system

Abstract

The system according to this embodiment aims to automatically provide personalized communication based on customer behavior data and lifecycle data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects customer behavior data and lifecycle data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates messages and coupons based on the data analyzed by the analysis unit. The distribution unit automatically distributes the messages and coupons generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, personalized communication based on customer behavior data or life cycle data has not been sufficiently carried out automatically, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically perform personalized communication based on customer behavior data or life cycle data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects customer behavior data and lifecycle data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates messages and coupons based on the data analyzed by the analysis unit. The distribution unit automatically distributes the messages and coupons generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically perform personalized communication based on customer behavior data and lifecycle data. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The personalized communication system according to an embodiment of the present invention is a system that automatically performs personalized communication based on customer behavior data and lifecycle using AI. This system collects customer behavior data and lifecycle data, analyzes it with AI to generate optimal messages and coupons for each customer, and automatically delivers them through a messaging platform. This enhances real-time engagement with customers, increases customer loyalty by providing coupons and loyalty programs at the optimal time, and promotes repeat purchases and engagement. For example, when collecting customer behavior data and lifecycle data, all data related to the customer is collected, such as purchase history, ad click data, and chat data from the messaging platform. For example, data such as which products a customer purchased, which ads they clicked, and what messages they sent and received on the messaging platform are collected. Next, the AI ​​analyzes the collected data. Based on the collected data, the AI ​​analyzes the customer's behavior patterns and lifecycle and generates optimal messages and coupons for each customer. For example, the AI ​​analyzes purchase history and generates coupons related to a specific product for customers who have purchased that product. It is also possible for the AI ​​to analyze lifecycle data and generate messages tailored to birthdays or special events. The generated messages and coupons are automatically delivered through the messaging platform. For example, AI-generated birthday messages and coupons are automatically sent to customers on their birthdays. Follow-up messages are also automatically sent after a purchase, sometimes offering campaign information and coupons to encourage future purchases. This system enhances real-time engagement with customers. Customers receive messages and coupons at the most opportune time for them, creating a sense of exclusivity. This increases customer loyalty and encourages repeat purchases and engagement. For instance, offering coupons related to a specific product to a customer who has purchased it can encourage repeat purchases.Furthermore, sending messages tailored to birthdays and special events deepens customer relationships and improves engagement. In addition, automating coupons and loyalty programs encourages customer re-engagement. For example, AI can send discount coupons or exclusive offers to customers who haven't made a purchase for a certain period, guiding them back to stores or online shops. This prevents customer churn and encourages repeat purchases. In this way, leveraging AI to automatically deliver personalized communications based on customer behavior data and lifecycle data strengthens real-time engagement with customers and ensures coupons and loyalty programs are delivered at the optimal time. This increases customer loyalty and encourages repeat purchases and engagement. Thus, personalized communication systems can automatically deliver personalized communications based on customer behavior data and lifecycle data.

[0029] The personalized communication system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects customer behavior data and lifecycle data. The collection unit can collect, for example, purchase history, ad click data, and messaging platform chat data. For example, the collection unit collects data such as which products a customer purchased, which ads they clicked, and what messages they sent and received on the messaging platform. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze customer behavior patterns and lifecycles based on the collected data. For example, the analysis unit can analyze purchase history and generate coupons related to specific products for customers who have purchased those products. The analysis unit can also analyze lifecycle data and generate messages tailored to birthdays or special events. The generation unit generates messages and coupons based on the data analyzed by the analysis unit. For example, the generation unit can generate optimal messages and coupons for each customer based on the analysis results. For example, the generation unit uses AI to generate messages and coupons based on customer behavior patterns and lifecycles. The distribution unit automatically distributes the messages and coupons generated by the generation unit. The distribution unit can, for example, automatically distribute messages and coupons through a messaging platform. For instance, the distribution unit can automatically send AI-generated birthday messages and coupons to customers on their birthdays. The distribution unit can also automatically send follow-up messages after a product purchase, providing campaign information and coupons to encourage future purchases. This allows the personalized communication system according to the embodiment to automatically perform personalized communication based on customer behavior data and lifecycle data. Some or all of the above-described processes in the collection unit, analysis unit, generation unit, and distribution unit may be performed using AI, for example, or without AI.For example, the collection unit inputs customer behavior data and lifecycle data into the AI, the AI ​​analyzes the data, the generation unit generates messages and coupons based on the analysis results, and the distribution unit automatically distributes them.

[0030] The data collection unit collects customer behavior and lifecycle data. For example, it can collect purchase history, ad click data, and messaging platform chat data. Specifically, it collects data such as which products customers purchased, which ads they clicked, and what messages they sent and received on messaging platforms. This allows for a detailed understanding of customer behavior patterns and interests. The data collection unit monitors customer online behavior in real time using website tracking tools and in-app event tracking. For example, it collects data such as the number of times a customer viewed a specific product page, the time spent on that page, which items were added to their cart, and which items were not purchased. Offline purchase data can also be integrated, including in-store purchase history and loyalty card usage history. Furthermore, the data collection unit also collects social media data to understand what customers post and which brands and products they mention. This allows for a deeper understanding of customer preferences and trends. The collected data is stored in a secure database and managed for access by the analysis and generation units. The frequency and scope of data collection are adjusted based on system settings and customer consent. This allows the data collection unit to efficiently and comprehensively collect customer behavioral data and lifecycle data, providing a foundation for personalized communication.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze customer behavior patterns and lifecycles based on the collected data. Specifically, the analysis unit analyzes purchase history and generates coupons related to specific products for customers who have purchased those products. The analysis unit can also analyze lifecycle data and generate messages tailored to birthdays and special events. The analysis unit uses AI to analyze data and gain a detailed understanding of customer behavior patterns and lifecycles. For example, it uses machine learning algorithms to predict products that customers are most likely to purchase next based on their purchase history. It also uses natural language processing technology to analyze chat data from messaging platforms and extract customer interests and needs. Furthermore, the analysis unit uses clustering algorithms to classify customers into groups with similar behavior patterns and lifecycles. This allows for the provision of optimal messages and coupons to each group. The analysis unit can analyze data in real time and respond quickly to changes in customer behavior and lifecycles. For example, if a customer starts frequently purchasing a particular product, it can instantly generate new coupons related to that product. Also, if a customer's birthday or a special event is approaching, it can prepare appropriate messages in advance and deliver them at the right time. This allows the analytics unit to effectively analyze customer behavior data and lifecycle data, providing a foundation for personalized communication.

[0032] The generation unit generates messages and coupons based on data analyzed by the analysis unit. For example, the generation unit can generate optimal messages and coupons for each customer based on the analysis results. Specifically, the generation unit uses AI to generate messages and coupons based on customer behavior patterns and lifecycles. For example, it uses natural language generation technology to automatically create personalized messages for customers. This allows for consistent and individually optimized communication for customers. The generation unit considers customer purchase history and behavior patterns to generate highly relevant coupons. For example, it provides discount coupons related to products to customers who frequently purchase specific items. It can also generate special messages and coupons for birthdays and anniversaries in line with customer lifecycle events. The generation unit uses AI to analyze customer behavior data and lifecycle data to generate messages and coupons at the optimal time. For example, if a customer adds a specific item to their cart but does not purchase it, it generates a coupon related to that item and sends a message encouraging purchase. Also, if a customer clicks on a specific advertisement, it generates a message providing information about products or services related to that advertisement. This allows the generation unit to effectively generate personalized messages and coupons based on customer behavior data and lifecycle data, thereby improving customer engagement.

