System

Abstract

An object of a system according to an embodiment is to analyze purchase data collected through a household account book app and plan a digital marketing strategy.SOLUTION: In accordance with an embodiment, a system comprises a purchase-data collector, a customer-data enhancer, and a digital marketing strategy planning. The purchase data collector may collect purchase data of the user through the account book app. The customer data enhancement unit analyzes the purchase data collected by the purchase data collection unit to enhance the customer data. The digital marketing strategy planning unit plans digital marketing strategies based on the customer information enhanced by the customer information enhancement unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With existing technology, there is room for improvement in effectively utilizing purchasing data collected through household accounting apps for digital marketing.

[0005] The system according to the embodiment aims to analyze purchase data collected through a household account book app and develop a digital marketing strategy. [Means for solving the problem]

[0006] The system according to the embodiment includes a purchase data collection unit, a customer data enhancement unit, and a digital marketing strategy planning unit. The purchase data collection unit collects user purchase data through a household accounting app. The customer data enhancement unit analyzes the purchase data collected by the purchase data collection unit to enhance the customer data. The digital marketing strategy planning unit plans a digital marketing strategy based on the customer data enhanced by the customer data enhancement unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze purchase data collected through a household account book app and develop a digital marketing strategy. [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 digital marketing system according to an embodiment of the present invention collects user purchase data through a household accounting app, and uses a generation AI to analyze and enhance customer data for digital marketing. As a result, the digital marketing system enhances customer data based on the user purchase data, develops more effective marketing strategies, and provides optimal advertisements and promotions to target customers.

[0029] A digital marketing system according to an embodiment includes a purchase data collection unit, a customer data enhancement unit, and a digital marketing strategy planning unit. The purchase data collection unit collects user purchase data through a household accounting app. For example, the purchase data collection unit collects data such as purchased items, purchase amounts, and purchase dates and times entered by the user into the household accounting app. The purchase data collection unit can also automatically classify and organize the data entered by the user into categories. For example, the purchase data collection unit can classify the data into categories such as groceries, clothing, and entertainment. The purchase data collection unit can also periodically update the user purchase data to maintain the latest data. For example, the data can be updated every weekend to reflect the latest purchase history. The customer data enhancement unit analyzes the purchase data collected by the purchase data collection unit to enhance the customer data. For example, the generation AI analyzes the user's purchase history to identify the user's preferences and behavioral patterns. The generation AI can also estimate the user's lifestyle and values ​​based on the user's purchase data. For example, the generation AI can identify health-conscious users or users with a strong interest in cafe culture. Furthermore, the Customer Data Enhancement Department can compare a user's purchasing data with other users to identify common preferences and behavioral patterns. For example, it can analyze the purchasing trends of users living in the same area and develop a marketing strategy specific to that area. The Digital Marketing Strategy Planning Department develops digital marketing strategies based on the customer data enhanced by the Customer Data Enhancement Department. For example, generative AI can generate optimal advertisements and promotions based on user preferences and behavioral patterns to effectively approach target customers. The Digital Marketing Strategy Planning Department can also propose marketing campaigns tailored to the season or event. For example, it could implement a special promotion during the Christmas season. Furthermore, the Digital Marketing Strategy Planning Department can analyze the results of digital marketing and evaluate its effectiveness. For example, it can analyze how many users a particular advertising campaign reached and how many conversions it achieved.As a result, the digital marketing system according to the embodiment can enhance customer data based on user purchase data, develop more effective marketing strategies, and provide optimal advertisements and promotions to target customers. For example, the output unit displays marketing results to marketers via a web application or mobile application. If marketers wish to receive feedback in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to marketers.

[0030] The purchasing data collection unit allows the generation AI to automatically suggest related products and services for the purchasing data entered by the user, thereby complementing the user's input. For example, when a user enters "I bought vegetables at the supermarket," the generation AI automatically suggests related recipes and seasonings. For example, it displays dressings and cooking methods that go well with vegetables. Furthermore, when a user enters "I bought coffee at a cafe," the generation AI automatically suggests new menu items at related cafes and information about nearby cafes. For example, it displays new dessert menu items and information about cafe events. Furthermore, when a user enters "I bought a book at a bookstore," the generation AI automatically suggests related books and other works by the author. For example, it displays bestsellers in the same genre and interviews with the author. This complements the user's input and improves the accuracy of the purchasing data.

[0031] The purchasing data collection unit uses the user's location information when collecting purchasing data to analyze purchasing trends by region and develop a region-specific marketing strategy. For example, when a user inputs "I bought vegetables at the supermarket," the purchasing data collection unit analyzes purchasing trends by region based on the location information and suggests vegetables and recipes that are popular in that region. For example, it displays dishes and ingredients that are unique to the region. Also, when a user inputs "I bought coffee at a cafe," the purchasing data collection unit analyzes popular menu items and event information for each region based on the location information and develops a marketing strategy for that region. For example, it displays promotions and events that are exclusive to the region. Also, when a user inputs "I bought a book at a bookstore," the purchasing data collection unit analyzes popular book rankings by region and information about author signings based on the location information and develops a marketing strategy for that region. For example, it displays book fairs and events that are exclusive to the region. This makes it possible to analyze purchasing trends by region and develop a region-specific marketing strategy.

[0032] The purchasing data collection unit can add a voice input function to the household accounting app, allowing the user to input purchasing data by voice. The purchasing data collection unit adds a function that allows the user to input, for example, "I bought vegetables at the supermarket" by voice. For example, using voice recognition technology, the user's voice is converted into text data and saved as purchasing data. The purchasing data collection unit also adds a function that allows the user to input, for example, "I bought coffee at a cafe" by voice. For example, a function is provided that automatically suggests related products and services when the user inputs by voice. The purchasing data collection unit also adds a function that allows the user to input, for example, "I bought a book at a bookstore" by voice. For example, a function is provided that automatically suggests related books and other works by the author when the user inputs by voice. This allows the user to input purchasing data by voice.

[0033] The purchase data collection unit links the household accounting app with other fitness apps and health management apps, and can analyze the user's health condition and purchase data in association with each other. For example, the purchase data collection unit links the household accounting app with a fitness app, and analyzes the user's exercise data and purchase data in association with each other. For example, the purchase data collection unit analyzes data on foods and drinks purchased after exercise and makes healthy dietary suggestions. The purchase data collection unit also links the household accounting app with a health management app, and analyzes the user's health condition and purchase data in association with each other. For example, the purchase data collection unit analyzes the user's weight and blood pressure data and purchase data and makes healthy lifestyle suggestions. The purchase data collection unit also links the household accounting app with a sleep management app, and analyzes the user's sleep data and purchase data in association with each other. For example, the purchase data collection unit analyzes the relationship between sleep quality and diet and makes dietary suggestions that promote good quality sleep. This allows the user's health condition and purchase data to be analyzed in association with each other.

[0034] The customer data enrichment unit uses the generation AI to estimate a user's lifestyle and values ​​based on purchasing data, allowing it to create a more detailed customer profile. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI estimates the user's health-consciousness and eating habits, creating a detailed customer profile. For example, the user may be classified as highly health-conscious. Furthermore, based on data entered by a user such as "I bought coffee at a cafe," the generation AI estimates the user's lifestyle and values, creating a detailed customer profile. For example, the user may be classified as highly interested in cafe culture. Furthermore, based on data entered by a user such as "I bought a book at a bookstore," the generation AI estimates the user's desire for knowledge and motivation to learn, creating a detailed customer profile. For example, the user may be classified as highly knowledge-conscious. This allows the generation AI to estimate a user's lifestyle and values, creating a detailed customer profile.

[0035] The customer data enrichment unit links user purchasing data with social media activity, enabling enhanced customer data that also takes online behavioral patterns into account. For example, the customer data enrichment unit links data entered by a user, such as "I bought vegetables at the supermarket," with health-related posts on social media, allowing the generation AI to reinforce the user's health consciousness. For example, the unit classifies the user as one who posts frequently about health. Similarly, the customer data enrichment unit links data entered by a user, such as "I bought coffee at a cafe," with cafe-related posts on social media, allowing the generation AI to reinforce the user's interest in cafe culture. For example, the unit classifies the user as one who posts frequently about cafes. Similarly, the customer data enrichment unit links data entered by a user, such as "I bought a book at a bookstore," with reading-related posts on social media, allowing the generation AI to reinforce the user's desire for knowledge. For example, the unit classifies the user as one who posts frequently about reading. This enables enhanced customer data that also takes online behavioral patterns into account.

