system

Abstract

The system according to the embodiment aims to enable even users who are reluctant to consult face-to-face to easily consult about financial products. [Solution] A system according to an embodiment includes a reception unit, a proposal unit, a provision unit, and a collection unit. The reception unit accepts questions from users. The proposal unit analyzes the questions accepted by the reception unit and proposes financial products that meet the user's needs. The provision unit provides detailed information about the financial products proposed by the proposal unit. The collection unit collects user feedback based on the information provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult to propose appropriate financial products to users who are reluctant to receive face-to-face financial consultations.

[0005] The system according to the embodiment aims to enable even users who are reluctant to consult face-to-face to easily consult about financial products. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a provision unit, and a collection unit. The reception unit accepts questions from users. The proposal unit analyzes the questions accepted by the reception unit and proposes financial products that meet the needs of the users. The provision unit provides detailed information about the financial products proposed by the proposal unit. The collection unit collects user feedback based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable even users who are reluctant to consult face-to-face to easily consult about financial products. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A financial planner service according to an embodiment of the present invention is a system that accepts and analyzes user questions, proposes appropriate financial products, provides detailed information, and collects feedback. In the financial planner service, a user inputs a question to an AI, which then analyzes the question and proposes financial products that meet the user's needs. For example, if a user inputs a question such as, "I want to know which insurance is right for me," the AI ​​analyzes the question and proposes an appropriate insurance product. The AI ​​then provides detailed information about the proposed financial products, such as the types and features of insurance and the risks and returns of investment trusts. This allows the user to obtain information to select a financial product that suits them. The AI ​​also collects user feedback and uses it to improve the service. For example, if a user inputs their thoughts or questions about the proposed financial products, the AI ​​can use that information to make its next proposal more appropriate. This allows the financial planner service to provide financial advice to people who are reluctant to receive face-to-face consultations. This allows the financial planner service to accept and analyze user questions, propose appropriate financial products, provide detailed information, and collect feedback, allowing even people who are reluctant to receive face-to-face consultations to provide financial advice to them. For example, the system can quickly and accurately analyze questions written by users and suggest appropriate financial products, reducing the burden on users. Users can also receive appropriate answers to their questions, which can be useful in selecting financial products.

[0029] A financial planner service according to an embodiment includes a reception unit, a proposal unit, a provision unit, and a collection unit. The reception unit accepts user questions. User questions may include, but are not limited to, text, audio, and images. The reception unit may accept, for example, text questions entered by the user. The reception unit may also accept user questions using voice input. For example, speech recognition technology may be used to convert speech into text and accept the text as a question. The reception unit may also accept user questions using image input. For example, image analysis technology may be used to analyze an image and accept the image as a question. The proposal unit analyzes the questions accepted by the reception unit and proposes financial products that meet the user's needs. The proposal unit may analyze the questions using natural language processing technology to identify the user's needs. The proposal unit may also use a machine learning algorithm to select optimal financial products based on past data and market trends. For example, the proposal unit may propose appropriate investment trusts based on the user's risk tolerance and investment goals. The provision unit provides detailed information about the financial products proposed by the proposal unit. The providing unit provides information such as types and features of insurance, and risks and returns of investment trusts. The providing unit can also provide detailed answers to user questions. For example, the providing unit provides detailed explanations of financial products proposed to the user. The collecting unit collects user feedback based on the information provided by the providing unit. For example, the collecting unit collects user impressions and questions about the proposed financial products. The collecting unit can also make next proposals more appropriate based on user feedback. For example, the collecting unit analyzes user feedback and improves the proposal algorithm. As a result, the financial planner service according to the embodiment can accept and analyze user questions, propose appropriate financial products, provide detailed information, and collect feedback.

[0030] The proposal unit can select appropriate financial products based on past data and market trends. The proposal unit selects financial products based on, for example, past trading history and market data. For example, the proposal unit analyzes past trading history to understand the user's investment trends. The proposal unit can also analyze market data to understand current market trends. For example, the proposal unit predicts market trends using economic indicators and trend analysis. The proposal unit can also select optimal financial products by combining past data and market trends. For example, the proposal unit proposes investment trusts suitable for the user based on past trading history and market data. This makes it possible to make more appropriate proposals to the user by selecting optimal financial products based on past data and market trends.

[0031] The providing unit can provide detailed information such as the types and characteristics of insurance, and the risks and returns of investment trusts. The providing unit provides detailed information about, for example, the types and characteristics of insurance. For example, the providing unit explains the types and characteristics of life insurance, medical insurance, property insurance, etc. The providing unit can also provide detailed information about the risks and returns of investment trusts. For example, the providing unit explains the types of risks of investment trusts and how to calculate returns. The providing unit can also provide detailed answers to questions from the user. For example, the providing unit provides a detailed explanation of financial products suggested to the user. By providing detailed information such as the types and characteristics of insurance, and the risks and returns of investment trusts, the user can obtain information to select a financial product that suits them.

[0032] The collection unit can collect users' impressions and questions about the proposed financial products. The collection unit, for example, collects users' impressions about the proposed financial products. For example, the collection unit collects users' evaluations and opinions about the proposed financial products. The collection unit can also collect users' questions about the proposed financial products. For example, the collection unit collects users' doubts and uncertainties about the proposed financial products. In this way, collecting users' impressions and questions about the proposed financial products can be useful for improving services.

[0033] The suggestion unit can suggest suitable investment trusts based on the user's risk tolerance and investment goals. The suggestion unit, for example, evaluates the user's risk tolerance and suggests appropriate investment trusts based on that. For example, the suggestion unit evaluates the user's risk tolerance using a questionnaire or risk profile analysis. The suggestion unit can also set the user's investment goals and suggest appropriate investment trusts based on those goals. For example, the suggestion unit sets the user's short-term goals, long-term goals, and profit goals and suggests investment trusts based on those goals. This makes it possible to suggest more appropriate investment trusts to the user by suggesting appropriate investment trusts based on the user's risk tolerance and investment goals.

[0034] The collection unit can make the next proposal more appropriate based on the collected feedback. For example, the collection unit collects the user's thoughts and questions about the proposed financial product and improves the next proposal based on the collected feedback. For example, the collection unit analyzes the user's feedback and improves the proposal algorithm. The collection unit can also customize the proposal content based on the user's feedback. For example, the collection unit reflects the user's evaluations and opinions to make the next proposal more appropriate. In this way, the accuracy of the service is improved by making the next proposal more appropriate based on the collected feedback.

