system

Abstract

The system according to the embodiment aims to analyze user information and propose appropriate investment products according to individual investment needs. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a NISA analysis unit, and a reception unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes appropriate investment products based on the analysis results obtained by the analysis unit. The NISA analysis unit analyzes information related to the New NISA. The reception unit accepts input from the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to propose appropriate investment products that meet the individual investment needs of users.

[0005] The system according to the embodiment aims to analyze user information and propose appropriate investment products according to individual investment needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a NISA analysis unit, and a reception unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes appropriate investment products based on the analysis results obtained by the analysis unit. The NISA analysis unit analyzes information related to the New NISA. The reception unit accepts input from the user. [Effects of the Invention]

[0007] The system according to the embodiment can analyze user information and propose appropriate investment products according to individual investment needs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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) The investment proposal system according to an embodiment of the present invention is a system that proposes recommended products through chat based on the user's age, available funds, and desired principal risk and return. This investment proposal system is particularly specialized for the New NISA and proposes recommended products for those eligible for the savings and growth investment limits. Furthermore, when the user inputs their current investment products and NISA product status, the system predicts future developments and makes future investment proposals. For example, the user inputs information such as their age, available funds, and risk tolerance. The system then collects and analyzes this information. Based on the analysis results, the system proposes optimal investment products. This system is particularly specialized for the New NISA and analyzes information regarding the savings and growth investment limits. The system also analyzes the current investment product information entered by the user to predict future developments and make investment proposals. This allows the system to provide more specific advice to the user. This allows the investment proposal system to collect and analyze user information and propose optimal investment products.

[0029] The investment proposal system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a NISA analysis unit, and a reception unit. The collection unit collects user information. The user information includes, but is not limited to, age, spare funds, and risk tolerance. The collection unit, for example, stores information entered by the user in a database and provides the information to the analysis unit. The analysis unit analyzes the collected information and determines the user's investment needs. The analysis unit analyzes the user's investment needs using, for example, statistical analysis or a machine learning algorithm. The proposal unit proposes optimal investment products based on the analysis results. The proposal unit proposes, for example, products according to risk tolerance or highly profitable products. The NISA analysis unit analyzes information related to the New NISA's savings limit and growth investment limit. The NISA analysis unit analyzes, for example, the annual savings limit and types of investment targets. The reception unit accepts input from the user. The reception unit accepts user information, for example, in the form of text input or multiple-choice options. This allows the investment proposal system to collect and analyze user information and propose optimal investment products.

[0030] The collection unit can collect the user's age, spare funds, risk tolerance, and other related information. For example, the collection unit stores information such as the user's age, spare funds, and risk tolerance inputted by the user in a database. The collection unit can also collect other related information such as the user's family structure and investment goals. For example, the collection unit provides the information inputted by the user to the analysis unit in real time, and the analysis unit performs analysis based on the information. In this way, the collection unit can make more appropriate investment suggestions by collecting information such as the user's age, spare funds, and risk tolerance.

[0031] The analysis unit can analyze the collected information and determine the user's investment needs. The analysis unit, for example, analyzes the collected information using statistical analysis or machine learning algorithms. For example, the analysis unit determines the user's investment needs based on information such as the user's age, spare funds, and risk tolerance. The analysis unit can also perform analysis taking into account information such as the user's investment goals and family composition. For example, the analysis unit can suggest high-risk investment products to users seeking short-term profits, and stable investment products to users aiming for long-term asset formation. In this way, the analysis unit can analyze the collected information and determine the user's investment needs, thereby making more appropriate investment suggestions.

[0032] The NISA analysis unit can analyze information regarding the savings limit and growth investment limit of the New NISA. The NISA analysis unit analyzes, for example, information regarding the savings limit and growth investment limit of the New NISA. For example, the NISA analysis unit analyzes the annual savings limit and types of investment targets and proposes investment products that are optimal for the user. The NISA analysis unit can also perform analysis taking into account information such as the user's investment goals and risk tolerance. For example, the NISA analysis unit proposes savings investment products that are appropriate for the user based on information regarding the savings limit, and proposes growth investment products that are appropriate for the user based on information regarding the growth investment limit. In this way, the NISA analysis unit can make more appropriate investment proposals by analyzing information regarding the savings limit and growth investment limit of the New NISA.

[0033] The suggestion unit can suggest optimal investment products based on the analysis results. The suggestion unit, for example, suggests optimal investment products based on the analysis results. For example, the suggestion unit suggests products that match the risk tolerance or highly profitable products. The suggestion unit can also make suggestions taking into consideration information such as the user's investment goals and family composition. For example, the suggestion unit suggests high-risk investment products to users seeking short-term profits, and stable investment products to users aiming for long-term asset formation. In this way, the suggestion unit can provide more specific advice to users by suggesting optimal investment products based on the analysis results.

[0034] The reception unit can receive input from a user. For example, the reception unit receives input from a user in the form of text input or multiple-choice options. For example, the reception unit stores the information input by the user in a database and provides it to the analysis unit. The reception unit can also provide the information input by the user to the analysis unit in real time, and the analysis unit can perform analysis based on that information. In this way, the reception unit can incorporate user information into the system by receiving input from the user.

