system

Abstract

The system according to this embodiment aims to provide differentiation and added value in payment applications and reduce the risk of fraud and crime. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a modification unit, and a reset unit. The collection unit collects purchase history. The analysis unit analyzes the purchase history collected by the collection unit. The generation unit generates an avatar based on the results analyzed by the analysis unit. The modification unit modifies the appearance of the avatar based on the KYC implementation status. The reset unit resets the avatar.

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, there is a problem that it is difficult to provide differentiation and added value in a payment application, and there is also a risk of fraud and crime.

[0005] The system according to the embodiment aims to provide differentiation and added value in a payment application and reduce the risk of fraud and crime.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a modification unit, and a reset unit. The collection unit collects purchase history. The analysis unit analyzes the purchase history collected by the collection unit. The generation unit generates an avatar based on the results of the analysis performed by the analysis unit. The modification unit modifies the appearance of the avatar based on the KYC implementation status. The reset unit resets the avatar. [Effects of the Invention]

[0007] The system according to this embodiment can provide differentiation and added value in payment applications and reduce the risk of fraud and crime. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 electronic payment system according to an embodiment of the present invention is a system that differentiates itself from other companies' payment methods and provides new added value to users by implementing an avatar creation function. When a user makes a purchase using this electronic payment system, their avatar changes based on their purchase history. The appearance and personality of this avatar change according to the purchase category, such as food, clothing, home appliances, sporting goods, miscellaneous goods, books, and cosmetics. For example, the avatar of a user who frequently purchases food will change to an appearance and personality related to food. Furthermore, the avatar's appearance can change not only to a human but also to an animal, and its actions and speech also change depending on its personality. This allows users to enjoy a unique avatar that matches their purchase history. In addition, the avatar's appearance and speech utilize image generation and language generation by a generation AI, and change in real time based on the user's purchase history. For example, the generation AI analyzes the user's purchase history and generates the avatar's appearance and personality based on the results. Also, avatars of users who have not performed KYC (Know Your Customer) tend to have an animal appearance, while avatars of users who have performed KYC generally maintain a human appearance. This promotes KYC (Know Your Customer) procedures and helps deter fraud and criminal use. Implementing this avatar creation function adds new value to the electronic payment system and increases user motivation. Furthermore, promoting KYC improves user safety and reduces the risk of fraud and criminal activity. For example, by completing KYC, users can ensure their avatars maintain a realistic appearance, allowing them to enjoy more attractive avatars. Additionally, an avatar reset function is provided, allowing users to reset their avatars at regular intervals, but they are restricted from resetting again for a certain period afterward. In this way, implementing an avatar creation function in the electronic payment system provides new value to users and promotes KYC. This, in turn, encourages the use of the electronic payment system and improves user safety.This allows the electronic payment system to generate an avatar based on the user's purchase history and change the avatar's appearance according to the KYC (Know Your Customer) status, thereby providing the user with new added value.

[0029] The electronic payment system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a modification unit, and a reset unit. The collection unit collects the user's purchase history. The collection unit collects data such as the date and time of purchases made by the user using the electronic payment system, the purchased items, and the purchase amount. The analysis unit analyzes the purchase history collected by the collection unit. The analysis unit analyzes the user's purchase patterns, for example, using data mining techniques. The analysis unit can also analyze the purchase frequency and purchase amount for each purchase category using statistical analysis techniques. The generation unit generates an avatar based on the results analyzed by the analysis unit. The generation unit generates the appearance and personality of the avatar based on the user's purchase history, for example, using a generation AI. The generation AI generates the appearance and personality of the avatar based on the user's purchase history, using a text generation AI (e.g., LLM) or an image generation AI. The modification unit modifies the appearance of the avatar based on the KYC implementation status. The modification unit, for example, changes the avatar of a user who has not completed KYC to the appearance of an animal, and changes the avatar of a user who has completed KYC to the appearance of a human. The reset unit provides a function to reset the avatar at regular intervals. The reset unit allows, for example, a user to return their avatar to its initial state at regular intervals. However, it restricts the user from resetting it again for a certain period after the initial reset. As a result, the electronic payment system according to this embodiment can provide new added value to the user by generating an avatar based on the user's purchase history and changing the appearance of the avatar according to the status of KYC completion.

[0030] The data collection unit collects users' purchase history. For example, the data collection unit collects data such as the date and time of purchases made by users using electronic payment systems, the purchased items, and the purchase amount. Specifically, the data collection unit obtains detailed purchase data in real time from electronic payment systems used by users when purchasing from online shopping sites or physical stores. This includes information such as the category, brand, quantity, payment method, and location of purchase of purchased items. Furthermore, the data collection unit can collect not only the user's past purchase history but also information on the ongoing purchase process and items added to the cart. This makes it possible to comprehensively understand the user's purchasing behavior and build a detailed database. The collected data is stored in secure cloud storage and managed so that the analysis and generation units can access it. The frequency and scope of data collection are adjusted based on the user's privacy settings and consent, so that necessary data can be efficiently collected while protecting the user's privacy.

[0031] The analysis unit analyzes the purchase history collected by the data collection unit. For example, the analysis unit uses data mining techniques to analyze user purchase patterns. Specifically, it uses clustering algorithms to group user purchase histories and identify user segments with common characteristics. It can also use association rule mining to analyze the frequency and patterns of purchases of specific products. Furthermore, the analysis unit can use statistical analysis techniques to analyze purchase frequency and amount for each purchase category. For example, it can use regression analysis to predict fluctuations in purchase amount over a specific period and time series analysis to understand seasonality and trends. This allows the analysis unit to analyze user purchasing behavior in detail and provide foundational data for proposing optimal marketing strategies and promotions for individual users. Additionally, the analysis unit can use AI to predict user purchasing behavior and detect anomalies. For example, it can use recurrent neural networks (RNNs) to predict future user purchasing behavior and detect abnormal purchasing patterns. This enables the analysis unit to monitor user purchasing behavior in real time and analyze it quickly and accurately.

[0032] The generation unit generates avatars based on the results analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate the avatar's appearance and personality based on the user's purchase history. Specifically, the generation AI uses a text generation AI (e.g., LLM) and an image generation AI to generate the avatar's appearance and personality based on the user's purchase history. The text generation AI analyzes keywords and phrases obtained from the user's purchase history and expresses the avatar's personality, hobbies, and preferences in text. For example, a user who frequently purchases fashion items will have an avatar with a stylish and trend-conscious personality. The image generation AI generates the avatar's appearance based on visual data obtained from the user's purchase history. For example, a user who frequently purchases sports equipment will have an avatar with an active and healthy appearance. By combining these AI technologies, the generation unit can generate unique and attractive avatars based on the user's purchase history. Furthermore, the generation unit provides an interface that allows users to customize the avatar's appearance and personality, enabling fine-tuning of the avatar to suit the user's preferences. This allows the generation unit to provide new added value to users and improve the user experience of the electronic payment system.

