system

Abstract

The system according to this embodiment aims to efficiently analyze the industry, surrounding information, and current social conditions of merchants that have introduced payment processing services, and to propose strategies for maximizing profits. [Solution] The system according to the embodiment comprises an analysis unit, an optimization unit, a proposal unit, and a provision unit. The analysis unit analyzes the industry, surrounding information, and current trends of merchants introducing payment processing services. The optimization unit performs pricing and price optimization based on the information analyzed by the analysis unit. The proposal unit proposes strategies to maximize revenue based on the results obtained by the optimization unit. The provision unit makes sales proposals based on the strategies proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, since the industry, peripheral information, and the current situation of the settlement agency-introduced franchise stores are manually analyzed, there is room for improvement in terms of quality and speed.

[0005] The system according to the embodiment aims to efficiently analyze the industry, peripheral information, and the current situation of the settlement agency-introduced franchise stores and propose a strategy for maximizing profits.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, an optimization unit, a proposal unit, and a provision unit. The analysis unit analyzes the industry, surrounding information, and current trends of merchants introducing payment processing services. The optimization unit performs pricing and price optimization based on the information analyzed by the analysis unit. The proposal unit proposes strategies to maximize revenue based on the results obtained by the optimization unit. The provision unit makes sales proposals based on the strategies proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently analyze the industry, surrounding information, and current trends of merchants that have introduced payment processing services, and propose strategies to maximize profits. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The payment strategy optimization system according to an embodiment of the present invention is a system that optimizes payment strategies using AI. The payment strategy optimization system analyzes the industry, surrounding information, and current trends of merchants that have adopted payment processing services, supports pricing and price optimization, and proposes strategies to maximize revenue. Furthermore, the payment strategy optimization system improves the quality and speed of analysis and utilizes it in sales proposals. For example, the payment strategy optimization system uses AI to analyze market trends, competitor activities, and consumer purchasing behavior. This allows merchants to grasp the current situation and future trends in their industry. Next, the payment strategy optimization system uses AI to calculate the optimal price based on past sales data and the market supply and demand balance. This enables merchants to set optimal prices to maximize revenue. Furthermore, the payment strategy optimization system uses AI to propose effective promotion and sales strategies based on market trends and competitor activities. This enables merchants to implement concrete action plans to maximize revenue. Finally, the payment strategy optimization system uses AI to monitor market fluctuations in real time and provides analysis results quickly. This enables sales representatives to make effective proposals based on the latest market information. This system enables merchants to implement optimal strategies to maximize their revenue and improves the quality and speed of their sales proposals. For example, the payment strategy optimization system uses AI to analyze market trends and propose optimal pricing, allowing merchants to maximize revenue while maintaining competitiveness. Furthermore, the system's AI monitors market fluctuations in real time and provides rapid analysis results, enabling sales representatives to make effective proposals. In summary, the payment strategy optimization system maximizes merchant revenue and improves the quality and speed of sales proposals.

[0029] The payment strategy optimization system according to this embodiment comprises an analysis unit, an optimization unit, a proposal unit, and a provision unit. The analysis unit analyzes the industry, surrounding information, and current social conditions of merchants introducing payment processing services. For example, the analysis unit analyzes market trends, the activities of competitors, and consumer purchasing behavior. For example, the analysis unit can use AI to analyze market trends and understand the pricing and promotional activities of competitors. The analysis unit can also analyze consumer purchasing behavior and understand purchase history, purchase frequency, and purchase channels. The optimization unit performs pricing and price optimization based on the information analyzed by the analysis unit. For example, the optimization unit calculates the optimal price based on past sales data and the market supply and demand balance. For example, the optimization unit can use AI to analyze past sales data and calculate the optimal price. The optimization unit can also analyze the market supply and demand balance and understand the status of the supply chain. The proposal unit proposes strategies to maximize profits based on the results obtained by the optimization unit. For example, the proposal unit proposes effective promotional strategies and sales strategies. The proposal department can, for example, use AI to propose promotional strategies based on market trends and competitor activities. The proposal department can also propose the selection of sales channels and the setting of sales targets. The supply department makes sales proposals based on the strategies proposed by the proposal department. The supply department can, for example, monitor market fluctuations in real time and provide analysis results quickly. The supply department can, for example, use AI to monitor market fluctuations in real time and provide the latest market information. Furthermore, the supply department can provide information to sales representatives to make effective proposals. As a result, the payment strategy optimization system according to this embodiment can maximize merchant revenue and improve the quality and speed of sales proposals.

[0030] The analytics department analyzes industry, surrounding information, and current trends for merchants using payment processing services. For example, the analytics department analyzes market trends, competitor activities, and consumer purchasing behavior. Specifically, the analytics department uses AI to analyze market trends and understand competitor pricing and promotional activities. The AI ​​uses natural language processing technology to analyze news articles and social media posts to extract current market trends and consumer interests. It also uses machine learning algorithms to analyze competitor pricing and promotional activity patterns and predict future trends. Furthermore, the analytics department analyzes consumer purchasing behavior to understand purchase history, purchase frequency, and purchase channels. For example, by collecting consumer online shopping data and analyzing purchase history, it can understand fluctuations in demand for specific products and services. By analyzing purchase frequency and purchase channel data, it can identify consumer purchasing patterns and preferences, providing insights for targeted marketing. In this way, the analytics department can provide merchants with a foundation to quickly respond to market fluctuations and competitor activities and develop strategies tailored to consumer needs.

[0031] The Optimization Department sets prices and optimizes pricing based on information analyzed by the Analysis Department. Specifically, the Optimization Department calculates the optimal price based on past sales data and the market supply and demand balance. It can use AI to analyze past sales data and calculate the optimal price. For example, it can use machine learning algorithms to model seasonal fluctuations in demand and the effects of promotions from past sales data to set the optimal price. The Optimization Department can also analyze the market supply and demand balance and understand the status of the supply chain. For example, it can collect supply chain data and assess inventory levels and supply delay risks to consider factors that affect pricing. Furthermore, the Optimization Department can monitor market fluctuations in real time and dynamically adjust pricing. For example, it can use AI to analyze market demand fluctuations in real time and maximize profits by raising prices when demand surges and lowering prices when demand declines. In this way, the Optimization Department can help franchisees respond quickly to market fluctuations and maximize profits by setting optimal prices.