[0035] The collection unit can analyze the user's past investment history and select the optimal information collection method. The collection unit, for example, analyzes the user's past investment history and selects the optimal information collection method. For example, the collection unit collects information based on the user's successful investment patterns in the past. The collection unit can also collect information to help the user avoid investment patterns that have failed in the past. The collection unit can also preferentially collect information about specific investment products from the user's past investment history. This allows the collection unit to select a more appropriate information collection method by analyzing the user's past investment history. The optimal information collection method can be determined, for example, by methods such as a questionnaire survey or analysis of behavioral history. For example, the collection unit can input the user's past investment history data into the generation AI and cause the generation AI to select the optimal information collection method.

[0036] The collection unit can filter information based on the user's current economic situation and areas of interest when collecting information. For example, the collection unit filters information based on the user's current economic situation and areas of interest when collecting information. For example, the collection unit collects appropriate investment product information based on the user's current income situation. The collection unit can also filter information based on the user's areas of interest (e.g., technology, medicine, etc.). The collection unit can also preferentially collect low-risk investment product information depending on the user's economic situation. This allows the collection unit to collect more relevant information by filtering information based on the user's current economic situation and areas of interest. Filtering is performed based on criteria such as economic situation indicators and interest categories. For example, the collection unit can input the user's economic situation data into the generation AI and have the generation AI perform filtering.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit collects information based on the economic situation in the area where the user lives. The collection unit can also prioritize collecting investment product information in areas that the user frequently visits. The collection unit can also collect market information related to the user's geographical location. In this way, the collection unit can prioritize collecting more relevant information by taking into account the user's geographical location information. Geographical location information is collected based on criteria such as GPS data or IP address. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0038] The collection unit can analyze the user's social media activity and collect relevant information when collecting information. For example, the collection unit can analyze the user's social media activity and collect relevant information when collecting information. For example, the collection unit can collect information about investment products in which the user has shown interest on social media. The collection unit can also collect opinions of investment experts followed by the user. The collection unit can also collect trend information for investment communities in which the user participates. This allows the collection unit to collect more relevant information by analyzing the user's social media activity. Social media activity is analyzed based on criteria such as the content of posts and the number of likes. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect relevant information.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the collected information during analysis. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information. The importance of information is evaluated based on criteria such as the reliability and relevance of the information. For example, the analysis unit can input importance data of the collected information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a specific analysis algorithm to information regarding stock investments. The analysis unit can also apply a different analysis algorithm to information regarding real estate investments. The analysis unit can also apply yet another analysis algorithm to information regarding bond investments. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of information. Analysis algorithms are realized using techniques such as regression analysis and clustering. For example, the analysis unit can input information category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0041] The analysis unit can determine the analysis priority based on when the information was collected during analysis. For example, the analysis unit determines the analysis priority based on when the information was collected during analysis. For example, the analysis unit prioritizes analyzing the latest information. The analysis unit can also postpone analyzing older information. The analysis unit can also analyze information of moderate newness with moderate priority. In this way, the analysis unit can provide more appropriate analysis results by determining the analysis priority based on when the information was collected. The information collection time is evaluated based on criteria such as the latest information and past data. For example, the analysis unit can input information collection time data into the generation AI and have the generation AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit can also postpone analysis of information with low relevance. The analysis unit can also analyze information with moderate relevance with moderate priority. In this way, the analysis unit can provide more appropriate analysis results by adjusting the order of analysis based on the relevance of information. The relevance of information is evaluated based on criteria such as commonalities and correlations between information. For example, the analysis unit can input information relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0043] The proposal unit can adjust the level of detail of the proposal based on the importance of the investment product when making a proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the investment product when making a proposal. For example, the proposal unit makes detailed proposals for investment products with high importance. The proposal unit can also make simplified proposals for investment products with low importance. The proposal unit can also make proposals with appropriate level of detail for investment products with medium importance. In this way, the proposal unit can provide more appropriate proposals by adjusting the level of detail of the proposal based on the importance of the investment product. The importance of an investment product is evaluated based on criteria such as risk assessment and profitability. For example, the proposal unit can input importance data of the investment product to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0044] The proposal unit can apply different proposal algorithms depending on the category of the investment product when making a proposal. For example, the proposal unit applies different proposal algorithms depending on the category of the investment product when making a proposal. For example, the proposal unit applies a specific proposal algorithm to proposals related to stock investments. The proposal unit can also apply a different proposal algorithm to proposals related to real estate investments. The proposal unit can also apply yet another proposal algorithm to proposals related to bond investments. In this way, the proposal unit can provide more appropriate proposals by applying different proposal algorithms depending on the category of the investment product. The proposal algorithm is realized by technologies such as a recommendation system or an optimization algorithm. For example, the proposal unit can input investment product category data into the generation AI and cause the generation AI to apply different proposal algorithms.

[0045] The proposal unit can determine the priority of proposals based on the submission date of investment products when making proposals. For example, the proposal unit determines the priority of proposals based on the submission date of investment products when making proposals. For example, the proposal unit prioritizes the latest investment products. The proposal unit can also postpone the proposal of older investment products. The proposal unit can also give moderate priority to the proposal of investment products that are of moderate newness. In this way, the proposal unit can provide more appropriate proposals by determining the priority of proposals based on the submission date of investment products. The submission date of investment products is evaluated based on criteria such as the most recent submission or a past submission. For example, the proposal unit can input the submission date data of investment products into the generation AI and have the generation AI determine the priority of proposals.

