system

Abstract

We provide the system. [Solution] A means of identifying information sharing infrastructure in a specific region by analyzing data collected from information and communication networks in various countries, A means of translating product information into multiple languages ​​using translation tools and disseminating it on an information sharing platform in a specific region, A means of monitoring responses to transmitted information, acquiring relevant response information, and responding accordingly. An analytical means for analyzing acquired reaction information and measuring the effectiveness of information dissemination, A means of presenting multilingual promotional information based on purchase history and interests, A means of visualizing the presented information in the user's preferred language and making it widely shareable within a specific region, 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When conducting social media marketing as an effective information sharing means in the global market, differences in languages and the diversity of platforms in each region are barriers. Furthermore, it is difficult to address these issues with limited human resources, and it is also difficult to respond in real time. Therefore, an efficient and automated method is required.

Means for Solving the Problems

[0005] This invention provides a means for analyzing data collected from an information and communication network to identify the most suitable information sharing platform for a specific region. It also includes means for translating product information into multiple languages ​​and appropriately disseminating it on the platform in the specific region. Furthermore, it includes means for automatically monitoring responses to the disseminated information, acquiring relevant response information, and responding accordingly. Finally, it automates comprehensive social media marketing through analytical means that analyze the acquired response information and measure the effectiveness of the information dissemination.

[0006] An "information and communication network" is a digital infrastructure for collecting, transmitting, and receiving data, and includes the internet and other means of communication.

[0007] An "information sharing platform" refers to an online server or web service that enables users to create, transmit, and share information.

[0008] "Translation methods" refer to technologies and software used to convert text and information between different languages, particularly those that utilize natural language processing technology.

[0009] "Reaction information" refers to user feedback such as impressions, opinions, and comments on content posted on an information sharing platform.

[0010] "Analysis tools" refer to algorithms and software tools used to analyze collected data and detect specific indicators or results. [Brief explanation of the drawing]

[0011] [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, which incorporates an emotion engine. [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]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled 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.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled 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.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] To implement this invention, it is necessary to build a system including a server, terminals, and users. The server is connected to a global information and communication network and configured to access information sharing platforms in each region. The server collects data from each country and uses analytical algorithms to identify the most widely used platform in that region. This makes it possible to provide the optimal digital strategy for the target market.

[0033] The terminal provides an interface for users to input product information. Users can input messages and information they wish to send through the terminal. The terminal sends the input information to a server, where it is translated into multiple languages ​​using translation tools. The server uses natural language processing technology to convert the text and adapt it to the language and culture of the specific market.

[0034] The multilingual information is automatically posted by the server to the appropriate information sharing platform. This posting process is completed without any user intervention, enabling efficient international communication. The server also monitors reactions to the posted content in real time and collects relevant feedback.

[0035] As a concrete example, when a new version of a product is released, users input product information using their devices. This information is translated into the target market's language, such as Japanese, Chinese, or French, and posted in a timely manner to popular platforms in each country. The server then collects user responses, analyzes how the information was consumed, and uses this information to improve the marketing strategy. This enables efficient use of human resources while increasing region-specific engagement.

[0036] The following describes the processing flow.

[0037] Step 1:

[0038] The server connects to a global information and communication network and collects data from various regions. This includes trend data and statistical information such as the number of users.

[0039] Step 2:

[0040] The server analyzes the collected data, using algorithms to identify the most widely used information-sharing platforms in each region.

[0041] Step 3:

[0042] Users input product information and marketing messages via their devices. This includes text, images, and other media content.

[0043] Step 4:

[0044] The terminal sends the entered information to the server. It ensures that the user's input data arrives at the server in the specified format.

[0045] Step 5:

[0046] The server uses translation tools to convert the received information into multiple languages. Natural language processing techniques are used to efficiently translate it into the target language.

[0047] Step 6:

[0048] The server automatically posts translated content to selected information sharing platforms according to a schedule. This ensures that marketing messages are published at the appropriate time.

[0049] Step 7:

[0050] The server monitors reactions to posts in real time. It automatically collects comments and engagement from users.

[0051] Step 8:

[0052] The server analyzes the collected response information. This allows it to measure the effectiveness of the information dissemination and determine what actions are necessary.

[0053] Step 9:

[0054] The server visually displays the analysis results on a dashboard that users can view, helping them plan their next marketing strategy.

[0055] (Example 1)

[0056] 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."

[0057] In international information dissemination, there are challenges in multilingualization and selecting information platforms appropriate for each region. Furthermore, monitoring responses after information is released in real time and developing effective marketing strategies requires significant resources.

[0058] 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.

[0059] In this invention, the server includes means for analyzing information collected from communication infrastructures in various countries to identify regional information exchange platforms, means for translating the information into multiple languages ​​and transmitting it to the regional information exchange platforms, and means for monitoring responses to the transmitted information, obtaining relevant response information, and responding. This enables efficient multilingual information dissemination and measurement of its effectiveness.

[0060] "Communication infrastructure" refers to the structure and equipment of a network intended for information transmission, and serves as the framework for sending and receiving data.

[0061] An "information exchange platform" refers to an application or website that serves as a foundation for users to share and disseminate information.

[0062] "Translation processing" refers to the procedures and techniques for appropriately converting given text or information into a different language.

[0063] A "generative AI model" refers to a model that uses artificial intelligence technology to generate text and adapt it to its context.

[0064] "Evaluation methods" refer to functions and processes for measuring the effectiveness of information dissemination and analyzing related data.

[0065] To implement this invention, it is necessary to construct a system including a server, terminals, and users. This system aims to improve the efficiency of international information dissemination and automates multilingual information dissemination and its effectiveness measurement. The embodiments of the invention are described below.

[0066] The server first collects information from the communication infrastructure of each country and identifies information exchange platforms in the region. In this process, it analyzes platform usage data in each region to reveal the platform that is most frequently used via a specific protocol.

[0067] The user uses a device to input the information they want to share as text. The device then transmits the user input to the server using a secure communication method. This information often includes descriptions of new products and promotional information.

[0068] The server translates the received information into multiple languages ​​using generative AI models and natural language processing technology. This ensures the information is translated to align with the language and culture of the target market. Translation APIs and AI models are used in this translation process.

[0069] Subsequently, the server posts the translated information to a selected information exchange platform using automated means. Specifically, it uploads the information in the appropriate format using the platform's API.

[0070] Furthermore, the server monitors responses to the transmitted information in real time and acquires relevant data. This data includes user reactions from the platform (e.g., comments, likes), and the effectiveness of the information dissemination is measured using evaluation methods.

[0071] As a concrete example, it is possible to translate new product release information for each country's market and post it on the appropriate platform. An example of a prompt would be, "Please translate the new product release information for each country's market and post it on the appropriate platform."

[0072] In this way, the embodiment of the invention makes it possible to efficiently disseminate information tailored to the target market and measure its effectiveness.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user inputs information on the device. This input consists of product descriptions and messages that the user wants to share. Specifically, the user writes information into text boxes through the device's interface. This information becomes the basis for subsequent processing.

[0076] Step 2:

[0077] The terminal sends information to the server. The entered information is sent to the server using a secure protocol such as HTTPS. The terminal converts the information into data packets and sends them to the specified endpoint. This allows the server to receive data from the user.

[0078] Step 3:

[0079] The server handles data translation and processing. Input data is translated into multiple languages ​​using generative AI models and natural language processing technologies. The server calls a translation API to convert input text into the target market language and generate output adapted to the culture and context.

[0080] Step 4:

[0081] The server posts the translated information to a selected information exchange platform using automated means. The server uses the platform's API to send the information in the specified format and publish it on the platform. This ensures that the information reaches the target market.

[0082] Step 5:

[0083] The system monitors reactions after posting and collects data. The server tracks reactions on the platform where the information was posted in real time and stores the collected data. The server uses APIs and scraping techniques to collect user reactions (comments, likes, shares, etc.) and uses evaluation tools to analyze the effectiveness of sending the information.

[0084] (Application Example 1)

[0085] 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."

[0086] In optimizing digital communication in international markets, multilingual translation and platform selection are crucial elements. However, traditional systems have not been able to adequately share information or efficiently provide promotional information tailored to the characteristics of each country. Therefore, in market expansion in various regions, there is a need to present promotions in the most appropriate language based on users' purchase history and interests, and to incorporate user responses in real time.

[0087] 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.

[0088] In this invention, the server includes means for analyzing data collected from information and communication networks in various countries to identify an information sharing platform in a specific region, means for translating product information into multiple languages ​​using translation means and transmitting it to the information sharing platform in the specific region, and means for presenting multilingual promotional information based on purchase history and interests. This makes it possible to provide information tailored to user preferences in each market and streamline international marketing strategies.

[0089] An "information and communication network" is a digital communication infrastructure for sending and receiving data, and a structure that enables information exchange between countries.

[0090] An "information sharing platform" is a collection of platforms used in a specific region, providing a space for users to exchange information.

[0091] A "translation tool" is a mechanism that uses natural language processing technology to convert text into multiple languages ​​and provide information in a format understandable to users of different languages.

[0092] "Promotional information" refers to information about product or service benefits and campaigns, provided to encourage users to make purchases.

[0093] "Purchase history" refers to records of purchases a user has made in the past, and is data used to provide personalized services.

[0094] "Preferences" refer to information that indicates a user's interests and concerns, and serve as a basis for providing content and services tailored to individual users.

[0095] To implement this invention, a system including a server, terminals, and users is utilized. The server is connected to a global information and communication network and can collect and analyze data from information sharing infrastructures in various countries, enabling the dissemination of information tailored to specific regions.

[0096] The server utilizes cloud platforms such as Google Cloud and AWS to perform data analysis and multilingual translation. For translation, it uses the Google Translate API and Azure's natural language processing services. Based on user purchase history and preferences, the server translates specific promotional information into multiple languages ​​and distributes it to a regional information sharing platform.

[0097] The device provides a user interface on smartphones and tablets for inputting information. Users can receive promotional information in real time and view visualized information in their specified language. This process enhances the user experience and contributes to the efficient execution of international campaigns.

[0098] For example, if a user wants to receive the latest promotional information about a specific product, that information will be customized based on the user's past purchase history and presented in languages ​​such as Japanese, English, and Chinese.

[0099] Examples of prompts to input into a generative AI model:

[0100] "Design a system to translate promotional notifications into multiple languages ​​and post them to an information sharing platform for the target market."

[0101] This approach enables efficient international marketing strategies and increases user engagement in each market.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] Users configure promotional notification settings through their devices. The devices then send data about the user's purchase history and preferences to the server. This data is used to analyze purchasing trends and identify areas of interest.

[0105] Step 2:

[0106] The server analyzes the received user data and identifies promotional information that requires multilingual translation. The server then uses the Google Translate API as input to translate this information into the appropriate language. This process generates text data for each market.