[0032] The Proposal Department proposes strategies to maximize revenue based on the results obtained by the Optimization Department. Specifically, the Proposal Department proposes effective promotion and sales strategies. For example, it can use AI to propose promotion strategies based on market trends and competitor activities. The AI ​​analyzes past promotion data to identify which promotions were most effective. It also monitors competitors' promotional activities and develops competitive promotion strategies. Furthermore, the Proposal Department can propose the selection of sales channels and the setting of sales targets. For example, it analyzes consumer purchasing behavior data to identify which sales channels are most effective. It also sets sales targets and proposes specific action plans to achieve them. In this way, the Proposal Department can provide franchisees with concrete strategies to maximize revenue and propose action plans that can be put into practice. In addition, the Proposal Department can monitor the effectiveness of the proposed strategies and modify them as needed. In this way, the Proposal Department can always provide effective strategies based on the latest information and support franchisees in maximizing their revenue.

[0033] The Supply Department makes sales proposals based on the strategies proposed by the Proposal Department. Specifically, the Supply Department monitors market fluctuations in real time and provides analysis results quickly. For example, it can use AI to monitor market fluctuations in real time and provide the latest market information. The AI ​​analyzes news articles and social media posts to grasp market fluctuations in real time. The Supply Department can also provide information to sales representatives to make effective proposals. For example, it can provide sales representatives with the latest market information and competitor trends when they make proposals to customers, supporting them in making effective proposals. Furthermore, the Supply Department can monitor the implementation status of proposed strategies and modify them as needed. For example, it can monitor the effectiveness of proposed promotional strategies and modify them if they are ineffective. The Supply Department can also collect feedback from sales representatives and continuously improve the accuracy and effectiveness of proposals. In this way, the Supply Department can support franchisees in responding quickly to market fluctuations and making effective sales proposals.

[0034] The analysis unit can analyze market trends, competitor activities, and consumer purchasing behavior. For example, to analyze market trends, the analysis unit uses sales data analysis and consumer survey results. For example, to analyze competitor activities, the analysis unit can understand competitor pricing and promotional activities. Furthermore, to analyze consumer purchasing behavior, the analysis unit can understand purchase history, purchase frequency, and purchase channels. This allows for the provision of more accurate information by analyzing market trends, competitor activities, and consumer purchasing behavior. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input sales data and consumer survey results into a generating AI, which can then analyze market trends.

[0035] The optimization unit can calculate the optimal price based on past sales data and the market supply and demand balance. For example, the optimization unit can calculate the optimal price based on past sales data. For example, the optimization unit can use AI to analyze past sales data and calculate the optimal price. The optimization unit can also calculate the optimal price based on the market supply and demand balance. For example, the optimization unit can understand the supply chain situation and calculate the optimal price. This makes it possible to set the optimal price to maximize profits by calculating the optimal price based on past sales data and the market supply and demand balance. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past sales data and the market supply and demand balance into a generating AI, which can then calculate the optimal price.

[0036] The proposal department can propose effective promotion and sales strategies. For example, the proposal department can propose effective promotion strategies. For example, the proposal department can propose promotion strategies based on market trends and competitor activities using AI. The proposal department can also propose sales strategies. For example, the proposal department can propose the selection of sales channels and the setting of sales targets. This enables the implementation of concrete action plans to maximize profits by proposing effective promotion and sales strategies. 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 market trends and competitor activities into a generating AI, which can then propose effective promotion and sales strategies.

[0037] The service provider can monitor market fluctuations in real time and provide analysis results quickly. For example, the service provider can monitor market fluctuations in real time. For example, the service provider can use AI to monitor market fluctuations in real time and provide the latest market information. The service provider can also provide analysis results quickly. For example, the service provider can use AI to provide analysis results quickly. This allows sales representatives to make effective proposals based on the latest market information by monitoring market fluctuations in real time and providing analysis results quickly. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input market fluctuation data into a generating AI, which can then monitor market fluctuations in real time and provide analysis results quickly.

[0038] The analysis unit can improve the accuracy of its analysis of market trends and competitor activities based on specific seasons and events. For example, the analysis unit can analyze consumer purchasing behavior based on specific events such as Christmas or Black Friday. For example, the analysis unit can analyze competitor pricing strategies by considering seasonal demand fluctuations. Furthermore, the analysis unit can analyze market trends during specific event periods and propose optimal sales strategies. This allows for the provision of more accurate information by improving the accuracy of the analysis based on specific seasons and events. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data related to specific seasons and events into a generating AI, which can then analyze market trends and competitor activities.

[0039] The analysis unit can improve the accuracy of its analysis by considering the consumer's past purchase history when analyzing consumer purchasing behavior. For example, the analysis unit can predict future purchasing behavior based on the consumer's past purchase history. For example, the analysis unit can analyze the demand for specific products or services from the consumer's purchase history. Furthermore, the analysis unit can analyze the consumer's purchase history and propose the optimal promotion strategy. By improving the accuracy of the analysis by considering the consumer's past purchase history, it becomes possible to predict purchasing behavior more accurately. 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 consumer's past purchase history into a generating AI, which can then analyze the consumer's purchasing behavior.

[0040] The analysis unit can perform analyses that take geographical factors into account when analyzing market trends and the actions of competitors. For example, the analysis unit can analyze consumer purchasing behavior in each region and propose the optimal sales strategy. For example, the analysis unit can analyze the pricing strategies of competitors, taking geographical factors into account. Furthermore, the analysis unit can analyze market trends in each region and propose the optimal promotion strategy. In this way, by performing analyses that take geographical factors into account, market trends in each region can be accurately grasped. 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 data on geographical factors into a generating AI, which can then analyze market trends and the actions of competitors.

[0041] The analysis unit can improve the accuracy of its analysis of consumer purchasing behavior by utilizing social media data. For example, the analysis unit can analyze consumer purchasing intent based on social media data. For example, the analysis unit can analyze social media trends and propose optimal promotion strategies. Furthermore, the analysis unit can predict consumer purchasing behavior by utilizing social media data. By improving the accuracy of the analysis using social media data, it is possible to more accurately understand consumer purchasing intent. 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 social media data into a generating AI, which can then analyze consumer purchasing behavior.

[0042] The optimization unit can calculate the optimal price based on past sales data, taking into account a specific promotion period. For example, the optimization unit calculates the optimal price based on sales data from past promotion periods. The optimization unit can perform price optimization, taking into account a specific promotion period. Furthermore, the optimization unit can calculate the optimal price by considering demand fluctuations during past promotion periods. This makes it possible to set the optimal price during promotion periods by optimizing while considering a specific promotion period. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input sales data from past promotion periods into a generating AI, which can then calculate the optimal price.

