System

Abstract

An object of the system according to the embodiment is to automatically generate a header, a content, and a target person list of an e-mail based on log data and to develop an efficient sales promotion strategy.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects log data. The analysis unit analyzes the log data collected by the collection unit. The generation unit generates a header, a content, and a target person list of the mail based on the analysis result obtained by the analysis unit. The providing unit provides the mail generated by the generating unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, email headlines, content, and target lists were generated manually, which was inefficient and made it difficult to develop promotional strategies that met user needs.

[0005] The system according to the embodiment aims to develop an efficient sales promotion strategy by automatically generating email titles, content, and target lists based on log data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects log data. The analysis unit analyzes the log data collected by the collection unit. The generation unit generates a mail title, content, and target recipient list based on the analysis results obtained by the analysis unit. The provision unit provides the mail generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate email titles, content, and target lists based on log data, enabling the development of efficient sales promotion strategies. [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) A system according to an embodiment of the present invention utilizes a generative AI to automatically generate email headlines, content, and target lists for commerce emails. This system collects log data and uses a generative AI to analyze it to understand current trends and user interests and predict future consumption trends. This allows companies to understand future consumption trends and develop appropriate sales promotion strategies. Furthermore, the generative AI automatically generates email headlines, content, and target lists based on the prediction results. For example, for users interested in a specific product, the system automatically generates emails containing promotional information related to that product. This allows users to receive attractive emails tailored to their interests. This reduces the cost of creating emails and improves both company and user satisfaction. For example, companies can conduct promotional activities more efficiently, and users can receive information tailored to their needs. Furthermore, the gap between market demand and supply is narrowed, improving overall market efficiency.

[0029] An email generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects log data. Examples of the log data include, but are not limited to, access logs, error logs, and transaction logs. The collection unit can collect data such as past purchase history, browsing history, and click rates. The analysis unit analyzes the log data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. The analysis unit can analyze social media and search keywords to understand current trends and user interests. The generation unit generates email titles, content, and target lists based on the analysis results obtained by the analysis unit. The generation can be performed based on, for example, the use of templates and the degree of personalization, but is not limited to these examples. The generation unit can generate emails containing promotional information related to a specific product for users interested in that product. The provision unit provides the emails generated by the generation unit to users. The provision can be performed based on, for example, but is not limited to these examples, the email sending method, timing, and delivery platform. The providing unit, for example, sends the generated email to a user, and the user can receive the email. As a result, the email generation system according to the embodiment automates the process from collecting and analyzing log data to generating and providing emails, thereby achieving efficient email marketing.

[0030] The collection unit can collect log data of past purchase history, browsing history, and click rate. The collection unit, for example, collects past purchase history. For example, the collection unit can collect data on products that the user has purchased in the past. The collection unit can also collect browsing history. For example, the collection unit can collect data on products that the user has browsed in the past. Furthermore, the collection unit can also collect log data of click rate. For example, the collection unit can collect data on products that the user has clicked on in the past. In this way, the collection unit can perform more accurate analysis by collecting past behavioral data of the user.

[0031] The analysis unit analyzes the log data collected by the collection unit to understand current trends and user interests. The analysis unit, for example, statistically analyzes the log data collected by the collection unit. For example, the analysis unit can understand current trends based on the collected data. The analysis unit can also understand user interests using machine learning algorithms. For example, the analysis unit can analyze social media and search keywords to understand user interests. Furthermore, the analysis unit can analyze user behavior patterns based on the collected data. For example, the analysis unit can understand user interests based on the user's past behavioral data. This allows the analysis unit to understand current trends and user interests, enabling more effective email marketing.

[0032] The generation unit can predict future consumption trends based on the analysis results obtained by the analysis unit. The generation unit can predict future consumption trends based on, for example, the analysis results obtained by the analysis unit. For example, the generation unit can predict future consumption trends using time series analysis. The generation unit can also predict future consumption trends using a prediction model. For example, the generation unit can predict how a particular product will sell in the future based on past data. Furthermore, the generation unit can predict what products will be popular based on the analysis results. For example, the generation unit can predict future consumption trends based on user interests. This allows the generation unit to predict future consumption trends, enabling forward-looking sales promotion strategies.

[0033] The generation unit can generate an email title, content, and target list based on the analysis results. The generation unit, for example, generates an email title based on the analysis results. For example, the generation unit can generate an email title using a template. The generation unit can also generate email content. For example, the generation unit can generate email content based on the degree of personalization. The generation unit can also generate a target list. For example, the generation unit can generate an email containing promotional information related to a particular product for a user who is interested in the product. In this way, the generation unit can provide emails that match the user's interests by generating emails based on the analysis results.

[0034] The providing unit can provide the email generated by the generating unit to the user. For example, the providing unit sends the email generated by the generating unit to the user. For example, the providing unit can provide the email based on the email sending method, timing, and delivery platform. The providing unit can also build a system for providing the generated email to the user. For example, the providing unit can send the generated email to the user, and the user can receive the email. In this way, the providing unit can provide the generated email to the user, thereby achieving effective email marketing.

[0035] The collection unit can analyze the user's past purchase history and browsing history and select a collection method. The collection unit, for example, analyzes the user's past purchase history. For example, the collection unit can analyze data on products the user has purchased in the past and select a collection method. The collection unit can also analyze the user's browsing history. For example, the collection unit can analyze data on products the user has browsed in the past and select a collection method. Furthermore, the collection unit can analyze click rates. For example, the collection unit can analyze data on products the user has clicked in the past and select a collection method. In this way, the collection unit can select the optimal data collection method by analyzing the user's past behavioral data.

[0036] When collecting log data, the collection unit can filter the log data based on the user's current interests. The collection unit, for example, identifies the user's current interests. For example, the collection unit can analyze social media and search keywords to identify the user's current interests. The collection unit can also filter the log data based on the user's current interests. For example, the collection unit can preferentially collect related log data based on product categories in which the user is currently interested. Furthermore, the collection unit can collect log data containing related keywords based on the user's recent search history. For example, the collection unit can analyze content shared by the user on social media and collect related log data. This allows the collection unit to collect more relevant data by filtering the data based on the user's current interests.

