system

Abstract

We provide the system. [Solution] A means to automatically detect the popularity of social networking platforms in each region, A means of translating product information into multiple languages, A means of automatically posting translated product information to detected social networking platforms, A means for automatically detecting product-related social media content and generating responses, A means of measuring the performance of marketing activities and generating reports, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order for a company to efficiently conduct marketing activities utilizing social media in the global market, it is required to transmit information suitable for different languages and cultures in each region. However, appropriately selecting different social networking platforms in each country, translating product information, and generating effective content require enormous resources and are difficult to achieve quickly. Also, it is not easy to conduct analysis for appropriately evaluating the results of marketing activities while enhancing real-time customer engagement through posts and comments related to products. Therefore, means for enabling effective and efficient marketing activities are required.

Means for Solving the Problems

[0005] This invention provides a means for automatically detecting the popularity of social networking platforms in each region and translating product information into the languages ​​of those countries. Furthermore, it constitutes a system that streamlines marketing activities by adapting the translated information to the culture of each region and automatically posting it to the appropriate platform. In addition, it includes means for automatically detecting product-related social media content, analyzing its relevance using natural language processing, and generating appropriate responses. This enables real-time customer engagement, and by providing a system for measuring the performance of marketing activities and generating reports, it enables effective and efficient global marketing activities.

[0006] A "social networking platform" is an online platform for users to share information and communicate with each other.

[0007] "Translation" refers to the process of replacing content expressed in one language with content expressed in another language.

[0008] "Content" refers to a collection of information, messages, or expressed ideas, delivered in various forms such as text, images, and videos.

[0009] "Engagement" refers to the reactions and involvement that users give to content, and it manifests itself in the form of comments, shares, likes, etc.

[0010] "Performance measurement" is the process of analyzing and evaluating metrics such as engagement rates, click-through rates, and conversion rates in order to assess the effectiveness of marketing activities.

[0011] "Report generation" is the process of analyzing collected data and presenting the results in a visually organized manner. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a system that automates and streamlines marketing activities in multiple countries. The system operates with a configuration including servers and terminals, and aims to effectively utilize a company's marketing resources.

[0034] The server first automatically detects popular social networking platforms in the target country by collecting trend data from the internet. This invention automates the selection of SNS platforms, eliminating the need for users to individually research popular platforms in each country.

[0035] The device receives product information provided by companies and translates it into multiple languages ​​using a translation API. During this process, the device generates content tailored to the culture and trends of each country, adjusting the translated content to ensure it is accepted seamlessly in each region. This process enables users to quickly deploy region-specific marketing content.

[0036] The server then uses its scheduling function to automatically post the translated product information to each country's social media platform at the appropriate time. This posting timing is calculated based on the time of day when users in the target country are most online. This allows users to deliver marketing messages to their target audience effectively and efficiently.

[0037] Furthermore, the server monitors product-related posts and comments in real time. This feature uses natural language processing technology to automatically detect highly relevant posts and generate appropriate automated responses, thereby increasing engagement with the product.

[0038] Finally, the server analyzes performance data based on all posts and engagements and generates reports periodically. These reports detail the effectiveness and areas for improvement of engagement, helping to improve a company's marketing strategy. Specifically, they include data showing which content was most effective on which social media platform.

[0039] Thus, by automating marketing activities in the global market, the present invention enables companies to efficiently overcome language and cultural barriers and optimize resource allocation.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server collects internet trend data from various countries and automatically detects popular social networking platforms. This provides the foundational information needed to effectively approach target markets.

[0043] Step 2:

[0044] The device receives product information provided by companies and translates it into multiple languages ​​using the latest translation APIs. In doing so, it generates content adapted to the culture and trends of each region, ensuring that information is delivered to local users without feeling out of place.

[0045] Step 3:

[0046] The server calculates the optimal posting time on each country's social networking platform based on the translated product information and creates an automated posting schedule. This maximizes the exposure of the content.

[0047] Step 4:

[0048] The server monitors product-related social media posts and comments in real time. During this process, natural language processing technology is used to detect highly relevant posts and comments and automatically generate appropriate responses. This allows users to quickly engage with customers.

[0049] Step 5:

[0050] The server collects and analyzes performance data from all posts and engagement activities. Regularly generated reports include performance evaluations based on various metrics, allowing users to optimize their marketing strategies.

[0051] (Example 1)

[0052] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0053] In the global market, companies face challenges in effectively promoting their products and conducting marketing activities in diverse regions with different languages ​​and cultural backgrounds. Traditionally, researching information sharing platforms across multiple regions, translating information, adapting to different cultures, scheduling content posting, and measuring effectiveness has been extremely time-consuming and costly. Therefore, automating and streamlining these processes is essential.

[0054] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0055] In this invention, the server includes means for automatically detecting the popularity of information sharing platforms in each region, means for converting information into multiple natural languages, and means for automatically providing the converted information to the detected information sharing platforms. This makes it possible to automate effective marketing activities in different regions and to optimally allocate corporate resources.

[0056] An "information sharing platform" is an online service that allows users to post, share, and interact with information.

[0057] "Natural language" refers to the language that humans use in their daily lives, and is the target language that machines need to understand and translate.

[0058] "Digital content" refers to forms of information and entertainment distributed and consumed via electronic media, and includes video, audio, text, etc.

[0059] "Linguistic analysis techniques" refer to computational methods and programming techniques used to understand, interpret, or analyze text data.

[0060] "Commercial activity" refers to a series of business processes aimed at distributing goods and services in the market and generating profits.

[0061] This system includes a server and terminals as its main components. The server utilizes information gathering technologies on the internet and has a mechanism to automatically detect the popularity of information sharing platforms in each region. Specifically, it applies "web scraping technology" and "data mining techniques," and tools such as "trend analysis APIs" are used in particular.

[0062] The server has the function of automatically posting company product information to an information sharing platform at the appropriate time, based on the analyzed trend data. For this purpose, "schedule management software" is introduced, and for example, an "automatic posting system" is used.

[0063] The device receives product information provided by companies and translates it into multiple natural languages ​​using "natural language processing" technology. Specifically, it utilizes "translation APIs" and "generative AI models," and uses, for example, "automatic translation services" and "language models" to generate content that is appropriate for the local culture.

[0064] By using this system, users can eliminate the manual processes of translating product information and adapting it to local areas, enabling them to quickly and effectively launch marketing activities tailored to each region.

[0065] For example, if a user uses this system to run a campaign for a new lifestyle product, the server automatically selects the relevant information sharing platform for that product, and the terminal translates the product information into multiple languages ​​and posts it in a timely manner. This allows users to execute rapid and efficient marketing strategies in the global market.

[0066] An example of a prompt message might be, "Generate a message to appeal to consumers in each country about the features of this new product." This system aims to streamline commercial activities and support market expansion in each region.

[0067] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0068] Step 1:

[0069] The server first collects trend data from information-sharing platforms in various regions on the internet. The input is raw data obtained from the web, and the output is a list of platforms whose popularity is expressed numerically. This process uses web scraping technology, and the collected data is analyzed through a trend analysis API. This clarifies the popularity trends of social networking services (SNS).

[0070] Step 2:

[0071] The server selects the most effective information-sharing platform in each country based on the analyzed data. The input is a list of popularity ratings generated in the previous step, and the output is the name of the preferred platform in each country. This data selection process uses statistical algorithms to optimize the content strategy.

[0072] Step 3:

[0073] The terminal receives product information from a company and begins translating it into multiple languages. The input is text data provided by the company, and the output is product information translated into multiple languages. This translation process uses natural language processing technology and a translation API, and utilizes a generative AI model during the translation process to convey information in a way that is appropriate for each country's culture.

[0074] Step 4:

[0075] The device generates region-appropriate digital content using translated information. The input is translated product information, and the output is content adapted to the culture of each country. Here, a generation AI model selects specific examples and appropriate vocabulary to support content that is relevant to the cultural context.

[0076] Step 5:

[0077] The server creates a posting schedule for the selected information sharing platform. The inputs are optimized content and the selected platform, and the output is the detailed posting schedule. This process uses scheduling software to automatically configure posting based on peak consumer activity times in each region.

[0078] Step 6:

[0079] The server monitors product-related digital content after posting and analyzes the response. Inputs are user feedback and comments, and outputs are analyzed data and generated responses. This process utilizes language analysis techniques, and an automated response system facilitates rapid interaction with users.

[0080] Step 7:

[0081] The server evaluates the results of commercial activities and generates regular reports. Inputs are submission and user engagement data, and output is a detailed performance report. Data analysis software is used for this analysis, providing information to support the company's marketing strategy.

[0082] (Application Example 1)

[0083] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0084] In content delivery services operating on a global scale, optimizing information resources to take into account the different communication infrastructures, cultures, and time zones of each region is required. However, doing this manually is extremely labor-intensive and inefficient. Furthermore, real-time response is difficult, and there are limits to performance improvement. Therefore, a system is needed to automate the delivery of information resources in a way that is appropriate for each region and to provide them efficiently.

[0085] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0086] In this invention, the server includes means for automatically detecting the penetration rate of information systems on communication infrastructure in each region, means for converting information resources into multiple natural languages, and means for optimizing the transmission schedule based on the time zone of each region. This enables the automation of information transmission tailored to each region and efficient information provision that takes into account culture and time zones.

[0087] "Communication infrastructure" refers to the entire infrastructure used for sending and receiving information, including the internet and mobile communication networks.

[0088] An "information system" is an integrated set of software and hardware for collecting, processing, storing, and distributing data.

[0089] "Information resources" refer to datasets and content that form the basis for providing information to users, and include a variety of formats such as text, images, and audio.

[0090] "Natural language" refers to the language that humans use on a daily basis, and it utilizes translation technologies and language analysis designed for computers to understand and process.

[0091] A "transmission schedule" refers to a plan for delivering information resources to recipients at specific times and frequencies.

[0092] This system consists of a server and client devices and is specifically designed to optimize the communication infrastructure for global information provision services. The server detects the penetration rate of information systems and efficiently provides information resources in the natural language of each region. To achieve this, it translates information resources and optimizes the transmission schedule based on the time zone of each region.

[0093] Specifically, the server scans data on the internet to understand the penetration status of communication infrastructure in the target area. Then, it translates information resources based on the culture and background of each region. The Google® Cloud Translation API is used for translation, and a generative AI model is utilized to improve translation accuracy. Next, the optimal transmission schedule is constructed by analyzing user activity times. CRON Jobs is used as the scheduling tool for this process.

[0094] Furthermore, client devices collect feedback provided by users and send it to the server. This allows users to receive personalized information. This feature ensures that users receive the information they need at the time that best suits their schedule.

[0095] As a concrete example, consider a case where a payment service provider wants to introduce a new feature to the Japanese market. The server analyzes trends on Japanese social media and identifies that the time when the most users are online is 8 PM. Based on this, it automatically posts the announcement content at that time. In this process, the following prompt is used for the generating AI model: "Generate the most effective promotional content for the Japanese market. This promotion should focus on the new payment service feature and take cultural elements into consideration."

[0096] In this way, the system can efficiently provide information and increase user engagement.

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The server collects data related to the target region from the internet. The input is region-specific filter settings, and the output is data on the prevalence of region-specific information systems. This data is then used to apply data analysis algorithms to identify which information systems are most widely used.

[0100] Step 2:

[0101] The server receives information resources provided by the user and translates them into multiple natural languages ​​using a translation API. The input is the original information resource, and the output is the information resource translated for each region. The Google Cloud Translation API is used for translation, and a generative AI model is used to generate translation results that are appropriate for the cultural context.

[0102] Step 3:

[0103] The server analyzes the online activity of users in the target region and identifies the most influential time slots. Inputs are SNS logs and activity data, and output is the optimal sending schedule. Data analysis tools are used to analyze users' peak activity times and optimize sending timing.

[0104] Step 4:

[0105] The terminal automatically posts information resources to content platforms based on the optimal schedule sent from the server. Inputs are translated information resources and the transmission schedule, while output is the posting to each platform. Information is transmitted according to the schedule using CRON Jobs.

