System

Abstract

An object of the system according to the embodiment is to analyze industry characteristics, peripheral information, and public opinion of a member store and predict an optimal settlement means.SOLUTION: A system according to an embodiment includes an industry characteristic analysis unit, a peripheral information analysis unit, a world analysis unit, and a settlement means prediction unit. The industry characteristic analysis unit analyzes an industry characteristic of a member store. The surrounding information analysis unit analyzes the surrounding information based on the industrial characteristic analyzed by the industrial characteristic analysis unit. The sentiment analysis unit analyzes a sentiment based on the surrounding information analyzed by the surrounding information analysis unit. The settlement means prediction unit predicts an optimal settlement means based on the sentiment analyzed by the sentiment analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately analyze the industry characteristics of affiliated stores, surrounding information, and current trends to predict the optimal payment method, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the industry characteristics of affiliated stores, surrounding information, and current circumstances, and predict the optimal payment method. [Means for solving the problem]

[0006] The system according to the embodiment includes an industry characteristic analysis unit, a peripheral information analysis unit, a social situation analysis unit, and a payment method prediction unit. The industry characteristic analysis unit analyzes the industry characteristics of affiliated stores. The peripheral information analysis unit analyzes the peripheral information based on the industry characteristics analyzed by the industry characteristic analysis unit. The social situation analysis unit analyzes the social situation based on the peripheral information analyzed by the peripheral information analysis unit. The payment method prediction unit predicts the optimal payment method based on the social situation analyzed by the social situation analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the industry characteristics of affiliated stores, surrounding information, and current circumstances, and predict the optimal payment method. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The payment method prediction system according to an embodiment of the present invention uses AI to analyze the industry, surrounding information, and current circumstances of existing member stores, and predicts which payment methods should be introduced. As a result, the payment method prediction system can propose optimal payment methods to member stores, thereby streamlining sales activities.

[0029] The payment method prediction system according to the embodiment includes an industry characteristic analysis unit, a peripheral information analysis unit, a social situation analysis unit, and a payment method prediction unit. The industry characteristic analysis unit analyzes the industry characteristics of the affiliated store. For example, the industry characteristic analysis unit understands industry characteristics such as the food and beverage industry, apparel industry, or service industry, and proposes an appropriate payment method based on the industry characteristics. The industry characteristic analysis unit also performs analysis based on information about the affiliated store's industry and past transaction data. The peripheral information analysis unit analyzes peripheral information based on the industry characteristics analyzed by the industry characteristic analysis unit. For example, the peripheral information analysis unit analyzes the age group and purchasing trends of surrounding consumers, the status of competing stores, and the like, and proposes the optimal payment method based on the analysis. The peripheral information analysis unit also performs analysis based on surrounding consumer data and information about competing stores. The social situation analysis unit analyzes the social situation based on the peripheral information analyzed by the peripheral information analysis unit. For example, the social situation analysis unit analyzes the economic situation, consumer trends, technological advances, and the like, and proposes the optimal payment method based on the analysis. The social situation analysis unit also performs analysis based on information about economic data and consumer trends. The payment method prediction unit predicts the optimal payment method based on the current state of affairs analyzed by the current state analysis unit. For example, the payment method prediction unit suggests the most appropriate option from among QR code payment, electronic money, credit card payment, etc. The payment method prediction unit also makes predictions using prompts based on the analysis results as input. This allows the payment method prediction system according to the embodiment to suggest the optimal payment method to affiliated stores and streamline sales activities. For example, it is possible to suggest QR code payment to restaurants and electronic money payment to apparel stores, making it possible to make suggestions tailored to the characteristics of each industry and region. Furthermore, by quickly responding to changes in the current state of affairs, it is possible to always make the latest suggestions.

[0030] The industry characteristic analysis unit can learn from past successes and failures and reflect them in the analysis of industry characteristics. For example, the industry characteristic analysis unit uses AI to learn from past successes and failures and reflect them in the analysis of industry characteristics. For example, it can learn from successful cases of payment method implementation in the restaurant industry and make suggestions for similar industries. The industry characteristic analysis unit can also learn from unsuccessful cases and make suggestions to avoid similar failures. For example, it can suggest alternative payment methods for similar industries based on past cases of unsuccessful implementation of payment methods. In this way, learning from past cases enables more accurate suggestions.

[0031] The industry characteristic analysis unit can reflect short-term trends by taking into account temporary factors such as seasonality and events. For example, the industry characteristic analysis unit uses AI to consider seasonality and propose payment methods that are in high demand at specific times of the year. For example, it might propose the introduction of gift cards during the Christmas season. The industry characteristic analysis unit also considers events and proposes payment methods tailored to specific events. For example, it might propose the introduction of cashless payments at sporting events. The industry characteristic analysis unit also reflects short-term trends and proposes payment methods that respond to changes in consumer behavior. For example, if sales of a particular product are rapidly increasing, it would propose a payment method that corresponds to that product. This makes it possible to make more timely proposals by reflecting short-term trends.

[0032] The peripheral information analysis unit analyzes consumers' social media activities and can reflect real-time consumer trends. For example, the peripheral information analysis unit uses AI to analyze consumers' social media activities and reflect real-time consumer trends. For example, it analyzes posts on Twitter and Instagram to understand consumers' interests. The peripheral information analysis unit also analyzes social media trends and predicts consumer trends. For example, it analyzes the frequency of use of specific hashtags and suggests payment methods based on those trends. The peripheral information analysis unit also analyzes comments and reviews on social media and reflects consumer feedback. For example, it selects the optimal payment method based on consumer opinions. This allows for more accurate suggestions by reflecting real-time consumer trends.

[0033] The surrounding information analysis unit can combine a geographic information system to take geographic factors into account. For example, AI uses a geographic information system (GIS) to take geographic factors into account when analyzing surrounding information. For example, it analyzes consumer trends in a specific area. The surrounding information analysis unit also detects geographic patterns and proposes payment methods based on the characteristics of each area. For example, it analyzes the differences in consumer trends between urban and suburban areas and makes proposals based on that. The surrounding information analysis unit also uses a geographic information system to take geographic factors such as transportation access and population density into account. For example, it proposes mobile payment in areas with good transportation access. By taking geographic factors into account, it becomes possible to make proposals that are more specific to the area.

