system

Abstract

The system according to this embodiment aims to collect and analyze customer data in an integrated manner and provide optimal proposals. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a settlement unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes optimal proposals based on the analysis results obtained by the analysis unit. The settlement unit performs electronic settlement.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, it has not been fully carried out to integrally collect and analyze customer data and make an optimal proposal, and there is room for improvement.

[0005] The system according to the embodiment aims to integrally collect and analyze customer data and make an optimal proposal.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a settlement unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes an optimal proposal based on the analysis result obtained by the analysis unit. The settlement unit performs electronic settlement.

Effects of the Invention

[0007] The system according to this embodiment can collect and analyze customer data in an integrated manner and make optimal proposals. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 3, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An integrated AI customer insights platform according to an embodiment of the present invention is a comprehensive AI-driven system that aggregates and analyzes customer data from all companies within a specific corporate group. This platform provides deep insights, enabling personalized customer experiences, targeted cross-selling, and data-driven business strategies. By leveraging the diverse customer base and vast data resources of the corporate group, it creates powerful synergies that improve customer satisfaction, loyalty, and revenue. For example, the integrated AI customer insights platform securely integrates customer data from all group companies and uses machine learning to deeply analyze customer behavior. It predicts customer needs and potential churn risks in real time and delivers personalized experiences at every customer touchpoint. It also suggests relevant products and services from across the group, achieving a comprehensive customer understanding across multiple industries and services. The platform aims to transform corporate group customer relationships, creating a personalized, predictive, and value-driven ecosystem where every interaction is personalized. This will improve customer satisfaction and loyalty, accelerate business growth and innovation across the enterprise, and set a new standard for customer-centricity in the digital age. This enables the integrated AI customer insights platform to consistently collect, analyze, suggest, and process customer data.

[0029] The integrated AI customer insight platform according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a payment unit. The data collection unit collects data. For example, the data collection unit collects customer data from multiple corporate groups. For example, the data collection unit can collect customer purchase history, personal information, behavioral data, etc. For example, the data collection unit can set the frequency and means of data collection. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected customer data and identifies customer behavior patterns. For example, the analysis unit analyzes the data based on the analysis algorithm used and the purpose of the analysis. For example, the analysis unit can identify behavioral patterns such as customer purchase frequency and number of visits. The proposal unit makes optimal proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit makes personalized proposals to customers. For example, the proposal unit can make proposals based on customer preferences and past behavioral history. For example, the proposal unit can predict customer churn risk and propose appropriate countermeasures. The payment unit performs electronic payments. The payment unit, for example, handles electronic payments for proposed goods and services. The payment unit can provide methods such as credit card payments and electronic money payments. This allows the integrated AI customer insight platform according to the embodiment to consistently collect, analyze, propose, and process payments for customer data.

[0030] The data collection unit collects data. For example, the data collection unit collects customer data from multiple corporate groups. Specifically, the data collection unit accesses each company's database and obtains customer purchase history, personal information, behavioral data, etc. This includes purchase history from online shopping sites, loyalty card usage history, and in-store purchase data. The data collection unit centrally manages this data and has the functionality to detect and correct data duplication and missing data. Furthermore, the data collection unit can set the frequency and method of data collection. For example, it is possible to set data to be collected in real time or at regular time intervals. Possible collection methods include data acquisition using APIs, importing CSV files, and data collection using scraping techniques. As a result, the data collection unit can efficiently collect data from diverse data sources and strengthen the data infrastructure of the entire platform.

[0031] The analytics department analyzes the data collected by the data collection department. Specifically, the analytics department analyzes the collected customer data to identify customer behavior patterns. For example, it analyzes data such as customer purchase frequency, number of visits, types of products purchased, and time of purchase to reveal customer preferences and behavior patterns. The analytics department analyzes the data based on the analytical algorithms used and the purpose of the analysis. For example, it uses clustering algorithms to segment customers and extract the characteristics of each segment. It can also predict customer purchasing behavior using regression analysis and decision trees. Furthermore, the analytics department can use AI to analyze data in real time and quickly grasp customer behavior patterns. This allows the analytics department to accurately grasp customer needs and preferences and provide valuable insights to optimize the marketing strategy for the entire platform.

[0032] The Proposal Department makes optimal proposals based on the analysis results obtained by the Analysis Department. Specifically, the Proposal Department makes personalized proposals to customers. For example, it recommends specific products or services based on the customer's preferences and past behavior history. The Proposal Department can use AI to predict customer preferences and make proposals at the optimal time. For example, it can recommend products similar to those the customer has purchased in the past, or suggest new products that the customer might be interested in. In addition, the Proposal Department can predict the risk of customer churn and propose appropriate countermeasures. For example, if a customer has not made any purchases for a certain period of time, it can encourage the customer to return by offering a special discount coupon. In this way, the Proposal Department can make effective proposals to improve customer satisfaction and increase customer loyalty.