[0043] The optimization unit can calculate the optimal price based on the market supply and demand balance, taking into account fluctuations in the supply chain. For example, the optimization unit can calculate the optimal price by considering fluctuations in the supply chain. For example, the optimization unit can calculate the optimal price based on the market supply and demand balance. Furthermore, the optimization unit can predict fluctuations in the supply chain and calculate the optimal price. This makes it possible to set prices more accurately by optimizing while considering fluctuations in the supply chain. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input supply chain fluctuation data into a generating AI, and the generating AI can calculate the optimal price based on the market supply and demand balance.

[0044] The optimization unit can calculate the optimal price based on past sales data, taking into account regional sales data. For example, the optimization unit can calculate the optimal price based on regional sales data. For example, the optimization unit can optimize prices by taking into account regional demand fluctuations. Furthermore, the optimization unit can analyze regional market trends and calculate the optimal price. As a result, by optimizing while considering regional sales data, it becomes possible to set the optimal price for each region. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input regional sales data into a generating AI, and the generating AI can calculate the optimal price.

[0045] The optimization unit can integrate online and offline sales data to calculate the optimal price based on the market supply and demand balance. For example, the optimization unit can integrate online and offline sales data to calculate the optimal price. For example, the optimization unit can optimize prices by considering online and offline demand fluctuations. Furthermore, the optimization unit can analyze online and offline market trends to calculate the optimal price. This allows for more accurate pricing by integrating and optimizing online and offline sales data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input online and offline sales data into a generating AI, which can then calculate the optimal price.

[0046] The proposal department can consider past promotional successes when proposing effective promotional strategies. For example, the proposal department can propose effective promotional strategies based on past promotional successes. For example, the proposal department can analyze past promotional failures and propose areas for improvement. Furthermore, the proposal department can propose optimal promotional strategies based on past promotional data. By considering past promotional successes, the proposal department can propose more effective promotional strategies. 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 past promotional data into a generating AI, which can then propose effective promotional strategies.

[0047] The proposal department can improve the accuracy of its sales strategy proposals based on a specific target market. For example, the proposal department can propose an optimal sales strategy based on a specific target market. For example, the proposal department can analyze the needs of the target market and propose an optimal sales strategy. Furthermore, the proposal department can propose an optimal sales strategy by considering the trends of competitors in the target market. This allows for the proposal of more effective sales strategies by improving the accuracy of proposals based on a specific target market. 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 on a specific target market into a generating AI, which can then propose an optimal sales strategy.

[0048] The proposal department can utilize social media data to propose effective promotional strategies. For example, the proposal department can propose effective promotional strategies based on social media data. For example, the proposal department can analyze social media trends and propose the optimal promotional strategy. Furthermore, the proposal department can utilize social media data to propose promotional strategies that increase consumer purchasing intent. In this way, by utilizing social media data to make proposals, more effective promotional strategies can be proposed. 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 social media data into a generating AI, and the generating AI can propose an effective promotional strategy.

[0049] The proposal department can consider the pricing strategies of competitors when proposing sales strategies. For example, the proposal department can propose the optimal sales strategy based on the pricing strategies of competitors. For example, the proposal department can propose the optimal sales strategy considering the price fluctuations of competitors. Furthermore, the proposal department can analyze the pricing strategies of competitors and propose areas for improvement. By considering the pricing strategies of competitors, the proposal department can propose more effective sales strategies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input competitor pricing strategy data into a generating AI, which can then propose the optimal sales strategy.

[0050] The service provider can improve the accuracy of its monitoring by considering specific events and news when monitoring market fluctuations in real time. For example, the service provider can monitor the impact of specific events and news on the market in real time. For example, when monitoring market fluctuations, the service provider can prioritize the analysis of data during specific event periods. Furthermore, the service provider can predict the impact of specific news on the market and improve the accuracy of its monitoring. This allows for the provision of more accurate information by improving the accuracy of monitoring by considering specific events and news. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data related to specific events and news into a generating AI, which can then monitor market fluctuations in real time and improve accuracy.

[0051] The information delivery unit can improve the accuracy of its information delivery by referring to past analysis results when providing analysis results quickly. For example, the information delivery unit can prioritize providing information that can be provided quickly based on past analysis results. For example, the information delivery unit can improve the accuracy of the information it provides by referring to past analysis results. In addition, the information delivery unit can analyze past analysis results and propose the optimal method of providing information. This makes it possible to provide more accurate information by improving the accuracy of the information delivery by referring to past analysis results. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input past analysis results into a generating AI, and the generating AI can quickly provide analysis results.

[0052] The service provider can monitor market fluctuations in real time, taking geographical factors into consideration. For example, the service provider can monitor regional market fluctuations in real time. For example, the service provider can prioritize monitoring market fluctuations in a specific region, taking geographical factors into consideration. Furthermore, the service provider can analyze regional market trends and provide optimal information. This allows for an accurate understanding of regional market trends by monitoring while considering geographical factors. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on geographical factors into a generating AI, which can then monitor market fluctuations in real time and improve accuracy.

[0053] The information delivery unit can improve the accuracy of its deliveries by utilizing social media data when providing analysis results quickly. For example, the information delivery unit can prioritize providing information that can be delivered quickly based on social media data. For example, the information delivery unit can analyze social media trends to improve the accuracy of the information it provides. Furthermore, the information delivery unit can use social media data to propose the optimal method of information delivery. By improving the accuracy of deliveries using social media data, it becomes possible to provide more accurate information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input social media data into a generating AI, which can then quickly provide analysis results.

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

[0055] The analysis unit can improve the accuracy of its analysis of market trends and competitor activities based on specific seasons or events. For example, it can analyze consumer purchasing behavior based on specific events such as Christmas or Black Friday. It can also analyze competitor pricing strategies by considering seasonal demand fluctuations. Furthermore, it can analyze market trends during specific event periods and propose optimal sales strategies. By improving the accuracy of analysis based on specific seasons or events, it can provide more accurate information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data related to specific seasons or events into a generating AI, which can then analyze market trends and competitor activities.

[0056] The analysis unit can improve the accuracy of its analysis by considering the consumer's past purchase history when analyzing consumer purchasing behavior. For example, it can predict future purchasing behavior based on the consumer's past purchase history. It can also analyze the demand for specific products or services from the consumer's purchase history. Furthermore, it can analyze the consumer's purchase history and propose the optimal promotion strategy. By improving the accuracy of the analysis by considering the consumer's past purchase history, it becomes possible to predict purchasing behavior more accurately. 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 consumer's past purchase history into a generating AI, which can then analyze the consumer's purchasing behavior.

[0057] The analysis unit can perform analyses that take geographical factors into account when analyzing market trends and the actions of competitors. For example, it can analyze consumer purchasing behavior in each region and propose the optimal sales strategy. It can also analyze the pricing strategies of competitors, taking geographical factors into account. Furthermore, it can analyze market trends in each region and propose the optimal promotion strategy. In this way, by performing analyses that take geographical factors into account, market trends in each region can be accurately grasped. 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 data on geographical factors into a generating AI, which can then analyze market trends and the actions of competitors.

