system

Abstract

The system according to the embodiment aims to provide loans and other services to users who have no credit history by making appropriate credit assessments. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a loan unit, and a provision unit. The collection unit collects usage data of a user's electronic payment app. The analysis unit analyzes the data collected by the collection unit and evaluates the user's creditworthiness. The loan unit sets an interest rate based on the creditworthiness evaluated by the analysis unit and provides the loan. The provision unit utilizes the evaluated creditworthiness to provide a service that requires "credit."

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult to perform an appropriate credit assessment on users who have no credit history, which limits the provision of loans and other services.

[0005] The system according to the embodiment aims to provide loans and other services to users who have no credit history by making appropriate credit assessments. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a loan unit, and a provision unit. The collection unit collects usage data of a user's electronic payment app. The analysis unit analyzes the data collected by the collection unit and evaluates the user's creditworthiness. The loan unit sets an interest rate based on the creditworthiness evaluated by the analysis unit and provides the loan. The provision unit utilizes the evaluated creditworthiness to provide a service that requires "credit." [Effects of the Invention]

[0007] The system according to the embodiment can perform appropriate credit evaluation even for users with no credit history, and can provide loans and other services. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A credit scoring system according to an embodiment of the present invention uses a generation AI to evaluate a user's creditworthiness based on usage data of an electronic payment app, and provides loans and other services at appropriate interest rates. The credit scoring system collects the user's usage data of the electronic payment app, and the generation AI analyzes the data to evaluate the user's creditworthiness. Based on the evaluation results, the system provides loans at appropriate interest rates and utilizes the evaluated creditworthiness to provide services such as real estate rental, insurance, employment, and guarantees. For example, the credit scoring system collects the user's usage data of the electronic payment app. For example, detailed data is collected, such as the type of transactions the user made, the stores where the user made purchases, and how frequently the user used the app. The credit scoring system then analyzes the collected data and evaluates the user's creditworthiness using a generation AI. The generation AI considers factors such as whether the user has a regular income, stable expenses, and no past outstanding payments to make the evaluation. The credit scoring system then provides loans at appropriate interest rates based on the evaluation results. For example, the system provides loans at low interest rates to users with high creditworthiness and loans at high interest rates to users with low creditworthiness. Finally, the credit scoring system provides services such as real estate rental, insurance, employment, and guarantees based on the evaluated creditworthiness. This allows the credit rating system to utilize the user's credit rating in various ways and provide various services. This allows the credit rating system to objectively evaluate the user's credit rating and provide loans and other services at appropriate interest rates. For example, even if a user has no past credit history, a quick and accurate credit rating can be achieved by evaluating the user's credit rating based on usage data of an electronic payment app. Furthermore, users can understand their own credit rating and receive appropriate services.

[0029] A credit evaluation system according to an embodiment includes a collection unit, an analysis unit, a financing unit, and a provision unit. The collection unit collects usage data of a user's electronic payment app. The usage data of the user's electronic payment app includes, but is not limited to, transaction history, purchase amount, and usage frequency. The collection unit collects detailed data, such as the type of transactions the user has made, the store where the purchase was made, and how frequently the user has used the app. The collection unit can also collect usage data of the user's electronic payment app in real time. For example, the collection unit periodically collects the user's transaction data and stores it in a database. The analysis unit uses a generation AI to analyze the data collected by the collection unit and evaluate the user's creditworthiness. The analysis unit considers factors such as whether the user has a regular income, stable expenses, and no past outstanding payments when making the evaluation. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and objectively evaluates the user's creditworthiness. For example, the generation AI analyzes the user's transaction data and calculates a credit score. The analysis unit can also use the generation AI to evaluate the user's creditworthiness in real time. For example, the analysis unit analyzes the user's transaction data in real time and updates the credit score. The lending unit sets an interest rate based on the creditworthiness evaluated by the analysis unit and provides a loan at an appropriate interest rate. For example, the lending unit provides a loan at a low interest rate to a user with a high creditworthiness and a loan at a high interest rate to a user with a low creditworthiness. The lending unit can also set an interest rate and determine the loan amount based on the user's credit score. The lending unit can also set a repayment period according to the user's creditworthiness. For example, the lending unit can set a long repayment period for a user with a high creditworthiness and a short repayment period for a user with a low creditworthiness. The provision unit provides services such as real estate rental, insurance, employment, and guarantees based on the evaluated creditworthiness. For example, the provision unit can provide good real estate rental contracts and insurance contracts to users with a high creditworthiness and appropriate services to users with a low creditworthiness. The provision unit can also determine the content of services based on the user's credit score. The provision unit can also adjust the method of providing services according to the user's creditworthiness.For example, the providing unit provides prompt service to users with high credit ratings and provides detailed explanations to users with low credit ratings. This allows the credit assessment system according to the embodiment to objectively assess a user's credit rating and provide loans and other services at appropriate interest rates. For example, even if a user has no past credit history, a prompt and accurate credit assessment can be achieved by assessing the user's credit rating based on usage data of an electronic payment app. Furthermore, users can understand their own credit rating and receive appropriate services.

[0030] The collection unit can collect detailed data including the user's purchase amount, usage frequency, and transaction type. Detailed data includes, but is not limited to, the purchase amount, usage frequency, and transaction type. For example, the collection unit collects how much money the user spends and how frequently. The collection unit can also collect what types of transactions the user makes. For example, the collection unit collects data such as the stores the user regularly uses, purchase amounts, and usage frequency. The collection unit can also collect user transaction data in real time. For example, the collection unit periodically collects user transaction data and stores it in a database. By collecting detailed user transaction data, the accuracy of credit assessment can be improved. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's transaction data into a generation AI and have the generation AI collect the data.

[0031] The analysis unit can evaluate the user's creditworthiness based on the collected data, including factors such as the user's regular income, the stability of expenses, and past unpaid bills. The analysis unit, for example, evaluates the user's regular income. For example, the analysis unit evaluates whether the user has a regular income. The analysis unit can also evaluate the stability of the user's expenses. For example, the analysis unit evaluates the user's monthly expenses and the fluctuation range of expenses. The analysis unit can also evaluate the user's past unpaid bills. For example, the analysis unit evaluates the number of unpaid bills and the amount of unpaid bills. This enables a more accurate creditworthiness evaluation by evaluating the user's creditworthiness from multiple angles. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's transaction data into the generation AI and have the generation AI perform a creditworthiness evaluation.

[0032] The lending unit can set an interest rate according to the assessed creditworthiness and provide a loan at an appropriate interest rate. The lending unit, for example, provides a loan at a low interest rate to a user with a high creditworthiness. For example, the lending unit provides a loan at a low interest rate to a user with a high creditworthiness. The lending unit can also provide a loan at a high interest rate to a user with a low creditworthiness. For example, the lending unit provides a loan at a high interest rate to a user with a low creditworthiness. The lending unit can also set a repayment period according to the user's creditworthiness. For example, the lending unit sets a long repayment period for a user with a high creditworthiness and a short repayment period for a user with a low creditworthiness. This makes it possible to provide a loan at an appropriate interest rate according to the user's creditworthiness. Some or all of the above-described processing in the lending unit may be performed, for example, using AI or may be performed without using AI. For example, the lending unit can input the user's credit score into a generation AI and have the generation AI set the interest rate and provide the loan.