[0033] The Payment Department handles electronic payments. Specifically, it processes electronic payments for proposed goods and services. For example, it can offer methods such as credit card payments, e-money payments, and bank transfers. The Payment Department implements the latest security technologies to ensure that customers can make payments safely and quickly. For example, it uses SSL / TLS encryption technology to protect customer payment information. The Payment Department also integrates multiple payment methods, allowing customers to choose the method that is most convenient for them. Furthermore, the Payment Department sends a confirmation email to customers after payment is completed, allowing them to verify the payment details. In this way, the Payment Department can provide an environment where customers can purchase goods and services with peace of mind, thereby improving the reliability of the entire platform.

[0034] The data collection unit can collect customer data from multiple corporate groups. For example, the data collection unit can collect customer data from multiple corporate groups. For example, the data collection unit can collect customer purchase history, personal information, behavioral data, etc. The data collection unit can set the frequency and method of data collection, for example. This allows for the acquisition of broader customer data by collecting data from multiple corporate groups. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input customer data collected from multiple corporate groups into AI and have the AI ​​perform data collection and integration.

[0035] The analysis department can analyze collected customer data and identify customer behavior patterns. For example, the analysis department can analyze collected customer data and identify customer behavior patterns. For example, the analysis department can identify behavior patterns such as customer purchase frequency and number of visits. The analysis department analyzes data based on the analysis algorithm used and the purpose of the analysis. This allows for more accurate analysis by identifying customer behavior patterns. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input collected customer data into AI and have the AI ​​perform the identification of customer behavior patterns.

[0036] The proposal department can make personalized suggestions to customers based on the analysis results. For example, the proposal department can make personalized suggestions to customers based on the analysis results. For example, the proposal department can make suggestions based on customer preferences and past behavioral history. For example, the proposal department can predict customer churn risk and propose appropriate countermeasures. By making personalized suggestions to customers, customer satisfaction is improved. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the analysis results into AI and have the AI ​​generate personalized suggestions.

[0037] The payment unit can perform electronic payments for the proposed goods and services. For example, the payment unit can provide payment methods such as credit card payments or electronic money payments. This streamlines the purchase process by enabling electronic payments for the proposed goods and services. Some or all of the above-described processes in the payment unit may be performed using AI, or not. For example, the payment unit can input payment data for the proposed goods and services into the AI ​​and have the AI ​​perform the electronic payment processing.

[0038] The proposal department can predict customer churn risk and propose appropriate countermeasures. For example, the proposal department can predict customer churn risk and propose appropriate countermeasures. For example, the proposal department can evaluate churn risk based on customer behavior history and satisfaction surveys. For example, the proposal department can propose special offers or services to reduce customer churn risk. In this way, customer churn can be prevented by predicting customer churn risk and proposing appropriate countermeasures. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data for predicting customer churn risk into AI and have the AI ​​execute proposals for appropriate countermeasures.

[0039] The analytics department can predict customer needs and provide strategic insights for new product development and market expansion. For example, the analytics department can predict customer needs and provide strategic insights for new product development and market expansion. For example, the analytics department can identify customer needs through surveys and behavioral data analysis. For example, the analytics department can propose strategies for new product development and market expansion based on customer needs. This promotes business growth by predicting customer needs and providing strategic insights for new product development and market expansion. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input data for predicting customer needs into AI and have the AI ​​provide strategic insights.

[0040] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize using data collection methods that the user has frequently used in the past. For example, the data collection unit may identify the most efficient collection timing from the user's past data collection history. For example, the data collection unit may customize the collection method based on the user's past data collection history. This allows the optimal collection method to be selected by analyzing the user's past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the user's past data collection history into AI and have the AI ​​select the optimal collection method.

[0041] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can collect only data related to the user's current activities. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's areas of interest. For example, the data collection unit can monitor the user's current activities in real time and collect appropriate data. This allows for the collection of highly relevant data by filtering the data based on the user's current activities and areas of interest. 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 data on the user's current activities and areas of interest into the AI ​​and have the AI ​​perform the data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will collect the most relevant data based on the user's current location. For example, the data collection unit can collect highly relevant data by considering the user's travel history. This allows for the priority collection of highly relevant data by considering the user's geographical location information. 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 geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can identify areas of interest from the user's social media activity and collect relevant data. For example, the data collection unit can analyze a user's statements on social media and collect relevant data. In this way, relevant data can be collected by analyzing the user's social media activity. 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 social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on high-importance data. For example, the analysis unit will perform a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes 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 data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a behavioral analysis algorithm to customer behavior data. For example, the analysis unit applies a purchase pattern analysis algorithm to purchase history data. For example, the analysis unit applies a sentiment analysis algorithm to social media data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. 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 data category into the AI ​​and have the AI ​​apply the appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analyses based on when the data was collected. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. For example, the analysis unit can analyze historical data to grasp long-term trends. For example, the analysis unit can adjust the priority of analyses according to when the data was collected. This allows for the provision of real-time insights by determining the priority of analyses based on when the data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the data collection dates into the AI ​​and have the AI ​​determine the priority of analyses.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize analyzing highly relevant data. For example, the analysis unit may postpone analyzing less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​adjust the order of analysis.