[0046] The proposal unit can adjust the order of proposals based on the relevance of investment products when making a proposal. For example, the proposal unit adjusts the order of proposals based on the relevance of investment products when making a proposal. For example, the proposal unit prioritizes proposing investment products with high relevance. The proposal unit can also postpone proposing investment products with low relevance. The proposal unit can also moderately prioritize proposing investment products with medium relevance. In this way, the proposal unit can provide more appropriate proposals by adjusting the order of proposals based on the relevance of investment products. The relevance of investment products is evaluated based on criteria such as commonalities and correlations between products. For example, the proposal unit can input relevance data of investment products into the generation AI and cause the generation AI to adjust the order of proposals.

[0047] When analyzing a NISA, the NISA analysis unit can predict the current analysis by referring to past NISA data. For example, when analyzing a NISA, the NISA analysis unit predicts the current analysis by referring to past NISA data. For example, the NISA analysis unit predicts the current investment situation based on past NISA data. The NISA analysis unit can also predict future investment trends from past NISA data. The NISA analysis unit can also adjust the current investment strategy by referring to past NISA data. In this way, the NISA analysis unit can more accurately predict the current analysis by referring to past NISA data. Past NISA data is referenced based on criteria such as past investment history and past market data. For example, the NISA analysis unit can input past NISA data into the generation AI and have the generation AI execute a current analysis prediction.

[0048] The NISA analysis unit can apply different analysis methods to each NISA category when analyzing a NISA. For example, the NISA analysis unit applies different analysis methods to each NISA category when analyzing a NISA. For example, the NISA analysis unit applies a specific analysis method to an analysis related to the savings limit. The NISA analysis unit can also apply a different analysis method to an analysis related to the growth investment limit. The NISA analysis unit can also apply yet another analysis method to an analysis related to other NISA categories. In this way, the NISA analysis unit can provide more appropriate analysis results by applying different analysis methods to each NISA category. NISA categories are analyzed based on criteria such as savings NISA and general NISA. For example, the NISA analysis unit can input NISA category data into the generation AI and have the generation AI apply different analysis methods.

[0049] The NISA analysis unit can analyze changes in the analysis based on the time of NISA submission when analyzing the NISA. For example, the NISA analysis unit can analyze changes in the analysis based on the time of NISA submission when analyzing the NISA. For example, the NISA analysis unit prioritizes analyzing the most recent NISA data. The NISA analysis unit can also postpone analyzing older NISA data. The NISA analysis unit can also moderately prioritize analyzing NISA data that is of moderate recency. This allows the NISA analysis unit to provide more appropriate analysis results by analyzing changes in the analysis based on the time of NISA submission. The time of NISA submission is evaluated based on criteria such as the most recent submission or a past submission. For example, the NISA analysis unit can input NISA submission time data into the generation AI and have the generation AI analyze changes in the analysis.

[0050] The NISA analysis unit can perform analysis by referring to NISA-related market data when analyzing a NISA. For example, the NISA analysis unit performs analysis by referring to NISA-related market data when analyzing a NISA. For example, the NISA analysis unit analyzes the current investment situation based on NISA-related market data. The NISA analysis unit can also predict future investment trends from NISA-related market data. The NISA analysis unit can also adjust the current investment strategy by referring to NISA-related market data. In this way, the NISA analysis unit can provide more appropriate analysis results by referring to NISA-related market data. NISA-related market data is referenced based on, for example, stock market data or bond market data. For example, the NISA analysis unit can input NISA-related market data into the generation AI and have the generation AI perform the analysis.

[0051] The reception unit can select the optimal reception method by referring to the user's past input history when receiving a request. For example, the reception unit can select the optimal reception method by referring to the user's past input history when receiving a request. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. This allows the reception unit to select a more appropriate reception method by referring to the user's past input history. The past input history is referenced based on criteria such as past inquiry content and past input data. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal reception method.

[0052] The reception unit can select the optimal reception method by taking into account the user's device information at the time of reception. For example, the reception unit selects the optimal reception method by taking into account the user's device information at the time of reception. For example, if the user is using a smartphone, the reception unit can provide a reception method that matches the screen size. Furthermore, if the user is using a tablet, the reception unit can provide a reception method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a simple and highly visible reception method. In this way, the reception unit can provide a more appropriate reception method by taking into account the user's device information. Device information is collected based on criteria such as the device type and OS version. For example, the reception unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal reception method.

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

[0054] The suggestion unit can analyze the user's investment behavior history and adjust the content of the suggestions based on past successes and failures. For example, the suggestion unit can prioritize suggestions based on investment patterns that the user has been successful in the past. The suggestion unit can also make suggestions to help the user avoid investment patterns that have failed them in the past. Furthermore, the suggestion unit can strengthen suggestions regarding specific investment products based on the user's past investment behavior. This allows the suggestion unit to make more appropriate investment suggestions by taking the user's investment behavior history into consideration.

[0055] The collection unit can monitor the user's health condition and adjust the timing of information collection based on the health condition. For example, the collection unit actively collects information when the user's health condition is good. The collection unit can also refrain from collecting information when the user's health condition is deteriorating. Furthermore, the collection unit can adjust the type of information to be collected depending on the user's health condition. This allows the collection unit to collect information at a more appropriate time by taking the user's health condition into consideration.

[0056] The NISA analysis unit can perform NISA analysis taking into account the user's geographic location information. For example, the NISA analysis unit performs analysis based on the economic situation in the area where the user lives. The NISA analysis unit can also prioritize analysis of investment product information in areas that the user frequently visits. Furthermore, the NISA analysis unit can analyze market information related to the user's geographic location. This allows the NISA analysis unit to provide more relevant analysis results by taking into account the user's geographic location information.

