system

Abstract

The system according to the embodiment aims to promote ethical investment and propose investment destinations based on the user's values. [Solution] A system according to an embodiment includes a selection unit, a proposal unit, a scoring unit, and a challenge unit. The selection unit selects investment targets based on ESG evaluation. The proposal unit proposes investment targets selected by the selection unit based on the user's values. The scoring unit assigns an ethical score based on the user's investment behavior. The challenge unit provides a mission or challenge to achieve a specific investment goal.

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 do not adequately promote ethical investing or suggest investment destinations based on users' values, and there is room for improvement.

[0005] The system according to the embodiment aims to promote ethical investment and propose investment destinations based on the user's values. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a proposal unit, a scoring unit, and a challenge unit. The selection unit selects investment targets based on ESG evaluation. The proposal unit proposes the investment targets selected by the selection unit based on the user's values. The scoring unit assigns an ethical score based on the user's investment behavior. The challenge unit provides a mission or challenge to achieve a specific investment goal. [Effects of the Invention]

[0007] The system according to the embodiment can promote ethical investment and suggest investment destinations based on the user's values. [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) An investment app according to an embodiment of the present invention proposes ethical investments while incorporating game elements. This investment app uses AI to select investments based on ESG (environmental, social, and governance) assessments and recommends investments tailored to the user's values. Furthermore, by incorporating game elements such as an ethical score, quests and challenges, a community, and educational content, users can learn about and practice investing while having fun. For example, the AI ​​evaluates companies' environmental initiatives, social responsibility, and governance status to select ethical investments. The AI ​​then recommends investments tailored to the user's values. Users can select investments based on their own values ​​and interests. For example, a user interested in environmental protection will be recommended environmentally conscious companies. Similarly, a user who values ​​social responsibility will be recommended companies that fulfill social responsibilities. Furthermore, the app incorporates game elements such as an ethical score, quests and challenges, a community, and educational content. The ethical score is a system that assigns a score based on the user's investment behavior. The quests and challenges provide missions and challenges for users to achieve specific investment goals. The community is a place where users can share information and interact with each other. Educational content is content for learning about investing. In this way, by proposing ethical investing while incorporating game elements, young people and beginners can learn about and practice investing while having fun. In addition, there are business opportunities due to changes in social values, growing interest in ethical initiatives, and increased interest in investing, making it highly significant for the company to enter the market. This allows investment apps to enable users to learn about and practice ethical investing while having fun.

[0029] An investment app according to an embodiment includes a selection unit, a proposal unit, a scoring unit, and a challenge unit. The selection unit selects investment targets based on ESG evaluation. The ESG evaluation is performed based on evaluation criteria for environmental, social, and governance factors. For example, the selection unit evaluates a company's environmental initiatives. Specifically, the evaluation criteria include CO2 emission reduction and renewable energy use. The selection unit also evaluates a company's social responsibility. Specifically, the evaluation criteria include improvements to the working environment and contributions to the local community. The selection unit also evaluates a company's governance status. Specifically, the evaluation criteria include the composition of the board of directors and internal control system. The proposal unit proposes investment targets selected by the selection unit based on a user's values. The user's values ​​are identified using surveys, profiling, past behavioral data, and the like. For example, the proposal unit proposes environmentally conscious companies to a user who is interested in environmental protection. The proposal unit also proposes socially responsible companies to a user who values ​​social responsibility. The scoring unit assigns an ethical score to the user's investment behavior. The ethical score is calculated based on evaluation criteria such as investment amount, investment type, and investment period. For example, the scoring unit assigns a high ethical score if the user invests in an environmentally conscious company. Furthermore, the scoring unit assigns a high ethical score if the user invests in a socially responsible company. The challenge unit provides missions and challenges for the user to achieve specific investment goals. The missions and challenges are provided based on achievement criteria such as investment amount, return target, and period. For example, the challenge unit provides a mission for the user to achieve a specific investment goal within a certain period of time. Furthermore, the challenge unit provides a challenge for the user to achieve a specific return target. In this way, the investment app according to the embodiment allows the user to learn about and practice ethical investment while having fun.

[0030] The selection department can evaluate a company's environmental efforts, social efforts, and governance status. For example, the selection department evaluates a company's environmental efforts. Specifically, evaluation criteria include CO2 emission reduction and use of renewable energy. The selection department also evaluates a company's social responsibility. Specifically, evaluation criteria include improvements to the working environment and contributions to the local community. The selection department also evaluates a company's governance status. Specifically, evaluation criteria include the composition of the board of directors and internal control system. In this way, ethical investment targets can be selected by evaluating a company's environmental efforts, social responsibility, and governance status. Some or all of the above-mentioned processing in the selection department may be performed using, or without, AI. For example, the selection department can input a company's environmental report and social responsibility report into AI, which then analyzes these reports and makes an evaluation.

[0031] The suggestion unit can suggest investment destinations based on the user's values ​​or interests. The suggestion unit, for example, collects the user's values ​​using a questionnaire or profiling. Specifically, the suggestion unit asks the user questions about environmental protection and social responsibility and identifies the user's values ​​based on the answers. The suggestion unit can also analyze the user's past behavioral data to identify the user's interests. For example, it can analyze what companies the user has invested in in the past and suggest investment destinations based on the results. This makes it possible to suggest investment destinations that are attractive to the user by suggesting investment destinations based on the user's values ​​and interests. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's questionnaire responses and behavioral data into AI, which can analyze this data and suggest investment destinations.

[0032] The scoring unit can calculate an ethical score based on the user's investment behavior. For example, the scoring unit calculates the user's score based on the ethical scores of companies in which the user has invested. Specifically, if the user invests in environmentally conscious companies, the scoring unit assigns a high ethical score. The scoring unit can also assign a high ethical score if the user invests in companies that fulfill social responsibility. Furthermore, the scoring unit can calculate a score based on evaluation criteria such as the user's investment amount and investment period. For example, if the user has invested in ethical companies over a long period of time, the scoring unit assigns a high score. In this way, the user's investment behavior can be evaluated by calculating an ethical score based on the user's investment behavior. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without AI. For example, the scoring unit can input the user's investment data into AI, which analyzes the data and calculates the score.

[0033] The challenge unit can provide a mission or challenge for the user to achieve a specific investment goal. For example, the challenge unit can provide a mission for the user to achieve a specific investment goal within a certain period of time. Specifically, the challenge unit can provide a challenge for the user to achieve a specific investment amount. The challenge unit can also provide a challenge for the user to achieve a specific return goal. Furthermore, the challenge unit can provide a mission for the user to invest in a specific industry. For example, if the user is interested in environmental protection, the challenge unit can provide a challenge for investing in environmentally friendly companies. This can support the user's investment activities by providing a mission or challenge for the user to achieve a specific investment goal. Some or all of the above-mentioned processing in the challenge unit can be performed, for example, using AI, or can be performed without using AI. For example, the challenge unit can input the user's investment goal data into AI, which can analyze this data and provide missions and challenges.

[0034] The suggestion unit can collect the user's values ​​using a questionnaire or profiling. For example, the suggestion unit asks the user questions about environmental protection or social responsibility and identifies the user's values ​​based on the answers. The suggestion unit can also analyze the user's past behavioral data to identify the user's interests. For example, it can analyze what companies the user has invested in in the past and suggest investment targets based on the results. By collecting the user's values ​​using a questionnaire or profiling, more accurate investment target suggestions become possible. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's questionnaire responses and behavioral data into AI, which can analyze this data and suggest investment targets.

[0035] The challenge unit can provide a forum for users to share information and interact with each other. The challenge unit can provide, for example, an online forum or chat room. Specifically, it can provide a forum for users to share information about investments and exchange opinions. The challenge unit can also provide a platform for users to share success stories and failure stories about investments. Furthermore, the challenge unit can provide a function for users to post questions about investments and receive answers from other users. For example, the challenge unit can allow users to post questions about specific investments and receive feedback from other users. This provides a community where users can share information and interact with each other, thereby promoting information exchange between users. Some or all of the above-mentioned processing in the challenge unit can be performed, for example, using AI, or can be performed without AI. For example, the challenge unit can input user-posted data into AI, which can analyze the data and provide appropriate feedback.

