system

Abstract

The system according to this embodiment aims to perform comprehensive and accurate credit assessments that reflect diverse economic activities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The evaluation unit performs a credit evaluation based on the analysis results obtained by the analysis unit. The provision unit provides the evaluation results obtained by the evaluation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a comprehensive and accurate credit evaluation reflecting various economic activities has not been performed, and there is room for improvement.

[0005] The system according to the embodiment aims to perform a comprehensive and accurate credit evaluation reflecting various economic activities.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The evaluation unit performs a credit evaluation based on the analysis result obtained by the analysis unit. The provision unit provides the evaluation result obtained by the evaluation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can perform comprehensive and accurate credit assessments that reflect diverse economic activities. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The credit evaluation system according to an embodiment of the present invention is a system that performs a more comprehensive and accurate credit evaluation by utilizing new data sources to reflect a diverse range of economic activities that could not be reflected in conventional credit scoring models. This credit evaluation system uses generative AI to perform credit scoring and aims to provide financial services to people who cannot have bank accounts or obtain loans from banks. Conventional credit scoring models only evaluated a limited amount of data, such as credit card payment history, employer and length of employment, annual income, other borrowings, and utility bill payment history. However, the present invention utilizes new data sources such as online shopping data, electronic money usage history, SNS data, education history, employment history, and digital trace information (website browsing history, etc.). This makes it possible to perform credit evaluations that reflect a wider range of economic activities in addition to conventional screening data. Specifically, the generative AI analyzes these new data sources and evaluates an individual's creditworthiness. For example, online shopping data is used to understand consumers' spending patterns and ability to pay, and electronic money usage history is used to analyze daily spending trends and ability to pay. In addition, SNS data is used to indirectly infer an individual's reliability and creditworthiness, and education history and employment history are used to evaluate future income prospects and job security. Furthermore, digital trace information will be used to understand individual interests and consumption trends. This system will partially disclose its evaluation algorithm, which is powered by generative AI, to ensure transparency for users. It will also collaborate with a group of partner companies that possess alternative data to build a credit scoring database. A perpetual analysis cycle will be established using generative AI, and service rates will be designed for all users. In addition, a mechanism will be built to continuously increase the amount of analytical data through blockchain, maintaining the profit and loss balance of the entire service. This system will make it possible to provide financial services to people who were difficult to evaluate with conventional credit scoring systems. For example, this could include non-regular employees, immigrants, and people with limited financial history. This will aim for financial inclusion and provide access to groups that have been excluded from conventional financial services.This allows credit rating systems to provide financial services to people who were previously difficult to evaluate using conventional credit scoring models.

[0029] The credit rating system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects data. The collection unit can collect data from a variety of data sources, such as online shopping data, electronic money usage history, SNS data, education history, employment history, and digital trace information. For example, to collect online shopping data, the collection unit obtains the user's purchase history and product reviews. The collection unit can also obtain transaction history and usage frequency to collect electronic money usage history. Furthermore, to collect SNS data, the collection unit can obtain the user's posts and friendships. For example, the collection unit analyzes the user's SNS posts and collects data to estimate reliability and creditworthiness. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses generative AI to analyze the collected data and evaluate the creditworthiness of individuals. For example, the analysis unit grasps the consumer's spending patterns and ability to pay from online shopping data. The analysis unit can also analyze daily spending trends and ability to pay from electronic money usage history. Furthermore, the analysis department can indirectly infer an individual's trustworthiness and credibility from social media data. For example, the analysis department analyzes social media data and calculates indicators for evaluating user trustworthiness. The evaluation department performs a credit assessment based on the analysis results obtained by the analysis department. For example, the evaluation department uses generative AI to calculate an individual's credit score based on the analysis results. For example, the evaluation department comprehensively evaluates online shopping data, electronic money usage history, social media data, etc., to calculate a credit score. The evaluation department can also include educational history, employment history, and digital trace information in its evaluation. For example, the evaluation department evaluates future income prospects and job security based on educational history and employment history. The service provider provides the evaluation results obtained by the evaluation department to users. For example, the service provider may partially disclose the evaluation algorithm to ensure transparency to users. For example, the service provider provides the evaluation results to users through web applications or mobile applications. The service provider can also provide the evaluation results via email or paper.For example, the service provider sends the evaluation results to the user via email, providing prompt feedback. This allows the credit evaluation system according to the embodiment to utilize diverse data sources and perform comprehensive and accurate credit evaluations.

[0030] The data collection unit collects data. The data collection unit can collect data from a variety of data sources, such as online shopping data, electronic money usage history, social media data, educational history, employment history, and digital trace information. Specifically, to collect online shopping data, it obtains users' purchase history and product reviews. This allows for an understanding of users' consumption trends and purchasing patterns. The data collection unit can also obtain transaction history and usage frequency to collect electronic money usage history. This allows for an understanding of users' daily spending trends and ability to pay. Furthermore, to collect social media data, the data collection unit can obtain users' posts and friendships. For example, the data collection unit analyzes users' social media posts and collects data to estimate reliability and credibility. This allows for an understanding of users' social credibility and network breadth. Regarding educational and employment history, the data collection unit obtains users' educational and work history to collect data for evaluating future income prospects and job security. Regarding digital trace information, it collects users' internet usage history and application usage to understand users' behavioral patterns and interests. This allows the data collection unit to gather a wide range of data from diverse data sources and comprehensively obtain the information necessary for user credit assessment. Furthermore, the data collection unit can centrally manage this data and link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and evaluation units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis department analyzes data collected by the data collection department. For example, the analysis department uses generative AI to analyze the collected data and evaluate individual creditworthiness. Specifically, it uses online shopping data to understand consumer spending patterns and payment ability. Generative AI analyzes users' purchase history and product reviews to calculate indicators for evaluating spending trends and payment ability. It can also analyze daily spending trends and payment ability from electronic money usage history. Generative AI analyzes transaction history and usage frequency to calculate indicators for evaluating users' payment ability and creditworthiness. Furthermore, it can indirectly infer individual reliability and creditworthiness from social media data. Generative AI analyzes users' posts and friendships to calculate indicators for evaluating reliability and creditworthiness. For example, it analyzes social media data to calculate indicators for evaluating user reliability. This allows the analysis department to quickly and accurately analyze collected data and provide information for evaluating individual creditworthiness. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term credit evaluations and trend analyses. For example, it can predict fluctuations in creditworthiness over a specific period based on past spending data and evaluate future credit risk. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to handle not only real-time credit assessment but also long-term credit risk management and anomaly detection, thereby improving the overall reliability and security of the system.