[0036] When enhancing customer data, the customer data enhancement unit not only uses the user's past purchasing data but also predicts future purchasing, allowing it to understand future needs. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI analyzes past purchasing data and predicts future purchasing. For example, it predicts seasonal vegetable purchasing trends and understands future needs. Similarly, based on data entered by a user such as "I bought coffee at a cafe," the generation AI analyzes past purchasing data and predicts future purchasing. For example, it predicts cafe usage trends during specific seasons or events and understands future needs. Similarly, based on data entered by a user such as "I bought a book at a bookstore," the generation AI analyzes past purchasing data and predicts future purchasing. For example, it predicts the release dates of new books and the popularity trends of specific genres and understands future needs. This allows it to understand future needs and enhance customer data.

[0037] To enhance customer data, the customer data enhancement unit compares a user's purchasing data with that of other users and identifies common preferences and behavioral patterns. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI compares the data with that of other users to identify common preferences and behavioral patterns. For example, the generation AI identifies a group of health-conscious users and analyzes the purchasing trends common to that group. Furthermore, based on data entered by a user such as "I bought coffee at a cafe," the generation AI compares the data with that of other users to identify common preferences and behavioral patterns. For example, the generation AI identifies a group of users who are highly interested in cafe culture and analyzes the purchasing trends common to that group. Furthermore, based on data entered by a user such as "I bought a book at a bookstore," the generation AI compares the data with that of other users to identify common preferences and behavioral patterns. For example, the generation AI identifies a group of users who are highly knowledgeable and analyzes the purchasing trends common to that group. This allows for the identification of common preferences and behavioral patterns and the enhancement of customer data.

[0038] The digital marketing strategy planning department can use the generation AI to automatically generate and deliver personalized advertisements to individual users based on the enhanced customer data. For example, based on data input by a user such as "I bought vegetables at the supermarket," the generation AI can automatically generate and deliver personalized advertisements for health foods to health-conscious users. For example, it can provide coupons and recipes for specific health foods. Furthermore, based on data input by a user such as "I bought coffee at a cafe," the generation AI can automatically generate and deliver personalized advertisements for new cafe menu items and event information to users who are interested in cafe culture. For example, it can provide information on new dessert menu items and cafe events. Furthermore, based on data input by a user such as "I bought a book at a bookstore," the generation AI can automatically generate and deliver personalized advertisements for new books and author interviews to users who are curious about knowledge. For example, it can provide information on new book releases and author interviews. This makes it possible to automatically generate and deliver personalized advertisements to individual users.

[0039] When formulating a digital marketing strategy, the digital marketing strategy planning department can propose marketing campaigns that are tailored not only to a user's purchasing history but also to seasons and events. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the digital marketing strategy planning department uses a generation AI to analyze seasonal vegetable purchasing trends and propose marketing campaigns that are tailored to the season. For example, in the summer, the generation AI proposes salad recipes and vegetables for barbecues. Also, based on data entered by a user such as "I bought coffee at a cafe," the digital marketing strategy planning department uses a generation AI to analyze cafe usage trends that are tailored to specific seasons and events and propose marketing campaigns. For example, during the Christmas season, the generation AI proposes special dessert menus and events. Also, based on data entered by a user such as "I bought a book at a bookstore," the generation AI analyzes book purchasing trends that are tailored to specific seasons and events and proposes marketing campaigns. For example, during summer vacation, the generation AI proposes travel guidebooks and books suitable for beach reading. This makes it possible to propose marketing campaigns that are tailored to seasons and events.

[0040] When formulating digital marketing strategies, the digital marketing strategy planning department can refer to data from different industries and fields to promote crossover innovation. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI can refer to data from different industries to promote crossover innovation. For example, it can refer to data from the health food industry to suggest new health foods. Similarly, based on data entered by a user such as "I bought coffee at a cafe," the generation AI can refer to data from different industries to promote crossover innovation. For example, it can refer to data from the fashion industry to suggest new cafe menus and events. Similarly, based on data entered by a user such as "I bought a book at a bookstore," the generation AI can refer to data from different industries to promote crossover innovation. For example, it can refer to data from the technology industry to suggest the theme and content of a new book. This allows crossover innovation to be promoted by referencing data from different industries and fields.

[0041] The digital marketing strategy planning unit uses the enhanced customer data to develop a marketing strategy tailored to the user's life stage and provide optimal advertisements to target customers. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI estimates the user's life stage and develops a marketing strategy tailored to that life stage. For example, for a user raising children, advertisements for healthy foods for children are provided. Furthermore, based on data entered by a user such as "I bought coffee at a cafe," the generation AI estimates the user's life stage and develops a marketing strategy tailored to that life stage. For example, for a student user, information on student discounts and events for students is provided. Furthermore, based on data entered by a user such as "I bought a book at a bookstore," the generation AI estimates the user's life stage and develops a marketing strategy tailored to that life stage. For example, for a retired user, advertisements for books on hobbies and self-improvement are provided. This allows for the development of a marketing strategy tailored to the user's life stage and the provision of optimal advertisements.

[0042] When analyzing the results of digital marketing, the digital marketing strategy planning department allows the generation AI to automatically identify areas for improvement and reflect them in the next campaign. For example, the digital marketing strategy planning department analyzes the results of a digital marketing campaign, and the generation AI automatically identifies areas for improvement. For example, it identifies ads with low click-through rates or conversion rates and analyzes the causes. The digital marketing strategy planning department also allows the generation AI to automatically identify areas for improvement and reflect them in the next campaign. For example, it analyzes the effectiveness of ads for a specific target group and adjusts the target group in the next campaign. The digital marketing strategy planning department also analyzes the results of a digital marketing campaign, and the generation AI automatically identifies areas for improvement and reflects them in the next campaign. For example, it adjusts the creative elements or message of an ad. In this way, the results of digital marketing can be analyzed and reflected in the next campaign.

[0043] When analyzing data, the digital marketing strategy planning department can take into account not only user purchasing behavior but also detailed data such as ad viewing time and click rate. For example, when analyzing the results of a digital marketing campaign, the digital marketing strategy planning department can take into account not only user purchasing behavior but also detailed data such as ad viewing time and click rate. For example, an advertisement that provides more detailed information is delivered to users who spend a long time viewing an advertisement. The digital marketing strategy planning department can also analyze the click rate and viewing time of an advertisement to evaluate the user's level of interest. For example, the digital marketing strategy planning department can identify elements of an advertisement that have a high click rate and strengthen those elements in the next campaign. The digital marketing strategy planning department can also analyze the correlation between user purchasing behavior and ad viewing time and click rate to identify areas for improvement in the marketing strategy. For example, the digital marketing strategy planning department can analyze how an advertisement with a long viewing time affects purchasing behavior. This allows the digital marketing strategy planning department to take into account not only user purchasing behavior but also detailed data such as ad viewing time and click rate.

[0044] The digital marketing strategy planning department can classify the results of data analysis by different marketing channels (e.g., social media, email, website) and evaluate the effectiveness of each channel. For example, the digital marketing strategy planning department analyzes the results of a digital marketing campaign and classifies them by different marketing channels. For example, the department evaluates the effectiveness of advertising for each social media, email, and website and identifies the most effective channel. The digital marketing strategy planning department also evaluates the effectiveness of each marketing channel and selects the optimal channel for the next campaign. For example, if advertising on social media is most effective, the department will distribute advertising mainly on social media in the next campaign. The digital marketing strategy planning department also evaluates the effectiveness of each different marketing channel and identifies areas for improvement for each channel. For example, if the click-through rate for email advertising is low, the department will analyze the cause and improve it in the next campaign. This makes it possible to evaluate the effectiveness of each different marketing channel.