[0036] The challenge unit can provide educational materials for learning investment knowledge. The challenge unit can provide, for example, online courses and video tutorials. Specifically, the challenge unit can provide educational materials for users to learn basic investment knowledge. The challenge unit can also provide educational materials for users to learn specific investment strategies and techniques. The challenge unit can also provide educational materials for users to learn the latest investment trends and market movements. For example, the challenge unit can provide an online course for users to learn investment strategies related to environmental protection. The challenge unit can also provide video tutorials for users to learn investment techniques related to social responsibility. This improves the user's investment knowledge by providing educational content for learning investment knowledge. Some or all of the above-mentioned processing in the challenge unit can be performed, for example, using AI or without AI. For example, the challenge unit can input the user's learning data into AI, which can analyze the data and provide appropriate educational materials.

[0037] The selection department can update a company's environmental initiatives, social initiatives, and governance status in real time and reflect them in the selection criteria. For example, the selection department obtains the company's latest environmental report and updates the company's environmental initiatives in real time. Specifically, the selection department periodically collects environmental reports and sustainability reports released by the company and reflects them in the evaluation. The selection department can also obtain the latest news and reports on corporate social responsibility and reflect them in the evaluation. Specifically, the selection department collects information on a company's social contribution activities and improvements to its working environment and reflects them in the evaluation. Furthermore, the selection department can obtain the latest information on a company's governance and reflect it in the evaluation criteria. Specifically, the selection department collects information on the composition of a company's board of directors and internal control system and reflects it in the evaluation. This allows for more accurate investment selection by reflecting the latest company information in real time. Some or all of the above-mentioned processing by the selection department can be performed using, for example, AI, or without AI. For example, the selection department can input the company's latest reports and news data into AI, which can analyze this data and update the evaluation.

[0038] When selecting a company, the selection unit can predict future performance by referring to the company's past ESG evaluation history. The selection unit, for example, analyzes the company's ESG evaluation history over the past five years to predict future performance of its environmental initiatives. Specifically, it analyzes the company's past environmental protection activities and predicts future performance based on the results. The selection unit can also predict future social responsibility performance based on the company's past evaluation history regarding social responsibility. Specifically, it analyzes the company's past social contribution activities and predicts future performance based on the results. The selection unit can also predict future governance performance by referring to the company's past governance evaluation history. Specifically, it analyzes the company's past evaluations of its board of directors and internal control system and predicts future performance based on the results. This allows future performance to be predicted and more appropriate investment targets to be selected by referring to the company's past ESG evaluation history. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or without AI. For example, the selection unit can input the company's past ESG evaluation data into AI, which then analyzes the data and predicts future performance.

[0039] The selection unit may select investment targets by taking into account the geographical scope of the company's influence. For example, if a company's activities have a significant impact on a specific region, the selection unit may consider the environmental and social conditions of that region when selecting investment targets. Specifically, the selection unit may evaluate the environmental protection status and social responsibility status of the region in which the company operates. Furthermore, if a company's products or services are in high demand in a specific region, the selection unit may also consider the market conditions of that region when selecting investment targets. Specifically, the selection unit may evaluate the region and the level of demand for the company's products or services. Furthermore, if a company's activities span multiple countries or regions, the selection unit may also consider the ESG status of each region when selecting investment targets. Specifically, the selection unit may evaluate the environmental, social, and governance status of each region in which the company operates. This allows the selection of investment targets appropriate for each region by taking into account the company's geographical scope of influence. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit may input data on the company's geographical influence into AI, which may then analyze the data to select investment targets.

[0040] When making a selection, the selection unit can analyze a company's social media activities and evaluate its ethical initiatives. For example, the selection unit can analyze a company's social media posts about its environmental protection activities and evaluate its ethical initiatives. Specifically, the selection unit collects information about environmental protection posted by the company on social media and evaluates its content. The selection unit can also analyze a company's social media posts about social responsibility and evaluate its ethical initiatives. Specifically, the selection unit collects information about social contribution activities posted by the company on social media and evaluates its content. The selection unit can also analyze a company's social media posts about governance and evaluate its ethical initiatives. Specifically, the selection unit collects information about governance posted by the company on social media and evaluates its content. This allows the analysis of a company's social media activities to evaluate its ethical initiatives and select appropriate investment destinations. Some or all of the above-mentioned processing by the selection unit can be performed using, for example, AI, or without AI. For example, the selection unit can input a company's social media data into AI, which can analyze the data and evaluate its ethical initiatives.

[0041] When making a proposal, the proposal unit can analyze the user's past investment history and select an appropriate proposal method. For example, the proposal unit analyzes the characteristics of investments in which the user has been successful in the past and proposes similar investments. Specifically, the proposal unit analyzes the characteristics of companies from which the user has received high returns in the past and proposes new investments based on the results. The proposal unit can also analyze the user's risk tolerance from the user's past investment history and propose investments with appropriate risk levels. Specifically, the proposal unit analyzes the level of risk the user has taken in past investments and adjusts the risk level based on the results. Furthermore, the proposal unit can make proposals that match the user's investment style based on the user's past investment history. Specifically, the proposal unit analyzes the user's preferred investment style in the past and proposes new investments based on the results. In this way, by analyzing the user's past investment history, the proposal method optimal for the user can be selected. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without AI. For example, the proposal unit can input the user's investment history data into AI, which can analyze the data and select an appropriate proposal method.

[0042] When making a proposal, the suggestion unit can customize the proposal content based on the user's current living situation or areas of interest. For example, if the user starts a new job, the suggestion unit can suggest investments related to that industry. Specifically, the suggestion unit can suggest companies in the user's new industry. If the user is interested in environmental protection, the suggestion unit can also suggest environmentally conscious companies. Specifically, the suggestion unit can suggest companies that engage in environmental protection activities or use renewable energy. If the user is interested in social responsibility, the suggestion unit can also suggest socially responsible companies. Specifically, the suggestion unit can suggest companies that improve working environments or contribute to local communities. This allows the proposal content to be customized based on the user's current living situation and areas of interest, enabling proposals that are highly relevant to the user. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's living situation data and area of ​​interest data into AI, which can then analyze the data and customize the proposal content.

[0043] When making a proposal, the suggestion unit can suggest highly relevant investments taking into account the user's geographical location information. For example, the suggestion unit prioritizes suggesting companies in the area where the user lives. Specifically, it suggests companies that are active in the area where the user lives or companies that contribute to the area. The suggestion unit can also suggest companies in areas frequently visited by the user. Specifically, it suggests companies that are active in areas frequently visited by the user or companies that are popular in the area. Furthermore, the suggestion unit can also suggest companies in areas in which the user is interested. Specifically, it suggests companies that are active in areas in which the user is interested or companies related to the area. In this way, by taking the user's geographical location information into consideration, it is possible to suggest highly relevant investments to the user. Some or all of the above-described processing by 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 data into AI, which can analyze the data and suggest highly relevant investments.

[0044] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related investments. For example, the suggestion unit can suggest companies that the user follows on social media. Specifically, the suggestion unit can suggest companies that the user follows on social media and investments related to those companies. The suggestion unit can also suggest companies related to topics that the user is interested in on social media. Specifically, the suggestion unit can suggest companies related to topics that the user is interested in on social media and investments related to those topics. Furthermore, the suggestion unit can also suggest companies related to communities that the user participates in on social media. Specifically, the suggestion unit can suggest companies related to communities that the user participates in on social media and investments related to those communities. In this way, by analyzing the user's social media activity, it is possible to suggest investments that are highly relevant to the user. Some or all of the above-mentioned processing by 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 social media data into AI, which can analyze the data and suggest related investments.