[0106] Step 5:

[0107] The server monitors user reactions to submitted content in real time and generates automated responses as needed. Input is user feedback and comments, and output is the generated automated response. Natural language processing tools are used to analyze comments and generate relevant responses.

[0108] Step 6:

[0109] The server aggregates the performance of all marketing activities and generates reports. Inputs are various types of engagement data, and outputs are performance analysis reports. Engagement data collected from the database is processed by analytical tools and visualized to clearly report results.

[0110] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0111] This invention combines a system that automates marketing activities using social networking platforms with an emotion engine that analyzes user emotions. This system utilizes servers, terminals, and emotion recognition technology to enable companies to personalize and efficiently conduct their marketing activities.

[0112] The server automatically analyzes the popularity of social networking platforms in each country and selects the platform best suited for marketing. In doing so, it also considers platforms that can reflect user sentiment, selecting the channel through which the brand message is most effectively conveyed.

[0113] The device translates product information provided by companies into multiple languages. The translated information is then adjusted to suit the culture and user sentiment of each region. For example, if the sentiment engine detects positive emotions from a user's post, more aggressive promotional language can be used.

[0114] The server uses an emotion engine to analyze product-related posts and comments on social media. The emotion engine analyzes the emotions expressed by users (positive, negative, neutral) in real time and generates automated responses based on that analysis. For example, these responses might say "Thank you for your great feedback" for positive responses, and provide careful follow-up, including suggestions for improvement, for negative responses.

[0115] Furthermore, the server can dynamically adjust marketing activities using the results of sentiment analysis. If a particular campaign or advertisement receives a negative response, the sentiment engine can analyze the feedback and suggest content changes or new approaches.

[0116] Finally, the server measures the overall performance of its activities and periodically generates analytical reports. These reports include analysis results from the sentiment engine and highlight areas for improvement in the marketing strategy. Specifically, they are based on data analysis that includes which products and messages evoked the most positive emotions in users.

[0117] Thus, by analyzing user emotions and conducting marketing activities based on those emotions, the present invention enables companies to build better relationships with target users and implement effective and efficient marketing activities.

[0118] The following describes the processing flow.

[0119] Step 1:

[0120] The server collects regional internet trend data and automatically detects popular social networking platforms in each country. Through this detection, it identifies the media that target users are most likely to use.

[0121] Step 2:

[0122] The device translates product information received from companies using a multilingual translation service. During this process, the translation is adjusted to take into account the cultural background and user sentiment of each region. For example, an emotion engine is used to customize expressions for different regions, generating content that is easily accepted by local users.

[0123] Step 3:

[0124] The server creates a posting schedule for the social networking platform. This schedule is configured to post at the optimal time based on the user's emotional state as detected by the sentiment engine.

[0125] Step 4:

[0126] The server uses an emotion engine to analyze product-related social media content, such as posts and comments, in real time. This analysis identifies the emotions expressed by users and classifies them as positive, negative, or neutral. Based on this, the server automatically generates an appropriate response and sends it back to the user.

[0127] Step 5:

[0128] The server dynamically adjusts marketing strategies based on user reactions and emotional feedback. It analyzes users' emotional responses to specific content and messages, and accordingly makes changes to content or suggests new campaigns.

[0129] Step 6:

[0130] The server measures the overall performance of marketing activities and generates detailed reports that include sentiment analysis results. These reports show which content had a positive impact on users and are used as a basis for future strategic improvements.

[0131] (Example 2)

[0132] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0133] Marketing activities utilizing information exchange services require flexible and effective marketing methods that adapt to the culture and language of each region while considering user emotions. However, current systems struggle with dynamic adjustments based on sentiment analysis, which prevents them from adequately improving the performance of marketing activities.

[0134] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0135] In this invention, the server includes means for automatically detecting the popularity of information exchange services in each region, means for automatically detecting product-related information exchange service content and generating responses based on sentiment analysis, and means for dynamically adjusting marketing activities based on sentiment analysis. This enables flexible and effective marketing activities that are tailored to the culture and user sentiment of each region.

[0136] An "information exchange service" refers to an online platform that allows users to share and interact with each other via the internet.

[0137] "Popularity" refers to an indicator that shows how widely a particular information exchange service is used and how much attention it receives in a given region or market.

[0138] "Product information" refers to detailed descriptions, features, and specifications related to a specific product or service.

[0139] "Translation" refers to the process of converting information expressed in one language into another language in an appropriate and meaningful way.

[0140] "Automatic" refers to a system or machine performing a task independently, without human intervention, based on pre-set conditions or algorithms.

[0141] "Sentiment analysis" refers to a technology that identifies and classifies the underlying emotions and attitudes from text data such as user posts and comments.

[0142] "Dynamic adjustment" refers to a method of optimizing a system's operation by appropriately modifying it in response to changes in circumstances or new information.

[0143] "Marketing performance" refers to the criteria and results used to evaluate how successful a marketing campaign is in achieving its set goals.

[0144] A "report" refers to a document that compiles information about a specific activity or process, such as survey results or analysis.

[0145] A "generative AI model" refers to a machine learning model that uses artificial intelligence technology to automatically generate answers or suggestions for specific tasks or problems.

[0146] A "prompt sentence" refers to the input sentence that a generative AI model uses to create appropriate responses or content.

[0147] This invention is a system for realizing effective marketing activities using information exchange services. The embodiments thereof are described in detail below.

[0148] The server automatically accesses information exchange service platforms and has the functionality to measure their popularity in each region. This involves using analytical software to collect user numbers and engagement metrics. This makes it possible to support the selection of the optimal platform. For example, in regions where visual content is important, a platform with a high volume of image sharing would be chosen.

[0149] The device has the ability to translate product information provided by companies into multiple languages ​​and automatically post it. Translation software utilizing natural language processing technology is used for the translation, enabling localization that appropriately reflects cultural nuances. For example, in regions where the user's emotions are determined to be positive, the translation will be rendered in a brighter tone.

[0150] Furthermore, the server combines generative AI models and sentiment analysis technology to analyze product-related posts on the information exchange service in real time. Based on this analysis, it generates appropriate responses to promote user engagement. For example, a positive response will receive an automated message such as "Thank you for your great feedback."

[0151] Furthermore, it's possible to measure the effectiveness of marketing activities and dynamically adjust strategies based on sentiment analysis data. This allows for the rapid development of countermeasures even in the event of negative reactions. This functionality contributes to the optimization of marketing campaigns.

[0152] To implement these processes, an example of a prompt might be: "Analyze social media posts about the new product and propose a marketing strategy based on positive or negative sentiment."

[0153] In this way, by making full use of the roles of user, server, and terminal, it becomes possible to provide effective marketing solutions through information exchange services.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The server accesses each platform of the information exchange service and measures its popularity in each region. The input is the number of users and posting data for each platform, and the output is a popularity rating for each region. Here, data analysis software is used to collect and evaluate engagement metrics.

[0157] Step 2:

[0158] The terminal inputs product information provided by companies into multilingual translation software and outputs translations appropriate to the language and cultural background of the target region. This process utilizes natural language processing technology to add nuances appropriate to the local culture to the translated text.

[0159] Step 3:

[0160] The server collects product-related content posted on an information exchange service and performs sentiment analysis using a generative AI model. Using the posted data as input, it obtains sentiment classifications such as positive, negative, and neutral as output. Here, a text analysis algorithm is used to identify the user's emotional state.

[0161] Step 4:

[0162] The server generates an automated response message for the user based on the sentiment analysis results. The input to this process is the sentiment analysis results, and the output is a dynamically generated response message. Specifically, it uses a generation AI model to select appropriate words and create a message that elicits a reaction.

[0163] Step 5:

[0164] The server dynamically adjusts marketing activities based on sentiment analysis data. Input is feedback data from each campaign, and output is the construction of an optimized marketing strategy. Here, higher-level management software is used to perform specific actions that rapidly implement situation-dependent strategic changes.

[0165] Step 6:

[0166] The server measures the overall performance of the activities and generates reports. The input is data on the results of each initiative, and the output is a report including analysis. Specifically, it utilizes statistical analysis software to process data from draft to final report creation.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0169] In modern marketing activities utilizing social networking platforms, it is crucial to respond individually to users' emotions, but this process is complex and time-consuming and costly when done manually. Furthermore, it is difficult to quickly provide appropriate content that reflects regional cultural elements to meet the demands of a global market. Therefore, there is a need for new methods that streamline marketing activities while effectively communicating with users.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] This invention includes a server that automatically detects the popularity of social networking platforms in each region, translates product information into multiple languages, automatically posts the translated product information to the detected social networking platforms, automatically detects product-related social media content and generates responses, personalizes responses based on user sentiment and dynamically adjusts advertising strategies, and measures the performance of marketing activities and generates reports. This enables efficient personalized marketing activities that respond to user sentiment and allows for appropriate adjustments to advertising strategies.

[0172] A "social networking platform" is an online service that allows individuals and groups to interact on the internet, enabling information sharing and communication.

[0173] "User sentiment" refers to the emotional reactions and feedback that users express on social media, and includes emotional states such as positive, negative, and neutral.

[0174] An "emotion engine" is an algorithm or tool used to analyze emotions from user posts and comments, supporting personalization in marketing activities.

[0175] Personalization refers to providing a more individualized experience by customizing information and services according to the characteristics and preferences of specific users.

[0176] "Advertising strategy" refers to the plans and methods used to effectively communicate products and services to the market, and is an important activity for companies to build relationships with consumers.

[0177] "Translation methods" refer to technologies and processes for converting information between different languages, and they support international communication.

[0178] "Means of detection" refer to the technologies and processes used to collect data and information and identify them based on specific criteria.

[0179] "Means of generation" refer to technologies and processes for creating new data, information, responses, etc., that enable output tailored to a specific purpose.

[0180] The system for implementing this invention mainly consists of a server, a terminal, and emotion recognition technology. The server has the function of collecting and analyzing data from the internet. First, it analyzes the popularity of social networking platforms used in each region and selects the optimal platform based on that data. The server also uses translation software to translate product information into multiple languages. In this translation process, it plays a role in adjusting the content to take into account the cultural background of each region.

[0181] Emotion recognition technology extracts emotions from content and comments posted by users on social media. Based on this information, the server generates responses tailored to the user's emotional state. For example, users who provide positive feedback may receive specific promotional information or thank-you messages, while users who express negative reactions may receive suggestions for improvement or apologies.

[0182] The device receives translated product information and response messages sent from the server and automatically posts them to social networking platforms. This process enables efficient and effective global marketing activities.

[0183] For example, if a user leaves an online review stating that a new product was "great to use," the server can detect this positive sentiment and offer that user a special discount coupon. This not only strengthens the relationship with the user but also increases their purchase intent.

[0184] An example of a prompt would be, "How can I analyze a user's recent product review and generate the most appropriate marketing response based on their sentiment?" Using this prompt helps guide the generative AI model to execute the appropriate response generation process.

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The server collects data publicly available on the internet. Specifically, it obtains information on the popularity of social networking platforms in each region. This information is obtained by analyzing user usage frequency and engagement data. The input is publicly available user activity data, and the output is popularity information for each platform.

[0188] Step 2:

[0189] The server translates product information provided by companies into multiple languages. The input is the original product information, and the output is the product information translated into multiple languages. Specifically, this involves using translation software to adjust the data, taking into account the cultural context of each region.

[0190] Step 3:

[0191] The server monitors user content posted on social networks and analyzes its sentiment using sentiment recognition technology. The input is user-submitted data, and the output is the sentiment information contained in the posts. Specifically, natural language processing is used to analyze the text of the posts and assign sentiment tags such as positive, negative, and neutral.

[0192] Step 4:

[0193] The server generates the most appropriate response for each user based on the sentiment analysis results. The input in this process is the sentiment analysis results, and the output is a personalized response message. For example, it might offer a special discount to users who exhibit positive emotions.

[0194] Step 5:

[0195] The terminal automatically posts translated product information and generated response messages received from the server to the social networking platform. The input is data from the server, and the output is the posted content. In this step, the content is accurately posted using each platform's API, completing the information delivery to the user.

[0196] Step 6:

[0197] The server measures the performance of marketing activities and generates periodic reports. The input is data on the activities themselves, and the output is an analytical report. Specific analysis includes user feedback and engagement data, which is used to guide future strategic improvements.