[0033] The distribution unit automatically distributes messages and coupons generated by the generation unit. For example, the distribution unit can automatically distribute messages and coupons through messaging platforms. Specifically, it can automatically send AI-generated birthday messages and coupons to customers on their birthdays. It can also automatically send follow-up messages after a purchase, providing campaign information and coupons to encourage future purchases. The distribution unit can distribute messages and coupons through multiple channels. For example, it can select and distribute through the channels most frequently used by customers, such as email, SMS, push notifications, and social media. This ensures that messages and coupons are delivered to customers at the optimal time and through the optimal channel. The distribution unit can distribute messages and coupons in real time and respond quickly to changes in customer behavior and lifecycle. For example, if a customer adds a specific product to their cart but does not purchase it, it can immediately send a coupon related to that product. Furthermore, it can prepare appropriate messages in advance and deliver them at the right time when a customer's birthday or special event is approaching. In addition, the distribution unit can monitor distribution results and evaluate the effectiveness of messages and coupons. For example, metrics such as open rates, click-through rates, and conversion rates are analyzed to optimize the delivery strategy. This allows the delivery department to provide effective communication to customers and improve engagement.

[0034] The data collection unit can collect purchase history, ad click data, and messaging platform chat data. For example, the data collection unit can collect purchase history. For example, the data collection unit can collect data such as the products purchased by the customer, the date and time of purchase, and the purchase amount. The data collection unit can also collect ad click data. For example, the data collection unit can collect data such as the type of ad clicked by the customer, the date and time of the click, and the page from which the click originated. The data collection unit can also collect messaging platform chat data. For example, the data collection unit can collect data such as the content of messages sent and received by the customer on the messaging platform, the date and time of the chat, and the person the chat was with. By collecting diverse data about the customer, more accurate analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input customer purchase history, ad click data, and messaging platform chat data into AI, and the AI ​​can analyze the data.

[0035] The analysis unit can analyze customer behavior patterns and lifecycles based on collected data. For example, the analysis unit can analyze customer behavior patterns based on collected data. For example, the analysis unit can analyze a customer's purchase history and generate coupons related to a specific product for customers who have purchased that product. The analysis unit can also analyze customer lifecycle data. For example, the analysis unit can generate messages tailored to a customer's birthday or special events. By analyzing customer behavior patterns and lifecycles, more personalized messages and coupons can be generated. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into AI, which can then analyze customer behavior patterns and lifecycles.

[0036] The generation unit can generate optimal messages and coupons for each customer based on the analysis results. For example, the generation unit can generate optimal messages for each customer based on the analysis results. For example, the generation unit can use AI to generate messages based on customer behavior patterns and lifecycles. The generation unit can also generate optimal coupons for each customer based on the analysis results. For example, the generation unit can use AI to generate coupons based on customer purchase history. By generating optimal messages and coupons for each customer, customer engagement can be increased. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input the analysis results into a generation AI, which can then generate optimal messages and coupons for each customer.

[0037] The distribution unit can automatically distribute generated messages and coupons through a messaging platform. For example, the distribution unit can automatically distribute generated messages through a messaging platform. For instance, the distribution unit can automatically send an AI-generated birthday message to a customer on their birthday. The distribution unit can also automatically distribute generated coupons through a messaging platform. For example, the distribution unit can automatically send a follow-up message after a product purchase, providing campaign information and coupons to encourage future purchases. This enhances real-time engagement with customers by automatically distributing generated messages and coupons. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input generated messages and coupons into an AI, which can then automatically distribute them.

[0038] The Loyalty Program Department can provide loyalty programs to encourage customer re-engagement. For example, the Loyalty Program Department can offer discount coupons or exclusive benefits to customers who have not made a purchase for a certain period of time. The Loyalty Program Department can also provide loyalty programs based on customer behavior data and lifecycle data. For example, the Loyalty Program Department can provide a points system based on a customer's purchase history and offer benefits according to the points accumulated. By providing loyalty programs, it is possible to encourage customer re-engagement and increase customer loyalty. Some or all of the above processes in the Loyalty Program Department may be performed using AI, for example, or not using AI. For example, the Loyalty Program Department can input customer behavior data and lifecycle data into AI, and the AI ​​can provide loyalty programs.

[0039] The data collection unit can analyze past customer behavior data and select the optimal data collection method. For example, the data collection unit can analyze past customer behavior data and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from devices that the customer has frequently used in the past. The data collection unit can also collect data during times that the customer has preferred to use in the past. Furthermore, the data collection unit can collect data through channels that the customer has shown high engagement with in the past. This allows the optimal data collection method to be selected by analyzing past customer behavior data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input past customer behavior data into AI, which can then select the optimal data collection method.

[0040] The data collection unit can filter data based on the customer's current purchasing intent and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to product categories that the customer is currently interested in. The data collection unit can also filter data based on keywords the customer has recently searched for. Furthermore, the data collection unit can collect data related to events the customer has recently attended. This allows for the collection of more relevant data by filtering data based on the customer's current purchasing intent and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's current purchasing intent and areas of interest into the AI, which can then filter the data.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location during data collection. For example, the data collection unit prioritizes the collection of highly relevant data by considering the customer's geographical location during data collection. For example, the data collection unit prioritizes the collection of store information in the area where the customer is currently located. The data collection unit can also collect relevant information about the travel destination if the customer is traveling. Furthermore, the data collection unit can collect data related to an event if the customer is participating in a specific event. This allows for the collection of more relevant data by considering the customer's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze customers' social media activity and collect relevant data during data collection. For example, the data collection unit can collect data based on content shared by customers on social media. The data collection unit can also analyze the activity of accounts that customers follow and collect relevant data. The data collection unit can also collect data based on the topics of social media groups that customers participate in. This allows for the collection of more relevant data by analyzing customers' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input customers' social media activity into AI, which can then collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and provide a detailed report. Alternatively, the analysis unit can perform a concise analysis on less important data and provide only the key points. Furthermore, the analysis unit can apply multiple analysis methods to highly important data to provide detailed results. This allows for more detailed analysis of more important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, which can then adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. It can also apply a click-through rate analysis algorithm to ad click data. It can also apply a natural language processing algorithm to chat data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data and provide real-time results. The analysis unit can also perform trend analysis based on historical data and provide long-term trends. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period and provide detailed results for that period. By determining the priority of analysis based on the data collection timing, more timely analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide detailed results. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can group highly relevant data and analyze them all at once. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, which can then adjust the order of analysis.

[0047] The generation unit can adjust the level of detail in messages and coupons based on customer behavior patterns. For example, the generation unit can generate detailed coupons for products that customers frequently purchase, and simpler coupons for products that customers rarely purchase. The generation unit can also generate coupons that are valid during specific time periods based on customer behavior patterns. By adjusting the level of detail based on customer behavior patterns, more appropriate messages and coupons can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer behavior patterns into AI, which can then adjust the level of detail in the generation.

[0048] The generation unit can apply different generation algorithms depending on the customer's lifecycle when generating messages and coupons. For example, the generation unit can generate a welcome message and a first-purchase coupon for new customers. It can also generate benefits based on a loyalty program for repeat customers. Furthermore, it can generate coupons to encourage re-engagement for churned customers. By applying different generation algorithms depending on the customer's lifecycle, more appropriate messages and coupons can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input customer lifecycle data into AI, and the AI ​​can apply different generation algorithms.