[0034] The Surrounding Information Analysis Unit compares the results of the analysis of surrounding information with data from different cities and countries, making proposals from a global perspective. For example, the AI ​​in the Surrounding Information Analysis Unit analyzes data from different cities and compares it with the results of the analysis of surrounding information. For example, it compares consumer trends in Tokyo and New York and makes proposals from a global perspective. The Surrounding Information Analysis Unit also analyzes data from different countries and makes proposals that take cultural differences into account. For example, it makes proposals based on the differences in consumer behavior between Japan and the United States. The Surrounding Information Analysis Unit also analyzes economic data from different regions and makes proposals based on the characteristics of each region. For example, it proposes specific payment methods for regions with significant economic growth. This makes it possible to make proposals from a global perspective.

[0035] The peripheral information analysis unit classifies the results of the peripheral information analysis based on the consumer's lifestyle and hobbies and preferences, allowing for more personalized suggestions. For example, the peripheral information analysis unit uses AI to analyze the consumer's lifestyle data and make personalized suggestions based on the results of the peripheral information analysis. For example, it proposes specific payment methods to consumers who love the outdoors. The peripheral information analysis unit also analyzes the consumer's hobbies and preferences and makes suggestions based on that. For example, it proposes payment methods that can be used at music events to consumers who love music. The peripheral information analysis unit also classifies based on the consumer's lifestyle and hobbies and preferences, allowing for more personalized suggestions. For example, it proposes health-related payment methods to health-conscious consumers. This makes it possible to make personalized suggestions based on the consumer's lifestyle and hobbies and preferences.

[0036] The social situation analysis unit analyzes news articles and social media posts in real time, and can reflect the latest trends. For example, the social situation analysis unit uses AI to analyze news articles in real time and reflect the latest trends. For example, it predicts consumer purchasing intentions based on economic news. The social situation analysis unit also analyzes social media posts in real time to understand consumer interests. For example, it analyzes posts on Twitter and Facebook to predict consumer trends. The social situation analysis unit also predicts changes in consumer behavior based on news articles and social media posts. For example, it analyzes the impact of specific news on consumer purchasing behavior. This makes it possible to make suggestions that reflect the latest trends.

[0037] The world situation analysis unit can combine economic indicators and stock price data and take into account fluctuations in the economic situation. For example, the world situation analysis unit uses AI to analyze economic indicators and reflect this in its analysis of the world situation. For example, it predicts consumer purchasing intent based on GDP and unemployment rate. The world situation analysis unit also analyzes stock price data and takes into account fluctuations in the economic situation. For example, it predicts consumer purchasing behavior based on fluctuations in stock prices. The world situation analysis unit also combines economic indicators and stock price data to comprehensively analyze fluctuations in the economic situation. For example, it predicts consumer purchasing intent based on economic indicators and stock price data. This makes it possible to make proposals that take fluctuations in the economic situation into account.

[0038] The world situation analysis unit compares the results of the world situation analysis with data from different countries and regions, and can make proposals from a global perspective. For example, the world situation analysis unit uses AI to analyze data from different countries and compare it with the results of the world situation analysis. For example, it makes proposals based on differences in consumer behavior between Japan and the United States. The world situation analysis unit also analyzes data from different regions and makes proposals based on the characteristics of each region. For example, it makes proposals based on differences in consumer trends between Asia and Europe. The world situation analysis unit also analyzes economic data from different countries and regions and makes proposals from a global perspective. For example, it can propose specific payment methods for regions with significant economic growth. This makes it possible to make proposals from a global perspective.

[0039] The world situation analysis unit classifies the results of the world situation analysis by industry or application, and can provide more specific insights. For example, the AI ​​classifies the results of the world situation analysis by industry to provide more specific insights. For example, it compares consumer trends in the food and beverage industry and the retail industry. The world situation analysis unit also classifies by application and makes suggestions for specific applications. For example, it compares consumer trends for home use and commercial use and makes suggestions based on that. The world situation analysis unit also classifies by industry or application to provide more specific insights. For example, it compares consumer trends for medical use and general use and makes suggestions based on that. This allows for more accurate suggestions by providing specific insights classified by industry or application.

[0040] The payment method prediction unit learns from past implementation cases and their success rates, enabling more accurate predictions. For example, the payment method prediction unit uses AI to learn from past implementation cases and their success rates, and reflects this in its payment method predictions. For example, it makes predictions based on successful implementation cases of payment methods in a specific industry. The payment method prediction unit also learns from failed cases and makes predictions to avoid similar failures. For example, based on past failed implementation cases of payment methods, it proposes other payment methods for similar industries. The payment method prediction unit also makes more accurate predictions based on cases with high success rates. For example, it proposes payment methods for similar industries based on payment methods with high success rates. In this way, by learning from past implementation cases and success rates, more accurate predictions are possible.

[0041] The payment method prediction unit can combine a consumer's purchase history and payment history to make optimal suggestions to individual consumers. For example, the payment method prediction unit uses AI to analyze a consumer's purchase history and predict the optimal payment method. For example, it makes suggestions based on frequently used payment methods. The payment method prediction unit also analyzes a consumer's payment history and makes suggestions based on that. For example, for a consumer who frequently uses a specific payment method, it will suggest a payment method that corresponds to that payment method. The payment method prediction unit also combines purchase history and payment history to make optimal suggestions to individual consumers. For example, for a consumer who frequently purchases a specific product, it will suggest a payment method that corresponds to that product. This makes it possible to make individual suggestions based on the consumer's purchase history and payment history.

[0042] The payment method prediction unit can compare the predicted results of payment methods with data from different industries and regions to find commonalities and differences. For example, the payment method prediction unit uses AI to analyze data from different industries and compare it with the predicted results of payment methods. For example, it can propose payment methods suitable for both industries based on the commonalities between the food and beverage and retail industries. The payment method prediction unit can also analyze data from different regions and make proposals based on the characteristics of each region. For example, it can analyze the differences in consumer trends between urban and suburban areas and make proposals based on that. The payment method prediction unit can also compare data from different industries and regions to find commonalities and differences. For example, it can make proposals based on payment methods that are commonly successful in a specific industry or region. This allows for more accurate proposals by comparing with data from different industries and regions.