[0033] The provision unit can provide services including real estate rental, insurance, employment, and guarantees based on the assessed credit rating. For example, the provision unit provides a good real estate rental contract or insurance contract to a user with a high credit rating. For example, the provision unit provides a good real estate rental contract or insurance contract to a user with a high credit rating. The provision unit can also provide an appropriate service to a user with a low credit rating. For example, the provision unit provides an appropriate service to a user with a low credit rating. The provision unit can also determine the content of the service based on the user's credit score. For example, the provision unit determines the content of the service based on the user's credit score. The provision unit can also adjust the method of providing the service according to the user's credit rating. For example, the provision unit provides quick service to a user with a high credit rating and provides detailed explanations to a user with a low credit rating. This makes it possible to utilize the user's credit rating in various ways and provide various services. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's credit score into a generation AI and cause the generation AI to provide the service.

[0034] The collection unit can analyze the user's past transaction history and select a data collection method. For example, the collection unit prioritizes collecting data from stores and services frequently used by the user. For example, the collection unit prioritizes collecting data from stores and services frequently used by the user. The collection unit can also focus on collecting transactions made during a specific time period from the user's transaction history. For example, the collection unit can focus on collecting transactions made during a specific time period from the user's transaction history. The collection unit can also analyze the user's past transaction patterns and select the most efficient data collection method. For example, the collection unit analyzes the user's past transaction patterns and selects the most efficient data collection method. This enables efficient data collection by analyzing the user's transaction history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's transaction history data to a generation AI and have the generation AI select a data collection method.

[0035] When collecting data, the collection unit can perform filtering based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting transaction data related to areas in which the user is currently interested. For example, the collection unit prioritizes collecting transaction data related to areas in which the user is currently interested. The collection unit can also filter relevant data according to the user's living situation (e.g., student, working adult). For example, the collection unit filters relevant data according to the user's living situation. The collection unit can also exclude unnecessary data based on the user's current living situation and perform efficient data collection. For example, the collection unit excludes unnecessary data based on the user's current living situation and performs efficient data collection. This enables data collection 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 AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest to the generation AI and have the generation AI perform data filtering.

[0036] When collecting data, the collection unit can select a collection means according to the user's input method. For example, when the user is using voice input, the collection unit prioritizes collecting voice data. For example, when the user is using voice input, the collection unit prioritizes collecting voice data. The collection unit can also prioritize collecting text data when the user is using text input. For example, when the user is using text input, the collection unit prioritizes collecting text data. The collection unit can also prioritize collecting image data when the user is using image input. For example, when the user is using image input, the collection unit prioritizes collecting image data. This enables optimal data collection according to the user's input method. Some or all of the above-mentioned 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 input data to a generation AI and cause the generation AI to select a collection means.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting transaction data related to the user's current location. For example, the collection unit prioritizes collecting transaction data related to the user's current location. The collection unit can also collect region-specific data based on the user's geographical location information. For example, the collection unit collects region-specific data based on the user's geographical location information. The collection unit can also prioritize collecting data related to places the user frequently visits. For example, the collection unit prioritizes collecting data related to places the user frequently visits. This enables efficient data collection 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 can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0038] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data related to places where the user has checked in on social media. For example, the collection unit collects data related to places where the user has checked in on social media. The collection unit can also analyze the user's social media posts and collect related transaction data. For example, the collection unit analyzes the user's social media posts and collects related transaction data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit collects related data by referring to the activities of the user's friends on social media. This makes it possible to collect data based on the user's social media activities. Some or all of the above-described processing by 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 social media data into a generation AI and cause the generation AI to collect related data.

[0039] The collection unit can adjust the collection method when collecting data by reflecting the user's past feedback. For example, the collection unit improves the data collection method based on feedback provided by the user in the past. For example, the collection unit improves the data collection method based on feedback provided by the user in the past. The collection unit can also adjust the type of data to be collected by reflecting the user's feedback. For example, the collection unit adjusts the type of data to be collected by reflecting the user's feedback. The collection unit can also adjust the frequency and timing of data collection based on the user's feedback. For example, the collection unit adjusts the frequency and timing of data collection based on the user's feedback. This makes it possible to customize the data collection method based on the user's feedback. 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 can input the user's feedback data to the generation AI and cause the generation AI to adjust the collection method.

[0040] During analysis, the analysis unit can change the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This enables efficient analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to change the level of detail of the analysis.

[0041] During analysis, the analysis unit can use different analysis algorithms depending on the data category. For example, the analysis unit applies a consumption pattern analysis algorithm to purchase data. For example, the analysis unit applies a consumption pattern analysis algorithm to purchase data. The analysis unit can also apply an income stability analysis algorithm to income data. For example, the analysis unit applies an income stability analysis algorithm to income data. The analysis unit can also apply an expense management analysis algorithm to expenditure data. For example, the analysis unit applies an expense management analysis algorithm to expenditure data. This enables optimal analysis according to the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can set analysis priorities based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. For example, the analysis unit postpones analysis of data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. For example, the analysis unit adjusts the analysis schedule based on the time of submission. This enables efficient analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI set the analysis priorities.

[0044] During analysis, the analysis unit can change the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. This enables efficient analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to change the order of analysis.

[0045] During analysis, the analysis unit can change the use of technical terminology in the analysis depending on the user's level of expertise. For example, the analysis unit provides analysis results in simple language to a user with little expertise. For example, the analysis unit provides analysis results in simple language to a user with little expertise. The analysis unit can also provide analysis results using detailed technical terminology to a user with extensive expertise. For example, the analysis unit provides analysis results using detailed technical terminology to a user with extensive expertise. The analysis unit can also adjust the way the analysis results are expressed depending on the user's level of expertise. For example, the analysis unit adjusts the way the analysis results are expressed depending on the user's level of expertise. This makes it possible to provide analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise to the generation AI and cause the generation AI to change the use of technical terminology.

[0046] When providing a loan, the loan unit can select a loan method by analyzing the user's past consumption behavior. The loan unit, for example, proposes an optimal loan amount based on the user's past consumption behavior. For example, the loan unit proposes an optimal loan amount based on the user's past consumption behavior. The loan unit can also analyze the user's consumption pattern and propose an optimal repayment plan. For example, the loan unit analyzes the user's consumption pattern and proposes an optimal repayment plan. The loan unit can also evaluate the risk of the loan by referring to the user's past consumption behavior. For example, the loan unit evaluates the risk of the loan by referring to the user's past consumption behavior. This makes it possible to provide an optimal loan method based on the user's consumption behavior. Some or all of the above-mentioned processing in the loan unit may be performed using, for example, AI, or may be performed without using AI. For example, the loan unit can input the user's consumption behavior data into a generation AI and have the generation AI select a loan method.

[0047] The loan unit can adjust the loan method based on the user's current living situation when providing a loan. For example, if the user is a student, the loan unit provides a loan plan specialized for tuition fees and living expenses. For example, if the user is a student, the loan unit provides a loan plan specialized for tuition fees and living expenses. Furthermore, if the user is a working adult, the loan unit can provide a loan plan tailored to the user's income. For example, if the user is a working adult, the loan unit can provide a loan plan tailored to the user's income. Furthermore, the loan unit can customize the optimal loan method based on the user's living situation. For example, the loan unit customizes the optimal loan method based on the user's living situation. This makes it possible to provide the optimal loan method tailored to the user's living situation. Some or all of the above-described processing in the loan unit may be performed using, for example, AI, or may be performed without using AI. For example, the loan unit can input the user's living situation data into a generation AI and have the generation AI adjust the loan method.

[0048] The loan unit can improve the loan method by reflecting user feedback when providing a loan. The loan unit, for example, improves the loan procedure method based on user feedback. For example, the loan unit improves the loan procedure method based on user feedback. The loan unit can also adjust the loan terms by reflecting user feedback. For example, the loan unit adjusts the loan terms by reflecting user feedback. The loan unit can also strengthen loan risk management by referring to user feedback. For example, the loan unit strengthens loan risk management by referring to user feedback. This makes it possible to improve the loan method based on user feedback. Some or all of the above-mentioned processing in the loan unit may be performed using AI, for example, or may be performed without using AI. For example, the loan unit can input user feedback data into a generation AI and cause the generation AI to improve the loan method.