[0049] The generation unit can determine the generation priority of messages and coupons based on the customer's purchase history. For example, the generation unit can prioritize generating coupons for products that the customer frequently purchases. It can also prioritize generating coupons related to products the customer has recently purchased. Furthermore, the generation unit can prioritize generating coupons for specific product categories based on the customer's purchase history. This allows for the provision of more appropriate messages and coupons by determining the generation priority based on the customer's purchase history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's purchase history into AI, which can then determine the generation priority.

[0050] The generation unit can adjust the generation order of messages and coupons based on customer relevance. For example, the generation unit can prioritize generating coupons for products that the customer has shown high interest in. It can also prioritize generating coupons related to products that the customer has previously purchased. Furthermore, the generation unit can prioritize generating coupons for specific product categories based on the customer's behavior patterns. By adjusting the generation order based on customer relevance, it is possible to provide more appropriate messages and coupons. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer relevance into AI, which can then adjust the generation order.

[0051] The distribution unit can adjust the level of detail in its messages based on customer behavior patterns during distribution. For example, the distribution unit can send detailed messages for products that customers frequently purchase, and concise messages for products that customers rarely purchase. The distribution unit can also send messages that are effective at specific times based on customer behavior patterns. By adjusting the level of detail in messages based on customer behavior patterns, more appropriate messages and coupons can be delivered. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input customer behavior patterns into AI, which can then adjust the level of detail in its messages.

[0052] The distribution unit can apply different distribution algorithms depending on the customer's lifecycle at the time of distribution. For example, the distribution unit can send a welcome message and a first-purchase coupon to new customers. It can also send benefits based on a loyalty program to repeat customers. Furthermore, it can send messages encouraging re-engagement to churned customers. By applying different distribution algorithms depending on the customer's lifecycle, more appropriate messages and coupons can be delivered. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input customer lifecycle data into AI, and the AI ​​can apply different distribution algorithms.

[0053] The distribution unit can prioritize the delivery of highly relevant messages and coupons by considering the customer's geographical location during distribution. For example, the distribution unit can prioritize the delivery of highly relevant messages and coupons by considering the customer's geographical location during distribution. For example, the distribution unit can prioritize the delivery of store information in the area where the customer is currently located. The distribution unit can also deliver relevant information about the customer's travel destination if the customer is traveling. Furthermore, the distribution unit can deliver messages and coupons related to an event if the customer is participating in a specific event. This allows for the delivery of more relevant messages and coupons by considering the customer's geographical location. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input the customer's geographical location information into AI, which can then prioritize the delivery of highly relevant messages and coupons.

[0054] The distribution department can analyze customers' social media activity at the time of distribution and deliver relevant messages and coupons. For example, the distribution department can deliver messages and coupons based on content shared by customers on social media. The distribution department can also analyze the activity of accounts that customers follow and deliver relevant messages and coupons. Furthermore, the distribution department can deliver messages and coupons based on topics in social media groups that customers participate in. This allows for the delivery of more relevant messages and coupons by analyzing customers' social media activity. Some or all of the above processing in the distribution department may be performed using AI, for example, or not. For example, the distribution department can input customers' social media activity into AI, which can then deliver relevant messages and coupons.

[0055] The follow-up unit can adjust the level of detail in follow-up based on the customer's behavior patterns. For example, the follow-up unit can provide detailed follow-up messages for products that the customer frequently purchases, and concise follow-up messages for products that the customer rarely purchases. The follow-up unit can also provide follow-up messages that are effective at specific times based on the customer's behavior patterns. This allows for more appropriate follow-up by adjusting the level of detail based on the customer's behavior patterns. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the customer's behavior patterns into AI, which can then adjust the level of detail in the follow-up.

[0056] The follow-up unit can prioritize highly relevant follow-ups by considering the customer's geographical location during the follow-up process. For example, the follow-up unit can prioritize following up with store information in the customer's current location. It can also follow up with relevant information about the customer's travel destination if the customer is traveling. Furthermore, if the customer is participating in a specific event, the follow-up unit can perform follow-ups related to that event. This allows for more relevant follow-ups by considering the customer's geographical location. Some or all of the above processing in the follow-up unit may be performed using AI, or not. For example, the follow-up unit can input the customer's geographical location into the AI, which can then prioritize highly relevant follow-ups.

[0057] The loyalty program department can adjust the level of detail of a loyalty program based on customer behavior patterns when providing it. For example, the loyalty program department can provide a detailed loyalty program for products that customers frequently purchase. It can also provide a simpler loyalty program for products that customers do not purchase often. Furthermore, the loyalty program department can provide a loyalty program that is effective during specific time periods based on customer behavior patterns. By adjusting the level of detail of the program based on customer behavior patterns, a more appropriate loyalty program can be provided. Some or all of the above processing in the loyalty program department may be performed using AI, for example, or not using AI. For example, the loyalty program department can input customer behavior patterns into AI, and the AI ​​can adjust the level of detail of the program.

[0058] The loyalty program department can prioritize providing highly relevant programs to customers by considering their geographical location when offering loyalty programs. For example, the loyalty program department can prioritize providing store information in the area where the customer is currently located. Furthermore, if the customer is traveling, the loyalty program department can provide relevant information about their travel destination. Also, if the customer is participating in a specific event, the loyalty program department can provide loyalty programs related to that event. This allows for the provision of more relevant loyalty programs by considering the customer's geographical location. Some or all of the above processing in the loyalty program department may be performed using AI, for example, or without AI. For example, the loyalty program department can input the customer's geographical location information into AI, which can then prioritize providing highly relevant programs.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] A personalized communication system can further incorporate a social media analytics unit that analyzes customers' social media activity. This unit analyzes content shared by customers on social media and the activity of accounts they follow to understand their interests. For example, if a customer frequently posts about a particular brand or product, it can generate coupons related to that brand or product. It can also generate relevant messages based on the topics of social media groups the customer participates in. This allows for the delivery of more personalized messages and coupons based on the customer's social media activity.

[0061] A personalized communication system can further incorporate a geographic information analysis unit that considers the customer's geographic location. This unit collects the customer's current location information and generates relevant messages and coupons based on that location. For example, if a customer is in a specific region, it can provide information about local shops and events. If a customer is traveling, it can also provide tourist information and special offers for their travel destination. This allows for the delivery of more relevant messages and coupons based on the customer's geographic location.

[0062] A personalized communication system can further incorporate a purchase intent estimation unit that estimates customer purchase intent in real time. This unit analyzes the customer's current behavioral data and past purchase history to estimate their purchase intent. For example, if a customer frequently searches for a particular product, it can offer coupons related to that product. It can also deliver messages tailored to a customer's tendencies to increase during certain times of the day. This allows for the delivery of more effective messages and coupons based on the customer's purchase intent.

[0063] A personalized communication system can also include a lifestyle data collection unit that gathers customer lifestyle data. This unit collects data on the customer's hobbies, interests, and daily activities to understand their lifestyle. For example, if a customer is interested in fitness, it can offer coupons for fitness-related products and services. Similarly, if a customer enjoys traveling, it can offer travel-related benefits. This allows for the delivery of more personalized messages and coupons based on the customer's lifestyle.