[0048] The proposal department can adjust the level of detail in a proposal based on the importance of the product. For example, the proposal department will provide a detailed proposal for a high-importance product, and a simplified proposal for a low-importance product. The proposal department can also determine the priority of proposals based on the importance of the product. This allows for efficient proposals by adjusting the level of detail based on the importance of the product. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the product into the AI ​​and have the AI ​​adjust the level of detail of the proposal.

[0049] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, for electronic products, the suggestion unit will make suggestions that include technical details. For example, for fashion products, the suggestion unit will make suggestions that take style and trends into consideration. For example, for food products, the suggestion unit will make suggestions that include nutritional information and recipes. By applying different suggestion algorithms depending on the product category, more accurate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the product category into the AI ​​and have the AI ​​apply the appropriate suggestion algorithm.

[0050] The proposal department can determine the priority of proposals based on the timing of product submission. For example, the proposal department may prioritize the newest products. For example, the proposal department may propose seasonal products at the appropriate time. For example, the proposal department may adjust the priority of proposals according to the timing of product submission. This enables timely proposals by determining the priority of proposals based on the timing of product submission. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the timing of product submission into the AI ​​and have the AI ​​determine the priority of proposals.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the products when making a proposal. For example, the proposal unit may prioritize proposing highly relevant products. For example, the proposal unit may postpone proposing less relevant products. The proposal unit can adjust the order of proposals according to the relevance of the products. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the products. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of products into the AI ​​and have the AI ​​perform the adjustment of the order of proposals.

[0052] The payment unit can analyze the user's past payment history to select the optimal payment method at the time of payment. For example, the payment unit may prioritize suggesting payment methods the user has used in the past. For example, the payment unit may identify the most efficient payment method from the user's past payment history. For example, the payment unit may customize payment methods based on the user's past payment history. This allows the optimal payment method to be selected by analyzing the user's past payment history. Some or all of the above processes in the payment unit may be performed using AI, for example, or without AI. For example, the payment unit may input the user's past payment history into AI and have the AI ​​select the optimal payment method.

[0053] The payment unit can customize payment methods based on the user's current living situation at the time of payment. For example, if the user is traveling, the payment unit will provide a payment method that supports international payments. For example, if the user is at home, the payment unit will provide a standard payment method. For example, the payment unit can suggest the most suitable payment method according to the user's living situation. This allows for the provision of a more appropriate payment method by customizing the payment method based on the user's current living situation. Some or all of the above processing in the payment unit may be performed using AI, for example, or without AI. For example, the payment unit can input the user's current living situation into the AI ​​and have the AI ​​perform the customization of the payment method.

[0054] The payment unit can select the optimal payment method at the time of payment, taking into account the user's geographical location information. For example, if the user is in a specific region, the payment unit will provide a payment method suitable for that region. For example, the payment unit will propose the most efficient payment method based on the user's current location. For example, the payment unit can select the optimal payment method by taking into account the user's travel history. In this way, the optimal payment method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the payment unit may be performed using AI, for example, or without using AI. For example, the payment unit can input the user's geographical location information into AI and have the AI ​​perform the selection of the optimal payment method.

[0055] The payment unit can analyze the user's social media activity and suggest payment methods at the time of payment. For example, the payment unit can suggest relevant payment methods based on information shared by the user on social media. For example, the payment unit can identify the optimal payment method from the user's social media activity. For example, the payment unit can analyze the user's social media posts and suggest relevant payment methods. In this way, relevant payment methods can be suggested by analyzing the user's social media activity. Some or all of the above processing in the payment unit may be performed using AI, for example, or without AI. For example, the payment unit can input the user's social media activity data into AI and have the AI ​​suggest payment methods.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize using data collection methods that the user has frequently used in the past. For example, the data collection unit may identify the most efficient collection timing from the user's past data collection history. For example, the data collection unit may customize the collection method based on the user's past data collection history. This allows the optimal collection method to be selected by analyzing the user's past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the user's past data collection history into AI and have the AI ​​select the optimal collection method.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on high-importance data. For example, the analysis unit will perform a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes 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 data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0059] The proposal department can adjust the level of detail in a proposal based on the importance of the product. For example, the proposal department will provide a detailed proposal for a high-importance product, and a simplified proposal for a low-importance product. The proposal department can also determine the priority of proposals based on the importance of the product. This allows for efficient proposals by adjusting the level of detail based on the importance of the product. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the product into the AI ​​and have the AI ​​adjust the level of detail of the proposal.