[0058] The optimization unit can optimize prices by considering specific promotion periods when calculating the optimal price based on past sales data. For example, it can calculate the optimal price based on sales data from past promotion periods. It can also optimize prices by considering specific promotion periods. Furthermore, it can calculate the optimal price by considering demand fluctuations during past promotion periods. This makes it possible to set the optimal price during promotion periods by optimizing while considering specific promotion periods. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input sales data from past promotion periods into a generating AI, which can then calculate the optimal price.

[0059] The optimization unit can calculate the optimal price based on the market supply and demand balance, taking into account fluctuations in the supply chain. For example, it can calculate the optimal price by considering fluctuations in the supply chain. It can also calculate the optimal price based on the market supply and demand balance. Furthermore, it can predict fluctuations in the supply chain and calculate the optimal price. By optimizing while considering fluctuations in the supply chain, more accurate pricing becomes possible. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input supply chain fluctuation data into a generating AI, which can then calculate the optimal price based on the market supply and demand balance.

[0060] The optimization unit can optimize prices by considering regional sales data when calculating the optimal price based on past sales data. For example, it can calculate the optimal price based on regional sales data. It can also optimize prices by considering regional demand fluctuations. Furthermore, it can analyze regional market trends and calculate the optimal price. As a result, by optimizing while considering regional sales data, it becomes possible to set optimal prices for each region. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input regional sales data into a generating AI, and the generating AI can calculate the optimal price.

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

[0062] Step 1: The analytics department analyzes the industry, surrounding information, and current trends of merchants implementing the payment processing service. For example, it analyzes market trends, competitor activities, and consumer purchasing behavior. The analytics department uses AI to analyze market trends and understand competitor pricing and promotional activities. It can also analyze consumer purchasing behavior to understand purchase history, purchase frequency, and purchase channels. Step 2: The optimization unit performs pricing and price optimization based on the information analyzed by the analysis unit. For example, it calculates the optimal price based on past sales data and the market supply and demand balance. The optimization unit can use AI to analyze past sales data and calculate the optimal price. It can also analyze the market supply and demand balance and understand the situation of the supply chain. Step 3: The proposal department proposes strategies to maximize revenue based on the results obtained by the optimization department. For example, it proposes effective promotion and sales strategies. The proposal department can use AI to propose promotion strategies based on market trends and competitor activities. It can also propose the selection of sales channels and the setting of sales targets. Step 4: The supply department makes sales proposals based on the strategies proposed by the proposal department. For example, they monitor market fluctuations in real time and provide analysis results quickly. The supply department can use AI to monitor market fluctuations in real time and provide the latest market information. They can also provide information to sales representatives to make effective proposals.

[0063] (Example of form 2) The payment strategy optimization system according to an embodiment of the present invention is a system that optimizes payment strategies using AI. The payment strategy optimization system analyzes the industry, surrounding information, and current trends of merchants that have adopted payment processing services, supports pricing and price optimization, and proposes strategies to maximize revenue. Furthermore, the payment strategy optimization system improves the quality and speed of analysis and utilizes it in sales proposals. For example, the payment strategy optimization system uses AI to analyze market trends, competitor activities, and consumer purchasing behavior. This allows merchants to grasp the current situation and future trends in their industry. Next, the payment strategy optimization system uses AI to calculate the optimal price based on past sales data and the market supply and demand balance. This enables merchants to set optimal prices to maximize revenue. Furthermore, the payment strategy optimization system uses AI to propose effective promotion and sales strategies based on market trends and competitor activities. This enables merchants to implement concrete action plans to maximize revenue. Finally, the payment strategy optimization system uses AI to monitor market fluctuations in real time and provides analysis results quickly. This enables sales representatives to make effective proposals based on the latest market information. This system enables merchants to implement optimal strategies to maximize their revenue and improves the quality and speed of their sales proposals. For example, the payment strategy optimization system uses AI to analyze market trends and propose optimal pricing, allowing merchants to maximize revenue while maintaining competitiveness. Furthermore, the system's AI monitors market fluctuations in real time and provides rapid analysis results, enabling sales representatives to make effective proposals. In summary, the payment strategy optimization system maximizes merchant revenue and improves the quality and speed of sales proposals.

[0064] The payment strategy optimization system according to this embodiment comprises an analysis unit, an optimization unit, a proposal unit, and a provision unit. The analysis unit analyzes the industry, surrounding information, and current social conditions of merchants introducing payment processing services. For example, the analysis unit analyzes market trends, the activities of competitors, and consumer purchasing behavior. For example, the analysis unit can use AI to analyze market trends and understand the pricing and promotional activities of competitors. The analysis unit can also analyze consumer purchasing behavior and understand purchase history, purchase frequency, and purchase channels. The optimization unit performs pricing and price optimization based on the information analyzed by the analysis unit. For example, the optimization unit calculates the optimal price based on past sales data and the market supply and demand balance. For example, the optimization unit can use AI to analyze past sales data and calculate the optimal price. The optimization unit can also analyze the market supply and demand balance and understand the status of the supply chain. The proposal unit proposes strategies to maximize profits based on the results obtained by the optimization unit. For example, the proposal unit proposes effective promotional strategies and sales strategies. The proposal department can, for example, use AI to propose promotional strategies based on market trends and competitor activities. The proposal department can also propose the selection of sales channels and the setting of sales targets. The supply department makes sales proposals based on the strategies proposed by the proposal department. The supply department can, for example, monitor market fluctuations in real time and provide analysis results quickly. The supply department can, for example, use AI to monitor market fluctuations in real time and provide the latest market information. Furthermore, the supply department can provide information to sales representatives to make effective proposals. As a result, the payment strategy optimization system according to this embodiment can maximize merchant revenue and improve the quality and speed of sales proposals.

[0065] The analytics department analyzes industry, surrounding information, and current trends for merchants using payment processing services. For example, the analytics department analyzes market trends, competitor activities, and consumer purchasing behavior. Specifically, the analytics department uses AI to analyze market trends and understand competitor pricing and promotional activities. The AI ​​uses natural language processing technology to analyze news articles and social media posts to extract current market trends and consumer interests. It also uses machine learning algorithms to analyze competitor pricing and promotional activity patterns and predict future trends. Furthermore, the analytics department analyzes consumer purchasing behavior to understand purchase history, purchase frequency, and purchase channels. For example, by collecting consumer online shopping data and analyzing purchase history, it can understand fluctuations in demand for specific products and services. By analyzing purchase frequency and purchase channel data, it can identify consumer purchasing patterns and preferences, providing insights for targeted marketing. In this way, the analytics department can provide merchants with a foundation to quickly respond to market fluctuations and competitor activities and develop strategies tailored to consumer needs.