[0049] The loan unit can select a loan method taking into account the user's geographical location information when providing a loan. For example, if the user lives in an urban area, the loan unit provides a loan plan specific to the urban area. For example, if the user lives in an urban area, the loan unit provides a loan plan specific to the urban area. Furthermore, if the user lives in a rural area, the loan unit can provide a loan plan specific to the rural area. For example, if the user lives in a rural area, the loan unit provides a loan plan specific to the rural area. Furthermore, the loan unit can select an optimal loan method based on the user's geographical location information. For example, the loan unit selects an optimal loan method based on the user's geographical location information. This makes it possible to provide an optimal loan method based on the user's geographical location information. Some or all of the above-described processing in the loan unit may be performed using, for example, AI, or may be performed without using AI. For example, the loan unit can input the user's geographical location information to a generation AI and cause the generation AI to select a loan method.

[0050] The lending unit can analyze the user's social media activity and provide a loan option when providing a loan. For example, if the user is active on social media, the lending unit can suggest a loan option through social media. For example, if the user is active on social media, the lending unit can suggest a loan option through social media. The lending unit can also analyze the user's social media posts and suggest the optimal loan option. For example, the lending unit can analyze the user's social media posts and suggest the optimal loan option. The lending unit can also suggest related loan options based on the activities of the user's friends on social media. For example, the lending unit can suggest related loan options based on the user's social media activities. This makes it possible to suggest the optimal loan option based on the user's social media activity. Some or all of the above-described processing in the lending unit can be performed using, for example, AI, or can be performed without using AI. For example, the lending unit can input the user's social media data into a generation AI and cause the generation AI to provide the loan option.

[0051] The loan unit can adjust the loan method by reflecting the user's past feedback when providing a loan. For example, the loan unit improves the loan procedure method based on the user's past feedback. For example, the loan unit improves the loan procedure method based on the user's past feedback. The loan unit can also adjust the loan terms by reflecting the user's feedback. For example, the loan unit adjusts the loan terms by reflecting the user's feedback. The loan unit can also strengthen loan risk management by referring to the user's feedback. For example, the loan unit strengthens loan risk management by referring to the user's feedback. This makes it possible to customize the loan method based on the user's feedback. Some or all of the above-mentioned processing in the loan unit may be performed using AI, for example, or may be performed without using AI. For example, the loan unit can input user feedback data into a generation AI and have the generation AI adjust the loan method.

[0052] When providing a service, the providing unit can select a service provision method by analyzing a user's past behavioral history. The providing unit, for example, selects an optimal service provision method based on the user's past behavioral history. For example, the providing unit selects an optimal service provision method based on the user's past behavioral history. The providing unit can also analyze a user's behavioral pattern and select an optimal service provision method. For example, the providing unit analyzes a user's behavioral pattern and selects an optimal service provision method. The providing unit can also customize the service provision method by referring to the user's past behavioral history. For example, the providing unit customizes the service provision method by referring to the user's past behavioral history. This makes it possible to select an optimal service provision method based on the user's behavioral history. 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 the user's behavioral history data into a generation AI and cause the generation AI to select a service provision method.

[0053] The providing unit can adjust the means of providing the service based on the user's current living situation when providing the service. For example, if the user is a student, the providing unit provides a service related to their studies. For example, if the user is a student, the providing unit provides a service related to their studies. Furthermore, if the user is a working adult, the providing unit can also provide a service related to their work. For example, if the user is a working adult, the providing unit can provide a service related to their work. Furthermore, the providing unit can customize the optimal means of providing the service based on the user's living situation. For example, the providing unit customizes the optimal means of providing the service based on the user's living situation. This makes it possible to customize the optimal means of providing the service according to the user's living situation. 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 the user's living situation data into a generating AI and cause the generating AI to adjust the means of providing the service.

[0054] The provision unit can improve the method of providing a service by reflecting user feedback when providing a service. The provision unit, for example, improves the method of providing a service based on user feedback. For example, the provision unit improves the method of providing a service based on user feedback. The provision unit can also adjust the content of the service provided by reflecting user feedback. For example, the provision unit adjusts the content of the service provided by reflecting user feedback. The provision unit can also improve the quality of the service provided by referring to user feedback. For example, the provision unit improves the quality of the service provided by referring to user feedback. This makes it possible to improve the method of providing a service based on user feedback. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input user feedback data into a generation AI and cause the generation AI to improve the method of providing a service.

[0055] The providing unit can select a service provision method taking into consideration the user's geographical location information when providing a service. For example, if the user lives in an urban area, the providing unit selects a service provision method specific to urban areas. For example, if the user lives in an urban area, the providing unit selects a service provision method specific to urban areas. Furthermore, if the user lives in a rural area, the providing unit can select a service provision method specific to rural areas. For example, if the user lives in a rural area, the providing unit selects a service provision method specific to rural areas. Furthermore, the providing unit can select an optimal service provision method based on the user's geographical location information. For example, the providing unit selects an optimal service provision method based on the user's geographical location information. This makes it possible to select an optimal service provision method based on the user's geographical location 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 the user's geographical location information to a generation AI and cause the generation AI to select a service provision method.

[0056] The providing unit can analyze the user's social media activities and provide a means for providing the service when providing the service. For example, if the user is active on social media, the providing unit can suggest a means for providing the service through social media. For example, if the user is active on social media, the providing unit can suggest a means for providing the service through social media. The providing unit can also analyze the content posted by the user on social media and suggest an optimal means for providing the service. For example, the providing unit can analyze the content posted by the user on social media and suggest an optimal means for providing the service. The providing unit can also suggest related means for providing the service by referring to the activities of the user's friends on social media. For example, the providing unit can suggest related means for providing the service by referring to the activities of the user's friends on social media. This makes it possible to suggest an optimal means for providing the service based on the user's social media activities. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to provide the means for providing the service.

[0057] The provision unit can adjust the method of providing a service by reflecting the user's past feedback when providing a service. The provision unit, for example, improves the method of providing a service based on the user's past feedback. For example, the provision unit improves the method of providing a service based on the user's past feedback. The provision unit can also adjust the content of the service by reflecting the user's feedback. For example, the provision unit adjusts the content of the service by reflecting the user's feedback. The provision unit can also improve the quality of the service by referring to the user's feedback. For example, the provision unit improves the quality of the service by referring to the user's feedback. This makes it possible to customize the method of providing a service based on the user's feedback. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input user feedback data into a generation AI and cause the generation AI to adjust the method of providing a service.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The credit scoring system can further collect user health data, which the analysis unit can reflect in the credit scoring. For example, the collection unit can collect data from the user's fitness tracker or smart watch. The collection unit can also collect the user's medical records and health checkup results. The analysis unit evaluates the impact of the user's health condition on the credit score based on this health data. For example, the analysis unit can increase the credit score for users in good health. This enables more accurate credit scoring that takes the user's health condition into account.

[0060] The credit rating system can also collect data on a user's hobbies and interests, which the analysis unit can reflect in the credit rating. For example, the collection unit collects data on events the user attends and hobby-related products the user purchases. The collection unit can also identify the user's hobbies and interests from the content the user posts on social media. The analysis unit uses this data to evaluate the impact that the user's lifestyle has on the credit rating. For example, it can increase the credit rating of users who have stable hobbies. This enables more accurate credit rating that takes the user's lifestyle into account.