[0060] The payment unit can analyze the user's past payment history to select the optimal payment method at the time of payment. For example, the payment unit may prioritize suggesting payment methods the user has used in the past. For example, the payment unit may identify the most efficient payment method from the user's past payment history. For example, the payment unit may customize payment methods based on the user's past payment history. This allows the optimal payment method to be selected by analyzing the user's past payment history. Some or all of the above processes in the payment unit may be performed using AI, for example, or without AI. For example, the payment unit may input the user's past payment history into AI and have the AI ​​select the optimal payment method.

[0061] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can collect only data related to the user's current activities. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's areas of interest. For example, the data collection unit can monitor the user's current activities in real time and collect appropriate data. This allows for the collection of highly relevant data by filtering the data based on the user's current activities and areas of interest. 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 data on the user's current activities and areas of interest into the AI ​​and have the AI ​​perform the data filtering.

[0062] The analysis unit can determine the priority of analyses based on when the data was collected. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. For example, the analysis unit can analyze historical data to grasp long-term trends. For example, the analysis unit can adjust the priority of analyses according to when the data was collected. This allows for the provision of real-time insights by determining the priority of analyses based on when the data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the data collection dates into the AI ​​and have the AI ​​determine the priority of analyses.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects data. The collection unit collects customer data from, for example, multiple corporate groups. The collection unit can collect, for example, customer purchase history, personal information, behavioral data, etc. The collection unit can set, for example, the frequency and method of data collection. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected customer data to identify customer behavior patterns. The analysis unit analyzes the data based on the analysis algorithms used and the purpose of the analysis. For example, the analysis unit can identify customer behavior patterns such as purchase frequency and visit frequency. Step 3: The proposal department makes optimal proposals based on the analysis results obtained by the analysis department. For example, the proposal department makes personalized proposals to customers. For example, the proposal department can make proposals based on customer preferences and past behavior history. For example, the proposal department can predict customer churn risk and propose appropriate countermeasures. Step 4: The payment department handles electronic payments. The payment department handles electronic payments for the proposed goods or services, for example. The payment department can provide methods such as credit card payments or electronic money payments.

[0065] (Example of form 2) An integrated AI customer insights platform according to an embodiment of the present invention is a comprehensive AI-driven system that aggregates and analyzes customer data from all companies within a specific corporate group. This platform provides deep insights, enabling personalized customer experiences, targeted cross-selling, and data-driven business strategies. By leveraging the diverse customer base and vast data resources of the corporate group, it creates powerful synergies that improve customer satisfaction, loyalty, and revenue. For example, the integrated AI customer insights platform securely integrates customer data from all group companies and uses machine learning to deeply analyze customer behavior. It predicts customer needs and potential churn risks in real time and delivers personalized experiences at every customer touchpoint. It also suggests relevant products and services from across the group, achieving a comprehensive customer understanding across multiple industries and services. The platform aims to transform corporate group customer relationships, creating a personalized, predictive, and value-driven ecosystem where every interaction is personalized. This will improve customer satisfaction and loyalty, accelerate business growth and innovation across the enterprise, and set a new standard for customer-centricity in the digital age. This enables the integrated AI customer insights platform to consistently collect, analyze, suggest, and process customer data.

[0066] The integrated AI customer insight platform according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a payment unit. The data collection unit collects data. For example, the data collection unit collects customer data from multiple corporate groups. For example, the data collection unit can collect customer purchase history, personal information, behavioral data, etc. For example, the data collection unit can set the frequency and means of data collection. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected customer data and identifies customer behavior patterns. For example, the analysis unit analyzes the data based on the analysis algorithm used and the purpose of the analysis. For example, the analysis unit can identify behavioral patterns such as customer purchase frequency and number of visits. The proposal unit makes optimal proposals based on the analysis results obtained by the analysis unit. For example, the proposal unit makes personalized proposals to customers. For example, the proposal unit can make proposals based on customer preferences and past behavioral history. For example, the proposal unit can predict customer churn risk and propose appropriate countermeasures. The payment unit performs electronic payments. The payment unit, for example, handles electronic payments for proposed goods and services. The payment unit can provide methods such as credit card payments and electronic money payments. This allows the integrated AI customer insight platform according to the embodiment to consistently collect, analyze, propose, and process payments for customer data.

[0067] The data collection unit collects data. For example, the data collection unit collects customer data from multiple corporate groups. Specifically, the data collection unit accesses each company's database and obtains customer purchase history, personal information, behavioral data, etc. This includes purchase history from online shopping sites, loyalty card usage history, and in-store purchase data. The data collection unit centrally manages this data and has the functionality to detect and correct data duplication and missing data. Furthermore, the data collection unit can set the frequency and method of data collection. For example, it is possible to set data to be collected in real time or at regular time intervals. Possible collection methods include data acquisition using APIs, importing CSV files, and data collection using scraping techniques. As a result, the data collection unit can efficiently collect data from diverse data sources and strengthen the data infrastructure of the entire platform.

