system

Abstract

The system according to the embodiment aims to generate optimal subject lines based on customer behavior data and improve the open rate of emails. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a delivery unit, a monitoring unit, and an improvement unit. The collection unit collects customer behavioral data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a subject line based on the analysis results obtained by the analysis unit. The delivery unit delivers emails using the subject line generated by the generation unit. The monitoring unit monitors the open rate of emails delivered by the delivery unit. The improvement unit improves the subject line based on the data obtained by the monitoring unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, email subject lines were often not appealing to customers, resulting in low open rates.

[0005] The system according to the embodiment aims to generate optimal subject lines based on customer behavior data and improve the open rate of emails. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a delivery unit, a monitoring unit, and an improvement unit. The collection unit collects customer behavior data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a subject line based on the analysis results obtained by the analysis unit. The delivery unit delivers emails using the subject line generated by the generation unit. The monitoring unit monitors the open rate of emails delivered by the delivery unit. The improvement unit improves the subject line based on the data obtained by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment generates optimal subject lines based on customer behavior data, thereby improving the email open rate. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An email delivery system according to an embodiment of the present invention generates optimal subject lines for each customer and improves the open rate. The email delivery system collects customer behavior data, analyzes it using AI to generate optimal subject lines, delivers emails, and monitors the open rate. Furthermore, the effectiveness of the subject lines is continuously improved and optimized by implementing a PDCA cycle. For example, the email delivery system analyzes customers' past behavioral data and interests and generates optimal subject lines based on that data. Then, emails are delivered using the generated subject lines and the open rate is monitored. Furthermore, the effectiveness of the subject lines is continuously improved and optimized by implementing a PDCA cycle. This is expected to improve the open rate and ultimately contribute to the conversion rate (CV) of each service. This allows the email delivery system to generate optimal subject lines for each customer and improve the open rate. For example, improving the open rate can lead to more customers clicking links in emails, increasing the likelihood of them using or purchasing services.

[0029] An email delivery system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a delivery unit, a monitoring unit, and an improvement unit. The collection unit collects customer behavioral data. Examples of customer behavioral data include, but are not limited to, website browsing history, purchase history, and click history. The collection unit collects data such as the subject lines of emails opened by customers in the past, links clicked, and pages viewed. The collection unit can also collect data such as customers' social media activity and geographic location information. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze customer behavioral data and identify patterns of subject lines that are likely to interest customers. For example, the analysis unit can identify patterns such as specific keywords, phrases, and writing styles. The generation unit generates subject lines based on the analysis results obtained by the analysis unit. The generation unit, for example, uses AI to generate subject lines that are optimal for each customer. For example, if a particular customer is likely to respond to keywords such as "sale" or "limited edition," the generation unit generates subject lines that include those keywords. The delivery unit delivers emails using the subject lines generated by the generation unit. The delivery unit, for example, delivers emails using the generated subject lines and tracks the open rate in real time. The monitoring unit monitors the open rate of emails delivered by the delivery unit. For example, the monitoring unit allows the email delivery system to track the open rate in real time and evaluate which subject line is most effective. The improvement unit improves the subject lines based on the data obtained by the monitoring unit. For example, the improvement unit uses AI to generate new subject line patterns based on the open rate data and delivers the email again. In this way, the email delivery system according to the embodiment can generate optimal subject lines for each customer and improve the open rate. For example, improving the open rate can increase the number of customers who click on links in emails, increasing the likelihood that they will use or purchase services.

[0030] The collection unit can collect at least one of data on the subject lines of emails opened in the past, links clicked, and pages viewed. The collection unit, for example, collects the subject lines of emails opened in the past. For example, the collection unit can collect data for a period such as the past month or the past year. The collection unit can also collect data on links clicked. For example, the collection unit can collect data on advertising links, internal links, external links, etc. The collection unit can also collect data on pages viewed. For example, the collection unit can collect data on product pages, blog articles, homepages, etc. By collecting data on customers' past behavior, more accurate subject line generation becomes possible. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input customer behavior data into AI and have the AI ​​collect the data.

[0031] The analysis unit can analyze the collected data and identify patterns of subject lines that are likely to interest customers. The analysis unit can, for example, analyze the collected data and identify patterns of subject lines that are likely to interest customers. For example, the analysis unit can identify patterns of specific keywords, phrases, writing styles, etc. The analysis unit can also identify patterns of subject lines that are likely to interest customers based on customer behavior data. For example, the analysis unit can analyze data such as the subjects of emails opened in the past, links clicked, and pages viewed, to identify patterns of subject lines that are likely to interest customers. This can improve the open rate by identifying patterns of subject lines that are likely to interest customers. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, AI, for example. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0032] The generation unit can generate a subject line appropriate for each customer based on the identified pattern. For example, if a particular customer is likely to respond to keywords such as "sale" or "limited," the generation unit generates a subject line including those keywords. The generation unit can also generate personalized subject lines based on the customer's interests. For example, the generation unit generates a subject line that is likely to interest the customer based on the customer's past behavioral data. This can improve the open rate by generating a subject line that is optimal for each customer. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer behavioral data into AI and have the AI ​​generate the subject line.

[0033] The delivery unit can deliver emails using the generated subject lines. The delivery unit, for example, delivers emails using the generated subject lines. For example, the delivery unit delivers emails using the generated subject lines and tracks the open rate in real time. The delivery unit can also deliver personalized emails based on customer interests. For example, the delivery unit delivers emails with content that is likely to interest customers based on past behavioral data of the customers. In this way, by delivering emails using the generated subject lines, the open rate can be improved. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the generated subject lines into AI and have the AI ​​execute email delivery.

[0034] The monitoring unit can track the open rate of delivered emails in real time. The monitoring unit, for example, tracks the open rate of delivered emails in real time. For example, the monitoring unit may have an email delivery system that tracks the open rate in real time and evaluate which subject line is most effective. The monitoring unit may also further refine effective subject line patterns based on the open rate data. For example, the monitoring unit may analyze the open rate data and identify the most effective subject line pattern. In this way, by tracking the open rate of delivered emails in real time, effective subject line patterns can be further refined. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI, for example. For example, the monitoring unit may input open rate data into AI and have the AI ​​analyze the data.

[0035] The improvement department can generate a new subject line pattern based on the open rate data and send the email again. For example, the improvement department can generate a new subject line pattern based on the open rate data and send the email again. For example, the improvement department can use AI to generate a new subject line pattern based on the open rate data and send the email again. The improvement department can also continuously improve and optimize the effectiveness of subject lines by running the PDCA cycle. For example, the improvement department can use AI to generate a new subject line pattern based on the open rate data and send the email again. By repeating this process, the open rate can be maximized. In this way, the open rate can be maximized by generating a new subject line pattern based on the open rate data and sending the email again. Some or all of the above-mentioned processing in the improvement department can be performed using AI, for example, or without AI. For example, the improvement department can input open rate data into AI and have the AI ​​generate the subject lines.

[0036] The collection unit can analyze the user's past behavioral data and select the optimal data collection method. The collection unit, for example, analyzes the user's past behavioral data and selects the optimal data collection method. For example, the collection unit prioritizes data collection on pages that the user has frequently accessed in the past. The collection unit can also collect related data based on links the user has clicked in the past. The collection unit can also analyze the user's past behavioral patterns and determine the optimal timing for data collection. In this way, the optimal data collection method can be selected by analyzing the user's past behavioral data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral data into AI and have the AI ​​select the data collection method.

[0037] The collection unit can filter data based on the user's current interests when collecting data. For example, the collection unit can filter data based on the user's current interests when collecting data. For example, the collection unit can prioritize collecting data related to topics in which the user is currently interested. The collection unit can also filter data based on keywords recently searched by the user. The collection unit can also analyze the user's social media activities and collect related data. This allows for more relevant data to be collected by filtering data based on the user's current interests. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's interest data into AI and have the AI ​​perform data filtering.

[0038] The collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting data. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Also, if the user is using image input, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into AI and have the AI ​​select the collection means.

[0039] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting data related to the user's current location. The collection unit can also collect data related to places the user has visited in the past. The collection unit can also collect data related to travel destinations the user is planning. In this way, highly relevant data can be collected by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into AI and cause the AI ​​to collect data.

[0040] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect data related to content shared by the user on social media. The collection unit can also collect data by referring to the activities of the user's friends on social media. The collection unit can also analyze the content posted by the user on social media and collect related data. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into AI and have the AI ​​collect the data.

[0041] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit adjusts the data collection method based on feedback provided by the user in the past. The collection unit can also preferentially use a data collection method that the user previously preferred. The collection unit can also analyze the user's past feedback and suggest an optimal data collection method. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into AI and have the AI ​​customize the collection method.