[0037] When collecting log data, the collection unit can select a collection means according to the user's input method. The collection unit, for example, identifies the user's input method. For example, the collection unit can identify methods such as voice input, text input, and image input. The collection unit can also select a collection means according to the user's input method. For example, if the user is using voice input, the collection unit can preferentially collect and analyze voice data. Furthermore, if the user is using text input, the collection unit can also focus on collecting and analyzing text data. For example, if the user is uploading an image, the collection unit can collect image data and perform image analysis. This allows the collection unit to select the optimal collection means according to the user's input method, enabling efficient data collection.

[0038] When collecting log data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, ascertains the user's geographical location information. For example, the collection unit can analyze GPS data or IP addresses to ascertain the user's geographical location information. The collection unit can also prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data on products and services related to that area. Furthermore, when the user is traveling, the collection unit can also collect data related to the user's travel destination. For example, when the user is at home, the collection unit can collect data related to stores and services around the user's home. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0039] When collecting log data, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, analyzes the user's social media activities. For example, the collection unit can collect data related to products and services shared by the user on social media. The collection unit can also analyze the content of the user's posts on social media and collect related data. For example, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, the collection unit can collect highly relevant data by analyzing the user's social media activities.

[0040] When collecting log data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, reflects the user's past feedback. For example, the collection unit can adjust the type and amount of data to be collected based on feedback provided by the user in the past. Furthermore, if the user's feedback indicates that a particular data collection method is preferred, the collection unit can preferentially use that method. For example, the collection unit can analyze the user's feedback and continuously improve the collection method. This allows the collection unit to customize the collection method by reflecting the user's past feedback, enabling more effective data collection.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the log data. The analysis unit, for example, evaluates the importance of the log data. For example, the analysis unit can evaluate the importance of the log data using criteria such as access frequency and error log occurrence frequency. The analysis unit can also perform a detailed analysis on data with high importance. For example, the analysis unit can perform a detailed analysis on data with high importance to provide more accurate results. Furthermore, the analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can perform a simplified analysis on data with low importance to achieve efficient analysis. As a result, the analysis unit can adjust the level of detail of the analysis based on the importance of the log data, thereby enabling efficient analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of log data. The analysis unit, for example, classifies the categories of log data. For example, the analysis unit can classify the log data based on categories such as error logs, access logs, and transaction logs. The analysis unit can also apply different analysis algorithms depending on the category of log data. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. Furthermore, the analysis unit can also apply a browsing behavior analysis algorithm to browsing history data. For example, the analysis unit can apply a click behavior analysis algorithm to click rate data. In this way, the analysis unit can apply different analysis algorithms depending on the category of log data, enabling more accurate analysis.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, refers to the user's past analysis results. For example, the analysis unit can improve accuracy by adjusting the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the analysis. For example, the analysis unit can utilize the user's past analysis results as a feedback loop to continuously improve the analysis accuracy. In this way, the analysis unit can improve the analysis accuracy by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time when the log data was collected. The analysis unit, for example, evaluates the time when the log data was collected. For example, the analysis unit can evaluate the time when the log data was collected using criteria such as the collection frequency and collection timing. The analysis unit can also prioritize analysis of the most recent log data. For example, the analysis unit can prioritize analysis based on the most recent log data and provide results in real time. Furthermore, the analysis unit can also perform analysis by focusing on the most recent data while referring to past log data. For example, the analysis unit can prioritize analysis of data collected during a specific period. This allows the analysis unit to determine the priority of analysis based on the time when the log data was collected, thereby enabling efficient analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the log data. The analysis unit, for example, evaluates the relevance of the log data. For example, the analysis unit can evaluate the relevance of the log data using criteria such as co-occurrence network analysis or correlation analysis. The analysis unit can also prioritize analysis of data with high relevance. For example, the analysis unit can prioritize analysis based on data with high relevance, thereby providing more accurate results. Furthermore, the analysis unit can also analyze data with a medium degree of relevance next. For example, the analysis unit can analyze data with a medium degree of relevance next, thereby achieving efficient analysis. As a result, the analysis unit can adjust the order of analysis based on the relevance of the log data, thereby enabling efficient analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit can select terms according to the user's level of expertise. The analysis unit can also provide analysis results that make extensive use of technical terms to users with high levels of expertise. For example, the analysis unit can provide analysis results that use detailed technical terms to users with high levels of expertise. Furthermore, the analysis unit can also provide analysis results that are explained in simple terms to users with low levels of expertise. For example, the analysis unit can provide analysis results that are explained in simple terms to users with low levels of expertise. In this way, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easier to understand.

[0047] The generation unit can adjust the level of detail of the email to be generated based on the importance of the analysis result during generation. The generation unit, for example, evaluates the importance of the analysis result. For example, the generation unit can evaluate the importance of the analysis result using criteria such as impact and relevance. The generation unit can also generate a detailed email based on an analysis result with a high importance. For example, the generation unit can generate an email including a detailed explanation based on an analysis result with a high importance. Furthermore, the generation unit can also generate a simplified email based on an analysis result with a low importance. For example, the generation unit can generate a simplified email based on an analysis result with a low importance. In this way, the generation unit can adjust the level of detail of the email based on the importance of the analysis result, thereby enabling efficient email generation.

[0048] The generation unit can apply different generation algorithms depending on the category of the analysis results during generation. The generation unit, for example, classifies the categories of the analysis results. For example, the generation unit can classify the analysis results based on categories such as trend analysis and user behavior analysis. The generation unit can also apply different generation algorithms depending on the category of the analysis results. For example, the generation unit can apply an algorithm that generates a purchase promotion email to analysis results based on purchase history. Furthermore, the generation unit can also apply an algorithm that generates a view promotion email to analysis results based on browsing history. For example, the generation unit can apply an algorithm that generates a click promotion email to analysis results based on click rate. In this way, the generation unit can apply different generation algorithms depending on the category of the analysis results, thereby enabling more effective email generation.