[0033] The customization feature changes the appearance of avatars based on the user's KYC (Know Your Customer) status. For example, it might change the avatar of a user who hasn't completed KYC to an animal and the avatar of a user who has completed KYC to a human. Specifically, the customization feature periodically checks the user's KYC information and automatically updates the avatar's appearance according to that status. Users who haven't completed KYC are assigned animal or character avatars, while users who have completed KYC are provided with more detailed and realistic human avatars. This helps users recognize the importance of KYC and encourages them to complete it. Furthermore, the customization feature provides a function that allows users to customize their avatar's appearance even after completing KYC, enabling them to change their avatar to match their personality and preferences. For example, users can freely select their avatar's hairstyle, clothing, accessories, etc., to create their own original avatar. In this way, the customization feature can encourage users to complete KYC while providing a more personalized experience through avatar customization.

[0034] The reset unit provides a function to reset avatars at regular intervals. For example, the reset unit allows users to return their avatars to their initial state at regular intervals. However, it restricts users from resetting their avatars again for a certain period after the initial reset. Specifically, the reset unit provides an interface that allows users to easily return their avatars to their initial state when they wish to reset them. After a reset, users are restricted from resetting their avatars again for a certain period (for example, one month). This restriction encourages users to reset their avatars carefully, thus maintaining system stability. Furthermore, the reset unit also provides a function to back up the avatar data before the reset and allow users to revert to the previous avatar if they wish. This allows users to reset their avatars with peace of mind. In addition, the reset unit allows users to customize the frequency and timing of resets according to their usage, flexibly responding to user needs. In this way, the reset unit provides users with an avatar reset function, improving system flexibility and user experience.

[0035] The data collection unit can collect data according to purchase categories such as food, clothing, home appliances, sporting goods, general merchandise, books, and cosmetics. For example, if a user purchases food, the data collection unit can collect their purchase history. The data collection unit can also collect the purchase history if a user purchases clothing. Furthermore, if a user purchases home appliances, the data collection unit can collect their purchase history. This allows the data collection unit to improve the accuracy of avatar generation by collecting data according to purchase categories. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history into AI, which can automatically classify the data according to the purchase category.

[0036] The analysis unit can analyze the collected data and identify the appearance and personality of avatars corresponding to the purchase category. For example, the analysis unit can use data mining techniques to analyze the user's purchase patterns. For instance, if a user frequently purchases food, the analysis unit can identify the appearance and personality of avatars related to food. The analysis unit can also use statistical analysis techniques to analyze the purchase frequency and amount for each purchase category. For example, if a user frequently purchases clothing, the analysis unit can identify the appearance and personality of avatars related to clothing. Furthermore, if a user frequently purchases home appliances, the analysis unit can also identify the appearance and personality of avatars related to home appliances. This allows the analysis unit to more accurately reflect the appearance and personality of avatars through analysis tailored to the purchase category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into an AI, which can then identify the appearance and personality of avatars corresponding to the purchase category.

[0037] The generation unit can generate avatars based on specified appearances and personalities. For example, the generation unit can use a generation AI to generate the appearance and personality of an avatar based on the user's purchase history. The generation AI can use a text generation AI (e.g., LLM) or an image generation AI to generate the appearance and personality of an avatar based on the user's purchase history. For example, if the user frequently purchases food, the generation unit will generate an avatar related to food. The generation unit can also generate an avatar related to clothing if the user frequently purchases clothing. Furthermore, if the user frequently purchases home appliances, the generation unit can generate an avatar related to home appliances. In this way, the generation unit can provide the user with a unique avatar by generating avatars based on specified appearances and personalities. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input specified appearances and personalities into an AI, and the AI ​​can generate an avatar.

[0038] The modification unit can change the appearance of avatars based on the status of KYC implementation. For example, the modification unit can change the appearance of avatars of users who have not performed KYC to that of animals, and change the appearance of avatars of users who have performed KYC to that of humans. For example, the modification unit can change the appearance of avatars of users who have not performed KYC to that of cats. The modification unit can also change the appearance of avatars of users who have performed KYC to that of humans. Furthermore, the modification unit can also change the appearance of avatars of users who have not performed KYC to that of dogs. In this way, the modification unit can promote KYC by changing the appearance of avatars according to the status of KYC implementation. Some or all of the above processing in the modification unit may be performed using AI, for example, or without using AI. For example, the modification unit can input the status of KYC implementation into AI, and the AI ​​can change the appearance of avatars.

[0039] The reset unit provides a function to reset the avatar at regular intervals, but it can restrict the user from resetting it again for a certain period after the initial reset. For example, the reset unit can allow the user to return the avatar to its initial state at regular intervals. For example, the reset unit can allow the user to reset the avatar every month. The reset unit can also restrict the user from resetting it again for one month after the initial reset. Furthermore, the reset unit can allow the user to reset the avatar every week. In this way, the reset unit allows the user to enjoy a new avatar through the avatar reset function. Some or all of the above processing in the reset unit may be performed using AI, for example, or without AI. For example, the reset unit can input the reset timing to the AI, and the AI ​​can control the reset.

[0040] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, the data collection unit can prioritize collecting items in categories that the user frequently purchases. The data collection unit can also analyze a user's purchase patterns and concentrate data collection during specific time periods. Furthermore, based on the user's past purchase history, the data collection unit can intensify data collection during specific events or sales periods. In this way, the data collection unit can select the optimal data collection method by analyzing past purchase history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's past purchase history into an AI, which can then select the optimal data collection method.

[0041] The data collection unit can filter purchase history based on the user's current lifestyle and areas of interest. For example, if the user is health-conscious, the unit can prioritize collecting purchase history of health foods and fitness products. It can also prioritize collecting travel-related purchase history if the user is traveling. Furthermore, if the user has started a new hobby, the unit can prioritize collecting purchase history related to that hobby. This allows the unit to collect more relevant purchase history through filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's lifestyle and areas of interest into an AI, which can then perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant purchase history by considering the user's geographical location when collecting purchase history. For example, if the user is in a specific region, the data collection unit will prioritize the collection of purchase history in that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of purchase history at their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of purchase history around their home. This allows the data collection unit to collect highly relevant purchase history by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant history.