[0107] Step 3:

[0108] The server distributes translated promotional information to information sharing platforms in each country. The server then uses this translated data as input to send to specific communities and platforms, enabling information to be displayed in each region.

[0109] Step 4:

[0110] The user's device receives the distributed information and displays it visually in the language specified by the user. The device uses translated promotional data as input and outputs it through the user interface. This allows the user to intuitively understand the promotional content.

[0111] Step 5:

[0112] The server monitors the effectiveness of promotions in real time and collects user responses. The server takes user clicks and feedback as input data and analyzes marketing effectiveness information as output. This process contributes to future strategic improvements.

[0113] 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.

[0114] To implement this invention, it is necessary to incorporate an emotion engine into a system consisting of a server, terminals, and users. This system collects and analyzes data from information and communication networks in various countries to identify the optimal information sharing platform in that region.

[0115] Initially, users can input product information and messages via their device. This information is sent to a server and translated using natural language processing technology. The translated information is then automatically posted to an information sharing platform identified in a specific region.

[0116] The server monitors user reactions to posted information in real time. A sentiment engine is used to analyze this reaction information. The sentiment engine recognizes user emotions from the reaction information and understands their tendencies. For example, it can identify positive, negative, and neutral emotions.

[0117] As a concrete example, consider a marketing campaign launched for a new product announcement. Users input messages using their devices, and the server translates them into multiple languages ​​and posts them to local information sharing platforms. The server then collects response information from the platforms, and an emotion engine analyzes user responses to visualize overall sentiment trends. Based on this information, the server can automatically generate and provide suggestions for improving the marketing strategy to users.

[0118] Thus, by combining an emotion engine, the present invention enhances the effectiveness of information dissemination and realizes more effective global social media marketing.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] Users input information and marketing messages about new products through their devices. This includes multimedia content such as text and images.

[0122] Step 2:

[0123] The terminal sends the entered information to the server. The transmitted data is stored on the server as backup data for information transmission.

[0124] Step 3:

[0125] The server translates received information into multiple languages ​​using translation tools. Natural language processing technology is used to translate it in a format best suited to the target market's language.

[0126] Step 4:

[0127] The server posts the translated information to the local information sharing platform identified by the analysis algorithm, according to a schedule.

[0128] Step 5:

[0129] The server monitors user reactions in real time after a post is made and collects reaction information from the information sharing platform.

[0130] Step 6:

[0131] The server uses an emotion engine to analyze the collected response information. Here, it identifies emotional tendencies in the responses.

[0132] Step 7:

[0133] Based on the analysis results from the emotion engine, the server generates visualized emotional trends and suggestions for improving marketing strategies.

[0134] Step 8:

[0135] The server displays the generated analysis results and suggestions on a dashboard that users can view. This provides users with reference material when formulating their next strategies.

[0136] (Example 2)

[0137] 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 as the "terminal".

[0138] In today's global information dissemination environment, identifying the optimal information sharing platform that accommodates different regions, cultures, and languages, and effectively disseminating information, is challenging. Furthermore, understanding user reactions to disseminated information and formulating appropriate marketing strategies based on that information is also complex. A means to overcome these challenges and achieve more effective information sharing and marketing is needed.

[0139] 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.

[0140] In this invention, the server includes means for analyzing data collected from information and communication networks in various countries to identify an information sharing infrastructure in a specific region; means for translating product information into multiple languages ​​using translation means and transmitting it to the information sharing infrastructure in the specific region; means for monitoring reactions to the transmitted information, acquiring relevant reaction information, and responding; means for analyzing the acquired reaction information using sentiment analysis technology to classify the user's emotions; and means for measuring the effectiveness of information transmission using the sentiment analysis results and proposing strategies using a generative AI model. This enables strategic information transmission that reflects the emotional reactions of users while adapting to different regions and languages.

[0141] An "information and communication network" refers to all the technological infrastructure for sending, receiving, and sharing data, and includes means of communication that connect multiple geographically separated points.

[0142] An "information sharing platform" refers to a platform or system for managing and distributing information within a specific region, and serves as a place for users to exchange data with each other.

[0143] "Translation methods" refer to software or systems used to convert input text data from one language to another, often employing natural language processing.

[0144] "Sentiment analysis technology" refers to techniques for identifying and classifying user emotions from expressions within text data, and includes analysis based on natural language processing.

[0145] A "generative AI model" is a model that processes data based on artificial intelligence technology to automatically generate new information and suggestions, and it utilizes machine learning algorithms.

[0146] To implement this invention, a system consisting of a server, a terminal, and a user is required. The terminal provides an interface for the user to input product information and messages. A common information processing device such as a smartphone or a personal computer can be used as the terminal.

[0147] The server is the core of this system and performs multiple functions. First, the server collects data from information and communication networks in various countries. The collected data is stored in a database, which serves as the basis for later analysis. In data analysis, the server uses machine learning algorithms to identify information sharing infrastructure optimized for specific regions.

[0148] As a translation method, the server utilizes natural language processing technology, such as a language processing service known as a machine translation API. This allows the collected text data to be translated into multiple languages. For example, an English message entered by a user might be translated into French or Spanish and delivered to users who speak those respective languages.

[0149] Next, the server monitors user reactions to the transmitted information. This monitoring utilizes streaming tools that process data in real time to collect user comments and feedback. Through sentiment analysis technology, the server analyzes these reactions and identifies the user's emotions. In this process, emotions are classified as positive, negative, or neutral, as described in the claims.

[0150] Finally, the server uses a generative AI model to create strategic suggestions based on the sentiment analysis results. The generative AI model is then input with prompts such as the following:

[0151] "Based on the results of the emotion engine, please propose specific strategies to enhance the effectiveness of our new product campaign."

[0152] This allows users to access information based on sentiment data, enabling them to strengthen strategies in areas such as marketing. The server then feeds the suggested results back to the user, and the entire system functions as a cycle.

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] Users use a terminal to input product information and messages. The input is primarily in text data format. For example, they might enter a review including features and pricing information for a new product. This input data is immediately sent to the server. At this stage, the output is the text data that has reached the server.

[0156] Step 2:

[0157] The server processes the received text data through a translation tool. In this process, natural language processing techniques are used to translate the input data into the target language. For example, data entered in English will be translated into French. Specifically, the server calls a machine translation API and retrieves the translated data. The output is multilingual text data.

[0158] Step 3:

[0159] The server automatically posts the translated information to a localized information sharing platform. Here, the server uses an API to send data to, for example, the posting interface of a social media platform. This posting shares the information with users in a specific region. The output is a notification indicating that the posting is complete.

[0160] Step 4:

[0161] The server collects and monitors user reactions to posts. For this purpose, it utilizes a real-time data streaming tool. It retrieves comments and rating data from the platform and records them in a database. At this stage, reaction data is output.

[0162] Step 5:

[0163] The server processes the collected response data using emotion analysis technology. Specifically, it uses an emotion engine to classify emotions into positive, negative, and neutral. This analysis process generates emotion indicators using text analysis techniques. The final output is data representing the emotion classification results.

[0164] Step 6:

[0165] The server utilizes a generative AI model to generate strategic suggestions based on sentiment classification results. During this process, prompts are input to the generative AI model to create marketing strategy proposals based on sentiment data. For example, a prompt might read, "Based on the sentiment engine results, please propose specific strategies to enhance the effectiveness of the new product campaign." Finally, the generated strategic suggestions are output and provided to the user.

[0166] (Application Example 2)

[0167] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0168] In global marketing, there is a challenge in disseminating information optimally based on the cultural and linguistic differences of each country. Furthermore, there is a need to evaluate the effectiveness of advertising in real time and quickly implement strategic improvements based on those results.

[0169] 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.

[0170] In this invention, the server includes means for analyzing data collected from information and communication networks in various countries to identify information sharing platforms in specific regions, means for translating product information into multiple languages ​​using translation means and transmitting it to the information sharing platforms in specific regions, means for analyzing acquired response information, measuring the effectiveness of information dissemination, and generating improvement suggestions based on response trends in each region, and means for evaluating the effectiveness of advertising campaigns in real time and optimizing promotional activities in designated regions. This enables effective information dissemination to users in each country and rapid and appropriate marketing improvements.

[0171] "Information and communication networks" refer to data communication infrastructure in each country, including the internet and mobile communications.

[0172] An "information sharing platform" refers to a digital space where users can exchange and share information with each other, and mainly includes social media and online forums.

[0173] "Translation methods" refer to tools and software used to translate product information into multiple languages, and which utilize natural language processing technology.

[0174] "Reaction information" refers to data that includes emotions and opinions, such as user feedback and comments on information disseminated.

[0175] "Information dissemination effectiveness" is an indicator that measures the degree to which disseminated information is perceived by consumers as intended and influences their behavior, as well as the success rate of such information dissemination.

[0176] "Methods for generating improvement suggestions" refers to methods that revise marketing strategies based on analyzed response information and propose specific action plans to enhance their effectiveness.

[0177] "Means of visualization and display" refers to methods of converting data into graphs, charts, and other forms of visualization so that people can intuitively understand the information.

[0178] The system implementing this invention consists of a server, a terminal, and a user. The server collects data from information and communication networks in various countries, analyzes this data, and identifies the most suitable information sharing platform for a specific region. The user inputs product information using the terminal, and the server translates the product information into multiple languages ​​using a translation means and transmits it to the information sharing platform.

[0179] The server monitors user reactions to the information disseminated and acquires this reaction information. It then analyzes this reaction information using an emotion engine, measures the effectiveness of the information dissemination, and generates improvement suggestions based on regional reaction trends. In this process, the server uses Google Translate API and Azure Translation Service as natural language processing technologies, and IBM Watson®'s Natural Language Understanding API for sentiment analysis. PostgreSQL is used as the database.

[0180] As a concrete example of the system, promotional information for a music festival event entered on a smartphone is posted to social media in various countries. The server analyzes the reactions to these posts in real time and provides additional event information and campaigns to regions that receive many positive responses.

[0181] Examples of prompts for a generative AI model are as follows:

[0182] Post promotional material for the new music festival event on social media platforms in various countries and analyze fan reactions in real time. Provide further event information to regions that receive a high level of positive response.

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The server collects data from information and communication networks in various countries. This data includes region-specific user trends and cultural characteristics, and this information is used to analyze which information-sharing platform is most appropriate. The input is country-specific internet data, and the output is a list of the most suitable information-sharing platforms for a given region.

[0186] Step 2:

[0187] Users input product information and promotional content via their terminals. Product information can include text, images, and videos, and this information is sent to the server. The input is the product information provided by the user, while the output is the unprocessed digital content stored on the server.

[0188] Step 3:

[0189] The server uses natural language processing technology to translate product information into multiple languages. Specifically, it utilizes the Google Translate API to convert the input information into an internationally compatible format. The input is the original text received from the user, and the output is the translated multilingual information.

[0190] Step 4:

[0191] The server transmits the translated information to a designated information-sharing platform, which is the one deemed most effective in each region. The input is the translated advertising content, and the output is the advertisement published on the platform.

