system

Abstract

The system according to the embodiment aims to analyze the user's asset information and provide advice on appropriate asset management and accumulation methods. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects asset information of a user. The analysis unit analyzes the asset information collected by the collection unit. The provision unit provides advice on asset management or accumulation methods based on the prediction results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of not being able to effectively analyze a user's asset information and provide adequate advice on appropriate asset management and accumulation methods.

[0005] The system according to the embodiment aims to analyze the user's asset information and provide advice on appropriate asset management and accumulation methods. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects asset information of a user. The analysis unit analyzes the asset information collected by the collection unit. The provision unit provides advice on asset management or accumulation methods based on the prediction results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's asset information and provide advice on appropriate asset management and accumulation methods. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An asset management support system according to an embodiment of the present invention links a user's asset information to a database and provides advice on how to manage future asset amounts and how to accumulate assets on a daily basis. The asset management support system collects the user's asset information and stores it in a database. The system then analyzes the collected asset information and predicts future asset amounts. Based on the prediction results, the system provides advice on optimal asset management methods and daily asset accumulation methods. This system enables users to efficiently manage their assets and supports future asset formation. For example, the asset management support system collects information such as the user's bank account balance, real estate appraised value, and stock holdings. This information is stored in a database. The system then analyzes the collected asset information. AI is used for the analysis. The AI ​​predicts future asset amounts based on past asset data and market trends. For example, past stock price fluctuations and real estate market trends are analyzed to predict future asset increases and decreases. Based on the prediction results, the system provides advice on optimal asset management methods and daily asset accumulation methods. For example, if there is a prospect of future asset increases, the system suggests taking risks and investing aggressively. In addition, if there is a prospect of assets decreasing, the system will suggest investment methods with reduced risk. This system allows users to efficiently manage their assets and support future asset formation. For example, users of the younger generation can actively manage their assets in order to build assets for the future. On the other hand, users of the retirement generation can select investment methods with reduced risk. In this way, the asset management support system can efficiently manage users' assets and support future asset formation.

[0029] The asset management support system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects asset information of a user. The asset information of a user includes, for example, bank account balances, real estate appraised values, and stock holdings, but is not limited to these examples. For example, the collection unit acquires bank account balances via an API. The collection unit can also acquire real estate appraised values ​​from a public database. The collection unit can also acquire stock holdings from a securities company's system. For example, the collection unit periodically acquires bank account balances and stores them in a database. The collection unit can also periodically update real estate appraised values ​​and update the database with the latest information. The collection unit can also acquire stock holdings in real time and store them in the database. The analysis unit analyzes the asset information collected by the collection unit. AI is used for the analysis. The AI ​​predicts future asset values ​​based on past asset data and market trends. For example, the analysis unit analyzes past stock price fluctuations and predicts future stock prices. The analysis unit can also analyze real estate market trends and predict future real estate appraised values. Furthermore, the analysis unit can predict increases or decreases in the user's overall assets and predict future asset values. For example, the analysis unit predicts future stock price fluctuations based on past stock price data. Furthermore, the analysis unit can predict future real estate appraised values ​​based on real estate market trends. Furthermore, the analysis unit can predict increases or decreases in the user's overall assets and predict future asset values. The provision unit provides advice on asset management methods and accumulation methods based on the prediction results obtained by the analysis unit. For example, if assets are expected to increase in the future, the provision unit can suggest aggressive investments that take risks. Furthermore, if assets are expected to decrease, the provision unit can suggest investment methods with reduced risk. Furthermore, the provision unit can provide advice according to the user's life stage. For example, the provision unit can suggest aggressive investments for future asset formation to younger users. Furthermore, the provision unit can suggest investment methods with reduced risk to users approaching retirement.As a result, the asset management support system according to the embodiment can efficiently manage the user's assets and support future asset formation.

[0030] The collection unit can collect information such as bank account balances, real estate appraised values, and stock holdings. The collection unit, for example, obtains bank account balances through an API. For example, the collection unit can access a user's bank account and obtain balance information. The collection unit can also obtain real estate appraised values ​​from a public database. For example, the collection unit can periodically update the real estate appraised values ​​and update the database with the latest information. The collection unit can also obtain stock holdings from a securities company's system. For example, the collection unit can access a user's securities account and obtain information about stock holdings. This allows the collection unit to comprehensively collect a variety of asset information about the user. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, to obtain the bank account balance, the collection unit can access the user's bank account using AI and obtain the balance information.

[0031] The analysis unit can predict future asset values ​​based on past asset data and market trends. The analysis unit, for example, analyzes past stock price fluctuations and predicts future stock prices. For example, the analysis unit can predict future stock price fluctuations based on past stock price data. The analysis unit can also analyze real estate market trends and predict future real estate appraised values. For example, the analysis unit can predict future real estate appraised values ​​based on real estate market trends. Furthermore, the analysis unit can predict increases or decreases in the user's overall assets and predict future asset values. For example, the analysis unit can predict future asset increases or decreases based on the user's past asset data. This allows the analysis unit to improve the accuracy of predicting future asset values. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past asset data into AI and have the AI ​​predict future asset values.