[0032] The evaluation department conducts credit assessments based on the analysis results obtained by the analysis department. For example, the evaluation department uses generative AI to calculate an individual's credit score based on the analysis results. Specifically, it comprehensively evaluates online shopping data, electronic money usage history, and social media data to calculate the credit score. The generative AI integrates this data to calculate multiple indicators for evaluating the user's creditworthiness. For example, online shopping data is used to evaluate spending trends and payment ability, while electronic money usage history is used to evaluate daily spending trends and payment ability. Social media data is used to evaluate the user's reliability and the breadth of their social network. Furthermore, the evaluation department can also include educational history, employment history, and digital trace information in its evaluation. For example, educational history and employment history are used to evaluate future income prospects and job security. The generative AI analyzes this data to calculate indicators for evaluating the user's future credit risk. This allows the evaluation department to comprehensively evaluate the collected data and calculate an individual's credit score with high accuracy. Furthermore, the evaluation department can continuously revise the credit score based on real-time updated data to adapt to the latest situation. For example, if a user's spending habits or ability to pay changes, the rating department immediately incorporates the new data and updates the credit score. Furthermore, the rating department can perform more accurate credit assessments by considering regional characteristics and past credit history. This allows the rating department to always provide highly accurate credit assessments based on the latest information, supporting quick and appropriate responses.

[0033] The service provider will provide users with the evaluation results obtained by the evaluation provider. The service provider will ensure transparency to users by, for example, partially disclosing the evaluation algorithm. Specifically, evaluation results will be provided to users through web and mobile applications. Users can check their credit score and evaluation results through these applications. The service provider can also provide evaluation results via email or paper. For example, the service provider can send evaluation results to users via email to provide quick feedback. Furthermore, the service provider can provide evaluation results in paper format, allowing users to physically review the results. This allows the service provider to provide evaluation results to users in diverse ways, improving user convenience. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the evaluation algorithm and delivery methods. For example, they can collect user opinions and requests regarding evaluation results and revise the evaluation algorithm and improve delivery methods. The service provider can also reliably transmit information using multiple communication methods. For example, they can reliably deliver important information using not only notifications from web and mobile applications, but also voice calls, SMS, and email. This allows the service provider to deliver evaluation results to users quickly and reliably, thereby improving users' understanding of and trust in credit ratings.

[0034] The evaluation unit can analyze online shopping data, e-money usage history, social media data, education history, employment history, and digital trace information. For example, the evaluation unit can analyze online shopping data to understand consumers' spending patterns and ability to pay. For example, the evaluation unit can analyze e-money usage history to assess daily spending trends and ability to pay. For example, the evaluation unit can analyze social media data to indirectly infer an individual's reliability and creditworthiness. For example, the evaluation unit can analyze education history and employment history to assess future income prospects and job security. For example, the evaluation unit can analyze digital trace information to understand an individual's interests and consumption trends. This allows the evaluation unit to perform more accurate credit assessments by analyzing diverse data sources. Some or all of the above processing in the evaluation unit may be performed using, for example, generative AI, or without generative AI. For example, the evaluation unit can input online shopping data, e-money usage history, social media data, etc., into a generative AI, which can then analyze this data to perform a credit assessment.

[0035] The provider can ensure transparency to users by partially disclosing the evaluation algorithm. For example, the provider can publish part of the evaluation algorithm on a website or application so that users can understand how they are being evaluated. The provider can also publish, for example, the models and calculation methods used in the evaluation algorithm. In this way, the provider can gain user trust by ensuring transparency in the evaluation algorithm. Some or all of the above processing by the provider may be performed using, for example, generative AI, or not using generative AI. For example, the provider can provide transparency to users by publishing part of the evaluation algorithm based on generative AI.

[0036] The data collection unit can collaborate with a group of partner companies that possess alternative data. For example, the data collection unit can partner with companies that provide third-party data or publicly available data to collect a wider variety of data. The data collection unit can also, for example, create a list of partner companies and clarify how data is shared. This allows the data collection unit to collect a wider variety of data by collaborating with a group of partner companies. Some or all of the processing described above in the data collection unit may be performed using, for example, generative AI, or not using generative AI. For example, the data collection unit can input data provided by partner companies into a generative AI, which can then analyze and collect this data.

[0037] The analysis department can build a system that continuously increases the amount of analysis data through blockchain. For example, the analysis department can use blockchain technology to prevent data tampering and ensure transparency. For example, the analysis department can record data on the blockchain to improve data reliability. In this way, the analysis department can improve data reliability and transparency by utilizing blockchain. Some or all of the above processes in the analysis department may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis department can input data recorded on the blockchain into a generative AI, and the generative AI can analyze this data to continuously increase the amount of analysis data.

[0038] The service provider can build a persistent analysis cycle using generative AI and design service rates for all users. For example, the service provider can use generative AI to perform periodic data updates and continuous learning. The service provider can also set service rates for all users and design usage-based pricing or fixed rates. This allows the service provider to improve the accuracy and efficiency of the service by building a persistent analysis cycle. Some or all of the above processes in the service provider may be performed using generative AI, for example, or without generative AI. For example, the service provider can design service rates based on the analysis results from generative AI and provide them to all users.