[0045] The digital marketing strategy planning department can provide the results of the data analysis as a visual dashboard, allowing marketers to intuitively understand. The digital marketing strategy planning department, for example, provides the results of a digital marketing campaign as a visual dashboard, allowing marketers to intuitively understand. For example, click-through rates and conversion rates are displayed in graphs and charts. The digital marketing strategy planning department also uses the visual dashboard to monitor the effectiveness of a marketing campaign in real time. For example, the effectiveness of an advertisement is displayed over time to grasp the progress of the campaign. The digital marketing strategy planning department also provides the results of the data analysis as a visual dashboard, allowing marketers to intuitively understand. For example, the effectiveness of each marketing channel is displayed in a color-coded manner. In this way, the results of the data analysis can be provided as a visual dashboard, allowing marketers to intuitively understand.

[0046] Generative AI can automatically anonymize and encrypt data to protect user privacy. For example, generative AI can automatically anonymize a user's purchasing data to prevent personal identification. For example, it can delete user IDs and personal information and analyze only the anonymized data. Generative AI can also automatically encrypt data to prevent unauthorized access and data leaks. For example, it can encrypt and store purchasing data so that only users with authorized access can access it. Generative AI can also automatically anonymize and encrypt data to build a system that protects user privacy. For example, it can perform anonymization and encryption throughout the entire process, from data collection to analysis. This allows data to be anonymized and encrypted to protect user privacy.

[0047] Generative AI can detect signs of unauthorized access or data leaks in real time and take immediate countermeasures. For example, generative AI will build a system that detects signs of unauthorized access or data leaks in real time and takes immediate countermeasures. For example, it will detect abnormal access patterns or unauthorized data transfers and issue an alert. For data security purposes, generative AI will also monitor network traffic in real time and detect signs of unauthorized access. For example, it will detect abnormal data transfer volumes or access from unauthorized IP addresses. Generative AI will also develop a system that detects signs of unauthorized access or data leaks in real time and takes immediate countermeasures. For example, if unauthorized access is detected, it will automatically block access and notify the security team. This makes it possible to detect signs of unauthorized access or data leaks in real time and take immediate countermeasures.

[0048] For privacy protection, a dashboard can be provided that allows users to check their own data usage status. For privacy protection, for example, a system is constructed that provides a dashboard that allows users to check their own data usage status. For example, which data is being used and how it is being used is visually displayed. Furthermore, for privacy protection, a dashboard is provided that allows users to check their own data usage status in real time. For example, usage status at each stage of data collection, analysis, and storage is displayed. Furthermore, for privacy protection, a dashboard is provided that allows users to check their own data usage status, thereby enhancing privacy protection. For example, data usage history and access permissions can be displayed, allowing users to manage their own data. In this way, a dashboard can be provided that allows users to check their own data usage status.

[0049] Generative AI can periodically conduct security diagnosis and identify and fix vulnerabilities. For example, generative AI can periodically conduct security diagnosis and build a system that identifies and fixes vulnerabilities. For example, it can periodically conduct security diagnosis of networks and databases and fix vulnerabilities. Generative AI can also periodically conduct security diagnosis and identify and fix vulnerabilities for data security purposes. For example, it can apply security patches and review settings. Generative AI can also periodically conduct security diagnosis and develop a system that identifies and fixes vulnerabilities. For example, it can automatically implement security measures based on the diagnosis results. This makes it possible to periodically conduct security diagnosis and identify and fix vulnerabilities.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The purchasing data collection unit allows the generation AI to automatically suggest related products and services for the purchasing data entered by the user, complementing the user's input. For example, when a user enters "I bought vegetables at the supermarket," the generation AI automatically suggests related recipes and seasonings. For example, it might display dressings and cooking methods that go well with vegetables. Furthermore, when a user enters "I bought coffee at a cafe," the generation AI automatically suggests new menu items at related cafes and information about nearby cafes. For example, it might display new dessert menu items or information about cafe events. Furthermore, when a user enters "I bought a book at a bookstore," the generation AI automatically suggests related books and other works by the author. For example, it might display bestsellers in the same genre or interviews with the author. This complements the user's input and improves the accuracy of purchasing data.

[0052] The purchasing data collection unit uses the user's location information when collecting purchasing data to analyze purchasing trends by region and develop a region-specific marketing strategy. For example, when a user inputs "I bought vegetables at the supermarket," the purchasing data collection unit analyzes purchasing trends by region based on the location information and suggests vegetables and recipes that are popular in that region. For example, it displays dishes and ingredients that are unique to the region. Also, when a user inputs "I bought coffee at a cafe," the purchasing data collection unit analyzes popular menu items and event information for each region based on the location information and develops a marketing strategy for that region. For example, it displays promotions and events that are exclusive to the region. Also, when a user inputs "I bought a book at a bookstore," the purchasing data collection unit analyzes popular book rankings by region and information about author signings based on the location information and develops a marketing strategy for that region. For example, it displays book fairs and events that are exclusive to the region. This makes it possible to analyze purchasing trends by region and develop a region-specific marketing strategy.

[0053] The purchasing data collection unit can add a voice input function to the household accounting app, allowing the user to input purchasing data by voice. For example, a function is added that allows the user to input by voice, "I bought vegetables at the supermarket." For example, speech recognition technology is used to convert the user's voice into text data and save it as purchasing data. The purchasing data collection unit also adds a function that allows the user to input by voice, "I bought coffee at a cafe." For example, a function is provided that automatically suggests related products and services when the user inputs by voice. The purchasing data collection unit also adds a function that allows the user to input by voice, "I bought a book at a bookstore." For example, a function is provided that automatically suggests related books and other works by the author when the user inputs by voice. This allows the user to input purchasing data by voice.

[0054] The purchase data collection unit links the household accounting app with other fitness apps and health management apps, and can analyze the user's health condition and purchase data in association with each other. For example, the household accounting app and fitness app can be linked, and the user's exercise data and purchase data can be analyzed in association with each other. For example, data on foods and drinks purchased after exercise can be analyzed to suggest healthy meals. The purchase data collection unit also links the household accounting app with a health management app, and analyzes the user's health condition and purchase data in association with each other. For example, the purchase data collection unit analyzes the user's weight and blood pressure data and purchase data to suggest healthy lifestyle habits. The purchase data collection unit also links the household accounting app with a sleep management app, and analyzes the user's sleep data and purchase data in association with each other. For example, the purchase data collection unit analyzes the relationship between sleep quality and diet and suggests meals that promote good quality sleep. This makes it possible to analyze the user's health condition and purchase data in association with each other.

[0055] The customer data enhancement unit uses the generation AI to estimate a user's lifestyle and values ​​based on purchasing data, allowing it to create a more detailed customer profile. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI estimates the user's health-consciousness and eating habits, creating a detailed customer profile. For example, it classifies the user as being highly health-conscious. Furthermore, based on data entered by a user such as "I bought coffee at a cafe," the customer data enhancement unit uses the generation AI to estimate a user's lifestyle and values, creating a detailed customer profile. For example, it classifies the user as being highly interested in cafe culture. Furthermore, based on data entered by a user such as "I bought a book at a bookstore," the generation AI estimates the user's desire for knowledge and motivation to learn, creating a detailed customer profile. For example, it classifies the user as being highly knowledge-conscious. This allows it to estimate a user's lifestyle and values, creating a detailed customer profile.

[0056] The customer data enrichment unit links user purchasing data with social media activity, enabling enhanced customer data that also takes online behavioral patterns into account. For example, by linking data entered by a user such as "I bought vegetables at the supermarket" with health-related posts on social media, the generation AI will reinforce the user's health consciousness. For example, the user will be classified as one who posts frequently on health-related topics. Similarly, the customer data enrichment unit links data entered by a user such as "I bought coffee at a cafe" with cafe-related posts on social media, enabling the generation AI to reinforce the user's interest in cafe culture. For example, the user will be classified as one who posts frequently on cafes. Similarly, the customer data enrichment unit links data entered by a user such as "I bought a book at a bookstore" with reading-related posts on social media, enabling the generation AI to reinforce the user's desire for knowledge. For example, the user will be classified as one who posts frequently on reading-related topics. This allows enhanced customer data that also takes online behavioral patterns into account.