[0198] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0199] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0204] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0205] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0206] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0207] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0208] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0209] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0210] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0211] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0212] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0213] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0214] This invention provides a system that automates and streamlines marketing activities in multiple countries. The system operates with a configuration including servers and terminals, and aims to effectively utilize a company's marketing resources.

[0215] The server first automatically detects popular social networking platforms in the target country by collecting trend data from the internet. This invention automates the selection of SNS platforms, eliminating the need for users to individually research popular platforms in each country.

[0216] The device receives product information provided by companies and translates it into multiple languages ​​using a translation API. During this process, the device generates content tailored to the culture and trends of each country, adjusting the translated content to ensure it is accepted seamlessly in each region. This process enables users to quickly deploy region-specific marketing content.

[0217] The server then uses its scheduling function to automatically post the translated product information to each country's social media platform at the appropriate time. This posting timing is calculated based on the time of day when users in the target country are most online. This allows users to deliver marketing messages to their target audience effectively and efficiently.

[0218] Furthermore, the server monitors product-related posts and comments in real time. This feature uses natural language processing technology to automatically detect highly relevant posts and generate appropriate automated responses, thereby increasing engagement with the product.

[0219] Finally, the server analyzes performance data based on all posts and engagements and generates reports periodically. These reports detail the effectiveness and areas for improvement of engagement, helping to improve a company's marketing strategy. Specifically, they include data showing which content was most effective on which social media platform.

[0220] Thus, by automating marketing activities in the global market, the present invention enables companies to efficiently overcome language and cultural barriers and optimize resource allocation.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] The server collects internet trend data from various countries and automatically detects popular social networking platforms. This provides the foundational information needed to effectively approach target markets.

[0224] Step 2:

[0225] The device receives product information provided by companies and translates it into multiple languages ​​using the latest translation APIs. In doing so, it generates content adapted to the culture and trends of each region, ensuring that information is delivered to local users without feeling out of place.

[0226] Step 3:

[0227] The server calculates the optimal posting time on each country's social networking platform based on the translated product information and creates an automated posting schedule. This maximizes the exposure of the content.

[0228] Step 4:

[0229] The server monitors product-related social media posts and comments in real time. During this process, natural language processing technology is used to detect highly relevant posts and comments and automatically generate appropriate responses. This allows users to quickly engage with customers.

[0230] Step 5:

[0231] The server collects and analyzes performance data from all posts and engagement activities. Regularly generated reports include performance evaluations based on various metrics, allowing users to optimize their marketing strategies.

[0232] (Example 1)

[0233] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0234] In the global market, companies face challenges in effectively promoting their products and conducting marketing activities in diverse regions with different languages ​​and cultural backgrounds. Traditionally, researching information sharing platforms across multiple regions, translating information, adapting to different cultures, scheduling content posting, and measuring effectiveness has been extremely time-consuming and costly. Therefore, automating and streamlining these processes is essential.

[0235] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0236] In this invention, the server includes means for automatically detecting the popularity of information sharing platforms in each region, means for converting information into multiple natural languages, and means for automatically providing the converted information to the detected information sharing platforms. This makes it possible to automate effective marketing activities in different regions and to optimally allocate corporate resources.

[0237] An "information sharing platform" is an online service that allows users to post, share, and interact with information.

[0238] "Natural language" refers to the language that humans use in their daily lives, and is the target language that machines need to understand and translate.

[0239] "Digital content" refers to forms of information and entertainment distributed and consumed via electronic media, and includes video, audio, text, etc.

[0240] "Linguistic analysis techniques" refer to computational methods and programming techniques used to understand, interpret, or analyze text data.

[0241] "Commercial activity" refers to a series of business processes aimed at distributing goods and services in the market and generating profits.

[0242] This system includes a server and terminals as its main components. The server utilizes information gathering technologies on the internet and has a mechanism to automatically detect the popularity of information sharing platforms in each region. Specifically, it applies "web scraping technology" and "data mining techniques," and tools such as "trend analysis APIs" are used in particular.

[0243] The server has the function of automatically posting company product information to an information sharing platform at the appropriate time, based on the analyzed trend data. For this purpose, "schedule management software" is introduced, and for example, an "automatic posting system" is used.

[0244] The device receives product information provided by companies and translates it into multiple natural languages ​​using "natural language processing" technology. Specifically, it utilizes "translation APIs" and "generative AI models," and uses, for example, "automatic translation services" and "language models" to generate content that is appropriate for the local culture.

[0245] By using this system, users can eliminate the manual processes of translating product information and adapting it to local areas, enabling them to quickly and effectively launch marketing activities tailored to each region.

[0246] For example, if a user uses this system to run a campaign for a new lifestyle product, the server automatically selects the relevant information sharing platform for that product, and the terminal translates the product information into multiple languages ​​and posts it in a timely manner. This allows users to execute rapid and efficient marketing strategies in the global market.

[0247] An example of a prompt message might be, "Generate a message to appeal to consumers in each country about the features of this new product." This system aims to streamline commercial activities and support market expansion in each region.

[0248] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0249] Step 1:

[0250] The server first collects trend data from information-sharing platforms in various regions on the internet. The input is raw data obtained from the web, and the output is a list of platforms whose popularity is expressed numerically. This process uses web scraping technology, and the collected data is analyzed through a trend analysis API. This clarifies the popularity trends of social networking services (SNS).

[0251] Step 2:

[0252] The server selects the most effective information-sharing platform in each country based on the analyzed data. The input is a list of popularity ratings generated in the previous step, and the output is the name of the preferred platform in each country. This data selection process uses statistical algorithms to optimize the content strategy.

[0253] Step 3:

[0254] The terminal receives product information from a company and begins translating it into multiple languages. The input is text data provided by the company, and the output is product information translated into multiple languages. This translation process uses natural language processing technology and a translation API, and utilizes a generative AI model during the translation process to convey information in a way that is appropriate for each country's culture.

[0255] Step 4:

[0256] The device generates region-appropriate digital content using translated information. The input is translated product information, and the output is content adapted to the culture of each country. Here, a generation AI model selects specific examples and appropriate vocabulary to support content that is relevant to the cultural context.

[0257] Step 5:

[0258] The server creates a posting schedule for the selected information sharing platform. The inputs are optimized content and the selected platform, and the output is the detailed posting schedule. This process uses scheduling software to automatically configure posting based on peak consumer activity times in each region.

[0259] Step 6:

[0260] The server monitors product-related digital content after posting and analyzes the response. Inputs are user feedback and comments, and outputs are analyzed data and generated responses. This process utilizes language analysis techniques, and an automated response system facilitates rapid interaction with users.

[0261] Step 7:

[0262] The server evaluates the results of commercial activities and generates regular reports. Inputs are submission and user engagement data, and output is a detailed performance report. Data analysis software is used for this analysis, providing information to support the company's marketing strategy.

[0263] (Application Example 1)

[0264] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0265] In content delivery services operating on a global scale, optimizing information resources to take into account the different communication infrastructures, cultures, and time zones of each region is required. However, doing this manually is extremely labor-intensive and inefficient. Furthermore, real-time response is difficult, and there are limits to performance improvement. Therefore, a system is needed to automate the delivery of information resources in a way that is appropriate for each region and to provide them efficiently.

[0266] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0267] In this invention, the server includes means for automatically detecting the penetration rate of information systems on communication infrastructure in each region, means for converting information resources into multiple natural languages, and means for optimizing the transmission schedule based on the time zone of each region. This enables the automation of information transmission tailored to each region and efficient information provision that takes into account culture and time zones.

[0268] "Communication infrastructure" refers to the entire infrastructure used for sending and receiving information, including the internet and mobile communication networks.

[0269] An "information system" is an integrated set of software and hardware for collecting, processing, storing, and distributing data.

[0270] "Information resources" refer to datasets and content that form the basis for providing information to users, and include a variety of formats such as text, images, and audio.

[0271] "Natural language" refers to the language that humans use on a daily basis, and it utilizes translation technologies and language analysis designed for computers to understand and process.

[0272] A "transmission schedule" refers to a plan for delivering information resources to recipients at specific times and frequencies.

[0273] This system consists of a server and client devices and is specifically designed to optimize the communication infrastructure for global information provision services. The server detects the penetration rate of information systems and efficiently provides information resources in the natural language of each region. To achieve this, it translates information resources and optimizes the transmission schedule based on the time zone of each region.

[0274] Specifically, the server scans data on the internet to understand the penetration rate of communication infrastructure in the target area. Then, it translates information resources based on the culture and background of each region. The Google Cloud Translation API is used for translation, and generative AI models are utilized to improve translation accuracy. Next, the system analyzes user activity times to create an optimal transmission schedule. CRON Jobs is used as the scheduling tool for this process.

[0275] Furthermore, client devices collect feedback provided by users and send it to the server. This allows users to receive personalized information. This feature ensures that users receive the information they need at the time that best suits their schedule.

[0276] As a concrete example, consider a case where a payment service provider wants to introduce a new feature to the Japanese market. The server analyzes trends on Japanese social media and identifies that the time when the most users are online is 8 PM. Based on this, it automatically posts the announcement content at that time. In this process, the following prompt is used for the generating AI model: "Generate the most effective promotional content for the Japanese market. This promotion should focus on the new payment service feature and take cultural elements into consideration."

[0277] In this way, the system can efficiently provide information and increase user engagement.

[0278] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0279] Step 1:

[0280] The server collects data related to the target region from the Internet. The input is the filter setting for region specification, and the output is data regarding the popularity of the region-specific information system. Apply a data analysis algorithm to this data to identify which information systems are most utilized.

[0281] Step 2:

[0282] The server receives the information resources provided by the user and converts them into multiple natural languages using a translation API. The input is the original information resource, and the output is the information resources translated for each region. Google Cloud Translation API is used for translation, and here a generative AI model is utilized to generate translation results tailored to the cultural background.

[0283] Step 3:

[0284] The server analyzes the online activities of users in the target region and identifies the most influential time periods. The input is SNS logs and activity data, and the output is the optimal transmission schedule. Use a data analysis tool to analyze the peak activity times of users and optimize the transmission timing.

[0285] Step 4:

[0286] The terminal automatically posts the information resources to the content platform based on the optimal schedule sent from the server. The input is the translated information resources and the transmission schedule, and the output is the posting to each platform. Use CRON Jobs to send information according to the schedule.

[0287] [[ID=3,4]] Step 5:

[0288] The server monitors user reactions to submitted content in real time and generates automated responses as needed. Input is user feedback and comments, and output is the generated automated response. Natural language processing tools are used to analyze comments and generate relevant responses.

[0289] Step 6:

[0290] The server aggregates the performance of all marketing activities and generates reports. Inputs are various types of engagement data, and outputs are performance analysis reports. Engagement data collected from the database is processed by analytical tools and visualized to clearly report results.

[0291] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0292] This invention combines a system that automates marketing activities using social networking platforms with an emotion engine that analyzes user emotions. This system utilizes servers, terminals, and emotion recognition technology to enable companies to personalize and efficiently conduct their marketing activities.

[0293] The server automatically analyzes the popularity of social networking platforms in each country and selects the platform best suited for marketing. In doing so, it also considers platforms that can reflect user sentiment, selecting the channel through which the brand message is most effectively conveyed.

[0294] The device translates product information provided by companies into multiple languages. The translated information is then adjusted to suit the culture and user sentiment of each region. For example, if the sentiment engine detects positive emotions from a user's post, more aggressive promotional language can be used.

[0295] The server uses an emotion engine to analyze product-related posts and comments on social media. The emotion engine analyzes the emotions expressed by users (positive, negative, neutral) in real time and generates automated responses based on that analysis. For example, these responses might say "Thank you for your great feedback" for positive responses, and provide careful follow-up, including suggestions for improvement, for negative responses.

[0296] Furthermore, the server can dynamically adjust marketing activities using the results of sentiment analysis. If a particular campaign or advertisement receives a negative response, the sentiment engine can analyze the feedback and suggest content changes or new approaches.