[0049] During generation, the generation unit can improve the accuracy of generation based on the user's past email generation results. The generation unit, for example, refers to the user's past email generation results. For example, the generation unit can improve accuracy by adjusting the generation algorithm based on the user's past email generation results. The generation unit can also extract specific patterns from the user's past email generation results and reflect them in the generation. For example, the generation unit can utilize the user's past email generation results as a feedback loop to continuously improve the generation accuracy. In this way, the generation unit can improve the generation accuracy by referring to the user's past email generation results.

[0050] At the time of generation, the generation unit can determine the priority of emails to be generated based on the time when the analysis results were collected. The generation unit, for example, evaluates the time when the analysis results were collected. For example, the generation unit can evaluate the time when the analysis results were collected using criteria such as the collection frequency and collection timing. The generation unit can also generate emails preferentially based on the latest analysis results. For example, the generation unit can generate emails preferentially based on the latest analysis results and provide results in real time. Furthermore, the generation unit can generate emails by focusing on the latest data while referring to past analysis results. For example, the generation unit can generate emails preferentially based on data collected during a specific period. This allows the generation unit to determine the priority of emails based on the time when the analysis results were collected, thereby enabling efficient email generation.

[0051] The generation unit can adjust the order of emails to be generated based on the relevance of the analysis results during generation. The generation unit, for example, evaluates the relevance of the analysis results. For example, the generation unit can evaluate the relevance of the analysis results using criteria such as co-occurrence network analysis or correlation analysis. The generation unit can also generate emails preferentially based on analysis results with high relevance. For example, the generation unit can generate emails preferentially based on analysis results with high relevance, thereby providing more accurate results. The generation unit can also generate emails next based on analysis results with medium relevance. For example, the generation unit can generate emails next based on analysis results with medium relevance, thereby achieving efficient email generation. As a result, the generation unit can adjust the order of emails to be generated based on the relevance of the analysis results, thereby enabling efficient email generation.

[0052] The generation unit can adjust the use of technical terms in the email to be generated according to the user's level of expertise at the time of generation. The generation unit, for example, evaluates the user's level of expertise. For example, the generation unit can select terms according to the user's level of expertise. The generation unit can also generate an email that uses a lot of technical terms for a user with high level of expertise. For example, the generation unit can generate an email that uses detailed technical terms for a user with high level of expertise. Furthermore, the generation unit can also generate an email that provides explanations in simple terms for a user with low level of expertise. For example, the generation unit can generate an email that provides explanations in simple terms for a user with low level of expertise. In this way, the generation unit can provide an email that is easier to understand by adjusting the use of technical terms in the email to be generated according to the user's level of expertise.

[0053] When providing email, the providing unit can select a delivery method based on the user's past email reception history. The providing unit, for example, refers to the user's past email reception history. For example, the providing unit can select the optimal delivery method based on the format of emails that the user has preferred to receive in the past. The providing unit can also provide emails during a specific time period based on the user's past email reception history. For example, the providing unit can analyze the user's past email reception history and select the most effective delivery method. As a result, the providing unit can select the optimal delivery method by referring to the user's past email reception history.

[0054] When providing emails, the providing unit can customize the content of the emails based on the user's current interests. The providing unit, for example, identifies the user's current interests. For example, the providing unit can analyze social media and search keywords to identify the user's current interests. The providing unit can also customize the content of the emails based on the user's current interests. For example, the providing unit can provide relevant emails based on product categories in which the user is currently interested. Furthermore, the providing unit can provide emails containing related keywords based on the user's recent search history. For example, the providing unit can analyze content shared by the user on social media and provide related emails. This allows the providing unit to customize the content of the emails based on the user's current interests, thereby providing more relevant emails.

[0055] The providing unit can improve the delivery method by reflecting user feedback when providing emails. The providing unit, for example, reflects user feedback. For example, the providing unit can adjust the format and content of the email to be provided based on feedback provided by the user in the past. Furthermore, if a specific delivery method is preferred based on the user feedback, the providing unit can also preferentially use that method. For example, the providing unit can analyze user feedback and continuously improve the delivery method. In this way, the providing unit can continuously improve the delivery method by reflecting user feedback.

[0056] When providing email, the providing unit can select a delivery method based on the user's geographical location information. The providing unit, for example, grasps the user's geographical location information. For example, the providing unit can analyze GPS data or an IP address to grasp the user's geographical location information. The providing unit can also select a delivery method based on the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide emails about products and services related to that area. Furthermore, if the user is traveling, the providing unit can also provide emails related to the user's travel destination. For example, if the user is at home, the providing unit can provide emails related to stores and services near the user's home. This allows the providing unit to select the optimal delivery method by taking the user's geographical location information into consideration.

[0057] When providing emails, the providing unit can analyze the user's social media activity and customize the content to be provided. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit can provide emails related to products or services that the user has shared on social media. The providing unit can also analyze the content of the user's social media posts and provide related emails. For example, the providing unit can provide related emails by referring to the activities of the user's friends on social media. In this way, the providing unit can provide highly relevant emails by analyzing the user's social media activity.

[0058] The providing unit can customize the delivery method by reflecting the user's past feedback when delivering emails. The providing unit, for example, reflects the user's past feedback. For example, the providing unit can adjust the format and content of the email to be delivered based on the user's past feedback. Furthermore, if the user's feedback indicates that a particular delivery method is preferred, the providing unit can preferentially use that method. For example, the providing unit can analyze the user's feedback and continuously improve the delivery method. As a result, the providing unit can customize the delivery method by reflecting the user's past feedback, enabling more effective email delivery.