[0057] The reception unit can select the optimal reception method by taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method that matches the screen size. Also, if the user is using a tablet, the reception unit can provide a reception method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a simple and highly visible reception method. In this way, the reception unit can provide a more appropriate reception method by taking into account the user's device information.

[0058] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also postpone the analysis of older information. Furthermore, the analysis unit can also give moderate priority to the analysis of information that is of intermediate recency. In this way, the analysis unit can provide more appropriate analysis results by determining the priority of analysis based on the time when the information was collected.

[0059] When analyzing NISA, the NISA analysis unit can predict the current analysis by referring to past NISA data. For example, the NISA analysis unit predicts the current investment situation based on past NISA data. The NISA analysis unit can also predict future investment trends from past NISA data. Furthermore, the NISA analysis unit can adjust the current investment strategy by referring to past NISA data. In this way, the NISA analysis unit can more accurately predict the current analysis by referring to past NISA data.

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

[0061] Step 1: The collection unit collects user information. The user information includes, for example, age, available funds, risk tolerance, etc. The collection unit stores the information entered by the user in a database and provides it to the analysis unit. Step 2: The analysis unit analyzes the collected information and determines the user's investment needs. The analysis unit uses statistical analysis and machine learning algorithms to analyze the user's investment needs. Step 3: The proposal department proposes optimal investment products based on the analysis results. The proposal department proposes products that match risk tolerance and are highly profitable. Step 4: The NISA analysis unit analyzes information on the New NISA's savings limit and growth investment limit. The NISA analysis unit analyzes the annual savings limit and types of investment targets. Step 5: The reception unit receives input from the user. The reception unit receives user information in the form of text input or selection options.

[0062] (Example 2) The investment proposal system according to an embodiment of the present invention is a system that proposes recommended products through chat based on the user's age, available funds, and desired principal risk and return. This investment proposal system is particularly specialized for the New NISA and proposes recommended products for those eligible for the savings and growth investment limits. Furthermore, when the user inputs their current investment products and NISA product status, the system predicts future developments and makes future investment proposals. For example, the user inputs information such as their age, available funds, and risk tolerance. The system then collects and analyzes this information. Based on the analysis results, the system proposes optimal investment products. This system is particularly specialized for the New NISA and analyzes information regarding the savings and growth investment limits. The system also analyzes the current investment product information entered by the user to predict future developments and make investment proposals. This allows the system to provide more specific advice to the user. This allows the investment proposal system to collect and analyze user information and propose optimal investment products.

[0063] The investment proposal system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, a NISA analysis unit, and a reception unit. The collection unit collects user information. The user information includes, but is not limited to, age, spare funds, and risk tolerance. The collection unit, for example, stores information entered by the user in a database and provides the information to the analysis unit. The analysis unit analyzes the collected information and determines the user's investment needs. The analysis unit analyzes the user's investment needs using, for example, statistical analysis or a machine learning algorithm. The proposal unit proposes optimal investment products based on the analysis results. The proposal unit proposes, for example, products according to risk tolerance or highly profitable products. The NISA analysis unit analyzes information related to the New NISA's savings limit and growth investment limit. The NISA analysis unit analyzes, for example, the annual savings limit and types of investment targets. The reception unit accepts input from the user. The reception unit accepts user information, for example, in the form of text input or multiple-choice options. This allows the investment proposal system to collect and analyze user information and propose optimal investment products.

[0064] The collection unit can collect the user's age, spare funds, risk tolerance, and other related information. For example, the collection unit stores information such as the user's age, spare funds, and risk tolerance inputted by the user in a database. The collection unit can also collect other related information such as the user's family structure and investment goals. For example, the collection unit provides the information inputted by the user to the analysis unit in real time, and the analysis unit performs analysis based on the information. In this way, the collection unit can make more appropriate investment suggestions by collecting information such as the user's age, spare funds, and risk tolerance.

[0065] The analysis unit can analyze the collected information and determine the user's investment needs. The analysis unit, for example, analyzes the collected information using statistical analysis or machine learning algorithms. For example, the analysis unit determines the user's investment needs based on information such as the user's age, spare funds, and risk tolerance. The analysis unit can also perform analysis taking into account information such as the user's investment goals and family composition. For example, the analysis unit can suggest high-risk investment products to users seeking short-term profits, and stable investment products to users aiming for long-term asset formation. In this way, the analysis unit can analyze the collected information and determine the user's investment needs, thereby making more appropriate investment suggestions.

[0066] The NISA analysis unit can analyze information regarding the savings limit and growth investment limit of the New NISA. The NISA analysis unit analyzes, for example, information regarding the savings limit and growth investment limit of the New NISA. For example, the NISA analysis unit analyzes the annual savings limit and types of investment targets and proposes investment products that are optimal for the user. The NISA analysis unit can also perform analysis taking into account information such as the user's investment goals and risk tolerance. For example, the NISA analysis unit proposes savings investment products that are appropriate for the user based on information regarding the savings limit, and proposes growth investment products that are appropriate for the user based on information regarding the growth investment limit. In this way, the NISA analysis unit can make more appropriate investment proposals by analyzing information regarding the savings limit and growth investment limit of the New NISA.

[0067] The suggestion unit can suggest optimal investment products based on the analysis results. The suggestion unit, for example, suggests optimal investment products based on the analysis results. For example, the suggestion unit suggests products that match the risk tolerance or highly profitable products. The suggestion unit can also make suggestions taking into consideration information such as the user's investment goals and family composition. For example, the suggestion unit suggests high-risk investment products to users seeking short-term profits, and stable investment products to users aiming for long-term asset formation. In this way, the suggestion unit can provide more specific advice to users by suggesting optimal investment products based on the analysis results.