[0039] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize collecting data sources that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection timing based on the user's past data collection history. The data collection unit can also customize the collection method by analyzing the user's past data collection history. This allows the data collection unit to select the optimal collection method by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the user's past data collection history into a generative AI, which can then analyze this data to select the optimal collection method.

[0040] The data collection unit can filter data based on the user's current economic situation and areas of interest during data collection. For example, the data collection unit can prioritize the collection of relevant data based on the user's current income. The data collection unit can also filter and collect relevant data based on the user's areas of interest. The data collection unit can also adjust the range of data to be collected according to the user's economic situation. This allows the data collection unit to collect more relevant data by filtering the data based on the user's economic situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data about the user's economic situation and areas of interest into a generative AI, which can then analyze and filter this data.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also filter and collect highly relevant data based on the user's current location, for example. The data collection unit can also prioritize the collection of highly relevant data by considering the user's travel history, for example. In this way, the data collection unit can collect more relevant data by collecting data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, and the generative AI can analyze this data and prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant data. The data collection unit can also consider the user's social media friendships and collect relevant data. For example, the data collection unit can analyze the frequency of the user's social media activity and collect relevant data. This allows the data collection unit to collect more relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then analyze this data and collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also, for example, determine the priority of the analysis based on the importance of the data. The analysis unit can also, for example, optimally allocate analysis resources based on the importance of the data. This enables efficient analysis by allowing the analysis unit to adjust the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can analyze this data and adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a consumption pattern analysis algorithm to online shopping data. For example, the analysis unit can also apply a reliability evaluation algorithm to social media data. For example, the analysis unit can apply a future income prospect evaluation algorithm to educational history data. This improves the accuracy of the analysis by allowing the analysis unit to apply the appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data categories into a generative AI, which can then analyze this data and apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also optimize the allocation of analysis resources based on the data collection timing. For example, the analysis unit may adjust the level of detail of the analysis according to the data collection timing. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the data collection timing into a generative AI, which can then analyze this data to determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also optimize the allocation of analysis resources based on the relevance of the data. The analysis unit can also adjust the level of detail of the analysis according to the relevance of the data. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, which can then analyze this data and adjust the order of analysis.

[0047] The evaluation unit can improve the accuracy of its evaluations based on the interrelationships between data. For example, the evaluation unit may consider the interrelationships between online shopping data and electronic money usage history when performing its evaluation. The evaluation unit may also consider the interrelationships between social media data and educational history when performing its evaluation. The evaluation unit may also consider the interrelationships between employment history and digital trace information when performing its evaluation. As a result, the evaluation unit can improve the accuracy of its evaluations by considering the interrelationships between data. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input the interrelationships between data into a generative AI, and the generative AI can analyze this data to improve the accuracy of the evaluation.

[0048] The evaluation unit can perform evaluations based on the attribute information of the data submitter. For example, the evaluation unit may consider the age of the data submitter when performing the evaluation. The evaluation unit may also consider the occupation of the data submitter when performing the evaluation. The evaluation unit may also consider the place of residence of the data submitter when performing the evaluation. This allows the evaluation unit to perform more accurate evaluations by considering the attribute information of the data submitter. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input the attribute information of the data submitter into a generative AI, and the generative AI can analyze this data and perform the evaluation.

[0049] The evaluation unit can perform evaluations based on the geographical distribution of the data during the evaluation process. For example, the evaluation unit can perform evaluations for each region based on the geographical distribution of the data. The evaluation unit can also adjust the evaluation criteria, for example, by taking the geographical distribution into consideration. The evaluation unit can also adjust the level of detail of the evaluation, for example, according to the geographical distribution. This allows the evaluation unit to perform evaluations for each region by considering the geographical distribution of the data. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input the geographical distribution of the data into a generative AI, and the generative AI can analyze this data and perform evaluations.

[0050] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the data during the evaluation process. For example, the evaluation unit can adjust the evaluation criteria by referring to relevant literature on the data. The evaluation unit can also adjust the level of detail of the evaluation based on relevant literature. The evaluation unit can also improve the accuracy of its evaluation by referring to relevant literature. As a result, the evaluation unit improves the accuracy of its evaluation by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input relevant literature on the data into a generative AI, and the generative AI can analyze this data to improve the accuracy of the evaluation.

[0051] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can provide the most frequently used display method based on the user's past operation history. The service provider can also analyze the user's past operation history and provide a customized display method. The service provider can also optimize the display method based on the user's past operation history. In this way, the service provider can provide the optimal display method by referring to past operation history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's past operation history into a generation AI, and the generation AI can analyze this data to select the optimal display method.

[0052] The service provider can select the optimal display method based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can also provide a concise and highly visible display method. This allows the service provider to provide more appropriate information by selecting a display method based on the user's device information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's device information into a generative AI, and the generative AI can analyze this data to select the optimal display method.

[0053] The service provider can provide multilingual information at the time of delivery, according to the user's language settings. For example, the service provider can automatically set the language of the information based on the language settings of the user's device. For example, the service provider can also provide a language switching function if the user uses multiple languages. For example, the service provider can provide information in a specific language if the user selects a particular language. This allows the service provider to provide more appropriate information by providing multilingual information according to the user's language settings. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's language settings into a generative AI, and the generative AI can analyze this data to provide multilingual information.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize collecting data sources that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection timing based on the user's past data collection history. The data collection unit can also customize the collection method by analyzing the user's past data collection history. This allows the data collection unit to select the optimal collection method by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the user's past data collection history into a generative AI, which can then analyze this data to select the optimal collection method.