[0061] The credit rating system can further collect environmental data about the user, which the analysis unit can reflect in the credit rating. For example, the collection unit collects data about the user's living environment and work environment. The collection unit can also collect data about the user's commuting time and means of transportation. The analysis unit evaluates the impact of the user's living environment on the credit rating based on this environmental data. For example, the credit rating can be increased for users with stable living environments. This enables more accurate credit rating that takes the user's living environment into account.

[0062] The credit rating system can also collect user educational data, which the analysis unit can reflect in the credit rating. For example, the collection unit collects data on the user's educational background and acquired qualifications. The collection unit can also collect the user's learning history and online course attendance history. Based on this educational data, the analysis unit evaluates the impact of the user's educational background on the credit rating. For example, the credit rating can be increased for users with a high level of education and many qualifications. This enables more accurate credit rating that takes the user's educational background into account.

[0063] The credit rating system can further collect social network data of the user, and the analysis unit can reflect this in the credit rating. For example, the collection unit can collect data on the user's relationships with friends and family. The collection unit can also collect data on the user's relationships at work and community activities. The analysis unit evaluates the impact of the user's social stability on the credit rating based on this social network data. For example, the analysis unit can increase the credit rating of a user with a strong social network. This enables a more accurate credit rating that takes into account the user's social stability.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects the user's usage data of the electronic payment app. The user's usage data of the electronic payment app includes transaction history, purchase amount, and usage frequency. The collection unit collects detailed data such as what transactions the user made, which store they purchased from, and how frequently they used the app. The collection unit can also collect the user's usage data of the electronic payment app in real time. For example, the collection unit periodically collects the user's transaction data and stores it in a database. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and evaluate the user's creditworthiness. The analysis unit makes the evaluation taking into account factors such as whether the user has a regular income, whether their expenses are stable, and whether they have any past outstanding payments. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and objectively evaluates the user's creditworthiness. For example, the generation AI analyzes the user's transaction data and calculates a credit score. The analysis unit can also use the generation AI to evaluate the user's creditworthiness in real time. For example, the analysis unit analyzes the user's transaction data in real time and updates the credit score. Step 3: The loan unit sets an interest rate based on the creditworthiness evaluated by the analysis unit and provides a loan at an appropriate interest rate. The loan unit provides a loan at a low interest rate to a user with a high creditworthiness and a loan at a high interest rate to a user with a low creditworthiness. The loan unit sets an interest rate and determines the loan amount based on the user's credit score. The loan unit can also set a repayment period according to the user's creditworthiness. For example, the loan unit sets a long repayment period for a user with a high creditworthiness and a short repayment period for a user with a low creditworthiness. Step 4: The provision unit provides services such as real estate rental, insurance, employment, and guarantees based on the assessed credit rating. The provision unit provides good real estate rental contracts and insurance contracts to users with high credit ratings, and provides appropriate services to users with low credit ratings. The provision unit determines the content of the services based on the user's credit score. The provision unit can also adjust the method of providing services depending on the user's credit rating. For example, the provision unit provides quick service to users with high credit ratings and provides detailed explanations to users with low credit ratings.

[0066] (Example 2) A credit scoring system according to an embodiment of the present invention uses a generation AI to evaluate a user's creditworthiness based on usage data of an electronic payment app, and provides loans and other services at appropriate interest rates. The credit scoring system collects the user's usage data of the electronic payment app, and the generation AI analyzes the data to evaluate the user's creditworthiness. Based on the evaluation results, the system provides loans at appropriate interest rates and utilizes the evaluated creditworthiness to provide services such as real estate rental, insurance, employment, and guarantees. For example, the credit scoring system collects the user's usage data of the electronic payment app. For example, detailed data is collected, such as the type of transactions the user made, the stores where the user made purchases, and how frequently the user used the app. The credit scoring system then analyzes the collected data and evaluates the user's creditworthiness using a generation AI. The generation AI considers factors such as whether the user has a regular income, stable expenses, and no past outstanding payments to make the evaluation. The credit scoring system then provides loans at appropriate interest rates based on the evaluation results. For example, the system provides loans at low interest rates to users with high creditworthiness and loans at high interest rates to users with low creditworthiness. Finally, the credit scoring system provides services such as real estate rental, insurance, employment, and guarantees based on the evaluated creditworthiness. This allows the credit rating system to utilize the user's credit rating in various ways and provide various services. This allows the credit rating system to objectively evaluate the user's credit rating and provide loans and other services at appropriate interest rates. For example, even if a user has no past credit history, a quick and accurate credit rating can be achieved by evaluating the user's credit rating based on usage data of an electronic payment app. Furthermore, users can understand their own credit rating and receive appropriate services.

[0067] A credit evaluation system according to an embodiment includes a collection unit, an analysis unit, a financing unit, and a provision unit. The collection unit collects usage data of a user's electronic payment app. The usage data of the user's electronic payment app includes, but is not limited to, transaction history, purchase amount, and usage frequency. The collection unit collects detailed data, such as the type of transactions the user has made, the store where the purchase was made, and how frequently the user has used the app. The collection unit can also collect usage data of the user's electronic payment app in real time. For example, the collection unit periodically collects the user's transaction data and stores it in a database. The analysis unit uses a generation AI to analyze the data collected by the collection unit and evaluate the user's creditworthiness. The analysis unit considers factors such as whether the user has a regular income, stable expenses, and no past outstanding payments when making the evaluation. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and objectively evaluates the user's creditworthiness. For example, the generation AI analyzes the user's transaction data and calculates a credit score. The analysis unit can also use the generation AI to evaluate the user's creditworthiness in real time. For example, the analysis unit analyzes the user's transaction data in real time and updates the credit score. The lending unit sets an interest rate based on the creditworthiness evaluated by the analysis unit and provides a loan at an appropriate interest rate. For example, the lending unit provides a loan at a low interest rate to a user with a high creditworthiness and a loan at a high interest rate to a user with a low creditworthiness. The lending unit can also set an interest rate and determine the loan amount based on the user's credit score. The lending unit can also set a repayment period according to the user's creditworthiness. For example, the lending unit can set a long repayment period for a user with a high creditworthiness and a short repayment period for a user with a low creditworthiness. The provision unit provides services such as real estate rental, insurance, employment, and guarantees based on the evaluated creditworthiness. For example, the provision unit can provide good real estate rental contracts and insurance contracts to users with a high creditworthiness and appropriate services to users with a low creditworthiness. The provision unit can also determine the content of services based on the user's credit score. The provision unit can also adjust the method of providing services according to the user's creditworthiness.For example, the providing unit provides prompt service to users with high credit ratings and provides detailed explanations to users with low credit ratings. This allows the credit assessment system according to the embodiment to objectively assess a user's credit rating and provide loans and other services at appropriate interest rates. For example, even if a user has no past credit history, a prompt and accurate credit assessment can be achieved by assessing the user's credit rating based on usage data of an electronic payment app. Furthermore, users can understand their own credit rating and receive appropriate services.

[0068] The collection unit can collect detailed data including the user's purchase amount, usage frequency, and transaction type. Detailed data includes, but is not limited to, the purchase amount, usage frequency, and transaction type. For example, the collection unit collects how much money the user spends and how frequently. The collection unit can also collect what types of transactions the user makes. For example, the collection unit collects data such as the stores the user regularly uses, purchase amounts, and usage frequency. The collection unit can also collect user transaction data in real time. For example, the collection unit periodically collects user transaction data and stores it in a database. By collecting detailed user transaction data, the accuracy of credit assessment can be improved. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's transaction data into a generation AI and have the generation AI collect the data.