[0032] The providing unit can provide advice on asset management methods or daily asset accumulation methods based on the prediction results. For example, if assets are expected to increase in the future, the providing unit can suggest aggressive asset management by taking risks. For example, the providing unit can suggest aggressive asset management methods such as stock investment or real estate investment to the user. Furthermore, if assets are expected to decrease, the providing unit can suggest low-risk asset management methods. For example, the providing unit can suggest low-risk asset management methods such as fixed-term deposits or bond investment to the user. Furthermore, the providing unit can provide advice according to the user's life stage. For example, the providing unit can suggest aggressive asset management for future asset formation to younger users. Furthermore, the providing unit can suggest low-risk asset management methods to users approaching retirement. This allows the providing unit to provide the user with advice on optimal asset management methods and accumulation methods. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the prediction results into AI and have the AI ​​execute advice on optimal asset management methods and accumulation methods.

[0033] The collection unit can analyze the user's past asset information collection history and select the optimal collection method. For example, the collection unit prioritizes the selection of a collection method (API, manual input, etc.) that the user has frequently used in the past. For example, the collection unit can analyze the user's past collection history and suggest the most efficient collection method. The collection unit can also optimize the collection frequency based on the user's past collection history. For example, the collection unit can determine the optimal collection frequency based on the user's past collection history. This allows the collection unit to select the optimal collection method based on the user's past collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past collection history data into the generation AI and have the generation AI select the optimal collection method.

[0034] When collecting asset information, the collection unit can filter the asset information based on the user's current living situation and areas of interest. For example, if the user purchases a new property, the collection unit prioritizes collecting that information. For example, the collection unit can collect related asset information based on the user's real estate purchase information. Furthermore, if the user is interested in stock investment, the collection unit can collect detailed information about the user's stock holdings. For example, the collection unit can collect related asset information based on information about the user's stock investment. Furthermore, if the user is approaching retirement, the collection unit can prioritize collecting information about pensions and retirement benefits. For example, the collection unit can collect related asset information based on information about the user's retirement. This allows the collection unit to collect asset information according to the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data about the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0035] When collecting asset information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting real estate information for that area. For example, the collection unit can prioritize collecting real estate information for that area based on the user's geographical location information. Furthermore, if the user is traveling, the collection unit can prioritize collecting asset information for the travel destination. For example, the collection unit can prioritize collecting asset information for the travel destination based on the user's geographical location information. Furthermore, if the user is planning to move, the collection unit can prioritize collecting asset information for the user's new residence. For example, the collection unit can prioritize collecting asset information for the new residence based on the user's geographical location information. This allows the collection unit to collect highly relevant asset information based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0036] When collecting asset information, the collection unit can analyze the user's social media activity and collect related information. For example, if the user posts about stock investment on social media, the collection unit collects related information. For example, the collection unit can collect information about stock investment based on the user's social media activity. In addition, if the user shares information about real estate, the collection unit can collect related information. For example, the collection unit can collect information about real estate based on the user's social media activity. Furthermore, if the user shows interest in a particular financial product, the collection unit can collect related information. For example, the collection unit can collect information about financial products based on the user's social media activity. This allows the collection unit to collect related asset information based on the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0037] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the asset. For example, the analysis unit performs a detailed analysis on assets with high importance. For example, the analysis unit can perform a detailed analysis on assets with high importance. The analysis unit can also perform a simplified analysis on assets with low importance. For example, the analysis unit can perform a simplified analysis on assets with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the asset. For example, the analysis unit can determine the priority of the analysis based on the importance of the asset. This allows the analysis unit to optimize the level of detail of the analysis based on the importance of the asset. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input asset importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the asset category. For example, for stocks, the analysis unit applies an analysis algorithm based on past stock price fluctuations. For example, for stocks, the analysis unit can apply an analysis algorithm based on past stock price fluctuations. Furthermore, for real estate, the analysis unit can apply an analysis algorithm based on market trends. For example, for real estate, the analysis unit can apply an analysis algorithm based on market trends. Furthermore, for bank accounts, the analysis unit can apply an analysis algorithm based on past transaction history. For example, for bank accounts, the analysis unit can apply an analysis algorithm based on past transaction history. This allows the analysis unit to apply an optimal analysis algorithm depending on the asset category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input asset category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the collection time of the asset information. The analysis unit, for example, prioritizes analysis of the most recent asset information. For example, the analysis unit can prioritize analysis of the most recent asset information based on the collection time of the asset information. The analysis unit can also determine the priority of analysis for past asset information based on its importance. For example, the analysis unit can determine the priority of analysis for past asset information based on its importance. Furthermore, the analysis unit can adjust the analysis schedule based on the collection time of the asset information. For example, the analysis unit can adjust the analysis schedule based on the collection time of the asset information. This allows the analysis unit to optimize the priority of analysis based on the collection time of the asset information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input asset information collection time data to the generation AI and cause the generation AI to determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of assets. The analysis unit, for example, prioritizes analysis of highly relevant assets. For example, the analysis unit can prioritize analysis of highly relevant assets based on the relevance of assets. The analysis unit can also postpone analysis of less relevant assets. For example, the analysis unit can postpone analysis of less relevant assets based on the relevance of assets. Furthermore, the analysis unit can also optimize the order of analysis based on the relevance of assets. For example, the analysis unit can optimize the order of analysis based on the relevance of assets. This allows the analysis unit to optimize the order of analysis based on the relevance of assets. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input asset relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the asset. For example, the providing unit can provide detailed advice for an asset with high importance. For example, the providing unit can provide detailed advice for an asset with high importance. Furthermore, the providing unit can provide simplified advice for an asset with low importance. For example, the providing unit can provide simplified advice for an asset with low importance. Furthermore, the providing unit can determine the priority of the advice according to the importance of the asset. For example, the providing unit can determine the priority of the advice based on the importance of the asset. This allows the providing unit to optimize the level of detail of the advice according to the importance of the asset. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input asset importance data to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0042] When providing advice, the providing unit can apply different advice algorithms depending on the asset category. For example, for stocks, the providing unit applies an advice algorithm based on past stock price fluctuations. For example, for stocks, the providing unit can apply an advice algorithm based on past stock price fluctuations. Furthermore, for real estate, the providing unit can apply an advice algorithm based on market trends. For example, for real estate, the providing unit can apply an advice algorithm based on market trends. Furthermore, for bank accounts, the providing unit can apply an advice algorithm based on past transaction history. For example, for bank accounts, the providing unit can apply an advice algorithm based on past transaction history. This allows the providing unit to apply an optimal advice algorithm depending on the asset category. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input asset category data to the generation AI and cause the generation AI to apply different advice algorithms.