[0192] Step 5:

[0193] The server monitors responses to transmitted information in real time. It collects user comments and reactions and stores relevant response data. The input is user responses from the platform, and the output is the response information collected for analysis.

[0194] Step 6:

[0195] The server analyzes response information using an emotion engine and measures the effectiveness of the information dissemination. This process uses IBM Watson's Natural Language Understanding API to perform sentiment analysis and distinguish between positive, negative, and neutral emotions. The input is the acquired response information, and the output is an index of the emotion distribution and the results of the effectiveness measurement.

[0196] Step 7:

[0197] The server generates suggestions for improving advertising campaigns based on regional response trends. Specifically, it sends prompts to a generative AI model to generate suggestions. The input is the sentiment analysis results, and the output is a strategic report including improvement suggestions.

[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 type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), 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] To implement this invention, it is necessary to build a system including a server, terminals, and users. The server is connected to a global information and communication network and configured to access information sharing platforms in each region. The server collects data from each country and uses analytical algorithms to identify the most widely used platform in that region. This makes it possible to provide the optimal digital strategy for the target market.

[0215] The terminal provides an interface for users to input product information. Users can input messages and information they wish to send through the terminal. The terminal sends the input information to a server, where it is translated into multiple languages ​​using translation tools. The server uses natural language processing technology to convert the text and adapt it to the language and culture of the specific market.

[0216] The multilingual information is automatically posted by the server to the appropriate information sharing platform. This posting process is completed without any user intervention, enabling efficient international communication. The server also monitors reactions to the posted content in real time and collects relevant feedback.

[0217] As a concrete example, when a new version of a product is released, users input product information using their devices. This information is translated into the target market's language, such as Japanese, Chinese, or French, and posted in a timely manner to popular platforms in each country. The server then collects user responses, analyzes how the information was consumed, and uses this information to improve the marketing strategy. This enables efficient use of human resources while increasing region-specific engagement.

[0218] The following describes the processing flow.

[0219] Step 1:

[0220] The server connects to a global information and communication network and collects data from various regions. This includes trend data and statistical information such as the number of users.

[0221] Step 2:

[0222] The server analyzes the collected data, using algorithms to identify the most widely used information-sharing platforms in each region.

[0223] Step 3:

[0224] Users input product information and marketing messages via their devices. This includes text, images, and other media content.

[0225] Step 4:

[0226] The terminal sends the entered information to the server. It ensures that the user's input data arrives at the server in the specified format.

[0227] Step 5:

[0228] The server uses translation tools to convert the received information into multiple languages. Natural language processing techniques are used to efficiently translate it into the target language.

[0229] Step 6:

[0230] The server automatically posts translated content to selected information sharing platforms according to a schedule. This ensures that marketing messages are published at the appropriate time.

[0231] Step 7:

[0232] The server monitors reactions to posts in real time. It automatically collects comments and engagement from users.

[0233] Step 8:

[0234] The server analyzes the collected response information. This allows it to measure the effectiveness of the information dissemination and determine what actions are necessary.

[0235] Step 9:

[0236] The server visually displays the analysis results on a dashboard that users can view, helping them plan their next marketing strategy.

[0237] (Example 1)

[0238] 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."

[0239] In international information dissemination, there are challenges in multilingualization and selecting information platforms appropriate for each region. Furthermore, monitoring responses after information is released in real time and developing effective marketing strategies requires significant resources.

[0240] 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.

[0241] In this invention, the server includes means for analyzing information collected from communication infrastructures in various countries to identify regional information exchange platforms, means for translating the information into multiple languages ​​and transmitting it to the regional information exchange platforms, and means for monitoring responses to the transmitted information, obtaining relevant response information, and responding. This enables efficient multilingual information dissemination and measurement of its effectiveness.

[0242] "Communication infrastructure" refers to the structure and equipment of a network intended for information transmission, and serves as the framework for sending and receiving data.

[0243] An "information exchange platform" refers to an application or website that serves as a foundation for users to share and disseminate information.

[0244] "Translation processing" refers to the procedures and techniques for appropriately converting given text or information into a different language.

[0245] A "generative AI model" refers to a model that uses artificial intelligence technology to generate text and adapt it to its context.

[0246] "Evaluation methods" refer to functions and processes for measuring the effectiveness of information dissemination and analyzing related data.

[0247] To implement this invention, it is necessary to construct a system including a server, terminals, and users. This system aims to improve the efficiency of international information dissemination and automates multilingual information dissemination and its effectiveness measurement. The embodiments of the invention are described below.

[0248] The server first collects information from the communication infrastructure of each country and identifies information exchange platforms in the region. In this process, it analyzes platform usage data in each region to reveal the platform that is most frequently used via a specific protocol.

[0249] The user uses a device to input the information they want to share as text. The device then transmits the user input to the server using a secure communication method. This information often includes descriptions of new products and promotional information.

[0250] The server translates the received information into multiple languages ​​using generative AI models and natural language processing technology. This ensures the information is translated to align with the language and culture of the target market. Translation APIs and AI models are used in this translation process.

[0251] Subsequently, the server posts the translated information to a selected information exchange platform using automated means. Specifically, it uploads the information in the appropriate format using the platform's API.

[0252] Furthermore, the server monitors responses to the transmitted information in real time and acquires relevant data. This data includes user reactions from the platform (e.g., comments, likes), and the effectiveness of the information dissemination is measured using evaluation methods.

[0253] As a concrete example, it is possible to translate new product release information for each country's market and post it on the appropriate platform. An example of a prompt would be, "Please translate the new product release information for each country's market and post it on the appropriate platform."

[0254] In this way, the embodiment of the invention makes it possible to efficiently disseminate information tailored to the target market and measure its effectiveness.

[0255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0256] Step 1:

[0257] The user inputs information on the device. This input consists of product descriptions and messages that the user wants to share. Specifically, the user writes information into text boxes through the device's interface. This information becomes the basis for subsequent processing.

[0258] Step 2:

[0259] The terminal sends information to the server. The entered information is sent to the server using a secure protocol such as HTTPS. The terminal converts the information into data packets and sends them to the specified endpoint. This allows the server to receive data from the user.

[0260] Step 3:

[0261] The server handles data translation and processing. Input data is translated into multiple languages ​​using generative AI models and natural language processing technologies. The server calls a translation API to convert input text into the target market language and generate output adapted to the culture and context.

[0262] Step 4:

[0263] The server posts the translated information to a selected information exchange platform using automated means. The server uses the platform's API to send the information in the specified format and publish it on the platform. This ensures that the information reaches the target market.

[0264] Step 5:

[0265] The system monitors reactions after posting and collects data. The server tracks reactions on the platform where the information was posted in real time and stores the collected data. The server uses APIs and scraping techniques to collect user reactions (comments, likes, shares, etc.) and uses evaluation tools to analyze the effectiveness of sending the information.

[0266] (Application Example 1)

[0267] 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."

[0268] In optimizing digital communication in international markets, multilingual translation and platform selection are crucial elements. However, traditional systems have not been able to adequately share information or efficiently provide promotional information tailored to the characteristics of each country. Therefore, in market expansion in various regions, there is a need to present promotions in the most appropriate language based on users' purchase history and interests, and to incorporate user responses in real time.

[0269] 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.

[0270] In this invention, the server includes means for analyzing data collected from information and communication networks in various countries to identify an information sharing platform in a specific region, means for translating product information into multiple languages ​​using translation means and transmitting it to the information sharing platform in the specific region, and means for presenting multilingual promotional information based on purchase history and interests. This makes it possible to provide information tailored to user preferences in each market and streamline international marketing strategies.

[0271] An "information and communication network" is a digital communication infrastructure for sending and receiving data, and a structure that enables information exchange between countries.

[0272] An "information sharing platform" is a collection of platforms used in a specific region, providing a space for users to exchange information.

[0273] A "translation tool" is a mechanism that uses natural language processing technology to convert text into multiple languages ​​and provide information in a format understandable to users of different languages.

[0274] "Promotional information" refers to information about product or service benefits and campaigns, provided to encourage users to make purchases.

[0275] "Purchase history" refers to records of purchases a user has made in the past, and is data used to provide personalized services.

[0276] "Preferences" refer to information that indicates a user's interests and concerns, and serve as a basis for providing content and services tailored to individual users.

[0277] To implement this invention, a system including a server, terminals, and users is utilized. The server is connected to a global information and communication network and can collect and analyze data from information sharing infrastructures in various countries, enabling the dissemination of information tailored to specific regions.

[0278] The server utilizes cloud platforms such as Google Cloud and AWS to perform data analysis and multilingual translation. For translation, it uses Google Translate API or Azure's natural language processing services. Based on the user's purchase history and preferences, the server multilingualizes specific promotion information and broadcasts it on the information sharing platform of a specific region.

[0279] The terminal provides an interface for the user to input information on smartphones and tablets. The user can receive promotion information in real-time and view visualization information according to the specified language. This process improves the user experience and contributes to the implementation of efficient international campaigns.

[0280] For example, when a user wants to receive the latest promotion information about a specific product, the information is customized based on the user's past purchase history and presented in languages such as Japanese, English, Chinese, etc.

[0281] Example of a prompt sentence for input into the generative AI model:

[0282] "Design a system that multilingualizes promotion notifications and posts them to the information sharing platform of the target market."

[0283] This approach enables an efficient international marketing strategy and enhances user engagement in each market.

[0284] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The user sets the promotion notification through the terminal. The terminal sends data regarding the user's purchase history and preferences to the server as input. This data is used for analyzing purchase trends and identifying interests.

[0287] Step 2:

[0288] The server analyzes the received user data and identifies promotional information that requires multilingual translation. The server then uses the Google Translate API as input to translate this information into the appropriate language. This process generates text data for each market.

[0289] Step 3:

[0290] The server distributes translated promotional information to information sharing platforms in each country. The server then uses this translated data as input to send to specific communities and platforms, enabling information to be displayed in each region.

[0291] Step 4:

[0292] The user's device receives the distributed information and displays it visually in the language specified by the user. The device uses translated promotional data as input and outputs it through the user interface. This allows the user to intuitively understand the promotional content.

[0293] Step 5:

[0294] The server monitors the effectiveness of promotions in real time and collects user responses. The server takes user clicks and feedback as input data and analyzes marketing effectiveness information as output. This process contributes to future strategic improvements.

[0295] 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.

[0296] To implement this invention, it is necessary to incorporate an emotion engine into a system consisting of a server, terminals, and users. This system collects and analyzes data from information and communication networks in various countries to identify the optimal information sharing platform in that region.

[0297] Initially, users can input product information and messages via their device. This information is sent to a server and translated using natural language processing technology. The translated information is then automatically posted to an information sharing platform identified in a specific region.