[0035] The reception unit can analyze the user's past question history and select an appropriate reception method. The reception unit, for example, analyzes the user's past question history and selects the optimal reception method. For example, the reception unit preferentially suggests question formats that the user has frequently used in the past. The reception unit can also select the optimal reception method for a specific time period from the user's past question history. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has preferred in the past. In this way, the optimal reception method can be selected by analyzing the user's past question history.

[0036] When receiving a question, the reception unit can filter the questions based on the user's current financial situation and areas of interest. For example, the reception unit analyzes the user's current financial situation and receives only relevant questions. For example, the reception unit analyzes the user's income, expenses, asset status, etc., and filters out related questions. The reception unit can also preferentially receive questions about specific financial products based on the user's areas of interest. For example, the reception unit identifies areas of interest based on the user's past search history and survey results, and filters out related questions. The reception unit can also filter out related questions based on the user's past financial transaction history. For example, the reception unit analyzes the user's past transaction history and receives related questions preferentially. In this way, by filtering questions based on the user's current financial situation and areas of interest, it is possible to receive only highly relevant questions.

[0037] When accepting a question, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, when the user uses voice input, the acceptance unit accepts the question using voice recognition technology. For example, the acceptance unit converts the voice into text using voice recognition software and accepts the text as a question. Furthermore, when the user uses text input, the acceptance unit can also accept the question using text analysis technology. For example, the acceptance unit analyzes the text entered by the user and accepts it as a question. Furthermore, when the user uses an image to ask a question, the acceptance unit can also accept the question using image analysis technology. For example, the acceptance unit analyzes the image sent by the user and accepts it as a question. In this way, by selecting the optimal acceptance means depending on the user's input method, it is possible to accept questions in a manner that is easy for the user to use.

[0038] When accepting a question, the acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, when the user is in a specific area, the acceptance unit prioritizes accepting questions about financial products related to that area. For example, the acceptance unit filters questions about financial products related to that area based on the user's geographical location information. Furthermore, when the user is traveling, the acceptance unit can also prioritize accepting questions about financial products at the user's travel destination. For example, the acceptance unit filters questions about financial products related to the user's travel destination based on the user's geographical location information. Furthermore, when the user is at home, the acceptance unit can also prioritize accepting questions about financial products near the user's home. For example, the acceptance unit filters questions about financial products related to the user's home based on the user's geographical location information. In this way, highly relevant questions can be prioritized by taking the user's geographical location information into account.

[0039] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. The reception unit, for example, analyzes the user's social media activity and receives related questions. For example, the reception unit preferentially receives questions about financial products in which the user has shown interest on social media. The reception unit can also analyze the content of the user's social media posts and receive related questions. For example, the reception unit analyzes the content of the user's social media posts and filters related questions. The reception unit can also receive related questions by referring to the activity of the user's friends on social media. For example, the reception unit preferentially receives questions about financial products in which the user's friends have shown interest. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related questions.

[0040] When receiving a question, the reception unit can customize an appropriate reception method by reflecting the user's past feedback. The reception unit customizes the optimal reception method based on the user's past feedback, for example. For example, the reception unit preferentially suggests reception methods that the user has preferred in the past. The reception unit can also improve a specific reception method based on the user's past feedback. For example, the reception unit customizes the reception method by reflecting the user's ratings and comments. In this way, the optimal reception method can be customized by reflecting the user's past feedback.

[0041] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the financial product. For example, the proposal unit evaluates the importance of the financial product and adjusts the level of detail of the proposal based on that. For example, the proposal unit evaluates the importance of the financial product based on an evaluation of risk and return, and provides detailed information for financial products with high importance. The proposal unit can also adjust the level of detail of the proposal based on the user's interest. For example, the proposal unit provides detailed information for financial products with high interest to the user, and provides concise information for financial products with low interest. In this way, by adjusting the level of detail of the proposal based on the importance of the financial product, appropriate information can be provided to the user.

[0042] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the financial product. For example, the proposal unit applies different proposal algorithms depending on the category of the financial product. For example, the proposal unit applies a proposal algorithm that emphasizes risk and return to insurance products. The proposal unit can also apply a proposal algorithm that emphasizes past performance to investment trusts. Furthermore, the proposal unit can apply a proposal algorithm that emphasizes safety to savings products. In this way, by applying different proposal algorithms depending on the category of the financial product, more appropriate proposals can be made.

[0043] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit analyzes the user's past proposal results and reflects them in the next proposal. For example, the suggestion unit analyzes the results of suggestions the user has received in the past and improves the proposal algorithm. The suggestion unit can also select the optimal suggestion method from the user's past proposal results. For example, the suggestion unit customizes the proposal content based on the user's past proposal results. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results.

[0044] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the financial product. The proposal unit, for example, evaluates the submission date of the financial product and determines the priority of the proposal based on that. For example, the proposal unit evaluates the submission date of the financial product based on the submission date or submission deadline, and gives priority to proposing financial products whose submission date is approaching. The proposal unit can also postpone financial products whose submission date is further away. In this way, by determining the priority of the proposal based on the submission date of the financial product, it is possible to make proposals at the optimal time for the user.

[0045] The suggestion unit can adjust the order of suggestions based on the relevance of financial products when making suggestions. The suggestion unit, for example, evaluates the relevance of financial products and adjusts the order of suggestions based on the evaluation. For example, the suggestion unit evaluates the relevance of financial products based on the same category or related risks, and prioritizes suggesting highly relevant financial products. The suggestion unit can also adjust the order of suggestions based on the user's level of interest. For example, the suggestion unit prioritizes suggesting financial products that the user is more interested in, and postpones financial products that the user is less interested in. In this way, adjusting the order of suggestions based on the relevance of financial products makes it possible to make suggestions in the order that is optimal for the user.

[0046] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, evaluates the user's level of expertise and adjusts the use of technical terminology in the proposal based on the evaluation. For example, if the user has technical expertise, the suggestion unit makes a proposal that uses a lot of technical terminology. In addition, if the user does not have technical expertise, the suggestion unit can also explain in simple terms. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to make a proposal that is easy for the user to understand.