[0068] The reception unit can receive input from a user. For example, the reception unit receives input from a user in the form of text input or multiple-choice options. For example, the reception unit stores the information input by the user in a database and provides it to the analysis unit. The reception unit can also provide the information input by the user to the analysis unit in real time, and the analysis unit can perform analysis based on that information. In this way, the reception unit can incorporate user information into the system by receiving input from the user.

[0069] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects information during a relaxed time. Furthermore, if the user is relaxed, the collection unit can also start collecting information immediately. Furthermore, if the user is in a hurry, the collection unit can collect information quickly. In this way, the collection unit can collect information at a more appropriate time by adjusting the timing of information collection based on the user's emotions. Emotion estimation is performed using, for example, technologies such as facial expression recognition and voice analysis. For example, the collection unit can input the user's facial expression data into a generation AI and cause the generation AI to estimate emotions.

[0070] The collection unit can analyze the user's past investment history and select the optimal information collection method. The collection unit, for example, analyzes the user's past investment history and selects the optimal information collection method. For example, the collection unit collects information based on the user's successful investment patterns in the past. The collection unit can also collect information to help the user avoid investment patterns that have failed in the past. The collection unit can also preferentially collect information about specific investment products from the user's past investment history. This allows the collection unit to select a more appropriate information collection method by analyzing the user's past investment history. The optimal information collection method can be determined, for example, by methods such as a questionnaire survey or analysis of behavioral history. For example, the collection unit can input the user's past investment history data into the generation AI and cause the generation AI to select the optimal information collection method.

[0071] The collection unit can filter information based on the user's current economic situation and areas of interest when collecting information. For example, the collection unit filters information based on the user's current economic situation and areas of interest when collecting information. For example, the collection unit collects appropriate investment product information based on the user's current income situation. The collection unit can also filter information based on the user's areas of interest (e.g., technology, medicine, etc.). The collection unit can also preferentially collect low-risk investment product information depending on the user's economic situation. This allows the collection unit to collect more relevant information by filtering information based on the user's current economic situation and areas of interest. Filtering is performed based on criteria such as economic situation indicators and interest categories. For example, the collection unit can input the user's economic situation data into the generation AI and have the generation AI perform filtering.

[0072] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can prioritize collecting low-risk investment product information. Also, if the user is excited, the collection unit can prioritize collecting high-risk, high-return investment product information. Also, if the user is relaxed, the collection unit can collect balanced investment product information. In this way, the collection unit can prioritize collecting more appropriate information by determining the priority of information to be collected based on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the collection unit can input the user's facial expression data into a generation AI and cause the generation AI to estimate emotions.

[0073] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, when collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit collects information based on the economic situation in the area where the user lives. The collection unit can also prioritize collecting investment product information in areas that the user frequently visits. The collection unit can also collect market information related to the user's geographical location. In this way, the collection unit can prioritize collecting more relevant information by taking into account the user's geographical location information. Geographical location information is collected based on criteria such as GPS data or IP address. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect highly relevant information.

[0074] The collection unit can analyze the user's social media activity and collect relevant information when collecting information. For example, the collection unit can analyze the user's social media activity and collect relevant information when collecting information. For example, the collection unit can collect information about investment products in which the user has shown interest on social media. The collection unit can also collect opinions of investment experts followed by the user. The collection unit can also collect trend information for investment communities in which the user participates. This allows the collection unit to collect more relevant information by analyzing the user's social media activity. Social media activity is analyzed based on criteria such as the content of posts and the number of likes. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect relevant information.

[0075] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can provide simple and easy-to-understand analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. This allows the analysis unit to provide more appropriate analysis results by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the collected information during analysis. For example, the analysis unit performs a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on information of medium importance. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the collected information. The importance of information is evaluated based on criteria such as the reliability and relevance of the information. For example, the analysis unit can input importance data of the collected information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a specific analysis algorithm to information regarding stock investments. The analysis unit can also apply a different analysis algorithm to information regarding real estate investments. The analysis unit can also apply yet another analysis algorithm to information regarding bond investments. In this way, the analysis unit can provide more appropriate analysis results by applying different analysis algorithms depending on the category of information. Analysis algorithms are realized using techniques such as regression analysis and clustering. For example, the analysis unit can input information category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0078] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. Alternatively, if the user is relaxed, the analysis unit can provide a detailed analysis result. Alternatively, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows the analysis unit to provide more appropriate analysis results by adjusting the length of the analysis based on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the analysis unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0079] The analysis unit can determine the analysis priority based on when the information was collected during analysis. For example, the analysis unit determines the analysis priority based on when the information was collected during analysis. For example, the analysis unit prioritizes analyzing the latest information. The analysis unit can also postpone analyzing older information. The analysis unit can also analyze information of moderate newness with moderate priority. In this way, the analysis unit can provide more appropriate analysis results by determining the analysis priority based on when the information was collected. The information collection time is evaluated based on criteria such as the latest information and past data. For example, the analysis unit can input information collection time data into the generation AI and have the generation AI determine the analysis priority.