[0043] When providing advice, the providing unit can determine the priority of the advice based on the collection time of the asset information. The providing unit, for example, provides advice based on the latest asset information. For example, the providing unit can provide advice based on the latest asset information based on the collection time of the asset information. The providing unit can also determine the priority of the advice for past asset information based on its importance. For example, the providing unit can determine the priority of the advice for past asset information based on its importance. Furthermore, the providing unit can adjust the advice schedule based on the collection time of the asset information. For example, the providing unit can adjust the advice schedule based on the collection time of the asset information. This allows the providing unit to optimize the priority of the advice based on the collection time of the asset information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input asset information collection time data to the generation AI and cause the generation AI to determine the priority of the advice.

[0044] When providing advice, the providing unit can adjust the order of advice based on the relevance of assets. The providing unit, for example, prioritizes advice for highly relevant assets. For example, the providing unit can prioritize advice for highly relevant assets based on the relevance of assets. The providing unit can also postpone advice for less relevant assets. For example, the providing unit can postpone advice for less relevant assets based on the relevance of assets. Furthermore, the providing unit can also optimize the order of advice based on the relevance of assets. For example, the providing unit can optimize the order of advice based on the relevance of assets. In this way, the providing unit can optimize the order of advice based on the relevance of assets. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input asset relevance data to a generation AI and cause the generation AI to adjust the order of advice.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] When analyzing a user's asset information, the analysis unit can adjust the accuracy of the analysis taking into account the user's health condition. For example, if the user's health condition is good, a detailed analysis can be performed to proactively evaluate future asset management risks. Also, if the user's health condition is deteriorating, an analysis that reduces risks can be performed to suggest a conservative asset management method. Furthermore, the analysis unit can adjust the priority of asset management based on the user's health condition. For example, if the user's health condition is deteriorating, short-term asset management can be prioritized, and if the user's health condition is good, long-term asset management can be prioritized. This allows the analysis unit to provide the optimal asset management method according to the user's health condition.

[0047] When collecting a user's asset information, the collection unit can adjust the type of information to be collected based on the user's hobbies and interests. For example, if the user is interested in art, art-related asset information can be preferentially collected. Also, if the user is interested in technology, technology-related asset information can be collected in detail. Furthermore, if the user enjoys traveling, real estate information and investment opportunities in travel destinations can be preferentially collected. This allows the collection unit to collect asset information according to the user's hobbies and interests.

[0048] When providing advice on asset management to a user, the providing unit can adjust the content of the advice based on the user's family structure and life events. For example, if the user has a new child, the providing unit can suggest ways to save for education. Also, if the user is planning to get married, the providing unit can suggest ways to prepare for marriage. Furthermore, if the user is approaching retirement, the providing unit can suggest ways to manage living expenses after retirement. In this way, the providing unit can provide optimal asset management advice according to the user's family structure and life events.

[0049] When analyzing the user's asset information, the analysis unit can adjust the analysis algorithm taking into account the user's past investment history. For example, based on the user's successful investment patterns in the past, similar patterns can be reflected in the analysis. The analysis algorithm can also be adjusted to avoid investment patterns that the user has failed in the past. Furthermore, the risk tolerance can be reflected in the analysis based on the user's investment history. This allows the analysis unit to provide an optimal analysis algorithm based on the user's past investment history.