[0057] When enhancing customer data, the customer data enhancement unit not only uses the user's past purchasing data but also predicts future purchasing, allowing it to understand future needs. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI analyzes the past purchasing data and makes future purchasing predictions. For example, it predicts seasonal vegetable purchasing trends and understands future needs. Similarly, based on data entered by a user such as "I bought coffee at a cafe," the generation AI analyzes the past purchasing data and makes future purchasing predictions. For example, it predicts cafe usage trends during specific seasons or events and understands future needs. Similarly, based on data entered by a user such as "I bought a book at a bookstore," the generation AI analyzes the past purchasing data and makes future purchasing predictions. For example, it predicts the release dates of new books and the popularity trends of specific genres and understands future needs. This allows it to understand future needs and enhance customer data.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The purchasing data collection unit collects the user's purchasing data through the household accounting app. For example, it collects data such as the items purchased, the purchase amount, and the purchase date and time entered by the user into the household accounting app. The purchasing data collection unit can also automatically classify the data entered by the user and organize it by category. For example, it can classify the data into categories such as groceries, clothing, and entertainment. Furthermore, the purchasing data collection unit can periodically update the user's purchasing data to keep it up to date. For example, it can update the data every weekend to reflect the latest purchasing history. Step 2: The Customer Data Enhancement Unit analyzes the purchase data collected by the Purchase Data Collection Unit to enhance the customer data. For example, the Generation AI analyzes a user's purchase history to identify their preferences and behavioral patterns. The Generation AI can also estimate a user's lifestyle and values ​​based on their purchase data. For example, it can identify users who are highly health-conscious or who are interested in cafe culture. Furthermore, the Customer Data Enhancement Unit can compare a user's purchase data with other users to identify common preferences and behavioral patterns. For example, it can analyze the purchasing trends of users living in the same area and develop a marketing strategy specific to that area. Step 3: The Digital Marketing Strategy Planning Department develops a digital marketing strategy based on the customer data enhanced by the Customer Data Enhancement Department. For example, the generative AI generates optimal advertisements and promotions based on user preferences and behavioral patterns, effectively approaching target customers. The Digital Marketing Strategy Planning Department can also propose marketing campaigns tailored to the season or event. For example, a special promotion could be implemented during the Christmas season. Furthermore, the Digital Marketing Strategy Planning Department can analyze the results of digital marketing and evaluate its effectiveness. For example, it could analyze how many users a particular advertising campaign reached and how many conversions it achieved.

[0060] (Example 2) A digital marketing system according to an embodiment of the present invention collects user purchase data through a household accounting app, and uses a generation AI to analyze and enhance customer data for digital marketing. As a result, the digital marketing system enhances customer data based on the user purchase data, develops more effective marketing strategies, and provides optimal advertisements and promotions to target customers.

[0061] A digital marketing system according to an embodiment includes a purchase data collection unit, a customer data enhancement unit, and a digital marketing strategy planning unit. The purchase data collection unit collects user purchase data through a household accounting app. For example, the purchase data collection unit collects data such as purchased items, purchase amounts, and purchase dates and times entered by the user into the household accounting app. The purchase data collection unit can also automatically classify and organize the data entered by the user into categories. For example, the purchase data collection unit can classify the data into categories such as groceries, clothing, and entertainment. The purchase data collection unit can also periodically update the user purchase data to maintain the latest data. For example, the data can be updated every weekend to reflect the latest purchase history. The customer data enhancement unit analyzes the purchase data collected by the purchase data collection unit to enhance the customer data. For example, the generation AI analyzes the user's purchase history to identify the user's preferences and behavioral patterns. The generation AI can also estimate the user's lifestyle and values ​​based on the user's purchase data. For example, the generation AI can identify health-conscious users or users with a strong interest in cafe culture. Furthermore, the Customer Data Enhancement Department can compare a user's purchasing data with other users to identify common preferences and behavioral patterns. For example, it can analyze the purchasing trends of users living in the same area and develop a marketing strategy specific to that area. The Digital Marketing Strategy Planning Department develops digital marketing strategies based on the customer data enhanced by the Customer Data Enhancement Department. For example, generative AI can generate optimal advertisements and promotions based on user preferences and behavioral patterns to effectively approach target customers. The Digital Marketing Strategy Planning Department can also propose marketing campaigns tailored to the season or event. For example, it could implement a special promotion during the Christmas season. Furthermore, the Digital Marketing Strategy Planning Department can analyze the results of digital marketing and evaluate its effectiveness. For example, it can analyze how many users a particular advertising campaign reached and how many conversions it achieved.As a result, the digital marketing system according to the embodiment can enhance customer data based on user purchase data, develop more effective marketing strategies, and provide optimal advertisements and promotions to target customers. For example, the output unit displays marketing results to marketers via a web application or mobile application. If marketers wish to receive feedback in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to marketers.

[0062] The purchasing data collection unit allows the generation AI to automatically suggest related products and services for the purchasing data entered by the user, thereby complementing the user's input. For example, when a user enters "I bought vegetables at the supermarket," the generation AI automatically suggests related recipes and seasonings. For example, it displays dressings and cooking methods that go well with vegetables. Furthermore, when a user enters "I bought coffee at a cafe," the generation AI automatically suggests new menu items at related cafes and information about nearby cafes. For example, it displays new dessert menu items and information about cafe events. Furthermore, when a user enters "I bought a book at a bookstore," the generation AI automatically suggests related books and other works by the author. For example, it displays bestsellers in the same genre and interviews with the author. This complements the user's input and improves the accuracy of the purchasing data.

[0063] The purchasing data collection unit uses the user's location information when collecting purchasing data to analyze purchasing trends by region and develop a region-specific marketing strategy. For example, when a user inputs "I bought vegetables at the supermarket," the purchasing data collection unit analyzes purchasing trends by region based on the location information and suggests vegetables and recipes that are popular in that region. For example, it displays dishes and ingredients that are unique to the region. Also, when a user inputs "I bought coffee at a cafe," the purchasing data collection unit analyzes popular menu items and event information for each region based on the location information and develops a marketing strategy for that region. For example, it displays promotions and events that are exclusive to the region. Also, when a user inputs "I bought a book at a bookstore," the purchasing data collection unit analyzes popular book rankings by region and information about author signings based on the location information and develops a marketing strategy for that region. For example, it displays book fairs and events that are exclusive to the region. This makes it possible to analyze purchasing trends by region and develop a region-specific marketing strategy.

[0064] The purchasing data collection unit can use the emotion estimation function to analyze the emotions of a user when entering purchasing data and provide an interface for eliciting positive emotions. For example, when a user enters "I bought vegetables at the supermarket," the purchasing data collection unit uses the emotion estimation function to analyze the user's facial expression and voice and provide an interface for eliciting positive emotions. For example, an encouraging message or positive feedback is displayed. Furthermore, when a user enters "I bought coffee at a cafe," the purchasing data collection unit uses the emotion estimation function to analyze the user's emotions and provide an interface for eliciting positive emotions. For example, positive comments about the cafe's atmosphere and the aroma of the coffee are displayed. Furthermore, when a user enters "I bought a book at a bookstore," the purchasing data collection unit uses the emotion estimation function to analyze the user's emotions and provide an interface for eliciting positive emotions. For example, positive comments about the enjoyment of reading and the joy of gaining new knowledge are displayed. This can elicit positive emotions from the user and increase motivation for input.

[0065] The purchasing data collection unit can add a voice input function to the household accounting app, allowing the user to input purchasing data by voice. The purchasing data collection unit adds a function that allows the user to input, for example, "I bought vegetables at the supermarket" by voice. For example, using voice recognition technology, the user's voice is converted into text data and saved as purchasing data. The purchasing data collection unit also adds a function that allows the user to input, for example, "I bought coffee at a cafe" by voice. For example, a function is provided that automatically suggests related products and services when the user inputs by voice. The purchasing data collection unit also adds a function that allows the user to input, for example, "I bought a book at a bookstore" by voice. For example, a function is provided that automatically suggests related books and other works by the author when the user inputs by voice. This allows the user to input purchasing data by voice.

[0066] The purchase data collection unit links the household accounting app with other fitness apps and health management apps, and can analyze the user's health condition and purchase data in association with each other. For example, the purchase data collection unit links the household accounting app with a fitness app, and analyzes the user's exercise data and purchase data in association with each other. For example, the purchase data collection unit analyzes data on foods and drinks purchased after exercise and makes healthy dietary suggestions. The purchase data collection unit also links the household accounting app with a health management app, and analyzes the user's health condition and purchase data in association with each other. For example, the purchase data collection unit analyzes the user's weight and blood pressure data and purchase data and makes healthy lifestyle suggestions. The purchase data collection unit also links the household accounting app with a sleep management app, and analyzes the user's sleep data and purchase data in association with each other. For example, the purchase data collection unit analyzes the relationship between sleep quality and diet and makes dietary suggestions that promote good quality sleep. This allows the user's health condition and purchase data to be analyzed in association with each other.