[0056] The data collection unit can filter data based on the user's current economic situation and areas of interest during data collection. For example, the data collection unit can prioritize the collection of relevant data based on the user's current income. The data collection unit can also filter and collect relevant data based on the user's areas of interest. The data collection unit can also adjust the range of data to be collected according to the user's economic situation. This allows the data collection unit to collect more relevant data by filtering the data based on the user's economic situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data about the user's economic situation and areas of interest into a generative AI, which can then analyze and filter this data.

[0057] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also filter and collect highly relevant data based on the user's current location, for example. The data collection unit can also prioritize the collection of highly relevant data by considering the user's travel history, for example. In this way, the data collection unit can collect more relevant data by collecting data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, and the generative AI can analyze this data and prioritize the collection of highly relevant data.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also, for example, determine the priority of the analysis based on the importance of the data. The analysis unit can also, for example, optimally allocate analysis resources based on the importance of the data. This enables efficient analysis by allowing the analysis unit to adjust the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can analyze this data and adjust the level of detail of the analysis.

[0059] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a consumption pattern analysis algorithm to online shopping data. For example, the analysis unit can also apply a reliability evaluation algorithm to social media data. For example, the analysis unit can apply a future income prospect evaluation algorithm to educational history data. This improves the accuracy of the analysis by allowing the analysis unit to apply the appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data categories into a generative AI, which can then analyze this data and apply an appropriate analysis algorithm.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects data. The data collection unit can collect data from a variety of data sources, such as online shopping data, e-money usage history, SNS data, education history, employment history, and digital trace information. For example, to collect online shopping data, the data collection unit obtains users' purchase history and product reviews. The data collection unit can also obtain transaction history and usage frequency to collect e-money usage history. Furthermore, to collect SNS data, the data collection unit can obtain users' posts and friendships. For example, the data collection unit analyzes users' SNS posts and collects data to estimate reliability and credibility. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department analyzes the collected data and evaluates the creditworthiness of individuals, for example, using generative AI. The analysis department can, for example, understand consumer spending patterns and ability to pay from online shopping data. The analysis department can also analyze daily spending trends and ability to pay from e-money usage history. Furthermore, the analysis department can indirectly infer the trustworthiness and creditworthiness of individuals from social media data. For example, the analysis department analyzes social media data and calculates an index to evaluate user trustworthiness. Step 3: The evaluation department performs a credit assessment based on the analysis results obtained by the analysis department. The evaluation department calculates an individual's credit score based on the analysis results, for example, by using generative AI. The evaluation department calculates a credit score by comprehensively evaluating data such as online shopping data, e-money usage history, and social media data. The evaluation department can also include educational history, employment history, and digital trace information in its evaluation. For example, the evaluation department evaluates future income prospects and job security based on educational history and employment history. Step 4: The service provider provides the evaluation results obtained by the evaluation provider to the user. The service provider ensures transparency to the user by, for example, partially disclosing the evaluation algorithm. The service provider provides the evaluation results to the user by, for example, a web application or a mobile application. The service provider can also provide the evaluation results by email or in paper format. For example, the service provider sends the evaluation results to the user by email to provide prompt feedback.

[0062] (Example of form 2) The credit evaluation system according to an embodiment of the present invention is a system that performs a more comprehensive and accurate credit evaluation by utilizing new data sources to reflect a diverse range of economic activities that could not be reflected in conventional credit scoring models. This credit evaluation system uses generative AI to perform credit scoring and aims to provide financial services to people who cannot have bank accounts or obtain loans from banks. Conventional credit scoring models only evaluated a limited amount of data, such as credit card payment history, employer and length of employment, annual income, other borrowings, and utility bill payment history. However, the present invention utilizes new data sources such as online shopping data, electronic money usage history, SNS data, education history, employment history, and digital trace information (website browsing history, etc.). This makes it possible to perform credit evaluations that reflect a wider range of economic activities in addition to conventional screening data. Specifically, the generative AI analyzes these new data sources and evaluates an individual's creditworthiness. For example, online shopping data is used to understand consumers' spending patterns and ability to pay, and electronic money usage history is used to analyze daily spending trends and ability to pay. In addition, SNS data is used to indirectly infer an individual's reliability and creditworthiness, and education history and employment history are used to evaluate future income prospects and job security. Furthermore, digital trace information will be used to understand individual interests and consumption trends. This system will partially disclose its evaluation algorithm, which is powered by generative AI, to ensure transparency for users. It will also collaborate with a group of partner companies that possess alternative data to build a credit scoring database. A perpetual analysis cycle will be established using generative AI, and service rates will be designed for all users. In addition, a mechanism will be built to continuously increase the amount of analytical data through blockchain, maintaining the profit and loss balance of the entire service. This system will make it possible to provide financial services to people who were difficult to evaluate with conventional credit scoring systems. For example, this could include non-regular employees, immigrants, and people with limited financial history. This will aim for financial inclusion and provide access to groups that have been excluded from conventional financial services.This allows credit rating systems to provide financial services to people who were previously difficult to evaluate using conventional credit scoring models.