[0298] The server monitors user reactions to posted information in real time. A sentiment engine is used to analyze this reaction information. The sentiment engine recognizes user emotions from the reaction information and understands their tendencies. For example, it can identify positive, negative, and neutral emotions.

[0299] As a concrete example, consider a marketing campaign launched for a new product announcement. Users input messages using their devices, and the server translates them into multiple languages ​​and posts them to local information sharing platforms. The server then collects response information from the platforms, and an emotion engine analyzes user responses to visualize overall sentiment trends. Based on this information, the server can automatically generate and provide suggestions for improving the marketing strategy to users.

[0300] Thus, by combining an emotion engine, the present invention enhances the effectiveness of information dissemination and realizes more effective global social media marketing.

[0301] The following describes the processing flow.

[0302] Step 1:

[0303] The user inputs information about new products and marketing messages through the terminal. This includes multimedia content such as text and images.

[0304] Step 2:

[0305] The terminal sends the input information to the server. The transmitted data is stored on the server as preliminary data for information dissemination.

[0306] Step 3:

[0307] The server converts the received information into multiple languages using translation means. It is translated into the optimal form for the language of the target market using natural language processing technology.

[0308] Step 4:

[0309] The server schedules and posts the translated information to the information sharing platform of that region identified by the analysis algorithm.

[0310] Step 5:

[0311] The server monitors the user's reaction in real time after posting and collects reaction information from the information sharing platform.

[0312] Step 6:

[0313] The server analyzes the collected reaction information using an emotion engine. Here, the emotional tendency in the reaction is identified.

[0314] Step 7:

[0315] Based on the analysis results of the emotion engine, the server generates visualized emotional tendencies and proposals for improving marketing strategies.

[0316] Step 8:

[0317] The server displays the generated analysis results and suggestions on a dashboard that users can view. This provides users with reference material when formulating their next strategies.

[0318] (Example 2)

[0319] 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".

[0320] In today's global information dissemination environment, identifying the optimal information sharing platform that accommodates different regions, cultures, and languages, and effectively disseminating information, is challenging. Furthermore, understanding user reactions to disseminated information and formulating appropriate marketing strategies based on that information is also complex. A means to overcome these challenges and achieve more effective information sharing and marketing is needed.

[0321] 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.

[0322] In this invention, the server includes means for analyzing data collected from information and communication networks in various countries to identify an information sharing infrastructure in a specific region; means for translating product information into multiple languages ​​using translation means and transmitting it to the information sharing infrastructure in the specific region; means for monitoring reactions to the transmitted information, acquiring relevant reaction information, and responding; means for analyzing the acquired reaction information using sentiment analysis technology to classify the user's emotions; and means for measuring the effectiveness of information transmission using the sentiment analysis results and proposing strategies using a generative AI model. This enables strategic information transmission that reflects the emotional reactions of users while adapting to different regions and languages.

[0323] An "information and communication network" refers to all the technological infrastructure for sending, receiving, and sharing data, and includes means of communication that connect multiple geographically separated points.

[0324] An "information sharing platform" refers to a platform or system for managing and distributing information within a specific region, and serves as a place for users to exchange data with each other.

[0325] "Translation methods" refer to software or systems used to convert input text data from one language to another, often employing natural language processing.

[0326] "Sentiment analysis technology" refers to techniques for identifying and classifying user emotions from expressions within text data, and includes analysis based on natural language processing.

[0327] A "generative AI model" is a model that processes data based on artificial intelligence technology to automatically generate new information and suggestions, and it utilizes machine learning algorithms.

[0328] To implement this invention, a system consisting of a server, a terminal, and a user is required. The terminal provides an interface for the user to input product information and messages. A common information processing device such as a smartphone or a personal computer can be used as the terminal.

[0329] The server is the core of this system and performs multiple functions. First, the server collects data from information and communication networks in various countries. The collected data is stored in a database, which serves as the basis for later analysis. In data analysis, the server uses machine learning algorithms to identify information sharing infrastructure optimized for specific regions.

[0330] As a translation method, the server utilizes natural language processing technology, such as a language processing service known as a machine translation API. This allows the collected text data to be translated into multiple languages. For example, an English message entered by a user might be translated into French or Spanish and delivered to users who speak those respective languages.

[0331] Next, the server monitors user reactions to the transmitted information. This monitoring utilizes streaming tools that process data in real time to collect user comments and feedback. Through sentiment analysis technology, the server analyzes these reactions and identifies the user's emotions. In this process, emotions are classified as positive, negative, or neutral, as described in the claims.

[0332] Finally, the server uses a generative AI model to create strategic suggestions based on the sentiment analysis results. The generative AI model is then input with prompts such as the following:

[0333] "Based on the results of the emotion engine, please propose specific strategies to enhance the effectiveness of our new product campaign."

[0334] This allows users to access information based on sentiment data, enabling them to strengthen strategies in areas such as marketing. The server then feeds the suggested results back to the user, and the entire system functions as a cycle.

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

[0336] Step 1:

[0337] Users use a terminal to input product information and messages. The input is primarily in text data format. For example, they might enter a review including features and pricing information for a new product. This input data is immediately sent to the server. At this stage, the output is the text data that has reached the server.

[0338] Step 2:

[0339] The server processes the received text data through a translation tool. In this process, natural language processing techniques are used to translate the input data into the target language. For example, data entered in English will be translated into French. Specifically, the server calls a machine translation API and retrieves the translated data. The output is multilingual text data.

[0340] Step 3:

[0341] The server automatically posts the translated information to a localized information sharing platform. Here, the server uses an API to send data to, for example, the posting interface of a social media platform. This posting shares the information with users in a specific region. The output is a notification indicating that the posting is complete.

[0342] Step 4:

[0343] The server collects and monitors user reactions to posts. For this purpose, it utilizes a real-time data streaming tool. It retrieves comments and rating data from the platform and records them in a database. At this stage, reaction data is output.

[0344] Step 5:

[0345] The server processes the collected response data using emotion analysis technology. Specifically, it uses an emotion engine to classify emotions into positive, negative, and neutral. This analysis process generates emotion indicators using text analysis techniques. The final output is data representing the emotion classification results.

[0346] Step 6:

[0347] The server utilizes a generative AI model to generate strategic suggestions based on sentiment classification results. During this process, prompts are input to the generative AI model to create marketing strategy proposals based on sentiment data. For example, a prompt might read, "Based on the sentiment engine results, please propose specific strategies to enhance the effectiveness of the new product campaign." Finally, the generated strategic suggestions are output and provided to the user.

[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 global marketing, there is a challenge in disseminating information optimally based on the cultural and linguistic differences of each country. Furthermore, there is a need to evaluate the effectiveness of advertising in real time and quickly implement strategic improvements based on those results.

[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] In this invention, the server includes means for analyzing data collected from information and communication networks in various countries to identify information sharing platforms in specific regions, means for translating product information into multiple languages ​​using translation means and transmitting it to the information sharing platforms in specific regions, means for analyzing acquired response information, measuring the effectiveness of information dissemination, and generating improvement suggestions based on response trends in each region, and means for evaluating the effectiveness of advertising campaigns in real time and optimizing promotional activities in designated regions. This enables effective information dissemination to users in each country and rapid and appropriate marketing improvements.

[0353] "Information and communication networks" refer to data communication infrastructure in each country, including the internet and mobile communications.

[0354] An "information sharing platform" refers to a digital space where users can exchange and share information with each other, and mainly includes social media and online forums.

[0355] "Translation methods" refer to tools and software used to translate product information into multiple languages, and which utilize natural language processing technology.

[0356] "Reaction information" refers to data that includes emotions and opinions, such as user feedback and comments on information disseminated.

[0357] "Information dissemination effectiveness" is an indicator that measures the degree to which disseminated information is perceived by consumers as intended and influences their behavior, as well as the success rate of such information dissemination.

[0358] "Methods for generating improvement suggestions" refers to methods that revise marketing strategies based on analyzed response information and propose specific action plans to enhance their effectiveness.

[0359] "Means of visualization and display" refers to methods of converting data into graphs, charts, and other forms of visualization so that people can intuitively understand the information.

[0360] The system implementing this invention consists of a server, a terminal, and a user. The server collects data from information and communication networks in various countries, analyzes this data, and identifies the most suitable information sharing platform for a specific region. The user inputs product information using the terminal, and the server translates the product information into multiple languages ​​using a translation means and transmits it to the information sharing platform.

[0361] The server monitors user reactions to the information disseminated and acquires this reaction information. It then analyzes this reaction information using an emotion engine, measures the effectiveness of the information dissemination, and generates improvement suggestions based on regional reaction trends. In this process, the server uses Google Translate API and Azure Translation Service as natural language processing technologies, and IBM Watson's Natural Language Understanding API for sentiment analysis. PostgreSQL is used as the database.

[0362] As a concrete example of the system, promotional information for a music festival event entered on a smartphone is posted to social media in various countries. The server analyzes the reactions to these posts in real time and provides additional event information and campaigns to regions that receive many positive responses.

[0363] Examples of prompts for a generative AI model are as follows:

[0364] Post promotional material for the new music festival event on social media platforms in various countries and analyze fan reactions in real time. Provide further event information to regions that receive a high level of positive response.

[0365] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0366] Step 1:

[0367] The server collects data from information and communication networks in various countries. This data includes region-specific user trends and cultural characteristics, and this information is used to analyze which information-sharing platform is most appropriate. The input is country-specific internet data, and the output is a list of the most suitable information-sharing platforms for a given region.

[0368] Step 2:

[0369] Users input product information and promotional content via their terminals. Product information can include text, images, and videos, and this information is sent to the server. The input is the product information provided by the user, while the output is the unprocessed digital content stored on the server.

[0370] Step 3:

[0371] The server uses natural language processing technology to translate product information into multiple languages. Specifically, it utilizes the Google Translate API to convert the input information into an internationally compatible format. The input is the original text received from the user, and the output is the translated multilingual information.

[0372] Step 4:

[0373] The server transmits the translated information to a designated information-sharing platform, which is the one deemed most effective in each region. The input is the translated advertising content, and the output is the advertisement published on the platform.

[0374] Step 5:

[0375] The server monitors responses to transmitted information in real time. It collects user comments and reactions and stores relevant response data. The input is user responses from the platform, and the output is the response information collected for analysis.

[0376] Step 6:

[0377] The server analyzes response information using an emotion engine and measures the effectiveness of the information dissemination. This process uses IBM Watson's Natural Language Understanding API to perform sentiment analysis and distinguish between positive, negative, and neutral emotions. The input is the acquired response information, and the output is an index of the emotion distribution and the results of the effectiveness measurement.

[0378] Step 7:

[0379] The server generates suggestions for improving advertising campaigns based on regional response trends. Specifically, it sends prompts to a generative AI model to generate suggestions. The input is the sentiment analysis results, and the output is a strategic report including improvement suggestions.

[0380] 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.

[0381] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0382] 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.

[0383] [Third Embodiment]

[0384] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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".