[0067] The purchasing data collection unit uses the emotion estimation function to provide real-time feedback on the user's emotions when entering purchasing data, thereby increasing motivation for entering data. For example, when a user enters "I bought vegetables at the supermarket," the purchasing data collection unit uses the emotion estimation function to analyze the user's emotions in real time and provide positive feedback. For example, an encouraging message is displayed according to the input content. Furthermore, when a user enters "I bought coffee at a cafe," the purchasing data collection unit uses the emotion estimation function to analyze the user's emotions in real time and provide positive feedback. For example, a positive comment is displayed according to the input content. Furthermore, when a user enters "I bought a book at a bookstore," the purchasing data collection unit uses the emotion estimation function to analyze the user's emotions in real time and provide positive feedback. For example, an encouraging message or positive comment is displayed according to the input content. In this way, the user's emotions are fed back in real time, thereby increasing motivation for entering data.

[0068] The customer data enrichment unit uses the generation AI to estimate a user's lifestyle and values ​​based on purchasing data, allowing it to create a more detailed customer profile. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI estimates the user's health-consciousness and eating habits, creating a detailed customer profile. For example, the user may be classified as highly health-conscious. Furthermore, based on data entered by a user such as "I bought coffee at a cafe," the generation AI estimates the user's lifestyle and values, creating a detailed customer profile. For example, the user may be classified as highly interested in cafe culture. Furthermore, based on data entered by a user such as "I bought a book at a bookstore," the generation AI estimates the user's desire for knowledge and motivation to learn, creating a detailed customer profile. For example, the user may be classified as highly knowledge-conscious. This allows the generation AI to estimate a user's lifestyle and values, creating a detailed customer profile.

[0069] The customer data enrichment unit links user purchasing data with social media activity, enabling enhanced customer data that also takes online behavioral patterns into account. For example, the customer data enrichment unit links data entered by a user, such as "I bought vegetables at the supermarket," with health-related posts on social media, allowing the generation AI to reinforce the user's health consciousness. For example, the unit classifies the user as one who posts frequently about health. Similarly, the customer data enrichment unit links data entered by a user, such as "I bought coffee at a cafe," with cafe-related posts on social media, allowing the generation AI to reinforce the user's interest in cafe culture. For example, the unit classifies the user as one who posts frequently about cafes. Similarly, the customer data enrichment unit links data entered by a user, such as "I bought a book at a bookstore," with reading-related posts on social media, allowing the generation AI to reinforce the user's desire for knowledge. For example, the unit classifies the user as one who posts frequently about reading. This enables enhanced customer data that also takes online behavioral patterns into account.

[0070] The customer data enhancement unit can use the emotion estimation function to analyze changes in emotions accompanying a user's purchasing behavior and enhance customer data based on emotions. For example, the customer data enhancement unit analyzes the emotion when a user inputs, "I bought vegetables at the supermarket," and if the emotion is strong, the customer data is enhanced as a user with a high level of health consciousness. For example, the user is classified as having positive emotions toward healthy eating. The customer data enhancement unit also analyzes the emotion when a user inputs, "I bought coffee at a cafe," and if the emotion is strong, the customer data is enhanced as a user with a high level of interest in cafe culture. For example, the user is classified as having positive emotions toward relaxing at cafes. The customer data enhancement unit also analyzes the emotion when a user inputs, "I bought a book at a bookstore," and if the emotion is strong, the customer data is enhanced as a user with a high level of knowledge. For example, the user is classified as having positive emotions toward reading. This allows customer data based on user emotions to be enhanced.

[0071] When enhancing customer data, the customer data enhancement unit not only uses the user's past purchasing data but also predicts future purchasing, allowing it to understand future needs. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI analyzes past purchasing data and predicts future purchasing. For example, it predicts seasonal vegetable purchasing trends and understands future needs. Similarly, based on data entered by a user such as "I bought coffee at a cafe," the generation AI analyzes past purchasing data and predicts future purchasing. For example, it predicts cafe usage trends during specific seasons or events and understands future needs. Similarly, based on data entered by a user such as "I bought a book at a bookstore," the generation AI analyzes past purchasing data and predicts future purchasing. For example, it predicts the release dates of new books and the popularity trends of specific genres and understands future needs. This allows it to understand future needs and enhance customer data.

[0072] To enhance customer data, the customer data enhancement unit compares a user's purchasing data with that of other users and identifies common preferences and behavioral patterns. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI compares the data with that of other users to identify common preferences and behavioral patterns. For example, the generation AI identifies a group of health-conscious users and analyzes the purchasing trends common to that group. Furthermore, based on data entered by a user such as "I bought coffee at a cafe," the generation AI compares the data with that of other users to identify common preferences and behavioral patterns. For example, the generation AI identifies a group of users who are highly interested in cafe culture and analyzes the purchasing trends common to that group. Furthermore, based on data entered by a user such as "I bought a book at a bookstore," the generation AI compares the data with that of other users to identify common preferences and behavioral patterns. For example, the generation AI identifies a group of users who are highly knowledgeable and analyzes the purchasing trends common to that group. This allows for the identification of common preferences and behavioral patterns and the enhancement of customer data.

[0073] The customer data enhancement unit uses the emotion estimation function to analyze emotional responses to users' purchasing behavior and develop emotion-based marketing strategies. For example, the customer data enhancement unit analyzes the emotion when a user inputs, for example, "I bought vegetables at the supermarket." If the emotion is strong, the customer data enhancement unit suggests health food promotions to health-conscious users. For example, it provides coupons for health foods and recipes. The customer data enhancement unit also analyzes the emotion when a user inputs, for example, "I bought coffee at a cafe." If the emotion is strong, the customer data enhancement unit provides new cafe menu items and event information to users who are interested in cafe culture. For example, it delivers information on new dessert menu items and cafe events. The customer data enhancement unit also analyzes the emotion when a user inputs, for example, "I bought a book at a bookstore." If the emotion is strong, the customer data enhancement unit provides new books and author interviews to users who are curious about knowledge. For example, it delivers information on new book releases and author interviews. This makes it possible to develop marketing strategies based on user emotions.

[0074] The digital marketing strategy planning department can use the generation AI to automatically generate and deliver personalized advertisements to individual users based on the enhanced customer data. For example, based on data input by a user such as "I bought vegetables at the supermarket," the generation AI can automatically generate and deliver personalized advertisements for health foods to health-conscious users. For example, it can provide coupons and recipes for specific health foods. Furthermore, based on data input by a user such as "I bought coffee at a cafe," the generation AI can automatically generate and deliver personalized advertisements for new cafe menu items and event information to users who are interested in cafe culture. For example, it can provide information on new dessert menu items and cafe events. Furthermore, based on data input by a user such as "I bought a book at a bookstore," the generation AI can automatically generate and deliver personalized advertisements for new books and author interviews to users who are curious about knowledge. For example, it can provide information on new book releases and author interviews. This makes it possible to automatically generate and deliver personalized advertisements to individual users.

[0075] When formulating a digital marketing strategy, the digital marketing strategy planning department can propose marketing campaigns that are tailored not only to a user's purchasing history but also to seasons and events. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the digital marketing strategy planning department uses a generation AI to analyze seasonal vegetable purchasing trends and propose marketing campaigns that are tailored to the season. For example, in the summer, the generation AI proposes salad recipes and vegetables for barbecues. Also, based on data entered by a user such as "I bought coffee at a cafe," the digital marketing strategy planning department uses a generation AI to analyze cafe usage trends that are tailored to specific seasons and events and propose marketing campaigns. For example, during the Christmas season, the generation AI proposes special dessert menus and events. Also, based on data entered by a user such as "I bought a book at a bookstore," the generation AI analyzes book purchasing trends that are tailored to specific seasons and events and proposes marketing campaigns. For example, during summer vacation, the generation AI proposes travel guidebooks and books suitable for beach reading. This makes it possible to propose marketing campaigns that are tailored to seasons and events.