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

[0060] The collection unit can prioritize collecting data related to a specific region by taking into account the user's geographical location information. For example, when the user is in a specific region, data on products and services related to that region can be prioritized. Also, when the user is traveling, data related to the travel destination can be collected. Furthermore, when the user is at home, data related to stores and services around the home can be collected. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0061] The providing unit can select the optimal delivery method based on the user's past email reception history. For example, the optimal delivery method can be selected based on the format of emails that the user has preferred in the past. Also, emails can be provided at specific time periods based on the user's past email reception history. Furthermore, the user's past email reception history can be analyzed to select the most effective delivery method. This allows the providing unit to select the optimal delivery method by referring to the user's past email reception history.

[0062] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, it can provide analysis results that use a lot of technical terms to a user with a high level of expertise. It can also provide analysis results that are explained in simple terms to a user with a low level of expertise. Furthermore, it can select terms according to the user's level of expertise. This allows the analysis unit to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0063] The generation unit can determine the priority of emails to be generated based on the time when the analysis results were collected. For example, emails can be generated preferentially based on the latest analysis results. Emails can also be generated by prioritizing the latest data while referring to past analysis results. Furthermore, emails can be generated preferentially based on data collected during a specific period. This allows the generation unit to determine the priority of emails based on the time when the analysis results were collected, enabling efficient email generation.

[0064] The analysis unit can apply different analysis algorithms depending on the category of the log data. For example, a purchase pattern analysis algorithm can be applied to purchase history data. A browsing behavior analysis algorithm can also be applied to browsing history data. Furthermore, a click behavior analysis algorithm can be applied to click rate data. This allows the analysis unit to apply different analysis algorithms depending on the category of the log data, enabling more accurate analysis.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects log data. The log data includes access logs, error logs, transaction logs, etc. The collection unit can also collect data such as past purchase history, browsing history, and click rates. Step 2: The analysis unit analyzes the log data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit can analyze social media and search keywords to understand current trends and user interests. Step 3: The generator generates email headers, content, and target lists based on the analysis results obtained by the analyzer. The generation is performed based on the use of templates and the degree of personalization. The generator can generate emails containing promotional information related to a specific product for users who are interested in that product. Step 4: The providing unit provides the email generated by the generating unit to the user. The provision is performed based on the email sending method, timing, and delivery platform. The providing unit sends the generated email to the user, and the user can receive the email.

[0067] (Example 2) A system according to an embodiment of the present invention utilizes a generative AI to automatically generate email headlines, content, and target lists for commerce emails. This system collects log data and uses a generative AI to analyze it to understand current trends and user interests and predict future consumption trends. This allows companies to understand future consumption trends and develop appropriate sales promotion strategies. Furthermore, the generative AI automatically generates email headlines, content, and target lists based on the prediction results. For example, for users interested in a specific product, the system automatically generates emails containing promotional information related to that product. This allows users to receive attractive emails tailored to their interests. This reduces the cost of creating emails and improves both company and user satisfaction. For example, companies can conduct promotional activities more efficiently, and users can receive information tailored to their needs. Furthermore, the gap between market demand and supply is narrowed, improving overall market efficiency.

[0068] An email generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects log data. Examples of the log data include, but are not limited to, access logs, error logs, and transaction logs. The collection unit can collect data such as past purchase history, browsing history, and click rates. The analysis unit analyzes the log data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. The analysis unit can analyze social media and search keywords to understand current trends and user interests. The generation unit generates email titles, content, and target lists based on the analysis results obtained by the analysis unit. The generation can be performed based on, for example, the use of templates and the degree of personalization, but is not limited to these examples. The generation unit can generate emails containing promotional information related to a specific product for users interested in that product. The provision unit provides the emails generated by the generation unit to users. The provision can be performed based on, for example, but is not limited to these examples, the email sending method, timing, and delivery platform. The providing unit, for example, sends the generated email to a user, and the user can receive the email. As a result, the email generation system according to the embodiment automates the process from collecting and analyzing log data to generating and providing emails, thereby achieving efficient email marketing.

[0069] The collection unit can collect log data of past purchase history, browsing history, and click rate. The collection unit, for example, collects past purchase history. For example, the collection unit can collect data on products that the user has purchased in the past. The collection unit can also collect browsing history. For example, the collection unit can collect data on products that the user has browsed in the past. Furthermore, the collection unit can also collect log data of click rate. For example, the collection unit can collect data on products that the user has clicked on in the past. In this way, the collection unit can perform more accurate analysis by collecting past behavioral data of the user.

[0070] The analysis unit analyzes the log data collected by the collection unit to understand current trends and user interests. The analysis unit, for example, statistically analyzes the log data collected by the collection unit. For example, the analysis unit can understand current trends based on the collected data. The analysis unit can also understand user interests using machine learning algorithms. For example, the analysis unit can analyze social media and search keywords to understand user interests. Furthermore, the analysis unit can analyze user behavior patterns based on the collected data. For example, the analysis unit can understand user interests based on the user's past behavioral data. This allows the analysis unit to understand current trends and user interests, enabling more effective email marketing.

[0071] The generation unit can predict future consumption trends based on the analysis results obtained by the analysis unit. The generation unit can predict future consumption trends based on, for example, the analysis results obtained by the analysis unit. For example, the generation unit can predict future consumption trends using time series analysis. The generation unit can also predict future consumption trends using a prediction model. For example, the generation unit can predict how a particular product will sell in the future based on past data. Furthermore, the generation unit can predict what products will be popular based on the analysis results. For example, the generation unit can predict future consumption trends based on user interests. This allows the generation unit to predict future consumption trends, enabling forward-looking sales promotion strategies.

[0072] The generation unit can generate an email title, content, and target list based on the analysis results. The generation unit, for example, generates an email title based on the analysis results. For example, the generation unit can generate an email title using a template. The generation unit can also generate email content. For example, the generation unit can generate email content based on the degree of personalization. The generation unit can also generate a target list. For example, the generation unit can generate an email containing promotional information related to a particular product for a user who is interested in the product. In this way, the generation unit can provide emails that match the user's interests by generating emails based on the analysis results.