[0050] When collecting a user's asset information, the collection unit can adjust the range of information to be collected taking into account the user's geographical movement patterns. For example, if the user frequently travels on business, asset information for the travel destination can be collected preferentially. Also, if the user stays in a specific area for a long period of time, asset information for that area can be collected in detail. Furthermore, if the user plans to move, asset information for the user's new residence can be collected preferentially. This allows the collection unit to collect asset information according to the user's geographical movement patterns.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The collection unit collects the user's asset information. The user's asset information includes bank account balances, real estate appraised values, stock holdings, etc. The collection unit obtains bank account balances via an API, real estate appraised values ​​from a public database, and stock holdings from a securities company's system. This information is updated regularly and stored in a database. Step 2: The analysis unit analyzes the asset information collected by the collection unit. AI is used for the analysis, and future asset values ​​are predicted based on past asset data and market trends. For example, past stock price fluctuations and real estate market trends are analyzed to predict future stock prices and real estate appraised values. The analysis unit also predicts the increase or decrease in the user's total assets, and predicts future asset values. Step 3: The provider provides advice on asset management and accumulation methods based on the prediction results obtained by the analyzer. For example, if assets are expected to increase in the future, it will suggest aggressive management, and if assets are expected to decrease, it will suggest a low-risk management method. It also provides advice according to the user's life stage, suggesting aggressive management for younger generations and low-risk management methods for those approaching retirement.

[0053] (Example 2) An asset management support system according to an embodiment of the present invention links a user's asset information to a database and provides advice on how to manage future asset amounts and how to accumulate assets on a daily basis. The asset management support system collects the user's asset information and stores it in a database. The system then analyzes the collected asset information and predicts future asset amounts. Based on the prediction results, the system provides advice on optimal asset management methods and daily asset accumulation methods. This system enables users to efficiently manage their assets and supports future asset formation. For example, the asset management support system collects information such as the user's bank account balance, real estate appraised value, and stock holdings. This information is stored in a database. The system then analyzes the collected asset information. AI is used for the analysis. The AI ​​predicts future asset amounts based on past asset data and market trends. For example, past stock price fluctuations and real estate market trends are analyzed to predict future asset increases and decreases. Based on the prediction results, the system provides advice on optimal asset management methods and daily asset accumulation methods. For example, if there is a prospect of future asset increases, the system suggests taking risks and investing aggressively. In addition, if there is a prospect of assets decreasing, the system will suggest investment methods with reduced risk. This system allows users to efficiently manage their assets and support future asset formation. For example, users of the younger generation can actively manage their assets in order to build assets for the future. On the other hand, users of the retirement generation can select investment methods with reduced risk. In this way, the asset management support system can efficiently manage users' assets and support future asset formation.

[0054] The asset management support system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects asset information of a user. The asset information of a user includes, for example, bank account balances, real estate appraised values, and stock holdings, but is not limited to these examples. For example, the collection unit acquires bank account balances via an API. The collection unit can also acquire real estate appraised values ​​from a public database. The collection unit can also acquire stock holdings from a securities company's system. For example, the collection unit periodically acquires bank account balances and stores them in a database. The collection unit can also periodically update real estate appraised values ​​and update the database with the latest information. The collection unit can also acquire stock holdings in real time and store them in the database. The analysis unit analyzes the asset information collected by the collection unit. AI is used for the analysis. The AI ​​predicts future asset values ​​based on past asset data and market trends. For example, the analysis unit analyzes past stock price fluctuations and predicts future stock prices. The analysis unit can also analyze real estate market trends and predict future real estate appraised values. Furthermore, the analysis unit can predict increases or decreases in the user's overall assets and predict future asset values. For example, the analysis unit predicts future stock price fluctuations based on past stock price data. Furthermore, the analysis unit can predict future real estate appraised values ​​based on real estate market trends. Furthermore, the analysis unit can predict increases or decreases in the user's overall assets and predict future asset values. The provision unit provides advice on asset management methods and accumulation methods based on the prediction results obtained by the analysis unit. For example, if assets are expected to increase in the future, the provision unit can suggest aggressive investments that take risks. Furthermore, if assets are expected to decrease, the provision unit can suggest investment methods with reduced risk. Furthermore, the provision unit can provide advice according to the user's life stage. For example, the provision unit can suggest aggressive investments for future asset formation to younger users. Furthermore, the provision unit can suggest investment methods with reduced risk to users approaching retirement.As a result, the asset management support system according to the embodiment can efficiently manage the user's assets and support future asset formation.