[0045] When calculating the score, the scoring unit can analyze the user's past investment behavior to improve the accuracy of the score. For example, the scoring unit analyzes the user's past investment behavior and reflects it in the calculation of the ethical score. Specifically, the scoring unit analyzes the types of companies the user has invested in in the past and calculates the score based on the results. The scoring unit can also analyze the user's risk tolerance from the user's past investment behavior to improve the accuracy of the score. Specifically, the scoring unit analyzes the level of risk the user has taken in past investments and adjusts the score based on the results. Furthermore, the scoring unit can calculate a score that matches the user's investment style based on the user's past investment behavior. Specifically, the scoring unit analyzes the user's preferred investment style in the past and calculates the score based on the results. In this way, the accuracy of the score can be improved by analyzing the user's past investment behavior. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input the user's investment history data into AI, which can then analyze the data and calculate the score.

[0046] When calculating the score, the scoring unit can weight the score based on the user's current investment situation. For example, the scoring unit analyzes the user's current investment situation and weights the ethical score. Specifically, it analyzes the types of companies the user is currently investing in and adjusts the score based on the results. The scoring unit can also analyze the user's risk tolerance based on the user's current investment situation and weight the score. Specifically, it analyzes the level of risk the user is currently taking in their investments and adjusts the score based on the results. Furthermore, the scoring unit can weight the score to match the user's investment style based on the user's current investment situation. Specifically, it analyzes the user's current preferred investment style and adjusts the score based on the results. This allows for weighting the score based on the user's current investment situation, thereby providing a more accurate score. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without AI. For example, the scoring unit can input the user's current investment data into AI, which then analyzes the data and weights the score.

[0047] The scoring unit can take the user's geographical location information into consideration when calculating the score. For example, the scoring unit prioritizes calculating the ethical scores of companies in the area where the user lives. Specifically, it calculates the scores of companies active in the area where the user lives and companies that contribute to the area. The scoring unit can also calculate the ethical scores of companies in areas frequently visited by the user. Specifically, it calculates the scores of companies active in the area frequently visited by the user and companies that are popular in the area. Furthermore, the scoring unit can also calculate the ethical scores of companies in areas of interest to the user. Specifically, it calculates the scores of companies active in the area of ​​interest to the user and companies related to the area. This makes it possible to provide a score appropriate for the area by taking the user's geographical location information into consideration. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input the user's geographical location information data into AI, which can then analyze the data to calculate the score.

[0048] The scoring unit can analyze the user's social media activity when calculating the score to improve the accuracy of the score. For example, the scoring unit calculates the ethical scores of companies the user follows on social media. Specifically, it calculates the scores of companies the user follows on social media and investments related to those companies. The scoring unit can also calculate the ethical scores of companies related to topics the user is interested in on social media. Specifically, it calculates the scores of companies related to topics the user is interested in on social media and investments related to those topics. Furthermore, the scoring unit can also calculate the ethical scores of companies related to communities the user participates in on social media. Specifically, it calculates the scores of companies related to communities the user participates in on social media and investments related to those communities. This allows the accuracy of the score to be improved by analyzing the user's social media activity. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input the user's social media data into AI, which analyzes the data and calculates the score.

[0049] When providing a challenge, the challenge unit can provide an appropriate challenge by referring to the user's past challenge history. For example, the challenge unit analyzes the characteristics of challenges that the user has successfully completed in the past and provides a similar challenge. Specifically, the challenge unit provides a new challenge based on the user's past investment goals and returns. The challenge unit can also adjust the difficulty of the challenge based on the user's past challenge history. Specifically, the challenge unit analyzes the level of difficulty of challenges the user has achieved in the past and sets the difficulty of the new challenge based on the results. Furthermore, the challenge unit can provide a challenge that the user is likely to be interested in based on the user's past challenge history. Specifically, the challenge unit provides a new challenge based on the investment field or theme in which the user has shown interest in the past. In this way, by referring to the user's past challenge history, the optimal challenge can be provided for the user. Some or all of the above-described processing in the challenge unit may be performed, for example, using AI, or may be performed without using AI. For example, the challenge unit can input the user's challenge history data into AI, which can analyze this data and provide an appropriate challenge.

[0050] When providing a challenge, the challenge unit can customize the challenge content based on the user's current investment goals. For example, if the user is aiming for short-term profits, the challenge unit can provide a challenge that matches the user's short-term investment goals. Specifically, the challenge unit can set an investment goal that the user can achieve in a short period of time and provide a challenge that matches that goal. Furthermore, if the user is aiming for long-term profits, the challenge unit can also provide a challenge that matches the user's long-term investment goals. Specifically, the challenge unit can set an investment goal that the user can achieve in a long period of time and provide a challenge that matches that goal. Furthermore, if the user is interested in a particular industry, the challenge unit can also provide a challenge related to that industry. Specifically, the challenge unit can provide a challenge for investing in companies in the industry in which the user is interested. This allows the challenge content to be customized based on the user's current investment goals, thereby providing a challenge that is highly relevant to the user. Some or all of the above-described processing in the challenge unit may be performed, for example, using AI, or may be performed without AI. For example, the challenge unit can input the user's investment goal data into AI, which can analyze this data to customize the challenge content.

[0051] When providing a challenge, the challenge unit can provide an appropriate challenge by taking into account the user's geographical location information. For example, the challenge unit can provide a challenge related to companies in the area where the user lives. Specifically, the challenge unit can provide a challenge to invest in companies active in the area where the user lives or companies that contribute to the area. The challenge unit can also provide a challenge related to companies in an area frequently visited by the user. Specifically, the challenge unit can provide a challenge to invest in companies active in an area frequently visited by the user or companies that are popular in the area. The challenge unit can also provide a challenge related to companies in an area in which the user is interested. Specifically, the challenge unit can provide a challenge to invest in companies active in an area in which the user is interested or companies related to the area. In this way, by taking the user's geographical location information into consideration, a challenge appropriate for the area can be provided. Some or all of the above-described processing in the challenge unit can be performed, for example, using AI, or can be performed without using AI. For example, the challenge unit can input the user's geographical location information data into AI, which can analyze this data to provide an appropriate challenge.

[0052] When providing a challenge, the challenge unit can analyze the user's social media activity and provide a relevant challenge. For example, the challenge unit can provide a challenge related to a company the user follows on social media. Specifically, the challenge unit can provide a challenge to invest in a company the user follows on social media or an investment related to that company. The challenge unit can also provide a challenge related to a topic the user is interested in on social media. Specifically, the challenge unit can provide a challenge to invest in a company related to a topic the user is interested in on social media or an investment related to that topic. Furthermore, the challenge unit can also provide a challenge related to a community the user is participating in on social media. Specifically, the challenge unit can provide a challenge to invest in a company related to a community the user is participating in on social media or an investment related to that community. In this way, by analyzing the user's social media activity, it is possible to provide a challenge that is highly relevant to the user. Some or all of the above-described processing in the challenge unit may be performed, for example, using AI, or may be performed without using AI. For example, the challenge unit can input the user's social media data into AI, which can analyze the data and provide a relevant challenge.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The selection department can also evaluate a company's entire supply chain to determine whether it is taking ethical initiatives. For example, it can evaluate whether the suppliers of raw materials used by the company are environmentally conscious. It can also evaluate the working environment and the fulfillment of social responsibility throughout the supply chain. It can also evaluate the state of governance throughout the supply chain. This allows for a more comprehensive ethical evaluation by evaluating a company's entire supply chain.

[0055] The selection department can also evaluate companies' contributions to the Sustainable Development Goals (SDGs) and select investment targets. For example, it can evaluate the extent to which a company contributes to climate change countermeasures. It can also evaluate the extent to which a company contributes to poverty reduction and the spread of education. It can also evaluate the extent to which a company contributes to gender equality and health promotion. In this way, by evaluating a company's contribution to the SDGs, it can select investment targets that aim to realize a sustainable society.