[0064] A personalized communication system can further include a loyalty benefits customization unit that customizes loyalty program benefits based on the customer's purchase history. This unit analyzes the customer's purchase history and provides the most suitable benefits. For example, if a customer frequently purchases products from a particular brand, it can provide benefits related to that brand's products. Similarly, if a customer prefers to purchase products from a specific category, it can provide benefits related to products in that category. This allows for a more personalized loyalty program based on the customer's purchase history.

[0065] A personalized communication system can also include a behavioral monitoring unit that monitors customer behavior data in real time. This unit collects customer website browsing history and app usage history in real time to understand the customer's current interests. For example, if a customer frequently views a particular product page, a coupon related to that product can be offered immediately. Similarly, if a customer frequently uses a particular app feature, benefits related to that feature can be offered. This real-time monitoring of customer behavior data allows for the delivery of more timely messages and coupons.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects customer behavior and lifecycle data. For example, it can collect purchase history, ad click data, and messaging platform chat data. Specifically, it collects data such as which products customers purchased, which ads they clicked on, and what messages they sent and received on messaging platforms. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can analyze customer behavior patterns and lifecycles based on the collected data. Specifically, it can analyze purchase history and generate coupons related to specific products for customers who have purchased those products. It can also analyze lifecycle data and generate messages tailored to birthdays or special events. Step 3: The generation unit generates messages and coupons based on the data analyzed by the analysis unit. For example, it can generate optimal messages and coupons for each customer based on the analysis results. Specifically, it uses AI to generate messages and coupons based on customer behavior patterns and lifecycles. Step 4: The distribution unit automatically distributes the messages and coupons generated by the generation unit. For example, messages and coupons can be automatically distributed through a messaging platform. Specifically, AI-generated birthday messages and coupons can be automatically sent to customers on their birthdays. It can also automatically send follow-up messages after a product purchase, providing campaign information and coupons to encourage future purchases.

[0068] (Example of form 2) The personalized communication system according to an embodiment of the present invention is a system that automatically performs personalized communication based on customer behavior data and lifecycle using AI. This system collects customer behavior data and lifecycle data, analyzes it with AI to generate optimal messages and coupons for each customer, and automatically delivers them through a messaging platform. This enhances real-time engagement with customers, increases customer loyalty by providing coupons and loyalty programs at the optimal time, and promotes repeat purchases and engagement. For example, when collecting customer behavior data and lifecycle data, all data related to the customer is collected, such as purchase history, ad click data, and chat data from the messaging platform. For example, data such as which products a customer purchased, which ads they clicked, and what messages they sent and received on the messaging platform are collected. Next, the AI ​​analyzes the collected data. Based on the collected data, the AI ​​analyzes the customer's behavior patterns and lifecycle and generates optimal messages and coupons for each customer. For example, the AI ​​analyzes purchase history and generates coupons related to a specific product for customers who have purchased that product. It is also possible for the AI ​​to analyze lifecycle data and generate messages tailored to birthdays or special events. The generated messages and coupons are automatically delivered through the messaging platform. For example, AI-generated birthday messages and coupons are automatically sent to customers on their birthdays. Follow-up messages are also automatically sent after a purchase, sometimes offering campaign information and coupons to encourage future purchases. This system enhances real-time engagement with customers. Customers receive messages and coupons at the most opportune time for them, creating a sense of exclusivity. This increases customer loyalty and encourages repeat purchases and engagement. For instance, offering coupons related to a specific product to a customer who has purchased it can encourage repeat purchases.Furthermore, sending messages tailored to birthdays and special events deepens customer relationships and improves engagement. In addition, automating coupons and loyalty programs encourages customer re-engagement. For example, AI can send discount coupons or exclusive offers to customers who haven't made a purchase for a certain period, guiding them back to stores or online shops. This prevents customer churn and encourages repeat purchases. In this way, leveraging AI to automatically deliver personalized communications based on customer behavior data and lifecycle data strengthens real-time engagement with customers and ensures coupons and loyalty programs are delivered at the optimal time. This increases customer loyalty and encourages repeat purchases and engagement. Thus, personalized communication systems can automatically deliver personalized communications based on customer behavior data and lifecycle data.

[0069] The personalized communication system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects customer behavior data and lifecycle data. The collection unit can collect, for example, purchase history, ad click data, and messaging platform chat data. For example, the collection unit collects data such as which products a customer purchased, which ads they clicked, and what messages they sent and received on the messaging platform. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze customer behavior patterns and lifecycles based on the collected data. For example, the analysis unit can analyze purchase history and generate coupons related to specific products for customers who have purchased those products. The analysis unit can also analyze lifecycle data and generate messages tailored to birthdays or special events. The generation unit generates messages and coupons based on the data analyzed by the analysis unit. For example, the generation unit can generate optimal messages and coupons for each customer based on the analysis results. For example, the generation unit uses AI to generate messages and coupons based on customer behavior patterns and lifecycles. The distribution unit automatically distributes the messages and coupons generated by the generation unit. The distribution unit can, for example, automatically distribute messages and coupons through a messaging platform. For instance, the distribution unit can automatically send AI-generated birthday messages and coupons to customers on their birthdays. The distribution unit can also automatically send follow-up messages after a product purchase, providing campaign information and coupons to encourage future purchases. This allows the personalized communication system according to the embodiment to automatically perform personalized communication based on customer behavior data and lifecycle data. Some or all of the above-described processes in the collection unit, analysis unit, generation unit, and distribution unit may be performed using AI, for example, or without AI.For example, the collection unit inputs customer behavior data and lifecycle data into the AI, the AI ​​analyzes the data, the generation unit generates messages and coupons based on the analysis results, and the distribution unit automatically distributes them.

[0070] The data collection unit collects customer behavior and lifecycle data. For example, it can collect purchase history, ad click data, and messaging platform chat data. Specifically, it collects data such as which products customers purchased, which ads they clicked, and what messages they sent and received on messaging platforms. This allows for a detailed understanding of customer behavior patterns and interests. The data collection unit monitors customer online behavior in real time using website tracking tools and in-app event tracking. For example, it collects data such as the number of times a customer viewed a specific product page, the time spent on that page, which items were added to their cart, and which items were not purchased. Offline purchase data can also be integrated, including in-store purchase history and loyalty card usage history. Furthermore, the data collection unit also collects social media data to understand what customers post and which brands and products they mention. This allows for a deeper understanding of customer preferences and trends. The collected data is stored in a secure database and managed for access by the analysis and generation units. The frequency and scope of data collection are adjusted based on system settings and customer consent. This allows the data collection unit to efficiently and comprehensively collect customer behavioral data and lifecycle data, providing a foundation for personalized communication.

[0071] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze customer behavior patterns and lifecycles based on the collected data. Specifically, the analysis unit analyzes purchase history and generates coupons related to specific products for customers who have purchased those products. The analysis unit can also analyze lifecycle data and generate messages tailored to birthdays and special events. The analysis unit uses AI to analyze data and gain a detailed understanding of customer behavior patterns and lifecycles. For example, it uses machine learning algorithms to predict products that customers are most likely to purchase next based on their purchase history. It also uses natural language processing technology to analyze chat data from messaging platforms and extract customer interests and needs. Furthermore, the analysis unit uses clustering algorithms to classify customers into groups with similar behavior patterns and lifecycles. This allows for the provision of optimal messages and coupons to each group. The analysis unit can analyze data in real time and respond quickly to changes in customer behavior and lifecycles. For example, if a customer starts frequently purchasing a particular product, it can instantly generate new coupons related to that product. Also, if a customer's birthday or a special event is approaching, it can prepare appropriate messages in advance and deliver them at the right time. This allows the analytics unit to effectively analyze customer behavior data and lifecycle data, providing a foundation for personalized communication.