[0043] The data collection unit can analyze a user's social media activity and collect relevant history when collecting purchase history. For example, the data collection unit can prioritize collecting purchase history of products that the user has shared on social media. It can also prioritize collecting purchase history of brands and products that the user follows on social media. Furthermore, it can prioritize collecting purchase history of products that the user has "liked" on social media. In this way, the data collection unit can collect relevant purchase history by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity into AI, and the AI ​​can collect relevant history.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the purchased genres during the analysis. For example, the analysis unit can perform a detailed analysis for genres of high importance. It can also perform a simplified analysis for genres of low importance. Furthermore, the analysis unit can adjust the frequency of analysis according to importance. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the purchased genres. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the purchased genres into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the purchase category during analysis. For example, the analysis unit can apply nutritional value and calorie analysis algorithms to food purchase history. It can also apply fashion trend analysis algorithms to clothing purchase history. Furthermore, it can apply energy efficiency and performance analysis algorithms to home appliance purchase history. This allows the analysis unit to provide more accurate analysis results by applying analysis algorithms appropriate to the purchase category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input analysis algorithms appropriate to the purchase category into the AI, and the AI ​​can perform the analysis.

[0046] The generation unit can adjust the level of detail of the avatar based on the importance of the purchased genre during generation. For example, the generation unit can generate detailed avatars for genres of high importance. It can also generate simpler avatars for genres of low importance. Furthermore, the generation unit can adjust the frequency of avatar generation according to importance. This allows the generation unit to provide more appropriate avatars by adjusting the level of detail based on the importance of the purchased genre. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the purchased genre into the AI, and the AI ​​can adjust the level of detail of the avatar.

[0047] The generation unit can apply different generation algorithms depending on the purchase category during generation. For example, the generation unit can apply an algorithm to generate avatars related to food to food purchase history. It can also apply an algorithm to generate avatars related to fashion to clothing purchase history. Furthermore, it can apply an algorithm to generate avatars related to home appliances to home appliance purchase history. In this way, the generation unit can provide more appropriate avatars by applying generation algorithms according to the purchase category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input a generation algorithm according to the purchase category into an AI, and the AI ​​can generate avatars.

[0048] The modification unit can adjust the level of detail of the avatar based on the KYC (Know Your Customer) status when making changes. For example, the modification unit can provide a detailed avatar to users who have completed KYC. It can also provide a simplified avatar to users who have not completed KYC. Furthermore, the modification unit can adjust the frequency of avatar changes according to the KYC status. This allows the modification unit to provide a more appropriate avatar by adjusting the level of detail based on the KYC status. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input the KYC status into AI, and the AI ​​can adjust the level of detail of the avatar.

[0049] The modification unit can apply different modification algorithms depending on the KYC (Know Your Customer) status during modification. For example, the modification unit can apply a detailed appearance modification algorithm to users who have completed KYC. It can also apply a simplified appearance modification algorithm to users who have not completed KYC. Furthermore, the modification unit can adjust the frequency of appearance modifications depending on the KYC status. This allows the modification unit to provide a more appropriate avatar by applying a modification algorithm tailored to the KYC status. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input the KYC status into AI, which can then apply different modification algorithms.

[0050] The modification unit can determine the priority of avatar appearance changes based on the timing of KYC (Know Your Customer) procedures. For example, the modification unit might prioritize changing the avatars of users who have recently completed KYC. It can also postpone changing the avatars of users who have completed KYC a long time ago. Furthermore, the modification unit can adjust the frequency of avatar appearance changes according to the timing of KYC. This allows the modification unit to provide more appropriate avatars by prioritizing appearance changes based on the timing of KYC. Some or all of the above processing in the modification unit may be performed using AI, for example, or not. For example, the modification unit can input the timing of KYC into the AI, which can then determine the priority of avatar appearance changes.

[0051] The modification unit can adjust the order of avatar appearance changes based on the KYC implementation status during the modification process. For example, the modification unit can prioritize changing the avatars of users who have completed KYC. It can also postpone changing the avatars of users who have not completed KYC. Furthermore, the modification unit can dynamically adjust the order of avatar appearance changes according to the KYC implementation status. This allows the modification unit to provide more appropriate avatars by adjusting the order of appearance changes based on the KYC implementation status. Some or all of the above processing in the modification unit may be performed using AI, for example, or not. For example, the modification unit can input the KYC implementation status into the AI, which can then adjust the order of avatar appearance changes.

[0052] The reset unit can select the optimal reset method by referring to past reset history during a reset. For example, the reset unit may prioritize providing the user's preferred reset method in the past. The reset unit can also select the most effective reset method from the user's past reset history. Furthermore, the reset unit can analyze the user's past reset history and customize the reset method. This allows the reset unit to select the optimal reset method by referring to past reset history. Some or all of the above processing in the reset unit may be performed using AI, for example, or without AI. For example, the reset unit can input past reset history into AI, which can then select the optimal reset method.

[0053] The reset unit can select the optimal reset method by considering the user's device information during a reset. For example, if the user is using a smartphone, the reset unit can provide a reset method optimized for the smartphone. Furthermore, if the user is using a tablet, the reset unit can also provide a reset method optimized for the tablet. In addition, if the user is using a smartwatch, the reset unit can provide a reset method optimized for the smartwatch. This allows the reset unit to provide a more appropriate reset method by selecting one that takes device information into account. Some or all of the above-described processes in the reset unit may be performed using AI, for example, or without AI. For example, the reset unit can input the user's device information into the AI, which can then select the optimal reset method.

[0054] The reset unit can select the optimal reset method by referring to the user's past reset history during a reset. For example, the reset unit may prioritize providing the user's preferred reset method in the past. The reset unit can also select the most effective reset method from the user's past reset history. Furthermore, the reset unit can analyze the user's past reset history and customize the reset method. This allows the reset unit to select the optimal reset method by referring to past reset history. Some or all of the above processing in the reset unit may be performed using AI, for example, or without AI. For example, the reset unit can input past reset history into AI, and the AI ​​can select the optimal reset method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The electronic payment system may also include a health data acquisition unit that obtains user health data. This unit can acquire data such as heart rate, steps taken, and sleep data from the user's smartwatch or fitness tracker. An analysis unit can analyze the acquired health data and modify the avatar's appearance and personality based on the user's health status. For example, if the user leads a healthy lifestyle, the avatar will change to a more energetic appearance and personality. If the user is not getting enough exercise, the avatar can make statements or take actions that encourage exercise. This allows users to visually confirm their health status and gain motivation to lead a healthier life.

[0057] Electronic payment systems can also include a promotional information section that provides promotional information to further enhance users' purchasing intent. This section provides discount and campaign information on relevant products based on the user's purchase history and interests. For example, if a product category that a user frequently buys is on sale, the system can notify the user through their avatar. Furthermore, if a user prefers a particular brand, the system can provide information on new products from that brand. This ensures users don't miss out on good deals and increases their purchasing intent.