[0043] The payment method prediction unit can visualize the predicted results of payment methods to enable sales representatives to intuitively understand them. For example, the payment method prediction unit uses AI to visualize the predicted results of payment methods in graphs and charts to enable sales representatives to intuitively understand them. For example, it visually displays consumer trends by industry. The payment method prediction unit also uses visualization to make proposals easier for sales representatives to understand. For example, it displays consumers' purchase history and payment history in graphs and makes proposals based on that. The payment method prediction unit also uses visualization to make proposals more persuasive. For example, it displays successful and unsuccessful cases in charts and makes proposals based on that. In this way, visualization allows sales representatives to intuitively understand.

[0044] The sales proposal department can learn from past proposals and their success rates, and make more accurate proposals. For example, the sales proposal department uses AI to learn from past sales proposals and their success rates, and reflect this in its proposals. For example, it makes new proposals based on proposals that were successful in a particular industry. The sales proposal department can also learn from unsuccessful proposals and make proposals to avoid similar failures. For example, it can make a different proposal for a similar industry based on proposals that failed in the past. The sales proposal department can also make more accurate proposals based on proposals with a high success rate. For example, it can make a proposal for a similar industry based on proposals with a high success rate. In this way, by learning from past proposals and their success rates, more accurate proposals become possible.

[0045] The sales proposal department can reflect consumer feedback in real time and dynamically adjust the content of proposals. For example, the sales proposal department uses AI to collect consumer feedback in real time and reflect it in the content of proposals. For example, it collects consumer opinions using online surveys and comment functions. The sales proposal department also adjusts the content of proposals based on consumer feedback. For example, it selects the optimal content of proposals based on consumer opinions. The sales proposal department also dynamically adjusts the content of proposals based on real-time feedback. For example, if the consumer's opinion changes, it changes the content of proposals accordingly. This makes it possible to make dynamic proposals based on consumer feedback.

[0046] The sales proposal department can compare the content of sales proposals with data from different industries and regions to find commonalities and differences. For example, the sales proposal department uses AI to analyze data from different industries and compare it with the content of sales proposals. For example, it generates proposals suitable for both industries based on the commonalities between the food and beverage and retail industries. The sales proposal department also analyzes data from different regions and makes proposals based on the characteristics of each region. For example, it analyzes the differences in consumer trends between urban and suburban areas and makes proposals based on that. The sales proposal department also compares data from different industries and regions to find commonalities and differences. For example, it makes proposals based on proposals that have been successful in a particular industry or region. This allows for more accurate proposals by comparing with data from different industries and regions.

[0047] The sales proposal department can visualize the content of sales proposals to enable sales representatives to intuitively understand them. For example, the sales proposal department uses AI to visualize the content of sales proposals using graphs and charts to enable sales representatives to intuitively understand them. For example, consumer trends by industry are visually displayed. The sales proposal department also uses visualization to make the content of proposals easier for sales representatives to understand. For example, they can display consumers' purchasing history and payment history in graphs and make proposals based on that. The sales proposal department also uses visualization to make the content of proposals more persuasive. For example, they can display success stories and failure stories in charts and make proposals based on that. In this way, visualization allows sales representatives to intuitively understand.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The payment method prediction system can further include a purchase history analysis unit that analyzes the consumer's purchase history. The purchase history analysis unit analyzes the consumer's past purchase history and identifies the consumer's preferred payment method. For example, it makes suggestions based on frequently used payment methods. The purchase history analysis unit also analyzes the consumer's purchase history for specific products and services and makes suggestions based on that. For example, for a consumer who frequently purchases a specific product, it suggests a payment method that corresponds to that product. The purchase history analysis unit also analyzes the consumer's purchasing patterns and makes suggestions tailored to the consumer's lifestyle. For example, for a consumer who frequently purchases outdoor equipment, it suggests payment methods that can be used at outdoor shops. This enables more personalized suggestions based on the consumer's purchase history.

[0050] The payment method prediction system can further include a feedback collection unit that collects consumer feedback. The feedback collection unit collects feedback from consumers in real time and reflects it in the proposal content. For example, it collects consumer opinions using an online survey or comment function. The feedback collection unit also adjusts the proposal content based on the consumer feedback. For example, it selects the optimal proposal content based on the consumer opinions. The feedback collection unit also dynamically adjusts the proposal content based on the real-time feedback. For example, if the consumer opinion changes, it changes the proposal content accordingly. This makes it possible to make dynamic proposals based on consumer feedback.

[0051] The payment method prediction system can further include a lifestyle analysis unit that analyzes the consumer's lifestyle data. The lifestyle analysis unit analyzes the consumer's lifestyle data and makes suggestions based on the consumer's lifestyle habits. For example, it suggests health-related payment methods to a health-conscious consumer. The lifestyle analysis unit also analyzes the consumer's hobbies and preferences and makes suggestions based on the results. For example, it suggests payment methods that can be used at music events to a consumer who loves music. The lifestyle analysis unit also classifies the consumer based on their lifestyle and hobbies and preferences to make more personalized suggestions. For example, it suggests a specific payment method to a consumer who loves the outdoors. This makes it possible to make personalized suggestions based on the consumer's lifestyle and hobbies and preferences.

[0052] The payment method prediction system can further include a social media analysis unit that analyzes consumers' social media activities. The social media analysis unit analyzes consumers' social media activities to reflect real-time consumer trends. For example, it analyzes posts on Twitter and Instagram to understand consumers' interests. The social media analysis unit also analyzes social media trends to predict consumer trends. For example, it analyzes the frequency of use of specific hashtags and suggests payment methods based on those trends. The social media analysis unit also analyzes comments and reviews on social media to reflect consumer feedback. For example, it selects the optimal payment method based on consumer opinions. This allows for more accurate suggestions by reflecting real-time consumer trends.