[0072] The generation unit generates messages and coupons based on data analyzed by the analysis unit. For example, the generation unit can generate optimal messages and coupons for each customer based on the analysis results. Specifically, the generation unit uses AI to generate messages and coupons based on customer behavior patterns and lifecycles. For example, it uses natural language generation technology to automatically create personalized messages for customers. This allows for consistent and individually optimized communication for customers. The generation unit considers customer purchase history and behavior patterns to generate highly relevant coupons. For example, it provides discount coupons related to products to customers who frequently purchase specific items. It can also generate special messages and coupons for birthdays and anniversaries in line with customer lifecycle events. The generation unit uses AI to analyze customer behavior data and lifecycle data to generate messages and coupons at the optimal time. For example, if a customer adds a specific item to their cart but does not purchase it, it generates a coupon related to that item and sends a message encouraging purchase. Also, if a customer clicks on a specific advertisement, it generates a message providing information about products or services related to that advertisement. This allows the generation unit to effectively generate personalized messages and coupons based on customer behavior data and lifecycle data, thereby improving customer engagement.

[0073] The distribution unit automatically distributes messages and coupons generated by the generation unit. For example, the distribution unit can automatically distribute messages and coupons through messaging platforms. Specifically, it can automatically send AI-generated birthday messages and coupons to customers on their birthdays. It can also automatically send follow-up messages after a purchase, providing campaign information and coupons to encourage future purchases. The distribution unit can distribute messages and coupons through multiple channels. For example, it can select and distribute through the channels most frequently used by customers, such as email, SMS, push notifications, and social media. This ensures that messages and coupons are delivered to customers at the optimal time and through the optimal channel. The distribution unit can distribute messages and coupons in real time and respond quickly to changes in customer behavior and lifecycle. For example, if a customer adds a specific product to their cart but does not purchase it, it can immediately send a coupon related to that product. Furthermore, it can prepare appropriate messages in advance and deliver them at the right time when a customer's birthday or special event is approaching. In addition, the distribution unit can monitor distribution results and evaluate the effectiveness of messages and coupons. For example, metrics such as open rates, click-through rates, and conversion rates are analyzed to optimize the delivery strategy. This allows the delivery department to provide effective communication to customers and improve engagement.

[0074] The data collection unit can collect purchase history, ad click data, and messaging platform chat data. For example, the data collection unit can collect purchase history. For example, the data collection unit can collect data such as the products purchased by the customer, the date and time of purchase, and the purchase amount. The data collection unit can also collect ad click data. For example, the data collection unit can collect data such as the type of ad clicked by the customer, the date and time of the click, and the page from which the click originated. The data collection unit can also collect messaging platform chat data. For example, the data collection unit can collect data such as the content of messages sent and received by the customer on the messaging platform, the date and time of the chat, and the person the chat was with. By collecting diverse data about the customer, more accurate analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input customer purchase history, ad click data, and messaging platform chat data into AI, and the AI ​​can analyze the data.

[0075] The analysis unit can analyze customer behavior patterns and lifecycles based on collected data. For example, the analysis unit can analyze customer behavior patterns based on collected data. For example, the analysis unit can analyze a customer's purchase history and generate coupons related to a specific product for customers who have purchased that product. The analysis unit can also analyze customer lifecycle data. For example, the analysis unit can generate messages tailored to a customer's birthday or special events. By analyzing customer behavior patterns and lifecycles, more personalized messages and coupons can be generated. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into AI, which can then analyze customer behavior patterns and lifecycles.

[0076] The generation unit can generate optimal messages and coupons for each customer based on the analysis results. For example, the generation unit can generate optimal messages for each customer based on the analysis results. For example, the generation unit can use AI to generate messages based on customer behavior patterns and lifecycles. The generation unit can also generate optimal coupons for each customer based on the analysis results. For example, the generation unit can use AI to generate coupons based on customer purchase history. By generating optimal messages and coupons for each customer, customer engagement can be increased. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input the analysis results into a generation AI, which can then generate optimal messages and coupons for each customer.

[0077] The distribution unit can automatically distribute generated messages and coupons through a messaging platform. For example, the distribution unit can automatically distribute generated messages through a messaging platform. For instance, the distribution unit can automatically send an AI-generated birthday message to a customer on their birthday. The distribution unit can also automatically distribute generated coupons through a messaging platform. For example, the distribution unit can automatically send a follow-up message after a product purchase, providing campaign information and coupons to encourage future purchases. This enhances real-time engagement with customers by automatically distributing generated messages and coupons. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input generated messages and coupons into an AI, which can then automatically distribute them.

[0078] The follow-up unit can perform follow-up based on messages distributed by the distribution unit. For example, the follow-up unit can automatically send a follow-up message after a customer receives a message. The follow-up unit can also perform follow-up based on customer behavior data and lifecycle data. For example, the follow-up unit can send a follow-up message after a customer purchases a specific product, providing campaign information or coupons to encourage future purchases. This allows for deeper customer relationships and promotes repeat purchases and engagement. Some or all of the above processing in the follow-up unit may be performed using emotion estimation functions, such as using an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the follow-up unit can input customer emotion data into an emotion engine or generative AI, which can then estimate the customer's emotion and perform follow-up based on the results.

[0079] The Loyalty Program Department can provide loyalty programs to encourage customer re-engagement. For example, the Loyalty Program Department can offer discount coupons or exclusive benefits to customers who have not made a purchase for a certain period of time. The Loyalty Program Department can also provide loyalty programs based on customer behavior data and lifecycle data. For example, the Loyalty Program Department can provide a points system based on a customer's purchase history and offer benefits according to the points accumulated. By providing loyalty programs, it is possible to encourage customer re-engagement and increase customer loyalty. Some or all of the above processes in the Loyalty Program Department may be performed using AI, for example, or not using AI. For example, the Loyalty Program Department can input customer behavior data and lifecycle data into AI, and the AI ​​can provide loyalty programs.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily delay data collection and resume it when the user is relaxed. The data collection unit can also collect data in real time and reflect it immediately if the user is excited. The data collection unit can also minimize data collection if the user is tired and collect more detailed data later. By adjusting the timing of data collection based on the user's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an emotion engine or generative AI, which can then estimate the user's emotions and adjust the timing of data collection based on the results.

[0081] The data collection unit can analyze past customer behavior data and select the optimal data collection method. For example, the data collection unit can analyze past customer behavior data and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from devices that the customer has frequently used in the past. The data collection unit can also collect data during times that the customer has preferred to use in the past. Furthermore, the data collection unit can collect data through channels that the customer has shown high engagement with in the past. This allows the optimal data collection method to be selected by analyzing past customer behavior data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input past customer behavior data into AI, which can then select the optimal data collection method.