[0055] The collection unit can collect information such as bank account balances, real estate appraised values, and stock holdings. The collection unit, for example, obtains bank account balances through an API. For example, the collection unit can access a user's bank account and obtain balance information. The collection unit can also obtain real estate appraised values ​​from a public database. For example, the collection unit can periodically update the real estate appraised values ​​and update the database with the latest information. The collection unit can also obtain stock holdings from a securities company's system. For example, the collection unit can access a user's securities account and obtain information about stock holdings. This allows the collection unit to comprehensively collect a variety of asset information about the user. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, to obtain the bank account balance, the collection unit can access the user's bank account using AI and obtain the balance information.

[0056] The analysis unit can predict future asset values ​​based on past asset data and market trends. The analysis unit, for example, analyzes past stock price fluctuations and predicts future stock prices. For example, the analysis unit can predict future stock price fluctuations based on past stock price data. The analysis unit can also analyze real estate market trends and predict future real estate appraised values. For example, the analysis unit can predict future real estate appraised values ​​based on real estate market trends. Furthermore, the analysis unit can predict increases or decreases in the user's overall assets and predict future asset values. For example, the analysis unit can predict future asset increases or decreases based on the user's past asset data. This allows the analysis unit to improve the accuracy of predicting future asset values. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past asset data into AI and have the AI ​​predict future asset values.

[0057] The providing unit can provide advice on asset management methods or daily asset accumulation methods based on the prediction results. For example, if assets are expected to increase in the future, the providing unit can suggest aggressive asset management by taking risks. For example, the providing unit can suggest aggressive asset management methods such as stock investment or real estate investment to the user. Furthermore, if assets are expected to decrease, the providing unit can suggest low-risk asset management methods. For example, the providing unit can suggest low-risk asset management methods such as fixed-term deposits or bond investment to the user. Furthermore, the providing unit can provide advice according to the user's life stage. For example, the providing unit can suggest aggressive asset management for future asset formation to younger users. Furthermore, the providing unit can suggest low-risk asset management methods to users approaching retirement. This allows the providing unit to provide the user with advice on optimal asset management methods and accumulation methods. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the prediction results into AI and have the AI ​​execute advice on optimal asset management methods and accumulation methods.

[0058] The collection unit can estimate the user's emotions and adjust the timing of asset information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing and collects asset information when the user is relaxed. For example, the collection unit can estimate the user's emotions and adjust the collection timing when the user is feeling stressed. Furthermore, if the user is relaxed, the collection unit can immediately collect asset information and update the database with the latest information. For example, the collection unit can collect asset information when the user is relaxed and store the information in the database. Furthermore, if the user is busy, the collection unit can adjust the collection timing to match the user's schedule. For example, the collection unit can select the optimal collection timing based on the user's schedule. This allows the collection unit to optimize the timing of asset information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0059] The collection unit can analyze the user's past asset information collection history and select the optimal collection method. For example, the collection unit prioritizes the selection of a collection method (API, manual input, etc.) that the user has frequently used in the past. For example, the collection unit can analyze the user's past collection history and suggest the most efficient collection method. The collection unit can also optimize the collection frequency based on the user's past collection history. For example, the collection unit can determine the optimal collection frequency based on the user's past collection history. This allows the collection unit to select the optimal collection method based on the user's past collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past collection history data into the generation AI and have the generation AI select the optimal collection method.

[0060] When collecting asset information, the collection unit can filter the asset information based on the user's current living situation and areas of interest. For example, if the user purchases a new property, the collection unit prioritizes collecting that information. For example, the collection unit can collect related asset information based on the user's real estate purchase information. Furthermore, if the user is interested in stock investment, the collection unit can collect detailed information about the user's stock holdings. For example, the collection unit can collect related asset information based on information about the user's stock investment. Furthermore, if the user is approaching retirement, the collection unit can prioritize collecting information about pensions and retirement benefits. For example, the collection unit can collect related asset information based on information about the user's retirement. This allows the collection unit to collect asset information according to the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data about the user's living situation and areas of interest into the generation AI and have the generation AI perform filtering.

