System

Abstract

An object of the system according to the embodiment is to analyze customer data and a purchase history and propose, evaluate, and improve an optimal loyalty program.SOLUTION: A system includes a data collection part, an analysis part, a proposal part, an evaluation part, and an improvement part. The data collection part collects customer data and purchase history. The analysis unit analyzes the customer data and the purchase history collected by the data collection unit. The proposal unit proposes an optimum program or privilege on the basis of the result analyzed by the analysis unit. The evaluation unit evaluates the effect of the program proposed by the proposal unit. The improvement unit improves the program on the basis of a result evaluated by the evaluation 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] Previous technology had the problem of not adequately implementing a process for proposing, evaluating, and improving optimal programs and benefits to maximize the effectiveness of customer loyalty programs.

[0005] The system according to the embodiment aims to analyze customer data and purchase history to propose, evaluate, and improve optimal loyalty programs. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a proposal unit, an evaluation unit, and an improvement unit. The data collection unit collects customer data and purchase history. The analysis unit analyzes the customer data and purchase history collected by the data collection unit. The proposal unit proposes optimal programs and benefits based on the results of the analysis by the analysis unit. The evaluation unit evaluates the effectiveness of the program proposed by the proposal unit. The improvement unit improves the program based on the results of the evaluation by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze customer data and purchase history to propose, evaluate, and improve optimal loyalty programs. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 customer loyalty program optimization system according to an embodiment of the present invention uses AI to analyze customer data and purchase history, propose optimal programs and benefits, evaluate their effectiveness, and make continuous improvements. As a result, the customer loyalty program optimization system can provide benefits tailored to customer preferences, quantitatively and qualitatively evaluate the effectiveness of the program, and make continuous improvements.

[0029] A customer loyalty program optimization system according to an embodiment includes a data collection unit, an analysis unit, a proposal unit, an evaluation unit, and an improvement unit. The data collection unit collects customer data and purchase history. For example, the data collection unit collects data such as a customer's age, gender, address, and purchase history. The data collection unit can also collect online shopping history and point card usage history. For example, the online shopping history may include data such as purchase date and time, purchased items, and purchase amount. The point card usage history may include point acquisition history and usage history. The analysis unit analyzes the customer data and purchase history collected by the data collection unit. For example, the generation AI may analyze customer purchasing patterns using data mining technology. The generation AI may also analyze customer preferences using statistical analysis technology. The generation AI may also predict customer purchases using a machine learning algorithm. For example, the data mining technology may include grouping customers using a clustering algorithm. The statistical analysis technology may include analyzing customer preferences using regression analysis. The machine learning algorithm may include random forests to predict customer purchases. The proposal unit proposes optimal programs and benefits based on the results of the analysis by the analysis unit. For example, the generation AI proposes benefits related to specific products based on the customer's purchasing history. The generation AI can also propose benefits tailored to seasons and events based on the customer's preferences. The generation AI can also customize benefits based on the customer's age and gender. For example, as a benefit related to a specific product, it proposes a discount coupon for that product. As a benefit tailored to seasons and events, it proposes benefits related to Christmas or Valentine's Day. As a benefit based on age and gender, it proposes trendy products for young people and health-related products for seniors. The evaluation unit evaluates the effectiveness of the programs proposed by the proposal unit. For example, the generation AI analyzes the program participation rate and the benefit usage rate to quantitatively evaluate the effectiveness. The generation AI can also analyze customer feedback to qualitatively evaluate the effectiveness. The generation AI can also analyze customers' social media activity to evaluate their response to the program.For example, the program participation rate is calculated as the percentage of customers who participated in a specific program. The reward usage rate is analyzed as the degree to which the provided rewards were used. Customer feedback is analyzed by analyzing survey results and reviews to identify program satisfaction. Customer social media activity is analyzed by analyzing the content of posts and the number of likes to evaluate reactions to the program. The improvement unit improves the program based on the evaluation results by the evaluation unit. For example, the generation AI analyzes program effectiveness data and generates specific proposals for improvement. The generation AI can also analyze customer feedback and generate specific proposals for improvement. The generation AI can also analyze customer social media activity and improve the program based on trends and topics. For example, program effectiveness data is analyzed as an increase in sales and an improvement in customer satisfaction to identify areas for improvement. Customer feedback is analyzed as positive and negative comments to identify areas for improvement. Customer social media activity is provided with rewards related to periods when a specific hashtag is frequently used. As a result, the customer loyalty program optimization system according to the embodiment can provide rewards tailored to customer preferences, quantitatively and qualitatively evaluate the effectiveness of the program, and make continuous improvements. For example, the output unit displays the evaluation results and improvement suggestions via a web or mobile application. If feedback is desired in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending the results directly to customers or marketing personnel.

[0030] The analysis unit can analyze customers' social media activities and identify trends and topics related to their purchasing history. For example, the analysis unit uses generative AI to analyze customers' social media posts and identify trends and topics related to their purchasing history. For example, it finds a tendency for purchases of a particular product to increase during periods when there are more posts about that product. The analysis unit also analyzes social media activities and identifies trends and topics that customers are interested in. For example, purchases of related products increase during periods when a particular hashtag is used frequently. The analysis unit also integrates customers' social media activities with their purchasing history and analyzes the impact of trends and topics on purchasing behavior. For example, purchases of products related to a particular event increase during periods when there are more posts about that event. This enables more effective marketing by analyzing customers' social media activities and identifying trends and topics related to their purchasing history.

[0031] The analysis unit can take into account a customer's life events and predict purchasing patterns based on them. For example, the analysis unit uses generative AI to collect customer life event data and predict purchasing patterns based on that data. For example, it analyzes the tendency to purchase specific products around birthdays. The analysis unit also takes into account a customer's life events and predicts changes in purchasing patterns. For example, it finds a tendency for purchases of household goods to increase after marriage. The analysis unit also integrates life event data with purchase history to analyze the impact of a customer's life events on purchasing behavior. For example, it identifies changes in purchasing patterns in a new area after moving. This allows for more personalized suggestions by taking into account a customer's life events and predicting purchasing patterns based on that information.