[0053] The payment method prediction system can further incorporate a geographic information analysis unit that takes geographic factors into account by combining a geographic information system (GIS). The geographic information analysis unit uses the geographic information system to take geographic factors into account when analyzing surrounding information. For example, it may analyze consumer trends in a specific area. The geographic information analysis unit also detects geographic patterns and proposes payment methods based on the characteristics of each area. For example, it may analyze the differences in consumer trends between urban and suburban areas and make proposals based on these. The geographic information analysis unit also uses the geographic information system to consider geographic factors such as transportation access and population density. For example, it may propose mobile payment to areas with good transportation access. By taking geographic factors into account, it becomes possible to make proposals that are more specific to the area.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The Industry Characteristics Analysis Department analyzes the industry characteristics of the affiliated store. For example, it understands the industry characteristics of the restaurant, apparel, service, etc., and proposes appropriate payment methods based on that. It also performs analysis based on information about the affiliated store's industry and past transaction data. Step 2: The Surrounding Information Analysis Unit analyzes surrounding information based on the industry characteristics analyzed by the Industry Characteristics Analysis Unit. For example, it analyzes the age groups and purchasing trends of surrounding consumers, the status of competing stores, etc., and proposes the optimal payment method based on this. It also performs analysis based on surrounding consumer data and information on competing stores. Step 3: The Social Situation Analysis Unit analyzes the social situation based on the peripheral information analyzed by the Peripheral Information Analysis Unit. For example, it analyzes the economic situation, consumer trends, technological advances, etc., and proposes the optimal payment method based on that information. It also performs analysis based on information on economic data and consumer trends. Step 4: The payment method prediction unit predicts the optimal payment method based on the current state of affairs analyzed by the current state analysis unit. For example, it suggests the most appropriate option from among QR code payment, electronic money, credit card payment, etc. It also makes predictions using prompts based on the analysis results as input.

[0056] (Example 2) The payment method prediction system according to an embodiment of the present invention uses AI to analyze the industry, surrounding information, and current circumstances of existing member stores, and predicts which payment methods should be introduced. As a result, the payment method prediction system can propose optimal payment methods to member stores, thereby streamlining sales activities.

[0057] The payment method prediction system according to the embodiment includes an industry characteristic analysis unit, a peripheral information analysis unit, a social situation analysis unit, and a payment method prediction unit. The industry characteristic analysis unit analyzes the industry characteristics of the affiliated store. For example, the industry characteristic analysis unit understands industry characteristics such as the food and beverage industry, apparel industry, or service industry, and proposes an appropriate payment method based on the industry characteristics. The industry characteristic analysis unit also performs analysis based on information about the affiliated store's industry and past transaction data. The peripheral information analysis unit analyzes peripheral information based on the industry characteristics analyzed by the industry characteristic analysis unit. For example, the peripheral information analysis unit analyzes the age group and purchasing trends of surrounding consumers, the status of competing stores, and the like, and proposes the optimal payment method based on the analysis. The peripheral information analysis unit also performs analysis based on surrounding consumer data and information about competing stores. The social situation analysis unit analyzes the social situation based on the peripheral information analyzed by the peripheral information analysis unit. For example, the social situation analysis unit analyzes the economic situation, consumer trends, technological advances, and the like, and proposes the optimal payment method based on the analysis. The social situation analysis unit also performs analysis based on information about economic data and consumer trends. The payment method prediction unit predicts the optimal payment method based on the current state of affairs analyzed by the current state analysis unit. For example, the payment method prediction unit suggests the most appropriate option from among QR code payment, electronic money, credit card payment, etc. The payment method prediction unit also makes predictions using prompts based on the analysis results as input. This allows the payment method prediction system according to the embodiment to suggest the optimal payment method to affiliated stores and streamline sales activities. For example, it is possible to suggest QR code payment to restaurants and electronic money payment to apparel stores, making it possible to make suggestions tailored to the characteristics of each industry and region. Furthermore, by quickly responding to changes in the current state of affairs, it is possible to always make the latest suggestions.

[0058] The industry characteristic analysis unit can learn from past successes and failures and reflect them in the analysis of industry characteristics. For example, the industry characteristic analysis unit uses AI to learn from past successes and failures and reflect them in the analysis of industry characteristics. For example, it can learn from successful cases of payment method implementation in the restaurant industry and make suggestions for similar industries. The industry characteristic analysis unit can also learn from unsuccessful cases and make suggestions to avoid similar failures. For example, it can suggest alternative payment methods for similar industries based on past cases of unsuccessful implementation of payment methods. In this way, learning from past cases enables more accurate suggestions.

[0059] The industry characteristic analysis unit can reflect short-term trends by taking into account temporary factors such as seasonality and events. For example, the industry characteristic analysis unit uses AI to consider seasonality and propose payment methods that are in high demand at specific times of the year. For example, it might propose the introduction of gift cards during the Christmas season. The industry characteristic analysis unit also considers events and proposes payment methods tailored to specific events. For example, it might propose the introduction of cashless payments at sporting events. The industry characteristic analysis unit also reflects short-term trends and proposes payment methods that respond to changes in consumer behavior. For example, if sales of a particular product are rapidly increasing, it would propose a payment method that corresponds to that product. This makes it possible to make more timely proposals by reflecting short-term trends.

[0060] The industry characteristic analysis unit uses the emotion estimation function to analyze consumer emotions within an industry and can suggest payment methods based on those emotions. For example, the industry characteristic analysis unit uses AI to analyze consumer emotions and suggest specific payment methods for industries with strong positive emotions. For example, it suggests payment methods with cashback for events that make consumers happy. The industry characteristic analysis unit also suggests appropriate payment methods for industries with strong negative emotions. For example, it suggests payment methods with enhanced security for situations that make consumers feel anxious. The industry characteristic analysis unit also uses the emotion estimation function to suggest payment methods based on consumer emotions. For example, it selects the optimal payment method based on the consumer's emotion score. This makes it possible to make suggestions based on consumer emotions.

[0061] The peripheral information analysis unit analyzes consumers' social media activities and can reflect real-time consumer trends. For example, the peripheral information analysis unit uses AI to analyze consumers' social media activities and reflect real-time consumer trends. For example, it analyzes posts on Twitter and Instagram to understand consumers' interests. The peripheral information analysis unit also analyzes social media trends and predicts consumer trends. For example, it analyzes the frequency of use of specific hashtags and suggests payment methods based on those trends. The peripheral information analysis unit also analyzes comments and reviews on social media and reflects consumer feedback. For example, it selects the optimal payment method based on consumer opinions. This allows for more accurate suggestions by reflecting real-time consumer trends.