[0069] The analysis unit can evaluate the user's creditworthiness based on the collected data, including factors such as the user's regular income, the stability of expenses, and past unpaid bills. The analysis unit, for example, evaluates the user's regular income. For example, the analysis unit evaluates whether the user has a regular income. The analysis unit can also evaluate the stability of the user's expenses. For example, the analysis unit evaluates the user's monthly expenses and the fluctuation range of expenses. The analysis unit can also evaluate the user's past unpaid bills. For example, the analysis unit evaluates the number of unpaid bills and the amount of unpaid bills. This enables a more accurate creditworthiness evaluation by evaluating the user's creditworthiness from multiple angles. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's transaction data into the generation AI and have the generation AI perform a creditworthiness evaluation.

[0070] The lending unit can set an interest rate according to the assessed creditworthiness and provide a loan at an appropriate interest rate. The lending unit, for example, provides a loan at a low interest rate to a user with a high creditworthiness. For example, the lending unit provides a loan at a low interest rate to a user with a high creditworthiness. The lending unit can also provide a loan at a high interest rate to a user with a low creditworthiness. For example, the lending unit provides a loan at a high interest rate to a user with a low creditworthiness. The lending unit can also set a repayment period according to the user's creditworthiness. For example, the lending unit sets a long repayment period for a user with a high creditworthiness and a short repayment period for a user with a low creditworthiness. This makes it possible to provide a loan at an appropriate interest rate according to the user's creditworthiness. Some or all of the above-described processing in the lending unit may be performed, for example, using AI or may be performed without using AI. For example, the lending unit can input the user's credit score into a generation AI and have the generation AI set the interest rate and provide the loan.

[0071] The provision unit can provide services including real estate rental, insurance, employment, and guarantees based on the assessed credit rating. For example, the provision unit provides a good real estate rental contract or insurance contract to a user with a high credit rating. For example, the provision unit provides a good real estate rental contract or insurance contract to a user with a high credit rating. The provision unit can also provide an appropriate service to a user with a low credit rating. For example, the provision unit provides an appropriate service to a user with a low credit rating. The provision unit can also determine the content of the service based on the user's credit score. For example, the provision unit determines the content of the service based on the user's credit score. The provision unit can also adjust the method of providing the service according to the user's credit rating. For example, the provision unit provides quick service to a user with a high credit rating and provides detailed explanations to a user with a low credit rating. This makes it possible to utilize the user's credit rating in various ways and provide various services. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's credit score into a generation AI and cause the generation AI to provide the service.

[0072] The collection unit can estimate the user's emotions and determine the timing of data collection based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the user's burden. The collection unit can also collect detailed data to improve the accuracy of the creditworthiness assessment when the user is relaxed. For example, when the user is relaxed, the collection unit collects detailed data to improve the accuracy of the creditworthiness assessment. The collection unit can also quickly collect only the minimum amount of data necessary when the user is in a hurry. For example, when the user is in a hurry, the collection unit quickly collects only the minimum amount of data necessary. This adjusts the timing of data collection according to the user's emotions, reducing the user's burden and improving the accuracy of data collection. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. 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 have the generation AI determine the timing of data collection.

[0073] The collection unit can analyze the user's past transaction history and select a data collection method. For example, the collection unit prioritizes collecting data from stores and services frequently used by the user. For example, the collection unit prioritizes collecting data from stores and services frequently used by the user. The collection unit can also focus on collecting transactions made during a specific time period from the user's transaction history. For example, the collection unit can focus on collecting transactions made during a specific time period from the user's transaction history. The collection unit can also analyze the user's past transaction patterns and select the most efficient data collection method. For example, the collection unit analyzes the user's past transaction patterns and selects the most efficient data collection method. This enables efficient data collection by analyzing the user's transaction history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's transaction history data to a generation AI and have the generation AI select a data collection method.

[0074] When collecting data, the collection unit can perform filtering based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting transaction data related to areas in which the user is currently interested. For example, the collection unit prioritizes collecting transaction data related to areas in which the user is currently interested. The collection unit can also filter relevant data according to the user's living situation (e.g., student, working adult). For example, the collection unit filters relevant data according to the user's living situation. The collection unit can also exclude unnecessary data based on the user's current living situation and perform efficient data collection. For example, the collection unit excludes unnecessary data based on the user's current living situation and performs efficient data collection. This enables data collection 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 AI, for example, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest to the generation AI and have the generation AI perform data filtering.

[0075] When collecting data, the collection unit can select a collection means according to the user's input method. For example, when the user is using voice input, the collection unit prioritizes collecting voice data. For example, when the user is using voice input, the collection unit prioritizes collecting voice data. The collection unit can also prioritize collecting text data when the user is using text input. For example, when the user is using text input, the collection unit prioritizes collecting text data. The collection unit can also prioritize collecting image data when the user is using image input. For example, when the user is using image input, the collection unit prioritizes collecting image data. This enables optimal data collection according to the user's input method. Some or all of the above-mentioned 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 input data to a generation AI and cause the generation AI to select a collection means.

[0076] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit postpones the collection of less important data. For example, when the user is feeling stressed, the collection unit postpones the collection of less important data. The collection unit can also prioritize the collection of detailed data when the user is relaxed. For example, when the user is relaxed, the collection unit prioritizes the collection of detailed data. The collection unit can also prioritize the collection of the minimum necessary data when the user is in a hurry. For example, when the user is in a hurry, the collection unit prioritizes the collection of the minimum necessary data. This enables efficient data collection by determining the priority of data 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 collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI determine the priority of the data.

[0077] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting transaction data related to the user's current location. For example, the collection unit prioritizes collecting transaction data related to the user's current location. The collection unit can also collect region-specific data based on the user's geographical location information. For example, the collection unit collects region-specific data based on the user's geographical location information. The collection unit can also prioritize collecting data related to places the user frequently visits. For example, the collection unit prioritizes collecting data related to places the user frequently visits. This enables efficient data collection 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 can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0078] During data collection, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit collects data related to places where the user has checked in on social media. For example, the collection unit collects data related to places where the user has checked in on social media. The collection unit can also analyze the user's social media posts and collect related transaction data. For example, the collection unit analyzes the user's social media posts and collects related transaction data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit collects related data by referring to the activities of the user's friends on social media. This makes it possible to collect data based on the user's social media activities. Some or all of the above-described processing by 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 social media data into a generation AI and cause the generation AI to collect related data.

[0079] The collection unit can adjust the collection method when collecting data by reflecting the user's past feedback. For example, the collection unit improves the data collection method based on feedback provided by the user in the past. For example, the collection unit improves the data collection method based on feedback provided by the user in the past. The collection unit can also adjust the type of data to be collected by reflecting the user's feedback. For example, the collection unit adjusts the type of data to be collected by reflecting the user's feedback. The collection unit can also adjust the frequency and timing of data collection based on the user's feedback. For example, the collection unit adjusts the frequency and timing of data collection based on the user's feedback. This makes it possible to customize the data collection method based on the user's feedback. 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 can input the user's feedback data to the generation AI and cause the generation AI to adjust the collection method.

[0080] The analysis unit can estimate the user's emotions and change the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. Furthermore, the analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can also provide a concise analysis result that focuses on the main points. For example, if the user is in a hurry, the analysis unit provides a concise analysis result that focuses on the main points. This makes it possible to provide an analysis result that corresponds to the user's emotions. The 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 these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI change the way the analysis is expressed.

[0081] During analysis, the analysis unit can change the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This enables efficient analysis according to the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to change the level of detail of the analysis.