[0058] Electronic payment systems can also include a predictive unit that forecasts user purchasing behavior. This unit analyzes the user's past purchase history and behavioral patterns to predict the next product they are most likely to purchase. For example, if a user purchases a specific product every month, the system can predict and notify the user when they will need that product. Furthermore, if a user tends to purchase specific products during certain seasons, the system can provide information on related products as that season approaches. This allows users to purchase necessary items in a timely manner.

[0059] The electronic payment system may also include a feedback unit that provides personalized feedback to users based on their purchase history. This feedback unit analyzes the user's purchase history and provides ratings and reviews of purchased products. For example, it might guide users through the use and maintenance of purchased products via an avatar. Users can also share their ratings of purchased products with other users. This allows users to gain a deeper understanding of their purchased products and increase their satisfaction.

[0060] The electronic payment system may also include a recommendation unit that provides personalized recommendations to users based on their purchase history. This recommendation unit analyzes the user's purchase history and provides recommendations for related products. For example, it might guide users through an avatar to related products they have purchased or similar products purchased by other users. It can also recommend new products that the user might be interested in. This makes it easier for users to find products that match their interests.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects the user's purchase history. For example, the data collection unit collects data such as the date and time of purchases made by the user using an electronic payment system, the purchased items, and the purchase amount. Step 2: The analysis unit analyzes the purchase history collected by the collection unit. The analysis unit analyzes the user's purchase patterns, for example, using data mining techniques. The analysis unit can also analyze the purchase frequency and amount for each purchase category using statistical analysis techniques. Step 3: The generation unit generates an avatar based on the results analyzed by the analysis unit. The generation unit generates the avatar's appearance and personality based on the user's purchase history, for example, using a generation AI. The generation AI generates the avatar's appearance and personality based on the user's purchase history, using a text generation AI (e.g., LLM) or an image generation AI. Step 4: The modification unit changes the appearance of the avatar based on the KYC implementation status. For example, the modification unit changes the avatar of a user who has not completed KYC to an animal appearance, and changes the avatar of a user who has completed KYC to a human appearance. Step 5: The reset section provides a function to reset the avatar at regular intervals. For example, the reset section allows the user to return the avatar to its initial state at regular intervals. However, it restricts the user from resetting the avatar again for a certain period after the initial reset.

[0063] (Example of form 2) The electronic payment system according to an embodiment of the present invention is a system that differentiates itself from other companies' payment methods and provides new added value to users by implementing an avatar creation function. When a user makes a purchase using this electronic payment system, their avatar changes based on their purchase history. The appearance and personality of this avatar change according to the purchase category, such as food, clothing, home appliances, sporting goods, miscellaneous goods, books, and cosmetics. For example, the avatar of a user who frequently purchases food will change to an appearance and personality related to food. Furthermore, the avatar's appearance can change not only to a human but also to an animal, and its actions and speech also change depending on its personality. This allows users to enjoy a unique avatar that matches their purchase history. In addition, the avatar's appearance and speech utilize image generation and language generation by a generation AI, and change in real time based on the user's purchase history. For example, the generation AI analyzes the user's purchase history and generates the avatar's appearance and personality based on the results. Also, avatars of users who have not performed KYC (Know Your Customer) tend to have an animal appearance, while avatars of users who have performed KYC generally maintain a human appearance. This promotes KYC (Know Your Customer) procedures and helps deter fraud and criminal use. Implementing this avatar creation function adds new value to the electronic payment system and increases user motivation. Furthermore, promoting KYC improves user safety and reduces the risk of fraud and criminal activity. For example, by completing KYC, users can ensure their avatars maintain a realistic appearance, allowing them to enjoy more attractive avatars. Additionally, an avatar reset function is provided, allowing users to reset their avatars at regular intervals, but they are restricted from resetting again for a certain period afterward. In this way, implementing an avatar creation function in the electronic payment system provides new value to users and promotes KYC. This, in turn, encourages the use of the electronic payment system and improves user safety.This allows the electronic payment system to generate an avatar based on the user's purchase history and change the avatar's appearance according to the KYC (Know Your Customer) status, thereby providing the user with new added value.

[0064] The electronic payment system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a modification unit, and a reset unit. The collection unit collects the user's purchase history. The collection unit collects data such as the date and time of purchases made by the user using the electronic payment system, the purchased items, and the purchase amount. The analysis unit analyzes the purchase history collected by the collection unit. The analysis unit analyzes the user's purchase patterns, for example, using data mining techniques. The analysis unit can also analyze the purchase frequency and purchase amount for each purchase category using statistical analysis techniques. The generation unit generates an avatar based on the results analyzed by the analysis unit. The generation unit generates the appearance and personality of the avatar based on the user's purchase history, for example, using a generation AI. The generation AI generates the appearance and personality of the avatar based on the user's purchase history, using a text generation AI (e.g., LLM) or an image generation AI. The modification unit modifies the appearance of the avatar based on the KYC implementation status. The modification unit, for example, changes the avatar of a user who has not completed KYC to the appearance of an animal, and changes the avatar of a user who has completed KYC to the appearance of a human. The reset unit provides a function to reset the avatar at regular intervals. The reset unit allows, for example, a user to return their avatar to its initial state at regular intervals. However, it restricts the user from resetting it again for a certain period after the initial reset. As a result, the electronic payment system according to this embodiment can provide new added value to the user by generating an avatar based on the user's purchase history and changing the appearance of the avatar according to the status of KYC completion.

[0065] The data collection unit collects users' purchase history. For example, the data collection unit collects data such as the date and time of purchases made by users using electronic payment systems, the purchased items, and the purchase amount. Specifically, the data collection unit obtains detailed purchase data in real time from electronic payment systems used by users when purchasing from online shopping sites or physical stores. This includes information such as the category, brand, quantity, payment method, and location of purchase of purchased items. Furthermore, the data collection unit can collect not only the user's past purchase history but also information on the ongoing purchase process and items added to the cart. This makes it possible to comprehensively understand the user's purchasing behavior and build a detailed database. The collected data is stored in secure cloud storage and managed so that the analysis and generation units can access it. The frequency and scope of data collection are adjusted based on the user's privacy settings and consent, so that necessary data can be efficiently collected while protecting the user's privacy.