[0062] The surrounding information analysis unit can combine a geographic information system to take geographic factors into account. For example, AI uses a geographic information system (GIS) to take geographic factors into account when analyzing surrounding information. For example, it analyzes consumer trends in a specific area. The surrounding information analysis unit also detects geographic patterns and proposes payment methods based on the characteristics of each area. For example, it analyzes the differences in consumer trends between urban and suburban areas and makes proposals based on that. The surrounding information analysis unit also uses a geographic information system to take geographic factors such as transportation access and population density into account. For example, it proposes mobile payment in areas with good transportation access. By taking geographic factors into account, it becomes possible to make proposals that are more specific to the area.

[0063] The surrounding information analysis unit can use the emotion estimation function to analyze the emotions of surrounding consumers and suggest payment methods based on their emotions. For example, the surrounding information analysis unit uses AI's emotion estimation function to analyze the emotions of surrounding consumers and suggest payment methods based on their emotions. For example, it selects the optimal payment method based on the consumer's emotion score. The surrounding information analysis unit also analyzes differences in emotions by region and makes suggestions based on that. For example, if positive emotions are strong in a particular region, it will suggest payment methods appropriate for that region. The surrounding information analysis unit also analyzes consumer emotions toward a particular event and makes suggestions based on that. For example, if event participants feel happy, it will suggest payment methods appropriate for that event. This makes it possible to make suggestions based on the emotions of surrounding consumers.

[0064] The Surrounding Information Analysis Unit compares the results of the analysis of surrounding information with data from different cities and countries, making proposals from a global perspective. For example, the AI ​​in the Surrounding Information Analysis Unit analyzes data from different cities and compares it with the results of the analysis of surrounding information. For example, it compares consumer trends in Tokyo and New York and makes proposals from a global perspective. The Surrounding Information Analysis Unit also analyzes data from different countries and makes proposals that take cultural differences into account. For example, it makes proposals based on the differences in consumer behavior between Japan and the United States. The Surrounding Information Analysis Unit also analyzes economic data from different regions and makes proposals based on the characteristics of each region. For example, it proposes specific payment methods for regions with significant economic growth. This makes it possible to make proposals from a global perspective.

[0065] The peripheral information analysis unit classifies the results of the peripheral information analysis based on the consumer's lifestyle and hobbies and preferences, allowing for more personalized suggestions. For example, the peripheral information analysis unit uses AI to analyze the consumer's lifestyle data and make personalized suggestions based on the results of the peripheral information analysis. For example, it proposes specific payment methods to consumers who love the outdoors. The peripheral information analysis unit also analyzes the consumer's hobbies and preferences and makes suggestions based on that. For example, it proposes payment methods that can be used at music events to consumers who love music. The peripheral information analysis unit also classifies based on the consumer's lifestyle and hobbies and preferences, allowing for more personalized suggestions. For example, it proposes health-related payment methods to health-conscious consumers. This makes it possible to make personalized suggestions based on the consumer's lifestyle and hobbies and preferences.

[0066] The surrounding information analysis unit uses an emotion estimation function to monitor the emotional reactions of surrounding consumers in real time and dynamically adjust the content of suggestions. For example, the surrounding information analysis unit uses an emotion estimation function to monitor the emotional reactions of surrounding consumers in real time. For example, the content of suggestions is adjusted based on the consumer's emotion score. The surrounding information analysis unit also monitors consumer emotions using real-time facial expression analysis. For example, it analyzes the consumer's facial expressions using a camera and calculates an emotion score. The surrounding information analysis unit also monitors consumer emotions using voice analysis. For example, it analyzes the tone and speed of the consumer's voice and calculates an emotion score. This makes it possible to make dynamic suggestions based on the emotional reactions of surrounding consumers.

[0067] The social situation analysis unit analyzes news articles and social media posts in real time, and can reflect the latest trends. For example, the social situation analysis unit uses AI to analyze news articles in real time and reflect the latest trends. For example, it predicts consumer purchasing intentions based on economic news. The social situation analysis unit also analyzes social media posts in real time to understand consumer interests. For example, it analyzes posts on Twitter and Facebook to predict consumer trends. The social situation analysis unit also predicts changes in consumer behavior based on news articles and social media posts. For example, it analyzes the impact of specific news on consumer purchasing behavior. This makes it possible to make suggestions that reflect the latest trends.

[0068] The world situation analysis unit can combine economic indicators and stock price data and take into account fluctuations in the economic situation. For example, the world situation analysis unit uses AI to analyze economic indicators and reflect this in its analysis of the world situation. For example, it predicts consumer purchasing intent based on GDP and unemployment rate. The world situation analysis unit also analyzes stock price data and takes into account fluctuations in the economic situation. For example, it predicts consumer purchasing behavior based on fluctuations in stock prices. The world situation analysis unit also combines economic indicators and stock price data to comprehensively analyze fluctuations in the economic situation. For example, it predicts consumer purchasing intent based on economic indicators and stock price data. This makes it possible to make proposals that take fluctuations in the economic situation into account.

[0069] The social climate analysis unit uses an emotion estimation function to analyze consumer emotions regarding the social climate and can propose payment methods based on those emotions. For example, the social climate analysis unit uses an AI emotion estimation function to analyze consumer emotions regarding the social climate and propose payment methods based on those emotions. For example, it selects the optimal payment method based on the consumer's emotion score. The social climate analysis unit also analyzes consumer emotions regarding the economic situation and makes proposals based on that. For example, if economic anxiety is increasing, it proposes payment methods with enhanced security. The social climate analysis unit also analyzes consumer emotions regarding political trends and makes proposals based on that. For example, if political anxiety is increasing, it proposes payment methods with high stability. This makes it possible to make proposals based on consumer emotions regarding the social climate.

[0070] The world situation analysis unit compares the results of the world situation analysis with data from different countries and regions, and can make proposals from a global perspective. For example, the world situation analysis unit uses AI to analyze data from different countries and compare it with the results of the world situation analysis. For example, it makes proposals based on differences in consumer behavior between Japan and the United States. The world situation analysis unit also analyzes data from different regions and makes proposals based on the characteristics of each region. For example, it makes proposals based on differences in consumer trends between Asia and Europe. The world situation analysis unit also analyzes economic data from different countries and regions and makes proposals from a global perspective. For example, it can propose specific payment methods for regions with significant economic growth. This makes it possible to make proposals from a global perspective.