[0042] The analysis unit can adjust the level of detail of the analysis based on the customer's interests during data analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the customer's interests during data analysis. For example, if the customer is interested in a specific topic, the analysis unit can perform a detailed analysis of that topic. Alternatively, if the customer has broad interests, the analysis unit can perform a comprehensive analysis. The analysis unit can also adjust the scope of the analysis based on the customer's interests. This allows for more relevant analysis by adjusting the level of detail of the analysis based on the customer's interests. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer interest data into AI and have the AI ​​adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the customer category when analyzing data. For example, the analysis unit applies different analysis algorithms depending on the customer category when analyzing data. For example, if the customer is a business user, the analysis unit applies a business-oriented analysis algorithm. Also, if the customer is a general consumer, the analysis unit can apply a consumer-oriented analysis algorithm. The analysis unit can also select the optimal analysis algorithm depending on the customer category. This enables more accurate analysis by applying the optimal analysis algorithm depending on the customer category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input customer category data into AI and have the AI ​​apply the analysis algorithm.

[0044] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results when analyzing data. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results when analyzing data. For example, the analysis unit can adjust the current analysis based on the user's past analysis results. The analysis unit can also improve the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by utilizing the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI and have the AI ​​improve the accuracy of the analysis.

[0045] The analysis unit can adjust the order of analysis based on the customer's behavioral history during data analysis. For example, the analysis unit can adjust the order of analysis based on the customer's behavioral history during data analysis. For example, the analysis unit can prioritize analyzing pages that the customer has frequently accessed in the past. The analysis unit can also prioritize analyzing related data based on links the customer has clicked in the past. The analysis unit can also analyze the customer's behavioral history and determine the optimal analysis order. This enables more efficient data analysis by adjusting the analysis order based on the customer's behavioral history. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer behavioral history data into AI and have the AI ​​adjust the analysis order.

[0046] The analysis unit can adjust the order of analysis based on customer relevance during data analysis. For example, the analysis unit can adjust the order of analysis based on customer relevance during data analysis. For example, the analysis unit can prioritize analyzing data related to topics in which the customer is currently interested. The analysis unit can also prioritize analyzing data based on keywords recently searched by the customer. The analysis unit can also adjust the order of analysis based on customer relevance. By adjusting the order of analysis based on customer relevance, more relevant data analysis is possible. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer relevance data into AI and have the AI ​​adjust the order of analysis.

[0047] The analysis unit can adjust the level of detail of the analysis according to the user's level of expertise during data analysis. For example, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise during data analysis. For example, the analysis unit provides a detailed analysis when the user has expertise. The analysis unit can also provide a concise analysis when the user is a beginner. The analysis unit can also adjust the level of detail of the analysis according to the user's level of expertise. This enables more appropriate data analysis by adjusting the level of detail of the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI and have the AI ​​adjust the level of detail of the analysis.

[0048] The generation unit can adjust the level of detail of the subject line based on the customer's interests when generating the subject line. For example, the generation unit adjusts the level of detail of the subject line based on the customer's interests when generating the subject line. For example, if the customer is interested in a specific topic, the generation unit generates a detailed subject line related to that topic. Alternatively, if the customer has broad interests, the generation unit can generate a general subject line. The generation unit can also adjust the scope of the subject line based on the customer's interests. In this way, by adjusting the level of detail of the subject line based on the customer's interests, more effective subject lines can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer interest data into AI and have the AI ​​adjust the level of detail of the subject line.

[0049] The generation unit can apply different generation algorithms depending on the customer category when generating a subject line. For example, the generation unit can apply different generation algorithms depending on the customer category when generating a subject line. For example, if the customer is a business user, the generation unit can apply a business-oriented subject line generation algorithm. Also, if the customer is a general consumer, the generation unit can apply a consumer-oriented subject line generation algorithm. The generation unit can also select the optimal subject line generation algorithm depending on the customer category. In this way, more effective subjects can be generated by applying the optimal generation algorithm depending on the customer category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input customer category data into AI and have the AI ​​apply the generation algorithm.

[0050] The generation unit can improve the accuracy of subject generation by referring to the user's past generation results. For example, the generation unit can improve the accuracy of subject generation by referring to the user's past generation results. For example, the generation unit can adjust the current subject generation based on the user's past subject generation results. The generation unit can also improve the generation algorithm by referring to the user's past subject generation results. The generation unit can also improve the accuracy of generation by utilizing the user's past subject generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's past generation result data into AI and have the AI ​​improve the accuracy of generation.

[0051] The generation unit can determine the priority of subjects based on the customer's behavioral history when generating subjects. For example, the generation unit determines the priority of subjects based on the customer's behavioral history when generating subjects. For example, the generation unit prioritizes subject patterns that the customer has frequently opened in the past. The generation unit can also prioritize subject lines related to links that the customer has clicked in the past. The generation unit can also analyze the customer's behavioral history and determine the priority of optimal subjects. In this way, more effective subject lines can be generated by prioritizing subjects based on the customer's behavioral history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer behavioral history data into AI and have the AI ​​determine the priority of subjects.

[0052] The generation unit can adjust the order of subjects based on the relevance of the customer when generating subjects. For example, the generation unit adjusts the order of subjects based on the relevance of the customer when generating subjects. For example, the generation unit prioritizes generating subjects related to topics in which the customer is currently interested. The generation unit can also prioritize generating subjects based on keywords recently searched by the customer. The generation unit can also adjust the order of subjects based on the relevance of the customer. In this way, more effective subjects can be generated by adjusting the order of subjects based on the relevance of the customer. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer relevance data into AI and have the AI ​​adjust the order of the subjects.

[0053] The generation unit can adjust the use of technical terms in the subject line according to the user's level of expertise when generating the subject line. For example, the generation unit can adjust the use of technical terms in the subject line according to the user's level of expertise when generating the subject line. For example, if the user has technical expertise, the generation unit can generate a subject line that includes technical terms. Also, if the user is a beginner, the generation unit can generate a concise and easy-to-understand subject line. The generation unit can also adjust the use of technical terms in the subject line according to the user's level of expertise. In this way, more effective subject lines can be generated by adjusting the use of technical terms in the subject line according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without AI. For example, the generation unit can input the user's level of expertise data into AI and cause the AI ​​to adjust the use of technical terms in the subject line.

[0054] The delivery unit can adjust the level of detail in delivery based on the customer's interests when delivering emails. The delivery unit, for example, adjusts the level of detail in delivery based on the customer's interests when delivering emails. For example, if a customer is interested in a specific topic, the delivery unit can deliver emails containing detailed information about that topic. Furthermore, if a customer has broad interests, the delivery unit can deliver emails containing general information. The delivery unit can also adjust the level of detail in delivery based on the customer's interests. This enables more effective email delivery by adjusting the level of detail in delivery based on the customer's interests. Some or all of the above-described processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input customer interest data into AI and have the AI ​​adjust the level of detail in delivery.

[0055] The delivery unit can apply different delivery algorithms depending on the customer category when delivering emails. For example, the delivery unit can apply different delivery algorithms depending on the customer category when delivering emails. For example, if the customer is a business user, the delivery unit can apply a business-oriented delivery algorithm. Also, if the customer is a general consumer, the delivery unit can apply a consumer-oriented delivery algorithm. The delivery unit can also select the optimal delivery algorithm depending on the customer category. This enables more effective email delivery by applying the optimal delivery algorithm depending on the customer category. Some or all of the above-mentioned processing in the delivery unit can be performed using, for example, AI, or can be performed without using AI. For example, the delivery unit can input customer category data into AI and have the AI ​​apply the delivery algorithm.

[0056] The delivery unit can improve the accuracy of delivery by referring to the user's past delivery results when delivering emails. For example, the delivery unit can improve the accuracy of delivery by referring to the user's past delivery results when delivering emails. For example, the delivery unit can adjust the current delivery based on the user's past delivery results. The delivery unit can also improve the delivery algorithm by referring to the user's past delivery results. The delivery unit can also improve the accuracy of delivery by utilizing the user's past delivery results. In this way, the accuracy of delivery can be improved by referring to the user's past delivery results. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the user's past delivery result data into AI and have the AI ​​improve the accuracy of delivery.

[0057] The delivery unit can adjust the delivery order based on the customer's behavioral history when delivering emails. For example, the delivery unit can adjust the delivery order based on the customer's behavioral history when delivering emails. For example, the delivery unit can prioritize delivery of information related to pages that the customer has frequently accessed in the past. The delivery unit can also prioritize delivery of information related to links that the customer has clicked in the past. The delivery unit can also analyze the customer's behavioral history and determine the optimal delivery order. This enables more effective email delivery by adjusting the delivery order based on the customer's behavioral history. Some or all of the above-described processing in the delivery unit can be performed using, for example, AI, or can be performed without using AI. For example, the delivery unit can input customer behavioral history data into AI and have the AI ​​adjust the delivery order.