[0080] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of information during analysis. For example, the analysis unit prioritizes analysis of information with high relevance. The analysis unit can also postpone analysis of information with low relevance. The analysis unit can also analyze information with moderate relevance with moderate priority. In this way, the analysis unit can provide more appropriate analysis results by adjusting the order of analysis based on the relevance of information. The relevance of information is evaluated based on criteria such as commonalities and correlations between information. For example, the analysis unit can input information relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0081] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and adjusts the way in which suggestions are expressed based on the estimated user emotions. For example, if the user is feeling anxious, the suggestion unit can provide simple and easy-to-understand suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually appealing suggestions. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way in which suggestions are expressed based on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0082] The proposal unit can adjust the level of detail of the proposal based on the importance of the investment product when making a proposal. For example, the proposal unit adjusts the level of detail of the proposal based on the importance of the investment product when making a proposal. For example, the proposal unit makes detailed proposals for investment products with high importance. The proposal unit can also make simplified proposals for investment products with low importance. The proposal unit can also make proposals with appropriate level of detail for investment products with medium importance. In this way, the proposal unit can provide more appropriate proposals by adjusting the level of detail of the proposal based on the importance of the investment product. The importance of an investment product is evaluated based on criteria such as risk assessment and profitability. For example, the proposal unit can input importance data of the investment product to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0083] The proposal unit can apply different proposal algorithms depending on the category of the investment product when making a proposal. For example, the proposal unit applies different proposal algorithms depending on the category of the investment product when making a proposal. For example, the proposal unit applies a specific proposal algorithm to proposals related to stock investments. The proposal unit can also apply a different proposal algorithm to proposals related to real estate investments. The proposal unit can also apply yet another proposal algorithm to proposals related to bond investments. In this way, the proposal unit can provide more appropriate proposals by applying different proposal algorithms depending on the category of the investment product. The proposal algorithm is realized by technologies such as a recommendation system or an optimization algorithm. For example, the proposal unit can input investment product category data into the generation AI and cause the generation AI to apply different proposal algorithms.

[0084] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can provide a short and to-the-point suggestion. If the user is relaxed, the suggestion unit can also provide a detailed suggestion. If the user is excited, the suggestion unit can also provide a visually stimulating suggestion. This allows the suggestion unit to provide more appropriate suggestions by adjusting the length of the suggestion based on the user's emotion. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] The proposal unit can determine the priority of proposals based on the submission date of investment products when making proposals. For example, the proposal unit determines the priority of proposals based on the submission date of investment products when making proposals. For example, the proposal unit prioritizes the latest investment products. The proposal unit can also postpone the proposal of older investment products. The proposal unit can also give moderate priority to the proposal of investment products that are of moderate newness. In this way, the proposal unit can provide more appropriate proposals by determining the priority of proposals based on the submission date of investment products. The submission date of investment products is evaluated based on criteria such as the most recent submission or a past submission. For example, the proposal unit can input the submission date data of investment products into the generation AI and have the generation AI determine the priority of proposals.

[0086] The proposal unit can adjust the order of proposals based on the relevance of investment products when making a proposal. For example, the proposal unit adjusts the order of proposals based on the relevance of investment products when making a proposal. For example, the proposal unit prioritizes proposing investment products with high relevance. The proposal unit can also postpone proposing investment products with low relevance. The proposal unit can also moderately prioritize proposing investment products with medium relevance. In this way, the proposal unit can provide more appropriate proposals by adjusting the order of proposals based on the relevance of investment products. The relevance of investment products is evaluated based on criteria such as commonalities and correlations between products. For example, the proposal unit can input relevance data of investment products into the generation AI and cause the generation AI to adjust the order of proposals.

[0087] The NISA analysis unit can estimate the user's emotions and adjust the display method of the NISA analysis based on the estimated user emotions. For example, the NISA analysis unit can estimate the user's emotions and adjust the display method of the NISA analysis based on the estimated user emotions. For example, if the user is feeling anxious, the NISA analysis unit can provide a simple and easy-to-understand display method. Also, if the user is relaxed, the NISA analysis unit can provide a display method that includes detailed information. Also, if the user is excited, the NISA analysis unit can provide a visually appealing display method. This allows the NISA analysis unit to provide more appropriate analysis results by adjusting the display method of the NISA analysis based on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the NISA analysis unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0088] When analyzing a NISA, the NISA analysis unit can predict the current analysis by referring to past NISA data. For example, when analyzing a NISA, the NISA analysis unit predicts the current analysis by referring to past NISA data. For example, the NISA analysis unit predicts the current investment situation based on past NISA data. The NISA analysis unit can also predict future investment trends from past NISA data. The NISA analysis unit can also adjust the current investment strategy by referring to past NISA data. In this way, the NISA analysis unit can more accurately predict the current analysis by referring to past NISA data. Past NISA data is referenced based on criteria such as past investment history and past market data. For example, the NISA analysis unit can input past NISA data into the generation AI and have the generation AI execute a current analysis prediction.

[0089] The NISA analysis unit can apply different analysis methods to each NISA category when analyzing a NISA. For example, the NISA analysis unit applies different analysis methods to each NISA category when analyzing a NISA. For example, the NISA analysis unit applies a specific analysis method to an analysis related to the savings limit. The NISA analysis unit can also apply a different analysis method to an analysis related to the growth investment limit. The NISA analysis unit can also apply yet another analysis method to an analysis related to other NISA categories. In this way, the NISA analysis unit can provide more appropriate analysis results by applying different analysis methods to each NISA category. NISA categories are analyzed based on criteria such as savings NISA and general NISA. For example, the NISA analysis unit can input NISA category data into the generation AI and have the generation AI apply different analysis methods.