[0032] The analysis unit can analyze a customer's health data and identify associations between their health condition and purchasing history. For example, the analysis unit may use generative AI to analyze data from a customer's fitness tracker and identify associations between their health condition and purchasing history. For example, it may find a tendency for purchases of health foods to increase during periods when the amount of exercise increases. The analysis unit may also analyze the health data and identify the impact of a customer's health condition on purchasing behavior. For example, it may analyze a tendency for customers to purchase specific products during periods when their sleep quality improves. The analysis unit may also integrate the health data and purchasing history to analyze how health condition affects purchasing behavior. For example, it may find a tendency for customers to purchase relaxation products during periods when their stress levels are low. This enables effective marketing to health-conscious customers by analyzing a customer's health data and identifying associations between their health condition and purchasing history.

[0033] The analysis unit can analyze customer transportation data and identify stores and services associated with purchase history. For example, using generative AI, the analysis unit can analyze a customer's commute route and movement patterns to identify stores and services associated with purchase history. For example, it can find a tendency for purchases to be made at stores along a specific commute route. The analysis unit can also analyze transportation data to identify the impact that customer movement patterns have on purchasing behavior. For example, it can analyze the tendency for customers with specific movement patterns to use specific services. The analysis unit can also integrate transportation data and purchase history to analyze how movement patterns affect purchasing behavior. For example, it can find a tendency for customers with long commute times to use online shopping. This enables more effective store marketing by analyzing customer transportation data and identifying stores and services associated with purchase history.

[0034] The suggestion unit can predict future purchases based on a customer's purchase history and suggest benefits based on that prediction. The suggestion unit, for example, uses generative AI to analyze a customer's purchase history and make future purchase predictions. For example, it identifies products that are likely to be purchased next based on past purchase patterns. The suggestion unit also predicts a customer's future purchasing behavior based on the purchase history and suggests benefits based on that prediction. For example, it provides benefits related to frequently purchased products. The suggestion unit also integrates the purchase history and future purchase predictions to suggest benefits tailored to the customer's needs in real time. For example, it provides benefits related to products purchased in a specific season. In this way, by making future purchase predictions based on a customer's purchase history and suggesting benefits based on that prediction, it is possible to provide benefits that meet the customer's needs.

[0035] The suggestion unit can analyze a customer's lifestyle data and suggest benefits based on it. For example, the suggestion unit uses generative AI to analyze a customer's lifestyle data and suggest benefits based on it. For example, it can offer benefits related to hobbies. The suggestion unit can also analyze the lifestyle data and suggest benefits based on the customer's interests and concerns. For example, it can offer invitations to events related to specific hobbies. The suggestion unit can also integrate lifestyle data with purchase history to suggest benefits tailored to the customer's lifestyle in real time. For example, it can offer benefits for health-related products to health-conscious customers. In this way, by analyzing a customer's lifestyle data and suggesting benefits based on it, it is possible to provide benefits that match the customer's interests and concerns.

[0036] The suggestion unit can analyze a customer's social media activity and suggest benefits based on trends and topics. For example, the suggestion unit uses generative AI to analyze a customer's social media activity and suggest benefits based on trends and topics. For example, it can offer benefits related to periods when a specific hashtag is frequently used. The suggestion unit also analyzes social media activity and suggests benefits based on trends and topics that interest the customer. For example, it can offer benefits related to a specific event. The suggestion unit also integrates social media data with purchase history to suggest benefits based on trends and topics in real time. For example, it can offer benefits for products related to a specific trend. In this way, by analyzing a customer's social media activity and suggesting benefits based on trends and topics, it is possible to provide benefits that attract the customer's interest.

[0037] The proposal unit can analyze a customer's health data and propose benefits according to their health condition. For example, the proposal unit uses generative AI to analyze a customer's health data and propose benefits according to their health condition. For example, a health food benefit is offered when the amount of exercise increases. The proposal unit also analyzes the health data and proposes benefits based on the customer's health condition. For example, a relaxation benefit is offered when the quality of sleep improves. The proposal unit also integrates health data with purchase history to propose benefits according to their health condition in real time. For example, a relaxation product benefit is offered when stress levels are low. This enables effective marketing to health-conscious customers by analyzing a customer's health data and proposing benefits according to their health condition.

[0038] The evaluation unit can analyze the program participation rate and the benefit usage rate to quantitatively evaluate the effectiveness. The evaluation unit, for example, uses generative AI to analyze the program participation rate and quantitatively evaluate the effectiveness. For example, it calculates the percentage of customers who participated in a specific program. The evaluation unit also analyzes the benefit usage rate and quantitatively evaluates the effectiveness of the program. For example, it analyzes how much the provided benefits were used. The evaluation unit also integrates the program participation rate and the benefit usage rate to quantitatively evaluate the effectiveness of the program. For example, it identifies the correlation between programs with high participation rates and benefits with high usage rates. This makes it possible to objectively grasp the effectiveness of the program by analyzing the program participation rate and the benefit usage rate and quantitatively evaluating the effectiveness.

[0039] The evaluation department can analyze customer feedback and qualitatively evaluate the effectiveness of the program. The evaluation department, for example, uses generative AI to analyze customer feedback and qualitatively evaluate the effectiveness of the program. For example, it analyzes customer comments and reviews and identifies the level of program satisfaction. The evaluation department also analyzes feedback data and evaluates the effectiveness of the program based on customer opinions and impressions. For example, it identifies programs that receive a lot of positive feedback. The evaluation department also integrates the feedback data with program effectiveness data and analyzes how customer opinions affect the effectiveness of the program. For example, it identifies areas for improvement for programs that receive a lot of negative feedback. In this way, by analyzing customer feedback and qualitatively evaluating the effectiveness of the program, it is possible to identify areas for improvement in the program.

[0040] The evaluation department can analyze customers' social media activities and evaluate their reactions to the program. The evaluation department, for example, uses generative AI to analyze customers' social media activities and evaluate their reactions to the program. For example, it analyzes whether the program is more effective during periods when a specific hashtag is used frequently. The evaluation department also analyzes social media activities and identifies how customers are reacting to the program. For example, it identifies programs with many positive comments. The evaluation department also integrates social media data with program effectiveness data and analyzes how customer reactions affect the program's effectiveness. For example, it identifies areas for improvement in programs with many negative comments. In this way, the effectiveness of the program can be understood by analyzing customers' social media activities and evaluating their reactions to the program.

[0041] The evaluation unit can analyze the customer's health data and evaluate the health effects of the program. The evaluation unit, for example, uses generative AI to analyze the customer's health data and evaluate the health effects of the program. For example, it analyzes whether the program is more effective when the amount of exercise increases. The evaluation unit also analyzes the health data and identifies the impact of the customer's health condition on the effectiveness of the program. For example, it analyzes whether the program is more effective when the quality of sleep improves. The evaluation unit also integrates the health data and program effect data to analyze how the health condition affects the effectiveness of the program. For example, it identifies whether the program is more effective when stress levels are low. This enables effective marketing to health-conscious customers by analyzing the customer's health data and evaluating the health effects of the program.