[0082] During analysis, the analysis unit can use different analysis algorithms depending on the data category. For example, the analysis unit applies a consumption pattern analysis algorithm to purchase data. For example, the analysis unit applies a consumption pattern analysis algorithm to purchase data. The analysis unit can also apply an income stability analysis algorithm to income data. For example, the analysis unit applies an income stability analysis algorithm to income data. The analysis unit can also apply an expense management analysis algorithm to expenditure data. For example, the analysis unit applies an expense management analysis algorithm to expenditure data. This enables optimal analysis according to the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the data category to the generation AI and cause the generation AI to apply the analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the user's past analysis results. For example, the analysis unit improves the accuracy of the analysis by referring to the past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and change the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. This allows the length of the analysis result to be adjusted according to the user's emotions, thereby providing an analysis result suitable for the user. 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 such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input user emotion data into the generation AI and cause the generation AI to change the length of the analysis.

[0085] During analysis, the analysis unit can set analysis priorities based on the time of data submission. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. For example, the analysis unit postpones analysis of data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. For example, the analysis unit adjusts the analysis schedule based on the time of submission. This enables efficient analysis based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of data submission to the generation AI and have the generation AI set the analysis priorities.

[0086] During analysis, the analysis unit can change the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. This enables efficient analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to change the order of analysis.

[0087] During analysis, the analysis unit can change the use of technical terminology in the analysis depending on the user's level of expertise. For example, the analysis unit provides analysis results in simple language to a user with little expertise. For example, the analysis unit provides analysis results in simple language to a user with little expertise. The analysis unit can also provide analysis results using detailed technical terminology to a user with extensive expertise. For example, the analysis unit provides analysis results using detailed technical terminology to a user with extensive expertise. The analysis unit can also adjust the way the analysis results are expressed depending on the user's level of expertise. For example, the analysis unit adjusts the way the analysis results are expressed depending on the user's level of expertise. This makes it possible to provide analysis results according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise to the generation AI and cause the generation AI to change the use of technical terminology.

[0088] The loan unit can estimate the user's emotions and change the loan method based on the estimated user's emotions. For example, if the user is nervous, the loan unit provides a simple and easy-to-understand loan method. For example, if the user is nervous, the loan unit provides a simple and easy-to-understand loan method. The loan unit can also provide detailed loan options if the user is relaxed. For example, if the user is relaxed, the loan unit provides detailed loan options. The loan unit can also quickly provide the loan if the user is in a hurry. For example, if the user is in a hurry, the loan unit quickly provides the loan. This makes it possible to provide a loan method 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 loan unit may be performed using AI, or may be performed without AI. For example, the loan unit can input the user's emotion data into the generation AI and cause the generation AI to change the loan method.

[0089] When providing a loan, the loan unit can select a loan method by analyzing the user's past consumption behavior. The loan unit, for example, proposes an optimal loan amount based on the user's past consumption behavior. For example, the loan unit proposes an optimal loan amount based on the user's past consumption behavior. The loan unit can also analyze the user's consumption pattern and propose an optimal repayment plan. For example, the loan unit analyzes the user's consumption pattern and proposes an optimal repayment plan. The loan unit can also evaluate the risk of the loan by referring to the user's past consumption behavior. For example, the loan unit evaluates the risk of the loan by referring to the user's past consumption behavior. This makes it possible to provide an optimal loan method based on the user's consumption behavior. Some or all of the above-mentioned processing in the loan unit may be performed using, for example, AI, or may be performed without using AI. For example, the loan unit can input the user's consumption behavior data into a generation AI and have the generation AI select a loan method.

[0090] The loan unit can adjust the loan method based on the user's current living situation when providing a loan. For example, if the user is a student, the loan unit provides a loan plan specialized for tuition fees and living expenses. For example, if the user is a student, the loan unit provides a loan plan specialized for tuition fees and living expenses. Furthermore, if the user is a working adult, the loan unit can provide a loan plan tailored to the user's income. For example, if the user is a working adult, the loan unit can provide a loan plan tailored to the user's income. Furthermore, the loan unit can customize the optimal loan method based on the user's living situation. For example, the loan unit customizes the optimal loan method based on the user's living situation. This makes it possible to provide the optimal loan method tailored to the user's living situation. Some or all of the above-described processing in the loan unit may be performed using, for example, AI, or may be performed without using AI. For example, the loan unit can input the user's living situation data into a generation AI and have the generation AI adjust the loan method.

[0091] The loan unit can improve the loan method by reflecting user feedback when providing a loan. The loan unit, for example, improves the loan procedure method based on user feedback. For example, the loan unit improves the loan procedure method based on user feedback. The loan unit can also adjust the loan terms by reflecting user feedback. For example, the loan unit adjusts the loan terms by reflecting user feedback. The loan unit can also strengthen loan risk management by referring to user feedback. For example, the loan unit strengthens loan risk management by referring to user feedback. This makes it possible to improve the loan method based on user feedback. Some or all of the above-mentioned processing in the loan unit may be performed using AI, for example, or may be performed without using AI. For example, the loan unit can input user feedback data into a generation AI and cause the generation AI to improve the loan method.

[0092] The loan unit can estimate the user's emotions and set loan priorities based on the estimated user emotions. For example, if the user is nervous, the loan unit lowers the loan priority and prioritizes loans for other users. For example, if the user is nervous, the loan unit lowers the loan priority and prioritizes loans for other users. Furthermore, if the user is relaxed, the loan unit can raise the loan priority and provide the loan quickly. For example, if the user is relaxed, the loan unit can raise the loan priority and provide the loan quickly. Furthermore, if the user is in a hurry, the loan unit can prioritize the loan as the highest priority and provide the loan immediately. For example, if the user is in a hurry, the loan unit prioritizes the loan as the highest priority and provides the loan immediately. This makes it possible to determine loan priorities 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 loan unit may be performed using, for example, AI, or may be performed without using AI. For example, the loan unit may input user emotion data into the generation AI and have the generation AI set loan priorities.

[0093] The loan unit can select a loan method taking into account the user's geographical location information when providing a loan. For example, if the user lives in an urban area, the loan unit provides a loan plan specific to the urban area. For example, if the user lives in an urban area, the loan unit provides a loan plan specific to the urban area. Furthermore, if the user lives in a rural area, the loan unit can provide a loan plan specific to the rural area. For example, if the user lives in a rural area, the loan unit provides a loan plan specific to the rural area. Furthermore, the loan unit can select an optimal loan method based on the user's geographical location information. For example, the loan unit selects an optimal loan method based on the user's geographical location information. This makes it possible to provide an optimal loan method based on the user's geographical location information. Some or all of the above-described processing in the loan unit may be performed using, for example, AI, or may be performed without using AI. For example, the loan unit can input the user's geographical location information to a generation AI and cause the generation AI to select a loan method.

[0094] The lending unit can analyze the user's social media activity and provide a loan option when providing a loan. For example, if the user is active on social media, the lending unit can suggest a loan option through social media. For example, if the user is active on social media, the lending unit can suggest a loan option through social media. The lending unit can also analyze the user's social media posts and suggest the optimal loan option. For example, the lending unit can analyze the user's social media posts and suggest the optimal loan option. The lending unit can also suggest related loan options based on the activities of the user's friends on social media. For example, the lending unit can suggest related loan options based on the user's social media activities. This makes it possible to suggest the optimal loan option based on the user's social media activity. Some or all of the above-described processing in the lending unit can be performed using, for example, AI, or can be performed without using AI. For example, the lending unit can input the user's social media data into a generation AI and cause the generation AI to provide the loan option.