[0076] The digital marketing strategy planning unit uses the emotion estimation function to generate advertisements based on the user's emotions and develop marketing strategies that are likely to resonate emotionally with the user. For example, the digital marketing strategy planning unit analyzes the user's emotions when the user enters, for example, "I bought vegetables at the supermarket." If the user's emotions are strong, the digital marketing strategy planning unit generates an advertisement for health foods that is likely to resonate emotionally with health-conscious users. For example, the digital marketing strategy planning unit delivers an advertisement including a message encouraging a healthy lifestyle. The digital marketing strategy planning unit also analyzes the user's emotions when the user enters, for example, "I bought coffee at a cafe." If the user's emotions are strong, the digital marketing strategy planning unit generates an advertisement for new cafe menu items or event information that is likely to resonate emotionally with users who are interested in cafe culture. For example, the digital marketing strategy planning unit delivers an advertisement with a theme of relaxation and healing. The digital marketing strategy planning unit also analyzes the user's emotions when the user enters, for example, "I bought a book at a bookstore." If the user's emotions are strong, the digital marketing strategy planning unit generates an advertisement for new books or author interviews that are likely to resonate emotionally with users who are curious about knowledge. For example, the digital marketing strategy planning unit delivers an advertisement with a theme of the pursuit of knowledge and the joy of learning. This allows us to generate advertisements based on users' emotions and develop marketing strategies that are likely to resonate with users emotionally.

[0077] When formulating digital marketing strategies, the digital marketing strategy planning department can refer to data from different industries and fields to promote crossover innovation. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI can refer to data from different industries to promote crossover innovation. For example, it can refer to data from the health food industry to suggest new health foods. Similarly, based on data entered by a user such as "I bought coffee at a cafe," the generation AI can refer to data from different industries to promote crossover innovation. For example, it can refer to data from the fashion industry to suggest new cafe menus and events. Similarly, based on data entered by a user such as "I bought a book at a bookstore," the generation AI can refer to data from different industries to promote crossover innovation. For example, it can refer to data from the technology industry to suggest the theme and content of a new book. This allows crossover innovation to be promoted by referencing data from different industries and fields.

[0078] The digital marketing strategy planning unit uses the enhanced customer data to develop a marketing strategy tailored to the user's life stage and provide optimal advertisements to target customers. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI estimates the user's life stage and develops a marketing strategy tailored to that life stage. For example, for a user raising children, advertisements for healthy foods for children are provided. Furthermore, based on data entered by a user such as "I bought coffee at a cafe," the generation AI estimates the user's life stage and develops a marketing strategy tailored to that life stage. For example, for a student user, information on student discounts and events for students is provided. Furthermore, based on data entered by a user such as "I bought a book at a bookstore," the generation AI estimates the user's life stage and develops a marketing strategy tailored to that life stage. For example, for a retired user, advertisements for books on hobbies and self-improvement are provided. This allows for the development of a marketing strategy tailored to the user's life stage and the provision of optimal advertisements.

[0079] The digital marketing strategy planning unit uses the emotion estimation function to generate advertisements based on user emotions in real time, maximizing the effectiveness of marketing strategies. For example, the digital marketing strategy planning unit analyzes the emotions expressed when a user enters, for example, "I bought vegetables at the supermarket." If the emotion is strong, the digital marketing strategy planning unit generates an advertisement for health foods in real time that is likely to resonate with health-conscious users. For example, the digital marketing strategy planning unit delivers an advertisement including a message encouraging healthy living. The digital marketing strategy planning unit also analyzes the emotions expressed when a user enters, for example, "I bought coffee at a cafe." If the emotion is strong, the digital marketing strategy planning unit generates an advertisement for new cafe menu items or event information in real time that is likely to resonate with users who are interested in cafe culture. For example, the digital marketing strategy planning unit delivers an advertisement with a theme of relaxation or healing. The digital marketing strategy planning unit also analyzes the emotions expressed when a user enters, for example, "I bought a book at a bookstore." If the emotion is strong, the digital marketing strategy planning unit generates an advertisement for new books or author interviews in real time that are likely to resonate with users who are curious about knowledge. For example, the digital marketing strategy planning unit delivers an advertisement with a theme of the pursuit of knowledge and the joy of learning. This allows for real-time generation of advertisements based on user emotions, maximizing the effectiveness of marketing strategies.

[0080] When analyzing the results of digital marketing, the digital marketing strategy planning department allows the generation AI to automatically identify areas for improvement and reflect them in the next campaign. For example, the digital marketing strategy planning department analyzes the results of a digital marketing campaign, and the generation AI automatically identifies areas for improvement. For example, it identifies ads with low click-through rates or conversion rates and analyzes the causes. The digital marketing strategy planning department also allows the generation AI to automatically identify areas for improvement and reflect them in the next campaign. For example, it analyzes the effectiveness of ads for a specific target group and adjusts the target group in the next campaign. The digital marketing strategy planning department also analyzes the results of a digital marketing campaign, and the generation AI automatically identifies areas for improvement and reflects them in the next campaign. For example, it adjusts the creative elements or message of an ad. In this way, the results of digital marketing can be analyzed and reflected in the next campaign.

[0081] When analyzing data, the digital marketing strategy planning department can take into account not only user purchasing behavior but also detailed data such as ad viewing time and click rate. For example, when analyzing the results of a digital marketing campaign, the digital marketing strategy planning department can take into account not only user purchasing behavior but also detailed data such as ad viewing time and click rate. For example, an advertisement that provides more detailed information is delivered to users who spend a long time viewing an advertisement. The digital marketing strategy planning department can also analyze the click rate and viewing time of an advertisement to evaluate the user's level of interest. For example, the digital marketing strategy planning department can identify elements of an advertisement that have a high click rate and strengthen those elements in the next campaign. The digital marketing strategy planning department can also analyze the correlation between user purchasing behavior and ad viewing time and click rate to identify areas for improvement in the marketing strategy. For example, the digital marketing strategy planning department can analyze how an advertisement with a long viewing time affects purchasing behavior. This allows the digital marketing strategy planning department to take into account not only user purchasing behavior but also detailed data such as ad viewing time and click rate.

[0082] The digital marketing strategy planning unit can use the emotion estimation function to analyze a user's emotional response to an advertising campaign and provide emotion-based feedback. For example, the digital marketing strategy planning unit analyzes a user's emotional response to an advertising campaign, and if positive emotions are strong, strengthens elements of the advertisement. For example, the emotion estimation function is used to analyze a user's facial expressions and voice to identify elements that elicit positive emotions. The digital marketing strategy planning unit also uses the emotion estimation function to analyze a user's emotional response to an advertising campaign in real time, and if negative emotions are strong, improves elements of the advertisement. For example, it identifies elements that cause negative emotions and eliminates those elements in the next campaign. The digital marketing strategy planning unit also analyzes a user's emotional response to an advertising campaign and provides emotion-based feedback. For example, it strengthens elements of an advertisement that elicit strong positive emotions in the next campaign and improves elements of an advertisement that elicit strong negative emotions. In this way, it is possible to analyze a user's emotional response to an advertising campaign and provide emotion-based feedback.

[0083] The digital marketing strategy planning department can classify the results of data analysis by different marketing channels (e.g., social media, email, website) and evaluate the effectiveness of each channel. For example, the digital marketing strategy planning department analyzes the results of a digital marketing campaign and classifies them by different marketing channels. For example, the department evaluates the effectiveness of advertising for each social media, email, and website and identifies the most effective channel. The digital marketing strategy planning department also evaluates the effectiveness of each marketing channel and selects the optimal channel for the next campaign. For example, if advertising on social media is most effective, the department will distribute advertising mainly on social media in the next campaign. The digital marketing strategy planning department also evaluates the effectiveness of each different marketing channel and identifies areas for improvement for each channel. For example, if the click-through rate for email advertising is low, the department will analyze the cause and improve it in the next campaign. This makes it possible to evaluate the effectiveness of each different marketing channel.