[0056] The suggestion unit can also suggest investments taking into account the user's health data. For example, if the user is interested in health, health-related companies can be suggested. If the user is interested in fitness, fitness-related companies can be suggested. Furthermore, if the user is interested in health foods, health food-related companies can be suggested. In this way, by taking the user's health data into consideration, it is possible to suggest investments that are highly relevant to the user.

[0057] The scoring unit can also analyze trends in the ethical score based on the user's investment behavior and predict future scores. For example, it can analyze what companies the user has invested in in the past and predict future scores based on the results. It can also analyze the user's risk tolerance from their investment behavior and predict future scores. It can also predict future scores that match their investment style based on the user's investment behavior. This makes it possible to provide users with more accurate scores by predicting future scores based on their investment behavior.

[0058] The challenge unit can also dynamically adjust the difficulty of the challenge based on the user's investment behavior. For example, it can analyze the characteristics of challenges that the user has successfully completed in the past and set the difficulty of the next challenge. It can also analyze the user's risk tolerance from their investment behavior and adjust the difficulty of the challenge. Furthermore, it can set the difficulty of the challenge to suit the user's investment style based on the user's investment behavior. This makes it possible to provide the user with the most suitable challenge by dynamically adjusting the difficulty of the challenge based on the user's investment behavior.

[0059] The suggestion unit can also suggest investments that suit the characteristics of a region, taking into account the user's geographical location information. For example, it can suggest investments that take into account the economic situation and industrial structure of the region where the user lives. It can also suggest investments that take into account the economic activity and market trends of regions that the user frequently visits. It can also suggest investments that take into account the social and environmental conditions of regions in which the user is interested. In this way, it is possible to suggest investments that suit the characteristics of a region by taking into account the user's geographical location information.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The selection department selects investment targets based on ESG evaluation. ESG evaluation is based on evaluation criteria for environmental, social, and governance factors. Specifically, it evaluates a company's environmental efforts (CO2 emission reduction, use of renewable energy, etc.), social responsibility (improvement of working conditions, contributions to the local community, etc.), and governance status (composition of the board of directors, internal control system, etc.). Step 2: The proposal unit proposes investment targets selected by the selection unit based on the user's values. The user's values ​​are identified using questionnaires, profiling, past behavioral data, etc. For example, environmentally conscious companies are proposed to a user who is interested in environmental protection, and socially responsible companies are proposed to a user who values ​​social responsibility. Step 3: The scoring unit assigns an ethical score based on the user's investment behavior. The ethical score is calculated based on evaluation criteria such as the investment amount, type of investment, and investment period. For example, a high ethical score is assigned if the user invests in an environmentally conscious company or a company that fulfills social responsibility. Step 4: The challenge section provides missions and challenges for users to achieve specific investment goals. Missions and challenges are provided based on achievement criteria such as investment amount, return target, and period. For example, a mission to achieve a specific investment target within a certain period of time or a challenge to achieve a specific return target is provided.

[0062] (Example 2) An investment app according to an embodiment of the present invention proposes ethical investments while incorporating game elements. This investment app uses AI to select investments based on ESG (environmental, social, and governance) assessments and recommends investments tailored to the user's values. Furthermore, by incorporating game elements such as an ethical score, quests and challenges, a community, and educational content, users can learn about and practice investing while having fun. For example, the AI ​​evaluates companies' environmental initiatives, social responsibility, and governance status to select ethical investments. The AI ​​then recommends investments tailored to the user's values. Users can select investments based on their own values ​​and interests. For example, a user interested in environmental protection will be recommended environmentally conscious companies. Similarly, a user who values ​​social responsibility will be recommended companies that fulfill social responsibilities. Furthermore, the app incorporates game elements such as an ethical score, quests and challenges, a community, and educational content. The ethical score is a system that assigns a score based on the user's investment behavior. The quests and challenges provide missions and challenges for users to achieve specific investment goals. The community is a place where users can share information and interact with each other. Educational content is content for learning about investing. In this way, by proposing ethical investing while incorporating game elements, young people and beginners can learn about and practice investing while having fun. In addition, there are business opportunities due to changes in social values, growing interest in ethical initiatives, and increased interest in investing, making it highly significant for the company to enter the market. This allows investment apps to enable users to learn about and practice ethical investing while having fun.

[0063] An investment app according to an embodiment includes a selection unit, a proposal unit, a scoring unit, and a challenge unit. The selection unit selects investment targets based on ESG evaluation. The ESG evaluation is performed based on evaluation criteria for environmental, social, and governance factors. For example, the selection unit evaluates a company's environmental initiatives. Specifically, the evaluation criteria include CO2 emission reduction and renewable energy use. The selection unit also evaluates a company's social responsibility. Specifically, the evaluation criteria include improvements to the working environment and contributions to the local community. The selection unit also evaluates a company's governance status. Specifically, the evaluation criteria include the composition of the board of directors and internal control system. The proposal unit proposes investment targets selected by the selection unit based on a user's values. The user's values ​​are identified using surveys, profiling, past behavioral data, and the like. For example, the proposal unit proposes environmentally conscious companies to a user who is interested in environmental protection. The proposal unit also proposes socially responsible companies to a user who values ​​social responsibility. The scoring unit assigns an ethical score to the user's investment behavior. The ethical score is calculated based on evaluation criteria such as investment amount, investment type, and investment period. For example, the scoring unit assigns a high ethical score if the user invests in an environmentally conscious company. Furthermore, the scoring unit assigns a high ethical score if the user invests in a socially responsible company. The challenge unit provides missions and challenges for the user to achieve specific investment goals. The missions and challenges are provided based on achievement criteria such as investment amount, return target, and period. For example, the challenge unit provides a mission for the user to achieve a specific investment goal within a certain period of time. Furthermore, the challenge unit provides a challenge for the user to achieve a specific return target. In this way, the investment app according to the embodiment allows the user to learn about and practice ethical investment while having fun.

[0064] The selection department can evaluate a company's environmental efforts, social efforts, and governance status. For example, the selection department evaluates a company's environmental efforts. Specifically, evaluation criteria include CO2 emission reduction and use of renewable energy. The selection department also evaluates a company's social responsibility. Specifically, evaluation criteria include improvements to the working environment and contributions to the local community. The selection department also evaluates a company's governance status. Specifically, evaluation criteria include the composition of the board of directors and internal control system. In this way, ethical investment targets can be selected by evaluating a company's environmental efforts, social responsibility, and governance status. Some or all of the above-mentioned processing in the selection department may be performed using, or without, AI. For example, the selection department can input a company's environmental report and social responsibility report into AI, which then analyzes these reports and makes an evaluation.

[0065] The suggestion unit can suggest investment destinations based on the user's values ​​or interests. The suggestion unit, for example, collects the user's values ​​using a questionnaire or profiling. Specifically, the suggestion unit asks the user questions about environmental protection and social responsibility and identifies the user's values ​​based on the answers. The suggestion unit can also analyze the user's past behavioral data to identify the user's interests. For example, it can analyze what companies the user has invested in in the past and suggest investment destinations based on the results. This makes it possible to suggest investment destinations that are attractive to the user by suggesting investment destinations based on the user's values ​​and interests. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's questionnaire responses and behavioral data into AI, which can analyze this data and suggest investment destinations.

[0066] The scoring unit can calculate an ethical score based on the user's investment behavior. For example, the scoring unit calculates the user's score based on the ethical scores of companies in which the user has invested. Specifically, if the user invests in environmentally conscious companies, the scoring unit assigns a high ethical score. The scoring unit can also assign a high ethical score if the user invests in companies that fulfill social responsibility. Furthermore, the scoring unit can calculate a score based on evaluation criteria such as the user's investment amount and investment period. For example, if the user has invested in ethical companies over a long period of time, the scoring unit assigns a high score. In this way, the user's investment behavior can be evaluated by calculating an ethical score based on the user's investment behavior. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without AI. For example, the scoring unit can input the user's investment data into AI, which analyzes the data and calculates the score.