[0073] The providing unit can provide the email generated by the generating unit to the user. For example, the providing unit sends the email generated by the generating unit to the user. For example, the providing unit can provide the email based on the email sending method, timing, and delivery platform. The providing unit can also build a system for providing the generated email to the user. For example, the providing unit can send the generated email to the user, and the user can receive the email. In this way, the providing unit can provide the generated email to the user, thereby achieving effective email marketing.

[0074] The collection unit can estimate the user's emotion and adjust the timing of collecting log data based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion. For example, the collection unit can estimate the user's emotion using an emotion analysis algorithm. The collection unit can also estimate the user's emotion using facial expression recognition technology. For example, the collection unit can analyze the user's facial expression and estimate the emotion. The collection unit can also adjust the timing of collecting log data based on the estimated user's emotion. For example, if the user is feeling stressed, the collection unit can delay the collection timing and collect log data when the user is relaxed. If the user is excited, the collection unit can immediately collect log data and perform real-time analysis. If the user is tired, the collection unit can adjust the collection timing and collect log data while the user is resting. This allows the collection unit to adjust the collection timing according to the user's emotion, thereby enabling more appropriate data collection.

[0075] The collection unit can analyze the user's past purchase history and browsing history and select a collection method. The collection unit, for example, analyzes the user's past purchase history. For example, the collection unit can analyze data on products the user has purchased in the past and select a collection method. The collection unit can also analyze the user's browsing history. For example, the collection unit can analyze data on products the user has browsed in the past and select a collection method. Furthermore, the collection unit can analyze click rates. For example, the collection unit can analyze data on products the user has clicked in the past and select a collection method. In this way, the collection unit can select the optimal data collection method by analyzing the user's past behavioral data.

[0076] When collecting log data, the collection unit can filter the log data based on the user's current interests. The collection unit, for example, identifies the user's current interests. For example, the collection unit can analyze social media and search keywords to identify the user's current interests. The collection unit can also filter the log data based on the user's current interests. For example, the collection unit can preferentially collect related log data based on product categories in which the user is currently interested. Furthermore, the collection unit can collect log data containing related keywords based on the user's recent search history. For example, the collection unit can analyze content shared by the user on social media and collect related log data. This allows the collection unit to collect more relevant data by filtering the data based on the user's current interests.

[0077] When collecting log data, the collection unit can select a collection means according to the user's input method. The collection unit, for example, identifies the user's input method. For example, the collection unit can identify methods such as voice input, text input, and image input. The collection unit can also select a collection means according to the user's input method. For example, if the user is using voice input, the collection unit can preferentially collect and analyze voice data. Furthermore, if the user is using text input, the collection unit can also focus on collecting and analyzing text data. For example, if the user is uploading an image, the collection unit can collect image data and perform image analysis. This allows the collection unit to select the optimal collection means according to the user's input method, enabling efficient data collection.

[0078] The collection unit can estimate a user's emotion and determine the priority of log data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion. For example, the collection unit can estimate the user's emotion using an emotion analysis algorithm. The collection unit can also estimate the user's emotion using facial expression recognition technology. For example, the collection unit can analyze the user's facial expression and estimate the emotion. The collection unit can also determine the priority of log data to be collected based on the estimated user's emotion. For example, the collection unit can prioritize collecting real-time behavioral data when the user is excited. The collection unit can also prioritize collecting past purchase history and browsing history when the user is relaxed. The collection unit can also prioritize collecting data related to stress reduction when the user is feeling stressed. In this way, the collection unit can prioritize collecting more important data by determining the priority of data to be collected according to the user's emotion.

[0079] When collecting log data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, ascertains the user's geographical location information. For example, the collection unit can analyze GPS data or IP addresses to ascertain the user's geographical location information. The collection unit can also prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data on products and services related to that area. Furthermore, when the user is traveling, the collection unit can also collect data related to the user's travel destination. For example, when the user is at home, the collection unit can collect data related to stores and services around the user's home. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0080] When collecting log data, the collection unit can analyze the user's social media activities and collect related data. The collection unit, for example, analyzes the user's social media activities. For example, the collection unit can collect data related to products and services shared by the user on social media. The collection unit can also analyze the content of the user's posts on social media and collect related data. For example, the collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, the collection unit can collect highly relevant data by analyzing the user's social media activities.

[0081] When collecting log data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, reflects the user's past feedback. For example, the collection unit can adjust the type and amount of data to be collected based on feedback provided by the user in the past. Furthermore, if the user's feedback indicates that a particular data collection method is preferred, the collection unit can preferentially use that method. For example, the collection unit can analyze the user's feedback and continuously improve the collection method. This allows the collection unit to customize the collection method by reflecting the user's past feedback, enabling more effective data collection.

[0082] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit can estimate the user's emotion using an emotion analysis algorithm. The analysis unit can also estimate the user's emotion using facial expression recognition technology. For example, the analysis unit can analyze the user's facial expression to estimate the emotion. Furthermore, the analysis unit can adjust the way the analysis is presented based on the estimated user's emotion. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide analysis results using visually stimulating graphs and charts when the user is excited. In this way, the analysis unit can provide more appropriate analysis results by adjusting the way the analysis is presented based on the user's emotion.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the log data. The analysis unit, for example, evaluates the importance of the log data. For example, the analysis unit can evaluate the importance of the log data using criteria such as access frequency and error log occurrence frequency. The analysis unit can also perform a detailed analysis on data with high importance. For example, the analysis unit can perform a detailed analysis on data with high importance to provide more accurate results. Furthermore, the analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can perform a simplified analysis on data with low importance to achieve efficient analysis. As a result, the analysis unit can adjust the level of detail of the analysis based on the importance of the log data, thereby enabling efficient analysis.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of log data. The analysis unit, for example, classifies the categories of log data. For example, the analysis unit can classify the log data based on categories such as error logs, access logs, and transaction logs. The analysis unit can also apply different analysis algorithms depending on the category of log data. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. Furthermore, the analysis unit can also apply a browsing behavior analysis algorithm to browsing history data. For example, the analysis unit can apply a click behavior analysis algorithm to click rate data. In this way, the analysis unit can apply different analysis algorithms depending on the category of log data, enabling more accurate analysis.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. The analysis unit, for example, refers to the user's past analysis results. For example, the analysis unit can improve accuracy by adjusting the analysis algorithm based on the user's past analysis results. The analysis unit can also extract specific patterns from the user's past analysis results and reflect them in the analysis. For example, the analysis unit can utilize the user's past analysis results as a feedback loop to continuously improve the analysis accuracy. In this way, the analysis unit can improve the analysis accuracy by referring to the user's past analysis results.