[0066] The Optimization Department sets prices and optimizes pricing based on information analyzed by the Analysis Department. Specifically, the Optimization Department calculates the optimal price based on past sales data and the market supply and demand balance. It can use AI to analyze past sales data and calculate the optimal price. For example, it can use machine learning algorithms to model seasonal fluctuations in demand and the effects of promotions from past sales data to set the optimal price. The Optimization Department can also analyze the market supply and demand balance and understand the status of the supply chain. For example, it can collect supply chain data and assess inventory levels and supply delay risks to consider factors that affect pricing. Furthermore, the Optimization Department can monitor market fluctuations in real time and dynamically adjust pricing. For example, it can use AI to analyze market demand fluctuations in real time and maximize profits by raising prices when demand surges and lowering prices when demand declines. In this way, the Optimization Department can help franchisees respond quickly to market fluctuations and maximize profits by setting optimal prices.

[0067] The Proposal Department proposes strategies to maximize revenue based on the results obtained by the Optimization Department. Specifically, the Proposal Department proposes effective promotion and sales strategies. For example, it can use AI to propose promotion strategies based on market trends and competitor activities. The AI ​​analyzes past promotion data to identify which promotions were most effective. It also monitors competitors' promotional activities and develops competitive promotion strategies. Furthermore, the Proposal Department can propose the selection of sales channels and the setting of sales targets. For example, it analyzes consumer purchasing behavior data to identify which sales channels are most effective. It also sets sales targets and proposes specific action plans to achieve them. In this way, the Proposal Department can provide franchisees with concrete strategies to maximize revenue and propose action plans that can be put into practice. In addition, the Proposal Department can monitor the effectiveness of the proposed strategies and modify them as needed. In this way, the Proposal Department can always provide effective strategies based on the latest information and support franchisees in maximizing their revenue.

[0068] The Supply Department makes sales proposals based on the strategies proposed by the Proposal Department. Specifically, the Supply Department monitors market fluctuations in real time and provides analysis results quickly. For example, it can use AI to monitor market fluctuations in real time and provide the latest market information. The AI ​​analyzes news articles and social media posts to grasp market fluctuations in real time. The Supply Department can also provide information to sales representatives to make effective proposals. For example, it can provide sales representatives with the latest market information and competitor trends when they make proposals to customers, supporting them in making effective proposals. Furthermore, the Supply Department can monitor the implementation status of proposed strategies and modify them as needed. For example, it can monitor the effectiveness of proposed promotional strategies and modify them if they are ineffective. The Supply Department can also collect feedback from sales representatives and continuously improve the accuracy and effectiveness of proposals. In this way, the Supply Department can support franchisees in responding quickly to market fluctuations and making effective sales proposals.

[0069] The analysis unit can analyze market trends, competitor activities, and consumer purchasing behavior. For example, to analyze market trends, the analysis unit uses sales data analysis and consumer survey results. For example, to analyze competitor activities, the analysis unit can understand competitor pricing and promotional activities. Furthermore, to analyze consumer purchasing behavior, the analysis unit can understand purchase history, purchase frequency, and purchase channels. This allows for the provision of more accurate information by analyzing market trends, competitor activities, and consumer purchasing behavior. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input sales data and consumer survey results into a generating AI, which can then analyze market trends.

[0070] The optimization unit can calculate the optimal price based on past sales data and the market supply and demand balance. For example, the optimization unit can calculate the optimal price based on past sales data. For example, the optimization unit can use AI to analyze past sales data and calculate the optimal price. The optimization unit can also calculate the optimal price based on the market supply and demand balance. For example, the optimization unit can understand the supply chain situation and calculate the optimal price. This makes it possible to set the optimal price to maximize profits by calculating the optimal price based on past sales data and the market supply and demand balance. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past sales data and the market supply and demand balance into a generating AI, which can then calculate the optimal price.

[0071] The proposal department can propose effective promotion and sales strategies. For example, the proposal department can propose effective promotion strategies. For example, the proposal department can propose promotion strategies based on market trends and competitor activities using AI. The proposal department can also propose sales strategies. For example, the proposal department can propose the selection of sales channels and the setting of sales targets. This enables the implementation of concrete action plans to maximize profits by proposing effective promotion and sales strategies. 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 market trends and competitor activities into a generating AI, which can then propose effective promotion and sales strategies.

[0072] The service provider can monitor market fluctuations in real time and provide analysis results quickly. For example, the service provider can monitor market fluctuations in real time. For example, the service provider can use AI to monitor market fluctuations in real time and provide the latest market information. The service provider can also provide analysis results quickly. For example, the service provider can use AI to provide analysis results quickly. This allows sales representatives to make effective proposals based on the latest market information by monitoring market fluctuations in real time and providing analysis results quickly. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input market fluctuation data into a generating AI, which can then monitor market fluctuations in real time and provide analysis results quickly.

[0073] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize analyzing and quickly provide important information. If the user is relaxed, for example, the analysis unit can perform a detailed analysis and provide comprehensive information. Also, if the user is in a hurry, the analysis unit can prioritize analyzing the most important data and quickly provide results. This allows for the provision of more appropriate information by adjusting the analysis priority 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the user's emotions and adjust the analysis priority.

[0074] The analysis unit can improve the accuracy of its analysis of market trends and competitor activities based on specific seasons and events. For example, the analysis unit can analyze consumer purchasing behavior based on specific events such as Christmas or Black Friday. For example, the analysis unit can analyze competitor pricing strategies by considering seasonal demand fluctuations. Furthermore, the analysis unit can analyze market trends during specific event periods and propose optimal sales strategies. This allows for the provision of more accurate information by improving the accuracy of the analysis based on specific seasons and events. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data related to specific seasons and events into a generating AI, which can then analyze market trends and competitor activities.

[0075] The analysis unit can improve the accuracy of its analysis by considering the consumer's past purchase history when analyzing consumer purchasing behavior. For example, the analysis unit can predict future purchasing behavior based on the consumer's past purchase history. For example, the analysis unit can analyze the demand for specific products or services from the consumer's purchase history. Furthermore, the analysis unit can analyze the consumer's purchase history and propose the optimal promotion strategy. By improving the accuracy of the analysis by considering the consumer's past purchase history, it becomes possible to predict purchasing behavior more accurately. 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 consumer's past purchase history into a generating AI, which can then analyze the consumer's purchasing behavior.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, more appropriate information can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the user's emotions and adjust the display method of the analysis results.