[0067] The challenge unit can provide a mission or challenge for the user to achieve a specific investment goal. For example, the challenge unit can provide a mission for the user to achieve a specific investment goal within a certain period of time. Specifically, the challenge unit can provide a challenge for the user to achieve a specific investment amount. The challenge unit can also provide a challenge for the user to achieve a specific return goal. Furthermore, the challenge unit can provide a mission for the user to invest in a specific industry. For example, if the user is interested in environmental protection, the challenge unit can provide a challenge for investing in environmentally friendly companies. This can support the user's investment activities by providing a mission or challenge for the user to achieve a specific investment goal. Some or all of the above-mentioned processing in the challenge unit can be performed, for example, using AI, or can be performed without using AI. For example, the challenge unit can input the user's investment goal data into AI, which can analyze this data and provide missions and challenges.

[0068] The suggestion unit can collect the user's values ​​using a questionnaire or profiling. For example, the suggestion unit asks the user questions about environmental protection or social responsibility and identifies the user's values ​​based on the answers. The suggestion unit can also analyze the user's past behavioral data to identify the user's interests. For example, it can analyze what companies the user has invested in in the past and suggest investment targets based on the results. By collecting the user's values ​​using a questionnaire or profiling, more accurate investment target suggestions become possible. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's questionnaire responses and behavioral data into AI, which can analyze this data and suggest investment targets.

[0069] The challenge unit can provide a forum for users to share information and interact with each other. The challenge unit can provide, for example, an online forum or chat room. Specifically, it can provide a forum for users to share information about investments and exchange opinions. The challenge unit can also provide a platform for users to share success stories and failure stories about investments. Furthermore, the challenge unit can provide a function for users to post questions about investments and receive answers from other users. For example, the challenge unit can allow users to post questions about specific investments and receive feedback from other users. This provides a community where users can share information and interact with each other, thereby promoting information exchange between users. Some or all of the above-mentioned processing in the challenge unit can be performed, for example, using AI, or can be performed without AI. For example, the challenge unit can input user-posted data into AI, which can analyze the data and provide appropriate feedback.

[0070] The challenge unit can provide educational materials for learning investment knowledge. The challenge unit can provide, for example, online courses and video tutorials. Specifically, the challenge unit can provide educational materials for users to learn basic investment knowledge. The challenge unit can also provide educational materials for users to learn specific investment strategies and techniques. The challenge unit can also provide educational materials for users to learn the latest investment trends and market movements. For example, the challenge unit can provide an online course for users to learn investment strategies related to environmental protection. The challenge unit can also provide video tutorials for users to learn investment techniques related to social responsibility. This improves the user's investment knowledge by providing educational content for learning investment knowledge. Some or all of the above-mentioned processing in the challenge unit can be performed, for example, using AI or without AI. For example, the challenge unit can input the user's learning data into AI, which can analyze the data and provide appropriate educational materials.

[0071] The selection unit can estimate the user's emotions and adjust the ESG evaluation criteria based on the estimated user's emotional state. For example, if the user shows a strong interest in environmental issues, the selection unit can set ESG evaluation criteria that emphasize environmental initiatives. Specifically, it can emphasize CO2 emission reduction and the use of renewable energy. Furthermore, if the user has a high awareness of social responsibility, the selection unit can also set ESG evaluation criteria that emphasize social responsibility. Specifically, it can emphasize improving the working environment and contributions to the local community. Furthermore, if the user requires a strict evaluation of governance, the selection unit can also set ESG evaluation criteria that emphasize the governance situation. Specifically, it can emphasize the composition of the board of directors and the internal control system. By adjusting the ESG evaluation criteria based on the user's emotions, it is possible to select investments that match the user's values. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may input user emotion data into AI, which may analyze the data and adjust the ESG evaluation criteria.

[0072] The selection department can update a company's environmental initiatives, social initiatives, and governance status in real time and reflect them in the selection criteria. For example, the selection department obtains the company's latest environmental report and updates the company's environmental initiatives in real time. Specifically, the selection department periodically collects environmental reports and sustainability reports released by the company and reflects them in the evaluation. The selection department can also obtain the latest news and reports on corporate social responsibility and reflect them in the evaluation. Specifically, the selection department collects information on a company's social contribution activities and improvements to its working environment and reflects them in the evaluation. Furthermore, the selection department can obtain the latest information on a company's governance and reflect it in the evaluation criteria. Specifically, the selection department collects information on the composition of a company's board of directors and internal control system and reflects it in the evaluation. This allows for more accurate investment selection by reflecting the latest company information in real time. Some or all of the above-mentioned processing by the selection department can be performed using, for example, AI, or without AI. For example, the selection department can input the company's latest reports and news data into AI, which can analyze this data and update the evaluation.

[0073] When selecting a company, the selection unit can predict future performance by referring to the company's past ESG evaluation history. The selection unit, for example, analyzes the company's ESG evaluation history over the past five years to predict future performance of its environmental initiatives. Specifically, it analyzes the company's past environmental protection activities and predicts future performance based on the results. The selection unit can also predict future social responsibility performance based on the company's past evaluation history regarding social responsibility. Specifically, it analyzes the company's past social contribution activities and predicts future performance based on the results. The selection unit can also predict future governance performance by referring to the company's past governance evaluation history. Specifically, it analyzes the company's past evaluations of its board of directors and internal control system and predicts future performance based on the results. This allows future performance to be predicted and more appropriate investment targets to be selected by referring to the company's past ESG evaluation history. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or without AI. For example, the selection unit can input the company's past ESG evaluation data into AI, which then analyzes the data and predicts future performance.

[0074] The selection unit can estimate the user's emotions and prioritize selected investments based on the estimated user's emotional state. For example, if the user is strongly concerned about environmental issues, the selection unit can prioritize environmentally conscious companies. Specifically, it can emphasize CO2 emission reduction and the use of renewable energy. Furthermore, if the user is highly conscious of social responsibility, the selection unit can also prioritize companies that fulfill their social responsibilities. Specifically, it can emphasize improving working environments and contributions to local communities. Furthermore, if the user requires strict evaluation of governance, the selection unit can prioritize companies with good governance. Specifically, it can emphasize the composition of the board of directors and internal control systems. By prioritizing investments based on the user's emotions, it is possible to prioritize and recommend investments that match the user's values. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may input user emotion data into AI, which may analyze the data and determine the priorities of investment destinations.

[0075] The selection unit may select investment targets by taking into account the geographical scope of the company's influence. For example, if a company's activities have a significant impact on a specific region, the selection unit may consider the environmental and social conditions of that region when selecting investment targets. Specifically, the selection unit may evaluate the environmental protection status and social responsibility status of the region in which the company operates. Furthermore, if a company's products or services are in high demand in a specific region, the selection unit may also consider the market conditions of that region when selecting investment targets. Specifically, the selection unit may evaluate the region and the level of demand for the company's products or services. Furthermore, if a company's activities span multiple countries or regions, the selection unit may also consider the ESG status of each region when selecting investment targets. Specifically, the selection unit may evaluate the environmental, social, and governance status of each region in which the company operates. This allows the selection of investment targets appropriate for each region by taking into account the company's geographical scope of influence. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit may input data on the company's geographical influence into AI, which may then analyze the data to select investment targets.