[0068] The analytics department analyzes the data collected by the data collection department. Specifically, the analytics department analyzes the collected customer data to identify customer behavior patterns. For example, it analyzes data such as customer purchase frequency, number of visits, types of products purchased, and time of purchase to reveal customer preferences and behavior patterns. The analytics department analyzes the data based on the analytical algorithms used and the purpose of the analysis. For example, it uses clustering algorithms to segment customers and extract the characteristics of each segment. It can also predict customer purchasing behavior using regression analysis and decision trees. Furthermore, the analytics department can use AI to analyze data in real time and quickly grasp customer behavior patterns. This allows the analytics department to accurately grasp customer needs and preferences and provide valuable insights to optimize the marketing strategy for the entire platform.

[0069] The Proposal Department makes optimal proposals based on the analysis results obtained by the Analysis Department. Specifically, the Proposal Department makes personalized proposals to customers. For example, it recommends specific products or services based on the customer's preferences and past behavior history. The Proposal Department can use AI to predict customer preferences and make proposals at the optimal time. For example, it can recommend products similar to those the customer has purchased in the past, or suggest new products that the customer might be interested in. In addition, the Proposal Department can predict the risk of customer churn and propose appropriate countermeasures. For example, if a customer has not made any purchases for a certain period of time, it can encourage the customer to return by offering a special discount coupon. In this way, the Proposal Department can make effective proposals to improve customer satisfaction and increase customer loyalty.

[0070] The Payment Department handles electronic payments. Specifically, it processes electronic payments for proposed goods and services. For example, it can offer methods such as credit card payments, e-money payments, and bank transfers. The Payment Department implements the latest security technologies to ensure that customers can make payments safely and quickly. For example, it uses SSL / TLS encryption technology to protect customer payment information. The Payment Department also integrates multiple payment methods, allowing customers to choose the method that is most convenient for them. Furthermore, the Payment Department sends a confirmation email to customers after payment is completed, allowing them to verify the payment details. In this way, the Payment Department can provide an environment where customers can purchase goods and services with peace of mind, thereby improving the reliability of the entire platform.

[0071] The data collection unit can collect customer data from multiple corporate groups. For example, the data collection unit can collect customer data from multiple corporate groups. For example, the data collection unit can collect customer purchase history, personal information, behavioral data, etc. The data collection unit can set the frequency and method of data collection, for example. This allows for the acquisition of broader customer data by collecting data from multiple corporate groups. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input customer data collected from multiple corporate groups into AI and have the AI ​​perform data collection and integration.

[0072] The analysis department can analyze collected customer data and identify customer behavior patterns. For example, the analysis department can analyze collected customer data and identify customer behavior patterns. For example, the analysis department can identify behavior patterns such as customer purchase frequency and number of visits. The analysis department analyzes data based on the analysis algorithm used and the purpose of the analysis. This allows for more accurate analysis by identifying customer behavior patterns. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input collected customer data into AI and have the AI ​​perform the identification of customer behavior patterns.

[0073] The proposal department can make personalized suggestions to customers based on the analysis results. For example, the proposal department can make personalized suggestions to customers based on the analysis results. For example, the proposal department can make suggestions based on customer preferences and past behavioral history. For example, the proposal department can predict customer churn risk and propose appropriate countermeasures. By making personalized suggestions to customers, customer satisfaction is improved. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the analysis results into AI and have the AI ​​generate personalized suggestions.

[0074] The payment unit can perform electronic payments for the proposed goods and services. For example, the payment unit can provide payment methods such as credit card payments or electronic money payments. This streamlines the purchase process by enabling electronic payments for the proposed goods and services. Some or all of the above-described processes in the payment unit may be performed using AI, or not. For example, the payment unit can input payment data for the proposed goods and services into the AI ​​and have the AI ​​perform the electronic payment processing.

[0075] The proposal department can predict customer churn risk and propose appropriate countermeasures. For example, the proposal department can predict customer churn risk and propose appropriate countermeasures. For example, the proposal department can evaluate churn risk based on customer behavior history and satisfaction surveys. For example, the proposal department can propose special offers or services to reduce customer churn risk. In this way, customer churn can be prevented by predicting customer churn risk and proposing appropriate countermeasures. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data for predicting customer churn risk into AI and have the AI ​​execute proposals for appropriate countermeasures.