[0396] To implement this invention, it is necessary to build a system including a server, terminals, and users. The server is connected to a global information and communication network and configured to access information sharing platforms in each region. The server collects data from each country and uses analytical algorithms to identify the most widely used platform in that region. This makes it possible to provide the optimal digital strategy for the target market.

[0397] The terminal provides an interface for users to input product information. Users can input messages and information they wish to send through the terminal. The terminal sends the input information to a server, where it is translated into multiple languages ​​using translation tools. The server uses natural language processing technology to convert the text and adapt it to the language and culture of the specific market.

[0398] The multilingual information is automatically posted by the server to the appropriate information sharing platform. This posting process is completed without any user intervention, enabling efficient international communication. The server also monitors reactions to the posted content in real time and collects relevant feedback.

[0399] As a concrete example, when a new version of a product is released, users input product information using their devices. This information is translated into the target market's language, such as Japanese, Chinese, or French, and posted in a timely manner to popular platforms in each country. The server then collects user responses, analyzes how the information was consumed, and uses this information to improve the marketing strategy. This enables efficient use of human resources while increasing region-specific engagement.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] The server connects to a global information and communication network and collects data from various regions. This includes trend data and statistical information such as the number of users.

[0403] Step 2:

[0404] The server analyzes the collected data, using algorithms to identify the most widely used information-sharing platforms in each region.

[0405] Step 3:

[0406] Users input product information and marketing messages via their devices. This includes text, images, and other media content.

[0407] Step 4:

[0408] The terminal sends the entered information to the server. It ensures that the user's input data arrives at the server in the specified format.

[0409] Step 5:

[0410] The server uses translation tools to convert the received information into multiple languages. Natural language processing techniques are used to efficiently translate it into the target language.

[0411] Step 6:

[0412] The server automatically posts translated content to selected information sharing platforms according to a schedule. This ensures that marketing messages are published at the appropriate time.

[0413] Step 7:

[0414] The server monitors reactions to posts in real time. It automatically collects comments and engagement from users.

[0415] Step 8:

[0416] The server analyzes the collected response information. This allows it to measure the effectiveness of the information dissemination and determine what actions are necessary.

[0417] Step 9:

[0418] The server visually displays the analysis results on a dashboard that users can view, helping them plan their next marketing strategy.

[0419] (Example 1)

[0420] 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."

[0421] In international information dissemination, there are challenges in multilingualization and selecting information platforms appropriate for each region. Furthermore, monitoring responses after information is released in real time and developing effective marketing strategies requires significant resources.

[0422] 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.

[0423] In this invention, the server includes means for analyzing information collected from communication infrastructures in various countries to identify regional information exchange platforms, means for translating the information into multiple languages ​​and transmitting it to the regional information exchange platforms, and means for monitoring responses to the transmitted information, obtaining relevant response information, and responding. This enables efficient multilingual information dissemination and measurement of its effectiveness.

[0424] "Communication infrastructure" refers to the structure and equipment of a network intended for information transmission, and serves as the framework for sending and receiving data.

[0425] An "information exchange platform" refers to an application or website that serves as a foundation for users to share and disseminate information.

[0426] "Translation processing" refers to the procedures and techniques for appropriately converting given text or information into a different language.

[0427] A "generative AI model" refers to a model that uses artificial intelligence technology to generate text and adapt it to its context.

[0428] "Evaluation methods" refer to functions and processes for measuring the effectiveness of information dissemination and analyzing related data.

[0429] To implement this invention, it is necessary to construct a system including a server, terminals, and users. This system aims to improve the efficiency of international information dissemination and automates multilingual information dissemination and its effectiveness measurement. The embodiments of the invention are described below.

[0430] The server first collects information from the communication infrastructure of each country and identifies information exchange platforms in the region. In this process, it analyzes platform usage data in each region to reveal the platform that is most frequently used via a specific protocol.

[0431] The user uses a device to input the information they want to share as text. The device then transmits the user input to the server using a secure communication method. This information often includes descriptions of new products and promotional information.

[0432] The server translates the received information into multiple languages ​​using generative AI models and natural language processing technology. This ensures the information is translated to align with the language and culture of the target market. Translation APIs and AI models are used in this translation process.

[0433] Subsequently, the server posts the translated information to a selected information exchange platform using automated means. Specifically, it uploads the information in the appropriate format using the platform's API.

[0434] Furthermore, the server monitors responses to the transmitted information in real time and acquires relevant data. This data includes user reactions from the platform (e.g., comments, likes), and the effectiveness of the information dissemination is measured using evaluation methods.

[0435] As a concrete example, it is possible to translate new product release information for each country's market and post it on the appropriate platform. An example of a prompt would be, "Please translate the new product release information for each country's market and post it on the appropriate platform."

[0436] In this way, the embodiment of the invention makes it possible to efficiently disseminate information tailored to the target market and measure its effectiveness.

[0437] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0438] Step 1:

[0439] The user inputs information on the device. This input consists of product descriptions and messages that the user wants to share. Specifically, the user writes information into text boxes through the device's interface. This information becomes the basis for subsequent processing.

[0440] Step 2:

[0441] The terminal sends information to the server. The entered information is sent to the server using a secure protocol such as HTTPS. The terminal converts the information into data packets and sends them to the specified endpoint. This allows the server to receive data from the user.

[0442] Step 3:

[0443] The server handles data translation and processing. Input data is translated into multiple languages ​​using generative AI models and natural language processing technologies. The server calls a translation API to convert input text into the target market language and generate output adapted to the culture and context.

[0444] Step 4:

[0445] The server posts the translated information to a selected information exchange platform using automated means. The server uses the platform's API to send the information in the specified format and publish it on the platform. This ensures that the information reaches the target market.

[0446] Step 5:

[0447] The system monitors reactions after posting and collects data. The server tracks reactions on the platform where the information was posted in real time and stores the collected data. The server uses APIs and scraping techniques to collect user reactions (comments, likes, shares, etc.) and uses evaluation tools to analyze the effectiveness of sending the information.

[0448] (Application Example 1)

[0449] 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."

[0450] In optimizing digital communication in international markets, multilingual translation and platform selection are crucial elements. However, traditional systems have not been able to adequately share information or efficiently provide promotional information tailored to the characteristics of each country. Therefore, in market expansion in various regions, there is a need to present promotions in the most appropriate language based on users' purchase history and interests, and to incorporate user responses in real time.

[0451] 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.

[0452] In this invention, the server includes means for analyzing data collected from information and communication networks in various countries to identify an information sharing platform in a specific region, means for translating product information into multiple languages ​​using translation means and transmitting it to the information sharing platform in the specific region, and means for presenting multilingual promotional information based on purchase history and interests. This makes it possible to provide information tailored to user preferences in each market and streamline international marketing strategies.

[0453] An "information and communication network" is a digital communication infrastructure for sending and receiving data, and a structure that enables information exchange between countries.

[0454] An "information sharing platform" is a collection of platforms used in a specific region, providing a space for users to exchange information.

[0455] A "translation tool" is a mechanism that uses natural language processing technology to convert text into multiple languages ​​and provide information in a format understandable to users of different languages.

[0456] "Promotional information" refers to information about product or service benefits and campaigns, provided to encourage users to make purchases.

[0457] "Purchase history" refers to records of purchases a user has made in the past, and is data used to provide personalized services.

[0458] "Preferences" refer to information that indicates a user's interests and concerns, and serve as a basis for providing content and services tailored to individual users.

[0459] To implement this invention, a system including a server, terminals, and users is utilized. The server is connected to a global information and communication network and can collect and analyze data from information sharing infrastructures in various countries, enabling the dissemination of information tailored to specific regions.

[0460] The server utilizes cloud platforms such as Google Cloud and AWS to perform data analysis and multilingual translation. Translation uses the Google Translate API and Azure's natural language processing services. Based on user purchase history and preferences, the server translates specific promotional information into multiple languages ​​and distributes it to a regional information sharing platform.

[0461] The device provides a user interface on smartphones and tablets for inputting information. Users can receive promotional information in real time and view visualized information in their specified language. This process enhances the user experience and contributes to the efficient execution of international campaigns.

[0462] For example, if a user wants to receive the latest promotional information about a specific product, that information will be customized based on the user's past purchase history and presented in languages ​​such as Japanese, English, and Chinese.

[0463] Examples of prompts to input into a generative AI model:

[0464] "Design a system to translate promotional notifications into multiple languages ​​and post them to an information sharing platform for the target market."

[0465] This approach enables efficient international marketing strategies and increases user engagement in each market.

[0466] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0467] Step 1:

[0468] Users configure promotional notification settings through their devices. The devices then send data about the user's purchase history and preferences to the server. This data is used to analyze purchasing trends and identify areas of interest.

[0469] Step 2:

[0470] The server analyzes the received user data and identifies promotional information that requires multilingual translation. The server then uses the Google Translate API as input to translate this information into the appropriate language. This process generates text data for each market.

[0471] Step 3:

[0472] The server distributes translated promotional information to information sharing platforms in each country. The server then uses this translated data as input to send to specific communities and platforms, enabling information to be displayed in each region.

[0473] Step 4:

[0474] The user's device receives the distributed information and displays it visually in the language specified by the user. The device uses translated promotional data as input and outputs it through the user interface. This allows the user to intuitively understand the promotional content.

[0475] Step 5:

[0476] The server monitors the effectiveness of promotions in real time and collects user responses. The server takes user clicks and feedback as input data and analyzes marketing effectiveness information as output. This process contributes to future strategic improvements.

[0477] 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.

[0478] To implement this invention, it is necessary to incorporate an emotion engine into a system consisting of a server, terminals, and users. This system collects and analyzes data from information and communication networks in various countries to identify the optimal information sharing platform in that region.

[0479] Initially, users can input product information and messages via their device. This information is sent to a server and translated using natural language processing technology. The translated information is then automatically posted to an information sharing platform identified in a specific region.

[0480] The server monitors user reactions to posted information in real time. A sentiment engine is used to analyze this reaction information. The sentiment engine recognizes user emotions from the reaction information and understands their tendencies. For example, it can identify positive, negative, and neutral emotions.

[0481] As a concrete example, consider a marketing campaign launched for a new product announcement. Users input messages using their devices, and the server translates them into multiple languages ​​and posts them to local information sharing platforms. The server then collects response information from the platforms, and an emotion engine analyzes user responses to visualize overall sentiment trends. Based on this information, the server can automatically generate and provide suggestions for improving the marketing strategy to users.

[0482] Thus, by combining an emotion engine, the present invention enhances the effectiveness of information dissemination and realizes more effective global social media marketing.

[0483] The following describes the processing flow.

[0484] Step 1:

[0485] Users input information and marketing messages about new products through their devices. This includes multimedia content such as text and images.

[0486] Step 2:

[0487] The terminal sends the entered information to the server. The transmitted data is stored on the server as backup data for information transmission.