[0077] The analysis unit can perform analyses that take geographical factors into account when analyzing market trends and the actions of competitors. For example, the analysis unit can analyze consumer purchasing behavior in each region and propose the optimal sales strategy. For example, the analysis unit can analyze the pricing strategies of competitors, taking geographical factors into account. Furthermore, the analysis unit can analyze market trends in each region and propose the optimal promotion strategy. In this way, by performing analyses that take geographical factors into account, market trends in each region can be accurately grasped. 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 data on geographical factors into a generating AI, which can then analyze market trends and the actions of competitors.

[0078] The analysis unit can improve the accuracy of its analysis of consumer purchasing behavior by utilizing social media data. For example, the analysis unit can analyze consumer purchasing intent based on social media data. For example, the analysis unit can analyze social media trends and propose optimal promotion strategies. Furthermore, the analysis unit can predict consumer purchasing behavior by utilizing social media data. By improving the accuracy of the analysis using social media data, it is possible to more accurately understand consumer purchasing intent. 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 social media data into a generating AI, which can then analyze consumer purchasing behavior.

[0079] The optimization unit can estimate the user's emotions and adjust the optimization parameters based on the estimated emotions. For example, if the user is stressed, the optimization unit can use simple optimization parameters. If the user is relaxed, for example, the optimization unit can use detailed optimization parameters. Also, if the user is in a hurry, the optimization unit can use parameters to provide optimization results quickly. This allows for more appropriate optimization results by adjusting the optimization parameters 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 optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generative AI, which can estimate the user's emotions and adjust the optimization parameters.

[0080] The optimization unit can calculate the optimal price based on past sales data, taking into account a specific promotion period. For example, the optimization unit calculates the optimal price based on sales data from past promotion periods. The optimization unit can perform price optimization, taking into account a specific promotion period. Furthermore, the optimization unit can calculate the optimal price by considering demand fluctuations during past promotion periods. This makes it possible to set the optimal price during promotion periods by optimizing while considering a specific promotion period. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input sales data from past promotion periods into a generating AI, which can then calculate the optimal price.

[0081] The optimization unit can calculate the optimal price based on the market supply and demand balance, taking into account fluctuations in the supply chain. For example, the optimization unit can calculate the optimal price by considering fluctuations in the supply chain. For example, the optimization unit can calculate the optimal price based on the market supply and demand balance. Furthermore, the optimization unit can predict fluctuations in the supply chain and calculate the optimal price. This makes it possible to set prices more accurately by optimizing while considering fluctuations in the supply chain. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input supply chain fluctuation data into a generating AI, and the generating AI can calculate the optimal price based on the market supply and demand balance.

[0082] The optimization unit can estimate the user's emotions and adjust the display method of the optimization results based on the estimated emotions. For example, if the user is nervous, the optimization unit can provide a simple and highly visible display method. For example, if the user is relaxed, the optimization unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the optimization unit can provide a display method that gets straight to the point. By adjusting the display method of the optimization results based on the user's emotions, more appropriate information can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generative AI, which can estimate the user's emotions and adjust the display method of the optimization results.

[0083] The optimization unit can calculate the optimal price based on past sales data, taking into account regional sales data. For example, the optimization unit can calculate the optimal price based on regional sales data. For example, the optimization unit can optimize prices by taking into account regional demand fluctuations. Furthermore, the optimization unit can analyze regional market trends and calculate the optimal price. As a result, by optimizing while considering regional sales data, it becomes possible to set the optimal price for each region. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input regional sales data into a generating AI, and the generating AI can calculate the optimal price.

[0084] The optimization unit can integrate online and offline sales data to calculate the optimal price based on the market supply and demand balance. For example, the optimization unit can integrate online and offline sales data to calculate the optimal price. For example, the optimization unit can optimize prices by considering online and offline demand fluctuations. Furthermore, the optimization unit can analyze online and offline market trends to calculate the optimal price. This allows for more accurate pricing by integrating and optimizing online and offline sales data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input online and offline sales data into a generating AI, which can then calculate the optimal price.

[0085] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can offer simple and easy-to-implement suggestions. If the user is relaxed, the suggestion unit can offer more detailed suggestions. If the user is in a hurry, the suggestion unit can offer suggestions that can be implemented quickly. By adjusting the content of suggestions based on the user's emotions, more appropriate suggestions can be made. 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 a generative AI, which can estimate the user's emotions and adjust the content of its suggestions.

[0086] The proposal department can consider past promotional successes when proposing effective promotional strategies. For example, the proposal department can propose effective promotional strategies based on past promotional successes. For example, the proposal department can analyze past promotional failures and propose areas for improvement. Furthermore, the proposal department can propose optimal promotional strategies based on past promotional data. By considering past promotional successes, the proposal department can propose more effective promotional strategies. 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 past promotional data into a generating AI, which can then propose effective promotional strategies.

[0087] The proposal department can improve the accuracy of its sales strategy proposals based on a specific target market. For example, the proposal department can propose an optimal sales strategy based on a specific target market. For example, the proposal department can analyze the needs of the target market and propose an optimal sales strategy. Furthermore, the proposal department can propose an optimal sales strategy by considering the trends of competitors in the target market. This allows for the proposal of more effective sales strategies by improving the accuracy of proposals based on a specific target market. 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 on a specific target market into a generating AI, which can then propose an optimal sales strategy.

[0088] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize important suggestions. If the user is relaxed, the suggestion unit will prioritize detailed suggestions. If the user is in a hurry, the suggestion unit will prioritize suggestions that can be implemented quickly. By prioritizing suggestions based on the user's emotions, more appropriate suggestions can be made. 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 a generative AI, which can estimate the user's emotions and determine the priority of suggestions.

[0089] The proposal department can utilize social media data to propose effective promotional strategies. For example, the proposal department can propose effective promotional strategies based on social media data. For example, the proposal department can analyze social media trends and propose the optimal promotional strategy. Furthermore, the proposal department can utilize social media data to propose promotional strategies that increase consumer purchasing intent. In this way, by utilizing social media data to make proposals, more effective promotional strategies can be proposed. 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 social media data into a generating AI, and the generating AI can propose an effective promotional strategy.

[0090] The proposal department can consider the pricing strategies of competitors when proposing sales strategies. For example, the proposal department can propose the optimal sales strategy based on the pricing strategies of competitors. For example, the proposal department can propose the optimal sales strategy considering the price fluctuations of competitors. Furthermore, the proposal department can analyze the pricing strategies of competitors and propose areas for improvement. By considering the pricing strategies of competitors, the proposal department can propose more effective sales strategies. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input competitor pricing strategy data into a generating AI, which can then propose the optimal sales strategy.