[0042] The improvement department can analyze the effectiveness of the program and generate specific proposals for improvement. For example, the improvement department uses generative AI to analyze program effectiveness data and generate specific proposals for improvement. For example, it makes proposals to review the content of benefits and the method of providing them. The improvement department also analyzes the effectiveness data, identifies areas for improvement in the program, and generates specific proposals. For example, it identifies areas for improvement in programs with low participation rates and proposes improvement measures. The improvement department also integrates the effectiveness data with customer feedback, identifies areas for improvement in the program, and generates specific proposals in real time. For example, it makes proposals to adjust the content of benefits based on the feedback. In this way, by analyzing the effectiveness of the program and generating specific proposals for improvement, it is possible to sustainably improve the effectiveness of the program.

[0043] The improvement department can analyze customer feedback and generate specific proposals for improvement. For example, the improvement department uses generative AI to analyze customer feedback and generate specific proposals for improvement. For example, the improvement department analyzes customer comments and reviews and makes proposals to improve program satisfaction. The improvement department also analyzes feedback data, identifies program improvement areas based on customer opinions and impressions, and generates specific proposals. For example, the improvement department identifies programs with a lot of positive feedback and makes proposals to apply those elements to other programs. The improvement department also integrates feedback data with program effectiveness data, analyzes how customer opinions affect program improvement, and generates specific proposals in real time. For example, the improvement department identifies improvement areas for programs with a lot of negative feedback and proposes improvement measures. In this way, by analyzing customer feedback and generating specific proposals for improvement, the effectiveness of the program can be sustainably improved.

[0044] The improvement department can analyze customers' social media activity and improve the program based on trends and topics. For example, the improvement department uses generative AI to analyze customers' social media activity and improve the program based on trends and topics. For example, providing benefits related to periods when a specific hashtag is used frequently. The improvement department also analyzes social media activity and improves the program based on trends and topics that customers are interested in. For example, providing benefits related to a specific event. The improvement department also integrates social media data with program effectiveness data and improves the program in real time based on trends and topics. For example, providing benefits for products related to a specific trend. In this way, by analyzing customers' social media activity and improving the program based on trends and topics, the effectiveness of the program can be continuously improved.

[0045] The improvement department can analyze customers' health data and improve the program based on their health condition. For example, the improvement department uses generative AI to analyze customers' health data and improve the program based on their health condition. For example, providing a health food perk when the amount of exercise increases. The improvement department also analyzes the health data and improves the program based on the customer's health condition. For example, providing a relaxation perk when the quality of sleep improves. The improvement department also integrates health data and program effect data and improves the program in real time based on the health condition. For example, providing a relaxation product perk when stress levels are low. In this way, analyzing customers' health data and improving the program based on their health condition enables effective marketing to health-conscious customers.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The customer loyalty program optimization system may further include a location information acquisition unit that acquires customer location information. The location information acquisition unit collects location information from customers' smartphones or GPS devices and analyzes their movement patterns and visit frequency. For example, for customers who frequently visit a particular store, it may suggest special offers that can be used at that store. The location information acquisition unit may also provide special offers related to a particular area when a customer is in that area. For example, it may provide discount coupons for tourist attractions or restaurants in a customer's travel destination to a customer who is traveling. This makes it possible to provide more personalized offers by utilizing the customer's location information.

[0048] The analysis unit can analyze customers' social media activities and identify trends and topics related to their purchasing history. For example, by analyzing customers' social media posts, it can find a tendency for purchases of a particular product to increase during periods when there are more posts about that product. It can also analyze that purchases of related products increase during periods when a particular hashtag is used frequently. Furthermore, it can integrate social media activities with purchasing history and analyze the impact of trends and topics on purchasing behavior. This allows for more effective marketing by analyzing customers' social media activities and identifying trends and topics related to their purchasing history.

[0049] The analysis unit can take into account a customer's life events and predict purchasing patterns based on them. For example, it collects life event data such as a customer's birthday or wedding anniversary and predicts purchasing patterns based on that data. It can analyze the tendency to purchase specific products around birthdays. It can also find a tendency for purchases of household goods to increase after marriage. Furthermore, it can integrate life event data with purchase history to analyze how a customer's life events affect purchasing behavior. This allows for more personalized suggestions by taking into account a customer's life events and predicting purchasing patterns based on them.

[0050] The analysis unit can analyze a customer's health data and identify correlations between health conditions and purchasing history. For example, by analyzing data from a customer's fitness tracker, it can find a tendency for purchases of health foods to increase when the amount of exercise increases. It can also analyze the tendency for purchases of specific products to occur when the quality of sleep improves. Furthermore, by integrating health data and purchasing history, it can analyze how health conditions affect purchasing behavior. This makes it possible to analyze a customer's health data and identify correlations between health conditions and purchasing history, enabling effective marketing to health-conscious customers.

[0051] The analysis unit can analyze customer transportation data and identify stores and services related to purchase history. For example, it can analyze a customer's commute route and movement patterns to find out that customers tend to make more purchases at stores along a particular commute route. It can also analyze the tendency for customers with particular movement patterns to use particular services. Furthermore, it can integrate transportation data with purchase history to analyze how movement patterns affect purchasing behavior. This allows for more effective store marketing by analyzing customer transportation data and identifying stores and services related to purchase history.

[0052] The suggestion unit can predict future purchases based on the customer's purchase history and suggest benefits based on that prediction. For example, it can identify products that a customer is likely to purchase next based on their past purchasing patterns. It can also provide benefits related to frequently purchased products. Furthermore, it can integrate the purchase history and future purchase predictions to suggest benefits tailored to the customer's needs in real time. This makes it possible to provide benefits that meet the customer's needs by predicting future purchases based on the customer's purchase history and suggesting benefits based on that prediction.

[0053] The suggestion unit can analyze a customer's lifestyle data and suggest benefits based on that data. For example, it can offer benefits related to the customer's hobbies. It can also offer invitations to specific events based on the customer's interests. Furthermore, it can integrate lifestyle data with purchase history to suggest benefits tailored to the customer's lifestyle in real time. This makes it possible to offer benefits that match the customer's interests by analyzing the customer's lifestyle data and suggesting benefits based on that data.