[0047] The providing unit can adjust the content of the information provided based on the level of detail of the financial product when providing the information. For example, the providing unit evaluates the level of detail of the financial product and adjusts the content of the information provided based on the level of detail. For example, the providing unit evaluates the level of detail of the financial product based on the product's features, risks, and returns, and provides detailed information for financial products with a high level of detail. The providing unit can also adjust the content of the information provided based on the user's level of interest. For example, the providing unit provides detailed information for financial products in which the user is highly interested, and provides concise information for financial products in which the user is less interested. In this way, by adjusting the content of the information provided based on the level of detail of the financial product, it is possible to provide optimal information to the user.

[0048] When providing information, the providing unit can apply different providing algorithms depending on the category of the financial product. The providing unit applies different providing algorithms depending on, for example, the category of the financial product. For example, the providing unit applies a providing algorithm that emphasizes risk and return to insurance products. The providing unit can also apply a providing algorithm that emphasizes past performance to investment trusts. Furthermore, the providing unit can apply a providing algorithm that emphasizes safety to savings products. In this way, by applying different providing algorithms depending on the category of the financial product, it is possible to provide optimal information to the user.

[0049] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past information provision results. The providing unit, for example, analyzes the user's past information provision results and reflects them in the next information provision. For example, the providing unit analyzes the results of information provided by the user in the past and improves the provision algorithm. The providing unit can also select the optimal provision method from the user's past information provision results. For example, the providing unit customizes the content of the provision based on the user's past information provision results. In this way, the accuracy of the information provided is improved by referring to the user's past information provision results.

[0050] When providing information, the providing unit can determine the priority of provision based on the submission time of the financial product. The providing unit, for example, evaluates the submission time of the financial product and determines the priority of provision based on that. For example, the providing unit evaluates the submission time of the financial product based on the submission date or submission deadline, and provides information preferentially for financial products whose submission time is approaching. The providing unit can also postpone financial products whose submission time is further away. In this way, by determining the priority of provision based on the submission time of the financial product, it is possible to provide information at the optimal time for the user.

[0051] When providing information, the providing unit can adjust the order of provision based on the relevance of financial products. The providing unit, for example, evaluates the relevance of financial products and adjusts the order of provision based on that. For example, the providing unit evaluates the relevance of financial products based on the same category or related risks, and provides information on highly relevant financial products preferentially. The providing unit can also adjust the order of provision based on the user's level of interest. For example, the providing unit provides information on financial products that the user is highly interested in preferentially, and financial products on which the user is less interested are provided later. In this way, by adjusting the order of provision based on the relevance of financial products, it is possible to provide information in the order that is optimal for the user.

[0052] When providing information, the providing unit can adjust the use of technical terms provided in accordance with the user's level of expertise. The providing unit, for example, evaluates the user's level of expertise and adjusts the use of technical terms provided based on the evaluation. For example, if the user has technical expertise, the providing unit provides information that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the providing unit can also explain the information in simple terms. In this way, by adjusting the use of technical terms provided in accordance with the user's level of expertise, it is possible to provide information that is easy for the user to understand.

[0053] When collecting feedback, the collection unit can select an appropriate collection method by referring to the user's past feedback history. The collection unit, for example, analyzes the user's past feedback history and selects the optimal collection method. For example, if the user provided detailed feedback in the past, the collection unit may request similar detailed feedback. Furthermore, if the user provided brief feedback in the past, the collection unit may request brief feedback. In this way, the optimal collection method can be selected by referring to the user's past feedback history.

[0054] When collecting feedback, the collection unit can customize the content of the collection based on the user's current financial situation. The collection unit, for example, analyzes the user's current financial situation and requests relevant feedback. For example, the collection unit analyzes the user's income, expenses, asset status, etc. and requests relevant feedback. The collection unit can also request feedback on specific financial products depending on the user's financial situation. For example, the collection unit customizes the content of the collection based on the user's current financial situation. In this way, by customizing the content of the collection based on the user's current financial situation, highly relevant feedback can be collected.

[0055] The collection unit can improve the collection method by reflecting the user's feedback when collecting feedback. The collection unit improves the collection method based on, for example, the user's past feedback. For example, the collection unit analyzes the user's ratings and comments and improves the collection method. The collection unit can also select the optimal collection method from the user's feedback. For example, the collection unit customizes the collection method by reflecting the user's feedback. In this way, the collection method can be improved by reflecting the user's feedback.

[0056] When collecting feedback, the collection unit can select the optimal collection method by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting feedback related to that area. For example, the collection unit collects feedback related to that area based on the user's geographical location information. Furthermore, when the user is traveling, the collection unit can also prioritize collecting feedback related to financial products at the travel destination. For example, the collection unit collects feedback related to the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the collection unit can also prioritize collecting feedback related to financial products near the user's home. For example, the collection unit collects feedback related to the area near the user's home based on the user's geographical location information. In this way, the optimal collection method can be selected by taking into account the user's geographical location information.

[0057] When collecting feedback, the collection unit can analyze the user's social media activities and suggest what to collect. The collection unit, for example, analyzes the user's social media activities and collects relevant feedback. For example, the collection unit prioritizes collecting feedback on financial products in which the user has shown interest on social media. The collection unit can also analyze the content of the user's posts on social media and collect related feedback. For example, the collection unit analyzes the content of the user's posts on social media and collect related feedback. The collection unit can also collect related feedback by referring to the activities of the user's friends on social media. For example, the collection unit prioritizes collecting feedback on financial products in which the user's friends have shown interest. In this way, by analyzing the user's social media activities, it is possible to prioritize collecting related feedback.

[0058] When collecting feedback, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit customizes the optimal collection method based on the user's past feedback, for example. For example, the collection unit preferentially suggests collection methods that the user has preferred in the past. The collection unit can also improve a specific collection method based on the user's past feedback. For example, the collection unit customizes the collection method by reflecting the user's ratings and comments. In this way, the optimal collection method can be customized by reflecting the user's past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The suggestion unit can also customize the content of the suggestions based on the user's past feedback. For example, if the user has previously given a high rating to a specific financial product, the suggestion unit can preferentially suggest similar financial products. Also, if the user has previously tended to avoid specific risks, the suggestion unit can suggest low-risk financial products. Furthermore, if the user has previously had specific investment goals, the suggestion unit can suggest financial products that match those goals. This makes it possible to make suggestions that reflect the user's past feedback.