[0076] When making a selection, the selection unit can analyze a company's social media activities and evaluate its ethical initiatives. For example, the selection unit can analyze a company's social media posts about its environmental protection activities and evaluate its ethical initiatives. Specifically, the selection unit collects information about environmental protection posted by the company on social media and evaluates its content. The selection unit can also analyze a company's social media posts about social responsibility and evaluate its ethical initiatives. Specifically, the selection unit collects information about social contribution activities posted by the company on social media and evaluates its content. The selection unit can also analyze a company's social media posts about governance and evaluate its ethical initiatives. Specifically, the selection unit collects information about governance posted by the company on social media and evaluates its content. This allows the analysis of a company's social media activities to evaluate its ethical initiatives and select appropriate investment destinations. Some or all of the above-mentioned processing by the selection unit can be performed using, for example, AI, or without AI. For example, the selection unit can input a company's social media data into AI, which can analyze the data and evaluate its ethical initiatives.

[0077] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on the estimated user's emotional state. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. Specifically, it can provide the user with detailed information about the investment and a risk assessment. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions that focus on the key points. Specifically, it can provide the user with key points about the investment and a return forecast. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions. Specifically, it can provide the user with visual information using graphs and charts. This allows the suggestion to be easily understood by adjusting the way the suggestions are presented based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotional data into the AI, which can then analyze this data and adjust how the suggestions are presented.

[0078] When making a proposal, the proposal unit can analyze the user's past investment history and select an appropriate proposal method. For example, the proposal unit analyzes the characteristics of investments in which the user has been successful in the past and proposes similar investments. Specifically, the proposal unit analyzes the characteristics of companies from which the user has received high returns in the past and proposes new investments based on the results. The proposal unit can also analyze the user's risk tolerance from the user's past investment history and propose investments with appropriate risk levels. Specifically, the proposal unit analyzes the level of risk the user has taken in past investments and adjusts the risk level based on the results. Furthermore, the proposal unit can make proposals that match the user's investment style based on the user's past investment history. Specifically, the proposal unit analyzes the user's preferred investment style in the past and proposes new investments based on the results. In this way, by analyzing the user's past investment history, the proposal method optimal for the user can be selected. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without AI. For example, the proposal unit can input the user's investment history data into AI, which can analyze the data and select an appropriate proposal method.

[0079] When making a proposal, the suggestion unit can customize the proposal content based on the user's current living situation or areas of interest. For example, if the user starts a new job, the suggestion unit can suggest investments related to that industry. Specifically, the suggestion unit can suggest companies in the user's new industry. If the user is interested in environmental protection, the suggestion unit can also suggest environmentally conscious companies. Specifically, the suggestion unit can suggest companies that engage in environmental protection activities or use renewable energy. If the user is interested in social responsibility, the suggestion unit can also suggest socially responsible companies. Specifically, the suggestion unit can suggest companies that improve working environments or contribute to local communities. This allows the proposal content to be customized based on the user's current living situation and areas of interest, enabling proposals that are highly relevant to the user. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the user's living situation data and area of ​​interest data into AI, which can then analyze the data and customize the proposal content.

[0080] The suggestion unit can estimate the user's emotions and prioritize proposals based on the estimated user's emotional state. For example, if the user is strongly interested in environmental issues, the suggestion unit can prioritize environmentally conscious companies. Specifically, the suggestion unit can emphasize CO2 emission reduction and the use of renewable energy. Furthermore, if the user is highly conscious of social responsibility, the suggestion unit can prioritize socially responsible companies. Specifically, the suggestion unit can emphasize improving working environments and contributions to the local community. Furthermore, if the user requires strict evaluation of governance, the suggestion unit can prioritize companies with good governance. Specifically, the suggestion unit can emphasize the composition of the board of directors and internal control systems. By prioritizing proposals based on the user's emotions, investment destinations that match the user's values ​​can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 may input user emotion data into AI, which may analyze the data and determine the priority of suggestions.

[0081] When making a proposal, the suggestion unit can suggest highly relevant investments taking into account the user's geographical location information. For example, the suggestion unit prioritizes suggesting companies in the area where the user lives. Specifically, it suggests companies that are active in the area where the user lives or companies that contribute to the area. The suggestion unit can also suggest companies in areas frequently visited by the user. Specifically, it suggests companies that are active in areas frequently visited by the user or companies that are popular in the area. Furthermore, the suggestion unit can also suggest companies in areas in which the user is interested. Specifically, it suggests companies that are active in areas in which the user is interested or companies related to the area. In this way, by taking the user's geographical location information into consideration, it is possible to suggest highly relevant investments to the user. Some or all of the above-described processing by 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 data into AI, which can analyze the data and suggest highly relevant investments.

[0082] When making a suggestion, the suggestion unit can analyze the user's social media activity and suggest related investments. For example, the suggestion unit can suggest companies that the user follows on social media. Specifically, the suggestion unit can suggest companies that the user follows on social media and investments related to those companies. The suggestion unit can also suggest companies related to topics that the user is interested in on social media. Specifically, the suggestion unit can suggest companies related to topics that the user is interested in on social media and investments related to those topics. Furthermore, the suggestion unit can also suggest companies related to communities that the user participates in on social media. Specifically, the suggestion unit can suggest companies related to communities that the user participates in on social media and investments related to those communities. In this way, by analyzing the user's social media activity, it is possible to suggest investments that are highly relevant to the user. Some or all of the above-mentioned processing by 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 social media data into AI, which can analyze the data and suggest related investments.

[0083] The scoring unit can estimate the user's emotions and adjust the calculation method of the ethical score based on the estimated emotional state of the user. For example, if the user shows a strong interest in environmental issues, the scoring unit can calculate an ethical score that emphasizes environmental efforts. Specifically, it can emphasize CO2 emission reduction and the use of renewable energy. If the user has a high awareness of social responsibility, the scoring unit can also calculate an ethical score that emphasizes social responsibility. Specifically, it can emphasize improving the working environment and contributions to the local community. Furthermore, if the user requires a strict evaluation of governance, the scoring unit can calculate an ethical score that emphasizes the governance situation. Specifically, it can emphasize the composition of the board of directors and the internal control system. By adjusting the calculation method of the ethical score based on the user's emotions, a score that matches the user's values ​​can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit may input user emotional data into AI, which may analyze this data and adjust the method for calculating the ethical score.

[0084] When calculating the score, the scoring unit can analyze the user's past investment behavior to improve the accuracy of the score. For example, the scoring unit analyzes the user's past investment behavior and reflects it in the calculation of the ethical score. Specifically, the scoring unit analyzes the types of companies the user has invested in in the past and calculates the score based on the results. The scoring unit can also analyze the user's risk tolerance from the user's past investment behavior to improve the accuracy of the score. Specifically, the scoring unit analyzes the level of risk the user has taken in past investments and adjusts the score based on the results. Furthermore, the scoring unit can calculate a score that matches the user's investment style based on the user's past investment behavior. Specifically, the scoring unit analyzes the user's preferred investment style in the past and calculates the score based on the results. In this way, the accuracy of the score can be improved by analyzing the user's past investment behavior. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input the user's investment history data into AI, which can then analyze the data and calculate the score.

[0085] When calculating the score, the scoring unit can weight the score based on the user's current investment situation. For example, the scoring unit analyzes the user's current investment situation and weights the ethical score. Specifically, it analyzes the types of companies the user is currently investing in and adjusts the score based on the results. The scoring unit can also analyze the user's risk tolerance based on the user's current investment situation and weight the score. Specifically, it analyzes the level of risk the user is currently taking in their investments and adjusts the score based on the results. Furthermore, the scoring unit can weight the score to match the user's investment style based on the user's current investment situation. Specifically, it analyzes the user's current preferred investment style and adjusts the score based on the results. This allows for weighting the score based on the user's current investment situation, thereby providing a more accurate score. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without AI. For example, the scoring unit can input the user's current investment data into AI, which then analyzes the data and weights the score.