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

[0055] Step 1: The data collection unit collects customer data and purchase history. For example, it collects data such as the customer's age, gender, address, and purchase history. The data collection unit can also collect online shopping history and point card usage history. For example, as online shopping history, it collects data such as purchase date and time, purchased items, and purchase amount. As point card usage history, it collects point acquisition history and usage history. Step 2: The analysis unit analyzes the customer data and purchase history collected by the data collection unit. For example, the generation AI uses data mining technology to analyze customer purchasing patterns. The generation AI can also use statistical analysis technology to analyze customer preferences. The generation AI can also use machine learning algorithms to predict customer purchases. For example, as a data mining technology, a clustering algorithm is used to group customers. As a statistical analysis technology, regression analysis is used to analyze customer preferences. As a machine learning algorithm, random forests are used to predict customer purchases. Step 3: The proposal unit proposes optimal programs and benefits based on the results of the analysis by the analysis unit. For example, the generation AI proposes benefits related to specific products based on the customer's purchasing history. The generation AI can also propose benefits that match the season or event based on the customer's preferences. The generation AI can also customize benefits based on the customer's age and gender. For example, as a benefit related to a specific product, it proposes a discount coupon for that product. As a benefit that matches the season or event, it proposes benefits related to Christmas or Valentine's Day. As a benefit based on age and gender, it proposes trendy products for young people and health-related products for seniors. Step 4: The evaluation unit evaluates the effectiveness of the program proposed by the proposal unit. For example, the generation AI analyzes the program participation rate and the rate at which benefits are used to quantitatively evaluate the effectiveness. The generation AI can also analyze customer feedback to qualitatively evaluate the effectiveness. The generation AI can also analyze customers' social media activity to evaluate their response to the program. For example, the program participation rate is calculated as the percentage of customers who participated in a specific program. The benefit usage rate is analyzed as to how often the provided benefits were used. Customer feedback is analyzed by analyzing survey results and reviews to identify program satisfaction. Customer social media activity is analyzed by analyzing the content of posts and the number of likes to evaluate their response to the program. Step 5: The improvement department improves the program based on the results of the evaluation by the evaluation department. For example, the generation AI analyzes program effectiveness data and generates specific suggestions for improvement. The generation AI can also analyze customer feedback and generate specific suggestions for improvement. The generation AI can also analyze customers' social media activity and improve the program based on trends and topics. For example, the program effectiveness data can be analyzed to identify areas for improvement, such as increases in sales and improvements in customer satisfaction. The customer feedback can be analyzed to identify areas for improvement, such as analyzing positive and negative comments. The customer's social media activity can be analyzed to identify areas for improvement, such as providing benefits related to periods when a specific hashtag is frequently used.

[0056] (Example 2) The customer loyalty program optimization system according to an embodiment of the present invention uses AI to analyze customer data and purchase history, propose optimal programs and benefits, evaluate their effectiveness, and make continuous improvements. As a result, the customer loyalty program optimization system can provide benefits tailored to customer preferences, quantitatively and qualitatively evaluate the effectiveness of the program, and make continuous improvements.

[0057] A customer loyalty program optimization system according to an embodiment includes a data collection unit, an analysis unit, a proposal unit, an evaluation unit, and an improvement unit. The data collection unit collects customer data and purchase history. For example, the data collection unit collects data such as a customer's age, gender, address, and purchase history. The data collection unit can also collect online shopping history and point card usage history. For example, the online shopping history may include data such as purchase date and time, purchased items, and purchase amount. The point card usage history may include point acquisition history and usage history. The analysis unit analyzes the customer data and purchase history collected by the data collection unit. For example, the generation AI may analyze customer purchasing patterns using data mining technology. The generation AI may also analyze customer preferences using statistical analysis technology. The generation AI may also predict customer purchases using a machine learning algorithm. For example, the data mining technology may include grouping customers using a clustering algorithm. The statistical analysis technology may include analyzing customer preferences using regression analysis. The machine learning algorithm may include random forests to predict customer purchases. The proposal unit proposes optimal programs and benefits based on the results of the analysis by the analysis unit. For example, the generation AI proposes benefits related to specific products based on the customer's purchasing history. The generation AI can also propose benefits tailored to seasons and events based on the customer's preferences. The generation AI can also customize benefits based on the customer's age and gender. For example, as a benefit related to a specific product, it proposes a discount coupon for that product. As a benefit tailored to seasons and events, it proposes benefits related to Christmas or Valentine's Day. As a benefit based on age and gender, it proposes trendy products for young people and health-related products for seniors. The evaluation unit evaluates the effectiveness of the programs proposed by the proposal unit. For example, the generation AI analyzes the program participation rate and the benefit usage rate to quantitatively evaluate the effectiveness. The generation AI can also analyze customer feedback to qualitatively evaluate the effectiveness. The generation AI can also analyze customers' social media activity to evaluate their response to the program.For example, the program participation rate is calculated as the percentage of customers who participated in a specific program. The reward usage rate is analyzed as the degree to which the provided rewards were used. Customer feedback is analyzed by analyzing survey results and reviews to identify program satisfaction. Customer social media activity is analyzed by analyzing the content of posts and the number of likes to evaluate reactions to the program. The improvement unit improves the program based on the evaluation results by the evaluation unit. For example, the generation AI analyzes program effectiveness data and generates specific proposals for improvement. The generation AI can also analyze customer feedback and generate specific proposals for improvement. The generation AI can also analyze customer social media activity and improve the program based on trends and topics. For example, program effectiveness data is analyzed as an increase in sales and an improvement in customer satisfaction to identify areas for improvement. Customer feedback is analyzed as positive and negative comments to identify areas for improvement. Customer social media activity is provided with rewards related to periods when a specific hashtag is frequently used. As a result, the customer loyalty program optimization system according to the embodiment can provide rewards tailored to customer preferences, quantitatively and qualitatively evaluate the effectiveness of the program, and make continuous improvements. For example, the output unit displays the evaluation results and improvement suggestions via a web or mobile application. If feedback is desired in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending the results directly to customers or marketing personnel.