[0061] The suggestion unit can also adjust the content of the suggestions based on the user's past investment performance. For example, if the user prefers investment products that have provided high returns in the past, the suggestion unit can suggest similar high-return products. Also, if the user has preferred low-risk products in the past, the suggestion unit can suggest low-risk products. Furthermore, if the user has invested in a specific market segment in the past, the suggestion unit can suggest products related to that market segment. This makes it possible to make suggestions that reflect the user's past investment performance.

[0062] The providing unit can also customize the method of providing information based on the user's past information provision history. For example, if the user has preferred detailed information in the past, the providing unit can provide detailed information preferentially. Also, if the user has preferred concise information in the past, the providing unit can provide concise information. Furthermore, if the user has preferred information in a particular format in the past, the providing unit can provide information in that format. This makes it possible to provide information that reflects the user's past information provision history.

[0063] The collection unit can also customize the feedback collection method based on the user's past feedback history. For example, if the user has provided detailed feedback in the past, the collection unit can request similar detailed feedback. If the user has provided brief feedback in the past, the collection unit can request brief feedback. Furthermore, if the user has provided feedback in a specific format in the past, the collection unit can request feedback in that format. This makes it possible to collect feedback that reflects the user's past feedback history.

[0064] The suggestion unit can also customize the content of the suggestions based on the user's current financial situation. For example, if the user currently has a high income, the suggestion unit can suggest high-risk, high-return products. If the user currently has a low income, the suggestion unit can suggest low-risk products. Furthermore, if the user currently holds a specific asset, the suggestion unit can suggest products related to that asset. This makes it possible to make suggestions that reflect the user's current financial situation.

[0065] The providing unit can also customize the content of the information provided based on the user's current financial situation. For example, if the user currently has a high income, the providing unit can provide information on high-risk, high-return products. If the user currently has a low income, the providing unit can provide information on low-risk products. Furthermore, if the user currently holds a specific asset, the providing unit can provide information related to that asset. This makes it possible to provide information that reflects the user's current financial situation.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives a user's question. The user's question may be in the form of text, voice, or image. For example, a text question entered by the user may be received. Voice recognition technology may also be used to convert voice into text, which may then be received as a question. Furthermore, image analysis technology may be used to analyze an image, which may then be received as a question. Step 2: The proposal unit analyzes the question received by the reception unit and proposes financial products that meet the user's needs. The proposal unit uses natural language processing technology to analyze the question and identify the user's needs. It can also use machine learning algorithms to select optimal financial products based on past data and market trends. For example, it proposes appropriate investment trusts based on the user's risk tolerance and investment goals. Step 3: The offering unit provides detailed information about the financial products proposed by the suggestion unit. For example, the offering unit provides information such as the type and characteristics of insurance, and the risks and returns of investment trusts. It can also provide detailed answers to the user's questions. For example, it provides a detailed explanation of the proposed financial products. Step 4: The collection unit collects user feedback based on the information provided by the provision unit. For example, the collection unit collects user feedback and questions about the proposed financial products. The collection unit can also use the user feedback to make future proposals more appropriate. For example, the collection unit analyzes the feedback and improves the proposal algorithm.

[0068] (Example 2) A financial planner service according to an embodiment of the present invention is a system that accepts and analyzes user questions, proposes appropriate financial products, provides detailed information, and collects feedback. In the financial planner service, a user inputs a question to an AI, which then analyzes the question and proposes financial products that meet the user's needs. For example, if a user inputs a question such as, "I want to know which insurance is right for me," the AI ​​analyzes the question and proposes an appropriate insurance product. The AI ​​then provides detailed information about the proposed financial products, such as the types and features of insurance and the risks and returns of investment trusts. This allows the user to obtain information to select a financial product that suits them. The AI ​​also collects user feedback and uses it to improve the service. For example, if a user inputs their thoughts or questions about the proposed financial products, the AI ​​can use that information to make its next proposal more appropriate. This allows the financial planner service to provide financial advice to people who are reluctant to receive face-to-face consultations. This allows the financial planner service to accept and analyze user questions, propose appropriate financial products, provide detailed information, and collect feedback, allowing even people who are reluctant to receive face-to-face consultations to provide financial advice to them. For example, the system can quickly and accurately analyze questions written by users and suggest appropriate financial products, reducing the burden on users. Users can also receive appropriate answers to their questions, which can be useful in selecting financial products.

[0069] A financial planner service according to an embodiment includes a reception unit, a proposal unit, a provision unit, and a collection unit. The reception unit accepts user questions. User questions may include, but are not limited to, text, audio, and images. The reception unit may accept, for example, text questions entered by the user. The reception unit may also accept user questions using voice input. For example, speech recognition technology may be used to convert speech into text and accept the text as a question. The reception unit may also accept user questions using image input. For example, image analysis technology may be used to analyze an image and accept the image as a question. The proposal unit analyzes the questions accepted by the reception unit and proposes financial products that meet the user's needs. The proposal unit may analyze the questions using natural language processing technology to identify the user's needs. The proposal unit may also use a machine learning algorithm to select optimal financial products based on past data and market trends. For example, the proposal unit may propose appropriate investment trusts based on the user's risk tolerance and investment goals. The provision unit provides detailed information about the financial products proposed by the proposal unit. The providing unit provides information such as types and features of insurance, and risks and returns of investment trusts. The providing unit can also provide detailed answers to user questions. For example, the providing unit provides detailed explanations of financial products proposed to the user. The collecting unit collects user feedback based on the information provided by the providing unit. For example, the collecting unit collects user impressions and questions about the proposed financial products. The collecting unit can also make next proposals more appropriate based on user feedback. For example, the collecting unit analyzes user feedback and improves the proposal algorithm. As a result, the financial planner service according to the embodiment can accept and analyze user questions, propose appropriate financial products, provide detailed information, and collect feedback.

[0070] The proposal unit can select appropriate financial products based on past data and market trends. The proposal unit selects financial products based on, for example, past trading history and market data. For example, the proposal unit analyzes past trading history to understand the user's investment trends. The proposal unit can also analyze market data to understand current market trends. For example, the proposal unit predicts market trends using economic indicators and trend analysis. The proposal unit can also select optimal financial products by combining past data and market trends. For example, the proposal unit proposes investment trusts suitable for the user based on past trading history and market data. This makes it possible to make more appropriate proposals to the user by selecting optimal financial products based on past data and market trends.