[0063] The credit rating system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects data. The collection unit can collect data from a variety of data sources, such as online shopping data, electronic money usage history, SNS data, education history, employment history, and digital trace information. For example, to collect online shopping data, the collection unit obtains the user's purchase history and product reviews. The collection unit can also obtain transaction history and usage frequency to collect electronic money usage history. Furthermore, to collect SNS data, the collection unit can obtain the user's posts and friendships. For example, the collection unit analyzes the user's SNS posts and collects data to estimate reliability and creditworthiness. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses generative AI to analyze the collected data and evaluate the creditworthiness of individuals. For example, the analysis unit grasps the consumer's spending patterns and ability to pay from online shopping data. The analysis unit can also analyze daily spending trends and ability to pay from electronic money usage history. Furthermore, the analysis department can indirectly infer an individual's trustworthiness and credibility from social media data. For example, the analysis department analyzes social media data and calculates indicators for evaluating user trustworthiness. The evaluation department performs a credit assessment based on the analysis results obtained by the analysis department. For example, the evaluation department uses generative AI to calculate an individual's credit score based on the analysis results. For example, the evaluation department comprehensively evaluates online shopping data, electronic money usage history, social media data, etc., to calculate a credit score. The evaluation department can also include educational history, employment history, and digital trace information in its evaluation. For example, the evaluation department evaluates future income prospects and job security based on educational history and employment history. The service provider provides the evaluation results obtained by the evaluation department to users. For example, the service provider may partially disclose the evaluation algorithm to ensure transparency to users. For example, the service provider provides the evaluation results to users through web applications or mobile applications. The service provider can also provide the evaluation results via email or paper.For example, the service provider sends the evaluation results to the user via email, providing prompt feedback. This allows the credit evaluation system according to the embodiment to utilize diverse data sources and perform comprehensive and accurate credit evaluations.

[0064] The data collection unit collects data. The data collection unit can collect data from a variety of data sources, such as online shopping data, electronic money usage history, social media data, educational history, employment history, and digital trace information. Specifically, to collect online shopping data, it obtains users' purchase history and product reviews. This allows for an understanding of users' consumption trends and purchasing patterns. The data collection unit can also obtain transaction history and usage frequency to collect electronic money usage history. This allows for an understanding of users' daily spending trends and ability to pay. Furthermore, to collect social media data, the data collection unit can obtain users' posts and friendships. For example, the data collection unit analyzes users' social media posts and collects data to estimate reliability and credibility. This allows for an understanding of users' social credibility and network breadth. Regarding educational and employment history, the data collection unit obtains users' educational and work history to collect data for evaluating future income prospects and job security. Regarding digital trace information, it collects users' internet usage history and application usage to understand users' behavioral patterns and interests. This allows the data collection unit to gather a wide range of data from diverse data sources and comprehensively obtain the information necessary for user credit assessment. Furthermore, the data collection unit can centrally manage this data and link it with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and evaluation units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0065] The analysis department analyzes data collected by the data collection department. For example, the analysis department uses generative AI to analyze the collected data and evaluate individual creditworthiness. Specifically, it uses online shopping data to understand consumer spending patterns and payment ability. Generative AI analyzes users' purchase history and product reviews to calculate indicators for evaluating spending trends and payment ability. It can also analyze daily spending trends and payment ability from electronic money usage history. Generative AI analyzes transaction history and usage frequency to calculate indicators for evaluating users' payment ability and creditworthiness. Furthermore, it can indirectly infer individual reliability and creditworthiness from social media data. Generative AI analyzes users' posts and friendships to calculate indicators for evaluating reliability and creditworthiness. For example, it analyzes social media data to calculate indicators for evaluating user reliability. This allows the analysis department to quickly and accurately analyze collected data and provide information for evaluating individual creditworthiness. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term credit evaluations and trend analyses. For example, it can predict fluctuations in creditworthiness over a specific period based on past spending data and evaluate future credit risk. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to handle not only real-time credit assessment but also long-term credit risk management and anomaly detection, thereby improving the overall reliability and security of the system.

[0066] The evaluation department conducts credit assessments based on the analysis results obtained by the analysis department. For example, the evaluation department uses generative AI to calculate an individual's credit score based on the analysis results. Specifically, it comprehensively evaluates online shopping data, electronic money usage history, and social media data to calculate the credit score. The generative AI integrates this data to calculate multiple indicators for evaluating the user's creditworthiness. For example, online shopping data is used to evaluate spending trends and payment ability, while electronic money usage history is used to evaluate daily spending trends and payment ability. Social media data is used to evaluate the user's reliability and the breadth of their social network. Furthermore, the evaluation department can also include educational history, employment history, and digital trace information in its evaluation. For example, educational history and employment history are used to evaluate future income prospects and job security. The generative AI analyzes this data to calculate indicators for evaluating the user's future credit risk. This allows the evaluation department to comprehensively evaluate the collected data and calculate an individual's credit score with high accuracy. Furthermore, the evaluation department can continuously revise the credit score based on real-time updated data to adapt to the latest situation. For example, if a user's spending habits or ability to pay changes, the rating department immediately incorporates the new data and updates the credit score. Furthermore, the rating department can perform more accurate credit assessments by considering regional characteristics and past credit history. This allows the rating department to always provide highly accurate credit assessments based on the latest information, supporting quick and appropriate responses.

[0067] The service provider will provide users with the evaluation results obtained by the evaluation provider. The service provider will ensure transparency to users by, for example, partially disclosing the evaluation algorithm. Specifically, evaluation results will be provided to users through web and mobile applications. Users can check their credit score and evaluation results through these applications. The service provider can also provide evaluation results via email or paper. For example, the service provider can send evaluation results to users via email to provide quick feedback. Furthermore, the service provider can provide evaluation results in paper format, allowing users to physically review the results. This allows the service provider to provide evaluation results to users in diverse ways, improving user convenience. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the evaluation algorithm and delivery methods. For example, they can collect user opinions and requests regarding evaluation results and revise the evaluation algorithm and improve delivery methods. The service provider can also reliably transmit information using multiple communication methods. For example, they can reliably deliver important information using not only notifications from web and mobile applications, but also voice calls, SMS, and email. This allows the service provider to deliver evaluation results to users quickly and reliably, thereby improving users' understanding of and trust in credit ratings.