[0086] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion. For example, the analysis unit can estimate the user's emotion using an emotion analysis algorithm. The analysis unit can also estimate the user's emotion using facial expression recognition technology. For example, the analysis unit can analyze the user's facial expression and estimate the emotion. The analysis unit can also adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. The analysis unit can provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually stimulating analysis result when the user is excited. In this way, the analysis unit can provide a more appropriate analysis result by adjusting the length of the analysis according to the user's emotion.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the time when the log data was collected. The analysis unit, for example, evaluates the time when the log data was collected. For example, the analysis unit can evaluate the time when the log data was collected using criteria such as the collection frequency and collection timing. The analysis unit can also prioritize analysis of the most recent log data. For example, the analysis unit can prioritize analysis based on the most recent log data and provide results in real time. Furthermore, the analysis unit can also perform analysis by focusing on the most recent data while referring to past log data. For example, the analysis unit can prioritize analysis of data collected during a specific period. This allows the analysis unit to determine the priority of analysis based on the time when the log data was collected, thereby enabling efficient analysis.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the log data. The analysis unit, for example, evaluates the relevance of the log data. For example, the analysis unit can evaluate the relevance of the log data using criteria such as co-occurrence network analysis or correlation analysis. The analysis unit can also prioritize analysis of data with high relevance. For example, the analysis unit can prioritize analysis based on data with high relevance, thereby providing more accurate results. Furthermore, the analysis unit can also analyze data with a medium degree of relevance next. For example, the analysis unit can analyze data with a medium degree of relevance next, thereby achieving efficient analysis. As a result, the analysis unit can adjust the order of analysis based on the relevance of the log data, thereby enabling efficient analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit can select terms according to the user's level of expertise. The analysis unit can also provide analysis results that make extensive use of technical terms to users with high levels of expertise. For example, the analysis unit can provide analysis results that use detailed technical terms to users with high levels of expertise. Furthermore, the analysis unit can also provide analysis results that are explained in simple terms to users with low levels of expertise. For example, the analysis unit can provide analysis results that are explained in simple terms to users with low levels of expertise. In this way, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easier to understand.

[0090] The generation unit can estimate the user's emotions and adjust the headline and content of the generated email based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions. For example, the generation unit can estimate the user's emotions using an emotion analysis algorithm. The generation unit can also estimate the user's emotions using facial expression recognition technology. For example, the generation unit can analyze the user's facial expressions and estimate the user's emotions. The generation unit can also adjust the headline and content of the generated email based on the estimated user's emotions. For example, the generation unit can generate a friendly headline and content when the user is relaxed. The generation unit can generate a concise and to-the-point headline and content when the user is in a hurry. The generation unit can also generate a visually stimulating headline and content when the user is excited. This allows the generation unit to adjust the headline and content of the email according to the user's emotions, enabling more effective email marketing.

[0091] The generation unit can adjust the level of detail of the email to be generated based on the importance of the analysis result during generation. The generation unit, for example, evaluates the importance of the analysis result. For example, the generation unit can evaluate the importance of the analysis result using criteria such as impact and relevance. The generation unit can also generate a detailed email based on an analysis result with a high importance. For example, the generation unit can generate an email including a detailed explanation based on an analysis result with a high importance. Furthermore, the generation unit can also generate a simplified email based on an analysis result with a low importance. For example, the generation unit can generate a simplified email based on an analysis result with a low importance. In this way, the generation unit can adjust the level of detail of the email based on the importance of the analysis result, thereby enabling efficient email generation.

[0092] The generation unit can apply different generation algorithms depending on the category of the analysis results during generation. The generation unit, for example, classifies the categories of the analysis results. For example, the generation unit can classify the analysis results based on categories such as trend analysis and user behavior analysis. The generation unit can also apply different generation algorithms depending on the category of the analysis results. For example, the generation unit can apply an algorithm that generates a purchase promotion email to analysis results based on purchase history. Furthermore, the generation unit can also apply an algorithm that generates a view promotion email to analysis results based on browsing history. For example, the generation unit can apply an algorithm that generates a click promotion email to analysis results based on click rate. In this way, the generation unit can apply different generation algorithms depending on the category of the analysis results, thereby enabling more effective email generation.

[0093] During generation, the generation unit can improve the accuracy of generation based on the user's past email generation results. The generation unit, for example, refers to the user's past email generation results. For example, the generation unit can improve accuracy by adjusting the generation algorithm based on the user's past email generation results. The generation unit can also extract specific patterns from the user's past email generation results and reflect them in the generation. For example, the generation unit can utilize the user's past email generation results as a feedback loop to continuously improve the generation accuracy. In this way, the generation unit can improve the generation accuracy by referring to the user's past email generation results.

[0094] The generation unit can estimate the user's emotion and adjust the length of the email to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. For example, the generation unit can estimate the user's emotion using an emotion analysis algorithm. The generation unit can also estimate the user's emotion using facial expression recognition technology. For example, the generation unit can analyze the user's facial expression and estimate the emotion. The generation unit can also adjust the length of the email to be generated based on the estimated user's emotion. For example, the generation unit can generate a short and to-the-point email when the user is in a hurry. The generation unit can generate a longer email with detailed explanations when the user is relaxed. The generation unit can also generate an email with visually stimulating effects when the user is excited. In this way, the generation unit can provide more appropriate emails by adjusting the length of the email according to the user's emotion.