[0058] The analysis unit can analyze customer emotional data and associate emotional fluctuations with purchase history. For example, the analysis unit uses generative AI to extract emotional data from customer reviews and feedback and associates the emotional fluctuations with purchase history. For example, it analyzes the purchasing patterns of customers who purchased products with many positive reviews. The analysis unit also analyzes customer emotional data and identifies the impact of emotional fluctuations on purchasing behavior. For example, it finds a tendency for purchases to decrease during periods of many negative feedback. The analysis unit also integrates emotional data with purchase history to analyze how a customer's emotional state affects purchasing behavior. For example, it can propose specific benefits to customers with large emotional fluctuations. This enables more precise targeting by analyzing customer emotional data and associating emotional fluctuations with purchase history.

[0059] The analysis unit can analyze customers' social media activities and identify trends and topics related to their purchasing history. For example, the analysis unit uses generative AI to analyze customers' social media posts and identify trends and topics related to their purchasing history. For example, it finds a tendency for purchases of a particular product to increase during periods when there are more posts about that product. The analysis unit also analyzes social media activities and identifies trends and topics that customers are interested in. For example, purchases of related products increase during periods when a particular hashtag is used frequently. The analysis unit also integrates customers' social media activities with their purchasing history and analyzes the impact of trends and topics on purchasing behavior. For example, purchases of products related to a particular event increase during periods when there are more posts about that event. This enables more effective marketing by analyzing customers' social media activities and identifying trends and topics related to their purchasing history.

[0060] The analysis unit can take into account a customer's life events and predict purchasing patterns based on them. For example, the analysis unit uses generative AI to collect customer life event data and predict purchasing patterns based on that data. For example, it analyzes the tendency to purchase specific products around birthdays. The analysis unit also takes into account a customer's life events and predicts changes in purchasing patterns. For example, it finds a tendency for purchases of household goods to increase after marriage. The analysis unit also integrates life event data with purchase history to analyze the impact of a customer's life events on purchasing behavior. For example, it identifies changes in purchasing patterns in a new area after moving. This allows for more personalized suggestions by taking into account a customer's life events and predicting purchasing patterns based on that information.

[0061] The analysis unit can analyze a customer's health data and identify associations between their health condition and purchasing history. For example, the analysis unit may use generative AI to analyze data from a customer's fitness tracker and identify associations between their health condition and purchasing history. For example, it may find a tendency for purchases of health foods to increase during periods when the amount of exercise increases. The analysis unit may also analyze the health data and identify the impact of a customer's health condition on purchasing behavior. For example, it may analyze a tendency for customers to purchase specific products during periods when their sleep quality improves. The analysis unit may also integrate the health data and purchasing history to analyze how health condition affects purchasing behavior. For example, it may find a tendency for customers to purchase relaxation products during periods when their stress levels are low. This enables effective marketing to health-conscious customers by analyzing a customer's health data and identifying associations between their health condition and purchasing history.

[0062] The analysis unit can analyze customer transportation data and identify stores and services associated with purchase history. For example, using generative AI, the analysis unit can analyze a customer's commute route and movement patterns to identify stores and services associated with purchase history. For example, it can find a tendency for purchases to be made at stores along a specific commute route. The analysis unit can also analyze transportation data to identify the impact that customer movement patterns have on purchasing behavior. For example, it can analyze the tendency for customers with specific movement patterns to use specific services. The analysis unit can also integrate transportation data and purchase history to analyze how movement patterns affect purchasing behavior. For example, it can find a tendency for customers with long commute times to use online shopping. This enables more effective store marketing by analyzing customer transportation data and identifying stores and services associated with purchase history.

[0063] The analysis unit can use the emotion estimation function to analyze a customer's emotional state in real time and identify purchasing patterns that correspond to emotional fluctuations. The analysis unit, for example, uses generative AI to analyze a customer's emotional state in real time and identify purchasing patterns that correspond to emotional fluctuations. For example, it can find a tendency to purchase specific products when in a positive emotional state. The analysis unit also uses the emotion estimation function to analyze in real time the impact of a customer's emotional state on purchasing behavior. For example, it can identify a tendency for purchases to decrease when in a negative emotional state. The analysis unit also integrates emotion data with purchase history to analyze in real time how emotional fluctuations affect purchasing behavior. For example, it can propose specific benefits during periods of significant emotional fluctuation. This enables more effective targeting by analyzing a customer's emotional state in real time and identifying purchasing patterns that correspond to emotional fluctuations.

[0064] The suggestion unit can analyze the customer's emotional data and suggest benefits according to emotional fluctuations. For example, the suggestion unit uses generative AI to analyze the customer's emotional data and suggest benefits according to emotional fluctuations. For example, a specific benefit is offered when the customer is in a positive emotional state. The suggestion unit also analyzes the emotional data and identifies the impact that the customer's emotional state has on the acceptance of benefits. For example, a relaxation benefit is offered when the customer is in a negative emotional state. The suggestion unit also integrates the emotional data with purchase history to suggest benefits in real time according to emotional fluctuations. For example, a specific benefit is offered during periods of significant emotional fluctuation. In this way, customer satisfaction can be improved by analyzing the customer's emotional data and suggesting benefits according to emotional fluctuations.

[0065] The suggestion unit can predict future purchases based on a customer's purchase history and suggest benefits based on that prediction. The suggestion unit, for example, uses generative AI to analyze a customer's purchase history and make future purchase predictions. For example, it identifies products that are likely to be purchased next based on past purchase patterns. The suggestion unit also predicts a customer's future purchasing behavior based on the purchase history and suggests benefits based on that prediction. For example, it provides benefits related to frequently purchased products. The suggestion unit also integrates the purchase history and future purchase predictions to suggest benefits tailored to the customer's needs in real time. For example, it provides benefits related to products purchased in a specific season. In this way, by making future purchase predictions based on a customer's purchase history and suggesting benefits based on that prediction, it is possible to provide benefits that meet the customer's needs.

[0066] The suggestion unit can analyze a customer's lifestyle data and suggest benefits based on it. For example, the suggestion unit uses generative AI to analyze a customer's lifestyle data and suggest benefits based on it. For example, it can offer benefits related to hobbies. The suggestion unit can also analyze the lifestyle data and suggest benefits based on the customer's interests and concerns. For example, it can offer invitations to events related to specific hobbies. The suggestion unit can also integrate lifestyle data with purchase history to suggest benefits tailored to the customer's lifestyle in real time. For example, it can offer benefits for health-related products to health-conscious customers. In this way, by analyzing a customer's lifestyle data and suggesting benefits based on it, it is possible to provide benefits that match the customer's interests and concerns.