[0058] The delivery unit can adjust the delivery order based on the relevance of the customer when delivering emails. For example, the delivery unit adjusts the delivery order based on the relevance of the customer when delivering emails. For example, the delivery unit prioritizes delivery of information related to topics in which the customer is currently interested. The delivery unit can also prioritize delivery of information based on keywords recently searched by the customer. The delivery unit can also adjust the delivery order based on the relevance of the customer. This enables more effective email delivery by adjusting the delivery order based on the relevance of the customer. Some or all of the above-described processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input customer relevance data into AI and have the AI ​​adjust the delivery order.

[0059] The delivery unit can adjust the level of detail of the delivery according to the user's level of expertise when delivering emails. For example, the delivery unit can adjust the level of detail of the delivery according to the user's level of expertise when delivering emails. For example, if the user has expertise, the delivery unit can deliver emails containing detailed information. Furthermore, if the user is a beginner, the delivery unit can deliver emails containing concise and easy-to-understand information. Furthermore, the delivery unit can adjust the level of detail of the delivery according to the user's level of expertise. This enables more effective email delivery by adjusting the level of detail of the delivery according to the user's level of expertise. Some or all of the above-described processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the user's level of expertise data into AI and have the AI ​​adjust the level of detail of the delivery.

[0060] The monitoring unit can adjust the level of detail of monitoring based on the customer's interests during monitoring. The monitoring unit, for example, adjusts the level of detail of monitoring based on the customer's interests during monitoring. For example, if the customer is interested in a specific topic, the monitoring unit performs detailed monitoring of that topic. Furthermore, if the customer has broad interests, the monitoring unit can also perform overall monitoring. Furthermore, the monitoring unit can adjust the scope of monitoring based on the customer's interests. This enables more appropriate monitoring by adjusting the level of detail of monitoring based on the customer's interests. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input customer interest data into AI and have the AI ​​adjust the level of detail of monitoring.

[0061] The monitoring unit can apply different monitoring algorithms depending on the customer category during monitoring. For example, the monitoring unit applies different monitoring algorithms depending on the customer category during monitoring. For example, if the customer is a business user, the monitoring unit applies a business-oriented monitoring algorithm. Also, if the customer is a general consumer, the monitoring unit can apply a consumer-oriented monitoring algorithm. The monitoring unit can also select the optimal monitoring algorithm depending on the customer category. This enables more appropriate monitoring by applying the optimal monitoring algorithm depending on the customer category. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input customer category data into AI and have the AI ​​apply the monitoring algorithm.

[0062] The monitoring unit can improve the accuracy of monitoring by referring to the user's past monitoring results during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to the user's past monitoring results during monitoring. For example, the monitoring unit can adjust the current monitoring based on the user's past monitoring results. The monitoring unit can also improve the monitoring algorithm by referring to the user's past monitoring results. The monitoring unit can also improve the accuracy of monitoring by utilizing the user's past monitoring results. In this way, the accuracy of monitoring can be improved by referring to the user's past monitoring results. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input the user's past monitoring result data into AI and have the AI ​​improve the accuracy of monitoring.

[0063] The monitoring unit can adjust the monitoring order based on the customer's behavioral history during monitoring. The monitoring unit, for example, adjusts the monitoring order based on the customer's behavioral history during monitoring. For example, the monitoring unit prioritizes monitoring data related to pages that the customer has frequently accessed in the past. The monitoring unit can also prioritize monitoring data related to links that the customer has clicked in the past. The monitoring unit can also analyze the customer's behavioral history and determine the optimal monitoring order. This enables more appropriate monitoring by adjusting the monitoring order based on the customer's behavioral history. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input customer behavioral history data into AI and have the AI ​​adjust the monitoring order.

[0064] The monitoring unit can adjust the monitoring order based on the relevance of the customer during monitoring. The monitoring unit, for example, adjusts the monitoring order based on the relevance of the customer during monitoring. For example, the monitoring unit prioritizes monitoring data related to topics in which the customer is currently interested. The monitoring unit can also prioritize monitoring data based on keywords recently searched by the customer. The monitoring unit can also adjust the monitoring order based on the relevance of the customer. This enables more appropriate monitoring by adjusting the monitoring order based on the relevance of the customer. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input customer relevance data into AI and have the AI ​​adjust the monitoring order.

[0065] The monitoring unit can adjust the level of detail of the monitoring during monitoring according to the user's level of expertise. For example, the monitoring unit adjusts the level of detail of the monitoring during monitoring according to the user's level of expertise. For example, the monitoring unit provides detailed monitoring when the user has expertise. The monitoring unit can also provide concise monitoring when the user is a beginner. The monitoring unit can also adjust the level of detail of the monitoring according to the user's level of expertise. This enables more appropriate monitoring by adjusting the level of detail of the monitoring according to the user's level of expertise. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input the user's level of expertise data into AI and have the AI ​​adjust the level of detail of the monitoring.

[0066] The improvement unit can adjust the level of detail of the improvement based on the customer's interests when making an improvement. For example, the improvement unit adjusts the level of detail of the improvement based on the customer's interests when making an improvement. For example, if the customer is interested in a specific topic, the improvement unit can make detailed improvement suggestions related to that topic. Also, if the customer has broad interests, the improvement unit can make overall improvement suggestions. The improvement unit can also adjust the scope of the improvement based on the customer's interests. This allows for more appropriate improvement by adjusting the level of detail of the improvement based on the customer's interests. Some or all of the above-mentioned processing in the improvement unit may be performed using, or without, AI, for example. For example, the improvement unit can input customer interest data into AI and have the AI ​​adjust the level of detail of the improvement.

[0067] The improvement unit can apply different improvement algorithms depending on the customer category during improvement. For example, the improvement unit applies different improvement algorithms depending on the customer category during improvement. For example, if the customer is a business user, the improvement unit applies a business improvement algorithm. Also, if the customer is a general consumer, the improvement unit can apply a consumer improvement algorithm. Also, the improvement unit can select the optimal improvement algorithm depending on the customer category. This enables more appropriate improvement by applying the optimal improvement algorithm depending on the customer category. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input customer category data into AI and have the AI ​​apply the improvement algorithm.

[0068] The improvement unit can improve the accuracy of the improvement by referring to the user's past improvement results when making an improvement. For example, the improvement unit can improve the accuracy of the improvement by referring to the user's past improvement results when making an improvement. For example, the improvement unit can adjust the current improvement based on the user's past improvement results. The improvement unit can also improve the improvement algorithm by referring to the user's past improvement results. The improvement unit can also improve the accuracy of the improvement by utilizing the user's past improvement results. In this way, the accuracy of the improvement can be improved by referring to the user's past improvement results. Some or all of the above-mentioned processing in the improvement unit can be performed using, for example, AI, or can be performed without using AI. For example, the improvement unit can input the user's past improvement result data into AI and have the AI ​​execute the improvement accuracy improvement.

[0069] The improvement unit can adjust the order of improvements based on the customer's behavioral history when making improvements. For example, the improvement unit adjusts the order of improvements based on the customer's behavioral history when making improvements. For example, the improvement unit prioritizes improving data related to pages that the customer has frequently accessed in the past. The improvement unit can also prioritize improving data related to links that the customer has clicked in the past. The improvement unit can also analyze the customer's behavioral history and determine the optimal order of improvements. This enables more appropriate improvements by adjusting the order of improvements based on the customer's behavioral history. Some or all of the above-described processing in the improvement unit may be performed using, or without, AI, for example. For example, the improvement unit can input customer behavioral history data into AI and have the AI ​​adjust the order of improvements.

[0070] The improvement unit can adjust the order of improvements based on customer relevance during improvement. For example, the improvement unit adjusts the order of improvements based on customer relevance during improvement. For example, the improvement unit prioritizes improving data related to topics in which the customer is currently interested. The improvement unit can also prioritize improving data based on keywords recently searched by the customer. The improvement unit can also adjust the order of improvements based on customer relevance. This enables more appropriate improvements by adjusting the order of improvements based on customer relevance. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input customer relevance data into AI and have the AI ​​adjust the order of improvements.