[0066] The analysis unit analyzes the purchase history collected by the data collection unit. For example, the analysis unit uses data mining techniques to analyze user purchase patterns. Specifically, it uses clustering algorithms to group user purchase histories and identify user segments with common characteristics. It can also use association rule mining to analyze the frequency and patterns of purchases of specific products. Furthermore, the analysis unit can use statistical analysis techniques to analyze purchase frequency and amount for each purchase category. For example, it can use regression analysis to predict fluctuations in purchase amount over a specific period and time series analysis to understand seasonality and trends. This allows the analysis unit to analyze user purchasing behavior in detail and provide foundational data for proposing optimal marketing strategies and promotions for individual users. Additionally, the analysis unit can use AI to predict user purchasing behavior and detect anomalies. For example, it can use recurrent neural networks (RNNs) to predict future user purchasing behavior and detect abnormal purchasing patterns. This enables the analysis unit to monitor user purchasing behavior in real time and analyze it quickly and accurately.

[0067] The generation unit generates avatars based on the results analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate the avatar's appearance and personality based on the user's purchase history. Specifically, the generation AI uses a text generation AI (e.g., LLM) and an image generation AI to generate the avatar's appearance and personality based on the user's purchase history. The text generation AI analyzes keywords and phrases obtained from the user's purchase history and expresses the avatar's personality, hobbies, and preferences in text. For example, a user who frequently purchases fashion items will have an avatar with a stylish and trend-conscious personality. The image generation AI generates the avatar's appearance based on visual data obtained from the user's purchase history. For example, a user who frequently purchases sports equipment will have an avatar with an active and healthy appearance. By combining these AI technologies, the generation unit can generate unique and attractive avatars based on the user's purchase history. Furthermore, the generation unit provides an interface that allows users to customize the avatar's appearance and personality, enabling fine-tuning of the avatar to suit the user's preferences. This allows the generation unit to provide new added value to users and improve the user experience of the electronic payment system.

[0068] The customization feature changes the appearance of avatars based on the user's KYC (Know Your Customer) status. For example, it might change the avatar of a user who hasn't completed KYC to an animal and the avatar of a user who has completed KYC to a human. Specifically, the customization feature periodically checks the user's KYC information and automatically updates the avatar's appearance according to that status. Users who haven't completed KYC are assigned animal or character avatars, while users who have completed KYC are provided with more detailed and realistic human avatars. This helps users recognize the importance of KYC and encourages them to complete it. Furthermore, the customization feature provides a function that allows users to customize their avatar's appearance even after completing KYC, enabling them to change their avatar to match their personality and preferences. For example, users can freely select their avatar's hairstyle, clothing, accessories, etc., to create their own original avatar. In this way, the customization feature can encourage users to complete KYC while providing a more personalized experience through avatar customization.

[0069] The reset unit provides a function to reset avatars at regular intervals. For example, the reset unit allows users to return their avatars to their initial state at regular intervals. However, it restricts users from resetting their avatars again for a certain period after the initial reset. Specifically, the reset unit provides an interface that allows users to easily return their avatars to their initial state when they wish to reset them. After a reset, users are restricted from resetting their avatars again for a certain period (for example, one month). This restriction encourages users to reset their avatars carefully, thus maintaining system stability. Furthermore, the reset unit also provides a function to back up the avatar data before the reset and allow users to revert to the previous avatar if they wish. This allows users to reset their avatars with peace of mind. In addition, the reset unit allows users to customize the frequency and timing of resets according to their usage, flexibly responding to user needs. In this way, the reset unit provides users with an avatar reset function, improving system flexibility and user experience.

[0070] The data collection unit can collect data according to purchase categories such as food, clothing, home appliances, sporting goods, general merchandise, books, and cosmetics. For example, if a user purchases food, the data collection unit can collect their purchase history. The data collection unit can also collect the purchase history if a user purchases clothing. Furthermore, if a user purchases home appliances, the data collection unit can collect their purchase history. This allows the data collection unit to improve the accuracy of avatar generation by collecting data according to purchase categories. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's purchase history into AI, which can automatically classify the data according to the purchase category.

[0071] The analysis unit can analyze the collected data and identify the appearance and personality of avatars corresponding to the purchase category. For example, the analysis unit can use data mining techniques to analyze the user's purchase patterns. For instance, if a user frequently purchases food, the analysis unit can identify the appearance and personality of avatars related to food. The analysis unit can also use statistical analysis techniques to analyze the purchase frequency and amount for each purchase category. For example, if a user frequently purchases clothing, the analysis unit can identify the appearance and personality of avatars related to clothing. Furthermore, if a user frequently purchases home appliances, the analysis unit can also identify the appearance and personality of avatars related to home appliances. This allows the analysis unit to more accurately reflect the appearance and personality of avatars through analysis tailored to the purchase category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into an AI, which can then identify the appearance and personality of avatars corresponding to the purchase category.

[0072] The generation unit can generate avatars based on specified appearances and personalities. For example, the generation unit can use a generation AI to generate the appearance and personality of an avatar based on the user's purchase history. The generation AI can use a text generation AI (e.g., LLM) or an image generation AI to generate the appearance and personality of an avatar based on the user's purchase history. For example, if the user frequently purchases food, the generation unit will generate an avatar related to food. The generation unit can also generate an avatar related to clothing if the user frequently purchases clothing. Furthermore, if the user frequently purchases home appliances, the generation unit can generate an avatar related to home appliances. In this way, the generation unit can provide the user with a unique avatar by generating avatars based on specified appearances and personalities. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input specified appearances and personalities into an AI, and the AI ​​can generate an avatar.

[0073] The modification unit can change the appearance of avatars based on the status of KYC implementation. For example, the modification unit can change the appearance of avatars of users who have not performed KYC to that of animals, and change the appearance of avatars of users who have performed KYC to that of humans. For example, the modification unit can change the appearance of avatars of users who have not performed KYC to that of cats. The modification unit can also change the appearance of avatars of users who have performed KYC to that of humans. Furthermore, the modification unit can also change the appearance of avatars of users who have not performed KYC to that of dogs. In this way, the modification unit can promote KYC by changing the appearance of avatars according to the status of KYC implementation. Some or all of the above processing in the modification unit may be performed using AI, for example, or without using AI. For example, the modification unit can input the status of KYC implementation into AI, and the AI ​​can change the appearance of avatars.

[0074] The reset unit provides a function to reset the avatar at regular intervals, but it can restrict the user from resetting it again for a certain period after the initial reset. For example, the reset unit can allow the user to return the avatar to its initial state at regular intervals. For example, the reset unit can allow the user to reset the avatar every month. The reset unit can also restrict the user from resetting it again for one month after the initial reset. Furthermore, the reset unit can allow the user to reset the avatar every week. In this way, the reset unit allows the user to enjoy a new avatar through the avatar reset function. Some or all of the above processing in the reset unit may be performed using AI, for example, or without AI. For example, the reset unit can input the reset timing to the AI, and the AI ​​can control the reset.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of purchase history collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay collection until the user is relaxed. Alternatively, if the user is excited, the data collection unit can immediately collect the purchase history and reflect it in the avatar in real time. Furthermore, if the user is tired, the data collection unit can adjust the timing to collect the data after the user has rested. This allows the data collection unit to collect purchase history at a more appropriate time by adjusting the timing 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 include, 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 AI, or not. For example, the data collection unit can input user emotion data into an AI, which can then adjust the collection timing.