[0086] The scoring unit can estimate the user's emotions and adjust the score display method based on the estimated user's emotional state. For example, when the user is relaxed, the scoring unit displays a score with detailed information. Specifically, it provides the user with a detailed breakdown of the score and evaluation criteria. Furthermore, when the user is in a hurry, the scoring unit can display a concise score that focuses on the key points. Specifically, it provides the user with key points of the score and evaluation results. Furthermore, when the user is excited, the scoring unit can display a visually appealing score. Specifically, it provides the user with visual information using graphs and charts. This allows the score display to be easily understood by adjusting the score display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 scoring unit can be performed, for example, using AI or without AI. For example, the scoring unit can input user emotional data into the AI, which can then analyze this data and adjust how the score is displayed.

[0087] The scoring unit can take the user's geographical location information into consideration when calculating the score. For example, the scoring unit prioritizes calculating the ethical scores of companies in the area where the user lives. Specifically, it calculates the scores of companies active in the area where the user lives and companies that contribute to the area. The scoring unit can also calculate the ethical scores of companies in areas frequently visited by the user. Specifically, it calculates the scores of companies active in the area frequently visited by the user and companies that are popular in the area. Furthermore, the scoring unit can also calculate the ethical scores of companies in areas of interest to the user. Specifically, it calculates the scores of companies active in the area of ​​interest to the user and companies related to the area. This makes it possible to provide a score appropriate for the area by taking the user's geographical location information into consideration. Some or all of the above-described processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input the user's geographical location information data into AI, which can then analyze the data to calculate the score.

[0088] The scoring unit can analyze the user's social media activity when calculating the score to improve the accuracy of the score. For example, the scoring unit calculates the ethical scores of companies the user follows on social media. Specifically, it calculates the scores of companies the user follows on social media and investments related to those companies. The scoring unit can also calculate the ethical scores of companies related to topics the user is interested in on social media. Specifically, it calculates the scores of companies related to topics the user is interested in on social media and investments related to those topics. Furthermore, the scoring unit can also calculate the ethical scores of companies related to communities the user participates in on social media. Specifically, it calculates the scores of companies related to communities the user participates in on social media and investments related to those communities. This allows the accuracy of the score to be improved by analyzing the user's social media activity. Some or all of the above-mentioned processing in the scoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the scoring unit can input the user's social media data into AI, which analyzes the data and calculates the score.

[0089] The challenge unit can estimate the user's emotions and adjust the content of the challenge based on the estimated emotional state of the user. For example, if the user is relaxed, the challenge unit can provide a more difficult challenge. Specifically, it can provide the user with an investment challenge aiming for a high return. Furthermore, if the user is feeling stressed, the challenge unit can also provide an easier challenge. Specifically, it can provide the user with a low-risk investment challenge. Furthermore, if the user is excited, the challenge unit can also provide a visually appealing challenge. Specifically, it can provide the user with a challenge that provides visual information using graphs and charts. By adjusting the content of the challenge based on the user's emotions, it is possible to provide an appropriate challenge for the user. The emotion estimation is realized 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 challenge unit can be performed using, for example, an AI, or without an AI. For example, the challenge unit can input the user's emotional data into an AI, which can analyze the data and adjust the content of the challenge.

[0090] When providing a challenge, the challenge unit can provide an appropriate challenge by referring to the user's past challenge history. For example, the challenge unit analyzes the characteristics of challenges that the user has successfully completed in the past and provides a similar challenge. Specifically, the challenge unit provides a new challenge based on the user's past investment goals and returns. The challenge unit can also adjust the difficulty of the challenge based on the user's past challenge history. Specifically, the challenge unit analyzes the level of difficulty of challenges the user has achieved in the past and sets the difficulty of the new challenge based on the results. Furthermore, the challenge unit can provide a challenge that the user is likely to be interested in based on the user's past challenge history. Specifically, the challenge unit provides a new challenge based on the investment field or theme in which the user has shown interest in the past. In this way, by referring to the user's past challenge history, the optimal challenge can be provided for the user. Some or all of the above-described processing in the challenge unit may be performed, for example, using AI, or may be performed without using AI. For example, the challenge unit can input the user's challenge history data into AI, which can analyze this data and provide an appropriate challenge.

[0091] When providing a challenge, the challenge unit can customize the challenge content based on the user's current investment goals. For example, if the user is aiming for short-term profits, the challenge unit can provide a challenge that matches the user's short-term investment goals. Specifically, the challenge unit can set an investment goal that the user can achieve in a short period of time and provide a challenge that matches that goal. Furthermore, if the user is aiming for long-term profits, the challenge unit can also provide a challenge that matches the user's long-term investment goals. Specifically, the challenge unit can set an investment goal that the user can achieve in a long period of time and provide a challenge that matches that goal. Furthermore, if the user is interested in a particular industry, the challenge unit can also provide a challenge related to that industry. Specifically, the challenge unit can provide a challenge for investing in companies in the industry in which the user is interested. This allows the challenge content to be customized based on the user's current investment goals, thereby providing a challenge that is highly relevant to the user. Some or all of the above-described processing in the challenge unit may be performed, for example, using AI, or may be performed without AI. For example, the challenge unit can input the user's investment goal data into AI, which can analyze this data to customize the challenge content.

[0092] The challenge unit can estimate the user's emotions and determine the priority of challenges based on the estimated emotional state of the user. For example, when the user is relaxed, the challenge unit can prioritize difficult challenges. Specifically, the challenge unit can prioritize investment challenges that aim for high returns. Furthermore, when the user is stressed, the challenge unit can prioritize easy challenges. Specifically, the challenge unit can prioritize low-risk investment challenges. Furthermore, when the user is excited, the challenge unit can prioritize visually appealing challenges. Specifically, the challenge unit can prioritize challenges that provide visual information using graphs and charts. In this way, by determining the priority of challenges based on the user's emotions, it is possible to prioritize challenges that are appropriate for the user. Emotion estimation is realized 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-mentioned processing in the challenge unit can be performed, for example, using AI or without AI. For example, the challenge unit can input the user's emotional data into the AI, which can then analyze this data and determine the priority of the challenges.

[0093] When providing a challenge, the challenge unit can provide an appropriate challenge by taking into account the user's geographical location information. For example, the challenge unit can provide a challenge related to companies in the area where the user lives. Specifically, the challenge unit can provide a challenge to invest in companies active in the area where the user lives or companies that contribute to the area. The challenge unit can also provide a challenge related to companies in an area frequently visited by the user. Specifically, the challenge unit can provide a challenge to invest in companies active in an area frequently visited by the user or companies that are popular in the area. The challenge unit can also provide a challenge related to companies in an area in which the user is interested. Specifically, the challenge unit can provide a challenge to invest in companies active in an area in which the user is interested or companies related to the area. In this way, by taking the user's geographical location information into consideration, a challenge appropriate for the area can be provided. Some or all of the above-described processing in the challenge unit can be performed, for example, using AI, or can be performed without using AI. For example, the challenge unit can input the user's geographical location information data into AI, which can analyze this data to provide an appropriate challenge.