[0095] At the time of generation, the generation unit can determine the priority of emails to be generated based on the time when the analysis results were collected. The generation unit, for example, evaluates the time when the analysis results were collected. For example, the generation unit can evaluate the time when the analysis results were collected using criteria such as the collection frequency and collection timing. The generation unit can also generate emails preferentially based on the latest analysis results. For example, the generation unit can generate emails preferentially based on the latest analysis results and provide results in real time. Furthermore, the generation unit can generate emails by focusing on the latest data while referring to past analysis results. For example, the generation unit can generate emails preferentially based on data collected during a specific period. This allows the generation unit to determine the priority of emails based on the time when the analysis results were collected, thereby enabling efficient email generation.

[0096] The generation unit can adjust the order of emails to be generated based on the relevance of the analysis results during generation. The generation unit, for example, evaluates the relevance of the analysis results. For example, the generation unit can evaluate the relevance of the analysis results using criteria such as co-occurrence network analysis or correlation analysis. The generation unit can also generate emails preferentially based on analysis results with high relevance. For example, the generation unit can generate emails preferentially based on analysis results with high relevance, thereby providing more accurate results. The generation unit can also generate emails next based on analysis results with medium relevance. For example, the generation unit can generate emails next based on analysis results with medium relevance, thereby achieving efficient email generation. As a result, the generation unit can adjust the order of emails to be generated based on the relevance of the analysis results, thereby enabling efficient email generation.

[0097] The generation unit can adjust the use of technical terms in the email to be generated according to the user's level of expertise at the time of generation. The generation unit, for example, evaluates the user's level of expertise. For example, the generation unit can select terms according to the user's level of expertise. The generation unit can also generate an email that uses a lot of technical terms for a user with high level of expertise. For example, the generation unit can generate an email that uses detailed technical terms for a user with high level of expertise. Furthermore, the generation unit can also generate an email that provides explanations in simple terms for a user with low level of expertise. For example, the generation unit can generate an email that provides explanations in simple terms for a user with low level of expertise. In this way, the generation unit can provide an email that is easier to understand by adjusting the use of technical terms in the email to be generated according to the user's level of expertise.

[0098] The providing unit can estimate the user's emotions and adjust the email delivery method based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit can estimate the user's emotions using an emotion analysis algorithm. The providing unit can also estimate the user's emotions using facial expression recognition technology. For example, the providing unit can analyze the user's facial expressions and estimate the user's emotions. Furthermore, the providing unit can adjust the email delivery method based on the estimated user's emotions. For example, the providing unit can deliver the email in a friendly tone if the user is relaxed. Furthermore, the providing unit can deliver the email in a concise and to-the-point tone if the user is in a hurry. Furthermore, the providing unit can deliver the email with visually stimulating effects if the user is excited. This allows the providing unit to adjust the email delivery method according to the user's emotions, enabling more effective email marketing.

[0099] When providing email, the providing unit can select a delivery method based on the user's past email reception history. The providing unit, for example, refers to the user's past email reception history. For example, the providing unit can select the optimal delivery method based on the format of emails that the user has preferred to receive in the past. The providing unit can also provide emails during a specific time period based on the user's past email reception history. For example, the providing unit can analyze the user's past email reception history and select the most effective delivery method. As a result, the providing unit can select the optimal delivery method by referring to the user's past email reception history.

[0100] When providing emails, the providing unit can customize the content of the emails based on the user's current interests. The providing unit, for example, identifies the user's current interests. For example, the providing unit can analyze social media and search keywords to identify the user's current interests. The providing unit can also customize the content of the emails based on the user's current interests. For example, the providing unit can provide relevant emails based on product categories in which the user is currently interested. Furthermore, the providing unit can provide emails containing related keywords based on the user's recent search history. For example, the providing unit can analyze content shared by the user on social media and provide related emails. This allows the providing unit to customize the content of the emails based on the user's current interests, thereby providing more relevant emails.

[0101] The providing unit can improve the delivery method by reflecting user feedback when providing emails. The providing unit, for example, reflects user feedback. For example, the providing unit can adjust the format and content of the email to be provided based on feedback provided by the user in the past. Furthermore, if a specific delivery method is preferred based on the user feedback, the providing unit can also preferentially use that method. For example, the providing unit can analyze user feedback and continuously improve the delivery method. In this way, the providing unit can continuously improve the delivery method by reflecting user feedback.

[0102] The providing unit can estimate the user's emotions and determine the priority of email delivery based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit can estimate the user's emotions using an emotion analysis algorithm. The providing unit can also estimate the user's emotions using facial expression recognition technology. For example, the providing unit can analyze the user's facial expressions and estimate the user's emotions. Furthermore, the providing unit can determine the priority of email delivery based on the estimated user's emotions. For example, the providing unit can prioritize delivery of emails in real time when the user is excited. Furthermore, the providing unit can provide emails based on the user's past purchase history or browsing history when the user is relaxed. Furthermore, the providing unit can prioritize delivery of emails related to stress reduction when the user is feeling stressed. In this way, the providing unit can determine the priority of email delivery based on the user's emotions, enabling more effective email marketing.

[0103] When providing email, the providing unit can select a delivery method based on the user's geographical location information. The providing unit, for example, grasps the user's geographical location information. For example, the providing unit can analyze GPS data or an IP address to grasp the user's geographical location information. The providing unit can also select a delivery method based on the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide emails about products and services related to that area. Furthermore, if the user is traveling, the providing unit can also provide emails related to the user's travel destination. For example, if the user is at home, the providing unit can provide emails related to stores and services near the user's home. This allows the providing unit to select the optimal delivery method by taking the user's geographical location information into consideration.

[0104] When providing emails, the providing unit can analyze the user's social media activity and customize the content to be provided. The providing unit, for example, analyzes the user's social media activity. For example, the providing unit can provide emails related to products or services that the user has shared on social media. The providing unit can also analyze the content of the user's social media posts and provide related emails. For example, the providing unit can provide related emails by referring to the activities of the user's friends on social media. In this way, the providing unit can provide highly relevant emails by analyzing the user's social media activity.

