor emotions regarding past market data and identify emotional trends. For example, it analyzes how investors felt during past market crashes. It can also predict future market trends based on emotional trends. This makes it possible to analyze investor emotions regarding past market data and identify emotional trends.

[0073] The Future Forecast Information Analysis Department can collect the opinions of experts from different industries and make future forecasts from multiple perspectives. For example, it can combine the opinions of technology industry experts and economists. It can also provide more diversified future forecasts based on the opinions of experts from different industries. This allows it to collect the opinions of experts from different industries and make future forecasts from multiple perspectives.

[0074] The future forecast information analysis unit uses the emotion estimation function to analyze market emotions regarding future forecast information and can provide emotionally positive forecasts with priority. For example, it prioritizes information with a large number of optimistic forecasts. The future forecast information analysis unit also prioritizes emotionally positive forecasts with priority. For example, it prioritizes highly profitable forecasts and low-risk forecasts. This makes it possible to analyze market emotions regarding future forecast information and provide emotionally positive forecasts with priority.

[0075] The future forecast information analysis unit can customize and individually provide future forecast information according to the user's investment style. For example, the future forecast information analysis unit can suggest stocks that are expected to have stable growth to a risk-averse user. The future forecast information analysis unit can also suggest stocks that are expected to have high returns to a risk-seeking user. This allows future forecast information to be customized and individually provided according to the user's investment style.

[0076] The future prediction information analysis unit can use the emotion estimation function to monitor the user's emotional response to future prediction information in real time and dynamically adjust the prediction results. For example, the emotion estimation function is used to monitor the user's emotional response to future prediction information in real time. For example, information to which the user has a positive response is preferentially provided. The future prediction information analysis unit also dynamically adjusts the prediction results based on the user's emotional response. For example, if the user has a negative response, low-risk investment candidates are suggested. In this way, the user's emotional response to future prediction information can be monitored in real time and the prediction results can be dynamically adjusted.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The needs gathering unit gathers the needs of individuals. For example, an individual can input a specific need such as "a company that has experienced fraud and whose stock price has plummeted, but whose stock price is expected to rise significantly in the future." Step 2: The Historical Information Analysis Department analyzes historical information based on the collected needs, such as past stock price trends, the history of fraudulent incidents, and the financial status of companies. Step 3: The future forecast information analysis unit analyzes future forecast information based on the information analyzed by the historical information analysis unit, for example, by analyzing investor reports and venture capital forecast data. Step 4: The information provider provides investment candidate stocks and risk information based on the information analyzed by the future forecast information analysis unit. For example, it provides specific information such as, "This company had a history of fraudulent activity, but has now improved, and its stock price is expected to rise in the future."

[0079] 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.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] 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.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

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

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

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

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0146] 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 needs gathering department that gathers individual needs; a historical information analysis unit that analyzes historical information based on the needs collected by the needs collection unit; a future prediction information analysis unit that analyzes future prediction information based on the information analyzed by the historical information analysis unit; a providing unit that provides investment candidate stocks and risk information based on the information analyzed by the future forecast information analyzing unit. A system characterized by:

2. The historical information analysis unit Analyze not only stock price data but also news articles and posts on the aforementioned social media platforms to provide more comprehensive historical information. The system of claim 1 .

3. The future prediction information analysis unit Using generation AI, the reliability of the future forecast information is evaluated, and only the most reliable information is selected and utilized. The system of claim 1 .

4. The needs collection unit When gathering user needs, provide more diverse input methods using voice input and image analysis. The system of claim 1 .

5. The needs collection unit Using the emotion estimation function, the system analyzes the emotions entered by the user regarding the needs and makes suggestions to elicit positive emotions. The system of claim 1 .

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