[0076] The analytics department can predict customer needs and provide strategic insights for new product development and market expansion. For example, the analytics department can predict customer needs and provide strategic insights for new product development and market expansion. For example, the analytics department can identify customer needs through surveys and behavioral data analysis. For example, the analytics department can propose strategies for new product development and market expansion based on customer needs. This promotes business growth by predicting customer needs and providing strategic insights for new product development and market expansion. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input data for predicting customer needs into AI and have the AI ​​provide strategic insights.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily stop data collection and resume it when the user is relaxed. For example, if the user is excited, the data collection unit can collect data in real time and immediately send it for analysis. For example, if the user is relaxed, the data collection unit can collect data periodically to grasp long-term trends. This allows for more appropriate data collection by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0078] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize using data collection methods that the user has frequently used in the past. For example, the data collection unit may identify the most efficient collection timing from the user's past data collection history. For example, the data collection unit may customize the collection method based on the user's past data collection history. This allows the optimal collection method to be selected by analyzing the user's past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the user's past data collection history into AI and have the AI ​​select the optimal collection method.

[0079] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can collect only data related to the user's current activities. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's areas of interest. For example, the data collection unit can monitor the user's current activities in real time and collect appropriate data. This allows for the collection of highly relevant data by filtering the data based on the user's current activities and areas of interest. 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 data on the user's current activities and areas of interest into the AI ​​and have the AI ​​perform the data filtering.

[0080] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. For example, if the user is relaxed, the data collection unit will prioritize the collection of detailed data. For example, if the user is in a hurry, the data collection unit will prioritize the collection of only the most important data. This ensures that important data is collected preferentially by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the data priority.

[0081] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will collect the most relevant data based on the user's current location. For example, the data collection unit can collect highly relevant data by considering the user's travel history. This allows for the priority collection of highly relevant data by considering the user's geographical location information. 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 geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0082] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can identify areas of interest from the user's social media activity and collect relevant data. For example, the data collection unit can analyze a user's statements on social media and collect relevant data. In this way, relevant data can be collected by analyzing the user's social media activity. 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 social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is in a hurry, the analysis unit provides a concise analysis result. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. 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 not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the presentation of the analysis.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on high-importance data. For example, the analysis unit will perform a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes 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 data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a behavioral analysis algorithm to customer behavior data. For example, the analysis unit applies a purchase pattern analysis algorithm to purchase history data. For example, the analysis unit applies a sentiment analysis algorithm to social media data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. 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 data category into the AI ​​and have the AI ​​apply the appropriate analysis algorithm.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. For example, if the user is relaxed, the analysis unit provides a detailed analysis. For example, if the user is excited, the analysis unit provides an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. 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 not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the length of the analysis.

[0087] The analysis unit can determine the priority of analyses based on when the data was collected. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. For example, the analysis unit can analyze historical data to grasp long-term trends. For example, the analysis unit can adjust the priority of analyses according to when the data was collected. This allows for the provision of real-time insights by determining the priority of analyses based on when the data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the data collection dates into the AI ​​and have the AI ​​determine the priority of analyses.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize analyzing highly relevant data. For example, the analysis unit may postpone analyzing less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​adjust the order of analysis.

[0089] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit will provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the way suggestions are presented.

[0090] The proposal department can adjust the level of detail in a proposal based on the importance of the product. For example, the proposal department will provide a detailed proposal for a high-importance product, and a simplified proposal for a low-importance product. The proposal department can also determine the priority of proposals based on the importance of the product. This allows for efficient proposals by adjusting the level of detail based on the importance of the product. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the product into the AI ​​and have the AI ​​adjust the level of detail of the proposal.

[0091] The suggestion unit can apply different suggestion algorithms depending on the product category when making suggestions. For example, for electronic products, the suggestion unit will make suggestions that include technical details. For example, for fashion products, the suggestion unit will make suggestions that take style and trends into consideration. For example, for food products, the suggestion unit will make suggestions that include nutritional information and recipes. By applying different suggestion algorithms depending on the product category, more accurate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the product category into the AI ​​and have the AI ​​apply the appropriate suggestion algorithm.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is excited, the suggestion unit will provide suggestions with visually stimulating effects. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the length of suggestions.

[0093] The proposal department can determine the priority of proposals based on the timing of product submission. For example, the proposal department may prioritize the newest products. For example, the proposal department may propose seasonal products at the appropriate time. For example, the proposal department may adjust the priority of proposals according to the timing of product submission. This enables timely proposals by determining the priority of proposals based on the timing of product submission. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the timing of product submission into the AI ​​and have the AI ​​determine the priority of proposals.

[0094] The proposal unit can adjust the order of proposals based on the relevance of the products when making a proposal. For example, the proposal unit may prioritize proposing highly relevant products. For example, the proposal unit may postpone proposing less relevant products. The proposal unit can adjust the order of proposals according to the relevance of the products. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the products. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of products into the AI ​​and have the AI ​​perform the adjustment of the order of proposals.