[0071] The world situation analysis unit classifies the results of the world situation analysis by industry or application, and can provide more specific insights. For example, the AI ​​classifies the results of the world situation analysis by industry to provide more specific insights. For example, it compares consumer trends in the food and beverage industry and the retail industry. The world situation analysis unit also classifies by application and makes suggestions for specific applications. For example, it compares consumer trends for home use and commercial use and makes suggestions based on that. The world situation analysis unit also classifies by industry or application to provide more specific insights. For example, it compares consumer trends for medical use and general use and makes suggestions based on that. This allows for more accurate suggestions by providing specific insights classified by industry or application.

[0072] The social sentiment analysis unit uses an emotion estimation function to monitor consumers' emotional reactions to social sentiment in real time and dynamically adjust the content of suggestions. For example, the social sentiment analysis unit uses an emotion estimation function to monitor consumers' emotional reactions to social sentiment in real time. For example, the content of suggestions is adjusted based on the consumer's emotion score. The social sentiment analysis unit also monitors consumers' emotions using real-time facial expression analysis. For example, it analyzes consumers' facial expressions using a camera and calculates an emotion score. The social sentiment analysis unit also monitors consumers' emotions using voice analysis. For example, it analyzes the tone and speed of the consumer's voice and calculates an emotion score. This makes it possible to make dynamic suggestions based on consumers' emotional reactions to social sentiment.

[0073] The payment method prediction unit learns from past implementation cases and their success rates, enabling more accurate predictions. For example, the payment method prediction unit uses AI to learn from past implementation cases and their success rates, and reflects this in its payment method predictions. For example, it makes predictions based on successful implementation cases of payment methods in a specific industry. The payment method prediction unit also learns from failed cases and makes predictions to avoid similar failures. For example, based on past failed implementation cases of payment methods, it proposes other payment methods for similar industries. The payment method prediction unit also makes more accurate predictions based on cases with high success rates. For example, it proposes payment methods for similar industries based on payment methods with high success rates. In this way, by learning from past implementation cases and success rates, more accurate predictions are possible.

[0074] The payment method prediction unit can combine a consumer's purchase history and payment history to make optimal suggestions to individual consumers. For example, the payment method prediction unit uses AI to analyze a consumer's purchase history and predict the optimal payment method. For example, it makes suggestions based on frequently used payment methods. The payment method prediction unit also analyzes a consumer's payment history and makes suggestions based on that. For example, for a consumer who frequently uses a specific payment method, it will suggest a payment method that corresponds to that payment method. The payment method prediction unit also combines purchase history and payment history to make optimal suggestions to individual consumers. For example, for a consumer who frequently purchases a specific product, it will suggest a payment method that corresponds to that product. This makes it possible to make individual suggestions based on the consumer's purchase history and payment history.

[0075] The payment method prediction unit can use the emotion estimation function to predict payment methods based on consumer emotions. For example, the payment method prediction unit uses the emotion estimation function with AI to predict payment methods based on consumer emotions. For example, it selects the optimal payment method based on the consumer's emotion score. The payment method prediction unit also analyzes consumer emotions and makes suggestions based on them. For example, it suggests payment methods with cashback for situations that make the consumer happy. The payment method prediction unit also makes predictions based on consumer emotions and makes suggestions according to the emotions. For example, it suggests payment methods with enhanced security for situations that make the consumer anxious. This makes it possible to predict payment methods based on consumer emotions.

[0076] The payment method prediction unit can compare the predicted results of payment methods with data from different industries and regions to find commonalities and differences. For example, the payment method prediction unit uses AI to analyze data from different industries and compare it with the predicted results of payment methods. For example, it can propose payment methods suitable for both industries based on the commonalities between the food and beverage and retail industries. The payment method prediction unit can also analyze data from different regions and make proposals based on the characteristics of each region. For example, it can analyze the differences in consumer trends between urban and suburban areas and make proposals based on that. The payment method prediction unit can also compare data from different industries and regions to find commonalities and differences. For example, it can make proposals based on payment methods that are commonly successful in a specific industry or region. This allows for more accurate proposals by comparing with data from different industries and regions.

[0077] The payment method prediction unit can visualize the predicted results of payment methods to enable sales representatives to intuitively understand them. For example, the payment method prediction unit uses AI to visualize the predicted results of payment methods in graphs and charts to enable sales representatives to intuitively understand them. For example, it visually displays consumer trends by industry. The payment method prediction unit also uses visualization to make proposals easier for sales representatives to understand. For example, it displays consumers' purchase history and payment history in graphs and makes proposals based on that. The payment method prediction unit also uses visualization to make proposals more persuasive. For example, it displays successful and unsuccessful cases in charts and makes proposals based on that. In this way, visualization allows sales representatives to intuitively understand.

[0078] The payment method prediction unit can use an emotion estimation function to monitor the consumer's emotional response in real time and dynamically adjust the content of proposals. For example, the payment method prediction unit uses an emotion estimation function with AI to monitor the consumer's emotional response in real time. For example, the proposal content is adjusted based on the consumer's emotion score. The payment method prediction unit also monitors the consumer's emotions using real-time facial expression analysis. For example, it analyzes the consumer's facial expressions using a camera and calculates an emotion score. The payment method prediction unit also monitors the consumer's emotions using voice analysis. For example, it analyzes the tone and speed of the consumer's voice and calculates an emotion score. This makes it possible to make dynamic proposals based on the consumer's emotional response.

[0079] The sales proposal department can learn from past proposals and their success rates, and make more accurate proposals. For example, the sales proposal department uses AI to learn from past sales proposals and their success rates, and reflect this in its proposals. For example, it makes new proposals based on proposals that were successful in a particular industry. The sales proposal department can also learn from unsuccessful proposals and make proposals to avoid similar failures. For example, it can make a different proposal for a similar industry based on proposals that failed in the past. The sales proposal department can also make more accurate proposals based on proposals with a high success rate. For example, it can make a proposal for a similar industry based on proposals with a high success rate. In this way, by learning from past proposals and their success rates, more accurate proposals become possible.