[0084] The digital marketing strategy planning department can provide the results of the data analysis as a visual dashboard, allowing marketers to intuitively understand. The digital marketing strategy planning department, for example, provides the results of a digital marketing campaign as a visual dashboard, allowing marketers to intuitively understand. For example, click-through rates and conversion rates are displayed in graphs and charts. The digital marketing strategy planning department also uses the visual dashboard to monitor the effectiveness of a marketing campaign in real time. For example, the effectiveness of an advertisement is displayed over time to grasp the progress of the campaign. The digital marketing strategy planning department also provides the results of the data analysis as a visual dashboard, allowing marketers to intuitively understand. For example, the effectiveness of each marketing channel is displayed in a color-coded manner. In this way, the results of the data analysis can be provided as a visual dashboard, allowing marketers to intuitively understand.

[0085] The digital marketing strategy planning department uses the emotion estimation function to monitor users' emotional responses to advertising campaigns in real time, thereby continuously improving the effectiveness of the campaigns. For example, the digital marketing strategy planning department develops a system that uses the emotion estimation function to monitor users' emotional responses to advertising campaigns in real time. For example, the digital marketing strategy planning department analyzes the user's facial expressions and voice to calculate an emotion score. The digital marketing strategy planning department also builds a system that continuously improves the effectiveness of advertising campaigns based on user emotional response data. For example, it strengthens elements of advertisements that generate many positive emotional responses and improves elements of advertisements that generate many negative emotional responses. The digital marketing strategy planning department also collects emotion estimation data in real time and develops a system that dynamically adjusts the effectiveness of advertising campaigns. For example, it adjusts the content and delivery timing of advertisements according to changes in the user's emotions. This makes it possible to monitor users' emotional responses to advertising campaigns in real time, thereby continuously improving the effectiveness of the campaigns.

[0086] Generative AI can automatically anonymize and encrypt data to protect user privacy. For example, generative AI can automatically anonymize a user's purchasing data to prevent personal identification. For example, it can delete user IDs and personal information and analyze only the anonymized data. Generative AI can also automatically encrypt data to prevent unauthorized access and data leaks. For example, it can encrypt and store purchasing data so that only users with authorized access can access it. Generative AI can also automatically anonymize and encrypt data to build a system that protects user privacy. For example, it can perform anonymization and encryption throughout the entire process, from data collection to analysis. This allows data to be anonymized and encrypted to protect user privacy.

[0087] Generative AI can detect signs of unauthorized access or data leaks in real time and take immediate countermeasures. For example, generative AI will build a system that detects signs of unauthorized access or data leaks in real time and takes immediate countermeasures. For example, it will detect abnormal access patterns or unauthorized data transfers and issue an alert. For data security purposes, generative AI will also monitor network traffic in real time and detect signs of unauthorized access. For example, it will detect abnormal data transfer volumes or access from unauthorized IP addresses. Generative AI will also develop a system that detects signs of unauthorized access or data leaks in real time and takes immediate countermeasures. For example, if unauthorized access is detected, it will automatically block access and notify the security team. This makes it possible to detect signs of unauthorized access or data leaks in real time and take immediate countermeasures.

[0088] The emotion estimation function can analyze the emotions a user has regarding privacy and suggest security measures to increase the sense of security. The emotion estimation function, for example, analyzes the emotions a user has regarding privacy and suggests security measures to increase the sense of security. For example, it identifies elements that make the user feel uneasy and improves those elements. The emotion estimation function also analyzes the emotions a user has regarding privacy in real time and suggests security measures to increase the sense of security. For example, it clarifies privacy policies and provides information on data handling. The emotion estimation function also builds a system that analyzes the emotions a user has regarding privacy and suggests security measures to increase the sense of security. For example, it dynamically adjusts security measures based on the user's emotion data. This makes it possible to analyze the emotions a user has regarding privacy and suggest security measures to increase the sense of security.

[0089] For privacy protection, a dashboard can be provided that allows users to check their own data usage status. For privacy protection, for example, a system is constructed that provides a dashboard that allows users to check their own data usage status. For example, which data is being used and how it is being used is visually displayed. Furthermore, for privacy protection, a dashboard is provided that allows users to check their own data usage status in real time. For example, usage status at each stage of data collection, analysis, and storage is displayed. Furthermore, for privacy protection, a dashboard is provided that allows users to check their own data usage status, thereby enhancing privacy protection. For example, data usage history and access permissions can be displayed, allowing users to manage their own data. In this way, a dashboard can be provided that allows users to check their own data usage status.

[0090] Generative AI can periodically conduct security diagnosis and identify and fix vulnerabilities. For example, generative AI can periodically conduct security diagnosis and build a system that identifies and fixes vulnerabilities. For example, it can periodically conduct security diagnosis of networks and databases and fix vulnerabilities. Generative AI can also periodically conduct security diagnosis and identify and fix vulnerabilities for data security purposes. For example, it can apply security patches and review settings. Generative AI can also periodically conduct security diagnosis and develop a system that identifies and fixes vulnerabilities. For example, it can automatically implement security measures based on the diagnosis results. This makes it possible to periodically conduct security diagnosis and identify and fix vulnerabilities.

[0091] The emotion estimation function monitors users' emotions regarding privacy in real time, making it possible to continuously improve the effectiveness of privacy protection. For example, the emotion estimation function develops a system that monitors users' emotions regarding privacy in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score regarding privacy. The emotion estimation function also builds a system that continuously improves the effectiveness of privacy protection based on user emotional response data. For example, it strengthens privacy protection measures that have a high positive emotional response and improves measures that have a high negative emotional response. The emotion estimation function also collects emotion estimation data in real time and develops a system that dynamically adjusts the effectiveness of privacy protection. For example, it adjusts privacy protection measures according to changes in the user's emotions. This makes it possible to monitor users' emotions regarding privacy in real time and continuously improve the effectiveness of privacy protection.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The purchasing data collection unit allows the generation AI to automatically suggest related products and services for the purchasing data entered by the user, complementing the user's input. For example, when a user enters "I bought vegetables at the supermarket," the generation AI automatically suggests related recipes and seasonings. For example, it might display dressings and cooking methods that go well with vegetables. Furthermore, when a user enters "I bought coffee at a cafe," the generation AI automatically suggests new menu items at related cafes and information about nearby cafes. For example, it might display new dessert menu items or information about cafe events. Furthermore, when a user enters "I bought a book at a bookstore," the generation AI automatically suggests related books and other works by the author. For example, it might display bestsellers in the same genre or interviews with the author. This complements the user's input and improves the accuracy of purchasing data.

[0094] The purchasing data collection unit uses the user's location information when collecting purchasing data to analyze purchasing trends by region and develop a region-specific marketing strategy. For example, when a user inputs "I bought vegetables at the supermarket," the purchasing data collection unit analyzes purchasing trends by region based on the location information and suggests vegetables and recipes that are popular in that region. For example, it displays dishes and ingredients that are unique to the region. Also, when a user inputs "I bought coffee at a cafe," the purchasing data collection unit analyzes popular menu items and event information for each region based on the location information and develops a marketing strategy for that region. For example, it displays promotions and events that are exclusive to the region. Also, when a user inputs "I bought a book at a bookstore," the purchasing data collection unit analyzes popular book rankings by region and information about author signings based on the location information and develops a marketing strategy for that region. For example, it displays book fairs and events that are exclusive to the region. This makes it possible to analyze purchasing trends by region and develop a region-specific marketing strategy.

[0095] The purchasing data collection unit can use the emotion estimation function to analyze the emotions of a user when entering purchasing data and provide an interface for eliciting positive emotions. For example, when a user enters "I bought vegetables at the supermarket," the emotion estimation function can be used to analyze the user's facial expressions and voice, providing an interface for eliciting positive emotions. For example, an encouraging message or positive feedback can be displayed. Furthermore, when a user enters "I bought coffee at a cafe," the purchasing data collection unit can use the emotion estimation function to analyze the user's emotions and provide an interface for eliciting positive emotions. For example, positive comments about the cafe's atmosphere and the aroma of the coffee can be displayed. Furthermore, when a user enters "I bought a book at a bookstore," the purchasing data collection unit can use the emotion estimation function to analyze the user's emotions and provide an interface for eliciting positive emotions. For example, positive comments about the enjoyment of reading and the joy of gaining new knowledge can be displayed. This can elicit positive emotions from the user and increase motivation for input.