[0068] The evaluation unit can analyze online shopping data, e-money usage history, social media data, education history, employment history, and digital trace information. For example, the evaluation unit can analyze online shopping data to understand consumers' spending patterns and ability to pay. For example, the evaluation unit can analyze e-money usage history to assess daily spending trends and ability to pay. For example, the evaluation unit can analyze social media data to indirectly infer an individual's reliability and creditworthiness. For example, the evaluation unit can analyze education history and employment history to assess future income prospects and job security. For example, the evaluation unit can analyze digital trace information to understand an individual's interests and consumption trends. This allows the evaluation unit to perform more accurate credit assessments by analyzing diverse data sources. Some or all of the above processing in the evaluation unit may be performed using, for example, generative AI, or without generative AI. For example, the evaluation unit can input online shopping data, e-money usage history, social media data, etc., into a generative AI, which can then analyze this data to perform a credit assessment.

[0069] The provider can ensure transparency to users by partially disclosing the evaluation algorithm. For example, the provider can publish part of the evaluation algorithm on a website or application so that users can understand how they are being evaluated. The provider can also publish, for example, the models and calculation methods used in the evaluation algorithm. In this way, the provider can gain user trust by ensuring transparency in the evaluation algorithm. Some or all of the above processing by the provider may be performed using, for example, generative AI, or not using generative AI. For example, the provider can provide transparency to users by publishing part of the evaluation algorithm based on generative AI.

[0070] The data collection unit can collaborate with a group of partner companies that possess alternative data. For example, the data collection unit can partner with companies that provide third-party data or publicly available data to collect a wider variety of data. The data collection unit can also, for example, create a list of partner companies and clarify how data is shared. This allows the data collection unit to collect a wider variety of data by collaborating with a group of partner companies. Some or all of the processing described above in the data collection unit may be performed using, for example, generative AI, or not using generative AI. For example, the data collection unit can input data provided by partner companies into a generative AI, which can then analyze and collect this data.

[0071] The analysis department can build a system that continuously increases the amount of analysis data through blockchain. For example, the analysis department can use blockchain technology to prevent data tampering and ensure transparency. For example, the analysis department can record data on the blockchain to improve data reliability. In this way, the analysis department can improve data reliability and transparency by utilizing blockchain. Some or all of the above processes in the analysis department may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis department can input data recorded on the blockchain into a generative AI, and the generative AI can analyze this data to continuously increase the amount of analysis data.

[0072] The service provider can build a persistent analysis cycle using generative AI and design service rates for all users. For example, the service provider can use generative AI to perform periodic data updates and continuous learning. The service provider can also set service rates for all users and design usage-based pricing or fixed rates. This allows the service provider to improve the accuracy and efficiency of the service by building a persistent analysis cycle. Some or all of the above processes in the service provider may be performed using generative AI, for example, or without generative AI. For example, the service provider can design service rates based on the analysis results from generative AI and provide them to all users.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily stop data collection and resume it when the user is relaxed. For example, if the user is relaxed, the data collection unit can actively collect data and gather detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data and process it quickly. This allows the data collection unit to collect more appropriate data by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can analyze this data and adjust the timing of data collection.

[0074] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize collecting data sources that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection timing based on the user's past data collection history. The data collection unit can also customize the collection method by analyzing the user's past data collection history. This allows the data collection unit to select the optimal collection method by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the user's past data collection history into a generative AI, which can then analyze this data to select the optimal collection method.

[0075] The data collection unit can filter data based on the user's current economic situation and areas of interest during data collection. For example, the data collection unit can prioritize the collection of relevant data based on the user's current income. The data collection unit can also filter and collect relevant data based on the user's areas of interest. The data collection unit can also adjust the range of data to be collected according to the user's economic situation. This allows the data collection unit to collect more relevant data by filtering the data based on the user's economic situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data about the user's economic situation and areas of interest into a generative AI, which can then analyze and filter this data.

[0076] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting only important data. If the user is relaxed, the data collection unit may prioritize collecting detailed data. If the user is in a hurry, the data collection unit may prioritize collecting data that can be collected quickly. In this way, the data collection unit can prioritize collecting more important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can analyze this data to determine the priority of the data.

[0077] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also filter and collect highly relevant data based on the user's current location, for example. The data collection unit can also prioritize the collection of highly relevant data by considering the user's travel history, for example. In this way, the data collection unit can collect more relevant data by collecting data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, and the generative AI can analyze this data and prioritize the collection of highly relevant data.

[0078] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant data. The data collection unit can also consider the user's social media friendships and collect relevant data. For example, the data collection unit can analyze the frequency of the user's social media activity and collect relevant data. This allows the data collection unit to collect more relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then analyze this data and collect relevant data.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the analysis unit can provide more easily understandable analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can analyze this data and adjust the presentation of the analysis.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also, for example, determine the priority of the analysis based on the importance of the data. The analysis unit can also, for example, optimally allocate analysis resources based on the importance of the data. This enables efficient analysis by allowing the analysis unit to adjust the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can analyze this data and adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a consumption pattern analysis algorithm to online shopping data. For example, the analysis unit can also apply a reliability evaluation algorithm to social media data. For example, the analysis unit can apply a future income prospect evaluation algorithm to educational history data. This improves the accuracy of the analysis by allowing the analysis unit to apply the appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data categories into a generative AI, which can then analyze this data and apply an appropriate analysis algorithm.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis. For example, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can analyze this data and adjust the length of the analysis.

[0083] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also optimize the allocation of analysis resources based on the data collection timing. For example, the analysis unit may adjust the level of detail of the analysis according to the data collection timing. This enables efficient analysis by allowing the analysis unit to determine the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the data collection timing into a generative AI, which can then analyze this data to determine the priority of analysis.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also optimize the allocation of analysis resources based on the relevance of the data. The analysis unit can also adjust the level of detail of the analysis according to the relevance of the data. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, which can then analyze this data and adjust the order of analysis.