[0071] The providing unit can provide detailed information such as the types and characteristics of insurance, and the risks and returns of investment trusts. The providing unit provides detailed information about, for example, the types and characteristics of insurance. For example, the providing unit explains the types and characteristics of life insurance, medical insurance, property insurance, etc. The providing unit can also provide detailed information about the risks and returns of investment trusts. For example, the providing unit explains the types of risks of investment trusts and how to calculate returns. The providing unit can also provide detailed answers to questions from the user. For example, the providing unit provides a detailed explanation of financial products suggested to the user. By providing detailed information such as the types and characteristics of insurance, and the risks and returns of investment trusts, the user can obtain information to select a financial product that suits them.

[0072] The collection unit can collect users' impressions and questions about the proposed financial products. The collection unit, for example, collects users' impressions about the proposed financial products. For example, the collection unit collects users' evaluations and opinions about the proposed financial products. The collection unit can also collect users' questions about the proposed financial products. For example, the collection unit collects users' doubts and uncertainties about the proposed financial products. In this way, collecting users' impressions and questions about the proposed financial products can be useful for improving services.

[0073] The suggestion unit can suggest suitable investment trusts based on the user's risk tolerance and investment goals. The suggestion unit, for example, evaluates the user's risk tolerance and suggests appropriate investment trusts based on that. For example, the suggestion unit evaluates the user's risk tolerance using a questionnaire or risk profile analysis. The suggestion unit can also set the user's investment goals and suggest appropriate investment trusts based on those goals. For example, the suggestion unit sets the user's short-term goals, long-term goals, and profit goals and suggests investment trusts based on those goals. This makes it possible to suggest more appropriate investment trusts to the user by suggesting appropriate investment trusts based on the user's risk tolerance and investment goals.

[0074] The collection unit can make the next proposal more appropriate based on the collected feedback. For example, the collection unit collects the user's thoughts and questions about the proposed financial product and improves the next proposal based on the collected feedback. For example, the collection unit analyzes the user's feedback and improves the proposal algorithm. The collection unit can also customize the proposal content based on the user's feedback. For example, the collection unit reflects the user's evaluations and opinions to make the next proposal more appropriate. In this way, the accuracy of the service is improved by making the next proposal more appropriate based on the collected feedback.

[0075] The reception unit can estimate the user's emotion and adjust the timing of question reception based on the estimated user emotion. The reception unit, for example, analyzes the user's facial expression to estimate the emotion. For example, it uses facial expression recognition technology to analyze the user's facial expression and estimate the emotion. The reception unit can also analyze the user's voice to estimate the emotion. For example, it uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The reception unit can also analyze the user's text to estimate the emotion. For example, it uses text analysis technology to analyze the user's sentences and estimate the emotion. In this way, the user's emotion can be estimated and the timing of question reception can be adjusted based on the estimated user emotion, allowing the question to be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The reception unit can analyze the user's past question history and select an appropriate reception method. The reception unit, for example, analyzes the user's past question history and selects the optimal reception method. For example, the reception unit preferentially suggests question formats that the user has frequently used in the past. The reception unit can also select the optimal reception method for a specific time period from the user's past question history. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has preferred in the past. In this way, the optimal reception method can be selected by analyzing the user's past question history.

[0077] When receiving a question, the reception unit can filter the questions based on the user's current financial situation and areas of interest. For example, the reception unit analyzes the user's current financial situation and receives only relevant questions. For example, the reception unit analyzes the user's income, expenses, asset status, etc., and filters out related questions. The reception unit can also preferentially receive questions about specific financial products based on the user's areas of interest. For example, the reception unit identifies areas of interest based on the user's past search history and survey results, and filters out related questions. The reception unit can also filter out related questions based on the user's past financial transaction history. For example, the reception unit analyzes the user's past transaction history and receives related questions preferentially. In this way, by filtering questions based on the user's current financial situation and areas of interest, it is possible to receive only highly relevant questions.

[0078] When accepting a question, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, when the user uses voice input, the acceptance unit accepts the question using voice recognition technology. For example, the acceptance unit converts the voice into text using voice recognition software and accepts the text as a question. Furthermore, when the user uses text input, the acceptance unit can also accept the question using text analysis technology. For example, the acceptance unit analyzes the text entered by the user and accepts it as a question. Furthermore, when the user uses an image to ask a question, the acceptance unit can also accept the question using image analysis technology. For example, the acceptance unit analyzes the image sent by the user and accepts it as a question. In this way, by selecting the optimal acceptance means depending on the user's input method, it is possible to accept questions in a manner that is easy for the user to use.

[0079] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. The reception unit, for example, analyzes the user's facial expression to estimate the emotions. For example, the reception unit uses facial expression recognition technology to analyze the user's facial expression and estimate the emotions. The reception unit can also analyze the user's voice to estimate the emotions. For example, the reception unit can use voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotions. The reception unit can also analyze the user's text to estimate the emotions. For example, the reception unit can use text analysis technology to analyze the user's sentences and estimate the emotions. In this way, the user's emotions are estimated and the priority of questions to be received based on the estimated user emotions, allowing questions with high urgency to be received preferentially. The 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.

[0080] When accepting a question, the acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, when the user is in a specific area, the acceptance unit prioritizes accepting questions about financial products related to that area. For example, the acceptance unit filters questions about financial products related to that area based on the user's geographical location information. Furthermore, when the user is traveling, the acceptance unit can also prioritize accepting questions about financial products at the user's travel destination. For example, the acceptance unit filters questions about financial products related to the user's travel destination based on the user's geographical location information. Furthermore, when the user is at home, the acceptance unit can also prioritize accepting questions about financial products near the user's home. For example, the acceptance unit filters questions about financial products related to the user's home based on the user's geographical location information. In this way, highly relevant questions can be prioritized by taking the user's geographical location information into account.

[0081] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. The reception unit, for example, analyzes the user's social media activity and receives related questions. For example, the reception unit preferentially receives questions about financial products in which the user has shown interest on social media. The reception unit can also analyze the content of the user's social media posts and receive related questions. For example, the reception unit analyzes the content of the user's social media posts and filters related questions. The reception unit can also receive related questions by referring to the activity of the user's friends on social media. For example, the reception unit preferentially receives questions about financial products in which the user's friends have shown interest. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related questions.