[0080] The sales proposal department can reflect consumer feedback in real time and dynamically adjust the content of proposals. For example, the sales proposal department uses AI to collect consumer feedback in real time and reflect it in the content of proposals. For example, it collects consumer opinions using online surveys and comment functions. The sales proposal department also adjusts the content of proposals based on consumer feedback. For example, it selects the optimal content of proposals based on consumer opinions. The sales proposal department also dynamically adjusts the content of proposals based on real-time feedback. For example, if the consumer's opinion changes, it changes the content of proposals accordingly. This makes it possible to make dynamic proposals based on consumer feedback.

[0081] The sales proposal department can use the emotion estimation function to make sales proposals based on the consumer's emotions. For example, the sales proposal department uses the emotion estimation function with AI to make sales proposals based on the consumer's emotions. For example, the optimal proposal content is selected based on the consumer's emotion score. The sales proposal department also analyzes the consumer's emotions and makes proposals based on them. For example, a specific proposal is made for situations that make the consumer feel happy. The sales proposal department also makes proposals based on the consumer's emotions and makes proposals according to the emotions. For example, a proposal that gives the consumer a sense of security in situations that make the consumer feel anxious is made. This makes it possible to make sales proposals based on the consumer's emotions.

[0082] The sales proposal department can compare the content of sales proposals with data from different industries and regions to find commonalities and differences. For example, the sales proposal department uses AI to analyze data from different industries and compare it with the content of sales proposals. For example, it generates proposals suitable for both industries based on the commonalities between the food and beverage and retail industries. The sales proposal department also analyzes data from different regions and makes proposals based on the characteristics of each region. For example, it analyzes the differences in consumer trends between urban and suburban areas and makes proposals based on that. The sales proposal department also compares data from different industries and regions to find commonalities and differences. For example, it makes proposals based on proposals that have been successful in a particular industry or region. This allows for more accurate proposals by comparing with data from different industries and regions.

[0083] The sales proposal department can visualize the content of sales proposals to enable sales representatives to intuitively understand them. For example, the sales proposal department uses AI to visualize the content of sales proposals using graphs and charts to enable sales representatives to intuitively understand them. For example, consumer trends by industry are visually displayed. The sales proposal department also uses visualization to make the content of proposals easier for sales representatives to understand. For example, they can display consumers' purchasing history and payment history in graphs and make proposals based on that. The sales proposal department also uses visualization to make the content of proposals more persuasive. For example, they can display success stories and failure stories in charts and make proposals based on that. In this way, visualization allows sales representatives to intuitively understand.

[0084] The sales proposal department can use the emotion estimation function to monitor consumers' emotional reactions in real time and dynamically adjust the content of proposals. For example, the sales proposal department uses the emotion estimation function to monitor consumers' emotional reactions in real time. For example, the sales proposal department adjusts the content of proposals based on the consumer's emotion score. The sales proposal department also uses real-time facial expression analysis to monitor consumers' emotions. For example, it analyzes the consumer's facial expressions with a camera and calculates an emotion score. The sales proposal department also uses voice analysis to monitor consumers' emotions. For example, it analyzes the tone and speed of the consumer's voice and calculates an emotion score. This makes it possible to make dynamic proposals based on the consumer's emotional reactions.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The payment method prediction system can further include a purchase history analysis unit that analyzes the consumer's purchase history. The purchase history analysis unit analyzes the consumer's past purchase history and identifies the consumer's preferred payment method. For example, it makes suggestions based on frequently used payment methods. The purchase history analysis unit also analyzes the consumer's purchase history for specific products and services and makes suggestions based on that. For example, for a consumer who frequently purchases a specific product, it suggests a payment method that corresponds to that product. The purchase history analysis unit also analyzes the consumer's purchasing patterns and makes suggestions tailored to the consumer's lifestyle. For example, for a consumer who frequently purchases outdoor equipment, it suggests payment methods that can be used at outdoor shops. This enables more personalized suggestions based on the consumer's purchase history.

[0087] The payment method prediction system can further include a feedback collection unit that collects consumer feedback. The feedback collection unit collects feedback from consumers in real time and reflects it in the proposal content. For example, it collects consumer opinions using an online survey or comment function. The feedback collection unit also adjusts the proposal content based on the consumer feedback. For example, it selects the optimal proposal content based on the consumer opinions. The feedback collection unit also dynamically adjusts the proposal content based on the real-time feedback. For example, if the consumer opinion changes, it changes the proposal content accordingly. This makes it possible to make dynamic proposals based on consumer feedback.

[0088] The payment method prediction system can further include a lifestyle analysis unit that analyzes the consumer's lifestyle data. The lifestyle analysis unit analyzes the consumer's lifestyle data and makes suggestions based on the consumer's lifestyle habits. For example, it suggests health-related payment methods to a health-conscious consumer. The lifestyle analysis unit also analyzes the consumer's hobbies and preferences and makes suggestions based on the results. For example, it suggests payment methods that can be used at music events to a consumer who loves music. The lifestyle analysis unit also classifies the consumer based on their lifestyle and hobbies and preferences to make more personalized suggestions. For example, it suggests a specific payment method to a consumer who loves the outdoors. This makes it possible to make personalized suggestions based on the consumer's lifestyle and hobbies and preferences.

[0089] The payment method prediction system can further include a social media analysis unit that analyzes consumers' social media activities. The social media analysis unit analyzes consumers' social media activities to reflect real-time consumer trends. For example, it analyzes posts on Twitter and Instagram to understand consumers' interests. The social media analysis unit also analyzes social media trends to predict consumer trends. For example, it analyzes the frequency of use of specific hashtags and suggests payment methods based on those trends. The social media analysis unit also analyzes comments and reviews on social media to reflect consumer feedback. For example, it selects the optimal payment method based on consumer opinions. This allows for more accurate suggestions by reflecting real-time consumer trends.