[0297] Finally, the server measures the overall performance of its activities and periodically generates analytical reports. These reports include analysis results from the sentiment engine and highlight areas for improvement in the marketing strategy. Specifically, they are based on data analysis that includes which products and messages evoked the most positive emotions in users.

[0298] Thus, by analyzing user emotions and conducting marketing activities based on those emotions, the present invention enables companies to build better relationships with target users and implement effective and efficient marketing activities.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The server collects Internet trend data for each region and automatically detects popular social networking platforms in each country. Through this detection, the medium that the target users are most likely to use is identified.

[0302] Step 2:

[0303] The terminal translates the product information received from the enterprise using a multilingual translation service. At this time, the translated content is adjusted in consideration of the cultural background of each region and the emotions of the users. For example, using an emotion engine, expressions in different regions are customized to generate content that is acceptable to local users.

[0304] Step 3:

[0305] The server creates a schedule for posting on the social networking platform. This schedule is set to post at the optimal timing based on the emotional state of the users perceived by the emotion engine.

[0306] Step 4:

[0307] The server uses an emotion engine to analyze product-related social media content such as posts and comments in real time. In this analysis, the emotions expressed by the users are identified and classified as positive, negative, neutral, etc. Based on this, the server automatically generates an appropriate response and replies to the users.

[0308] Step 5:

[0309] The server that has received reactions and emotional feedback from users dynamically adjusts its marketing strategy. It analyzes the emotional reactions of users to specific content and messages, and accordingly, proposes changes to the content or new campaigns.

[0310] Step 6:

[0311] The server measures the overall performance of marketing activities and generates detailed reports that include sentiment analysis results. These reports show which content had a positive impact on users and are used as a basis for future strategic improvements.

[0312] (Example 2)

[0313] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0314] Marketing activities utilizing information exchange services require flexible and effective marketing methods that adapt to the culture and language of each region while considering user emotions. However, current systems struggle with dynamic adjustments based on sentiment analysis, which prevents them from adequately improving the performance of marketing activities.

[0315] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0316] In this invention, the server includes means for automatically detecting the popularity of information exchange services in each region, means for automatically detecting product-related information exchange service content and generating responses based on sentiment analysis, and means for dynamically adjusting marketing activities based on sentiment analysis. This enables flexible and effective marketing activities that are tailored to the culture and user sentiment of each region.

[0317] An "information exchange service" refers to an online platform that allows users to share and interact with each other via the internet.

[0318] "Popularity" refers to an indicator that shows how widely a particular information exchange service is used and how much attention it receives in a given region or market.

[0319] "Product information" refers to detailed descriptions, features, and specifications related to a specific product or service.

[0320] "Translation" refers to the process of converting information expressed in one language into another language in an appropriate and meaningful way.

[0321] "Automatic" refers to a system or machine performing a task independently, without human intervention, based on pre-set conditions or algorithms.

[0322] "Sentiment analysis" refers to a technology that identifies and classifies the underlying emotions and attitudes from text data such as user posts and comments.

[0323] "Dynamic adjustment" refers to a method of optimizing a system's operation by appropriately modifying it in response to changes in circumstances or new information.

[0324] "Marketing performance" refers to the criteria and results used to evaluate how successful a marketing campaign is in achieving its set goals.

[0325] A "report" refers to a document that compiles information about a specific activity or process, such as survey results or analysis.

[0326] A "generative AI model" refers to a machine learning model that uses artificial intelligence technology to automatically generate answers or suggestions for specific tasks or problems.

[0327] A "prompt sentence" refers to the input sentence that a generative AI model uses to create appropriate responses or content.

[0328] This invention is a system for realizing effective marketing activities using information exchange services. The embodiments thereof are described in detail below.

[0329] The server automatically accesses information exchange service platforms and has the functionality to measure their popularity in each region. This involves using analytical software to collect user numbers and engagement metrics. This makes it possible to support the selection of the optimal platform. For example, in regions where visual content is important, a platform with a high volume of image sharing would be chosen.

[0330] The device has the ability to translate product information provided by companies into multiple languages ​​and automatically post it. Translation software utilizing natural language processing technology is used for the translation, enabling localization that appropriately reflects cultural nuances. For example, in regions where the user's emotions are determined to be positive, the translation will be rendered in a brighter tone.

[0331] Furthermore, the server combines generative AI models and sentiment analysis technology to analyze product-related posts on the information exchange service in real time. Based on this analysis, it generates appropriate responses to promote user engagement. For example, a positive response will receive an automated message such as "Thank you for your great feedback."

[0332] Furthermore, it's possible to measure the effectiveness of marketing activities and dynamically adjust strategies based on sentiment analysis data. This allows for the rapid development of countermeasures even in the event of negative reactions. This functionality contributes to the optimization of marketing campaigns.

[0333] To implement these processes, an example of a prompt might be: "Analyze social media posts about the new product and propose a marketing strategy based on positive or negative sentiment."

[0334] In this way, by making full use of the roles of user, server, and terminal, it becomes possible to provide effective marketing solutions through information exchange services.

[0335] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0336] Step 1:

[0337] The server accesses each platform of the information exchange service and measures its popularity in each region. The input is the number of users and posting data for each platform, and the output is a popularity rating for each region. Here, data analysis software is used to collect and evaluate engagement metrics.

[0338] Step 2:

[0339] The terminal inputs product information provided by companies into multilingual translation software and outputs translations appropriate to the language and cultural background of the target region. This process utilizes natural language processing technology to add nuances appropriate to the local culture to the translated text.

[0340] Step 3:

[0341] The server collects product-related content posted on an information exchange service and performs sentiment analysis using a generative AI model. Using the posted data as input, it obtains sentiment classifications such as positive, negative, and neutral as output. Here, a text analysis algorithm is used to identify the user's emotional state.

[0342] Step 4:

[0343] The server generates an automated response message for the user based on the sentiment analysis results. The input to this process is the sentiment analysis results, and the output is a dynamically generated response message. Specifically, it uses a generation AI model to select appropriate words and create a message that elicits a reaction.

[0344] Step 5:

[0345] The server dynamically adjusts marketing activities based on sentiment analysis data. Input is feedback data from each campaign, and output is the construction of an optimized marketing strategy. Here, higher-level management software is used to perform specific actions that rapidly implement situation-dependent strategic changes.

[0346] Step 6:

[0347] The server measures the overall performance of the activities and generates reports. The input is data on the results of each initiative, and the output is a report including analysis. Specifically, it utilizes statistical analysis software to process data from draft to final report creation.

[0348] (Application Example 2)

[0349] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0350] In modern marketing activities utilizing social networking platforms, it is crucial to respond individually to users' emotions, but this process is complex and time-consuming and costly when done manually. Furthermore, it is difficult to quickly provide appropriate content that reflects regional cultural elements to meet the demands of a global market. Therefore, there is a need for new methods that streamline marketing activities while effectively communicating with users.

[0351] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0352] This invention includes a server that automatically detects the popularity of social networking platforms in each region, translates product information into multiple languages, automatically posts the translated product information to the detected social networking platforms, automatically detects product-related social media content and generates responses, personalizes responses based on user sentiment and dynamically adjusts advertising strategies, and measures the performance of marketing activities and generates reports. This enables efficient personalized marketing activities that respond to user sentiment and allows for appropriate adjustments to advertising strategies.

[0353] A "social networking platform" is an online service that allows individuals and groups to interact on the internet, enabling information sharing and communication.

[0354] "User sentiment" refers to the emotional reactions and feedback that users express on social media, and includes emotional states such as positive, negative, and neutral.

[0355] An "emotion engine" is an algorithm or tool used to analyze emotions from user posts and comments, supporting personalization in marketing activities.

[0356] Personalization refers to providing a more individualized experience by customizing information and services according to the characteristics and preferences of specific users.

[0357] "Advertising strategy" refers to the plans and methods used to effectively communicate products and services to the market, and is an important activity for companies to build relationships with consumers.

[0358] "Translation methods" refer to technologies and processes for converting information between different languages, and they support international communication.

[0359] "Means of detection" refer to the technologies and processes used to collect data and information and identify them based on specific criteria.

[0360] "Means of generation" refer to technologies and processes for creating new data, information, responses, etc., that enable output tailored to a specific purpose.

[0361] The system for implementing this invention mainly consists of a server, a terminal, and emotion recognition technology. The server has the function of collecting and analyzing data from the internet. First, it analyzes the popularity of social networking platforms used in each region and selects the optimal platform based on that data. The server also uses translation software to translate product information into multiple languages. In this translation process, it plays a role in adjusting the content to take into account the cultural background of each region.

[0362] Emotion recognition technology extracts emotions from content and comments posted by users on social media. Based on this information, the server generates responses tailored to the user's emotional state. For example, users who provide positive feedback may receive specific promotional information or thank-you messages, while users who express negative reactions may receive suggestions for improvement or apologies.

[0363] The device receives translated product information and response messages sent from the server and automatically posts them to social networking platforms. This process enables efficient and effective global marketing activities.

[0364] For example, if a user leaves an online review stating that a new product was "great to use," the server can detect this positive sentiment and offer that user a special discount coupon. This not only strengthens the relationship with the user but also increases their purchase intent.

[0365] An example of a prompt would be, "How can I analyze a user's recent product review and generate the most appropriate marketing response based on their sentiment?" Using this prompt helps guide the generative AI model to execute the appropriate response generation process.

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The server collects data publicly available on the internet. Specifically, it obtains information on the popularity of social networking platforms in each region. This information is obtained by analyzing user usage frequency and engagement data. The input is publicly available user activity data, and the output is popularity information for each platform.

[0369] Step 2:

[0370] The server translates product information provided by companies into multiple languages. The input is the original product information, and the output is the product information translated into multiple languages. Specifically, this involves using translation software to adjust the data, taking into account the cultural context of each region.

[0371] Step 3:

[0372] The server monitors user content posted on social networks and analyzes its sentiment using sentiment recognition technology. The input is user-submitted data, and the output is the sentiment information contained in the posts. Specifically, natural language processing is used to analyze the text of the posts and assign sentiment tags such as positive, negative, and neutral.

[0373] Step 4:

[0374] The server generates the most appropriate response for each user based on the sentiment analysis results. The input in this process is the sentiment analysis results, and the output is a personalized response message. For example, it might offer a special discount to users who exhibit positive emotions.

[0375] Step 5:

[0376] The terminal automatically posts translated product information and generated response messages received from the server to the social networking platform. The input is data from the server, and the output is the posted content. In this step, the content is accurately posted using each platform's API, completing the information delivery to the user.

[0377] Step 6:

[0378] The server measures the performance of marketing activities and generates periodic reports. The input is data on the activities themselves, and the output is an analytical report. Specific analysis includes user feedback and engagement data, which is used to guide future strategic improvements.

[0379] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0380] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0381] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0382] [Third Embodiment]

[0383] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0384] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0385] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0386] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0387] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0388] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0389] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0390] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0391] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0392] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0393] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0394] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0395] This invention provides a system that automates and streamlines marketing activities in multiple countries. The system operates with a configuration including servers and terminals, and aims to effectively utilize a company's marketing resources.

[0396] The server first automatically detects popular social networking platforms in the target country by collecting trend data from the internet. This invention automates the selection of SNS platforms, eliminating the need for users to individually research popular platforms in each country.

[0397] The device receives product information provided by companies and translates it into multiple languages ​​using a translation API. During this process, the device generates content tailored to the culture and trends of each country, adjusting the translated content to ensure it is accepted seamlessly in each region. This process enables users to quickly deploy region-specific marketing content.

[0398] The server then uses its scheduling function to automatically post the translated product information to each country's social media platform at the appropriate time. This posting timing is calculated based on the time of day when users in the target country are most online. This allows users to deliver marketing messages to their target audience effectively and efficiently.

[0399] Furthermore, the server monitors product-related posts and comments in real time. This feature uses natural language processing technology to automatically detect highly relevant posts and generate appropriate automated responses, thereby increasing engagement with the product.

[0400] Finally, the server analyzes performance data based on all posts and engagements and generates reports periodically. These reports detail the effectiveness and areas for improvement of engagement, helping to improve a company's marketing strategy. Specifically, they include data showing which content was most effective on which social media platform.