[0061] The collection unit can estimate the user's emotions and determine the priority of asset information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit postpones collecting less important information and prioritizes collecting more important information. For example, the collection unit can estimate the user's emotions and prioritize collecting more important information when the user is feeling stressed. The collection unit can also collect all asset information evenly when the user is relaxed. For example, the collection unit can collect all asset information evenly when the user is relaxed. Furthermore, the collection unit can prioritize collecting the most important asset information when the user is in a hurry. For example, the collection unit can prioritize collecting the most important asset information when the user is in a hurry. This allows the collection unit to optimize the priority of asset information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0062] When collecting asset information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in a specific area, the collection unit prioritizes collecting real estate information for that area. For example, the collection unit can prioritize collecting real estate information for that area based on the user's geographical location information. Furthermore, if the user is traveling, the collection unit can prioritize collecting asset information for the travel destination. For example, the collection unit can prioritize collecting asset information for the travel destination based on the user's geographical location information. Furthermore, if the user is planning to move, the collection unit can prioritize collecting asset information for the user's new residence. For example, the collection unit can prioritize collecting asset information for the new residence based on the user's geographical location information. This allows the collection unit to collect highly relevant asset information based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0063] When collecting asset information, the collection unit can analyze the user's social media activity and collect related information. For example, if the user posts about stock investment on social media, the collection unit collects related information. For example, the collection unit can collect information about stock investment based on the user's social media activity. In addition, if the user shares information about real estate, the collection unit can collect related information. For example, the collection unit can collect information about real estate based on the user's social media activity. Furthermore, if the user shows interest in a particular financial product, the collection unit can collect related information. For example, the collection unit can collect information about financial products based on the user's social media activity. This allows the collection unit to collect related asset information based on the user's social media activity. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit may input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0064] The analysis unit can estimate the user's emotions and adjust the method of expressing the analysis based on the estimated user's emotions. For example, when the user is nervous, the analysis unit can provide a simple, highly visible analysis result. For example, when the user is nervous, the analysis unit can estimate the user's emotions and provide a simple, highly visible analysis result. Furthermore, when the user is relaxed, the analysis unit can provide a detailed analysis result. For example, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can provide a summary analysis result. For example, when the user is in a hurry, the analysis unit can provide a summary analysis result. This allows the analysis unit to optimize the method of expressing the analysis result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0065] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the asset. For example, the analysis unit performs a detailed analysis on assets with high importance. For example, the analysis unit can perform a detailed analysis on assets with high importance. The analysis unit can also perform a simplified analysis on assets with low importance. For example, the analysis unit can perform a simplified analysis on assets with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the asset. For example, the analysis unit can determine the priority of the analysis based on the importance of the asset. This allows the analysis unit to optimize the level of detail of the analysis based on the importance of the asset. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input asset importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0066] During analysis, the analysis unit can apply different analysis algorithms depending on the asset category. For example, for stocks, the analysis unit applies an analysis algorithm based on past stock price fluctuations. For example, for stocks, the analysis unit can apply an analysis algorithm based on past stock price fluctuations. Furthermore, for real estate, the analysis unit can apply an analysis algorithm based on market trends. For example, for real estate, the analysis unit can apply an analysis algorithm based on market trends. Furthermore, for bank accounts, the analysis unit can apply an analysis algorithm based on past transaction history. For example, for bank accounts, the analysis unit can apply an analysis algorithm based on past transaction history. This allows the analysis unit to apply an optimal analysis algorithm depending on the asset category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input asset category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0067] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. For example, if the user is in a hurry, the analysis unit can estimate the user's emotions and provide a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide an analysis result with visually stimulating effects when the user is excited. For example, the analysis unit can provide an analysis result with visually stimulating effects when the user is excited. This allows the analysis unit to optimize the length of the analysis result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0068] During analysis, the analysis unit can determine the priority of analysis based on the collection time of the asset information. The analysis unit, for example, prioritizes analysis of the most recent asset information. For example, the analysis unit can prioritize analysis of the most recent asset information based on the collection time of the asset information. The analysis unit can also determine the priority of analysis for past asset information based on its importance. For example, the analysis unit can determine the priority of analysis for past asset information based on its importance. Furthermore, the analysis unit can adjust the analysis schedule based on the collection time of the asset information. For example, the analysis unit can adjust the analysis schedule based on the collection time of the asset information. This allows the analysis unit to optimize the priority of analysis based on the collection time of the asset information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input asset information collection time data to the generation AI and cause the generation AI to determine the analysis priority.

[0069] During analysis, the analysis unit can adjust the order of analysis based on the relevance of assets. The analysis unit, for example, prioritizes analysis of highly relevant assets. For example, the analysis unit can prioritize analysis of highly relevant assets based on the relevance of assets. The analysis unit can also postpone analysis of less relevant assets. For example, the analysis unit can postpone analysis of less relevant assets based on the relevance of assets. Furthermore, the analysis unit can also optimize the order of analysis based on the relevance of assets. For example, the analysis unit can optimize the order of analysis based on the relevance of assets. This allows the analysis unit to optimize the order of analysis based on the relevance of assets. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input asset relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0070] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. For example, when the user is nervous, the providing unit can provide simple, highly visible advice. For example, when the user is nervous, the providing unit can estimate the user's emotions and provide simple, highly visible advice. The providing unit can also provide detailed advice when the user is relaxed. For example, the providing unit can provide detailed advice when the user is relaxed. Furthermore, when the user is in a hurry, the providing unit can provide advice that focuses on the main points. For example, when the user is in a hurry, the providing unit can provide advice that focuses on the main points. This allows the providing unit to optimize the way in which advice is expressed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0071] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the asset. For example, the providing unit can provide detailed advice for an asset with high importance. For example, the providing unit can provide detailed advice for an asset with high importance. Furthermore, the providing unit can provide simplified advice for an asset with low importance. For example, the providing unit can provide simplified advice for an asset with low importance. Furthermore, the providing unit can determine the priority of the advice according to the importance of the asset. For example, the providing unit can determine the priority of the advice based on the importance of the asset. This allows the providing unit to optimize the level of detail of the advice according to the importance of the asset. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input asset importance data to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0072] When providing advice, the providing unit can apply different advice algorithms depending on the asset category. For example, for stocks, the providing unit applies an advice algorithm based on past stock price fluctuations. For example, for stocks, the providing unit can apply an advice algorithm based on past stock price fluctuations. Furthermore, for real estate, the providing unit can apply an advice algorithm based on market trends. For example, for real estate, the providing unit can apply an advice algorithm based on market trends. Furthermore, for bank accounts, the providing unit can apply an advice algorithm based on past transaction history. For example, for bank accounts, the providing unit can apply an advice algorithm based on past transaction history. This allows the providing unit to apply an optimal advice algorithm depending on the asset category. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input asset category data to the generation AI and cause the generation AI to apply different advice algorithms.