[0091] The information provider can estimate the user's emotions and adjust the priority of the information provided based on the estimated emotions. For example, if the user is stressed, the provider can prioritize providing important information. If the user is relaxed, the provider can provide detailed information. Also, if the user is in a hurry, the provider can prioritize providing information that can be delivered quickly. By adjusting the priority of information provided based on the user's emotions, more appropriate information 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 information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into a generative AI, which can estimate the user's emotions and adjust the priority of the information provided.

[0092] The service provider can improve the accuracy of its monitoring by considering specific events and news when monitoring market fluctuations in real time. For example, the service provider can monitor the impact of specific events and news on the market in real time. For example, when monitoring market fluctuations, the service provider can prioritize the analysis of data during specific event periods. Furthermore, the service provider can predict the impact of specific news on the market and improve the accuracy of its monitoring. This allows for the provision of more accurate information by improving the accuracy of monitoring by considering specific events and news. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data related to specific events and news into a generating AI, which can then monitor market fluctuations in real time and improve accuracy.

[0093] The information delivery unit can improve the accuracy of its information delivery by referring to past analysis results when providing analysis results quickly. For example, the information delivery unit can prioritize providing information that can be provided quickly based on past analysis results. For example, the information delivery unit can improve the accuracy of the information it provides by referring to past analysis results. In addition, the information delivery unit can analyze past analysis results and propose the optimal method of providing information. This makes it possible to provide more accurate information by improving the accuracy of the information delivery by referring to past analysis results. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input past analysis results into a generating AI, and the generating AI can quickly provide analysis results.

[0094] The service provider can estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. If the user is in a hurry, the service provider can provide a display method that gets straight to the point. By adjusting the way the information is displayed based on the user's emotions, more appropriate information can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the user's emotions and adjust the way the information is displayed.

[0095] The service provider can monitor market fluctuations in real time, taking geographical factors into consideration. For example, the service provider can monitor regional market fluctuations in real time. For example, the service provider can prioritize monitoring market fluctuations in a specific region, taking geographical factors into consideration. Furthermore, the service provider can analyze regional market trends and provide optimal information. This allows for an accurate understanding of regional market trends by monitoring while considering geographical factors. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on geographical factors into a generating AI, which can then monitor market fluctuations in real time and improve accuracy.

[0096] The information delivery unit can improve the accuracy of its deliveries by utilizing social media data when providing analysis results quickly. For example, the information delivery unit can prioritize providing information that can be delivered quickly based on social media data. For example, the information delivery unit can analyze social media trends to improve the accuracy of the information it provides. Furthermore, the information delivery unit can use social media data to propose the optimal method of information delivery. By improving the accuracy of deliveries using social media data, it becomes possible to provide more accurate information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input social media data into a generating AI, which can then quickly provide analysis results.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, important information can be prioritized and provided quickly. If the user is relaxed, a detailed analysis can be performed to provide comprehensive information. Furthermore, if the user is in a hurry, the most important data can be prioritized and results can be provided quickly. In this way, more appropriate information can be provided by adjusting the analysis priority 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 analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the user's emotions and adjust the analysis priority.

[0099] The analysis unit can improve the accuracy of its analysis of market trends and competitor activities based on specific seasons or events. For example, it can analyze consumer purchasing behavior based on specific events such as Christmas or Black Friday. It can also analyze competitor pricing strategies by considering seasonal demand fluctuations. Furthermore, it can analyze market trends during specific event periods and propose optimal sales strategies. By improving the accuracy of analysis based on specific seasons or events, it can provide more accurate information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data related to specific seasons or events into a generating AI, which can then analyze market trends and competitor activities.

[0100] The analysis unit can improve the accuracy of its analysis by considering the consumer's past purchase history when analyzing consumer purchasing behavior. For example, it can predict future purchasing behavior based on the consumer's past purchase history. It can also analyze the demand for specific products or services from the consumer's purchase history. Furthermore, it can analyze the consumer's purchase history and propose the optimal promotion strategy. By improving the accuracy of the analysis by considering the consumer's past purchase history, it becomes possible to predict purchasing behavior more accurately. 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 consumer's past purchase history into a generating AI, which can then analyze the consumer's purchasing behavior.

[0101] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the analysis results based on the user's emotions, more appropriate information can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, the generative AI can estimate the user's emotions, and the display method of the analysis results can be adjusted.

[0102] The analysis unit can perform analyses that take geographical factors into account when analyzing market trends and the actions of competitors. For example, it can analyze consumer purchasing behavior in each region and propose the optimal sales strategy. It can also analyze the pricing strategies of competitors, taking geographical factors into account. Furthermore, it can analyze market trends in each region and propose the optimal promotion strategy. In this way, by performing analyses that take geographical factors into account, market trends in each region can be accurately grasped. 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 data on geographical factors into a generating AI, which can then analyze market trends and the actions of competitors.

[0103] The optimization unit can estimate the user's emotions and adjust the optimization parameters based on the estimated emotions. For example, if the user is stressed, simple optimization parameters can be used. If the user is relaxed, detailed optimization parameters can be used. Furthermore, if the user is in a hurry, parameters can be used to provide optimization results quickly. By adjusting the optimization parameters based on the user's emotions, more appropriate optimization 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 optimization unit may be performed using AI or not using AI. For example, the optimization unit can input user emotion data into a generative AI, which can estimate the user's emotions and adjust the optimization parameters.

[0104] The optimization unit can optimize prices by considering specific promotion periods when calculating the optimal price based on past sales data. For example, it can calculate the optimal price based on sales data from past promotion periods. It can also optimize prices by considering specific promotion periods. Furthermore, it can calculate the optimal price by considering demand fluctuations during past promotion periods. This makes it possible to set the optimal price during promotion periods by optimizing while considering specific promotion periods. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input sales data from past promotion periods into a generating AI, which can then calculate the optimal price.

[0105] The optimization unit can calculate the optimal price based on the market supply and demand balance, taking into account fluctuations in the supply chain. For example, it can calculate the optimal price by considering fluctuations in the supply chain. It can also calculate the optimal price based on the market supply and demand balance. Furthermore, it can predict fluctuations in the supply chain and calculate the optimal price. By optimizing while considering fluctuations in the supply chain, more accurate pricing becomes possible. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input supply chain fluctuation data into a generating AI, which can then calculate the optimal price based on the market supply and demand balance.

[0106] The optimization unit can estimate the user's emotions and adjust the display method of the optimization results based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method containing detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the optimization results based on the user's emotions, more appropriate information can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input user emotion data into a generative AI, the generative AI can estimate the user's emotions, and the display method of the optimization results can be adjusted.