[0090] The payment method prediction system can further incorporate a geographic information analysis unit that takes geographic factors into account by combining a geographic information system (GIS). The geographic information analysis unit uses the geographic information system to take geographic factors into account when analyzing surrounding information. For example, it may analyze consumer trends in a specific area. The geographic information analysis unit also detects geographic patterns and proposes payment methods based on the characteristics of each area. For example, it may analyze the differences in consumer trends between urban and suburban areas and make proposals based on these. The geographic information analysis unit also uses the geographic information system to consider geographic factors such as transportation access and population density. For example, it may propose mobile payment to areas with good transportation access. By taking geographic factors into account, it becomes possible to make proposals that are more specific to the area.

[0091] The payment method prediction system can further include an emotion analysis unit that analyzes consumer emotions. The emotion analysis unit analyzes consumer emotions and suggests payment methods based on those emotions. For example, it selects the optimal payment method based on the consumer's emotion score. The emotion analysis unit also analyzes consumer emotions and makes suggestions based on those emotions. For example, it suggests a payment method with cashback for situations that make the consumer happy. The emotion analysis unit also makes suggestions based on the consumer's emotions and makes suggestions according to the emotions. For example, it suggests a payment method with enhanced security for situations that make the consumer anxious. This makes it possible to suggest payment methods based on the consumer's emotions.

[0092] The payment method prediction system may further include an emotion monitoring unit that monitors the consumer's emotional response in real time. The emotion monitoring unit monitors the consumer's emotional response in real time and dynamically adjusts the content of suggestions. For example, the content of suggestions may be adjusted based on the consumer's emotion score. The emotion monitoring unit may also monitor the consumer's emotions using real-time facial expression analysis. For example, the emotion monitoring unit may analyze the consumer's facial expression using a camera and calculate an emotion score. The emotion monitoring unit may also monitor the consumer's emotions using voice analysis. For example, the emotion monitoring unit may analyze the tone and speed of the consumer's voice and calculate an emotion score. This enables dynamic suggestions based on the consumer's emotional response.

[0093] The payment method prediction system can further include a marketing strategy proposal unit that proposes a marketing strategy based on consumer emotions. The marketing strategy proposal unit analyzes consumer emotions and proposes a marketing strategy based on the emotions. For example, it selects an optimal marketing strategy based on the consumer's emotion score. The marketing strategy proposal unit also analyzes consumer emotions and makes proposals based on the analysis. For example, it proposes a specific marketing strategy for a situation in which the consumer feels joy. The marketing strategy proposal unit also makes proposals based on consumer emotions and proposes a marketing strategy that corresponds to the emotions. For example, it proposes a marketing strategy that gives the consumer a sense of security in a situation in which the consumer feels anxious. This makes it possible to propose a marketing strategy based on consumer emotions.

[0094] The payment method prediction system can further include a customer support unit that provides customer support based on the consumer's emotions. The customer support unit analyzes the consumer's emotions and provides customer support based on the emotions. For example, it selects the optimal support content based on the consumer's emotion score. The customer support unit also analyzes the consumer's emotions and provides support based on the analysis. For example, it provides support that gives the consumer a sense of security in situations where the consumer feels anxious. The customer support unit also provides support based on the consumer's emotions and provides support that corresponds to the emotions. For example, it provides specific support in situations where the consumer feels happy. This makes it possible to provide customer support based on the consumer's emotions.

[0095] The payment method prediction system can further include a promotion unit that carries out promotions based on consumer emotions. The promotion unit analyzes consumer emotions and carries out promotions based on the emotions. For example, it selects optimal promotion content based on the consumer's emotion score. The promotion unit also analyzes consumer emotions and carries out promotions based on the analysis. For example, it carries out specific promotions in situations that make the consumer feel happy. The promotion unit also carries out promotions based on the consumer's emotions and carries out promotions that correspond to the emotions. For example, it carries out promotions that give the consumer a sense of security in situations that make the consumer feel anxious. This makes it possible to carry out promotions based on the consumer's emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The Industry Characteristics Analysis Department analyzes the industry characteristics of the affiliated store. For example, it understands the industry characteristics of the restaurant, apparel, service, etc., and proposes appropriate payment methods based on that. It also performs analysis based on information about the affiliated store's industry and past transaction data. Step 2: The Surrounding Information Analysis Unit analyzes surrounding information based on the industry characteristics analyzed by the Industry Characteristics Analysis Unit. For example, it analyzes the age groups and purchasing trends of surrounding consumers, the status of competing stores, etc., and proposes the optimal payment method based on this. It also performs analysis based on surrounding consumer data and information on competing stores. Step 3: The Social Situation Analysis Unit analyzes the social situation based on the peripheral information analyzed by the Peripheral Information Analysis Unit. For example, it analyzes the economic situation, consumer trends, technological advances, etc., and proposes the optimal payment method based on that information. It also performs analysis based on information on economic data and consumer trends. Step 4: The payment method prediction unit predicts the optimal payment method based on the current state of affairs analyzed by the current state analysis unit. For example, it suggests the most appropriate option from among QR code payment, electronic money, credit card payment, etc. It also makes predictions using prompts based on the analysis results as input.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An industry characteristics analysis department that analyzes the industry characteristics of franchisees; a peripheral information analysis unit that analyzes peripheral information based on the industry characteristics analyzed by the industry characteristic analysis unit; a world situation analysis unit that analyzes world situations based on the peripheral information analyzed by the peripheral information analysis unit; a payment method prediction unit that predicts the optimal payment method based on the world situation analyzed by the world situation analysis unit; A system characterized by:

2. The industry characteristic analysis unit Learn from past successes and failures and reflect them in the analysis of industry characteristics 2. The system of claim 1.

3. The peripheral information analysis unit Analyze consumers' social media activity to reflect real-time consumer trends 2. The system of claim 1.

4. The world situation analysis unit Analyze news articles and social media posts in real time to reflect the latest trends 2. The system of claim 1.

5. The payment method prediction unit Learn from past implementation cases and their success rates to make more accurate predictions 2. The system of claim 1.

6. The industry characteristic analysis unit Use emotion estimation to analyze consumer sentiment within an industry and offer emotion-based payment options.

2. The system of claim 1.

7. The peripheral information analysis unit Using emotion estimation function, analyze the emotions of surrounding consumers and propose payment methods based on their emotions.

2. The system of claim 1.

8. The world situation analysis unit Using emotion estimation functionality, we analyze consumer sentiment regarding the current state of affairs and propose payment methods based on those sentiments.

2. The system of claim 1.

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