[0073] The providing unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide short, concise advice. For example, the providing unit can estimate the user's emotions and provide short, concise advice when the user is in a hurry. The providing unit can also provide detailed advice when the user is relaxed. For example, the providing unit can provide detailed advice when the user is relaxed. Furthermore, the providing unit can provide advice with visually stimulating effects when the user is excited. For example, the providing unit can provide advice with visually stimulating effects when the user is excited. This allows the providing unit to optimize the length of the advice according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0074] When providing advice, the providing unit can determine the priority of the advice based on the collection time of the asset information. The providing unit, for example, provides advice based on the latest asset information. For example, the providing unit can provide advice based on the latest asset information based on the collection time of the asset information. The providing unit can also determine the priority of the advice for past asset information based on its importance. For example, the providing unit can determine the priority of the advice for past asset information based on its importance. Furthermore, the providing unit can adjust the advice schedule based on the collection time of the asset information. For example, the providing unit can adjust the advice schedule based on the collection time of the asset information. This allows the providing unit to optimize the priority of the advice based on the collection time of the asset information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input asset information collection time data to the generation AI and cause the generation AI to determine the priority of the advice.

[0075] When providing advice, the providing unit can adjust the order of advice based on the relevance of assets. The providing unit, for example, prioritizes advice for highly relevant assets. For example, the providing unit can prioritize advice for highly relevant assets based on the relevance of assets. The providing unit can also postpone advice for less relevant assets. For example, the providing unit can postpone advice for less relevant assets based on the relevance of assets. Furthermore, the providing unit can also optimize the order of advice based on the relevance of assets. For example, the providing unit can optimize the order of advice based on the relevance of assets. In this way, the providing unit can optimize the order of advice based on the relevance of assets. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input asset relevance data to a generation AI and cause the generation AI to adjust the order of advice. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 is realized by the control unit 46A of the smart device 14 and collects information such as the user's bank account balance, real estate appraised value, and stock holdings. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected asset information to predict future asset values. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides advice on asset management methods and savings methods based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 is realized by the control unit 46A of the smart glasses 214 and collects information such as the user's bank account balance, real estate appraised value, and stock holdings. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected asset information to predict future asset values. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides advice on asset management methods and savings methods based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 is realized by the control unit 46A of the headset terminal 314 and collects information such as the user's bank account balance, real estate appraised value, and stock holdings. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected asset information to predict future asset values. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314 and provides advice on asset management methods and savings methods based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 is realized by the control unit 46A of the robot 414 and collects information such as the user's bank account balance, real estate appraised value, and stock holdings. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected asset information to predict future asset values. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides advice on asset management methods and savings methods based on the analysis results.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] When analyzing a user's asset information, the analysis unit can adjust the accuracy of the analysis taking into account the user's health condition. For example, if the user's health condition is good, a detailed analysis can be performed to proactively evaluate future asset management risks. Also, if the user's health condition is deteriorating, an analysis that reduces risks can be performed to suggest a conservative asset management method. Furthermore, the analysis unit can adjust the priority of asset management based on the user's health condition. For example, if the user's health condition is deteriorating, short-term asset management can be prioritized, and if the user's health condition is good, long-term asset management can be prioritized. This allows the analysis unit to provide the optimal asset management method according to the user's health condition.

[0078] The providing unit can estimate the user's emotions and adjust the timing of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the provision of advice can be delayed and provided when the user is relaxed. Also, if the user is relaxed, the advice can be provided immediately, providing information at a timing when the user is most receptive. Furthermore, if the user is busy, the provision of advice can be adjusted to suit the user's schedule. For example, the optimal timing for providing advice can be selected based on the user's schedule. This allows the providing unit to optimize the timing for providing advice according to the user's emotions.

[0079] When collecting a user's asset information, the collection unit can adjust the type of information to be collected based on the user's hobbies and interests. For example, if the user is interested in art, art-related asset information can be preferentially collected. Also, if the user is interested in technology, technology-related asset information can be collected in detail. Furthermore, if the user enjoys traveling, real estate information and investment opportunities in travel destinations can be preferentially collected. This allows the collection unit to collect asset information according to the user's hobbies and interests.