[0488] Step 3:

[0489] The server translates received information into multiple languages ​​using translation tools. Natural language processing technology is used to translate it in a format best suited to the target market's language.

[0490] Step 4:

[0491] The server posts the translated information to the local information sharing platform identified by the analysis algorithm, according to a schedule.

[0492] Step 5:

[0493] The server monitors user reactions in real time after a post is made and collects reaction information from the information sharing platform.

[0494] Step 6:

[0495] The server uses an emotion engine to analyze the collected response information. Here, it identifies emotional tendencies in the responses.

[0496] Step 7:

[0497] Based on the analysis results from the emotion engine, the server generates visualized emotional trends and suggestions for improving marketing strategies.

[0498] Step 8:

[0499] The server displays the generated analysis results and suggestions on a dashboard that users can view. This provides users with reference material when formulating their next strategies.

[0500] (Example 2)

[0501] 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."

[0502] In today's global information dissemination environment, identifying the optimal information sharing platform that accommodates different regions, cultures, and languages, and effectively disseminating information, is challenging. Furthermore, understanding user reactions to disseminated information and formulating appropriate marketing strategies based on that information is also complex. A means to overcome these challenges and achieve more effective information sharing and marketing is needed.

[0503] 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.

[0504] In this invention, the server includes means for analyzing data collected from information and communication networks in various countries to identify an information sharing infrastructure in a specific region; means for translating product information into multiple languages ​​using translation means and transmitting it to the information sharing infrastructure in the specific region; means for monitoring reactions to the transmitted information, acquiring relevant reaction information, and responding; means for analyzing the acquired reaction information using sentiment analysis technology to classify the user's emotions; and means for measuring the effectiveness of information transmission using the sentiment analysis results and proposing strategies using a generative AI model. This enables strategic information transmission that reflects the emotional reactions of users while adapting to different regions and languages.

[0505] An "information and communication network" refers to all the technological infrastructure for sending, receiving, and sharing data, and includes means of communication that connect multiple geographically separated points.

[0506] An "information sharing platform" refers to a platform or system for managing and distributing information within a specific region, and serves as a place for users to exchange data with each other.

[0507] "Translation methods" refer to software or systems used to convert input text data from one language to another, often employing natural language processing.

[0508] "Sentiment analysis technology" refers to techniques for identifying and classifying user emotions from expressions within text data, and includes analysis based on natural language processing.

[0509] A "generative AI model" is a model that processes data based on artificial intelligence technology to automatically generate new information and suggestions, and it utilizes machine learning algorithms.

[0510] To implement this invention, a system consisting of a server, a terminal, and a user is required. The terminal provides an interface for the user to input product information and messages. A common information processing device such as a smartphone or a personal computer can be used as the terminal.

[0511] The server is the core of this system and performs multiple functions. First, the server collects data from information and communication networks in various countries. The collected data is stored in a database, which serves as the basis for later analysis. In data analysis, the server uses machine learning algorithms to identify information sharing infrastructure optimized for specific regions.

[0512] As a translation method, the server utilizes natural language processing technology, such as a language processing service known as a machine translation API. This allows the collected text data to be translated into multiple languages. For example, an English message entered by a user might be translated into French or Spanish and delivered to users who speak those respective languages.

[0513] Next, the server monitors user reactions to the transmitted information. This monitoring utilizes streaming tools that process data in real time to collect user comments and feedback. Through sentiment analysis technology, the server analyzes these reactions and identifies the user's emotions. In this process, emotions are classified as positive, negative, or neutral, as described in the claims.

[0514] Finally, the server uses a generative AI model to create strategic suggestions based on the sentiment analysis results. The generative AI model is then input with prompts such as the following:

[0515] "Based on the results of the emotion engine, please propose specific strategies to enhance the effectiveness of our new product campaign."

[0516] This allows users to access information based on sentiment data, enabling them to strengthen strategies in areas such as marketing. The server then feeds the suggested results back to the user, and the entire system functions as a cycle.

[0517] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0518] Step 1:

[0519] Users use a terminal to input product information and messages. The input is primarily in text data format. For example, they might enter a review including features and pricing information for a new product. This input data is immediately sent to the server. At this stage, the output is the text data that has reached the server.

[0520] Step 2:

[0521] The server processes the received text data through a translation tool. In this process, natural language processing techniques are used to translate the input data into the target language. For example, data entered in English will be translated into French. Specifically, the server calls a machine translation API and retrieves the translated data. The output is multilingual text data.

[0522] Step 3:

[0523] The server automatically posts the translated information to a localized information sharing platform. Here, the server uses an API to send data to, for example, the posting interface of a social media platform. This posting shares the information with users in a specific region. The output is a notification indicating that the posting is complete.

[0524] Step 4:

[0525] The server collects and monitors user reactions to posts. For this purpose, it utilizes a real-time data streaming tool. It retrieves comments and rating data from the platform and records them in a database. At this stage, reaction data is output.

[0526] Step 5:

[0527] The server processes the collected response data using emotion analysis technology. Specifically, it uses an emotion engine to classify emotions into positive, negative, and neutral. This analysis process generates emotion indicators using text analysis techniques. The final output is data representing the emotion classification results.

[0528] Step 6:

[0529] The server utilizes a generative AI model to generate strategic suggestions based on sentiment classification results. During this process, prompts are input to the generative AI model to create marketing strategy proposals based on sentiment data. For example, a prompt might read, "Based on the sentiment engine results, please propose specific strategies to enhance the effectiveness of the new product campaign." Finally, the generated strategic suggestions are output and provided to the user.

[0530] (Application Example 2)

[0531] 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."

[0532] In global marketing, there is a challenge in disseminating information optimally based on the cultural and linguistic differences of each country. Furthermore, there is a need to evaluate the effectiveness of advertising in real time and quickly implement strategic improvements based on those results.

[0533] 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.

[0534] In this invention, the server includes means for analyzing data collected from information and communication networks in various countries to identify information sharing platforms in specific regions, means for translating product information into multiple languages ​​using translation means and transmitting it to the information sharing platforms in specific regions, means for analyzing acquired response information, measuring the effectiveness of information dissemination, and generating improvement suggestions based on response trends in each region, and means for evaluating the effectiveness of advertising campaigns in real time and optimizing promotional activities in designated regions. This enables effective information dissemination to users in each country and rapid and appropriate marketing improvements.

[0535] "Information and communication networks" refer to data communication infrastructure in each country, including the internet and mobile communications.

[0536] An "information sharing platform" refers to a digital space where users can exchange and share information with each other, and mainly includes social media and online forums.

[0537] "Translation methods" refer to tools and software used to translate product information into multiple languages, and which utilize natural language processing technology.

[0538] "Reaction information" refers to data that includes emotions and opinions, such as user feedback and comments on information disseminated.

[0539] "Information dissemination effectiveness" is an indicator that measures the degree to which disseminated information is perceived by consumers as intended and influences their behavior, as well as the success rate of such information dissemination.

[0540] "Methods for generating improvement suggestions" refers to methods that revise marketing strategies based on analyzed response information and propose specific action plans to enhance their effectiveness.

[0541] "Means of visualization and display" refers to methods of converting data into graphs, charts, and other forms of visualization so that people can intuitively understand the information.

[0542] The system implementing this invention consists of a server, a terminal, and a user. The server collects data from information and communication networks in various countries, analyzes this data, and identifies the most suitable information sharing platform for a specific region. The user inputs product information using the terminal, and the server translates the product information into multiple languages ​​using a translation means and transmits it to the information sharing platform.

[0543] The server monitors user reactions to the information disseminated and acquires this reaction information. It then analyzes this reaction information using an emotion engine, measures the effectiveness of the information dissemination, and generates improvement suggestions based on regional reaction trends. In this process, the server uses Google Translate API and Azure Translation Service as natural language processing technologies, and IBM Watson's Natural Language Understanding API for sentiment analysis. PostgreSQL is used as the database.

[0544] As a concrete example of the system, promotional information for a music festival event entered on a smartphone is posted to social media in various countries. The server analyzes the reactions to these posts in real time and provides additional event information and campaigns to regions that receive many positive responses.

[0545] Examples of prompts for a generative AI model are as follows:

[0546] Post promotional material for the new music festival event on social media platforms in various countries and analyze fan reactions in real time. Provide further event information to regions that receive a high level of positive response.

[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 from information and communication networks in various countries. This data includes region-specific user trends and cultural characteristics, and this information is used to analyze which information-sharing platform is most appropriate. The input is country-specific internet data, and the output is a list of the most suitable information-sharing platforms for a given region.

[0550] Step 2:

[0551] Users input product information and promotional content via their terminals. Product information can include text, images, and videos, and this information is sent to the server. The input is the product information provided by the user, while the output is the unprocessed digital content stored on the server.

[0552] Step 3:

[0553] The server uses natural language processing technology to translate product information into multiple languages. Specifically, it utilizes the Google Translate API to convert the input information into an internationally compatible format. The input is the original text received from the user, and the output is the translated multilingual information.

[0554] Step 4:

[0555] The server transmits the translated information to a designated information-sharing platform, which is the one deemed most effective in each region. The input is the translated advertising content, and the output is the advertisement published on the platform.

[0556] Step 5:

[0557] The server monitors responses to transmitted information in real time. It collects user comments and reactions and stores relevant response data. The input is user responses from the platform, and the output is the response information collected for analysis.

[0558] Step 6:

[0559] The server analyzes response information using an emotion engine and measures the effectiveness of the information dissemination. This process uses IBM Watson's Natural Language Understanding API to perform sentiment analysis and distinguish between positive, negative, and neutral emotions. The input is the acquired response information, and the output is an index of the emotion distribution and the results of the effectiveness measurement.

[0560] Step 7:

[0561] The server generates suggestions for improving advertising campaigns based on regional response trends. Specifically, it sends prompts to a generative AI model to generate suggestions. The input is the sentiment analysis results, and the output is a strategic report including improvement suggestions.

[0562] 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.

[0563] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0564] 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.

[0565] [Fourth Embodiment]

[0566] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0567] 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.

[0568] 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).

[0569] 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.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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".

[0579] To implement this invention, it is necessary to build a system including a server, terminals, and users. The server is connected to a global information and communication network and configured to access information sharing platforms in each region. The server collects data from each country and uses analytical algorithms to identify the most widely used platform in that region. This makes it possible to provide the optimal digital strategy for the target market.

[0580] The terminal provides an interface for users to input product information. Users can input messages and information they wish to send through the terminal. The terminal sends the input information to a server, where it is translated into multiple languages ​​using translation tools. The server uses natural language processing technology to convert the text and adapt it to the language and culture of the specific market.

[0581] The multilingual information is automatically posted by the server to the appropriate information sharing platform. This posting process is completed without any user intervention, enabling efficient international communication. The server also monitors reactions to the posted content in real time and collects relevant feedback.