[0090] The NISA analysis unit can estimate the user's emotions and adjust the importance of the NISA analysis based on the estimated user emotions. For example, the NISA analysis unit can estimate the user's emotions and adjust the importance of the NISA analysis based on the estimated user emotions. For example, if the user is feeling anxious, the NISA analysis unit prioritizes analysis of information with high importance. Furthermore, if the user is relaxed, the NISA analysis unit can also perform an analysis that includes detailed information. Furthermore, if the user is excited, the NISA analysis unit can perform a visually appealing analysis. This allows the NISA analysis unit to adjust the importance of the NISA analysis based on the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the NISA analysis unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0091] The NISA analysis unit can analyze changes in the analysis based on the time of NISA submission when analyzing the NISA. For example, the NISA analysis unit can analyze changes in the analysis based on the time of NISA submission when analyzing the NISA. For example, the NISA analysis unit prioritizes analyzing the most recent NISA data. The NISA analysis unit can also postpone analyzing older NISA data. The NISA analysis unit can also moderately prioritize analyzing NISA data that is of moderate recency. This allows the NISA analysis unit to provide more appropriate analysis results by analyzing changes in the analysis based on the time of NISA submission. The time of NISA submission is evaluated based on criteria such as the most recent submission or a past submission. For example, the NISA analysis unit can input NISA submission time data into the generation AI and have the generation AI analyze changes in the analysis.

[0092] The NISA analysis unit can perform analysis by referring to NISA-related market data when analyzing a NISA. For example, the NISA analysis unit performs analysis by referring to NISA-related market data when analyzing a NISA. For example, the NISA analysis unit analyzes the current investment situation based on NISA-related market data. The NISA analysis unit can also predict future investment trends from NISA-related market data. The NISA analysis unit can also adjust the current investment strategy by referring to NISA-related market data. In this way, the NISA analysis unit can provide more appropriate analysis results by referring to NISA-related market data. NISA-related market data is referenced based on, for example, stock market data or bond market data. For example, the NISA analysis unit can input NISA-related market data into the generation AI and have the generation AI perform the analysis.

[0093] The reception unit can estimate the user's emotions and adjust the reception method based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the reception method based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can provide a simple and easy-to-understand reception method. Furthermore, if the user is relaxed, the reception unit can provide a detailed reception method. Furthermore, if the user is excited, the reception unit can provide a visually appealing reception method. In this way, the reception unit can adjust the reception method based on the user's emotions and provide a more appropriate reception method. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the reception unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0094] The reception unit can select the optimal reception method by referring to the user's past input history when receiving a request. For example, the reception unit can select the optimal reception method by referring to the user's past input history when receiving a request. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. This allows the reception unit to select a more appropriate reception method by referring to the user's past input history. The past input history is referenced based on criteria such as past inquiry content and past input data. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI select the optimal reception method.

[0095] The reception unit can estimate the user's emotions and determine the reception priority based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and determine the reception priority based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can prioritize information of high importance. Furthermore, if the user is relaxed, the reception unit can provide a reception that includes detailed information. Furthermore, if the user is excited, the reception unit can provide a visually appealing reception. This allows the reception unit to determine the reception priority based on the user's emotions, thereby providing a more appropriate reception method. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the reception unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0096] The reception unit can select the optimal reception method by taking into account the user's device information at the time of reception. For example, the reception unit selects the optimal reception method by taking into account the user's device information at the time of reception. For example, if the user is using a smartphone, the reception unit can provide a reception method that matches the screen size. Furthermore, if the user is using a tablet, the reception unit can provide a reception method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a simple and highly visible reception method. In this way, the reception unit can provide a more appropriate reception method by taking into account the user's device information. Device information is collected based on criteria such as the device type and OS version. For example, the reception unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal reception method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, NISA analysis unit, and reception unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user information via the control unit 46A of the smart device 14 and provides it to the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and determines the user's investment needs. For example, the proposal unit proposes optimal investment products based on the analysis results via the specific processing unit 290 of the data processing device 12. For example, the NISA analysis unit analyzes information regarding the New NISA's savings limit and growth investment limit via the specific processing unit 290 of the data processing device 12. For example, the reception unit accepts input from the user via the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, NISA analysis unit, and reception unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user information via the control unit 46A of the smart glasses 214 and provides it to the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected via the specific processing unit 290 of the data processing device 12 and determines the user's investment needs. For example, the proposal unit proposes optimal investment products based on the analysis results via the specific processing unit 290 of the data processing device 12. For example, the NISA analysis unit analyzes information regarding the New NISA's savings limit and growth investment limit via the specific processing unit 290 of the data processing device 12. For example, the reception unit accepts input from the user via the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, NISA analysis unit, and reception unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects user information via the control unit 46A of the headset terminal 314 and provides it to the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and determines the user's investment needs. For example, the proposal unit proposes optimal investment products based on the analysis results via the specific processing unit 290 of the data processing device 12. For example, the NISA analysis unit analyzes information regarding the New NISA's savings limit and growth investment limit via the specific processing unit 290 of the data processing device 12. For example, the reception unit accepts input from the user via the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, NISA analysis unit, and reception unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user information via the control unit 46A of the robot 414 and provides it to the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected via the specific processing unit 290 of the data processing device 12 and determines the user's investment needs. For example, the proposal unit proposes optimal investment products based on the analysis results via the specific processing unit 290 of the data processing device 12. For example, the NISA analysis unit analyzes information regarding the New NISA's savings limit and growth investment limit via the specific processing unit 290 of the data processing device 12. For example, the reception unit accepts input from the user via the control unit 46A of the robot 414.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The suggestion unit can analyze the user's investment behavior history and adjust the content of the suggestions based on past successes and failures. For example, the suggestion unit can prioritize suggestions based on investment patterns that the user has been successful in the past. The suggestion unit can also make suggestions to help the user avoid investment patterns that have failed them in the past. Furthermore, the suggestion unit can strengthen suggestions regarding specific investment products based on the user's past investment behavior. This allows the suggestion unit to make more appropriate investment suggestions by taking the user's investment behavior history into consideration.