[0095] The payment unit can estimate the user's emotions and adjust the payment method based on the estimated emotions. For example, if the user is nervous, the payment unit provides a simple and easily visible payment method. For example, if the user is relaxed, the payment unit provides detailed payment options. For example, if the user is in a hurry, the payment unit provides a fast payment method. This allows for the provision of a more appropriate payment method by adjusting the payment method based on 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 payment unit may be performed using AI or not using AI. For example, the payment unit can input user emotion data into AI and have the AI ​​perform the adjustment of the payment method.

[0096] The payment unit can analyze the user's past payment history to select the optimal payment method at the time of payment. For example, the payment unit may prioritize suggesting payment methods the user has used in the past. For example, the payment unit may identify the most efficient payment method from the user's past payment history. For example, the payment unit may customize payment methods based on the user's past payment history. This allows the optimal payment method to be selected by analyzing the user's past payment history. Some or all of the above processes in the payment unit may be performed using AI, for example, or without AI. For example, the payment unit may input the user's past payment history into AI and have the AI ​​select the optimal payment method.

[0097] The payment unit can customize payment methods based on the user's current living situation at the time of payment. For example, if the user is traveling, the payment unit will provide a payment method that supports international payments. For example, if the user is at home, the payment unit will provide a standard payment method. For example, the payment unit can suggest the most suitable payment method according to the user's living situation. This allows for the provision of a more appropriate payment method by customizing the payment method based on the user's current living situation. Some or all of the above processing in the payment unit may be performed using AI, for example, or without AI. For example, the payment unit can input the user's current living situation into the AI ​​and have the AI ​​perform the customization of the payment method.

[0098] The payment unit can estimate the user's emotions and determine payment priorities based on those emotions. For example, if the user is stressed, the payment unit will prioritize high-priority payments. If the user is relaxed, the payment unit will offer detailed payment options. If the user is in a hurry, the payment unit will prioritize quick payments. This allows important payments to be prioritized by determining payment priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit can input user emotion data into an AI and have the AI ​​determine payment priorities.

[0099] The payment unit can select the optimal payment method at the time of payment, taking into account the user's geographical location information. For example, if the user is in a specific region, the payment unit will provide a payment method suitable for that region. For example, the payment unit will propose the most efficient payment method based on the user's current location. For example, the payment unit can select the optimal payment method by taking into account the user's travel history. In this way, the optimal payment method can be selected by taking into account the user's geographical location information. Some or all of the above processing in the payment unit may be performed using AI, for example, or without using AI. For example, the payment unit can input the user's geographical location information into AI and have the AI ​​perform the selection of the optimal payment method.

[0100] The payment unit can analyze the user's social media activity and suggest payment methods at the time of payment. For example, the payment unit can suggest relevant payment methods based on information shared by the user on social media. For example, the payment unit can identify the optimal payment method from the user's social media activity. For example, the payment unit can analyze the user's social media posts and suggest relevant payment methods. In this way, relevant payment methods can be suggested by analyzing the user's social media activity. Some or all of the above processing in the payment unit may be performed using AI, for example, or without AI. For example, the payment unit can input the user's social media activity data into AI and have the AI ​​suggest payment methods.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily stop data collection and resume it when the user is relaxed. For example, if the user is excited, the data collection unit can collect data in real time and immediately send it for analysis. For example, if the user is relaxed, the data collection unit can collect data periodically to grasp long-term trends. This allows for more appropriate data collection by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0103] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is in a hurry, the analysis unit provides a concise analysis result. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. 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 not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the presentation of the analysis.

[0104] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit will provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the way suggestions are presented.

[0105] The payment unit can estimate the user's emotions and adjust the payment method based on the estimated emotions. For example, if the user is nervous, the payment unit provides a simple and easily visible payment method. For example, if the user is relaxed, the payment unit provides detailed payment options. For example, if the user is in a hurry, the payment unit provides a fast payment method. This allows for the provision of a more appropriate payment method by adjusting the payment method based on 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 payment unit may be performed using AI or not using AI. For example, the payment unit can input user emotion data into AI and have the AI ​​perform the adjustment of the payment method.

[0106] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize using data collection methods that the user has frequently used in the past. For example, the data collection unit may identify the most efficient collection timing from the user's past data collection history. For example, the data collection unit may customize the collection method based on the user's past data collection history. This allows the optimal collection method to be selected by analyzing the user's past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit may input the user's past data collection history into AI and have the AI ​​select the optimal collection method.

[0107] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit will perform a detailed analysis on high-importance data. For example, the analysis unit will perform a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes 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 data into the AI ​​and have the AI ​​adjust the level of detail of the analysis.

[0108] The proposal department can adjust the level of detail in a proposal based on the importance of the product. For example, the proposal department will provide a detailed proposal for a high-importance product, and a simplified proposal for a low-importance product. The proposal department can also determine the priority of proposals based on the importance of the product. This allows for efficient proposals by adjusting the level of detail based on the importance of the product. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the product into the AI ​​and have the AI ​​adjust the level of detail of the proposal.