[0076] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, the data collection unit can prioritize collecting items in categories that the user frequently purchases. The data collection unit can also analyze a user's purchase patterns and concentrate data collection during specific time periods. Furthermore, based on the user's past purchase history, the data collection unit can intensify data collection during specific events or sales periods. In this way, the data collection unit can select the optimal data collection method by analyzing past purchase history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the user's past purchase history into an AI, which can then select the optimal data collection method.

[0077] The data collection unit can filter purchase history based on the user's current lifestyle and areas of interest. For example, if the user is health-conscious, the unit can prioritize collecting purchase history of health foods and fitness products. It can also prioritize collecting travel-related purchase history if the user is traveling. Furthermore, if the user has started a new hobby, the unit can prioritize collecting purchase history related to that hobby. This allows the unit to collect more relevant purchase history through filtering based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's lifestyle and areas of interest into an AI, which can then perform the filtering.

[0078] The data collection unit can estimate the user's emotions and determine the priority of purchase history to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting purchase history of products with a relaxing effect. Similarly, if the user is excited, the data collection unit may prioritize collecting purchase history of entertainment-related products. Furthermore, if the user is tired, the data collection unit may prioritize collecting purchase history of products related to health and rest. This allows the data collection unit to collect more appropriate purchase history by prioritizing 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 include, 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 AI, or not. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of purchase history to collect.

[0079] The data collection unit can prioritize the collection of highly relevant purchase history by considering the user's geographical location when collecting purchase history. For example, if the user is in a specific region, the data collection unit will prioritize the collection of purchase history in that region. Furthermore, if the user is traveling, the data collection unit can prioritize the collection of purchase history at their travel destination. Additionally, if the user is at home, the data collection unit can prioritize the collection of purchase history around their home. This allows the data collection unit to collect highly relevant purchase history by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant history.

[0080] The data collection unit can analyze a user's social media activity and collect relevant history when collecting purchase history. For example, the data collection unit can prioritize collecting purchase history of products that the user has shared on social media. It can also prioritize collecting purchase history of brands and products that the user follows on social media. Furthermore, it can prioritize collecting purchase history of products that the user has "liked" on social media. In this way, the data collection unit can collect relevant purchase history by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity into AI, and the AI ​​can collect relevant history.

[0081] 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 relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, the analysis unit can provide more appropriate 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 AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the presentation of the analysis.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the purchased genres during the analysis. For example, the analysis unit can perform a detailed analysis for genres of high importance. It can also perform a simplified analysis for genres of low importance. Furthermore, the analysis unit can adjust the frequency of analysis according to importance. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the purchased genres. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the purchased genres into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the purchase category during analysis. For example, the analysis unit can apply nutritional value and calorie analysis algorithms to food purchase history. It can also apply fashion trend analysis algorithms to clothing purchase history. Furthermore, it can apply energy efficiency and performance analysis algorithms to home appliance purchase history. This allows the analysis unit to provide more accurate analysis results by applying analysis algorithms appropriate to the purchase category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input analysis algorithms appropriate to the purchase category into the AI, and the AI ​​can perform the analysis.

[0084] The generation unit can estimate the user's emotions and adjust the avatar generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an avatar with a calm expression. It can also generate an avatar with an energetic expression if the user is excited. Furthermore, it can generate an avatar with a calm expression if the user is tired. This allows the generation unit to provide a more appropriate avatar by adjusting the avatar generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the AI, which can then adjust the avatar generation method.

[0085] The generation unit can adjust the level of detail of the avatar based on the importance of the purchased genre during generation. For example, the generation unit can generate detailed avatars for genres of high importance. It can also generate simpler avatars for genres of low importance. Furthermore, the generation unit can adjust the frequency of avatar generation according to importance. This allows the generation unit to provide more appropriate avatars by adjusting the level of detail based on the importance of the purchased genre. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the purchased genre into the AI, and the AI ​​can adjust the level of detail of the avatar.

[0086] The generation unit can apply different generation algorithms depending on the purchase category during generation. For example, the generation unit can apply an algorithm to generate avatars related to food to food purchase history. It can also apply an algorithm to generate avatars related to fashion to clothing purchase history. Furthermore, it can apply an algorithm to generate avatars related to home appliances to home appliance purchase history. In this way, the generation unit can provide more appropriate avatars by applying generation algorithms according to the purchase category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input a generation algorithm according to the purchase category into an AI, and the AI ​​can generate avatars.

[0087] The modification unit can estimate the user's emotions and adjust the avatar's appearance based on the estimated emotions. For example, if the user is relaxed, the modification unit can change the avatar to one with a calm expression. If the user is excited, the modification unit can also change the avatar to one with an energetic expression. Furthermore, if the user is tired, the modification unit can also change the avatar to one with a calm expression. In this way, the modification unit can provide a more appropriate avatar by adjusting the avatar's appearance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input the user's emotion data into an AI, which can then adjust the avatar's appearance.

[0088] The modification unit can adjust the level of detail of the avatar based on the KYC (Know Your Customer) status when making changes. For example, the modification unit can provide a detailed avatar to users who have completed KYC. It can also provide a simplified avatar to users who have not completed KYC. Furthermore, the modification unit can adjust the frequency of avatar changes according to the KYC status. This allows the modification unit to provide a more appropriate avatar by adjusting the level of detail based on the KYC status. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input the KYC status into AI, and the AI ​​can adjust the level of detail of the avatar.

[0089] The modification unit can apply different modification algorithms depending on the KYC (Know Your Customer) status during modification. For example, the modification unit can apply a detailed appearance modification algorithm to users who have completed KYC. It can also apply a simplified appearance modification algorithm to users who have not completed KYC. Furthermore, the modification unit can adjust the frequency of appearance modifications depending on the KYC status. This allows the modification unit to provide a more appropriate avatar by applying a modification algorithm tailored to the KYC status. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input the KYC status into AI, which can then apply different modification algorithms.

[0090] The modification unit can estimate the user's emotions and determine the priority of avatar appearance changes based on the estimated emotions. For example, if the user is relaxed, the modification unit will prioritize changing to an avatar with a calm expression. It can also prioritize changing to an avatar with an energetic expression if the user is excited. Furthermore, it can prioritize changing to an avatar with a calm expression if the user is tired. This allows the modification unit to provide a more appropriate avatar by prioritizing appearance changes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a 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 modification unit may be performed using AI, or not. For example, the modification unit can input user emotion data into an AI, which can then determine the priority of avatar appearance changes.