[0067] The suggestion unit can analyze a customer's social media activity and suggest benefits based on trends and topics. For example, the suggestion unit uses generative AI to analyze a customer's social media activity and suggest benefits based on trends and topics. For example, it can offer benefits related to periods when a specific hashtag is frequently used. The suggestion unit also analyzes social media activity and suggests benefits based on trends and topics that interest the customer. For example, it can offer benefits related to a specific event. The suggestion unit also integrates social media data with purchase history to suggest benefits based on trends and topics in real time. For example, it can offer benefits for products related to a specific trend. In this way, by analyzing a customer's social media activity and suggesting benefits based on trends and topics, it is possible to provide benefits that attract the customer's interest.

[0068] The proposal unit can analyze a customer's health data and propose benefits according to their health condition. For example, the proposal unit uses generative AI to analyze a customer's health data and propose benefits according to their health condition. For example, a health food benefit is offered when the amount of exercise increases. The proposal unit also analyzes the health data and proposes benefits based on the customer's health condition. For example, a relaxation benefit is offered when the quality of sleep improves. The proposal unit also integrates health data with purchase history to propose benefits according to their health condition in real time. For example, a relaxation product benefit is offered when stress levels are low. This enables effective marketing to health-conscious customers by analyzing a customer's health data and proposing benefits according to their health condition.

[0069] The suggestion unit can use the emotion estimation function to suggest benefits in real time according to the customer's emotional state. The suggestion unit, for example, uses generative AI to analyze the customer's emotional state in real time and suggest benefits according to emotional fluctuations. For example, a specific benefit is offered when the emotional state is positive. The suggestion unit also uses the emotion estimation function to analyze in real time the impact that the customer's emotional state has on the acceptance of benefits. For example, a relaxation benefit is offered when the emotional state is negative. The suggestion unit also integrates emotion data with purchase history to suggest benefits in real time according to emotional fluctuations. For example, a specific benefit is offered during periods of large emotional fluctuations. In this way, customer satisfaction can be improved by suggesting benefits in real time according to the customer's emotional state.

[0070] The evaluation unit can analyze customer emotional data and evaluate the effectiveness of the program based on emotional fluctuations. The evaluation unit, for example, uses generative AI to analyze customer emotional data and evaluate the effectiveness of the program based on emotional fluctuations. For example, it analyzes whether the program participation rate is high when the customer is in a positive emotional state. The evaluation unit also analyzes the emotional data and identifies the impact that the customer's emotional state has on the effectiveness of the program. For example, it analyzes whether the benefit usage rate is low when the customer is in a negative emotional state. The evaluation unit also integrates the emotional data with program effectiveness data and analyzes how emotional fluctuations affect the effectiveness of the program. For example, it identifies whether the program is more effective during periods of greater emotional fluctuation. In this way, by analyzing customer emotional data and evaluating the effectiveness of the program based on emotional fluctuations, the effectiveness of the program can be more accurately understood.

[0071] The evaluation unit can analyze the program participation rate and the benefit usage rate to quantitatively evaluate the effectiveness. The evaluation unit, for example, uses generative AI to analyze the program participation rate and quantitatively evaluate the effectiveness. For example, it calculates the percentage of customers who participated in a specific program. The evaluation unit also analyzes the benefit usage rate and quantitatively evaluates the effectiveness of the program. For example, it analyzes how much the provided benefits were used. The evaluation unit also integrates the program participation rate and the benefit usage rate to quantitatively evaluate the effectiveness of the program. For example, it identifies the correlation between programs with high participation rates and benefits with high usage rates. This makes it possible to objectively grasp the effectiveness of the program by analyzing the program participation rate and the benefit usage rate and quantitatively evaluating the effectiveness.

[0072] The evaluation department can analyze customer feedback and qualitatively evaluate the effectiveness of the program. The evaluation department, for example, uses generative AI to analyze customer feedback and qualitatively evaluate the effectiveness of the program. For example, it analyzes customer comments and reviews and identifies the level of program satisfaction. The evaluation department also analyzes feedback data and evaluates the effectiveness of the program based on customer opinions and impressions. For example, it identifies programs that receive a lot of positive feedback. The evaluation department also integrates the feedback data with program effectiveness data and analyzes how customer opinions affect the effectiveness of the program. For example, it identifies areas for improvement for programs that receive a lot of negative feedback. In this way, by analyzing customer feedback and qualitatively evaluating the effectiveness of the program, it is possible to identify areas for improvement in the program.

[0073] The evaluation department can analyze customers' social media activities and evaluate their reactions to the program. The evaluation department, for example, uses generative AI to analyze customers' social media activities and evaluate their reactions to the program. For example, it analyzes whether the program is more effective during periods when a specific hashtag is used frequently. The evaluation department also analyzes social media activities and identifies how customers are reacting to the program. For example, it identifies programs with many positive comments. The evaluation department also integrates social media data with program effectiveness data and analyzes how customer reactions affect the program's effectiveness. For example, it identifies areas for improvement in programs with many negative comments. In this way, the effectiveness of the program can be understood by analyzing customers' social media activities and evaluating their reactions to the program.

[0074] The evaluation unit can analyze the customer's health data and evaluate the health effects of the program. The evaluation unit, for example, uses generative AI to analyze the customer's health data and evaluate the health effects of the program. For example, it analyzes whether the program is more effective when the amount of exercise increases. The evaluation unit also analyzes the health data and identifies the impact of the customer's health condition on the effectiveness of the program. For example, it analyzes whether the program is more effective when the quality of sleep improves. The evaluation unit also integrates the health data and program effect data to analyze how the health condition affects the effectiveness of the program. For example, it identifies whether the program is more effective when stress levels are low. This enables effective marketing to health-conscious customers by analyzing the customer's health data and evaluating the health effects of the program.

[0075] The evaluation unit can use the emotion estimation function to evaluate the effectiveness of the program in real time based on the customer's emotional state. The evaluation unit, for example, uses generative AI to analyze the customer's emotional state in real time and evaluate the effectiveness of the program based on emotional fluctuations. For example, it analyzes whether the program participation rate is high when the customer is in a positive emotional state. The evaluation unit also uses the emotion estimation function to analyze in real time the impact of the customer's emotional state on the effectiveness of the program. For example, it analyzes whether the benefit usage rate is low when the customer is in a negative emotional state. The evaluation unit also integrates the emotion data and the program effectiveness data to analyze in real time how emotional fluctuations affect the effectiveness of the program. For example, it identifies whether the program is effective during periods of large emotional fluctuations. This allows the effectiveness of the program to be more accurately understood by evaluating the effectiveness of the program in real time based on the customer's emotional state.