[0401] Thus, by automating marketing activities in the global market, the present invention enables companies to efficiently overcome language and cultural barriers and optimize resource allocation.

[0402] The following describes the processing flow.

[0403] Step 1:

[0404] The server collects internet trend data from various countries and automatically detects popular social networking platforms. This provides the foundational information needed to effectively approach target markets.

[0405] Step 2:

[0406] The device receives product information provided by companies and translates it into multiple languages ​​using the latest translation APIs. In doing so, it generates content adapted to the culture and trends of each region, ensuring that information is delivered to local users without feeling out of place.

[0407] Step 3:

[0408] The server calculates the optimal posting time on each country's social networking platform based on the translated product information and creates an automated posting schedule. This maximizes the exposure of the content.

[0409] Step 4:

[0410] The server monitors product-related social media posts and comments in real time. During this process, natural language processing technology is used to detect highly relevant posts and comments and automatically generate appropriate responses. This allows users to quickly engage with customers.

[0411] Step 5:

[0412] The server collects and analyzes performance data from all posts and engagement activities. Regularly generated reports include performance evaluations based on various metrics, allowing users to optimize their marketing strategies.

[0413] (Example 1)

[0414] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0415] In the global market, companies face challenges in effectively promoting their products and conducting marketing activities in diverse regions with different languages ​​and cultural backgrounds. Traditionally, researching information sharing platforms across multiple regions, translating information, adapting to different cultures, scheduling content posting, and measuring effectiveness has been extremely time-consuming and costly. Therefore, automating and streamlining these processes is essential.

[0416] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0417] In this invention, the server includes means for automatically detecting the popularity of information sharing platforms in each region, means for converting information into multiple natural languages, and means for automatically providing the converted information to the detected information sharing platforms. This makes it possible to automate effective marketing activities in different regions and to optimally allocate corporate resources.

[0418] An "information sharing platform" is an online service that allows users to post, share, and interact with information.

[0419] "Natural language" refers to the language that humans use in their daily lives, and is the target language that machines need to understand and translate.

[0420] "Digital content" refers to forms of information and entertainment distributed and consumed via electronic media, and includes video, audio, text, etc.

[0421] "Linguistic analysis techniques" refer to computational methods and programming techniques used to understand, interpret, or analyze text data.

[0422] "Commercial activity" refers to a series of business processes aimed at distributing goods and services in the market and generating profits.

[0423] This system includes a server and terminals as its main components. The server utilizes information gathering technologies on the internet and has a mechanism to automatically detect the popularity of information sharing platforms in each region. Specifically, it applies "web scraping technology" and "data mining techniques," and tools such as "trend analysis APIs" are used in particular.

[0424] The server has the function of automatically posting company product information to an information sharing platform at the appropriate time, based on the analyzed trend data. For this purpose, "schedule management software" is introduced, and for example, an "automatic posting system" is used.

[0425] The device receives product information provided by companies and translates it into multiple natural languages ​​using "natural language processing" technology. Specifically, it utilizes "translation APIs" and "generative AI models," and uses, for example, "automatic translation services" and "language models" to generate content that is appropriate for the local culture.

[0426] By using this system, users can eliminate the manual processes of translating product information and adapting it to local areas, enabling them to quickly and effectively launch marketing activities tailored to each region.

[0427] For example, if a user uses this system to run a campaign for a new lifestyle product, the server automatically selects the relevant information sharing platform for that product, and the terminal translates the product information into multiple languages ​​and posts it in a timely manner. This allows users to execute rapid and efficient marketing strategies in the global market.

[0428] An example of a prompt message might be, "Generate a message to appeal to consumers in each country about the features of this new product." This system aims to streamline commercial activities and support market expansion in each region.

[0429] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0430] Step 1:

[0431] The server first collects trend data from information-sharing platforms in various regions on the internet. The input is raw data obtained from the web, and the output is a list of platforms whose popularity is expressed numerically. This process uses web scraping technology, and the collected data is analyzed through a trend analysis API. This clarifies the popularity trends of social networking services (SNS).

[0432] Step 2:

[0433] The server selects the most effective information-sharing platform in each country based on the analyzed data. The input is a list of popularity ratings generated in the previous step, and the output is the name of the preferred platform in each country. This data selection process uses statistical algorithms to optimize the content strategy.

[0434] Step 3:

[0435] The terminal receives product information from a company and begins translating it into multiple languages. The input is text data provided by the company, and the output is product information translated into multiple languages. This translation process uses natural language processing technology and a translation API, and utilizes a generative AI model during the translation process to convey information in a way that is appropriate for each country's culture.

[0436] Step 4:

[0437] The device generates region-appropriate digital content using translated information. The input is translated product information, and the output is content adapted to the culture of each country. Here, a generation AI model selects specific examples and appropriate vocabulary to support content that is relevant to the cultural context.

[0438] Step 5:

[0439] The server creates a posting schedule for the selected information sharing platform. The inputs are optimized content and the selected platform, and the output is the detailed posting schedule. This process uses scheduling software to automatically configure posting based on peak consumer activity times in each region.

[0440] Step 6:

[0441] The server monitors product-related digital content after posting and analyzes the response. Inputs are user feedback and comments, and outputs are analyzed data and generated responses. This process utilizes language analysis techniques, and an automated response system facilitates rapid interaction with users.

[0442] Step 7:

[0443] The server evaluates the results of commercial activities and generates regular reports. Inputs are submission and user engagement data, and output is a detailed performance report. Data analysis software is used for this analysis, providing information to support the company's marketing strategy.

[0444] (Application Example 1)

[0445] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0446] In content delivery services operating on a global scale, optimizing information resources to take into account the different communication infrastructures, cultures, and time zones of each region is required. However, doing this manually is extremely labor-intensive and inefficient. Furthermore, real-time response is difficult, and there are limits to performance improvement. Therefore, a system is needed to automate the delivery of information resources in a way that is appropriate for each region and to provide them efficiently.

[0447] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0448] In this invention, the server includes means for automatically detecting the penetration rate of information systems on communication infrastructure in each region, means for converting information resources into multiple natural languages, and means for optimizing the transmission schedule based on the time zone of each region. This enables the automation of information transmission tailored to each region and efficient information provision that takes into account culture and time zones.

[0449] "Communication infrastructure" refers to the entire infrastructure used for sending and receiving information, including the internet and mobile communication networks.

[0450] An "information system" is an integrated set of software and hardware for collecting, processing, storing, and distributing data.

[0451] "Information resources" refer to datasets and content that form the basis for providing information to users, and include a variety of formats such as text, images, and audio.

[0452] "Natural language" refers to the language that humans use on a daily basis, and it utilizes translation technologies and language analysis designed for computers to understand and process.

[0453] A "transmission schedule" refers to a plan for delivering information resources to recipients at specific times and frequencies.

[0454] This system consists of a server and client devices and is specifically designed to optimize the communication infrastructure for global information provision services. The server detects the penetration rate of information systems and efficiently provides information resources in the natural language of each region. To achieve this, it translates information resources and optimizes the transmission schedule based on the time zone of each region.

[0455] Specifically, the server scans data on the internet to understand the penetration rate of communication infrastructure in the target area. Then, it translates information resources based on the culture and background of each region. The Google Cloud Translation API is used for translation, and generative AI models are utilized to improve translation accuracy. Next, the system analyzes user activity times to create an optimal transmission schedule. CRON Jobs is used as the scheduling tool for this process.

[0456] Furthermore, client devices collect feedback provided by users and send it to the server. This allows users to receive personalized information. This feature ensures that users receive the information they need at the time that best suits their schedule.

[0457] As a concrete example, consider a case where a payment service provider wants to introduce a new feature to the Japanese market. The server analyzes trends on Japanese social media and identifies that the time when the most users are online is 8 PM. Based on this, it automatically posts the announcement content at that time. In this process, the following prompt is used for the generating AI model: "Generate the most effective promotional content for the Japanese market. This promotion should focus on the new payment service feature and take cultural elements into consideration."

[0458] In this way, the system can efficiently provide information and increase user engagement.

[0459] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0460] Step 1:

[0461] The server collects data related to the target region from the internet. The input is region-specific filter settings, and the output is data on the prevalence of region-specific information systems. This data is then used to apply data analysis algorithms to identify which information systems are most widely used.

[0462] Step 2:

[0463] The server receives information resources provided by the user and translates them into multiple natural languages ​​using a translation API. The input is the original information resource, and the output is the information resource translated for each region. The Google Cloud Translation API is used for translation, and a generative AI model is used to generate translation results that are appropriate for the cultural context.

[0464] Step 3:

[0465] The server analyzes the online activity of users in the target region and identifies the most influential time slots. Inputs are SNS logs and activity data, and output is the optimal sending schedule. Data analysis tools are used to analyze users' peak activity times and optimize sending timing.

[0466] Step 4:

[0467] The terminal automatically posts information resources to content platforms based on the optimal schedule sent from the server. Inputs are translated information resources and the transmission schedule, while output is the posting to each platform. Information is transmitted according to the schedule using CRON Jobs.

[0468] Step 5:

[0469] The server monitors user reactions to submitted content in real time and generates automated responses as needed. Input is user feedback and comments, and output is the generated automated response. Natural language processing tools are used to analyze comments and generate relevant responses.

[0470] Step 6:

[0471] The server aggregates the performance of all marketing activities and generates reports. Inputs are various types of engagement data, and outputs are performance analysis reports. Engagement data collected from the database is processed by analytical tools and visualized to clearly report results.

[0472] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0473] This invention combines a system that automates marketing activities using social networking platforms with an emotion engine that analyzes user emotions. This system utilizes servers, terminals, and emotion recognition technology to enable companies to personalize and efficiently conduct their marketing activities.

[0474] The server automatically analyzes the popularity of social networking platforms in each country and selects the platform best suited for marketing. In doing so, it also considers platforms that can reflect user sentiment, selecting the channel through which the brand message is most effectively conveyed.

[0475] The device translates product information provided by companies into multiple languages. The translated information is then adjusted to suit the culture and user sentiment of each region. For example, if the sentiment engine detects positive emotions from a user's post, more aggressive promotional language can be used.

[0476] The server uses an emotion engine to analyze product-related posts and comments on social media. The emotion engine analyzes the emotions expressed by users (positive, negative, neutral) in real time and generates automated responses based on that analysis. For example, these responses might say "Thank you for your great feedback" for positive responses, and provide careful follow-up, including suggestions for improvement, for negative responses.

[0477] Furthermore, the server can dynamically adjust marketing activities using the results of sentiment analysis. If a particular campaign or advertisement receives a negative response, the sentiment engine can analyze the feedback and suggest content changes or new approaches.

[0478] Finally, the server measures the overall performance of its activities and periodically generates analytical reports. These reports include analysis results from the sentiment engine and highlight areas for improvement in the marketing strategy. Specifically, they are based on data analysis that includes which products and messages evoked the most positive emotions in users.

[0479] Thus, by analyzing user emotions and conducting marketing activities based on those emotions, the present invention enables companies to build better relationships with target users and implement effective and efficient marketing activities.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] The server collects regional internet trend data and automatically detects popular social networking platforms in each country. Through this detection, it identifies the media that target users are most likely to use.

[0483] Step 2:

[0484] The device translates product information received from companies using a multilingual translation service. During this process, the translation is adjusted to take into account the cultural background and user sentiment of each region. For example, an emotion engine is used to customize expressions for different regions, generating content that is easily accepted by local users.

[0485] Step 3:

[0486] The server creates a posting schedule for the social networking platform. This schedule is configured to post at the optimal time based on the user's emotional state as detected by the sentiment engine.

[0487] Step 4:

[0488] The server uses an emotion engine to analyze product-related social media content, such as posts and comments, in real time. This analysis identifies the emotions expressed by users and classifies them as positive, negative, or neutral. Based on this, the server automatically generates an appropriate response and sends it back to the user.

[0489] Step 5:

[0490] The server dynamically adjusts marketing strategies based on user reactions and emotional feedback. It analyzes users' emotional responses to specific content and messages, and accordingly makes changes to content or suggests new campaigns.