[0582] As a concrete example, when a new version of a product is released, users input product information using their devices. This information is translated into the target market's language, such as Japanese, Chinese, or French, and posted in a timely manner to popular platforms in each country. The server then collects user responses, analyzes how the information was consumed, and uses this information to improve the marketing strategy. This enables efficient use of human resources while increasing region-specific engagement.

[0583] The following describes the processing flow.

[0584] Step 1:

[0585] The server connects to a global information and communication network and collects data from various regions. This includes trend data and statistical information such as the number of users.

[0586] Step 2:

[0587] The server analyzes the collected data, using algorithms to identify the most widely used information-sharing platforms in each region.

[0588] Step 3:

[0589] Users input product information and marketing messages via their devices. This includes text, images, and other media content.

[0590] Step 4:

[0591] The terminal sends the entered information to the server. It ensures that the user's input data arrives at the server in the specified format.

[0592] Step 5:

[0593] The server uses translation tools to convert the received information into multiple languages. Natural language processing techniques are used to efficiently translate it into the target language.

[0594] Step 6:

[0595] The server automatically posts translated content to selected information sharing platforms according to a schedule. This ensures that marketing messages are published at the appropriate time.

[0596] Step 7:

[0597] The server monitors reactions to posts in real time. It automatically collects comments and engagement from users.

[0598] Step 8:

[0599] The server analyzes the collected response information. This allows it to measure the effectiveness of the information dissemination and determine what actions are necessary.

[0600] Step 9:

[0601] The server visually displays the analysis results on a dashboard that users can view, helping them plan their next marketing strategy.

[0602] (Example 1)

[0603] 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".

[0604] In international information dissemination, there are challenges in multilingualization and selecting information platforms appropriate for each region. Furthermore, monitoring responses after information is released in real time and developing effective marketing strategies requires significant resources.

[0605] 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.

[0606] In this invention, the server includes means for analyzing information collected from communication infrastructures in various countries to identify regional information exchange platforms, means for translating the information into multiple languages ​​and transmitting it to the regional information exchange platforms, and means for monitoring responses to the transmitted information, obtaining relevant response information, and responding. This enables efficient multilingual information dissemination and measurement of its effectiveness.

[0607] "Communication infrastructure" refers to the structure and equipment of a network intended for information transmission, and serves as the framework for sending and receiving data.

[0608] An "information exchange platform" refers to an application or website that serves as a foundation for users to share and disseminate information.

[0609] "Translation processing" refers to the procedures and techniques for appropriately converting given text or information into a different language.

[0610] A "generative AI model" refers to a model that uses artificial intelligence technology to generate text and adapt it to its context.

[0611] "Evaluation methods" refer to functions and processes for measuring the effectiveness of information dissemination and analyzing related data.

[0612] To implement this invention, it is necessary to construct a system including a server, terminals, and users. This system aims to improve the efficiency of international information dissemination and automates multilingual information dissemination and its effectiveness measurement. The embodiments of the invention are described below.

[0613] The server first collects information from the communication infrastructure of each country and identifies information exchange platforms in the region. In this process, it analyzes platform usage data in each region to reveal the platform that is most frequently used via a specific protocol.

[0614] The user uses a device to input the information they want to share as text. The device then transmits the user input to the server using a secure communication method. This information often includes descriptions of new products and promotional information.

[0615] The server translates the received information into multiple languages ​​using generative AI models and natural language processing technology. This ensures the information is translated to align with the language and culture of the target market. Translation APIs and AI models are used in this translation process.

[0616] Subsequently, the server posts the translated information to a selected information exchange platform using automated means. Specifically, it uploads the information in the appropriate format using the platform's API.

[0617] Furthermore, the server monitors responses to the transmitted information in real time and acquires relevant data. This data includes user reactions from the platform (e.g., comments, likes), and the effectiveness of the information dissemination is measured using evaluation methods.

[0618] As a concrete example, it is possible to translate new product release information for each country's market and post it on the appropriate platform. An example of a prompt would be, "Please translate the new product release information for each country's market and post it on the appropriate platform."

[0619] In this way, the embodiment of the invention makes it possible to efficiently disseminate information tailored to the target market and measure its effectiveness.

[0620] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0621] Step 1:

[0622] The user inputs information on the device. This input consists of product descriptions and messages that the user wants to share. Specifically, the user writes information into text boxes through the device's interface. This information becomes the basis for subsequent processing.

[0623] Step 2:

[0624] The terminal sends information to the server. The entered information is sent to the server using a secure protocol such as HTTPS. The terminal converts the information into data packets and sends them to the specified endpoint. This allows the server to receive data from the user.

[0625] Step 3:

[0626] The server handles data translation and processing. Input data is translated into multiple languages ​​using generative AI models and natural language processing technologies. The server calls a translation API to convert input text into the target market language and generate output adapted to the culture and context.

[0627] Step 4:

[0628] The server posts the translated information to a selected information exchange platform using automated means. The server uses the platform's API to send the information in the specified format and publish it on the platform. This ensures that the information reaches the target market.

[0629] Step 5:

[0630] The system monitors reactions after posting and collects data. The server tracks reactions on the platform where the information was posted in real time and stores the collected data. The server uses APIs and scraping techniques to collect user reactions (comments, likes, shares, etc.) and uses evaluation tools to analyze the effectiveness of sending the information.

[0631] (Application Example 1)

[0632] 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".

[0633] In optimizing digital communication in international markets, multilingual translation and platform selection are crucial elements. However, traditional systems have not been able to adequately share information or efficiently provide promotional information tailored to the characteristics of each country. Therefore, in market expansion in various regions, there is a need to present promotions in the most appropriate language based on users' purchase history and interests, and to incorporate user responses in real time.

[0634] 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.

[0635] In this invention, the server includes means for analyzing data collected from information and communication networks in various countries to identify an information sharing platform in a specific region, means for translating product information into multiple languages ​​using translation means and transmitting it to the information sharing platform in the specific region, and means for presenting multilingual promotional information based on purchase history and interests. This makes it possible to provide information tailored to user preferences in each market and streamline international marketing strategies.

[0636] An "information and communication network" is a digital communication infrastructure for sending and receiving data, and a structure that enables information exchange between countries.

[0637] An "information sharing platform" is a collection of platforms used in a specific region, providing a space for users to exchange information.

[0638] A "translation tool" is a mechanism that uses natural language processing technology to convert text into multiple languages ​​and provide information in a format understandable to users of different languages.

[0639] "Promotional information" refers to information about product or service benefits and campaigns, provided to encourage users to make purchases.

[0640] "Purchase history" refers to records of purchases a user has made in the past, and is data used to provide personalized services.

[0641] "Preferences" refer to information that indicates a user's interests and concerns, and serve as a basis for providing content and services tailored to individual users.

[0642] To implement this invention, a system including a server, terminals, and users is utilized. The server is connected to a global information and communication network and can collect and analyze data from information sharing infrastructures in various countries, enabling the dissemination of information tailored to specific regions.

[0643] The server utilizes cloud platforms such as Google Cloud and AWS to perform data analysis and multilingual translation. Translation uses the Google Translate API and Azure's natural language processing services. Based on user purchase history and preferences, the server translates specific promotional information into multiple languages ​​and distributes it to a regional information sharing platform.

[0644] The device provides a user interface on smartphones and tablets for inputting information. Users can receive promotional information in real time and view visualized information in their specified language. This process enhances the user experience and contributes to the efficient execution of international campaigns.

[0645] For example, if a user wants to receive the latest promotional information about a specific product, that information will be customized based on the user's past purchase history and presented in languages ​​such as Japanese, English, and Chinese.

[0646] Examples of prompts to input into a generative AI model:

[0647] "Design a system to translate promotional notifications into multiple languages ​​and post them to an information sharing platform for the target market."

[0648] This approach enables efficient international marketing strategies and increases user engagement in each market.

[0649] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0650] Step 1:

[0651] Users configure promotional notification settings through their devices. The devices then send data about the user's purchase history and preferences to the server. This data is used to analyze purchasing trends and identify areas of interest.

[0652] Step 2:

[0653] The server analyzes the received user data and identifies promotional information that requires multilingual translation. The server then uses the Google Translate API as input to translate this information into the appropriate language. This process generates text data for each market.

[0654] Step 3:

[0655] The server distributes translated promotional information to information sharing platforms in each country. The server then uses this translated data as input to send to specific communities and platforms, enabling information to be displayed in each region.

[0656] Step 4:

[0657] The user's device receives the distributed information and displays it visually in the language specified by the user. The device uses translated promotional data as input and outputs it through the user interface. This allows the user to intuitively understand the promotional content.

[0658] Step 5:

[0659] The server monitors the effectiveness of promotions in real time and collects user responses. The server takes user clicks and feedback as input data and analyzes marketing effectiveness information as output. This process contributes to future strategic improvements.

[0660] 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.

[0661] To implement this invention, it is necessary to incorporate an emotion engine into a system consisting of a server, terminals, and users. This system collects and analyzes data from information and communication networks in various countries to identify the optimal information sharing platform in that region.

[0662] Initially, users can input product information and messages via their device. This information is sent to a server and translated using natural language processing technology. The translated information is then automatically posted to an information sharing platform identified in a specific region.

[0663] The server monitors user reactions to posted information in real time. A sentiment engine is used to analyze this reaction information. The sentiment engine recognizes user emotions from the reaction information and understands their tendencies. For example, it can identify positive, negative, and neutral emotions.

[0664] As a concrete example, consider a marketing campaign launched for a new product announcement. Users input messages using their devices, and the server translates them into multiple languages ​​and posts them to local information sharing platforms. The server then collects response information from the platforms, and an emotion engine analyzes user responses to visualize overall sentiment trends. Based on this information, the server can automatically generate and provide suggestions for improving the marketing strategy to users.

[0665] Thus, by combining an emotion engine, the present invention enhances the effectiveness of information dissemination and realizes more effective global social media marketing.

[0666] The following describes the processing flow.

[0667] Step 1:

[0668] Users input information and marketing messages about new products through their devices. This includes multimedia content such as text and images.

[0669] Step 2:

[0670] The terminal sends the entered information to the server. The transmitted data is stored on the server as backup data for information transmission.

[0671] Step 3:

[0672] The server translates received information into multiple languages ​​using translation tools. Natural language processing technology is used to translate it in a format best suited to the target market's language.

[0673] Step 4:

[0674] The server posts the translated information to the local information sharing platform identified by the analysis algorithm, according to a schedule.

[0675] Step 5:

[0676] The server monitors user reactions in real time after a post is made and collects reaction information from the information sharing platform.

[0677] Step 6:

[0678] The server uses an emotion engine to analyze the collected response information. Here, it identifies emotional tendencies in the responses.

[0679] Step 7:

[0680] Based on the analysis results from the emotion engine, the server generates visualized emotional trends and suggestions for improving marketing strategies.

[0681] Step 8:

[0682] The server displays the generated analysis results and suggestions on a dashboard that users can view. This provides users with reference material when formulating their next strategies.