[0105] The providing unit can customize the delivery method by reflecting the user's past feedback when delivering emails. The providing unit, for example, reflects the user's past feedback. For example, the providing unit can adjust the format and content of the email to be delivered based on the user's past feedback. Furthermore, if the user's feedback indicates that a particular delivery method is preferred, the providing unit can preferentially use that method. For example, the providing unit can analyze the user's feedback and continuously improve the delivery method. As a result, the providing unit can customize the delivery method by reflecting the user's past feedback, enabling more effective email delivery. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision 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 is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit 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 is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The provision unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is excited, real-time behavioral data can be analyzed preferentially to provide immediate feedback. If the user is relaxed, past purchase history and browsing history can be analyzed preferentially to provide detailed analysis results. Furthermore, if the user is feeling stressed, data related to stress reduction can be analyzed preferentially to provide suggestions for reducing the user's stress. In this way, the analysis unit can provide more effective analysis results by determining the priority of analysis according to the user's emotions.

[0108] The collection unit can prioritize collecting data related to a specific region by taking into account the user's geographical location information. For example, when the user is in a specific region, data on products and services related to that region can be prioritized. Also, when the user is traveling, data related to the travel destination can be collected. Furthermore, when the user is at home, data related to stores and services around the home can be collected. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0109] The generation unit can estimate the user's emotions and adjust the tone of the email to be generated based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate an email in a friendly tone. If the user is in a hurry, the generation unit can generate an email in a concise and to-the-point tone. Furthermore, if the user is excited, the generation unit can generate an email in a visually stimulating tone. This allows the generation unit to adjust the tone of the email according to the user's emotions, thereby enabling more effective email marketing.

[0110] The providing unit can select the optimal delivery method based on the user's past email reception history. For example, the optimal delivery method can be selected based on the format of emails that the user has preferred in the past. Also, emails can be provided at specific time periods based on the user's past email reception history. Furthermore, the user's past email reception history can be analyzed to select the most effective delivery method. This allows the providing unit to select the optimal delivery method by referring to the user's past email reception history.

[0111] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, it can provide analysis results that use a lot of technical terms to a user with a high level of expertise. It can also provide analysis results that are explained in simple terms to a user with a low level of expertise. Furthermore, it can select terms according to the user's level of expertise. This allows the analysis unit to provide analysis results that are easier to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0112] The collection unit can estimate the user's emotions and determine the priority of log data to be collected based on the estimated user's emotions. For example, if the user is excited, real-time behavioral data can be collected with priority. Also, if the user is relaxed, past purchase history and browsing history can be collected with priority. Furthermore, if the user is feeling stressed, data related to stress reduction can be collected with priority. In this way, the collection unit can prioritize collecting more important data by determining the priority of data to be collected according to the user's emotions.

[0113] The generation unit can determine the priority of emails to be generated based on the time when the analysis results were collected. For example, emails can be generated preferentially based on the latest analysis results. Emails can also be generated by prioritizing the latest data while referring to past analysis results. Furthermore, emails can be generated preferentially based on data collected during a specific period. This allows the generation unit to determine the priority of emails based on the time when the analysis results were collected, enabling efficient email generation.

[0114] The providing unit can estimate the user's emotions and determine the priority of email delivery based on the estimated user's emotions. For example, if the user is excited, real-time email delivery can be prioritized. Also, if the user is relaxed, emails can be delivered based on the user's past purchase history and browsing history. Furthermore, if the user is feeling stressed, emails related to stress reduction can be delivered with priority. This allows the providing unit to determine the priority of email delivery according to the user's emotions, enabling more effective email marketing.

[0115] The analysis unit can apply different analysis algorithms depending on the category of the log data. For example, a purchase pattern analysis algorithm can be applied to purchase history data. A browsing behavior analysis algorithm can also be applied to browsing history data. Furthermore, a click behavior analysis algorithm can be applied to click rate data. This allows the analysis unit to apply different analysis algorithms depending on the category of the log data, enabling more accurate analysis.

[0116] The generation unit can estimate the user's emotions and adjust the length of the email to be generated based on the estimated user emotions. For example, if the user is in a hurry, a short, to-the-point email can be generated. If the user is relaxed, a longer email including detailed explanations can be generated. Furthermore, if the user is excited, an email with visually stimulating effects can be generated. In this way, the generation unit can provide more appropriate emails by adjusting the length of the email according to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects log data. The log data includes access logs, error logs, transaction logs, etc. The collection unit can also collect data such as past purchase history, browsing history, and click rates. Step 2: The analysis unit analyzes the log data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. The analysis unit can analyze social media and search keywords to understand current trends and user interests. Step 3: The generator generates email headers, content, and target lists based on the analysis results obtained by the analyzer. The generation is performed based on the use of templates and the degree of personalization. The generator can generate emails containing promotional information related to a specific product for users who are interested in that product. Step 4: The providing unit provides the email generated by the generating unit to the user. The provision is performed based on the email sending method, timing, and delivery platform. The providing unit sends the generated email to the user, and the user can receive the email.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 log data; an analysis unit that analyzes the log data collected by the collection unit; a generation unit that generates a mail title, content, and target list based on the analysis result obtained by the analysis unit; a providing unit that provides the email generated by the generating unit to a user; Equipped with A system characterized by:

2. The collecting unit Collect log data on past purchase history, browsing history, and click rates 2. The system of claim 1.

3. The analysis unit Analyzing the log data collected by the collection unit to understand current trends and user interests.

2. The system of claim 1.

4. The generation unit Predicting future consumption trends based on the analysis results obtained by the analysis unit 2. The system of claim 1.

5. The generation unit Generate email headlines, content, and target lists based on the analysis results 2. The system of claim 1.

6. The providing unit Providing the email generated by the generation unit to the user 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of log data collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past purchase history and browsing history and select the collection method 2. The system of claim 1.

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