[0491] Step 6:

[0492] The server measures the overall performance of marketing activities and generates detailed reports that include sentiment analysis results. These reports show which content had a positive impact on users and are used as a basis for future strategic improvements.

[0493] (Example 2)

[0494] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0495] Marketing activities utilizing information exchange services require flexible and effective marketing methods that adapt to the culture and language of each region while considering user emotions. However, current systems struggle with dynamic adjustments based on sentiment analysis, which prevents them from adequately improving the performance of marketing activities.

[0496] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0497] In this invention, the server includes means for automatically detecting the popularity of information exchange services in each region, means for automatically detecting product-related information exchange service content and generating responses based on sentiment analysis, and means for dynamically adjusting marketing activities based on sentiment analysis. This enables flexible and effective marketing activities that are tailored to the culture and user sentiment of each region.

[0498] An "information exchange service" refers to an online platform that allows users to share and interact with each other via the internet.

[0499] "Popularity" refers to an indicator that shows how widely a particular information exchange service is used and how much attention it receives in a given region or market.

[0500] "Product information" refers to detailed descriptions, features, and specifications related to a specific product or service.

[0501] "Translation" refers to the process of converting information expressed in one language into another language in an appropriate and meaningful way.

[0502] "Automatic" refers to a system or machine performing a task independently, without human intervention, based on pre-set conditions or algorithms.

[0503] "Sentiment analysis" refers to a technology that identifies and classifies the underlying emotions and attitudes from text data such as user posts and comments.

[0504] "Dynamic adjustment" refers to a method of optimizing a system's operation by appropriately modifying it in response to changes in circumstances or new information.

[0505] "Marketing performance" refers to the criteria and results used to evaluate how successful a marketing campaign is in achieving its set goals.

[0506] A "report" refers to a document that compiles information about a specific activity or process, such as survey results or analysis.

[0507] A "generative AI model" refers to a machine learning model that uses artificial intelligence technology to automatically generate answers or suggestions for specific tasks or problems.

[0508] A "prompt sentence" refers to the input sentence that a generative AI model uses to create appropriate responses or content.

[0509] This invention is a system for realizing effective marketing activities using information exchange services. The embodiments thereof are described in detail below.

[0510] The server automatically accesses information exchange service platforms and has the functionality to measure their popularity in each region. This involves using analytical software to collect user numbers and engagement metrics. This makes it possible to support the selection of the optimal platform. For example, in regions where visual content is important, a platform with a high volume of image sharing would be chosen.

[0511] The device has the ability to translate product information provided by companies into multiple languages ​​and automatically post it. Translation software utilizing natural language processing technology is used for the translation, enabling localization that appropriately reflects cultural nuances. For example, in regions where the user's emotions are determined to be positive, the translation will be rendered in a brighter tone.

[0512] Furthermore, the server combines generative AI models and sentiment analysis technology to analyze product-related posts on the information exchange service in real time. Based on this analysis, it generates appropriate responses to promote user engagement. For example, a positive response will receive an automated message such as "Thank you for your great feedback."

[0513] Furthermore, it's possible to measure the effectiveness of marketing activities and dynamically adjust strategies based on sentiment analysis data. This allows for the rapid development of countermeasures even in the event of negative reactions. This functionality contributes to the optimization of marketing campaigns.

[0514] To implement these processes, an example of a prompt might be: "Analyze social media posts about the new product and propose a marketing strategy based on positive or negative sentiment."

[0515] In this way, by making full use of the roles of user, server, and terminal, it becomes possible to provide effective marketing solutions through information exchange services.

[0516] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0517] Step 1:

[0518] The server accesses each platform of the information exchange service and measures its popularity in each region. The input is the number of users and posting data for each platform, and the output is a popularity rating for each region. Here, data analysis software is used to collect and evaluate engagement metrics.

[0519] Step 2:

[0520] The terminal inputs product information provided by companies into multilingual translation software and outputs translations appropriate to the language and cultural background of the target region. This process utilizes natural language processing technology to add nuances appropriate to the local culture to the translated text.

[0521] Step 3:

[0522] The server collects product-related content posted on an information exchange service and performs sentiment analysis using a generative AI model. Using the posted data as input, it obtains sentiment classifications such as positive, negative, and neutral as output. Here, a text analysis algorithm is used to identify the user's emotional state.

[0523] Step 4:

[0524] The server generates an automated response message for the user based on the sentiment analysis results. The input to this process is the sentiment analysis results, and the output is a dynamically generated response message. Specifically, it uses a generation AI model to select appropriate words and create a message that elicits a reaction.

[0525] Step 5:

[0526] The server dynamically adjusts marketing activities based on sentiment analysis data. Input is feedback data from each campaign, and output is the construction of an optimized marketing strategy. Here, higher-level management software is used to perform specific actions that rapidly implement situation-dependent strategic changes.

[0527] Step 6:

[0528] The server measures the overall performance of the activities and generates reports. The input is data on the results of each initiative, and the output is a report including analysis. Specifically, it utilizes statistical analysis software to process data from draft to final report creation.

[0529] (Application Example 2)

[0530] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0531] In modern marketing activities utilizing social networking platforms, it is crucial to respond individually to users' emotions, but this process is complex and time-consuming and costly when done manually. Furthermore, it is difficult to quickly provide appropriate content that reflects regional cultural elements to meet the demands of a global market. Therefore, there is a need for new methods that streamline marketing activities while effectively communicating with users.

[0532] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0533] This invention includes a server that automatically detects the popularity of social networking platforms in each region, translates product information into multiple languages, automatically posts the translated product information to the detected social networking platforms, automatically detects product-related social media content and generates responses, personalizes responses based on user sentiment and dynamically adjusts advertising strategies, and measures the performance of marketing activities and generates reports. This enables efficient personalized marketing activities that respond to user sentiment and allows for appropriate adjustments to advertising strategies.

[0534] A "social networking platform" is an online service that allows individuals and groups to interact on the internet, enabling information sharing and communication.

[0535] "User sentiment" refers to the emotional reactions and feedback that users express on social media, and includes emotional states such as positive, negative, and neutral.

[0536] An "emotion engine" is an algorithm or tool used to analyze emotions from user posts and comments, supporting personalization in marketing activities.

[0537] Personalization refers to providing a more individualized experience by customizing information and services according to the characteristics and preferences of specific users.

[0538] "Advertising strategy" refers to the plans and methods used to effectively communicate products and services to the market, and is an important activity for companies to build relationships with consumers.

[0539] "Translation methods" refer to technologies and processes for converting information between different languages, and they support international communication.

[0540] "Means of detection" refer to the technologies and processes used to collect data and information and identify them based on specific criteria.

[0541] "Means of generation" refer to technologies and processes for creating new data, information, responses, etc., that enable output tailored to a specific purpose.

[0542] The system for implementing this invention mainly consists of a server, a terminal, and emotion recognition technology. The server has the function of collecting and analyzing data from the internet. First, it analyzes the popularity of social networking platforms used in each region and selects the optimal platform based on that data. The server also uses translation software to translate product information into multiple languages. In this translation process, it plays a role in adjusting the content to take into account the cultural background of each region.

[0543] Emotion recognition technology extracts emotions from content and comments posted by users on social media. Based on this information, the server generates responses tailored to the user's emotional state. For example, users who provide positive feedback may receive specific promotional information or thank-you messages, while users who express negative reactions may receive suggestions for improvement or apologies.

[0544] The device receives translated product information and response messages sent from the server and automatically posts them to social networking platforms. This process enables efficient and effective global marketing activities.

[0545] For example, if a user leaves an online review stating that a new product was "great to use," the server can detect this positive sentiment and offer that user a special discount coupon. This not only strengthens the relationship with the user but also increases their purchase intent.

[0546] An example of a prompt would be, "How can I analyze a user's recent product review and generate the most appropriate marketing response based on their sentiment?" Using this prompt helps guide the generative AI model to execute the appropriate response generation process.

[0547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0548] Step 1:

[0549] The server collects data publicly available on the internet. Specifically, it obtains information on the popularity of social networking platforms in each region. This information is obtained by analyzing user usage frequency and engagement data. The input is publicly available user activity data, and the output is popularity information for each platform.

[0550] Step 2:

[0551] The server translates product information provided by companies into multiple languages. The input is the original product information, and the output is the product information translated into multiple languages. Specifically, this involves using translation software to adjust the data, taking into account the cultural context of each region.

[0552] Step 3:

[0553] The server monitors user content posted on social networks and analyzes its sentiment using sentiment recognition technology. The input is user-submitted data, and the output is the sentiment information contained in the posts. Specifically, natural language processing is used to analyze the text of the posts and assign sentiment tags such as positive, negative, and neutral.

[0554] Step 4:

[0555] The server generates the most appropriate response for each user based on the sentiment analysis results. The input in this process is the sentiment analysis results, and the output is a personalized response message. For example, it might offer a special discount to users who exhibit positive emotions.

[0556] Step 5:

[0557] The terminal automatically posts translated product information and generated response messages received from the server to the social networking platform. The input is data from the server, and the output is the posted content. In this step, the content is accurately posted using each platform's API, completing the information delivery to the user.

[0558] Step 6:

[0559] The server measures the performance of marketing activities and generates periodic reports. The input is data on the activities themselves, and the output is an analytical report. Specific analysis includes user feedback and engagement data, which is used to guide future strategic improvements.

[0560] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0561] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0562] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0563] [Fourth Embodiment]

[0564] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0565] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0566] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0567] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0568] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0569] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0570] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0571] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0572] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0573] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0574] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0575] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0576] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0577] This invention provides a system that automates and streamlines marketing activities in multiple countries. The system operates with a configuration including servers and terminals, and aims to effectively utilize a company's marketing resources.

[0578] The server first automatically detects popular social networking platforms in the target country by collecting trend data from the internet. This invention automates the selection of SNS platforms, eliminating the need for users to individually research popular platforms in each country.

[0579] The device receives product information provided by companies and translates it into multiple languages ​​using a translation API. During this process, the device generates content tailored to the culture and trends of each country, adjusting the translated content to ensure it is accepted seamlessly in each region. This process enables users to quickly deploy region-specific marketing content.

[0580] The server then uses its scheduling function to automatically post the translated product information to each country's social media platform at the appropriate time. This posting timing is calculated based on the time of day when users in the target country are most online. This allows users to deliver marketing messages to their target audience effectively and efficiently.

[0581] Furthermore, the server monitors product-related posts and comments in real time. This feature uses natural language processing technology to automatically detect highly relevant posts and generate appropriate automated responses, thereby increasing engagement with the product.

[0582] Finally, the server analyzes performance data based on all posts and engagements and generates reports periodically. These reports detail the effectiveness and areas for improvement of engagement, helping to improve a company's marketing strategy. Specifically, they include data showing which content was most effective on which social media platform.

[0583] Thus, by automating marketing activities in the global market, the present invention enables companies to efficiently overcome language and cultural barriers and optimize resource allocation.

[0584] The following describes the processing flow.

[0585] Step 1:

[0586] The server collects internet trend data from various countries and automatically detects popular social networking platforms. This provides the foundational information needed to effectively approach target markets.

[0587] Step 2:

[0588] The device receives product information provided by companies and translates it into multiple languages ​​using the latest translation APIs. In doing so, it generates content adapted to the culture and trends of each region, ensuring that information is delivered to local users without feeling out of place.

[0589] Step 3:

[0590] The server calculates the optimal posting time on each country's social networking platform based on the translated product information and creates an automated posting schedule. This maximizes the exposure of the content.

[0591] Step 4:

[0592] The server monitors product-related social media posts and comments in real time. During this process, natural language processing technology is used to detect highly relevant posts and comments and automatically generate appropriate responses. This allows users to quickly engage with customers.

[0593] Step 5:

[0594] The server collects and analyzes performance data from all posts and engagement activities. Regularly generated reports include performance evaluations based on various metrics, allowing users to optimize their marketing strategies.

[0595] (Example 1)

[0596] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0597] In the global market, companies face challenges in effectively promoting their products and conducting marketing activities in diverse regions with different languages ​​and cultural backgrounds. Traditionally, researching information sharing platforms across multiple regions, translating information, adapting to different cultures, scheduling content posting, and measuring effectiveness has been extremely time-consuming and costly. Therefore, automating and streamlining these processes is essential.