[0091] The modification unit can determine the priority of avatar appearance changes based on the timing of KYC (Know Your Customer) procedures. For example, the modification unit might prioritize changing the avatars of users who have recently completed KYC. It can also postpone changing the avatars of users who have completed KYC a long time ago. Furthermore, the modification unit can adjust the frequency of avatar appearance changes according to the timing of KYC. This allows the modification unit to provide more appropriate avatars by prioritizing appearance changes based on the timing of KYC. Some or all of the above processing in the modification unit may be performed using AI, for example, or not. For example, the modification unit can input the timing of KYC into the AI, which can then determine the priority of avatar appearance changes.

[0092] The modification unit can adjust the order of avatar appearance changes based on the KYC implementation status during the modification process. For example, the modification unit can prioritize changing the avatars of users who have completed KYC. It can also postpone changing the avatars of users who have not completed KYC. Furthermore, the modification unit can dynamically adjust the order of avatar appearance changes according to the KYC implementation status. This allows the modification unit to provide more appropriate avatars by adjusting the order of appearance changes based on the KYC implementation status. Some or all of the above processing in the modification unit may be performed using AI, for example, or not. For example, the modification unit can input the KYC implementation status into the AI, which can then adjust the order of avatar appearance changes.

[0093] The reset unit can estimate the user's emotions and adjust the avatar reset method based on the estimated emotions. For example, if the user is relaxed, the reset unit can provide a gentle reset method. If the user is excited, the reset unit can also provide a visually appealing reset method. Furthermore, if the user is tired, the reset unit can provide a concise and quick reset method. In this way, the reset unit can provide a more appropriate reset method by adjusting the reset method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 reset unit may be performed using AI, for example, or without AI. For example, the reset unit can input user emotion data into the AI, and the AI ​​can adjust the reset method.

[0094] The reset unit can select the optimal reset method by referring to past reset history during a reset. For example, the reset unit may prioritize providing the user's preferred reset method in the past. The reset unit can also select the most effective reset method from the user's past reset history. Furthermore, the reset unit can analyze the user's past reset history and customize the reset method. This allows the reset unit to select the optimal reset method by referring to past reset history. Some or all of the above processing in the reset unit may be performed using AI, for example, or without AI. For example, the reset unit can input past reset history into AI, which can then select the optimal reset method.

[0095] The reset unit can estimate the user's emotions and adjust the reset frequency based on the estimated emotions. For example, if the user is relaxed, the reset unit may lower the reset frequency. Conversely, if the user is excited, the reset unit may increase the reset frequency. Furthermore, if the user is tired, the reset unit may adjust the reset frequency to reduce the user's burden. In this way, the reset unit can provide a more appropriate reset method by adjusting the reset frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 reset unit may be performed using AI, for example, or without AI. For example, the reset unit can input user emotion data into the AI, and the AI ​​can adjust the reset frequency.

[0096] The reset unit can select the optimal reset method by considering the user's device information during a reset. For example, if the user is using a smartphone, the reset unit can provide a reset method optimized for the smartphone. Furthermore, if the user is using a tablet, the reset unit can also provide a reset method optimized for the tablet. In addition, if the user is using a smartwatch, the reset unit can provide a reset method optimized for the smartwatch. This allows the reset unit to provide a more appropriate reset method by selecting one that takes device information into account. Some or all of the above-described processes in the reset unit may be performed using AI, for example, or without AI. For example, the reset unit can input the user's device information into the AI, which can then select the optimal reset method.

[0097] The reset unit can select the optimal reset method by referring to the user's past reset history during a reset. For example, the reset unit may prioritize providing the user's preferred reset method in the past. The reset unit can also select the most effective reset method from the user's past reset history. Furthermore, the reset unit can analyze the user's past reset history and customize the reset method. This allows the reset unit to select the optimal reset method by referring to past reset history. Some or all of the above processing in the reset unit may be performed using AI, for example, or without AI. For example, the reset unit can input past reset history into AI, and the AI ​​can select the optimal reset method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The electronic payment system may also include a health data acquisition unit that obtains user health data. This unit can acquire data such as heart rate, steps taken, and sleep data from the user's smartwatch or fitness tracker. An analysis unit can analyze the acquired health data and modify the avatar's appearance and personality based on the user's health status. For example, if the user leads a healthy lifestyle, the avatar will change to a more energetic appearance and personality. If the user is not getting enough exercise, the avatar can make statements or take actions that encourage exercise. This allows users to visually confirm their health status and gain motivation to lead a healthier life.

[0100] The electronic payment system can further estimate the user's emotions and adjust the avatar's behavior based on those emotions. For example, if the user is stressed, the avatar can offer advice on how to relax. If the user is happy, the avatar can display a congratulatory message. Furthermore, if the user is sad, the avatar can offer words of comfort. In this way, users can receive emotional support through the avatar.

[0101] Electronic payment systems can also include a promotional information section that provides promotional information to further enhance users' purchasing intent. This section provides discount and campaign information on relevant products based on the user's purchase history and interests. For example, if a product category that a user frequently buys is on sale, the system can notify the user through their avatar. Furthermore, if a user prefers a particular brand, the system can provide information on new products from that brand. This ensures users don't miss out on good deals and increases their purchasing intent.

[0102] Electronic payment systems can further estimate a user's emotions and provide analysis results of their purchase history based on those estimated emotions. For example, if a user is relaxed, a detailed analysis can be provided. If a user is in a hurry, a concise analysis can be provided. Furthermore, if a user is excited, a visually appealing analysis can be provided. This allows users to receive information appropriate to their emotions.

[0103] Electronic payment systems can also include a predictive unit that forecasts user purchasing behavior. This unit analyzes the user's past purchase history and behavioral patterns to predict the next product they are most likely to purchase. For example, if a user purchases a specific product every month, the system can predict and notify the user when they will need that product. Furthermore, if a user tends to purchase specific products during certain seasons, the system can provide information on related products as that season approaches. This allows users to purchase necessary items in a timely manner.

[0104] Electronic payment systems can further estimate a user's emotions and adjust how promotional information is delivered based on those estimates. For example, if a user is relaxed, detailed promotional information can be provided. If a user is in a hurry, concise promotional information can be provided. Furthermore, if a user is excited, visually appealing promotional information can be provided. This allows users to receive appropriate promotional information that matches their emotions.

[0105] The electronic payment system may also include a feedback unit that provides personalized feedback to users based on their purchase history. This feedback unit analyzes the user's purchase history and provides ratings and reviews of purchased products. For example, it might guide users through the use and maintenance of purchased products via an avatar. Users can also share their ratings of purchased products with other users. This allows users to gain a deeper understanding of their purchased products and increase their satisfaction.