[0080] The analysis unit can estimate the user's emotions and adjust the frequency of analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis frequency can be reduced and analysis can be performed when the user is relaxed. Also, if the user is relaxed, the analysis frequency can be increased and detailed analysis results can be provided. Furthermore, if the user is busy, the analysis frequency can be adjusted to suit the user's schedule. For example, the optimal analysis frequency can be selected based on the user's schedule. This allows the analysis unit to optimize the analysis frequency according to the user's emotions.

[0081] When providing advice on asset management to a user, the providing unit can adjust the content of the advice based on the user's family structure and life events. For example, if the user has a new child, the providing unit can suggest ways to save for education. Also, if the user is planning to get married, the providing unit can suggest ways to prepare for marriage. Furthermore, if the user is approaching retirement, the providing unit can suggest ways to manage living expenses after retirement. In this way, the providing unit can provide optimal asset management advice according to the user's family structure and life events.

[0082] The collection unit can estimate the user's emotions and adjust the accuracy of the information to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the accuracy of the information to be collected can be increased and only important information can be collected. Also, when the user is relaxed, the accuracy of the information to be collected can be decreased and a wide range of information can be collected. Furthermore, when the user is busy, the accuracy of the information to be collected can be adjusted to match the user's schedule. For example, the optimal accuracy of information collection can be selected based on the user's schedule. This allows the collection unit to optimize the accuracy of information collection according to the user's emotions.

[0083] When analyzing the user's asset information, the analysis unit can adjust the analysis algorithm taking into account the user's past investment history. For example, based on the user's successful investment patterns in the past, similar patterns can be reflected in the analysis. The analysis algorithm can also be adjusted to avoid investment patterns that the user has failed in the past. Furthermore, the risk tolerance can be reflected in the analysis based on the user's investment history. This allows the analysis unit to provide an optimal analysis algorithm based on the user's past investment history.

[0084] The providing unit can estimate the user's emotion and adjust the format of advice based on the estimated user's emotion. For example, if the user is nervous, advice can be provided in a visually simple and easy-to-understand format. If the user is relaxed, advice can be provided in a format including detailed data and graphs. Furthermore, if the user is excited, advice can be provided in an interactive format. In this way, the providing unit can provide the optimal format of advice depending on the user's emotion.

[0085] When collecting a user's asset information, the collection unit can adjust the range of information to be collected taking into account the user's geographical movement patterns. For example, if the user frequently travels on business, asset information for the travel destination can be collected preferentially. Also, if the user stays in a specific area for a long period of time, asset information for that area can be collected in detail. Furthermore, if the user plans to move, asset information for the user's new residence can be collected preferentially. This allows the collection unit to collect asset information according to the user's geographical movement patterns.

[0086] The analysis unit can estimate the user's emotions and adjust the method of visually presenting the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis results can be provided using simple, highly visible graphs and charts. If the user is relaxed, the analysis results can be provided using detailed data and complex graphs. Furthermore, if the user is excited, the analysis results can be provided with visually stimulating effects. This allows the analysis unit to optimize the method of visually presenting the analysis results according to the user's emotions.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The collection unit collects the user's asset information. The user's asset information includes bank account balances, real estate appraised values, stock holdings, etc. The collection unit obtains bank account balances via an API, real estate appraised values ​​from a public database, and stock holdings from a securities company's system. This information is updated regularly and stored in a database. Step 2: The analysis unit analyzes the asset information collected by the collection unit. AI is used for the analysis, and future asset values ​​are predicted based on past asset data and market trends. For example, past stock price fluctuations and real estate market trends are analyzed to predict future stock prices and real estate appraised values. The analysis unit also predicts the increase or decrease in the user's total assets, and predicts future asset values. Step 3: The provider provides advice on asset management and accumulation methods based on the prediction results obtained by the analyzer. For example, if assets are expected to increase in the future, it will suggest aggressive management, and if assets are expected to decrease, it will suggest a low-risk management method. It also provides advice according to the user's life stage, suggesting aggressive management for younger generations and low-risk management methods for those approaching retirement.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] 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.

[0160] [Explanation of symbols]

[0161] 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 asset information of users; an analysis unit that analyzes the asset information collected by the collection unit; a providing unit that provides advice on asset management methods or savings methods based on the prediction results obtained by the analysis unit. A system characterized by:

2. The collecting unit Collect information about your bank account balances, property appraised values, and stock holdings 2. The system of claim 1.

3. The analysis unit Predict future asset values ​​based on past asset data and market trends 2. The system of claim 1.

4. The providing unit Providing advice on asset management or daily asset accumulation methods based on the prediction results 2. The system of claim 1.

5. The collecting unit To estimate a user's emotion and adjust the timing of collecting asset information based on the estimated user's emotion.

2. The system of claim 1.

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

7. The collecting unit When collecting asset information, filter it based on the user's current living situation and areas of interest.

2. The system of claim 1.

8. The collecting unit The user's emotions are estimated, and the priority of asset information to be collected is determined based on the estimated user's emotions.

2. The system of claim 1.

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