[0085] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is tense, the evaluation unit may relax the evaluation criteria to reduce stress. For example, if the user is relaxed, the evaluation unit may apply detailed evaluation criteria. For example, if the user is in a hurry, the evaluation unit may apply criteria for a quick evaluation. This allows the evaluation unit to provide a more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the evaluation unit can input user emotion data into a generative AI, which can analyze this data and adjust the evaluation criteria.

[0086] The evaluation unit can improve the accuracy of its evaluations based on the interrelationships between data. For example, the evaluation unit may consider the interrelationships between online shopping data and electronic money usage history when performing its evaluation. The evaluation unit may also consider the interrelationships between social media data and educational history when performing its evaluation. The evaluation unit may also consider the interrelationships between employment history and digital trace information when performing its evaluation. As a result, the evaluation unit can improve the accuracy of its evaluations by considering the interrelationships between data. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input the interrelationships between data into a generative AI, and the generative AI can analyze this data to improve the accuracy of the evaluation.

[0087] The evaluation unit can perform evaluations based on the attribute information of the data submitter. For example, the evaluation unit may consider the age of the data submitter when performing the evaluation. The evaluation unit may also consider the occupation of the data submitter when performing the evaluation. The evaluation unit may also consider the place of residence of the data submitter when performing the evaluation. This allows the evaluation unit to perform more accurate evaluations by considering the attribute information of the data submitter. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input the attribute information of the data submitter into a generative AI, and the generative AI can analyze this data and perform the evaluation.

[0088] The evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated emotions. For example, if the user is nervous, the evaluation unit may display important results first and detailed results later. For example, if the user is relaxed, the evaluation unit may also display detailed results first. For example, if the user is in a hurry, the evaluation unit may also display concise results first. In this way, the evaluation unit can provide more appropriate information by adjusting the display order of evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or not using a generative AI. For example, the evaluation unit can input user emotion data into a generative AI, and the generative AI can analyze this data to adjust the display order of the evaluation results.

[0089] The evaluation unit can perform evaluations based on the geographical distribution of the data during the evaluation process. For example, the evaluation unit can perform evaluations for each region based on the geographical distribution of the data. The evaluation unit can also adjust the evaluation criteria, for example, by taking the geographical distribution into consideration. The evaluation unit can also adjust the level of detail of the evaluation, for example, according to the geographical distribution. This allows the evaluation unit to perform evaluations for each region by considering the geographical distribution of the data. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the evaluation unit can input the geographical distribution of the data into a generative AI, and the generative AI can analyze this data and perform evaluations.

[0090] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the data during the evaluation process. For example, the evaluation unit can adjust the evaluation criteria by referring to relevant literature on the data. The evaluation unit can also adjust the level of detail of the evaluation based on relevant literature. The evaluation unit can also improve the accuracy of its evaluation by referring to relevant literature. As a result, the evaluation unit improves the accuracy of its evaluation by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input relevant literature on the data into a generative AI, and the generative AI can analyze this data to improve the accuracy of the evaluation.

[0091] The service provider can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. If the user is in a hurry, the service provider can also provide a display method that gets straight to the point. This allows the service provider to provide more appropriate information by adjusting the way information is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can analyze this data to adjust the way information is displayed.

[0092] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider can provide the most frequently used display method based on the user's past operation history. The service provider can also analyze the user's past operation history and provide a customized display method. The service provider can also optimize the display method based on the user's past operation history. In this way, the service provider can provide the optimal display method by referring to past operation history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's past operation history into a generation AI, and the generation AI can analyze this data to select the optimal display method.

[0093] The service provider can estimate the user's emotions and adjust the instructions for operating the information it provides based on the estimated emotions. For example, if the user is tense, the service provider may simplify the instructions to reduce stress. For example, if the user is relaxed, the service provider may provide detailed instructions. For example, if the user is in a hurry, the service provider may provide instructions that allow for quick operation. This enables the service provider to provide more appropriate information by adjusting the instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, which can then analyze this data and adjust the instructions.

[0094] The service provider can select the optimal display method based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can also provide a concise and highly visible display method. This allows the service provider to provide more appropriate information by selecting a display method based on the user's device information. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's device information into a generative AI, and the generative AI can analyze this data to select the optimal display method.

[0095] The service provider can provide multilingual information at the time of delivery, according to the user's language settings. For example, the service provider can automatically set the language of the information based on the language settings of the user's device. For example, the service provider can also provide a language switching function if the user uses multiple languages. For example, the service provider can provide information in a specific language if the user selects a particular language. This allows the service provider to provide more appropriate information by providing multilingual information according to the user's language settings. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's language settings into a generative AI, and the generative AI can analyze this data to provide multilingual information.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily stop data collection and resume it when the user is relaxed. For example, if the user is relaxed, the data collection unit can actively collect data and gather detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data and process it quickly. This allows the data collection unit to collect more appropriate data by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can analyze this data and adjust the timing of data collection.

[0098] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize collecting data sources that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection timing based on the user's past data collection history. The data collection unit can also customize the collection method by analyzing the user's past data collection history. This allows the data collection unit to select the optimal collection method by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data collection unit can input the user's past data collection history into a generative AI, which can then analyze this data to select the optimal collection method.

[0099] The data collection unit can filter data based on the user's current economic situation and areas of interest during data collection. For example, the data collection unit can prioritize the collection of relevant data based on the user's current income. The data collection unit can also filter and collect relevant data based on the user's areas of interest. The data collection unit can also adjust the range of data to be collected according to the user's economic situation. This allows the data collection unit to collect more relevant data by filtering the data based on the user's economic situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data about the user's economic situation and areas of interest into a generative AI, which can then analyze and filter this data.