[0071] The improvement unit can adjust the level of detail of the improvement according to the user's level of expertise during improvement. For example, the improvement unit adjusts the level of detail of the improvement according to the user's level of expertise during improvement. For example, if the user has expertise, the improvement unit provides detailed improvement suggestions. Also, if the user is a beginner, the improvement unit can provide concise improvement suggestions. Also, the improvement unit can adjust the level of detail of the improvement according to the user's level of expertise. In this way, adjusting the level of detail of the improvement according to the user's level of expertise enables more appropriate improvement. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the user's level of expertise data into AI and have the AI ​​adjust the level of detail of the improvement.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The email delivery system can further include a recommended product generation unit based on the user's purchase history. The recommended product generation unit analyzes the user's past purchase history and identifies products that the user may be interested in. For example, it can recommend products similar to products the user has previously purchased. The recommended product generation unit can also recommend products based on seasons and trends. Furthermore, the recommended product generation unit can remind the user to purchase products regularly based on the user's purchase frequency. This enables more personalized product recommendations based on the user's purchase history.

[0074] The email delivery system may further include a social media analysis unit that analyzes a user's social media activity. The social media analysis unit analyzes content and comments shared by the user on social media to identify the user's interests. For example, the social media analysis unit may generate subject lines related to topics frequently shared by the user. The social media analysis unit may also generate relevant subject lines based on the activities of the user's friends. Furthermore, the social media analysis unit may analyze the content posted by the user on social media to generate personalized subject lines. This allows for the generation of more relevant subject lines based on the user's social media activity.

[0075] The email delivery system may further include a geographic information analysis unit that generates a subject line taking into account the user's geographic location information. The geographic information analysis unit analyzes data related to the user's current location and places the user has visited in the past to generate a subject line that is highly relevant to the user. For example, it may generate a subject line related to an event being held in the city the user is currently in. It may also generate a subject line that includes information related to tourist spots the user has visited in the past. It may also generate a subject line related to travel destinations the user is planning. This allows for the generation of more relevant subjects based on the user's geographic location information.

[0076] The email delivery system can further include a recommended product generation unit based on the user's purchase history. The recommended product generation unit analyzes the user's past purchase history and identifies products that the user may be interested in. For example, it can recommend products similar to products the user has previously purchased. The recommended product generation unit can also recommend products based on seasons and trends. Furthermore, the recommended product generation unit can remind the user to purchase products regularly based on the user's purchase frequency. This enables more personalized product recommendations based on the user's purchase history.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The collection department collects customer behavioral data, including website browsing history, purchase history, click history, email subject lines and links clicked on, pages viewed, social media activity, and geographic location information. Step 2: The analysis department analyzes the data collected by the collection department. The analysis department uses AI to analyze customer behavior data and identify patterns of subject lines that are likely to interest customers. It can identify patterns such as specific keywords, phrases, and writing styles. Step 3: The generator generates a subject line based on the analysis results obtained by the analyzer. The generator uses AI to generate the optimal subject line for each customer. For example, if a particular customer is likely to respond to keywords such as "sale" or "limited," it will generate a subject line that includes those keywords. Step 4: The delivery unit delivers the email using the subject line generated by the generation unit. The delivery unit delivers the email using the generated subject line and tracks the open rate in real time. Step 5: The monitoring department monitors the open rate of the emails sent by the delivery department. The monitoring department uses the email delivery system to track the open rate in real time and evaluate which subject line is most effective. Step 6: The Improvement Department improves the subject line based on the data obtained by the Monitoring Department. Based on the open rate data, the Improvement Department uses AI to generate new subject line patterns and send the email again.

[0079] (Example 2) An email delivery system according to an embodiment of the present invention generates optimal subject lines for each customer and improves the open rate. The email delivery system collects customer behavior data, analyzes it using AI to generate optimal subject lines, delivers emails, and monitors the open rate. Furthermore, the effectiveness of the subject lines is continuously improved and optimized by implementing a PDCA cycle. For example, the email delivery system analyzes customers' past behavioral data and interests and generates optimal subject lines based on that data. Then, emails are delivered using the generated subject lines and the open rate is monitored. Furthermore, the effectiveness of the subject lines is continuously improved and optimized by implementing a PDCA cycle. This is expected to improve the open rate and ultimately contribute to the conversion rate (CV) of each service. This allows the email delivery system to generate optimal subject lines for each customer and improve the open rate. For example, improving the open rate can lead to more customers clicking links in emails, increasing the likelihood of them using or purchasing services.

[0080] An email delivery system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a delivery unit, a monitoring unit, and an improvement unit. The collection unit collects customer behavioral data. Examples of customer behavioral data include, but are not limited to, website browsing history, purchase history, and click history. The collection unit collects data such as the subject lines of emails opened by customers in the past, links clicked, and pages viewed. The collection unit can also collect data such as customers' social media activity and geographic location information. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze customer behavioral data and identify patterns of subject lines that are likely to interest customers. For example, the analysis unit can identify patterns such as specific keywords, phrases, and writing styles. The generation unit generates subject lines based on the analysis results obtained by the analysis unit. The generation unit, for example, uses AI to generate subject lines that are optimal for each customer. For example, if a particular customer is likely to respond to keywords such as "sale" or "limited edition," the generation unit generates subject lines that include those keywords. The delivery unit delivers emails using the subject lines generated by the generation unit. The delivery unit, for example, delivers emails using the generated subject lines and tracks the open rate in real time. The monitoring unit monitors the open rate of emails delivered by the delivery unit. For example, the monitoring unit allows the email delivery system to track the open rate in real time and evaluate which subject line is most effective. The improvement unit improves the subject lines based on the data obtained by the monitoring unit. For example, the improvement unit uses AI to generate new subject line patterns based on the open rate data and delivers the email again. In this way, the email delivery system according to the embodiment can generate optimal subject lines for each customer and improve the open rate. For example, improving the open rate can increase the number of customers who click on links in emails, increasing the likelihood that they will use or purchase services.

[0081] The collection unit can collect at least one of data on the subject lines of emails opened in the past, links clicked, and pages viewed. The collection unit, for example, collects the subject lines of emails opened in the past. For example, the collection unit can collect data for a period such as the past month or the past year. The collection unit can also collect data on links clicked. For example, the collection unit can collect data on advertising links, internal links, external links, etc. The collection unit can also collect data on pages viewed. For example, the collection unit can collect data on product pages, blog articles, homepages, etc. By collecting data on customers' past behavior, more accurate subject line generation becomes possible. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input customer behavior data into AI and have the AI ​​collect the data.

[0082] The analysis unit can analyze the collected data and identify patterns of subject lines that are likely to interest customers. The analysis unit can, for example, analyze the collected data and identify patterns of subject lines that are likely to interest customers. For example, the analysis unit can identify patterns of specific keywords, phrases, writing styles, etc. The analysis unit can also identify patterns of subject lines that are likely to interest customers based on customer behavior data. For example, the analysis unit can analyze data such as the subjects of emails opened in the past, links clicked, and pages viewed, to identify patterns of subject lines that are likely to interest customers. This can improve the open rate by identifying patterns of subject lines that are likely to interest customers. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, AI, for example. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0083] The generation unit can generate a subject line appropriate for each customer based on the identified pattern. For example, if a particular customer is likely to respond to keywords such as "sale" or "limited," the generation unit generates a subject line including those keywords. The generation unit can also generate personalized subject lines based on the customer's interests. For example, the generation unit generates a subject line that is likely to interest the customer based on the customer's past behavioral data. This can improve the open rate by generating a subject line that is optimal for each customer. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer behavioral data into AI and have the AI ​​generate the subject line.

[0084] The delivery unit can deliver emails using the generated subject lines. The delivery unit, for example, delivers emails using the generated subject lines. For example, the delivery unit delivers emails using the generated subject lines and tracks the open rate in real time. The delivery unit can also deliver personalized emails based on customer interests. For example, the delivery unit delivers emails with content that is likely to interest customers based on past behavioral data of the customers. In this way, by delivering emails using the generated subject lines, the open rate can be improved. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the generated subject lines into AI and have the AI ​​execute email delivery.

[0085] The monitoring unit can track the open rate of delivered emails in real time. The monitoring unit, for example, tracks the open rate of delivered emails in real time. For example, the monitoring unit may have an email delivery system that tracks the open rate in real time and evaluate which subject line is most effective. The monitoring unit may also further refine effective subject line patterns based on the open rate data. For example, the monitoring unit may analyze the open rate data and identify the most effective subject line pattern. In this way, by tracking the open rate of delivered emails in real time, effective subject line patterns can be further refined. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI, for example. For example, the monitoring unit may input open rate data into AI and have the AI ​​analyze the data.