[0598] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0599] In this invention, the server includes means for automatically detecting the popularity of information sharing platforms in each region, means for converting information into multiple natural languages, and means for automatically providing the converted information to the detected information sharing platforms. This makes it possible to automate effective marketing activities in different regions and to optimally allocate corporate resources.

[0600] An "information sharing platform" is an online service that allows users to post, share, and interact with information.

[0601] "Natural language" refers to the language that humans use in their daily lives, and is the target language that machines need to understand and translate.

[0602] "Digital content" refers to forms of information and entertainment distributed and consumed via electronic media, and includes video, audio, text, etc.

[0603] "Linguistic analysis techniques" refer to computational methods and programming techniques used to understand, interpret, or analyze text data.

[0604] "Commercial activity" refers to a series of business processes aimed at distributing goods and services in the market and generating profits.

[0605] This system includes a server and terminals as its main components. The server utilizes information gathering technologies on the internet and has a mechanism to automatically detect the popularity of information sharing platforms in each region. Specifically, it applies "web scraping technology" and "data mining techniques," and tools such as "trend analysis APIs" are used in particular.

[0606] The server has the function of automatically posting company product information to an information sharing platform at the appropriate time, based on the analyzed trend data. For this purpose, "schedule management software" is introduced, and for example, an "automatic posting system" is used.

[0607] The device receives product information provided by companies and translates it into multiple natural languages ​​using "natural language processing" technology. Specifically, it utilizes "translation APIs" and "generative AI models," and uses, for example, "automatic translation services" and "language models" to generate content that is appropriate for the local culture.

[0608] By using this system, users can eliminate the manual processes of translating product information and adapting it to local areas, enabling them to quickly and effectively launch marketing activities tailored to each region.

[0609] For example, if a user uses this system to run a campaign for a new lifestyle product, the server automatically selects the relevant information sharing platform for that product, and the terminal translates the product information into multiple languages ​​and posts it in a timely manner. This allows users to execute rapid and efficient marketing strategies in the global market.

[0610] An example of a prompt message might be, "Generate a message to appeal to consumers in each country about the features of this new product." This system aims to streamline commercial activities and support market expansion in each region.

[0611] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0612] Step 1:

[0613] The server first collects trend data from information-sharing platforms in various regions on the internet. The input is raw data obtained from the web, and the output is a list of platforms whose popularity is expressed numerically. This process uses web scraping technology, and the collected data is analyzed through a trend analysis API. This clarifies the popularity trends of social networking services (SNS).

[0614] Step 2:

[0615] The server selects the most effective information-sharing platform in each country based on the analyzed data. The input is a list of popularity ratings generated in the previous step, and the output is the name of the preferred platform in each country. This data selection process uses statistical algorithms to optimize the content strategy.

[0616] Step 3:

[0617] The terminal receives product information from a company and begins translating it into multiple languages. The input is text data provided by the company, and the output is product information translated into multiple languages. This translation process uses natural language processing technology and a translation API, and utilizes a generative AI model during the translation process to convey information in a way that is appropriate for each country's culture.

[0618] Step 4:

[0619] The device generates region-appropriate digital content using translated information. The input is translated product information, and the output is content adapted to the culture of each country. Here, a generation AI model selects specific examples and appropriate vocabulary to support content that is relevant to the cultural context.

[0620] Step 5:

[0621] The server creates a posting schedule for the selected information sharing platform. The inputs are optimized content and the selected platform, and the output is the detailed posting schedule. This process uses scheduling software to automatically configure posting based on peak consumer activity times in each region.

[0622] Step 6:

[0623] The server monitors product-related digital content after posting and analyzes the response. Inputs are user feedback and comments, and outputs are analyzed data and generated responses. This process utilizes language analysis techniques, and an automated response system facilitates rapid interaction with users.

[0624] Step 7:

[0625] The server evaluates the results of commercial activities and generates regular reports. Inputs are submission and user engagement data, and output is a detailed performance report. Data analysis software is used for this analysis, providing information to support the company's marketing strategy.

[0626] (Application Example 1)

[0627] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] In content delivery services operating on a global scale, optimizing information resources to take into account the different communication infrastructures, cultures, and time zones of each region is required. However, doing this manually is extremely labor-intensive and inefficient. Furthermore, real-time response is difficult, and there are limits to performance improvement. Therefore, a system is needed to automate the delivery of information resources in a way that is appropriate for each region and to provide them efficiently.

[0629] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0630] In this invention, the server includes means for automatically detecting the penetration rate of information systems on communication infrastructure in each region, means for converting information resources into multiple natural languages, and means for optimizing the transmission schedule based on the time zone of each region. This enables the automation of information transmission tailored to each region and efficient information provision that takes into account culture and time zones.

[0631] "Communication infrastructure" refers to the entire infrastructure used for sending and receiving information, including the internet and mobile communication networks.

[0632] An "information system" is an integrated set of software and hardware for collecting, processing, storing, and distributing data.

[0633] "Information resources" refer to datasets and content that form the basis for providing information to users, and include a variety of formats such as text, images, and audio.

[0634] "Natural language" refers to the language that humans use on a daily basis, and it utilizes translation technologies and language analysis designed for computers to understand and process.

[0635] A "transmission schedule" refers to a plan for delivering information resources to recipients at specific times and frequencies.

[0636] This system consists of a server and client devices and is specifically designed to optimize the communication infrastructure for global information provision services. The server detects the penetration rate of information systems and efficiently provides information resources in the natural language of each region. To achieve this, it translates information resources and optimizes the transmission schedule based on the time zone of each region.

[0637] Specifically, the server scans data on the internet to understand the penetration rate of communication infrastructure in the target area. Then, it translates information resources based on the culture and background of each region. The Google Cloud Translation API is used for translation, and generative AI models are utilized to improve translation accuracy. Next, the system analyzes user activity times to create an optimal transmission schedule. CRON Jobs is used as the scheduling tool for this process.

[0638] Furthermore, client devices collect feedback provided by users and send it to the server. This allows users to receive personalized information. This feature ensures that users receive the information they need at the time that best suits their schedule.

[0639] As a concrete example, consider a case where a payment service provider wants to introduce a new feature to the Japanese market. The server analyzes trends on Japanese social media and identifies that the time when the most users are online is 8 PM. Based on this, it automatically posts the announcement content at that time. In this process, the following prompt is used for the generating AI model: "Generate the most effective promotional content for the Japanese market. This promotion should focus on the new payment service feature and take cultural elements into consideration."

[0640] In this way, the system can efficiently provide information and increase user engagement.

[0641] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0642] Step 1:

[0643] The server collects data related to the target region from the internet. The input is region-specific filter settings, and the output is data on the prevalence of region-specific information systems. This data is then used to apply data analysis algorithms to identify which information systems are most widely used.

[0644] Step 2:

[0645] The server receives information resources provided by the user and translates them into multiple natural languages ​​using a translation API. The input is the original information resource, and the output is the information resource translated for each region. The Google Cloud Translation API is used for translation, and a generative AI model is used to generate translation results that are appropriate for the cultural context.

[0646] Step 3:

[0647] The server analyzes the online activity of users in the target region and identifies the most influential time slots. Inputs are SNS logs and activity data, and output is the optimal sending schedule. Data analysis tools are used to analyze users' peak activity times and optimize sending timing.

[0648] Step 4:

[0649] The terminal automatically posts information resources to content platforms based on the optimal schedule sent from the server. Inputs are translated information resources and the transmission schedule, while output is the posting to each platform. Information is transmitted according to the schedule using CRON Jobs.

[0650] Step 5:

[0651] The server monitors user reactions to submitted content in real time and generates automated responses as needed. Input is user feedback and comments, and output is the generated automated response. Natural language processing tools are used to analyze comments and generate relevant responses.

[0652] Step 6:

[0653] The server aggregates the performance of all marketing activities and generates reports. Inputs are various types of engagement data, and outputs are performance analysis reports. Engagement data collected from the database is processed by analytical tools and visualized to clearly report results.

[0654] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0655] This invention combines a system that automates marketing activities using social networking platforms with an emotion engine that analyzes user emotions. This system utilizes servers, terminals, and emotion recognition technology to enable companies to personalize and efficiently conduct their marketing activities.

[0656] The server automatically analyzes the popularity of social networking platforms in each country and selects the platform best suited for marketing. In doing so, it also considers platforms that can reflect user sentiment, selecting the channel through which the brand message is most effectively conveyed.

[0657] The device translates product information provided by companies into multiple languages. The translated information is then adjusted to suit the culture and user sentiment of each region. For example, if the sentiment engine detects positive emotions from a user's post, more aggressive promotional language can be used.

[0658] The server uses an emotion engine to analyze product-related posts and comments on social media. The emotion engine analyzes the emotions expressed by users (positive, negative, neutral) in real time and generates automated responses based on that analysis. For example, these responses might say "Thank you for your great feedback" for positive responses, and provide careful follow-up, including suggestions for improvement, for negative responses.

[0659] Furthermore, the server can dynamically adjust marketing activities using the results of sentiment analysis. If a particular campaign or advertisement receives a negative response, the sentiment engine can analyze the feedback and suggest content changes or new approaches.

[0660] Finally, the server measures the overall performance of its activities and periodically generates analytical reports. These reports include analysis results from the sentiment engine and highlight areas for improvement in the marketing strategy. Specifically, they are based on data analysis that includes which products and messages evoked the most positive emotions in users.

[0661] Thus, by analyzing user emotions and conducting marketing activities based on those emotions, the present invention enables companies to build better relationships with target users and implement effective and efficient marketing activities.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The server collects regional internet trend data and automatically detects popular social networking platforms in each country. Through this detection, it identifies the media that target users are most likely to use.

[0665] Step 2:

[0666] The device translates product information received from companies using a multilingual translation service. During this process, the translation is adjusted to take into account the cultural background and user sentiment of each region. For example, an emotion engine is used to customize expressions for different regions, generating content that is easily accepted by local users.

[0667] Step 3:

[0668] The server creates a posting schedule for the social networking platform. This schedule is configured to post at the optimal time based on the user's emotional state as detected by the sentiment engine.

[0669] Step 4:

[0670] The server uses an emotion engine to analyze product-related social media content, such as posts and comments, in real time. This analysis identifies the emotions expressed by users and classifies them as positive, negative, or neutral. Based on this, the server automatically generates an appropriate response and sends it back to the user.

[0671] Step 5:

[0672] The server dynamically adjusts marketing strategies based on user reactions and emotional feedback. It analyzes users' emotional responses to specific content and messages, and accordingly makes changes to content or suggests new campaigns.

[0673] Step 6:

[0674] The server measures the overall performance of marketing activities and generates detailed reports that include sentiment analysis results. These reports show which content had a positive impact on users and are used as a basis for future strategic improvements.

[0675] (Example 2)

[0676] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0677] Marketing activities utilizing information exchange services require flexible and effective marketing methods that adapt to the culture and language of each region while considering user emotions. However, current systems struggle with dynamic adjustments based on sentiment analysis, which prevents them from adequately improving the performance of marketing activities.

[0678] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0679] In this invention, the server includes means for automatically detecting the popularity of information exchange services in each region, means for automatically detecting product-related information exchange service content and generating responses based on sentiment analysis, and means for dynamically adjusting marketing activities based on sentiment analysis. This enables flexible and effective marketing activities that are tailored to the culture and user sentiment of each region.

[0680] An "information exchange service" refers to an online platform that allows users to share and interact with each other via the internet.

[0681] "Popularity" refers to an indicator that shows how widely a particular information exchange service is used and how much attention it receives in a given region or market.

[0682] "Product information" refers to detailed descriptions, features, and specifications related to a specific product or service.

[0683] "Translation" refers to the process of converting information expressed in one language into another language in an appropriate and meaningful way.

[0684] "Automatic" refers to a system or machine performing a task independently, without human intervention, based on pre-set conditions or algorithms.

[0685] "Sentiment analysis" refers to a technology that identifies and classifies the underlying emotions and attitudes from text data such as user posts and comments.