[0095] The loan unit can adjust the loan method by reflecting the user's past feedback when providing a loan. For example, the loan unit improves the loan procedure method based on the user's past feedback. For example, the loan unit improves the loan procedure method based on the user's past feedback. The loan unit can also adjust the loan terms by reflecting the user's feedback. For example, the loan unit adjusts the loan terms by reflecting the user's feedback. The loan unit can also strengthen loan risk management by referring to the user's feedback. For example, the loan unit strengthens loan risk management by referring to the user's feedback. This makes it possible to customize the loan method based on the user's feedback. Some or all of the above-mentioned processing in the loan unit may be performed using AI, for example, or may be performed without using AI. For example, the loan unit can input user feedback data into a generation AI and have the generation AI adjust the loan method.

[0096] The providing unit can estimate the user's emotions and change the service provision method based on the estimated user's emotions. For example, if the user is nervous, the providing unit selects a simple and easy-to-understand service provision method. For example, if the user is nervous, the providing unit selects a simple and easy-to-understand service provision method. The providing unit can also select a detailed service provision method if the user is relaxed. For example, if the user is relaxed, the providing unit selects a detailed service provision method. The providing unit can also select a method to provide a service quickly if the user is in a hurry. For example, if the user is in a hurry, the providing unit selects a method to provide a service quickly. This makes it possible to adjust the service provision method according to the user's emotions. The 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 emotional data into the generating AI and cause the generating AI to change the service provision method.

[0097] When providing a service, the providing unit can select a service provision method by analyzing a user's past behavioral history. The providing unit, for example, selects an optimal service provision method based on the user's past behavioral history. For example, the providing unit selects an optimal service provision method based on the user's past behavioral history. The providing unit can also analyze a user's behavioral pattern and select an optimal service provision method. For example, the providing unit analyzes a user's behavioral pattern and selects an optimal service provision method. The providing unit can also customize the service provision method by referring to the user's past behavioral history. For example, the providing unit customizes the service provision method by referring to the user's past behavioral history. This makes it possible to select an optimal service provision method based on the user's behavioral history. 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 the user's behavioral history data into a generation AI and cause the generation AI to select a service provision method.

[0098] The providing unit can adjust the means of providing the service based on the user's current living situation when providing the service. For example, if the user is a student, the providing unit provides a service related to their studies. For example, if the user is a student, the providing unit provides a service related to their studies. Furthermore, if the user is a working adult, the providing unit can also provide a service related to their work. For example, if the user is a working adult, the providing unit can provide a service related to their work. Furthermore, the providing unit can customize the optimal means of providing the service based on the user's living situation. For example, the providing unit customizes the optimal means of providing the service based on the user's living situation. This makes it possible to customize the optimal means of providing the service according to the user's living situation. 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 the user's living situation data into a generating AI and cause the generating AI to adjust the means of providing the service.

[0099] The provision unit can improve the method of providing a service by reflecting user feedback when providing a service. The provision unit, for example, improves the method of providing a service based on user feedback. For example, the provision unit improves the method of providing a service based on user feedback. The provision unit can also adjust the content of the service provided by reflecting user feedback. For example, the provision unit adjusts the content of the service provided by reflecting user feedback. The provision unit can also improve the quality of the service provided by referring to user feedback. For example, the provision unit improves the quality of the service provided by referring to user feedback. This makes it possible to improve the method of providing a service based on user feedback. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input user feedback data into a generation AI and cause the generation AI to improve the method of providing a service.

[0100] The providing unit can estimate the user's emotions and set a priority for providing services based on the estimated user's emotions. For example, if the user is nervous, the providing unit lowers the priority of providing services and prioritizes services provided by other users. For example, if the user is nervous, the providing unit lowers the priority of providing services and prioritizes services provided by other users. The providing unit can also raise the priority of providing services and provide services quickly when the user is relaxed. For example, if the user is relaxed, the providing unit raises the priority of providing services and provides services quickly. The providing unit can also give top priority to providing services and provide services immediately when the user is in a hurry. For example, if the user is in a hurry, the providing unit gives top priority to providing services and provides services immediately. This makes it possible to determine the priority of providing services according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generating AI and cause the generating AI to set priorities for providing services.

[0101] The providing unit can select a service provision method taking into consideration the user's geographical location information when providing a service. For example, if the user lives in an urban area, the providing unit selects a service provision method specific to urban areas. For example, if the user lives in an urban area, the providing unit selects a service provision method specific to urban areas. Furthermore, if the user lives in a rural area, the providing unit can select a service provision method specific to rural areas. For example, if the user lives in a rural area, the providing unit selects a service provision method specific to rural areas. Furthermore, the providing unit can select an optimal service provision method based on the user's geographical location information. For example, the providing unit selects an optimal service provision method based on the user's geographical location information. This makes it possible to select an optimal service provision method based on the user's geographical location 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 the user's geographical location information to a generation AI and cause the generation AI to select a service provision method.

[0102] The providing unit can analyze the user's social media activities and provide a means for providing the service when providing the service. For example, if the user is active on social media, the providing unit can suggest a means for providing the service through social media. For example, if the user is active on social media, the providing unit can suggest a means for providing the service through social media. The providing unit can also analyze the content posted by the user on social media and suggest an optimal means for providing the service. For example, the providing unit can analyze the content posted by the user on social media and suggest an optimal means for providing the service. The providing unit can also suggest related means for providing the service by referring to the activities of the user's friends on social media. For example, the providing unit can suggest related means for providing the service by referring to the activities of the user's friends on social media. This makes it possible to suggest an optimal means for providing the service based on the user's social media activities. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to provide the means for providing the service.

[0103] The provision unit can adjust the method of providing a service by reflecting the user's past feedback when providing a service. The provision unit, for example, improves the method of providing a service based on the user's past feedback. For example, the provision unit improves the method of providing a service based on the user's past feedback. The provision unit can also adjust the content of the service by reflecting the user's feedback. For example, the provision unit adjusts the content of the service by reflecting the user's feedback. The provision unit can also improve the quality of the service by referring to the user's feedback. For example, the provision unit improves the quality of the service by referring to the user's feedback. This makes it possible to customize the method of providing a service based on the user's feedback. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input user feedback data into a generation AI and cause the generation AI to adjust the method of providing a service. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, loan 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 computer 36 of the smart device 14 and the specific processing unit 290 of the data processing device 12 and collects usage data of the user's electronic payment app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI to evaluate the user's creditworthiness. The loan unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets an interest rate based on the evaluated creditworthiness and provides a loan at an appropriate interest rate. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12 and provides services such as real estate rental, insurance, employment, and guarantees based on the evaluated creditworthiness. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, financing 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 computer 36 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12, and collects usage data of the user's electronic payment app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI to evaluate the user's creditworthiness. The financing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sets an interest rate based on the evaluated creditworthiness and provides a loan at an appropriate interest rate. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12, and provides services such as real estate rental, insurance, employment, and guarantee based on the evaluated creditworthiness. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, financing 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 computer 36 of the headset terminal 314 and the specific processing unit 290 of the data processing device 12 and collects usage data of the user's electronic payment app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI to evaluate the user's creditworthiness. The financing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets an interest rate based on the assessed creditworthiness and provides a loan at an appropriate interest rate. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing device 12 and provides services such as real estate rental, insurance, employment, and guarantees based on the assessed creditworthiness. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, loan 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 computer 36 of the robot 414 and the specific processing unit 290 of the data processing device 12 and collects usage data of the user's electronic payment app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI to evaluate the user's creditworthiness. The loan unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets an interest rate based on the evaluated creditworthiness and provides a loan at an appropriate interest rate. The provision unit is realized, for example, by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12 and provides services such as real estate rental, insurance, employment, and guarantees based on the evaluated creditworthiness.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The credit scoring system can further collect user health data, which the analysis unit can reflect in the credit scoring. For example, the collection unit can collect data from the user's fitness tracker or smart watch. The collection unit can also collect the user's medical records and health checkup results. The analysis unit evaluates the impact of the user's health condition on the credit score based on this health data. For example, the analysis unit can increase the credit score for users in good health. This enables more accurate credit scoring that takes the user's health condition into account.