[0086] The improvement department can generate a new subject line pattern based on the open rate data and send the email again. For example, the improvement department can generate a new subject line pattern based on the open rate data and send the email again. For example, the improvement department can use AI to generate a new subject line pattern based on the open rate data and send the email again. The improvement department can also continuously improve and optimize the effectiveness of subject lines by running the PDCA cycle. For example, the improvement department can use AI to generate a new subject line pattern based on the open rate data and send the email again. By repeating this process, the open rate can be maximized. In this way, the open rate can be maximized by generating a new subject line pattern based on the open rate data and sending the email again. Some or all of the above-mentioned processing in the improvement department can be performed using AI, for example, or without AI. For example, the improvement department can input open rate data into AI and have the AI ​​generate the subject lines.

[0087] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can temporarily stop data collection and resume it later. This enables more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0088] The collection unit can analyze the user's past behavioral data and select the optimal data collection method. The collection unit, for example, analyzes the user's past behavioral data and selects the optimal data collection method. For example, the collection unit prioritizes data collection on pages that the user has frequently accessed in the past. The collection unit can also collect related data based on links the user has clicked in the past. The collection unit can also analyze the user's past behavioral patterns and determine the optimal timing for data collection. In this way, the optimal data collection method can be selected by analyzing the user's past behavioral data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral data into AI and have the AI ​​select the data collection method.

[0089] The collection unit can filter data based on the user's current interests when collecting data. For example, the collection unit can filter data based on the user's current interests when collecting data. For example, the collection unit can prioritize collecting data related to topics in which the user is currently interested. The collection unit can also filter data based on keywords recently searched by the user. The collection unit can also analyze the user's social media activities and collect related data. This allows for more relevant data to be collected by filtering data based on the user's current interests. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's interest data into AI and have the AI ​​perform data filtering.

[0090] The collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the user's input method when collecting data. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Also, if the user is using text input, the collection unit can prioritize collecting text data. Also, if the user is using image input, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into AI and have the AI ​​select the collection means.

[0091] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit can prioritize collecting data related to the latest trends. Furthermore, if the user is relaxed, the collection unit can prioritize collecting detailed analytical data. Furthermore, if the user is stressed, the collection unit can prioritize collecting concise data. This enables more appropriate data collection by determining the priority of data to be collected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI and have the AI ​​determine the priority of the data.

[0092] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting data related to the user's current location. The collection unit can also collect data related to places the user has visited in the past. The collection unit can also collect data related to travel destinations the user is planning. In this way, highly relevant data can be collected by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into AI and cause the AI ​​to collect data.

[0093] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect data related to content shared by the user on social media. The collection unit can also collect data by referring to the activities of the user's friends on social media. The collection unit can also analyze the content posted by the user on social media and collect related data. In this way, related data can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into AI and have the AI ​​collect the data.

[0094] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit adjusts the data collection method based on feedback provided by the user in the past. The collection unit can also preferentially use a data collection method that the user previously preferred. The collection unit can also analyze the user's past feedback and suggest an optimal data collection method. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into AI and have the AI ​​customize the collection method.

[0095] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, the analysis unit can perform a detailed data analysis when the user is relaxed. The analysis unit can also perform a concise data analysis when the user is in a hurry. The analysis unit can also perform a visually stimulating data analysis when the user is excited. This enables more appropriate data analysis by adjusting the data analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be 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-mentioned processing in the analysis unit can be performed using an AI, for example, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​adjust the data analysis method.

[0096] The analysis unit can adjust the level of detail of the analysis based on the customer's interests during data analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the customer's interests during data analysis. For example, if the customer is interested in a specific topic, the analysis unit can perform a detailed analysis of that topic. Alternatively, if the customer has broad interests, the analysis unit can perform a comprehensive analysis. The analysis unit can also adjust the scope of the analysis based on the customer's interests. This allows for more relevant analysis by adjusting the level of detail of the analysis based on the customer's interests. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer interest data into AI and have the AI ​​adjust the level of detail of the analysis.

[0097] The analysis unit can apply different analysis algorithms depending on the customer category when analyzing data. For example, the analysis unit applies different analysis algorithms depending on the customer category when analyzing data. For example, if the customer is a business user, the analysis unit applies a business-oriented analysis algorithm. Also, if the customer is a general consumer, the analysis unit can apply a consumer-oriented analysis algorithm. The analysis unit can also select the optimal analysis algorithm depending on the customer category. This enables more accurate analysis by applying the optimal analysis algorithm depending on the customer category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input customer category data into AI and have the AI ​​apply the analysis algorithm.

[0098] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results when analyzing data. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results when analyzing data. For example, the analysis unit can adjust the current analysis based on the user's past analysis results. The analysis unit can also improve the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by utilizing the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI and have the AI ​​improve the accuracy of the analysis.

[0099] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. For example, if the user is excited, the analysis unit can prioritize analyzing the most recent data. Furthermore, if the user is relaxed, the analysis unit can prioritize analyzing detailed data. Furthermore, if the user is stressed, the analysis unit can prioritize analyzing concise data. This enables more appropriate data analysis by determining the analysis priorities based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into an AI and have the AI ​​determine the analysis priorities.

[0100] The analysis unit can adjust the order of analysis based on the customer's behavioral history during data analysis. For example, the analysis unit can adjust the order of analysis based on the customer's behavioral history during data analysis. For example, the analysis unit can prioritize analyzing pages that the customer has frequently accessed in the past. The analysis unit can also prioritize analyzing related data based on links the customer has clicked in the past. The analysis unit can also analyze the customer's behavioral history and determine the optimal analysis order. This enables more efficient data analysis by adjusting the analysis order based on the customer's behavioral history. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer behavioral history data into AI and have the AI ​​adjust the analysis order.

[0101] The analysis unit can adjust the order of analysis based on customer relevance during data analysis. For example, the analysis unit can adjust the order of analysis based on customer relevance during data analysis. For example, the analysis unit can prioritize analyzing data related to topics in which the customer is currently interested. The analysis unit can also prioritize analyzing data based on keywords recently searched by the customer. The analysis unit can also adjust the order of analysis based on customer relevance. By adjusting the order of analysis based on customer relevance, more relevant data analysis is possible. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input customer relevance data into AI and have the AI ​​adjust the order of analysis.

[0102] The analysis unit can adjust the level of detail of the analysis according to the user's level of expertise during data analysis. For example, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise during data analysis. For example, the analysis unit provides a detailed analysis when the user has expertise. The analysis unit can also provide a concise analysis when the user is a beginner. The analysis unit can also adjust the level of detail of the analysis according to the user's level of expertise. This enables more appropriate data analysis by adjusting the level of detail of the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise data into AI and have the AI ​​adjust the level of detail of the analysis.

[0103] The generation unit can estimate the user's emotions and adjust the expression method for generating a subject line based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions and adjusts the expression method for generating a subject line based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a subject line with a gentle expression. If the user is in a hurry, the generation unit can generate a concise and to-the-point subject line. If the user is excited, the generation unit can generate a visually stimulating subject line. This allows for the generation of more effective subjects by adjusting the expression method for generating a subject line according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into an AI and have the AI ​​adjust the expression method for generating a subject line.

[0104] The generation unit can adjust the level of detail of the subject line based on the customer's interests when generating the subject line. For example, the generation unit adjusts the level of detail of the subject line based on the customer's interests when generating the subject line. For example, if the customer is interested in a specific topic, the generation unit generates a detailed subject line related to that topic. Alternatively, if the customer has broad interests, the generation unit can generate a general subject line. The generation unit can also adjust the scope of the subject line based on the customer's interests. In this way, by adjusting the level of detail of the subject line based on the customer's interests, more effective subject lines can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer interest data into AI and have the AI ​​adjust the level of detail of the subject line.

[0105] The generation unit can apply different generation algorithms depending on the customer category when generating a subject line. For example, the generation unit can apply different generation algorithms depending on the customer category when generating a subject line. For example, if the customer is a business user, the generation unit can apply a business-oriented subject line generation algorithm. Also, if the customer is a general consumer, the generation unit can apply a consumer-oriented subject line generation algorithm. The generation unit can also select the optimal subject line generation algorithm depending on the customer category. In this way, more effective subjects can be generated by applying the optimal generation algorithm depending on the customer category. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input customer category data into AI and have the AI ​​apply the generation algorithm.

[0106] The generation unit can improve the accuracy of subject generation by referring to the user's past generation results. For example, the generation unit can improve the accuracy of subject generation by referring to the user's past generation results. For example, the generation unit can adjust the current subject generation based on the user's past subject generation results. The generation unit can also improve the generation algorithm by referring to the user's past subject generation results. The generation unit can also improve the accuracy of generation by utilizing the user's past subject generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the user's past generation result data into AI and have the AI ​​improve the accuracy of generation.