[0082] When receiving a question, the reception unit can customize an appropriate reception method by reflecting the user's past feedback. The reception unit customizes the optimal reception method based on the user's past feedback, for example. For example, the reception unit preferentially suggests reception methods that the user has preferred in the past. The reception unit can also improve a specific reception method based on the user's past feedback. For example, the reception unit customizes the reception method by reflecting the user's ratings and comments. In this way, the optimal reception method can be customized by reflecting the user's past feedback.

[0083] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit, for example, analyzes the user's facial expression to estimate the emotion. For example, it uses facial expression recognition technology to analyze the user's facial expression and estimate the emotion. The suggestion unit can also analyze the user's voice to estimate the emotion. For example, it uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The suggestion unit can also analyze the user's text to estimate the emotion. For example, it uses text analysis technology to analyze the user's writing and estimate the emotion. This enables more appropriate suggestions by estimating the user's emotion and adjusting the way the suggestion is expressed based on the estimated user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0084] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the financial product. For example, the proposal unit evaluates the importance of the financial product and adjusts the level of detail of the proposal based on that. For example, the proposal unit evaluates the importance of the financial product based on an evaluation of risk and return, and provides detailed information for financial products with high importance. The proposal unit can also adjust the level of detail of the proposal based on the user's interest. For example, the proposal unit provides detailed information for financial products with high interest to the user, and provides concise information for financial products with low interest. In this way, by adjusting the level of detail of the proposal based on the importance of the financial product, appropriate information can be provided to the user.

[0085] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the financial product. For example, the proposal unit applies different proposal algorithms depending on the category of the financial product. For example, the proposal unit applies a proposal algorithm that emphasizes risk and return to insurance products. The proposal unit can also apply a proposal algorithm that emphasizes past performance to investment trusts. Furthermore, the proposal unit can apply a proposal algorithm that emphasizes safety to savings products. In this way, by applying different proposal algorithms depending on the category of the financial product, more appropriate proposals can be made.

[0086] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. For example, the suggestion unit analyzes the user's past proposal results and reflects them in the next proposal. For example, the suggestion unit analyzes the results of suggestions the user has received in the past and improves the proposal algorithm. The suggestion unit can also select the optimal suggestion method from the user's past proposal results. For example, the suggestion unit customizes the proposal content based on the user's past proposal results. In this way, the accuracy of the proposal is improved by referring to the user's past proposal results.

[0087] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, analyzes the user's facial expression to estimate the emotion. For example, it uses facial expression recognition technology to analyze the user's facial expression and estimate the emotion. The suggestion unit can also analyze the user's voice to estimate the emotion. For example, it uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The suggestion unit can also analyze the user's text to estimate the emotion. For example, it uses text analysis technology to analyze the user's sentences and estimate the emotion. This enables the user to estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion, thereby enabling the most optimal suggestion for the user. The emotion estimation is realized using an emotion estimation function, for example, using 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.

[0088] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the financial product. The proposal unit, for example, evaluates the submission date of the financial product and determines the priority of the proposal based on that. For example, the proposal unit evaluates the submission date of the financial product based on the submission date or submission deadline, and gives priority to proposing financial products whose submission date is approaching. The proposal unit can also postpone financial products whose submission date is further away. In this way, by determining the priority of the proposal based on the submission date of the financial product, it is possible to make proposals at the optimal time for the user.

[0089] The suggestion unit can adjust the order of suggestions based on the relevance of financial products when making suggestions. The suggestion unit, for example, evaluates the relevance of financial products and adjusts the order of suggestions based on the evaluation. For example, the suggestion unit evaluates the relevance of financial products based on the same category or related risks, and prioritizes suggesting highly relevant financial products. The suggestion unit can also adjust the order of suggestions based on the user's level of interest. For example, the suggestion unit prioritizes suggesting financial products that the user is more interested in, and postpones financial products that the user is less interested in. In this way, adjusting the order of suggestions based on the relevance of financial products makes it possible to make suggestions in the order that is optimal for the user.

[0090] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. The suggestion unit, for example, evaluates the user's level of expertise and adjusts the use of technical terminology in the proposal based on the evaluation. For example, if the user has technical expertise, the suggestion unit makes a proposal that uses a lot of technical terminology. In addition, if the user does not have technical expertise, the suggestion unit can also explain in simple terms. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to make a proposal that is easy for the user to understand.

[0091] The providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. The providing unit, for example, analyzes the user's facial expression to estimate the emotion. For example, it uses facial expression recognition technology to analyze the user's facial expression and estimate the emotion. The providing unit can also analyze the user's voice to estimate the emotion. For example, it uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The providing unit can also analyze the user's text to estimate the emotion. For example, it uses text analysis technology to analyze the user's writing and estimate the emotion. This makes it possible to provide optimal information to the user by estimating the user's emotion and adjusting the method of providing information based on the estimated user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0092] The providing unit can adjust the content of the information provided based on the level of detail of the financial product when providing the information. For example, the providing unit evaluates the level of detail of the financial product and adjusts the content of the information provided based on the level of detail. For example, the providing unit evaluates the level of detail of the financial product based on the product's features, risks, and returns, and provides detailed information for financial products with a high level of detail. The providing unit can also adjust the content of the information provided based on the user's level of interest. For example, the providing unit provides detailed information for financial products in which the user is highly interested, and provides concise information for financial products in which the user is less interested. In this way, by adjusting the content of the information provided based on the level of detail of the financial product, it is possible to provide optimal information to the user.

[0093] When providing information, the providing unit can apply different providing algorithms depending on the category of the financial product. The providing unit applies different providing algorithms depending on, for example, the category of the financial product. For example, the providing unit applies a providing algorithm that emphasizes risk and return to insurance products. The providing unit can also apply a providing algorithm that emphasizes past performance to investment trusts. Furthermore, the providing unit can apply a providing algorithm that emphasizes safety to savings products. In this way, by applying different providing algorithms depending on the category of the financial product, it is possible to provide optimal information to the user.

[0094] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past information provision results. The providing unit, for example, analyzes the user's past information provision results and reflects them in the next information provision. For example, the providing unit analyzes the results of information provided by the user in the past and improves the provision algorithm. The providing unit can also select the optimal provision method from the user's past information provision results. For example, the providing unit customizes the content of the provision based on the user's past information provision results. In this way, the accuracy of the information provided is improved by referring to the user's past information provision results.