[0096] The purchasing data collection unit can add a voice input function to the household accounting app, allowing the user to input purchasing data by voice. For example, a function is added that allows the user to input by voice, "I bought vegetables at the supermarket." For example, speech recognition technology is used to convert the user's voice into text data and save it as purchasing data. The purchasing data collection unit also adds a function that allows the user to input by voice, "I bought coffee at a cafe." For example, a function is provided that automatically suggests related products and services when the user inputs by voice. The purchasing data collection unit also adds a function that allows the user to input by voice, "I bought a book at a bookstore." For example, a function is provided that automatically suggests related books and other works by the author when the user inputs by voice. This allows the user to input purchasing data by voice.

[0097] The purchase data collection unit links the household accounting app with other fitness apps and health management apps, and can analyze the user's health condition and purchase data in association with each other. For example, the household accounting app and fitness app can be linked, and the user's exercise data and purchase data can be analyzed in association with each other. For example, data on foods and drinks purchased after exercise can be analyzed to suggest healthy meals. The purchase data collection unit also links the household accounting app with a health management app, and analyzes the user's health condition and purchase data in association with each other. For example, the purchase data collection unit analyzes the user's weight and blood pressure data and purchase data to suggest healthy lifestyle habits. The purchase data collection unit also links the household accounting app with a sleep management app, and analyzes the user's sleep data and purchase data in association with each other. For example, the purchase data collection unit analyzes the relationship between sleep quality and diet and suggests meals that promote good quality sleep. This makes it possible to analyze the user's health condition and purchase data in association with each other.

[0098] The purchasing data collection unit uses the emotion estimation function to provide real-time feedback on the emotions of the user when entering purchasing data, thereby increasing motivation for input. For example, when a user enters "I bought vegetables at the supermarket," the emotion estimation function is used to analyze the user's emotions in real time and provide positive feedback. For example, an encouraging message is displayed according to the input content. Furthermore, when a user enters "I bought coffee at a cafe," the purchasing data collection unit uses the emotion estimation function to analyze the user's emotions in real time and provide positive feedback. For example, a positive comment is displayed according to the input content. Furthermore, when a user enters "I bought a book at a bookstore," the purchasing data collection unit uses the emotion estimation function to analyze the user's emotions in real time and provide positive feedback. For example, an encouraging message or positive comment is displayed according to the input content. In this way, the user's emotions can be fed back in real time, increasing motivation for input.

[0099] The customer data enhancement unit uses the generation AI to estimate a user's lifestyle and values ​​based on purchasing data, allowing it to create a more detailed customer profile. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI estimates the user's health-consciousness and eating habits, creating a detailed customer profile. For example, it classifies the user as being highly health-conscious. Furthermore, based on data entered by a user such as "I bought coffee at a cafe," the customer data enhancement unit uses the generation AI to estimate a user's lifestyle and values, creating a detailed customer profile. For example, it classifies the user as being highly interested in cafe culture. Furthermore, based on data entered by a user such as "I bought a book at a bookstore," the generation AI estimates the user's desire for knowledge and motivation to learn, creating a detailed customer profile. For example, it classifies the user as being highly knowledge-conscious. This allows it to estimate a user's lifestyle and values, creating a detailed customer profile.

[0100] The customer data enrichment unit links user purchasing data with social media activity, enabling enhanced customer data that also takes online behavioral patterns into account. For example, by linking data entered by a user such as "I bought vegetables at the supermarket" with health-related posts on social media, the generation AI will reinforce the user's health consciousness. For example, the user will be classified as one who posts frequently on health-related topics. Similarly, the customer data enrichment unit links data entered by a user such as "I bought coffee at a cafe" with cafe-related posts on social media, enabling the generation AI to reinforce the user's interest in cafe culture. For example, the user will be classified as one who posts frequently on cafes. Similarly, the customer data enrichment unit links data entered by a user such as "I bought a book at a bookstore" with reading-related posts on social media, enabling the generation AI to reinforce the user's desire for knowledge. For example, the user will be classified as one who posts frequently on reading-related topics. This allows enhanced customer data that also takes online behavioral patterns into account.

[0101] The customer data enhancement unit can use the emotion estimation function to analyze changes in emotions accompanying a user's purchasing behavior and enhance customer data based on emotions. For example, the emotion when a user inputs "I bought vegetables at the supermarket" is analyzed, and if the emotion is strong, the customer data is enhanced as a user with a high level of health consciousness. For example, the user is classified as having positive emotions toward healthy eating. The customer data enhancement unit can also analyze the emotion when a user inputs "I bought coffee at a cafe," and if the emotion is strong, the customer data is enhanced as a user with a high level of interest in cafe culture. For example, the user is classified as having positive emotions toward relaxing at cafes. The customer data enhancement unit can also analyze the emotion when a user inputs "I bought a book at a bookstore," and if the emotion is strong, the customer data is enhanced as a user with a high level of knowledge. For example, the user is classified as having positive emotions toward reading. This makes it possible to enhance customer data based on user emotions.

[0102] When enhancing customer data, the customer data enhancement unit not only uses the user's past purchasing data but also predicts future purchasing, allowing it to understand future needs. For example, based on data entered by a user such as "I bought vegetables at the supermarket," the generation AI analyzes the past purchasing data and makes future purchasing predictions. For example, it predicts seasonal vegetable purchasing trends and understands future needs. Similarly, based on data entered by a user such as "I bought coffee at a cafe," the generation AI analyzes the past purchasing data and makes future purchasing predictions. For example, it predicts cafe usage trends during specific seasons or events and understands future needs. Similarly, based on data entered by a user such as "I bought a book at a bookstore," the generation AI analyzes the past purchasing data and makes future purchasing predictions. For example, it predicts the release dates of new books and the popularity trends of specific genres and understands future needs. This allows it to understand future needs and enhance customer data.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The purchasing data collection unit collects the user's purchasing data through the household accounting app. For example, it collects data such as the items purchased, the purchase amount, and the purchase date and time entered by the user into the household accounting app. The purchasing data collection unit can also automatically classify the data entered by the user and organize it by category. For example, it can classify the data into categories such as groceries, clothing, and entertainment. Furthermore, the purchasing data collection unit can periodically update the user's purchasing data to keep it up to date. For example, it can update the data every weekend to reflect the latest purchasing history. Step 2: The Customer Data Enhancement Unit analyzes the purchase data collected by the Purchase Data Collection Unit to enhance the customer data. For example, the Generation AI analyzes a user's purchase history to identify their preferences and behavioral patterns. The Generation AI can also estimate a user's lifestyle and values ​​based on their purchase data. For example, it can identify users who are highly health-conscious or who are interested in cafe culture. Furthermore, the Customer Data Enhancement Unit can compare a user's purchase data with other users to identify common preferences and behavioral patterns. For example, it can analyze the purchasing trends of users living in the same area and develop a marketing strategy specific to that area. Step 3: The Digital Marketing Strategy Planning Department develops a digital marketing strategy based on the customer data enhanced by the Customer Data Enhancement Department. For example, the generative AI generates optimal advertisements and promotions based on user preferences and behavioral patterns, effectively approaching target customers. The Digital Marketing Strategy Planning Department can also propose marketing campaigns tailored to the season or event. For example, a special promotion could be implemented during the Christmas season. Furthermore, the Digital Marketing Strategy Planning Department can analyze the results of digital marketing and evaluate its effectiveness. For example, it could analyze how many users a particular advertising campaign reached and how many conversions it achieved.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 AI 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

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

[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 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.

[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 AI 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 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[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 robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific 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 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.

[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 AI 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 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 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 purchasing data collection unit that collects purchasing data of users through a household account book application; a customer data enhancement unit that analyzes the purchase data collected by the purchase data collection unit and enhances customer data; a digital marketing strategy planning unit that plans a digital marketing strategy based on the customer data enhanced by the customer data enhancement unit. A system characterized by:

2. The purchasing data collection unit The generation AI automatically suggests related products and services based on the purchasing data entered by the user, thereby complementing the user's input.

2. The system of claim 1.

3. The purchasing data collection unit When collecting the purchase data, the location information of the user is used to analyze purchasing trends by region and develop a marketing strategy specific to the region.

2. The system of claim 1.

4. The purchasing data collection unit Analyzing the emotions of the user when entering the purchase data and providing an interface for eliciting positive emotions 2. The system of claim 1.

5. The purchasing data collection unit Add a voice input function to the household account book app, allowing the user to input the purchase data by voice.

2. The system of claim 1.

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