[0107] The generation unit can estimate the user's emotions and adjust the length of the subject line based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the subject line based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a longer subject line containing detailed information. If the user is in a hurry, the generation unit can generate a concise and short subject line. If the user is excited, the generation unit can generate a short, visually stimulating subject line. This allows for the generation of more effective subject lines by adjusting the length of the subject line according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the user's emotion data into an AI and have the AI ​​adjust the length of the subject line.

[0108] The generation unit can determine the priority of subjects based on the customer's behavioral history when generating subjects. For example, the generation unit determines the priority of subjects based on the customer's behavioral history when generating subjects. For example, the generation unit prioritizes subject patterns that the customer has frequently opened in the past. The generation unit can also prioritize subject lines related to links that the customer has clicked in the past. The generation unit can also analyze the customer's behavioral history and determine the priority of optimal subjects. In this way, more effective subject lines can be generated by prioritizing subjects based on the customer's behavioral history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer behavioral history data into AI and have the AI ​​determine the priority of subjects.

[0109] The generation unit can adjust the order of subjects based on the relevance of the customer when generating subjects. For example, the generation unit adjusts the order of subjects based on the relevance of the customer when generating subjects. For example, the generation unit prioritizes generating subjects related to topics in which the customer is currently interested. The generation unit can also prioritize generating subjects based on keywords recently searched by the customer. The generation unit can also adjust the order of subjects based on the relevance of the customer. In this way, more effective subjects can be generated by adjusting the order of subjects based on the relevance of the customer. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer relevance data into AI and have the AI ​​adjust the order of the subjects.

[0110] The generation unit can adjust the use of technical terms in the subject line according to the user's level of expertise when generating the subject line. For example, the generation unit can adjust the use of technical terms in the subject line according to the user's level of expertise when generating the subject line. For example, if the user has technical expertise, the generation unit can generate a subject line that includes technical terms. Also, if the user is a beginner, the generation unit can generate a concise and easy-to-understand subject line. The generation unit can also adjust the use of technical terms in the subject line according to the user's level of expertise. In this way, more effective subject lines can be generated by adjusting the use of technical terms in the subject line according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without AI. For example, the generation unit can input the user's level of expertise data into AI and cause the AI ​​to adjust the use of technical terms in the subject line.

[0111] The delivery unit can estimate a user's emotions and adjust the timing of email delivery based on the estimated user emotions. For example, the delivery unit estimates a user's emotions and adjusts the timing of email delivery based on the estimated user emotions. For example, if the user is relaxed, the delivery unit delivers the email immediately. If the user is in a hurry, the delivery unit can also adjust the schedule to deliver the email later. If the user is feeling stressed, the delivery unit can temporarily delay the delivery. This enables more effective email delivery by adjusting the timing of email delivery according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the delivery unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the delivery unit can input user emotion data into an AI and have the AI ​​adjust the timing of email delivery.

[0112] The delivery unit can adjust the level of detail in delivery based on the customer's interests when delivering emails. The delivery unit, for example, adjusts the level of detail in delivery based on the customer's interests when delivering emails. For example, if a customer is interested in a specific topic, the delivery unit can deliver emails containing detailed information about that topic. Furthermore, if a customer has broad interests, the delivery unit can deliver emails containing general information. The delivery unit can also adjust the level of detail in delivery based on the customer's interests. This enables more effective email delivery by adjusting the level of detail in delivery based on the customer's interests. Some or all of the above-described processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input customer interest data into AI and have the AI ​​adjust the level of detail in delivery.

[0113] The delivery unit can apply different delivery algorithms depending on the customer category when delivering emails. For example, the delivery unit can apply different delivery algorithms depending on the customer category when delivering emails. For example, if the customer is a business user, the delivery unit can apply a business-oriented delivery algorithm. Also, if the customer is a general consumer, the delivery unit can apply a consumer-oriented delivery algorithm. The delivery unit can also select the optimal delivery algorithm depending on the customer category. This enables more effective email delivery by applying the optimal delivery algorithm depending on the customer category. Some or all of the above-mentioned processing in the delivery unit can be performed using, for example, AI, or can be performed without using AI. For example, the delivery unit can input customer category data into AI and have the AI ​​apply the delivery algorithm.

[0114] The delivery unit can improve the accuracy of delivery by referring to the user's past delivery results when delivering emails. For example, the delivery unit can improve the accuracy of delivery by referring to the user's past delivery results when delivering emails. For example, the delivery unit can adjust the current delivery based on the user's past delivery results. The delivery unit can also improve the delivery algorithm by referring to the user's past delivery results. The delivery unit can also improve the accuracy of delivery by utilizing the user's past delivery results. In this way, the accuracy of delivery can be improved by referring to the user's past delivery results. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the user's past delivery result data into AI and have the AI ​​improve the accuracy of delivery.

[0115] The delivery unit can estimate the user's emotions and determine delivery priorities based on the estimated user emotions. The delivery unit, for example, estimates the user's emotions and determines delivery priorities based on the estimated user emotions. For example, if the user is excited, the delivery unit prioritizes delivery of the latest information. Furthermore, if the user is relaxed, the delivery unit can prioritize delivery of detailed information. Furthermore, if the user is stressed, the delivery unit can prioritize delivery of concise information. This enables more effective email delivery by determining delivery priorities based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the delivery unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the delivery unit may input the user's emotion data into an AI and have the AI ​​determine the delivery priorities.

[0116] The delivery unit can adjust the delivery order based on the customer's behavioral history when delivering emails. For example, the delivery unit can adjust the delivery order based on the customer's behavioral history when delivering emails. For example, the delivery unit can prioritize delivery of information related to pages that the customer has frequently accessed in the past. The delivery unit can also prioritize delivery of information related to links that the customer has clicked in the past. The delivery unit can also analyze the customer's behavioral history and determine the optimal delivery order. This enables more effective email delivery by adjusting the delivery order based on the customer's behavioral history. Some or all of the above-described processing in the delivery unit can be performed using, for example, AI, or can be performed without using AI. For example, the delivery unit can input customer behavioral history data into AI and have the AI ​​adjust the delivery order.

[0117] The delivery unit can adjust the delivery order based on the relevance of the customer when delivering emails. For example, the delivery unit adjusts the delivery order based on the relevance of the customer when delivering emails. For example, the delivery unit prioritizes delivery of information related to topics in which the customer is currently interested. The delivery unit can also prioritize delivery of information based on keywords recently searched by the customer. The delivery unit can also adjust the delivery order based on the relevance of the customer. This enables more effective email delivery by adjusting the delivery order based on the relevance of the customer. Some or all of the above-described processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input customer relevance data into AI and have the AI ​​adjust the delivery order.

[0118] The delivery unit can adjust the level of detail of the delivery according to the user's level of expertise when delivering emails. For example, the delivery unit can adjust the level of detail of the delivery according to the user's level of expertise when delivering emails. For example, if the user has expertise, the delivery unit can deliver emails containing detailed information. Furthermore, if the user is a beginner, the delivery unit can deliver emails containing concise and easy-to-understand information. Furthermore, the delivery unit can adjust the level of detail of the delivery according to the user's level of expertise. This enables more effective email delivery by adjusting the level of detail of the delivery according to the user's level of expertise. Some or all of the above-described processing in the delivery unit may be performed using, for example, AI, or may be performed without using AI. For example, the delivery unit can input the user's level of expertise data into AI and have the AI ​​adjust the level of detail of the delivery.

[0119] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. For example, the monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. For example, the monitoring unit can perform detailed monitoring when the user is relaxed. The monitoring unit can also perform brief monitoring when the user is in a hurry. The monitoring unit can also perform visually stimulating monitoring when the user is excited. This enables more appropriate monitoring by adjusting the monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the monitoring unit can be performed using an AI, for example, or without an AI. For example, the monitoring unit can input the user's emotion data into an AI and have the AI ​​adjust the monitoring method.

[0120] The monitoring unit can adjust the level of detail of monitoring based on the customer's interests during monitoring. The monitoring unit, for example, adjusts the level of detail of monitoring based on the customer's interests during monitoring. For example, if the customer is interested in a specific topic, the monitoring unit performs detailed monitoring of that topic. Furthermore, if the customer has broad interests, the monitoring unit can also perform overall monitoring. Furthermore, the monitoring unit can adjust the scope of monitoring based on the customer's interests. This enables more appropriate monitoring by adjusting the level of detail of monitoring based on the customer's interests. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input customer interest data into AI and have the AI ​​adjust the level of detail of monitoring.