[0076] The improvement department can analyze customer emotional data and improve the program based on emotional fluctuations. For example, using generative AI, the improvement department analyzes customer emotional data and improves the program based on emotional fluctuations. For example, providing a specific reward when the customer is in a positive emotional state. The improvement department also analyzes the emotional data and identifies the impact that the customer's emotional state has on program improvement. For example, proposing a relaxation reward when the customer is in a negative emotional state. The improvement department also integrates the emotional data with program effectiveness data and improves the program in real time based on emotional fluctuations. For example, providing a specific reward during periods of large emotional fluctuations. In this way, by analyzing customer emotional data and improving the program based on emotional fluctuations, the effectiveness of the program can be continuously improved.

[0077] The improvement department can analyze the effectiveness of the program and generate specific proposals for improvement. For example, the improvement department uses generative AI to analyze program effectiveness data and generate specific proposals for improvement. For example, it makes proposals to review the content of benefits and the method of providing them. The improvement department also analyzes the effectiveness data, identifies areas for improvement in the program, and generates specific proposals. For example, it identifies areas for improvement in programs with low participation rates and proposes improvement measures. The improvement department also integrates the effectiveness data with customer feedback, identifies areas for improvement in the program, and generates specific proposals in real time. For example, it makes proposals to adjust the content of benefits based on the feedback. In this way, by analyzing the effectiveness of the program and generating specific proposals for improvement, it is possible to sustainably improve the effectiveness of the program.

[0078] The improvement department can analyze customer feedback and generate specific proposals for improvement. For example, the improvement department uses generative AI to analyze customer feedback and generate specific proposals for improvement. For example, the improvement department analyzes customer comments and reviews and makes proposals to improve program satisfaction. The improvement department also analyzes feedback data, identifies program improvement areas based on customer opinions and impressions, and generates specific proposals. For example, the improvement department identifies programs with a lot of positive feedback and makes proposals to apply those elements to other programs. The improvement department also integrates feedback data with program effectiveness data, analyzes how customer opinions affect program improvement, and generates specific proposals in real time. For example, the improvement department identifies improvement areas for programs with a lot of negative feedback and proposes improvement measures. In this way, by analyzing customer feedback and generating specific proposals for improvement, the effectiveness of the program can be sustainably improved.

[0079] The improvement department can analyze customers' social media activity and improve the program based on trends and topics. For example, the improvement department uses generative AI to analyze customers' social media activity and improve the program based on trends and topics. For example, providing benefits related to periods when a specific hashtag is used frequently. The improvement department also analyzes social media activity and improves the program based on trends and topics that customers are interested in. For example, providing benefits related to a specific event. The improvement department also integrates social media data with program effectiveness data and improves the program in real time based on trends and topics. For example, providing benefits for products related to a specific trend. In this way, by analyzing customers' social media activity and improving the program based on trends and topics, the effectiveness of the program can be continuously improved.

[0080] The improvement department can analyze customers' health data and improve the program based on their health condition. For example, the improvement department uses generative AI to analyze customers' health data and improve the program based on their health condition. For example, providing a health food perk when the amount of exercise increases. The improvement department also analyzes the health data and improves the program based on the customer's health condition. For example, providing a relaxation perk when the quality of sleep improves. The improvement department also integrates health data and program effect data and improves the program in real time based on the health condition. For example, providing a relaxation product perk when stress levels are low. In this way, analyzing customers' health data and improving the program based on their health condition enables effective marketing to health-conscious customers.

[0081] The improvement department can use the emotion estimation function to improve the program in real time based on the customer's emotional state. For example, the improvement department can use generative AI to analyze the customer's emotional state in real time and improve the program based on emotional fluctuations. For example, a specific benefit can be provided when the customer is in a positive emotional state. The improvement department can also use the emotion estimation function to analyze in real time the impact of the customer's emotional state on program improvement. For example, a relaxation benefit can be suggested when the customer is in a negative emotional state. The improvement department can also integrate the emotion data with program effectiveness data to improve the program in real time based on emotional fluctuations. For example, a specific benefit can be provided during periods of large emotional fluctuations. In this way, by improving the program in real time based on the customer's emotional state, the effectiveness of the program can be continuously improved.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The customer loyalty program optimization system may further include a location information acquisition unit that acquires customer location information. The location information acquisition unit collects location information from customers' smartphones or GPS devices and analyzes their movement patterns and visit frequency. For example, for customers who frequently visit a particular store, it may suggest special offers that can be used at that store. The location information acquisition unit may also provide special offers related to a particular area when a customer is in that area. For example, it may provide discount coupons for tourist attractions or restaurants in a customer's travel destination to a customer who is traveling. This makes it possible to provide more personalized offers by utilizing the customer's location information.

[0084] The analysis unit can analyze customer emotional data and associate emotional fluctuations with purchase history. For example, it can extract emotional data from customer reviews and feedback and associate the emotional fluctuations with purchase history. It can analyze the purchasing patterns of customers who purchased products with many positive reviews. It can also find a trend of decreased purchases during periods of high negative feedback. Furthermore, it can integrate emotional data and purchase history to analyze how a customer's emotional state affects purchasing behavior. This allows for more accurate targeting by analyzing customer emotional data and associating emotional fluctuations with purchase history.

[0085] The analysis unit can analyze customers' social media activities and identify trends and topics related to their purchasing history. For example, by analyzing customers' social media posts, it can find a tendency for purchases of a particular product to increase during periods when there are more posts about that product. It can also analyze that purchases of related products increase during periods when a particular hashtag is used frequently. Furthermore, it can integrate social media activities with purchasing history and analyze the impact of trends and topics on purchasing behavior. This allows for more effective marketing by analyzing customers' social media activities and identifying trends and topics related to their purchasing history.

[0086] The analysis unit can take into account a customer's life events and predict purchasing patterns based on them. For example, it collects life event data such as a customer's birthday or wedding anniversary and predicts purchasing patterns based on that data. It can analyze the tendency to purchase specific products around birthdays. It can also find a tendency for purchases of household goods to increase after marriage. Furthermore, it can integrate life event data with purchase history to analyze how a customer's life events affect purchasing behavior. This allows for more personalized suggestions by taking into account a customer's life events and predicting purchasing patterns based on them.