[0107] The optimization unit can optimize prices by considering regional sales data when calculating the optimal price based on past sales data. For example, it can calculate the optimal price based on regional sales data. It can also optimize prices by considering regional demand fluctuations. Furthermore, it can analyze regional market trends and calculate the optimal price. As a result, by optimizing while considering regional sales data, it becomes possible to set optimal prices for each region. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input regional sales data into a generating AI, and the generating AI can calculate the optimal price.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The analytics department analyzes the industry, surrounding information, and current trends of merchants implementing the payment processing service. For example, it analyzes market trends, competitor activities, and consumer purchasing behavior. The analytics department uses AI to analyze market trends and understand competitor pricing and promotional activities. It can also analyze consumer purchasing behavior to understand purchase history, purchase frequency, and purchase channels. Step 2: The optimization unit performs pricing and price optimization based on the information analyzed by the analysis unit. For example, it calculates the optimal price based on past sales data and the market supply and demand balance. The optimization unit can use AI to analyze past sales data and calculate the optimal price. It can also analyze the market supply and demand balance and understand the situation of the supply chain. Step 3: The proposal department proposes strategies to maximize revenue based on the results obtained by the optimization department. For example, it proposes effective promotion and sales strategies. The proposal department can use AI to propose promotion strategies based on market trends and competitor activities. It can also propose the selection of sales channels and the setting of sales targets. Step 4: The supply department makes sales proposals based on the strategies proposed by the proposal department. For example, they monitor market fluctuations in real time and provide analysis results quickly. The supply department can use AI to monitor market fluctuations in real time and provide the latest market information. They can also provide information to sales representatives to make effective proposals.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the analysis unit, optimization unit, proposal unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 38B of the smart device 14 to detect market trends and the actions of competitors, and the control unit 46A analyzes them. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and calculates the optimal price based on past sales data and the market supply and demand balance. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes effective promotion and sales strategies. The provision unit is implemented in the control unit 46A of the smart device 14 and monitors market fluctuations in real time and provides analysis results quickly. 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.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the analysis unit, optimization unit, proposal unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the smart glasses 214 to detect market trends and the actions of competitors, and the control unit 46A analyzes them. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12, and calculates the optimal price based on past sales data and the market supply and demand balance. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes effective promotion and sales strategies. The provision unit is implemented in the control unit 46A of the smart glasses 214, and monitors market fluctuations in real time and provides analysis results quickly. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the analysis unit, optimization unit, proposal unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the headset terminal 314 to detect market trends and the actions of competitors, and the control unit 46A analyzes them. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12, and calculates the optimal price based on past sales data and the market supply and demand balance. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes effective promotion and sales strategies. The provision unit is implemented in the control unit 46A of the headset terminal 314, and monitors market fluctuations in real time and provides analysis results quickly. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the analysis unit, optimization unit, proposal unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the robot 414 to detect market trends and the actions of competitors, and the control unit 46A analyzes them. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and calculates the optimal price based on past sales data and the market supply and demand balance. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes effective promotion and sales strategies. The provision unit is implemented in the control unit 46A of the robot 414 and monitors market fluctuations in real time and provides analysis results quickly. 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) The analysis department analyzes industry and surrounding information, as well as current trends, for merchants that have adopted payment processing services. An optimization unit performs pricing and price optimization based on the information analyzed by the aforementioned analysis unit, A proposal unit proposes a strategy to maximize profits based on the results obtained by the optimization unit, The system comprises a provision department that makes sales proposals based on the strategies proposed by the proposal department. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze market trends, competitor activities, and consumer purchasing behavior. The system described in Appendix 1, characterized by the features described herein. (Note 3) The optimization unit, The optimal price is calculated based on past sales data and the balance of supply and demand in the market. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose effective promotion and sales strategies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We monitor market fluctuations in real time and provide analysis results quickly. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, When analyzing market trends and competitor activities, improve the accuracy of the analysis based on specific seasons or events. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing consumer purchasing behavior, consider the consumer's past purchase history to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing market trends and the activities of competitors, geographical factors should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing consumer purchasing behavior, we use social media data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The optimization unit, It estimates the user's emotions and adjusts the optimization parameters based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The optimization unit, When calculating the optimal price based on past sales data, the system optimizes the pricing by taking into account specific promotional periods. The system described in Appendix 1, characterized by the features described herein. (Note 14) The optimization unit, When calculating the optimal price based on the market supply and demand balance, the optimization process takes into account fluctuations in the supply chain. The system described in Appendix 1, characterized by the features described herein. (Note 15) The optimization unit, It estimates the user's emotions and adjusts how the optimization results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The optimization unit, When calculating the optimal price based on past sales data, the system optimizes the pricing by taking into account sales data specific to each region. The system described in Appendix 1, characterized by the features described herein. (Note 17) The optimization unit, When calculating the optimal price based on the market supply and demand balance, online and offline sales data are integrated and optimized. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the content of the suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When proposing an effective promotion strategy, we take into account past successful promotional examples. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When proposing sales strategies, improve the accuracy of the proposal based on specific target markets. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When proposing an effective promotion strategy, we utilize social media data to make our suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When proposing a sales strategy, we take into account the pricing strategies of our competitors. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the priority of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When monitoring market fluctuations in real time, consider specific events and news to improve the accuracy of the monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing analysis results quickly, we refer to past analysis results to improve the accuracy of the results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When monitoring market fluctuations in real time, geographical factors should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, To provide analysis results quickly, we utilize social media data to improve the accuracy of the results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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. The analysis department analyzes industry and surrounding information, as well as current trends, for merchants that have adopted payment processing services. An optimization unit performs pricing and price optimization based on the information analyzed by the aforementioned analysis unit, A proposal unit proposes a strategy to maximize profits based on the results obtained by the optimization unit, The system comprises a provision department that makes sales proposals based on the strategies proposed by the proposal department. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze market trends, competitor activities, and consumer purchasing behavior. The system according to feature 1.

3. The optimization unit, The optimal price is calculated based on past sales data and the balance of supply and demand in the market. The system according to feature 1.

4. The aforementioned proposal section is, We propose effective promotion and sales strategies. The system according to feature 1.

5. The aforementioned supply unit is, We monitor market fluctuations in real time and provide analysis results quickly. The system according to feature 1.

6. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit, When analyzing market trends and competitor activities, improve the accuracy of the analysis based on specific seasons or events. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing consumer purchasing behavior, consider the consumer's past purchase history to improve the accuracy of the analysis. The system according to feature 1.

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