[0121] The monitoring unit can apply different monitoring algorithms depending on the customer category during monitoring. For example, the monitoring unit applies different monitoring algorithms depending on the customer category during monitoring. For example, if the customer is a business user, the monitoring unit applies a business-oriented monitoring algorithm. Also, if the customer is a general consumer, the monitoring unit can apply a consumer-oriented monitoring algorithm. The monitoring unit can also select the optimal monitoring algorithm depending on the customer category. This enables more appropriate monitoring by applying the optimal monitoring algorithm depending on the customer category. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input customer category data into AI and have the AI ​​apply the monitoring algorithm.

[0122] The monitoring unit can improve the accuracy of monitoring by referring to the user's past monitoring results during monitoring. For example, the monitoring unit can improve the accuracy of monitoring by referring to the user's past monitoring results during monitoring. For example, the monitoring unit can adjust the current monitoring based on the user's past monitoring results. The monitoring unit can also improve the monitoring algorithm by referring to the user's past monitoring results. The monitoring unit can also improve the accuracy of monitoring by utilizing the user's past monitoring results. In this way, the accuracy of monitoring can be improved by referring to the user's past monitoring results. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input the user's past monitoring result data into AI and have the AI ​​improve the accuracy of monitoring.

[0123] The monitoring unit can estimate the user's emotions and determine the monitoring priorities based on the estimated user emotions. For example, the monitoring unit estimates the user's emotions and determines the monitoring priorities based on the estimated user emotions. For example, if the user is excited, the monitoring unit prioritizes monitoring the most recent data. Also, if the user is relaxed, the monitoring unit can prioritize monitoring detailed data. Also, if the user is stressed, the monitoring unit can prioritize monitoring concise data. This enables more appropriate monitoring by determining the monitoring priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the monitoring unit may be performed using an AI, for example, or without an AI. For example, the monitoring unit can input the user's emotion data into an AI and have the AI ​​determine the monitoring priorities.

[0124] The monitoring unit can adjust the monitoring order based on the customer's behavioral history during monitoring. The monitoring unit, for example, adjusts the monitoring order based on the customer's behavioral history during monitoring. For example, the monitoring unit prioritizes monitoring data related to pages that the customer has frequently accessed in the past. The monitoring unit can also prioritize monitoring data related to links that the customer has clicked in the past. The monitoring unit can also analyze the customer's behavioral history and determine the optimal monitoring order. This enables more appropriate monitoring by adjusting the monitoring order based on the customer's behavioral history. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input customer behavioral history data into AI and have the AI ​​adjust the monitoring order.

[0125] The monitoring unit can adjust the monitoring order based on the relevance of the customer during monitoring. The monitoring unit, for example, adjusts the monitoring order based on the relevance of the customer during monitoring. For example, the monitoring unit prioritizes monitoring data related to topics in which the customer is currently interested. The monitoring unit can also prioritize monitoring data based on keywords recently searched by the customer. The monitoring unit can also adjust the monitoring order based on the relevance of the customer. This enables more appropriate monitoring by adjusting the monitoring order based on the relevance of the customer. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input customer relevance data into AI and have the AI ​​adjust the monitoring order.

[0126] The monitoring unit can adjust the level of detail of the monitoring during monitoring according to the user's level of expertise. For example, the monitoring unit adjusts the level of detail of the monitoring during monitoring according to the user's level of expertise. For example, the monitoring unit provides detailed monitoring when the user has expertise. The monitoring unit can also provide concise monitoring when the user is a beginner. The monitoring unit can also adjust the level of detail of the monitoring according to the user's level of expertise. This enables more appropriate monitoring by adjusting the level of detail of the monitoring according to the user's level of expertise. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input the user's level of expertise data into AI and have the AI ​​adjust the level of detail of the monitoring.

[0127] The improvement unit can estimate the user's emotions and adjust the improvement method based on the estimated user's emotions. For example, the improvement unit estimates the user's emotions and adjusts the improvement method based on the estimated user's emotions. For example, the improvement unit can make detailed improvement suggestions when the user is relaxed. Furthermore, the improvement unit can make concise improvement suggestions when the user is in a hurry. Furthermore, the improvement unit can make visually stimulating improvement suggestions when the user is excited. This enables more appropriate improvement by adjusting the improvement method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the improvement unit may be performed using an AI, for example, or without an AI. For example, the improvement unit can input the user's emotion data into an AI and have the AI ​​adjust the improvement method.

[0128] The improvement unit can adjust the level of detail of the improvement based on the customer's interests when making an improvement. For example, the improvement unit adjusts the level of detail of the improvement based on the customer's interests when making an improvement. For example, if the customer is interested in a specific topic, the improvement unit can make detailed improvement suggestions related to that topic. Also, if the customer has broad interests, the improvement unit can make overall improvement suggestions. The improvement unit can also adjust the scope of the improvement based on the customer's interests. This allows for more appropriate improvement by adjusting the level of detail of the improvement based on the customer's interests. Some or all of the above-mentioned processing in the improvement unit may be performed using, or without, AI, for example. For example, the improvement unit can input customer interest data into AI and have the AI ​​adjust the level of detail of the improvement.

[0129] The improvement unit can apply different improvement algorithms depending on the customer category during improvement. For example, the improvement unit applies different improvement algorithms depending on the customer category during improvement. For example, if the customer is a business user, the improvement unit applies a business improvement algorithm. Also, if the customer is a general consumer, the improvement unit can apply a consumer improvement algorithm. Also, the improvement unit can select the optimal improvement algorithm depending on the customer category. This enables more appropriate improvement by applying the optimal improvement algorithm depending on the customer category. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input customer category data into AI and have the AI ​​apply the improvement algorithm.

[0130] The improvement unit can improve the accuracy of the improvement by referring to the user's past improvement results when making an improvement. For example, the improvement unit can improve the accuracy of the improvement by referring to the user's past improvement results when making an improvement. For example, the improvement unit can adjust the current improvement based on the user's past improvement results. The improvement unit can also improve the improvement algorithm by referring to the user's past improvement results. The improvement unit can also improve the accuracy of the improvement by utilizing the user's past improvement results. In this way, the accuracy of the improvement can be improved by referring to the user's past improvement results. Some or all of the above-mentioned processing in the improvement unit can be performed using, for example, AI, or can be performed without using AI. For example, the improvement unit can input the user's past improvement result data into AI and have the AI ​​execute the improvement accuracy improvement.

[0131] The improvement unit can estimate the user's emotions and determine the priority of improvements based on the estimated user emotions. For example, the improvement unit estimates the user's emotions and determines the priority of improvements based on the estimated user emotions. For example, if the user is excited, the improvement unit prioritizes improving the most recent data. Furthermore, if the user is relaxed, the improvement unit can prioritize improving detailed data. Furthermore, if the user is stressed, the improvement unit can prioritize improving concise data. This enables more appropriate improvements by determining the priority of improvements according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the improvement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the improvement unit may input the user's emotion data into an AI and have the AI ​​determine the priority of improvements.

[0132] The improvement unit can adjust the order of improvements based on the customer's behavioral history when making improvements. For example, the improvement unit adjusts the order of improvements based on the customer's behavioral history when making improvements. For example, the improvement unit prioritizes improving data related to pages that the customer has frequently accessed in the past. The improvement unit can also prioritize improving data related to links that the customer has clicked in the past. The improvement unit can also analyze the customer's behavioral history and determine the optimal order of improvements. This enables more appropriate improvements by adjusting the order of improvements based on the customer's behavioral history. Some or all of the above-described processing in the improvement unit may be performed using, or without, AI, for example. For example, the improvement unit can input customer behavioral history data into AI and have the AI ​​adjust the order of improvements.

[0133] The improvement unit can adjust the order of improvements based on customer relevance during improvement. For example, the improvement unit adjusts the order of improvements based on customer relevance during improvement. For example, the improvement unit prioritizes improving data related to topics in which the customer is currently interested. The improvement unit can also prioritize improving data based on keywords recently searched by the customer. The improvement unit can also adjust the order of improvements based on customer relevance. This enables more appropriate improvements by adjusting the order of improvements based on customer relevance. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input customer relevance data into AI and have the AI ​​adjust the order of improvements.