[0095] The providing unit can estimate the user's emotion and adjust the length of information provided based on the estimated user emotion. The providing unit, for example, analyzes the user's facial expression to estimate the emotion. For example, it uses facial expression recognition technology to analyze the user's facial expression and estimate the emotion. The providing unit can also analyze the user's voice to estimate the emotion. For example, it uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotion. The providing unit can also analyze the user's text to estimate the emotion. For example, it uses text analysis technology to analyze the user's writing and estimate the emotion. This makes it possible to provide optimal information to the user by estimating the user's emotion and adjusting the length of information provided based on the estimated user emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] When providing information, the providing unit can determine the priority of provision based on the submission time of the financial product. The providing unit, for example, evaluates the submission time of the financial product and determines the priority of provision based on that. For example, the providing unit evaluates the submission time of the financial product based on the submission date or submission deadline, and provides information preferentially for financial products whose submission time is approaching. The providing unit can also postpone financial products whose submission time is further away. In this way, by determining the priority of provision based on the submission time of the financial product, it is possible to provide information at the optimal time for the user.

[0097] When providing information, the providing unit can adjust the order of provision based on the relevance of financial products. The providing unit, for example, evaluates the relevance of financial products and adjusts the order of provision based on that. For example, the providing unit evaluates the relevance of financial products based on the same category or related risks, and provides information on highly relevant financial products preferentially. The providing unit can also adjust the order of provision based on the user's level of interest. For example, the providing unit provides information on financial products that the user is highly interested in preferentially, and financial products on which the user is less interested are provided later. In this way, by adjusting the order of provision based on the relevance of financial products, it is possible to provide information in the order that is optimal for the user.

[0098] When providing information, the providing unit can adjust the use of technical terms provided in accordance with the user's level of expertise. The providing unit, for example, evaluates the user's level of expertise and adjusts the use of technical terms provided based on the evaluation. For example, if the user has technical expertise, the providing unit provides information that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the providing unit can also explain the information in simple terms. In this way, by adjusting the use of technical terms provided in accordance with the user's level of expertise, it is possible to provide information that is easy for the user to understand.

[0099] The collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The collection unit, for example, analyzes the user's facial expressions to estimate the emotions. For example, it uses facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The collection unit can also analyze the user's voice to estimate the emotions. For example, it uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotions. The collection unit can also analyze the user's text to estimate the emotions. For example, it uses text analysis technology to analyze the user's writing and estimate the emotions. This makes it possible to estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions, thereby enabling optimal feedback collection for the user. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0100] When collecting feedback, the collection unit can select an appropriate collection method by referring to the user's past feedback history. The collection unit, for example, analyzes the user's past feedback history and selects the optimal collection method. For example, if the user provided detailed feedback in the past, the collection unit may request similar detailed feedback. Furthermore, if the user provided brief feedback in the past, the collection unit may request brief feedback. In this way, the optimal collection method can be selected by referring to the user's past feedback history.

[0101] When collecting feedback, the collection unit can customize the content of the collection based on the user's current financial situation. The collection unit, for example, analyzes the user's current financial situation and requests relevant feedback. For example, the collection unit analyzes the user's income, expenses, asset status, etc. and requests relevant feedback. The collection unit can also request feedback on specific financial products depending on the user's financial situation. For example, the collection unit customizes the content of the collection based on the user's current financial situation. In this way, by customizing the content of the collection based on the user's current financial situation, highly relevant feedback can be collected.

[0102] The collection unit can improve the collection method by reflecting the user's feedback when collecting feedback. The collection unit improves the collection method based on, for example, the user's past feedback. For example, the collection unit analyzes the user's ratings and comments and improves the collection method. The collection unit can also select the optimal collection method from the user's feedback. For example, the collection unit customizes the collection method by reflecting the user's feedback. In this way, the collection method can be improved by reflecting the user's feedback.

[0103] The collection unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. The collection unit, for example, analyzes the user's facial expressions to estimate the emotions. For example, it uses facial expression recognition technology to analyze the user's facial expressions and estimate the emotions. The collection unit can also analyze the user's voice to estimate the emotions. For example, it uses voice analysis technology to analyze the tone and speed of the user's voice and estimate the emotions. The collection unit can also analyze the user's text to estimate the emotions. For example, it uses text analysis technology to analyze the user's writing and estimate the emotions. In this way, the user's emotions are estimated and the priority of feedback is determined based on the estimated user emotions, so that feedback with high urgency can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, using 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.

[0104] When collecting feedback, the collection unit can select the optimal collection method by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting feedback related to that area. For example, the collection unit collects feedback related to that area based on the user's geographical location information. Furthermore, when the user is traveling, the collection unit can also prioritize collecting feedback related to financial products at the travel destination. For example, the collection unit collects feedback related to the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the collection unit can also prioritize collecting feedback related to financial products near the user's home. For example, the collection unit collects feedback related to the area near the user's home based on the user's geographical location information. In this way, the optimal collection method can be selected by taking into account the user's geographical location information.

[0105] When collecting feedback, the collection unit can analyze the user's social media activities and suggest what to collect. The collection unit, for example, analyzes the user's social media activities and collects relevant feedback. For example, the collection unit prioritizes collecting feedback on financial products in which the user has shown interest on social media. The collection unit can also analyze the content of the user's posts on social media and collect related feedback. For example, the collection unit analyzes the content of the user's posts on social media and collect related feedback. The collection unit can also collect related feedback by referring to the activities of the user's friends on social media. For example, the collection unit prioritizes collecting feedback on financial products in which the user's friends have shown interest. In this way, by analyzing the user's social media activities, it is possible to prioritize collecting related feedback.

[0106] When collecting feedback, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit customizes the optimal collection method based on the user's past feedback, for example. For example, the collection unit preferentially suggests collection methods that the user has preferred in the past. The collection unit can also improve a specific collection method based on the user's past feedback. For example, the collection unit customizes the collection method by reflecting the user's ratings and comments. In this way, the optimal collection method can be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, suggestion unit, provision unit, and collection unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and accepts text or voice questions from the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes financial products that meet the user's needs using natural language processing technology or machine learning algorithms. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides detailed information about the proposed financial products. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects user feedback to improve future proposals. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, suggestion unit, provision unit, and collection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a user's voice question. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes financial products that meet the user's needs using natural language processing techniques and machine learning algorithms. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides detailed information about the proposed financial products. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects user feedback to improve future proposals. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, provision unit, and collection unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice questions from the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes financial products that meet the user's needs using natural language processing techniques and machine learning algorithms. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and provides detailed information about the proposed financial products. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects user feedback to improve future proposals. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, provision unit, and collection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice questions from the user. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests financial products that meet the user's needs using natural language processing techniques and machine learning algorithms. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides detailed information about the proposed financial products. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects user feedback to improve future suggestions.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] 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 feeling stressed, the suggestion unit can temporarily refrain from making suggestions. If the user is relaxed, the suggestion unit can proactively make suggestions. Furthermore, if the user is excited, the suggestion unit can continue to attract the user's attention by providing detailed information. This allows for more effective suggestions by making suggestions at a timing that suits the user's emotions.