[0106] The credit rating system can also collect data on a user's hobbies and interests, which the analysis unit can reflect in the credit rating. For example, the collection unit collects data on events the user attends and hobby-related products the user purchases. The collection unit can also identify the user's hobbies and interests from the content the user posts on social media. The analysis unit uses this data to evaluate the impact that the user's lifestyle has on the credit rating. For example, it can increase the credit rating of users who have stable hobbies. This enables more accurate credit rating that takes the user's lifestyle into account.

[0107] The credit rating system can further collect environmental data about the user, which the analysis unit can reflect in the credit rating. For example, the collection unit collects data about the user's living environment and work environment. The collection unit can also collect data about the user's commuting time and means of transportation. The analysis unit evaluates the impact of the user's living environment on the credit rating based on this environmental data. For example, the credit rating can be increased for users with stable living environments. This enables more accurate credit rating that takes the user's living environment into account.

[0108] The credit rating system can also collect user educational data, which the analysis unit can reflect in the credit rating. For example, the collection unit collects data on the user's educational background and acquired qualifications. The collection unit can also collect the user's learning history and online course attendance history. Based on this educational data, the analysis unit evaluates the impact of the user's educational background on the credit rating. For example, the credit rating can be increased for users with a high level of education and many qualifications. This enables more accurate credit rating that takes the user's educational background into account.

[0109] The credit rating system can further collect social network data of the user, and the analysis unit can reflect this in the credit rating. For example, the collection unit can collect data on the user's relationships with friends and family. The collection unit can also collect data on the user's relationships at work and community activities. The analysis unit evaluates the impact of the user's social stability on the credit rating based on this social network data. For example, the analysis unit can increase the credit rating of a user with a strong social network. This enables a more accurate credit rating that takes into account the user's social stability.

[0110] The credit rating system can further estimate the user's emotions and adjust the criteria for credit rating based on the estimated user's emotions. For example, the analysis unit relaxes the criteria for credit rating when the user is feeling stressed. For example, the analysis unit relaxes the criteria for credit rating when the user is feeling stressed. The analysis unit can also tighten the criteria for credit rating when the user is relaxed. For example, the analysis unit tightens the criteria for credit rating when the user is relaxed. The analysis unit can also quickly evaluate the credit rating when the user is in a hurry. For example, the analysis unit quickly evaluates the credit rating when the user is in a hurry. This enables flexible credit rating evaluation according to the user's emotions.

[0111] The credit evaluation system can further estimate the user's emotions and adjust the loan conditions based on the estimated user's emotions. For example, the loan unit relaxes the loan conditions when the user is nervous. For example, the loan unit relaxes the loan conditions when the user is nervous. The loan unit can also tighten the loan conditions when the user is relaxed. For example, the loan unit tightens the loan conditions when the user is relaxed. The loan unit can also quickly provide the loan when the user is in a hurry. For example, the loan unit quickly provides the loan when the user is in a hurry. This makes it possible to provide flexible loan conditions according to the user's emotions.

[0112] The credit evaluation system can further estimate the user's emotions and adjust the content of the service provided based on the estimated user's emotions. For example, the providing unit provides a simple and easy-to-understand service when the user is nervous. For example, the providing unit provides a simple and easy-to-understand service when the user is nervous. The providing unit can also provide a detailed service when the user is relaxed. For example, the providing unit provides a detailed service when the user is relaxed. The providing unit can also provide a quick service when the user is in a hurry. For example, the providing unit provides a quick service when the user is in a hurry. This makes it possible to provide flexible services according to the user's emotions.

[0113] The credit rating system can further estimate the user's emotions and adjust the data collection method based on the estimated user's emotions. For example, the collection unit reduces the frequency of data collection when the user is feeling stressed. For example, the collection unit reduces the frequency of data collection when the user is feeling stressed. The collection unit can also collect detailed data when the user is relaxed. For example, the collection unit collects detailed data when the user is relaxed. The collection unit can also quickly collect only the minimum amount of data necessary when the user is in a hurry. For example, the collection unit quickly collects only the minimum amount of data necessary when the user is in a hurry. This enables flexible data collection according to the user's emotions.

[0114] The credit rating system can further estimate the user's emotions and adjust the method of providing the analysis results based on the estimated user's emotions. For example, the analysis unit provides a simple and highly visible analysis result when the user is nervous. For example, the analysis unit provides a simple and highly visible analysis result when the user is nervous. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, the analysis unit provides a detailed analysis result when the user is relaxed. The analysis unit can also provide a concise analysis result that focuses on the main points when the user is in a hurry. For example, the analysis unit provides a concise analysis result that focuses on the main points when the user is in a hurry. This makes it possible to provide flexible analysis results that correspond to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects the user's usage data of the electronic payment app. The user's usage data of the electronic payment app includes transaction history, purchase amount, and usage frequency. The collection unit collects detailed data such as what transactions the user made, which store they purchased from, and how frequently they used the app. The collection unit can also collect the user's usage data of the electronic payment app in real time. For example, the collection unit periodically collects the user's transaction data and stores it in a database. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and evaluate the user's creditworthiness. The analysis unit makes the evaluation taking into account factors such as whether the user has a regular income, whether their expenses are stable, and whether they have any past outstanding payments. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, and objectively evaluates the user's creditworthiness. For example, the generation AI analyzes the user's transaction data and calculates a credit score. The analysis unit can also use the generation AI to evaluate the user's creditworthiness in real time. For example, the analysis unit analyzes the user's transaction data in real time and updates the credit score. Step 3: The loan unit sets an interest rate based on the creditworthiness evaluated by the analysis unit and provides a loan at an appropriate interest rate. The loan unit provides a loan at a low interest rate to a user with a high creditworthiness and a loan at a high interest rate to a user with a low creditworthiness. The loan unit sets an interest rate and determines the loan amount based on the user's credit score. The loan unit can also set a repayment period according to the user's creditworthiness. For example, the loan unit sets a long repayment period for a user with a high creditworthiness and a short repayment period for a user with a low creditworthiness. Step 4: The provision unit provides services such as real estate rental, insurance, employment, and guarantees based on the assessed credit rating. The provision unit provides good real estate rental contracts and insurance contracts to users with high credit ratings, and provides appropriate services to users with low credit ratings. The provision unit determines the content of the services based on the user's credit score. The provision unit can also adjust the method of providing services depending on the user's credit rating. For example, the provision unit provides quick service to users with high credit ratings and provides detailed explanations to users with low credit ratings.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 usage data of a user's electronic payment application; an analysis unit that analyzes the data collected by the collection unit and evaluates the credibility of the user; a financing unit that sets an interest rate based on the creditworthiness evaluated by the analysis unit and provides financing; a providing unit that utilizes the evaluated credit rating to provide a service that requires credit; A system characterized by:

2. The collecting unit Collect detailed data, including user purchase amounts, frequency of use, and transaction types 2. The system of claim 1.

3. The analysis unit The collected data will be used to assess the user's creditworthiness based on factors including the user's regular income, stability of expenses, and past non-payment history.

2. The system of claim 1.

4. The financing department Set interest rates based on assessed creditworthiness and provide loans at appropriate interest rates 2. The system of claim 1.

5. The providing unit Based on the assessed credit score, services including real estate rental, insurance, employment, and guarantees are provided.

2. The system of claim 1.

6. The collecting unit Estimate user emotions and determine the timing of data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze users' past trading history and select data collection methods 2. The system of claim 1.

8. The collecting unit Filtering data collection based on the user's current life situation and interests 2. The system of claim 1.

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