[0134] The improvement unit can adjust the level of detail of the improvement according to the user's level of expertise during improvement. For example, the improvement unit adjusts the level of detail of the improvement according to the user's level of expertise during improvement. For example, if the user has expertise, the improvement unit provides detailed improvement suggestions. Also, if the user is a beginner, the improvement unit can provide concise improvement suggestions. Also, the improvement unit can adjust the level of detail of the improvement according to the user's level of expertise. In this way, adjusting the level of detail of the improvement according to the user's level of expertise enables more appropriate improvement. Some or all of the above-described processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the user's level of expertise data into AI and have the AI ​​adjust the level of detail of the improvement. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, delivery unit, monitoring unit, and improvement unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects customer behavior data using the camera 42 and microphone 38B of the smart device 14 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a subject line based on the analysis result. The delivery unit, realized, for example, by the control unit 46A of the smart device 14, delivers an email using the generated subject line. The monitoring unit, realized, for example, by the specific processing unit 290 of the data processing device 12, monitors the open rate of the delivered email. The improvement unit, realized, for example, by the specific processing unit 290 of the data processing device 12, improves the subject line based on the monitoring result. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, distribution unit, monitoring unit, and improvement unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects customer behavior data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a subject line based on the analysis result. The distribution unit, realized, for example, by the control unit 46A of the smart glasses 214, distributes emails using the generated subject line. The monitoring unit, realized, for example, by the specific processing unit 290 of the data processing device 12, monitors the open rate of the distributed emails. The improvement unit, realized, for example, by the specific processing unit 290 of the data processing device 12, improves the subject line based on the monitoring result. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, delivery unit, monitoring unit, and improvement unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects customer behavior data using the camera 42 and microphone 238 of the headset-type terminal 314 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a subject line based on the analysis results. The delivery unit, realized, for example, by the control unit 46A of the headset-type terminal 314, delivers emails using the generated subject line. The monitoring unit, realized, for example, by the specific processing unit 290 of the data processing device 12, monitors the open rate of delivered emails. The improvement unit, realized, for example, by the specific processing unit 290 of the data processing device 12, improves the subject line based on the monitoring results. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, distribution unit, monitoring unit, and improvement unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects customer behavior data using the camera 42 and microphone 238 of the robot 414 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a subject line based on the analysis result. The distribution unit, realized, for example, by the control unit 46A of the robot 414, distributes emails using the generated subject line. The monitoring unit, realized, for example, by the specific processing unit 290 of the data processing device 12, monitors the open rate of the distributed emails. The improvement unit, realized, for example, by the specific processing unit 290 of the data processing device 12, improves the subject line based on the monitoring result.

[0135] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0136] The email delivery system can further include a recommended product generation unit based on the user's purchase history. The recommended product generation unit analyzes the user's past purchase history and identifies products that the user may be interested in. For example, it can recommend products similar to products the user has previously purchased. The recommended product generation unit can also recommend products based on seasons and trends. Furthermore, the recommended product generation unit can remind the user to purchase products regularly based on the user's purchase frequency. This enables more personalized product recommendations based on the user's purchase history.

[0137] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the user's burden. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, data collection can be temporarily stopped and resumed later. This allows for more appropriate data collection by adjusting the frequency of data collection according to the user's emotions.

[0138] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is relaxed, detailed data analysis can be performed. If the user is in a hurry, brief data analysis can be performed. Furthermore, if the user is excited, visually stimulating data analysis can be performed. In this way, by adjusting the data analysis method according to the user's emotions, more appropriate data analysis can be performed.

[0139] The generation unit can estimate the user's emotions and adjust the expression method for generating a subject line based on the estimated emotions. For example, if the user is relaxed, a subject line with gentle expressions can be generated. If the user is in a hurry, a subject line that is concise and to the point can be generated. Furthermore, if the user is excited, a visually stimulating subject line can be generated. In this way, by adjusting the expression method for generating a subject line according to the user's emotions, more effective subject lines can be generated.

[0140] The delivery unit can estimate the user's emotions and adjust the timing of email delivery based on the estimated emotions. For example, if the user is relaxed, the email can be delivered immediately. If the user is in a hurry, the schedule can be adjusted to deliver the email later. Furthermore, if the user is feeling stressed, the delivery can be temporarily delayed. This allows for more effective email delivery by adjusting the timing of email delivery according to the user's emotions.

[0141] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated emotions. For example, if the user is relaxed, detailed monitoring can be performed. If the user is in a hurry, brief monitoring can be performed. Furthermore, if the user is excited, visually stimulating monitoring can be performed. In this way, more appropriate monitoring can be achieved by adjusting the monitoring method according to the user's emotions.

[0142] The improvement unit can estimate the user's emotions and adjust the improvement method based on the estimated emotions. For example, if the user is relaxed, detailed improvement suggestions can be made. If the user is in a hurry, concise improvement suggestions can be made. Furthermore, if the user is excited, visually stimulating improvement suggestions can be made. In this way, more appropriate improvements can be made by adjusting the improvement method according to the user's emotions.

[0143] The email delivery system may further include a social media analysis unit that analyzes a user's social media activity. The social media analysis unit analyzes content and comments shared by the user on social media to identify the user's interests. For example, the social media analysis unit may generate subject lines related to topics frequently shared by the user. The social media analysis unit may also generate relevant subject lines based on the activities of the user's friends. Furthermore, the social media analysis unit may analyze the content posted by the user on social media to generate personalized subject lines. This allows for the generation of more relevant subject lines based on the user's social media activity.

[0144] The email delivery system may further include a geographic information analysis unit that generates a subject line taking into account the user's geographic location information. The geographic information analysis unit analyzes data related to the user's current location and places the user has visited in the past to generate a subject line that is highly relevant to the user. For example, it may generate a subject line related to an event being held in the city the user is currently in. It may also generate a subject line that includes information related to tourist spots the user has visited in the past. It may also generate a subject line related to travel destinations the user is planning. This allows for the generation of more relevant subjects based on the user's geographic location information.

[0145] The email delivery system can further include a recommended product generation unit based on the user's purchase history. The recommended product generation unit analyzes the user's past purchase history and identifies products that the user may be interested in. For example, it can recommend products similar to products the user has previously purchased. The recommended product generation unit can also recommend products based on seasons and trends. Furthermore, the recommended product generation unit can remind the user to purchase products regularly based on the user's purchase frequency. This enables more personalized product recommendations based on the user's purchase history.

[0146] The processing flow of the second embodiment will be briefly explained below.

[0147] Step 1: The collection department collects customer behavioral data, including website browsing history, purchase history, click history, email subject lines and links clicked on, pages viewed, social media activity, and geographic location information. Step 2: The analysis department analyzes the data collected by the collection department. The analysis department uses AI to analyze customer behavior data and identify patterns of subject lines that are likely to interest customers. It can identify patterns such as specific keywords, phrases, and writing styles. Step 3: The generator generates a subject line based on the analysis results obtained by the analyzer. The generator uses AI to generate the optimal subject line for each customer. For example, if a particular customer is likely to respond to keywords such as "sale" or "limited," it will generate a subject line that includes those keywords. Step 4: The delivery unit delivers the email using the subject line generated by the generation unit. The delivery unit delivers the email using the generated subject line and tracks the open rate in real time. Step 5: The monitoring department monitors the open rate of the emails sent by the delivery department. The monitoring department uses the email delivery system to track the open rate in real time and evaluate which subject line is most effective. Step 6: The Improvement Department improves the subject line based on the data obtained by the Monitoring Department. Based on the open rate data, the Improvement Department uses AI to generate new subject line patterns and send the email again.

[0148] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0153] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0169] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0170] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0171] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0172] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0174] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0175] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0179] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0180] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0185] 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.

[0186] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0187] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0188] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0189] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0190] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0191] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0192] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0193] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0194] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0195] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0196] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0197] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0198] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0199] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0202] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0203] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0204] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0205] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0206] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0207] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0208] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0209] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0210] 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.

[0211] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0212] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0213] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0214] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0215] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0216] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0217] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0218] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0219] [Explanation of symbols]

[0220] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects customer behavior data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates a subject line based on the analysis result obtained by the analysis unit; a delivery unit that delivers an email using the subject generated by the generation unit; a monitoring unit that monitors the opening rate of emails delivered by the delivery unit; and an improvement unit that improves the subject line based on the data obtained by the monitoring unit. A system characterized by:

2. The collecting unit Collect data on at least one of the following: the subject of emails opened in the past, links clicked, and pages viewed 2. The system of claim 1.

3. The analysis unit Analyze the collected data to identify patterns in subject lines that are likely to interest customers 2. The system of claim 1.

4. The generation unit Generate personalized subject lines based on identified patterns 2. The system of claim 1.

5. The distribution unit Send emails with generated subject lines 2. The system of claim 1.

6. The monitoring unit Track the open rates of emails sent in real time 2. The system of claim 1.

7. The improvement unit Generate new subject line variations based on open rate data and send the email again 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

9. The collecting unit Analyze users' past behavioral data and select the optimal data collection method 2. The system of claim 1.

10. The collecting unit At the time of data collection, filtering based on the user's current interests 2. The system of claim 1.

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