[0106] Electronic payment systems can further estimate the user's emotions and adjust the content of feedback based on those emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is in a hurry, concise feedback can be provided. Furthermore, if the user is excited, visually appealing feedback can be provided. This allows users to receive appropriate feedback that matches their emotions.

[0107] The electronic payment system may also include a recommendation unit that provides personalized recommendations to users based on their purchase history. This recommendation unit analyzes the user's purchase history and provides recommendations for related products. For example, it might guide users through an avatar to related products they have purchased or similar products purchased by other users. It can also recommend new products that the user might be interested in. This makes it easier for users to find products that match their interests.

[0108] Electronic payment systems can further estimate a user's emotions and adjust recommendations based on those emotions. For example, if a user is relaxed, detailed recommendations can be provided. If a user is in a hurry, concise recommendations can be offered. Furthermore, if a user is excited, visually appealing recommendations can be provided. This allows users to receive appropriate recommendations that match their emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects the user's purchase history. For example, the data collection unit collects data such as the date and time of purchases made by the user using an electronic payment system, the purchased items, and the purchase amount. Step 2: The analysis unit analyzes the purchase history collected by the collection unit. The analysis unit analyzes the user's purchase patterns, for example, using data mining techniques. The analysis unit can also analyze the purchase frequency and amount for each purchase category using statistical analysis techniques. Step 3: The generation unit generates an avatar based on the results analyzed by the analysis unit. The generation unit generates the avatar's appearance and personality based on the user's purchase history, for example, using a generation AI. The generation AI generates the avatar's appearance and personality based on the user's purchase history, using a text generation AI (e.g., LLM) or an image generation AI. Step 4: The modification unit changes the appearance of the avatar based on the KYC implementation status. For example, the modification unit changes the avatar of a user who has not completed KYC to an animal appearance, and changes the avatar of a user who has completed KYC to a human appearance. Step 5: The reset section provides a function to reset the avatar at regular intervals. For example, the reset section allows the user to return the avatar to its initial state at regular intervals. However, it restricts the user from resetting the avatar again for a certain period after the initial reset.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, modification unit, and reset unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's purchase history. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected purchase history. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates an avatar using a generation AI. The modification unit is implemented by the specific processing unit 290 of the data processing device 12 and modifies the appearance of the avatar based on the KYC implementation status. The reset unit is implemented by the control unit 46A of the smart device 14 and provides a function to reset the avatar at regular intervals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, modification unit, and reset 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 the user's purchase history. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected purchase history. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates an avatar using generation AI. The modification unit is implemented by the identification processing unit 290 of the data processing unit 12 and modifies the appearance of the avatar based on the KYC implementation status. The reset unit is implemented by the control unit 46A of the smart glasses 214 and provides a function to reset the avatar at regular intervals. The correspondence between each unit and the device or control unit is not limited to the example described above and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, modification unit, and reset 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 the user's purchase history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected purchase history. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an avatar using a generation AI. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and modifies the appearance of the avatar based on the KYC implementation status. The reset unit is implemented by the control unit 46A of the headset terminal 314 and provides a function to reset the avatar at regular intervals. The correspondence between each unit and the device or control unit is not limited to the example described above and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, modification unit, and reset 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 the user's purchase history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected purchase history. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an avatar using a generation AI. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12 and modifies the appearance of the avatar based on the status of KYC implementation. The reset unit is implemented by the control unit 46A of the robot 414 and provides a function to reset the avatar at regular intervals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A collection unit that collects purchase history, An analysis unit analyzes the purchase history collected by the collection unit, A generation unit generates an avatar based on the results of the analysis performed by the aforementioned analysis unit, A modification section that changes the appearance of the avatar based on the KYC implementation status, It includes a reset unit for resetting the avatar. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data according to the purchase category, such as food, clothing, home appliances, sporting goods, general merchandise, books, and cosmetics. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to identify the appearance and personality of avatars based on the purchase category. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Avatars are generated based on identified appearance and personality. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned modified part is, Avatar appearance will be changed based on KYC (Know Your Customer) status. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reset unit The system will provide a function to reset avatars at regular intervals, but will restrict users from resetting them again for a certain period after the initial reset. 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 purchase history 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 purchase history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting purchase history, filtering is performed based on the user's current lifestyle 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 determines the priority of purchase history to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting purchase history, the system prioritizes collecting highly relevant history by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting purchase history, the system analyzes the user's social media activity and collects relevant history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the purchased genre. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the purchase category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the avatar generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the level of detail of the avatar is adjusted based on the importance of the purchased genre. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, different generation algorithms are applied depending on the purchased genre. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned modified part is, It estimates the user's emotions and adjusts how the avatar's appearance is changed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned modified part is, When making changes, the level of detail in the avatar will be adjusted based on the KYC (Know Your Customer) status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned modified part is, When making changes, different change algorithms are applied depending on the status of KYC implementation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned modified part is, The system estimates the user's emotions and determines the priority of avatar appearance changes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned modified part is, When making changes, the priority of avatar appearance changes will be determined based on the timing of KYC (Know Your Customer) verification. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned modified part is, When making changes, the order of avatar appearance changes will be adjusted based on the KYC (Know Your Customer) status. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reset unit It estimates the user's emotions and adjusts the avatar reset method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reset unit During a reset, the system will refer to past reset history to select the optimal reset method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reset unit It estimates the user's emotions and adjusts the reset frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reset unit During a reset, the system selects the optimal reset method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reset unit During a reset, the system selects the optimal reset method by referring to the user's past reset history. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 collection unit that collects purchase history, An analysis unit analyzes the purchase history collected by the collection unit, A generation unit generates an avatar based on the results of the analysis performed by the aforementioned analysis unit, A modification section that changes the appearance of the avatar based on the KYC implementation status, It includes a reset unit for resetting the avatar. A system characterized by the following features.

2. The aforementioned collection unit is We collect data according to the purchase category, such as food, clothing, home appliances, sporting goods, general merchandise, books, and cosmetics. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to identify the appearance and personality of avatars based on the purchase category. The system according to feature 1.

4. The generating unit is Avatars are generated based on identified appearance and personality. The system according to feature 1.

5. The aforementioned modified part is, Avatar appearance will be changed based on KYC (Know Your Customer) status. The system according to feature 1.

6. The aforementioned reset unit The system will provide a function to reset avatars at regular intervals, but will restrict users from resetting them again for a certain period after the initial reset. The system according to feature 1.

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

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

9. The aforementioned collection unit is When collecting purchase history, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of purchase history to collect based on the estimated user emotions. The system according to feature 1.

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