[0087] The analysis unit can analyze a customer's health data and identify correlations between health conditions and purchasing history. For example, by analyzing data from a customer's fitness tracker, it can find a tendency for purchases of health foods to increase when the amount of exercise increases. It can also analyze the tendency for purchases of specific products to occur when the quality of sleep improves. Furthermore, by integrating health data and purchasing history, it can analyze how health conditions affect purchasing behavior. This makes it possible to analyze a customer's health data and identify correlations between health conditions and purchasing history, enabling effective marketing to health-conscious customers.

[0088] The analysis unit can analyze customer transportation data and identify stores and services related to purchase history. For example, it can analyze a customer's commute route and movement patterns to find out that customers tend to make more purchases at stores along a particular commute route. It can also analyze the tendency for customers with particular movement patterns to use particular services. Furthermore, it can integrate transportation data with purchase history to analyze how movement patterns affect purchasing behavior. This allows for more effective store marketing by analyzing customer transportation data and identifying stores and services related to purchase history.

[0089] The analysis unit uses the emotion estimation function to analyze a customer's emotional state in real time and identify purchasing patterns that correspond to emotional fluctuations. For example, by analyzing a customer's emotional state in real time, it is possible to find a tendency for them to purchase certain products when they are in a positive emotional state. It is also possible to identify a tendency for purchases to decrease when they are in a negative emotional state. Furthermore, by integrating emotional data with purchase history, it is possible to analyze in real time how emotional fluctuations affect purchasing behavior. This allows for more effective targeting by analyzing a customer's emotional state in real time and identifying purchasing patterns that correspond to emotional fluctuations.

[0090] The suggestion unit can analyze the customer's emotional data and suggest benefits according to emotional fluctuations. For example, by analyzing the customer's emotional data, it can provide a specific benefit when the customer is in a positive emotional state. It can also suggest a relaxation benefit when the customer is in a negative emotional state. Furthermore, it can integrate the emotional data with the purchase history and suggest benefits in real time according to emotional fluctuations. In this way, customer satisfaction can be improved by analyzing the customer's emotional data and suggesting benefits according to emotional fluctuations.

[0091] The suggestion unit can predict future purchases based on the customer's purchase history and suggest benefits based on that prediction. For example, it can identify products that a customer is likely to purchase next based on their past purchasing patterns. It can also provide benefits related to frequently purchased products. Furthermore, it can integrate the purchase history and future purchase predictions to suggest benefits tailored to the customer's needs in real time. This makes it possible to provide benefits that meet the customer's needs by predicting future purchases based on the customer's purchase history and suggesting benefits based on that prediction.

[0092] The suggestion unit can analyze a customer's lifestyle data and suggest benefits based on that data. For example, it can offer benefits related to the customer's hobbies. It can also offer invitations to specific events based on the customer's interests. Furthermore, it can integrate lifestyle data with purchase history to suggest benefits tailored to the customer's lifestyle in real time. This makes it possible to offer benefits that match the customer's interests by analyzing the customer's lifestyle data and suggesting benefits based on that data.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The data collection unit collects customer data and purchase history. For example, it collects data such as the customer's age, gender, address, and purchase history. The data collection unit can also collect online shopping history and point card usage history. For example, as online shopping history, it collects data such as purchase date and time, purchased items, and purchase amount. As point card usage history, it collects point acquisition history and usage history. Step 2: The analysis unit analyzes the customer data and purchase history collected by the data collection unit. For example, the generation AI uses data mining technology to analyze customer purchasing patterns. The generation AI can also use statistical analysis technology to analyze customer preferences. The generation AI can also use machine learning algorithms to predict customer purchases. For example, as a data mining technology, a clustering algorithm is used to group customers. As a statistical analysis technology, regression analysis is used to analyze customer preferences. As a machine learning algorithm, random forests are used to predict customer purchases. Step 3: The proposal unit proposes optimal programs and benefits based on the results of the analysis by the analysis unit. For example, the generation AI proposes benefits related to specific products based on the customer's purchasing history. The generation AI can also propose benefits that match the season or event based on the customer's preferences. The generation AI can also customize benefits based on the customer's age and gender. For example, as a benefit related to a specific product, it proposes a discount coupon for that product. As a benefit that matches the season or event, it proposes benefits related to Christmas or Valentine's Day. As a benefit based on age and gender, it proposes trendy products for young people and health-related products for seniors. Step 4: The evaluation unit evaluates the effectiveness of the program proposed by the proposal unit. For example, the generation AI analyzes the program participation rate and the rate at which benefits are used to quantitatively evaluate the effectiveness. The generation AI can also analyze customer feedback to qualitatively evaluate the effectiveness. The generation AI can also analyze customers' social media activity to evaluate their response to the program. For example, the program participation rate is calculated as the percentage of customers who participated in a specific program. The benefit usage rate is analyzed as to how often the provided benefits were used. Customer feedback is analyzed by analyzing survey results and reviews to identify program satisfaction. Customer social media activity is analyzed by analyzing the content of posts and the number of likes to evaluate their response to the program. Step 5: The improvement department improves the program based on the results of the evaluation by the evaluation department. For example, the generation AI analyzes program effectiveness data and generates specific suggestions for improvement. The generation AI can also analyze customer feedback and generate specific suggestions for improvement. The generation AI can also analyze customers' social media activity and improve the program based on trends and topics. For example, the program effectiveness data can be analyzed to identify areas for improvement, such as increases in sales and improvements in customer satisfaction. The customer feedback can be analyzed to identify areas for improvement, such as analyzing positive and negative comments. The customer's social media activity can be analyzed to identify areas for improvement, such as providing benefits related to periods when a specific hashtag is frequently used.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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. a data collection unit that collects customer data and purchase history; an analysis unit that analyzes the customer data and the purchase history collected by the data collection unit; a proposal unit that proposes optimal programs and benefits based on the results of the analysis by the analysis unit; an evaluation unit that evaluates the effectiveness of the program proposed by the proposal unit; an improvement unit that improves the program based on the results of the evaluation by the evaluation unit. A system characterized by:

2. The analysis unit Analyze customer emotion data and associate emotion fluctuations with the purchase history 2. The system of claim 1.

3. The analysis unit Analyzing customers' social media activity to identify trends and topics related to said purchase history 2. The system of claim 1.

4. The analysis unit Consider customer life events and predict purchasing patterns based on them 2. The system of claim 1.

5. The analysis unit Analyzing the customer's health data and identifying the relationship between the health condition and the purchase history 2. The system of claim 1.

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