[0100] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting only important data. If the user is relaxed, the data collection unit may prioritize collecting detailed data. If the user is in a hurry, the data collection unit may prioritize collecting data that can be collected quickly. In this way, the data collection unit can prioritize collecting more important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can analyze this data to determine the priority of the data.

[0101] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also filter and collect highly relevant data based on the user's current location, for example. The data collection unit can also prioritize the collection of highly relevant data by considering the user's travel history, for example. In this way, the data collection unit can collect more relevant data by collecting data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, and the generative AI can analyze this data and prioritize the collection of highly relevant data.

[0102] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the analysis unit can provide more easily understandable analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can analyze this data and adjust the presentation of the analysis.

[0103] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a simplified analysis on less important data. The analysis unit can also, for example, determine the priority of the analysis based on the importance of the data. The analysis unit can also, for example, optimally allocate analysis resources based on the importance of the data. This enables efficient analysis by allowing the analysis unit to adjust the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can analyze this data and adjust the level of detail of the analysis.

[0104] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a consumption pattern analysis algorithm to online shopping data. For example, the analysis unit can also apply a reliability evaluation algorithm to social media data. For example, the analysis unit can apply a future income prospect evaluation algorithm to educational history data. This improves the accuracy of the analysis by allowing the analysis unit to apply the appropriate analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data categories into a generative AI, which can then analyze this data and apply an appropriate analysis algorithm.

[0105] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis. For example, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can analyze this data and adjust the length of the analysis.

[0106] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is tense, the evaluation unit may relax the evaluation criteria to reduce stress. For example, if the user is relaxed, the evaluation unit may apply detailed evaluation criteria. For example, if the user is in a hurry, the evaluation unit may apply criteria for a quick evaluation. This allows the evaluation unit to provide a more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the evaluation unit can input user emotion data into a generative AI, which can analyze this data and adjust the evaluation criteria.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects data. The data collection unit can collect data from a variety of data sources, such as online shopping data, e-money usage history, SNS data, education history, employment history, and digital trace information. For example, to collect online shopping data, the data collection unit obtains users' purchase history and product reviews. The data collection unit can also obtain transaction history and usage frequency to collect e-money usage history. Furthermore, to collect SNS data, the data collection unit can obtain users' posts and friendships. For example, the data collection unit analyzes users' SNS posts and collects data to estimate reliability and credibility. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department analyzes the collected data and evaluates the creditworthiness of individuals, for example, using generative AI. The analysis department can, for example, understand consumer spending patterns and ability to pay from online shopping data. The analysis department can also analyze daily spending trends and ability to pay from e-money usage history. Furthermore, the analysis department can indirectly infer the trustworthiness and creditworthiness of individuals from social media data. For example, the analysis department analyzes social media data and calculates an index to evaluate user trustworthiness. Step 3: The evaluation department performs a credit assessment based on the analysis results obtained by the analysis department. The evaluation department calculates an individual's credit score based on the analysis results, for example, by using generative AI. The evaluation department calculates a credit score by comprehensively evaluating data such as online shopping data, e-money usage history, and social media data. The evaluation department can also include educational history, employment history, and digital trace information in its evaluation. For example, the evaluation department evaluates future income prospects and job security based on educational history and employment history. Step 4: The service provider provides the evaluation results obtained by the evaluation provider to the user. The service provider ensures transparency to the user by, for example, partially disclosing the evaluation algorithm. The service provider provides the evaluation results to the user by, for example, a web application or a mobile application. The service provider can also provide the evaluation results by email or in paper format. For example, the service provider sends the evaluation results to the user by email to provide prompt feedback.

[0109] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects online shopping data, electronic money usage history, SNS data, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates a credit score based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the evaluation results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 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.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects online shopping data, electronic money usage history, SNS data, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generated AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates a credit score based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the evaluation results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects online shopping data, electronic money usage history, SNS data, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generated AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates a credit score based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the evaluation results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects online shopping data, electronic money usage history, SNS data, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generated AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and calculates a credit score based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides the evaluation results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that performs a credit evaluation based on the analysis results obtained by the aforementioned analysis unit, A providing unit that provides the evaluation results obtained by the evaluation unit to the user, Equipped with A system characterized by the following features. (Note 2) The evaluation unit, Analyze online shopping data, e-money usage history, social media data, education history, employment history, and digital trace information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We will partially disclose our evaluation algorithm to ensure transparency for users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We collaborate with a group of partner companies that possess alternative data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is We will build a system that continuously increases the amount of analytical data through blockchain. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We will build a persistent analysis cycle using generative AI and design service rates for all users. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current economic situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During evaluation, improve the accuracy of the evaluation based on the interrelationships between data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the evaluation process, the data will be evaluated based on the attribute information of the data submitter. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, It estimates the user's emotions and adjusts the order in which evaluation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During the evaluation, the evaluation will be based on the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During evaluation, we refer to relevant literature to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts the instructions for interacting with the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal display method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we will provide information in multiple languages ​​according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, An evaluation unit that performs a credit evaluation based on the analysis results obtained by the aforementioned analysis unit, A providing unit that provides the evaluation results obtained by the evaluation unit to the user, Equipped with A system characterized by the following features.

2. The evaluation unit, Analyze online shopping data, e-money usage history, social media data, education history, employment history, and digital trace information. The system according to feature 1.

3. The aforementioned supply unit is, We will partially disclose our evaluation algorithm to ensure transparency for users. The system according to feature 1.

4. The aforementioned collection unit is We collaborate with a group of partner companies that possess alternative data. The system according to feature 1.

5. The aforementioned analysis unit is We will build a system that continuously increases the amount of analytical data through blockchain. The system according to feature 1.

6. The aforementioned supply unit is, We will build a persistent analysis cycle using generative AI and design service rates for all users. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current economic situation and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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