[0109] The suggestion unit can also customize the content of the suggestions based on the user's past feedback. For example, if the user has previously given a high rating to a specific financial product, the suggestion unit can preferentially suggest similar financial products. Also, if the user has previously tended to avoid specific risks, the suggestion unit can suggest low-risk financial products. Furthermore, if the user has previously had specific investment goals, the suggestion unit can suggest financial products that match those goals. This makes it possible to make suggestions that reflect the user's past feedback.

[0110] The providing unit can also estimate the user's emotions and adjust the order of information provision based on the estimated user's emotions. For example, if the user is feeling anxious, the providing unit can first provide information that gives a sense of security. If the user is excited, the providing unit can prioritize providing detailed information. Furthermore, if the user is relaxed, the providing unit can start by providing general information. This allows for more effective information provision by providing information in an order according to the user's emotions.

[0111] The collection unit can also estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can request brief feedback. If the user is relaxed, the collection unit can request detailed feedback. If the user is excited, the collection unit can request specific opinions or suggestions. In this way, more useful feedback can be obtained by collecting feedback in a manner appropriate to the user's emotions.

[0112] The suggestion unit can also estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is feeling anxious, the suggestion unit can use expressions that give a sense of security. If the user is excited, the suggestion unit can continue to attract the user's attention by providing detailed information. If the user is relaxed, the suggestion unit can deepen understanding of the user by providing general information. This enables more effective suggestions to be made by using expressions that correspond to the user's emotions.

[0113] The suggestion unit can also adjust the content of the suggestions based on the user's past investment performance. For example, if the user prefers investment products that have provided high returns in the past, the suggestion unit can suggest similar high-return products. Also, if the user has preferred low-risk products in the past, the suggestion unit can suggest low-risk products. Furthermore, if the user has invested in a specific market segment in the past, the suggestion unit can suggest products related to that market segment. This makes it possible to make suggestions that reflect the user's past investment performance.

[0114] The providing unit can also customize the method of providing information based on the user's past information provision history. For example, if the user has preferred detailed information in the past, the providing unit can provide detailed information preferentially. Also, if the user has preferred concise information in the past, the providing unit can provide concise information. Furthermore, if the user has preferred information in a particular format in the past, the providing unit can provide information in that format. This makes it possible to provide information that reflects the user's past information provision history.

[0115] The collection unit can also customize the feedback collection method based on the user's past feedback history. For example, if the user has provided detailed feedback in the past, the collection unit can request similar detailed feedback. If the user has provided brief feedback in the past, the collection unit can request brief feedback. Furthermore, if the user has provided feedback in a specific format in the past, the collection unit can request feedback in that format. This makes it possible to collect feedback that reflects the user's past feedback history.

[0116] The suggestion unit can also customize the content of the suggestions based on the user's current financial situation. For example, if the user currently has a high income, the suggestion unit can suggest high-risk, high-return products. If the user currently has a low income, the suggestion unit can suggest low-risk products. Furthermore, if the user currently holds a specific asset, the suggestion unit can suggest products related to that asset. This makes it possible to make suggestions that reflect the user's current financial situation.

[0117] The providing unit can also customize the content of the information provided based on the user's current financial situation. For example, if the user currently has a high income, the providing unit can provide information on high-risk, high-return products. If the user currently has a low income, the providing unit can provide information on low-risk products. Furthermore, if the user currently holds a specific asset, the providing unit can provide information related to that asset. This makes it possible to provide information that reflects the user's current financial situation.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives a user's question. The user's question may be in the form of text, voice, or image. For example, a text question entered by the user may be received. Voice recognition technology may also be used to convert voice into text, which may then be received as a question. Furthermore, image analysis technology may be used to analyze an image, which may then be received as a question. Step 2: The proposal unit analyzes the question received by the reception unit and proposes financial products that meet the user's needs. The proposal unit uses natural language processing technology to analyze the question and identify the user's needs. It can also use machine learning algorithms to select optimal financial products based on past data and market trends. For example, it proposes appropriate investment trusts based on the user's risk tolerance and investment goals. Step 3: The offering unit provides detailed information about the financial products proposed by the suggestion unit. For example, the offering unit provides information such as the type and characteristics of insurance, and the risks and returns of investment trusts. It can also provide detailed answers to the user's questions. For example, it provides a detailed explanation of the proposed financial products. Step 4: The collection unit collects user feedback based on the information provided by the provision unit. For example, the collection unit collects user feedback and questions about the proposed financial products. The collection unit can also use the user feedback to make future proposals more appropriate. For example, the collection unit analyzes the feedback and improves the proposal algorithm.

[0120] 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.

[0121] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, the 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 reception unit that receives questions from users; a proposal unit that analyzes the questions received by the reception unit and proposes financial products that meet the needs of the user; a providing unit that provides detailed information about the financial products proposed by the proposal unit; a collection unit that collects user feedback based on the information provided by the provision unit. A system characterized by:

2. The proposal unit Select appropriate financial products based on past data and market trends 2. The system of claim 1.

3. The providing unit Provides detailed information on types and characteristics of insurance, risks and returns of investment trusts, etc.

2. The system of claim 1.

4. The collecting unit Collect user feedback and questions about proposed financial products 2. The system of claim 1.

5. The proposal unit Recommend suitable mutual funds based on the user's risk tolerance and investment goals 2. The system of claim 1.

6. The collecting unit Use the feedback you gather to refine your next proposal 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past question history and select the most appropriate reception method 2. The system of claim 1.

9. The reception unit When questions are submitted, they are filtered based on the user's current financial situation and areas of interest.

2. The system of claim 1.

10. The reception unit When accepting a question, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

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