[0099] The collection unit can monitor the user's health condition and adjust the timing of information collection based on the health condition. For example, the collection unit actively collects information when the user's health condition is good. The collection unit can also refrain from collecting information when the user's health condition is deteriorating. Furthermore, the collection unit can adjust the type of information to be collected depending on the user's health condition. This allows the collection unit to collect information at a more appropriate time by taking the user's health condition into consideration.

[0100] The analysis unit can estimate the user's emotions and determine the priorities of analysis based on the estimated user's emotions. For example, if the user is feeling anxious, the analysis unit can prioritize the analysis of low-risk investment products. Also, if the user is excited, the analysis unit can prioritize the analysis of high-risk, high-return investment products. Furthermore, if the user is relaxed, the analysis unit can analyze balanced investment products. In this way, the analysis unit can provide more appropriate analysis results by determining the priorities of analysis based on the user's emotions.

[0101] The NISA analysis unit can perform NISA analysis taking into account the user's geographic location information. For example, the NISA analysis unit performs analysis based on the economic situation in the area where the user lives. The NISA analysis unit can also prioritize analysis of investment product information in areas that the user frequently visits. Furthermore, the NISA analysis unit can analyze market information related to the user's geographic location. This allows the NISA analysis unit to provide more relevant analysis results by taking into account the user's geographic location information.

[0102] The suggestion unit can estimate the user's emotions and adjust the timing of the suggestion based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can make the suggestion during a relaxed time period. Also, if the user is relaxed, the suggestion unit can make the suggestion immediately. Furthermore, if the user is in a hurry, the suggestion unit can make the suggestion quickly. In this way, the suggestion unit can make the suggestion at a more appropriate time by adjusting the timing of the suggestion based on the user's emotions.

[0103] The reception unit can select the optimal reception method by taking into account the user's device information. For example, if the user is using a smartphone, the reception unit can provide a reception method that matches the screen size. Also, if the user is using a tablet, the reception unit can provide a reception method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a simple and highly visible reception method. In this way, the reception unit can provide a more appropriate reception method by taking into account the user's device information.

[0104] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, if the user is feeling anxious, the collection unit can preferentially collect low-risk investment product information. Also, if the user is excited, the collection unit can preferentially collect high-risk, high-return investment product information. Furthermore, if the user is relaxed, the collection unit can collect balanced investment product information. In this way, the collection unit can preferentially collect more appropriate information by determining the priority of information to be collected based on the user's emotions.

[0105] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also postpone the analysis of older information. Furthermore, the analysis unit can also give moderate priority to the analysis of information that is of intermediate recency. In this way, the analysis unit can provide more appropriate analysis results by determining the priority of analysis based on the time when the information was collected.

[0106] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. For example, if the user feels anxious, the suggestion unit can provide simple and easy-to-understand suggestions. If the user feels relaxed, the suggestion unit can also provide detailed suggestions. Furthermore, if the user feels excited, the suggestion unit can also provide visually appealing suggestions. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way in which suggestions are expressed based on the user's emotion.

[0107] When analyzing NISA, the NISA analysis unit can predict the current analysis by referring to past NISA data. For example, the NISA analysis unit predicts the current investment situation based on past NISA data. The NISA analysis unit can also predict future investment trends from past NISA data. Furthermore, the NISA analysis unit can adjust the current investment strategy by referring to past NISA data. In this way, the NISA analysis unit can more accurately predict the current analysis by referring to past NISA data.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The collection unit collects user information. The user information includes, for example, age, available funds, risk tolerance, etc. The collection unit stores the information entered by the user in a database and provides it to the analysis unit. Step 2: The analysis unit analyzes the collected information and determines the user's investment needs. The analysis unit uses statistical analysis and machine learning algorithms to analyze the user's investment needs. Step 3: The proposal department proposes optimal investment products based on the analysis results. The proposal department proposes products that match risk tolerance and are highly profitable. Step 4: The NISA analysis unit analyzes information on the New NISA's savings limit and growth investment limit. The NISA analysis unit analyzes the annual savings limit and types of investment targets. Step 5: The reception unit receives input from the user. The reception unit receives user information in the form of text input or selection options.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0140] In the 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.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 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.

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

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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, in order to avoid confusion and to 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user information; an analysis unit that analyzes the information collected by the collection unit; a proposal unit that proposes appropriate investment products based on the analysis results obtained by the analysis unit; The NISA Analysis Department analyzes information about the new NISA. A reception unit that receives input from a user. A system characterized by:

2. The collecting unit Collect your age, financial resources, risk tolerance, and other relevant information 2. The system of claim 1.

3. The analysis unit Analyze the collected information to determine your investment needs 2. The system of claim 1.

4. The NISA analysis unit Analyzing information on the new NISA's savings and growth investment limits 2. The system of claim 1.

5. The proposal unit Propose optimal investment products based on analysis results 2. The system of claim 1.

6. The reception unit Accepting input from the user 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past investment history and select the optimal information collection method 2. The system of claim 1.

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