[0082] The data collection unit can filter data based on the customer's current purchasing intent and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to product categories that the customer is currently interested in. The data collection unit can also filter data based on keywords the customer has recently searched for. Furthermore, the data collection unit can collect data related to events the customer has recently attended. This allows for the collection of more relevant data by filtering data based on the customer's current purchasing intent and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's current purchasing intent and areas of interest into the AI, which can then filter the data.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed data. If the user is in a hurry, the data collection unit may prioritize collecting only important data. If the user is excited, the data collection unit may prioritize collecting data that is fluctuating in real time. This allows for the priority collection of more important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an emotion engine or generative AI, which can estimate the user's emotions and determine the priority of data to collect based on the result.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the customer's geographical location during data collection. For example, the data collection unit prioritizes the collection of highly relevant data by considering the customer's geographical location during data collection. For example, the data collection unit prioritizes the collection of store information in the area where the customer is currently located. The data collection unit can also collect relevant information about the travel destination if the customer is traveling. Furthermore, the data collection unit can collect data related to an event if the customer is participating in a specific event. This allows for the collection of more relevant data by considering the customer's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the customer's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0085] The data collection unit can analyze customers' social media activity and collect relevant data during data collection. For example, the data collection unit can collect data based on content shared by customers on social media. The data collection unit can also analyze the activity of accounts that customers follow and collect relevant data. The data collection unit can also collect data based on the topics of social media groups that customers participate in. This allows for the collection of more relevant data by analyzing customers' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input customers' social media activity into AI, which can then collect relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. It can also provide concise analysis results that get straight to the point when the user is in a hurry. It can also provide analysis results using visually appealing graphs or charts when the user is excited. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an emotion engine or generative AI, which can then estimate the user's emotions and adjust the method of representing the analysis based on the results.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and provide a detailed report. Alternatively, the analysis unit can perform a concise analysis on less important data and provide only the key points. Furthermore, the analysis unit can apply multiple analysis methods to highly important data to provide detailed results. This allows for more detailed analysis of more important data by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, which can then adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. It can also apply a click-through rate analysis algorithm to ad click data. It can also apply a natural language processing algorithm to chat data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply different analysis algorithms.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can provide a detailed analysis report if the user is relaxed. It can also provide a short, concise analysis report if the user is in a hurry. It can also provide a visually appealing analysis report if the user is excited. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an emotion engine or generative AI, which can estimate the user's emotions and adjust the length of the analysis based on the result.

[0090] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data and provide real-time results. The analysis unit can also perform trend analysis based on historical data and provide long-term trends. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period and provide detailed results for that period. By determining the priority of analysis based on the data collection timing, more timely analysis results can be provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide detailed results. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can group highly relevant data and analyze them all at once. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, which can then adjust the order of analysis.

[0092] The generation unit can estimate the user's emotions and adjust the way messages and coupons are presented based on those emotions. For example, the generation unit can use friendly language when the user is relaxed, or concise and to-the-point language when the user is in a hurry, or visually appealing design when the user is excited. By adjusting the presentation of messages and coupons based on the user's emotions, more appropriate messages and coupons can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an emotion engine or generation AI, which can then estimate the user's emotions and adjust the way messages and coupons are presented based on the results.

[0093] The generation unit can adjust the level of detail in messages and coupons based on customer behavior patterns. For example, the generation unit can generate detailed coupons for products that customers frequently purchase, and simpler coupons for products that customers rarely purchase. The generation unit can also generate coupons that are valid during specific time periods based on customer behavior patterns. By adjusting the level of detail based on customer behavior patterns, more appropriate messages and coupons can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer behavior patterns into AI, which can then adjust the level of detail in the generation.

[0094] The generation unit can apply different generation algorithms depending on the customer's lifecycle when generating messages and coupons. For example, the generation unit can generate a welcome message and a first-purchase coupon for new customers. It can also generate benefits based on a loyalty program for repeat customers. Furthermore, it can generate coupons to encourage re-engagement for churned customers. By applying different generation algorithms depending on the customer's lifecycle, more appropriate messages and coupons can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input customer lifecycle data into AI, and the AI ​​can apply different generation algorithms.

[0095] The generation unit can estimate the user's emotions and adjust the length of messages and coupons based on the estimated emotions. For example, the generation unit can provide detailed messages and coupons when the user is relaxed. It can also provide short, to-the-point messages and coupons when the user is in a hurry. It can also provide visually appealing messages and coupons when the user is excited. By adjusting the length of messages and coupons based on the user's emotions, more appropriate messages and coupons can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into an emotion engine or generation AI, which can then estimate the user's emotions and adjust the length of messages or coupons based on the results.

[0096] The generation unit can determine the generation priority of messages and coupons based on the customer's purchase history. For example, the generation unit can prioritize generating coupons for products that the customer frequently purchases. It can also prioritize generating coupons related to products the customer has recently purchased. Furthermore, the generation unit can prioritize generating coupons for specific product categories based on the customer's purchase history. This allows for the provision of more appropriate messages and coupons by determining the generation priority based on the customer's purchase history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the customer's purchase history into AI, which can then determine the generation priority.

[0097] The generation unit can adjust the generation order of messages and coupons based on customer relevance. For example, the generation unit can prioritize generating coupons for products that the customer has shown high interest in. It can also prioritize generating coupons related to products that the customer has previously purchased. Furthermore, the generation unit can prioritize generating coupons for specific product categories based on the customer's behavior patterns. By adjusting the generation order based on customer relevance, it is possible to provide more appropriate messages and coupons. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer relevance into AI, which can then adjust the generation order.

[0098] The delivery unit can estimate the user's emotions and adjust the timing of delivery based on the estimated emotions. For example, the delivery unit can provide detailed messages or coupons when the user is relaxed. It can also provide short, to-the-point messages or coupons when the user is in a hurry. It can also provide visually appealing messages or coupons when the user is excited. By adjusting the timing of delivery based on the user's emotions, messages and coupons can be delivered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the distribution unit can input user emotion data into an emotion engine or generative AI, which can then estimate the user's emotions and adjust the timing of distribution based on the results.

[0099] The distribution unit can adjust the level of detail in its messages based on customer behavior patterns during distribution. For example, the distribution unit can send detailed messages for products that customers frequently purchase, and concise messages for products that customers rarely purchase. The distribution unit can also send messages that are effective at specific times based on customer behavior patterns. By adjusting the level of detail in messages based on customer behavior patterns, more appropriate messages and coupons can be delivered. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input customer behavior patterns into AI, which can then adjust the level of detail in its messages.

[0100] The distribution unit can apply different distribution algorithms depending on the customer's lifecycle at the time of distribution. For example, the distribution unit can send a welcome message and a first-purchase coupon to new customers. It can also send benefits based on a loyalty program to repeat customers. Furthermore, it can send messages encouraging re-engagement to churned customers. By applying different distribution algorithms depending on the customer's lifecycle, more appropriate messages and coupons can be delivered. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input customer lifecycle data into AI, and the AI ​​can apply different distribution algorithms.

[0101] The delivery unit can estimate the user's emotions and determine the priority of deliveries based on those estimated emotions. For example, if the user is relaxed, the delivery unit will prioritize delivering detailed messages and coupons. If the user is in a hurry, the delivery unit can also prioritize delivering short, concise messages and coupons. If the user is excited, the delivery unit can also prioritize delivering visually appealing messages and coupons. This allows for the delivery of more appropriate messages and coupons by prioritizing deliveries based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input user emotion data into an emotion engine or generative AI, which can then estimate the user's emotions and determine the distribution priority based on the results.

[0102] The distribution unit can prioritize the delivery of highly relevant messages and coupons by considering the customer's geographical location during distribution. For example, the distribution unit can prioritize the delivery of highly relevant messages and coupons by considering the customer's geographical location during distribution. For example, the distribution unit can prioritize the delivery of store information in the area where the customer is currently located. The distribution unit can also deliver relevant information about the customer's travel destination if the customer is traveling. Furthermore, the distribution unit can deliver messages and coupons related to an event if the customer is participating in a specific event. This allows for the delivery of more relevant messages and coupons by considering the customer's geographical location. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input the customer's geographical location information into AI, which can then prioritize the delivery of highly relevant messages and coupons.