[0109] The payment unit can analyze the user's past payment history to select the optimal payment method at the time of payment. For example, the payment unit may prioritize suggesting payment methods the user has used in the past. For example, the payment unit may identify the most efficient payment method from the user's past payment history. For example, the payment unit may customize payment methods based on the user's past payment history. This allows the optimal payment method to be selected by analyzing the user's past payment history. Some or all of the above processes in the payment unit may be performed using AI, for example, or without AI. For example, the payment unit may input the user's past payment history into AI and have the AI ​​select the optimal payment method.

[0110] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can collect only data related to the user's current activities. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's areas of interest. For example, the data collection unit can monitor the user's current activities in real time and collect appropriate data. This allows for the collection of highly relevant data by filtering the data based on the user's current activities and areas of interest. 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 data on the user's current activities and areas of interest into the AI ​​and have the AI ​​perform the data filtering.

[0111] The analysis unit can determine the priority of analyses based on when the data was collected. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. For example, the analysis unit can analyze historical data to grasp long-term trends. For example, the analysis unit can adjust the priority of analyses according to when the data was collected. This allows for the provision of real-time insights by determining the priority of analyses based on when the data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the data collection dates into the AI ​​and have the AI ​​determine the priority of analyses.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The collection unit collects data. The collection unit collects customer data from, for example, multiple corporate groups. The collection unit can collect, for example, customer purchase history, personal information, behavioral data, etc. The collection unit can set, for example, the frequency and method of data collection. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected customer data to identify customer behavior patterns. The analysis unit analyzes the data based on the analysis algorithms used and the purpose of the analysis. For example, the analysis unit can identify customer behavior patterns such as purchase frequency and visit frequency. Step 3: The proposal department makes optimal proposals based on the analysis results obtained by the analysis department. For example, the proposal department makes personalized proposals to customers. For example, the proposal department can make proposals based on customer preferences and past behavior history. For example, the proposal department can predict customer churn risk and propose appropriate countermeasures. Step 4: The payment department handles electronic payments. The payment department handles electronic payments for the proposed goods or services, for example. The payment department can provide methods such as credit card payments or electronic money payments.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and payment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects customer data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the identification unit 290 of the data processing unit 12 and analyzes the collected data to identify customer behavior patterns. The proposal unit is implemented in the identification unit 290 of the data processing unit 12 and makes personalized proposals based on the analysis results. The payment unit is implemented in the identification unit 46A of the smart device 14 and performs electronic payment for the proposed goods or services. 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.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and payment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects customer data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify customer behavior patterns. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and makes personalized proposals based on the analysis results. The payment unit is implemented, for example, by the control unit 46A of the smart glasses 214, and performs electronic payment for the proposed goods or services. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and payment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects customer data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to identify customer behavior patterns. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12 and makes personalized proposals based on the analysis results. The payment unit is implemented in the control unit 46A of the headset terminal 314 and performs electronic payment for the proposed goods or services. 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.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and payment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects customer data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to identify customer behavior patterns. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and makes personalized proposals based on the analysis results. The payment unit is implemented by, for example, the control unit 46A of the robot 414 and performs electronic payment for the proposed goods or services. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit that makes the optimal proposal based on the analysis results obtained by the aforementioned analysis unit, It includes a settlement unit that performs electronic payments. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect customer data from multiple corporate groups. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze collected customer data to identify customer behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we will make personalized suggestions to our customers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned settlement unit, Electronic payment for proposed goods and services The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We predict customer churn risk and propose appropriate countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We anticipate customer needs and provide strategic insights for new product development and market expansion. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the products are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned settlement unit, It estimates the user's emotions and adjusts the payment method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned settlement unit, During payment, the system analyzes the user's past payment history to select the most suitable payment method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned settlement unit, At the time of payment, the payment method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned settlement unit, It estimates the user's emotions and determines payment priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned settlement unit, During payment, the system selects the most suitable payment method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned settlement unit, At the time of payment, the system analyzes the user's social media activity and suggests payment methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit that makes the optimal proposal based on the analysis results obtained by the aforementioned analysis unit, It includes a settlement unit that performs electronic payments. A system characterized by the following features.

2. The aforementioned collection unit is Collect customer data from multiple corporate groups. The system according to feature 1.

3. The aforementioned analysis unit is We analyze collected customer data to identify customer behavior patterns. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we will make personalized suggestions to our customers. The system according to feature 1.

5. The aforementioned settlement unit, Electronic payment for proposed goods and services The system according to feature 1.

6. The aforementioned proposal section is, We predict customer churn risk and propose appropriate countermeasures. The system according to feature 1.

7. The aforementioned analysis unit is We anticipate customer needs and provide strategic insights for new product development and market expansion. The system according to feature 1.

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

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

10. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system according to feature 1.

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