[0094] When providing a challenge, the challenge unit can analyze the user's social media activity and provide a relevant challenge. For example, the challenge unit can provide a challenge related to a company the user follows on social media. Specifically, the challenge unit can provide a challenge to invest in a company the user follows on social media or an investment related to that company. The challenge unit can also provide a challenge related to a topic the user is interested in on social media. Specifically, the challenge unit can provide a challenge to invest in a company related to a topic the user is interested in on social media or an investment related to that topic. Furthermore, the challenge unit can also provide a challenge related to a community the user is participating in on social media. Specifically, the challenge unit can provide a challenge to invest in a company related to a community the user is participating in on social media or an investment related to that community. In this way, by analyzing the user's social media activity, it is possible to provide a challenge that is highly relevant to the user. Some or all of the above-described processing in the challenge unit may be performed, for example, using AI, or may be performed without using AI. For example, the challenge unit can input the user's social media data into AI, which can analyze the data and provide a relevant challenge. === Hard Collateral 1-1 === Each of the multiple elements, including the selection unit, suggestion unit, score unit, and challenge unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is implemented by the specific processing unit 290 of the data processing device 12 and selects investment targets based on ESG evaluation. The suggestion unit is implemented, for example, by the control unit 46A of the smart device 14 and suggests investment targets based on the user's values. The score unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and assigns an ethical score based on the user's investment behavior. The challenge unit is implemented, for example, by the control unit 46A of the smart device 14 and provides missions and challenges for the user to achieve specific investment goals. === Hard Collateral 1-2 === Each of the multiple elements, including the selection unit, suggestion unit, score unit, and challenge unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is implemented by the specific processing unit 290 of the data processing device 12 and selects investment targets based on ESG evaluation. The suggestion unit is implemented, for example, by the control unit 46A of the smart glasses 214 and suggests investment targets based on the user's values. The score unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and assigns an ethical score based on the user's investment behavior. The challenge unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides missions and challenges for the user to achieve specific investment goals. === Hard Collateral 1-3 === Each of the multiple elements including the selection unit, suggestion unit, scoring unit, and challenge unit described above is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the selection unit is implemented by the specific processing unit 290 of the data processing device 12 and selects investment targets based on ESG evaluation. The suggestion unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and suggests investment targets based on the user's values. The scoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and assigns an ethical score based on the user's investment behavior. The challenge unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and provides missions and challenges for the user to achieve specific investment goals. === Hard Collateral 1-4 === Each of the multiple elements including the selection unit, proposal unit, score unit, and challenge unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects investment destinations based on ESG evaluation. The proposal unit is realized, for example, by the control unit 46A of the robot 414 and proposes investment destinations based on the user's values. The score unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and assigns an ethical score based on the user's investment behavior. The challenge unit is realized, for example, by the control unit 46A of the robot 414 and provides missions and challenges for the user to achieve specific investment goals.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The suggestion unit can also estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can select the timing to provide detailed investment information. If the user is stressed, the suggestion unit can select the timing to provide concise information. If the user is excited, the suggestion unit can select the timing to provide visually appealing information. In this way, by adjusting the timing of suggestions based on the user's emotions, investment information can be provided at the optimal timing for the user.

[0097] The selection department can also evaluate a company's entire supply chain to determine whether it is taking ethical initiatives. For example, it can evaluate whether the suppliers of raw materials used by the company are environmentally conscious. It can also evaluate the working environment and the fulfillment of social responsibility throughout the supply chain. It can also evaluate the state of governance throughout the supply chain. This allows for a more comprehensive ethical evaluation by evaluating a company's entire supply chain.

[0098] The scoring unit can also estimate the user's emotions and adjust the method of providing score feedback based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is stressed, brief feedback can be provided. If the user is excited, visually appealing feedback can be provided. Thus, by adjusting the method of providing score feedback based on the user's emotions, feedback that is easy for the user to understand can be provided.

[0099] The challenge unit can also estimate the user's emotions and adjust the reward for the challenge based on the estimated user's emotions. For example, if the user is relaxed, a high reward can be provided. If the user is stressed, an easily achievable reward can be provided. Furthermore, if the user is excited, a visually attractive reward can be provided. In this way, by adjusting the reward for the challenge based on the user's emotions, it is possible to provide an attractive reward for the user.

[0100] The suggestion unit can also estimate the user's emotions and adjust the frequency of suggestions based on the estimated user's emotions. For example, if the user is relaxed, the frequency of suggestions can be increased. Also, if the user is stressed, the frequency of suggestions can be decreased. Furthermore, if the user is excited, the frequency of suggestions can be adjusted to provide visually appealing suggestions. In this way, by adjusting the frequency of suggestions based on the user's emotions, investment information can be provided at an optimal frequency for the user.

[0101] The selection department can also evaluate companies' contributions to the Sustainable Development Goals (SDGs) and select investment targets. For example, it can evaluate the extent to which a company contributes to climate change countermeasures. It can also evaluate the extent to which a company contributes to poverty reduction and the spread of education. It can also evaluate the extent to which a company contributes to gender equality and health promotion. In this way, by evaluating a company's contribution to the SDGs, it can select investment targets that aim to realize a sustainable society.

[0102] The suggestion unit can also suggest investments taking into account the user's health data. For example, if the user is interested in health, health-related companies can be suggested. If the user is interested in fitness, fitness-related companies can be suggested. Furthermore, if the user is interested in health foods, health food-related companies can be suggested. In this way, by taking the user's health data into consideration, it is possible to suggest investments that are highly relevant to the user.

[0103] The scoring unit can also analyze trends in the ethical score based on the user's investment behavior and predict future scores. For example, it can analyze what companies the user has invested in in the past and predict future scores based on the results. It can also analyze the user's risk tolerance from their investment behavior and predict future scores. It can also predict future scores that match their investment style based on the user's investment behavior. This makes it possible to provide users with more accurate scores by predicting future scores based on their investment behavior.

[0104] The challenge unit can also dynamically adjust the difficulty of the challenge based on the user's investment behavior. For example, it can analyze the characteristics of challenges that the user has successfully completed in the past and set the difficulty of the next challenge. It can also analyze the user's risk tolerance from their investment behavior and adjust the difficulty of the challenge. Furthermore, it can set the difficulty of the challenge to suit the user's investment style based on the user's investment behavior. This makes it possible to provide the user with the most suitable challenge by dynamically adjusting the difficulty of the challenge based on the user's investment behavior.

[0105] The suggestion unit can also suggest investments that suit the characteristics of a region, taking into account the user's geographical location information. For example, it can suggest investments that take into account the economic situation and industrial structure of the region where the user lives. It can also suggest investments that take into account the economic activity and market trends of regions that the user frequently visits. It can also suggest investments that take into account the social and environmental conditions of regions in which the user is interested. In this way, it is possible to suggest investments that suit the characteristics of a region by taking into account the user's geographical location information.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The selection department selects investment targets based on ESG evaluation. ESG evaluation is based on evaluation criteria for environmental, social, and governance factors. Specifically, it evaluates a company's environmental efforts (CO2 emission reduction, use of renewable energy, etc.), social responsibility (improvement of working conditions, contributions to the local community, etc.), and governance status (composition of the board of directors, internal control system, etc.). Step 2: The proposal unit proposes investment targets selected by the selection unit based on the user's values. The user's values ​​are identified using questionnaires, profiling, past behavioral data, etc. For example, environmentally conscious companies are proposed to a user who is interested in environmental protection, and socially responsible companies are proposed to a user who values ​​social responsibility. Step 3: The scoring unit assigns an ethical score based on the user's investment behavior. The ethical score is calculated based on evaluation criteria such as the investment amount, type of investment, and investment period. For example, a high ethical score is assigned if the user invests in an environmentally conscious company or a company that fulfills social responsibility. Step 4: The challenge section provides missions and challenges for users to achieve specific investment goals. Missions and challenges are provided based on achievement criteria such as investment amount, return target, and period. For example, a mission to achieve a specific investment target within a certain period of time or a challenge to achieve a specific return target is provided.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[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 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.

[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 (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).

[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] 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.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0122] 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.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 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 selection department that selects investment targets based on ESG evaluations; a proposal unit that proposes investment destinations selected by the selection unit based on the user's values; a score unit that assigns an ethics score based on the user's investment behavior; A challenge department that provides missions or challenges to achieve specific investment goals. A system characterized by:

2. The selection unit Evaluating a company's environmental efforts, social initiatives, and governance status 2. The system of claim 1.

3. The proposal unit Suggest investments based on your values ​​or interests 2. The system of claim 1.

4. The score section Calculate an ethical score based on the user's investment behavior 2. The system of claim 1.

5. The challenge section comprises: Providing missions or challenges for users to achieve specific investment goals 2. The system of claim 1.

6. The proposal unit Collecting user values ​​through surveys or profiling 2. The system of claim 1.

7. The challenge section comprises: Providing a place for users to share information and interact with each other 2. The system of claim 1.

8. The challenge section comprises: Providing educational materials for learning about investments 2. The system of claim 1.

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