[0103] The distribution department can analyze customers' social media activity at the time of distribution and deliver relevant messages and coupons. For example, the distribution department can deliver messages and coupons based on content shared by customers on social media. The distribution department can also analyze the activity of accounts that customers follow and deliver relevant messages and coupons. Furthermore, the distribution department can deliver messages and coupons based on topics in social media groups that customers participate in. This allows for the delivery of more relevant messages and coupons by analyzing customers' social media activity. Some or all of the above processing in the distribution department may be performed using AI, for example, or not. For example, the distribution department can input customers' social media activity into AI, which can then deliver relevant messages and coupons.

[0104] The follow-up unit can estimate the user's emotions and adjust the timing of follow-ups based on the estimated emotions. For example, the follow-up unit can provide a detailed follow-up message if the user is relaxed. It can also provide a short, concise follow-up message if the user is in a hurry. It can also provide a visually appealing follow-up message if the user is excited. By adjusting the timing of follow-ups based on the user's emotions, follow-ups can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the follow-up unit may be performed using AI or not. For example, the follow-up unit can input user emotion data into an emotion engine or generative AI, which can then estimate the user's emotions and adjust the timing of follow-up based on the results.

[0105] The follow-up unit can adjust the level of detail in follow-up based on the customer's behavior patterns. For example, the follow-up unit can provide detailed follow-up messages for products that the customer frequently purchases, and concise follow-up messages for products that the customer rarely purchases. The follow-up unit can also provide follow-up messages that are effective at specific times based on the customer's behavior patterns. This allows for more appropriate follow-up by adjusting the level of detail based on the customer's behavior patterns. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input the customer's behavior patterns into AI, which can then adjust the level of detail in the follow-up.

[0106] The follow-up unit can estimate the user's emotions and determine the priority of follow-ups based on the estimated emotions. For example, if the user is relaxed, the follow-up unit may prioritize providing detailed follow-up messages. If the user is in a hurry, the follow-up unit may prioritize providing short, concise follow-up messages. If the user is excited, the follow-up unit may prioritize providing visually appealing follow-up messages. This allows for more appropriate follow-ups by prioritizing follow-ups based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input user emotion data into an emotion engine or generative AI, which can then estimate the user's emotions and determine the priority of follow-up based on the results.

[0107] The follow-up unit can prioritize highly relevant follow-ups by considering the customer's geographical location during the follow-up process. For example, the follow-up unit can prioritize following up with store information in the customer's current location. It can also follow up with relevant information about the customer's travel destination if the customer is traveling. Furthermore, if the customer is participating in a specific event, the follow-up unit can perform follow-ups related to that event. This allows for more relevant follow-ups by considering the customer's geographical location. Some or all of the above processing in the follow-up unit may be performed using AI, or not. For example, the follow-up unit can input the customer's geographical location into the AI, which can then prioritize highly relevant follow-ups.

[0108] The loyalty program unit can estimate the user's emotions and adjust the way the loyalty program is delivered based on those estimated emotions. For example, the loyalty program unit can provide a detailed loyalty program when the user is relaxed. It can also provide a short, concise loyalty program when the user is in a hurry. It can also provide a visually appealing loyalty program when the user is excited. By adjusting the way the loyalty program is delivered based on the user's emotions, a more appropriate loyalty program can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the loyalty program unit may be performed using AI, for example, or without AI. For example, the loyalty program department can input user emotional data into an emotion engine or generative AI, which can then estimate the user's emotions and adjust the method of providing the loyalty program based on the results.

[0109] The loyalty program department can adjust the level of detail of a loyalty program based on customer behavior patterns when providing it. For example, the loyalty program department can provide a detailed loyalty program for products that customers frequently purchase. It can also provide a simpler loyalty program for products that customers do not purchase often. Furthermore, the loyalty program department can provide a loyalty program that is effective during specific time periods based on customer behavior patterns. By adjusting the level of detail of the program based on customer behavior patterns, a more appropriate loyalty program can be provided. Some or all of the above processing in the loyalty program department may be performed using AI, for example, or not using AI. For example, the loyalty program department can input customer behavior patterns into AI, and the AI ​​can adjust the level of detail of the program.

[0110] The loyalty program unit can estimate the user's emotions and prioritize loyalty programs based on those emotions. For example, the loyalty program unit might prioritize detailed loyalty programs when the user is relaxed. It might also prioritize short, concise loyalty programs when the user is in a hurry. Furthermore, it might prioritize visually appealing loyalty programs when the user is excited. This allows for the provision of more appropriate loyalty programs by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the loyalty program unit may be performed using AI or not. For example, the loyalty program department can input user emotional data into an emotion engine or generative AI, which then estimates the user's emotions and determines the priority of loyalty programs based on the results.

[0111] The loyalty program department can prioritize providing highly relevant programs to customers by considering their geographical location when offering loyalty programs. For example, the loyalty program department can prioritize providing store information in the area where the customer is currently located. Furthermore, if the customer is traveling, the loyalty program department can provide relevant information about their travel destination. Also, if the customer is participating in a specific event, the loyalty program department can provide loyalty programs related to that event. This allows for the provision of more relevant loyalty programs by considering the customer's geographical location. Some or all of the above processing in the loyalty program department may be performed using AI, for example, or without AI. For example, the loyalty program department can input the customer's geographical location information into AI, which can then prioritize providing highly relevant programs.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] A personalized communication system can further incorporate a social media analytics unit that analyzes customers' social media activity. This unit analyzes content shared by customers on social media and the activity of accounts they follow to understand their interests. For example, if a customer frequently posts about a particular brand or product, it can generate coupons related to that brand or product. It can also generate relevant messages based on the topics of social media groups the customer participates in. This allows for the delivery of more personalized messages and coupons based on the customer's social media activity.

[0114] A personalized communication system can further incorporate a geographic information analysis unit that considers the customer's geographic location. This unit collects the customer's current location information and generates relevant messages and coupons based on that location. For example, if a customer is in a specific region, it can provide information about local shops and events. If a customer is traveling, it can also provide tourist information and special offers for their travel destination. This allows for the delivery of more relevant messages and coupons based on the customer's geographic location.

[0115] A personalized communication system can further incorporate a purchase intent estimation unit that estimates customer purchase intent in real time. This unit analyzes the customer's current behavioral data and past purchase history to estimate their purchase intent. For example, if a customer frequently searches for a particular product, it can offer coupons related to that product. It can also deliver messages tailored to a customer's tendencies to increase during certain times of the day. This allows for the delivery of more effective messages and coupons based on the customer's purchase intent.

[0116] Personalized communication systems can further incorporate an emotion tone adjustment unit that estimates customer emotions and adjusts the tone of messages based on those estimated emotions. This unit analyzes chat data from the customer's messaging platform and social media posts to estimate their emotions. For example, if a customer is stressed, it can deliver a message in a relaxing tone. Conversely, if a customer is excited, it can deliver a message in an energetic tone. This allows for more effective communication by delivering messages tailored to the customer's emotions.