[0686] "Dynamic adjustment" refers to a method of optimizing a system's operation by appropriately modifying it in response to changes in circumstances or new information.

[0687] "Marketing performance" refers to the criteria and results used to evaluate how successful a marketing campaign is in achieving its set goals.

[0688] A "report" refers to a document that compiles information about a specific activity or process, such as survey results or analysis.

[0689] A "generative AI model" refers to a machine learning model that uses artificial intelligence technology to automatically generate answers or suggestions for specific tasks or problems.

[0690] A "prompt sentence" refers to the input sentence that a generative AI model uses to create appropriate responses or content.

[0691] This invention is a system for realizing effective marketing activities using information exchange services. The embodiments thereof are described in detail below.

[0692] The server automatically accesses information exchange service platforms and has the functionality to measure their popularity in each region. This involves using analytical software to collect user numbers and engagement metrics. This makes it possible to support the selection of the optimal platform. For example, in regions where visual content is important, a platform with a high volume of image sharing would be chosen.

[0693] The device has the ability to translate product information provided by companies into multiple languages ​​and automatically post it. Translation software utilizing natural language processing technology is used for the translation, enabling localization that appropriately reflects cultural nuances. For example, in regions where the user's emotions are determined to be positive, the translation will be rendered in a brighter tone.

[0694] Furthermore, the server combines generative AI models and sentiment analysis technology to analyze product-related posts on the information exchange service in real time. Based on this analysis, it generates appropriate responses to promote user engagement. For example, a positive response will receive an automated message such as "Thank you for your great feedback."

[0695] Furthermore, it's possible to measure the effectiveness of marketing activities and dynamically adjust strategies based on sentiment analysis data. This allows for the rapid development of countermeasures even in the event of negative reactions. This functionality contributes to the optimization of marketing campaigns.

[0696] To implement these processes, an example of a prompt might be: "Analyze social media posts about the new product and propose a marketing strategy based on positive or negative sentiment."

[0697] In this way, by making full use of the roles of user, server, and terminal, it becomes possible to provide effective marketing solutions through information exchange services.

[0698] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0699] Step 1:

[0700] The server accesses each platform of the information exchange service and measures its popularity in each region. The input is the number of users and posting data for each platform, and the output is a popularity rating for each region. Here, data analysis software is used to collect and evaluate engagement metrics.

[0701] Step 2:

[0702] The terminal inputs product information provided by companies into multilingual translation software and outputs translations appropriate to the language and cultural background of the target region. This process utilizes natural language processing technology to add nuances appropriate to the local culture to the translated text.

[0703] Step 3:

[0704] The server collects product-related content posted on an information exchange service and performs sentiment analysis using a generative AI model. Using the posted data as input, it obtains sentiment classifications such as positive, negative, and neutral as output. Here, a text analysis algorithm is used to identify the user's emotional state.

[0705] Step 4:

[0706] The server generates an automated response message for the user based on the sentiment analysis results. The input to this process is the sentiment analysis results, and the output is a dynamically generated response message. Specifically, it uses a generation AI model to select appropriate words and create a message that elicits a reaction.

[0707] Step 5:

[0708] The server dynamically adjusts marketing activities based on sentiment analysis data. Input is feedback data from each campaign, and output is the construction of an optimized marketing strategy. Here, higher-level management software is used to perform specific actions that rapidly implement situation-dependent strategic changes.

[0709] Step 6:

[0710] The server measures the overall performance of the activities and generates reports. The input is data on the results of each initiative, and the output is a report including analysis. Specifically, it utilizes statistical analysis software to process data from draft to final report creation.

[0711] (Application Example 2)

[0712] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0713] In modern marketing activities utilizing social networking platforms, it is crucial to respond individually to users' emotions, but this process is complex and time-consuming and costly when done manually. Furthermore, it is difficult to quickly provide appropriate content that reflects regional cultural elements to meet the demands of a global market. Therefore, there is a need for new methods that streamline marketing activities while effectively communicating with users.

[0714] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0715] This invention includes a server that automatically detects the popularity of social networking platforms in each region, translates product information into multiple languages, automatically posts the translated product information to the detected social networking platforms, automatically detects product-related social media content and generates responses, personalizes responses based on user sentiment and dynamically adjusts advertising strategies, and measures the performance of marketing activities and generates reports. This enables efficient personalized marketing activities that respond to user sentiment and allows for appropriate adjustments to advertising strategies.

[0716] A "social networking platform" is an online service that allows individuals and groups to interact on the internet, enabling information sharing and communication.

[0717] "User sentiment" refers to the emotional reactions and feedback that users express on social media, and includes emotional states such as positive, negative, and neutral.

[0718] An "emotion engine" is an algorithm or tool used to analyze emotions from user posts and comments, supporting personalization in marketing activities.

[0719] Personalization refers to providing a more individualized experience by customizing information and services according to the characteristics and preferences of specific users.

[0720] "Advertising strategy" refers to the plans and methods used to effectively communicate products and services to the market, and is an important activity for companies to build relationships with consumers.

[0721] "Translation methods" refer to technologies and processes for converting information between different languages, and they support international communication.

[0722] "Means of detection" refer to the technologies and processes used to collect data and information and identify them based on specific criteria.

[0723] "Means of generation" refer to technologies and processes for creating new data, information, responses, etc., that enable output tailored to a specific purpose.

[0724] The system for implementing this invention mainly consists of a server, a terminal, and emotion recognition technology. The server has the function of collecting and analyzing data from the internet. First, it analyzes the popularity of social networking platforms used in each region and selects the optimal platform based on that data. The server also uses translation software to translate product information into multiple languages. In this translation process, it plays a role in adjusting the content to take into account the cultural background of each region.

[0725] Emotion recognition technology extracts emotions from content and comments posted by users on social media. Based on this information, the server generates responses tailored to the user's emotional state. For example, users who provide positive feedback may receive specific promotional information or thank-you messages, while users who express negative reactions may receive suggestions for improvement or apologies.

[0726] The device receives translated product information and response messages sent from the server and automatically posts them to social networking platforms. This process enables efficient and effective global marketing activities.

[0727] For example, if a user leaves an online review stating that a new product was "great to use," the server can detect this positive sentiment and offer that user a special discount coupon. This not only strengthens the relationship with the user but also increases their purchase intent.

[0728] An example of a prompt would be, "How can I analyze a user's recent product review and generate the most appropriate marketing response based on their sentiment?" Using this prompt helps guide the generative AI model to execute the appropriate response generation process.

[0729] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0730] Step 1:

[0731] The server collects data publicly available on the internet. Specifically, it obtains information on the popularity of social networking platforms in each region. This information is obtained by analyzing user usage frequency and engagement data. The input is publicly available user activity data, and the output is popularity information for each platform.

[0732] Step 2:

[0733] The server translates product information provided by companies into multiple languages. The input is the original product information, and the output is the product information translated into multiple languages. Specifically, this involves using translation software to adjust the data, taking into account the cultural context of each region.

[0734] Step 3:

[0735] The server monitors user content posted on social networks and analyzes its sentiment using sentiment recognition technology. The input is user-submitted data, and the output is the sentiment information contained in the posts. Specifically, natural language processing is used to analyze the text of the posts and assign sentiment tags such as positive, negative, and neutral.

[0736] Step 4:

[0737] The server generates the most appropriate response for each user based on the sentiment analysis results. The input in this process is the sentiment analysis results, and the output is a personalized response message. For example, it might offer a special discount to users who exhibit positive emotions.

[0738] Step 5:

[0739] The terminal automatically posts translated product information and generated response messages received from the server to the social networking platform. The input is data from the server, and the output is the posted content. In this step, the content is accurately posted using each platform's API, completing the information delivery to the user.

[0740] Step 6:

[0741] The server measures the performance of marketing activities and generates periodic reports. The input is data on the activities themselves, and the output is an analytical report. Specific analysis includes user feedback and engagement data, which is used to guide future strategic improvements.

[0742] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0743] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0744] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0745] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0746] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0747] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0748] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0749] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0750] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0751] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0752] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0753] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0754] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0755] 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.

[0756] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0757] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0758] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0759] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0760] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0761] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0762] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0763] The following is further disclosed regarding the embodiments described above.

[0764] (Claim 1)

[0765] A means to automatically detect the popularity of social networking platforms in each region,

[0766] A means of translating product information into multiple languages,

[0767] A means of automatically posting translated product information to detected social networking platforms,

[0768] A means for automatically detecting product-related social media content and generating responses,

[0769] A means of measuring the performance of marketing activities and generating reports,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, further comprising means for generating content corresponding to the culture of each region in the translation of the product information.

[0773] (Claim 3)

[0774] The system according to claim 1, further comprising means for analyzing relevance using natural language processing in detecting social media content related to the aforementioned product.

[0775] "Example 1"

[0776] (Claim 1)

[0777] A means to automatically detect the popularity of information sharing platforms in each region,

[0778] A means of converting information into multiple natural languages,

[0779] A means of automatically providing the converted information to the detected information sharing platform,

[0780] A means for automatically detecting product-related digital content and generating responses,

[0781] A means of evaluating the results of commercial activities and preparing reports,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, further comprising means for generating content corresponding to the culture of each region in the conversion of the aforementioned information.

[0785] (Claim 3)

[0786] The system according to claim 1, further comprising means for analyzing the relevance using language analysis technology in detecting digital content related to the aforementioned product.

[0787] "Application Example 1"

[0788] (Claim 1)

[0789] A means for automatically detecting the penetration rate of information systems on communication infrastructure in each region,

[0790] A means of converting information resources into multiple natural languages,

[0791] A means for automatically transmitting the converted information resources to the detected communication infrastructure,

[0792] A means for automatically detecting information on a communication infrastructure related to information resources and generating a response,

[0793] A means of measuring the evaluation of market research activities and generating reports,

[0794] A means of optimizing the transmission schedule based on the time zone of each region,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, further comprising means for generating information corresponding to the social background of each region in the translation of the aforementioned information resources.

[0798] (Claim 3)

[0799] The system according to claim 1, further comprising means for analyzing relationships using multilingual understanding in the detection of information on a communication infrastructure related to the aforementioned information resources.

[0800] "Example 2 of combining an emotion engine"

[0801] (Claim 1)

[0802] A means to automatically detect the popularity of information exchange services in each region,

[0803] A means of translating product information into multiple languages,

[0804] A means of automatically posting translated product information to detected information exchange services,

[0805] A means for automatically detecting product-related information exchange service content and generating responses based on sentiment analysis,

[0806] A means of dynamically adjusting marketing activities based on sentiment analysis,

[0807] A means for measuring the performance of marketing activities and generating reports that include analysis results,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, further comprising means for translating the product information to generate content corresponding to the culture of each region and adjusting the tone based on the user's emotions.

[0811] (Claim 3)

[0812] The system according to claim 1, further comprising means for detecting product-related information exchange service content, analyzing the relationships using natural language processing, and generating prompt sentences using a generative AI model.

[0813] "Application example 2 when combining with an emotional engine"

[0814] (Claim 1)

[0815] A means to automatically detect the popularity of social networking platforms in each region,

[0816] A means of translating product information into multiple languages,

[0817] A means of automatically posting translated product information to detected social networking platforms,

[0818] A means for automatically detecting product-related social media content and generating responses,

[0819] A means of personalizing responses and dynamically adjusting advertising strategies based on user emotions,

[0820] A means of measuring the performance of marketing activities and generating reports,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, further comprising means for generating content corresponding to the culture of each region in the translation of the product information.

[0824] (Claim 3)

[0825] The system according to claim 1, further comprising means for analyzing relevance using natural language processing in detecting social media content related to the aforementioned product. [Explanation of symbols]

[0826] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means to automatically detect the popularity of social networking platforms in each region, A means of translating product information into multiple languages, A means of automatically posting translated product information to detected social networking platforms, A means for automatically detecting product-related social media content and generating responses, A means of measuring the performance of marketing activities and generating reports, A system that includes this.

2. The system according to claim 1, further comprising means for generating content corresponding to the culture of each region in the translation of the product information.

3. The system according to claim 1, further comprising means for analyzing relevance using natural language processing in detecting social media content related to the aforementioned product.

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