[0117] A personalized communication system can also include a lifestyle data collection unit that gathers customer lifestyle data. This unit collects data on the customer's hobbies, interests, and daily activities to understand their lifestyle. For example, if a customer is interested in fitness, it can offer coupons for fitness-related products and services. Similarly, if a customer enjoys traveling, it can offer travel-related benefits. This allows for the delivery of more personalized messages and coupons based on the customer's lifestyle.

[0118] A personalized communication system can further include an emotion expiration adjustment unit that estimates customer emotions and adjusts the coupon's expiration date based on those emotions. The emotion expiration adjustment unit analyzes customer emotion data and sets the coupon's expiration date according to the customer's emotions. For example, if the customer is relaxed, a longer expiration date can be set. Conversely, if the customer is in a hurry, a shorter expiration date can be set. This allows for more effective coupon delivery by adjusting the coupon's expiration date based on customer emotions.

[0119] A personalized communication system can further include a loyalty benefits customization unit that customizes loyalty program benefits based on the customer's purchase history. This unit analyzes the customer's purchase history and provides the most suitable benefits. For example, if a customer frequently purchases products from a particular brand, it can provide benefits related to that brand's products. Similarly, if a customer prefers to purchase products from a specific category, it can provide benefits related to products in that category. This allows for a more personalized loyalty program based on the customer's purchase history.

[0120] A personalized communication system can further include an emotion-based delivery channel selection unit that estimates customer emotions and selects the appropriate message delivery channel based on those emotions. This unit analyzes customer emotion data and selects the optimal delivery channel according to the customer's emotions. For example, if a customer is relaxed, it might select email or push notifications. If a customer is excited, it might deliver messages via social media. This allows for more effective message delivery by selecting the optimal delivery channel based on customer emotions.

[0121] A personalized communication system can also include a behavioral monitoring unit that monitors customer behavior data in real time. This unit collects customer website browsing history and app usage history in real time to understand the customer's current interests. For example, if a customer frequently views a particular product page, a coupon related to that product can be offered immediately. Similarly, if a customer frequently uses a particular app feature, benefits related to that feature can be offered. This real-time monitoring of customer behavior data allows for the delivery of more timely messages and coupons.

[0122] A personalized communication system can further include an emotion content customization unit that estimates the customer's emotions and customizes the message content based on those emotions. This unit analyzes customer emotion data and adjusts the message content according to the customer's emotions. For example, if the customer is relaxed, it can provide a message with detailed information. Conversely, if the customer is in a hurry, it can provide a concise and to-the-point message. This allows for more effective communication by customizing message content based on the customer's emotions.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects customer behavior and lifecycle data. For example, it can collect purchase history, ad click data, and messaging platform chat data. Specifically, it collects data such as which products customers purchased, which ads they clicked on, and what messages they sent and received on messaging platforms. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it can analyze customer behavior patterns and lifecycles based on the collected data. Specifically, it can analyze purchase history and generate coupons related to specific products for customers who have purchased those products. It can also analyze lifecycle data and generate messages tailored to birthdays or special events. Step 3: The generation unit generates messages and coupons based on the data analyzed by the analysis unit. For example, it can generate optimal messages and coupons for each customer based on the analysis results. Specifically, it uses AI to generate messages and coupons based on customer behavior patterns and lifecycles. Step 4: The distribution unit automatically distributes the messages and coupons generated by the generation unit. For example, messages and coupons can be automatically distributed through a messaging platform. Specifically, AI-generated birthday messages and coupons can be automatically sent to customers on their birthdays. It can also automatically send follow-up messages after a product purchase, providing campaign information and coupons to encourage future purchases.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and distribution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects customer behavior data and lifecycle data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates messages and coupons based on the analysis results. The distribution unit is implemented in the control unit 46A of the smart device 14 and automatically distributes the generated messages and coupons. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and distribution unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects customer behavior data and lifecycle data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates messages and coupons based on the analysis results. The distribution unit is implemented in the control unit 46A of the smart glasses 214 and automatically distributes the generated messages and coupons. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and distribution unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects customer behavior data and lifecycle data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates messages and coupons based on the analysis results. The distribution unit is implemented in the control unit 46A of the headset terminal 314 and automatically distributes the generated messages and coupons. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and distribution unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects customer behavior data and lifecycle data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates messages and coupons based on the analysis results. The distribution unit is implemented in the control unit 46A of the robot 414 and automatically distributes the generated messages and coupons. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A data collection unit that collects customer behavior data and lifecycle data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates messages and coupons based on the data analyzed by the analysis unit, The system includes a distribution unit that automatically distributes messages and coupons generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect purchase history, ad click data, and chat data from messaging platforms. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we analyze customer behavior patterns and lifecycles. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the analysis results, we generate the most suitable messages and coupons for each customer. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned distribution unit, The generated messages and coupons are automatically delivered through the messaging platform. The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes a follow-up unit that performs follow-up based on messages distributed by the distribution unit. The system described in Appendix 1, characterized by the features described herein. (Note 7) The company has a loyalty program department that provides loyalty programs to promote customer re-engagement. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze past customer behavior data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the customer's current purchasing intent and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, analyze customers' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the way messages and coupons are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating messages or coupons, adjust the level of detail based on customer behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating messages and coupons, different generation algorithms are applied depending on the customer's lifecycle. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts the length of messages and coupons based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating messages or coupons, the priority of generation is determined based on the customer's purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating messages and coupons, the generation order is adjusted based on customer relevance. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned distribution unit, It estimates the user's emotions and adjusts the delivery timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned distribution unit, When delivering a message, adjust the level of detail based on customer behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned distribution unit, When delivering content, different delivery algorithms are applied depending on the customer's lifecycle. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned distribution unit, It estimates user sentiment and determines delivery priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned distribution unit, When delivering messages, the system prioritizes sending highly relevant messages and coupons, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned distribution unit, During distribution, the system analyzes customers' social media activity and delivers relevant messages and coupons. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned follow-up unit is, It estimates the user's emotions and adjusts the timing of follow-up based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned follow-up unit is, During follow-up, adjust the level of detail based on the customer's behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned follow-up unit is, The system estimates the user's emotions and determines the priority of follow-up based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned follow-up unit is, During follow-up, we prioritize highly relevant follow-ups by considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned loyalty program department, We estimate user sentiment and adjust how we deliver our loyalty program based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned loyalty program department, When offering a loyalty program, adjust the level of detail in the program based on the customer's behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned loyalty program department, It estimates user sentiment and prioritizes loyalty programs based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned loyalty program department, When offering loyalty programs, we prioritize providing programs that are highly relevant to the customer, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects customer behavior data and lifecycle data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates messages and coupons based on the data analyzed by the analysis unit, The system includes a distribution unit that automatically distributes messages and coupons generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect purchase history, ad click data, and chat data from messaging platforms. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we analyze customer behavior patterns and lifecycles. The system according to feature 1.

4. The generating unit is Based on the analysis results, we generate the most suitable messages and coupons for each customer. The system according to feature 1.

5. The aforementioned distribution unit, The generated messages and coupons are automatically delivered through the messaging platform. The system according to feature 1.

6. The system includes a follow-up unit that performs follow-up based on the messages distributed by the aforementioned distribution unit. The system according to feature 1.

7. The company has a loyalty program department that provides loyalty programs to promote customer re-engagement. The system according to feature 1.

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

9. The aforementioned collection unit is Analyze past customer behavior data to select the optimal data collection method. The system according to feature 1.

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

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