[0683] (Example 2)

[0684] 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".

[0685] In today's global information dissemination environment, identifying the optimal information sharing platform that accommodates different regions, cultures, and languages, and effectively disseminating information, is challenging. Furthermore, understanding user reactions to disseminated information and formulating appropriate marketing strategies based on that information is also complex. A means to overcome these challenges and achieve more effective information sharing and marketing is needed.

[0686] 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.

[0687] In this invention, the server includes means for analyzing data collected from information and communication networks in various countries to identify an information sharing infrastructure in a specific region; means for translating product information into multiple languages ​​using translation means and transmitting it to the information sharing infrastructure in the specific region; means for monitoring reactions to the transmitted information, acquiring relevant reaction information, and responding; means for analyzing the acquired reaction information using sentiment analysis technology to classify the user's emotions; and means for measuring the effectiveness of information transmission using the sentiment analysis results and proposing strategies using a generative AI model. This enables strategic information transmission that reflects the emotional reactions of users while adapting to different regions and languages.

[0688] An "information and communication network" refers to all the technological infrastructure for sending, receiving, and sharing data, and includes means of communication that connect multiple geographically separated points.

[0689] An "information sharing platform" refers to a platform or system for managing and distributing information within a specific region, and serves as a place for users to exchange data with each other.

[0690] "Translation methods" refer to software or systems used to convert input text data from one language to another, often employing natural language processing.

[0691] "Sentiment analysis technology" refers to techniques for identifying and classifying user emotions from expressions within text data, and includes analysis based on natural language processing.

[0692] A "generative AI model" is a model that processes data based on artificial intelligence technology to automatically generate new information and suggestions, and it utilizes machine learning algorithms.

[0693] To implement this invention, a system consisting of a server, a terminal, and a user is required. The terminal provides an interface for the user to input product information and messages. A common information processing device such as a smartphone or a personal computer can be used as the terminal.

[0694] The server is the core of this system and performs multiple functions. First, the server collects data from information and communication networks in various countries. The collected data is stored in a database, which serves as the basis for later analysis. In data analysis, the server uses machine learning algorithms to identify information sharing infrastructure optimized for specific regions.

[0695] As a translation method, the server utilizes natural language processing technology, such as a language processing service known as a machine translation API. This allows the collected text data to be translated into multiple languages. For example, an English message entered by a user might be translated into French or Spanish and delivered to users who speak those respective languages.

[0696] Next, the server monitors user reactions to the transmitted information. This monitoring utilizes streaming tools that process data in real time to collect user comments and feedback. Through sentiment analysis technology, the server analyzes these reactions and identifies the user's emotions. In this process, emotions are classified as positive, negative, or neutral, as described in the claims.

[0697] Finally, the server uses a generative AI model to create strategic suggestions based on the sentiment analysis results. The generative AI model is then input with prompts such as the following:

[0698] "Based on the results of the emotion engine, please propose specific strategies to enhance the effectiveness of our new product campaign."

[0699] This allows users to access information based on sentiment data, enabling them to strengthen strategies in areas such as marketing. The server then feeds the suggested results back to the user, and the entire system functions as a cycle.

[0700] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0701] Step 1:

[0702] Users use a terminal to input product information and messages. The input is primarily in text data format. For example, they might enter a review including features and pricing information for a new product. This input data is immediately sent to the server. At this stage, the output is the text data that has reached the server.

[0703] Step 2:

[0704] The server processes the received text data through a translation tool. In this process, natural language processing techniques are used to translate the input data into the target language. For example, data entered in English will be translated into French. Specifically, the server calls a machine translation API and retrieves the translated data. The output is multilingual text data.

[0705] Step 3:

[0706] The server automatically posts the translated information to a localized information sharing platform. Here, the server uses an API to send data to, for example, the posting interface of a social media platform. This posting shares the information with users in a specific region. The output is a notification indicating that the posting is complete.

[0707] Step 4:

[0708] The server collects and monitors user reactions to posts. For this purpose, it utilizes a real-time data streaming tool. It retrieves comments and rating data from the platform and records them in a database. At this stage, reaction data is output.

[0709] Step 5:

[0710] The server processes the collected response data using emotion analysis technology. Specifically, it uses an emotion engine to classify emotions into positive, negative, and neutral. This analysis process generates emotion indicators using text analysis techniques. The final output is data representing the emotion classification results.

[0711] Step 6:

[0712] The server utilizes a generative AI model to generate strategic suggestions based on sentiment classification results. During this process, prompts are input to the generative AI model to create marketing strategy proposals based on sentiment data. For example, a prompt might read, "Based on the sentiment engine results, please propose specific strategies to enhance the effectiveness of the new product campaign." Finally, the generated strategic suggestions are output and provided to the user.

[0713] (Application Example 2)

[0714] 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".

[0715] In global marketing, there is a challenge in disseminating information optimally based on the cultural and linguistic differences of each country. Furthermore, there is a need to evaluate the effectiveness of advertising in real time and quickly implement strategic improvements based on those results.

[0716] 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.

[0717] In this invention, the server includes means for analyzing data collected from information and communication networks in various countries to identify information sharing platforms in specific regions, means for translating product information into multiple languages ​​using translation means and transmitting it to the information sharing platforms in specific regions, means for analyzing acquired response information, measuring the effectiveness of information dissemination, and generating improvement suggestions based on response trends in each region, and means for evaluating the effectiveness of advertising campaigns in real time and optimizing promotional activities in designated regions. This enables effective information dissemination to users in each country and rapid and appropriate marketing improvements.

[0718] "Information and communication networks" refer to data communication infrastructure in each country, including the internet and mobile communications.

[0719] An "information sharing platform" refers to a digital space where users can exchange and share information with each other, and mainly includes social media and online forums.

[0720] "Translation methods" refer to tools and software used to translate product information into multiple languages, and which utilize natural language processing technology.

[0721] "Reaction information" refers to data that includes emotions and opinions, such as user feedback and comments on information disseminated.

[0722] "Information dissemination effectiveness" is an indicator that measures the degree to which disseminated information is perceived by consumers as intended and influences their behavior, as well as the success rate of such information dissemination.

[0723] "Methods for generating improvement suggestions" refers to methods that revise marketing strategies based on analyzed response information and propose specific action plans to enhance their effectiveness.

[0724] "Means of visualization and display" refers to methods of converting data into graphs, charts, and other forms of visualization so that people can intuitively understand the information.

[0725] The system implementing this invention consists of a server, a terminal, and a user. The server collects data from information and communication networks in various countries, analyzes this data, and identifies the most suitable information sharing platform for a specific region. The user inputs product information using the terminal, and the server translates the product information into multiple languages ​​using a translation means and transmits it to the information sharing platform.

[0726] The server monitors user reactions to the information disseminated and acquires this reaction information. It then analyzes this reaction information using an emotion engine, measures the effectiveness of the information dissemination, and generates improvement suggestions based on regional reaction trends. In this process, the server uses Google Translate API and Azure Translation Service as natural language processing technologies, and IBM Watson's Natural Language Understanding API for sentiment analysis. PostgreSQL is used as the database.

[0727] As a concrete example of the system, promotional information for a music festival event entered on a smartphone is posted to social media in various countries. The server analyzes the reactions to these posts in real time and provides additional event information and campaigns to regions that receive many positive responses.

[0728] Examples of prompts for a generative AI model are as follows:

[0729] Post promotional material for the new music festival event on social media platforms in various countries and analyze fan reactions in real time. Provide further event information to regions that receive a high level of positive response.

[0730] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0731] Step 1:

[0732] The server collects data from information and communication networks in various countries. This data includes region-specific user trends and cultural characteristics, and this information is used to analyze which information-sharing platform is most appropriate. The input is country-specific internet data, and the output is a list of the most suitable information-sharing platforms for a given region.

[0733] Step 2:

[0734] Users input product information and promotional content via their terminals. Product information can include text, images, and videos, and this information is sent to the server. The input is the product information provided by the user, while the output is the unprocessed digital content stored on the server.

[0735] Step 3:

[0736] The server uses natural language processing technology to translate product information into multiple languages. Specifically, it utilizes the Google Translate API to convert the input information into an internationally compatible format. The input is the original text received from the user, and the output is the translated multilingual information.

[0737] Step 4:

[0738] The server transmits the translated information to a designated information-sharing platform, which is the one deemed most effective in each region. The input is the translated advertising content, and the output is the advertisement published on the platform.

[0739] Step 5:

[0740] The server monitors responses to transmitted information in real time. It collects user comments and reactions and stores relevant response data. The input is user responses from the platform, and the output is the response information collected for analysis.

[0741] Step 6:

[0742] The server analyzes response information using an emotion engine and measures the effectiveness of the information dissemination. This process uses IBM Watson's Natural Language Understanding API to perform sentiment analysis and distinguish between positive, negative, and neutral emotions. The input is the acquired response information, and the output is an index of the emotion distribution and the results of the effectiveness measurement.

[0743] Step 7:

[0744] The server generates suggestions for improving advertising campaigns based on regional response trends. Specifically, it sends prompts to a generative AI model to generate suggestions. The input is the sentiment analysis results, and the output is a strategic report including improvement suggestions.

[0745] 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.

[0746] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0747] 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 robot 414.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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."

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] The following is further disclosed regarding the embodiments described above.

[0767] (Claim 1)

[0768] A means of identifying information sharing platforms in a specific region by analyzing data collected from information and communication networks in various countries,

[0769] A means of translating product information into multiple languages ​​using translation tools and disseminating it on an information sharing platform in a specific region,

[0770] A means of monitoring responses to transmitted information, acquiring relevant response information, and responding accordingly.

[0771] An analytical means for analyzing acquired reaction information and measuring the effectiveness of information dissemination,

[0772] A system that includes this.

[0773] (Claim 2)

[0774] The system according to claim 1, wherein the translation means uses natural language processing technology to translate into a target language.

[0775] (Claim 3)

[0776] The system according to claim 1, further comprising means for visualizing and displaying the results of measuring the effectiveness of information dissemination.

[0777] "Example 1"

[0778] (Claim 1)

[0779] A means of identifying regional information exchange platforms by analyzing information collected from communication infrastructures in various countries,

[0780] A means of translating information into multiple languages ​​and transmitting it on a local information exchange platform,

[0781] Means for monitoring responses to transmitted information, obtaining relevant response information, and responding,

[0782] An evaluation means for analyzing acquired reaction information and measuring the effectiveness of information transmission,

[0783] A means for receiving user input information using a communication device and transmitting it to a server,

[0784] A means of adapting information to culture and context using generative AI models,

[0785] A means of posting information to an information exchange platform selected by automated means,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, wherein the translation process uses natural language processing technology to translate into a target language.

[0789] (Claim 3)

[0790] The system according to claim 1, further comprising means for visualizing and displaying the results of measuring the effectiveness of information transmission.

[0791] "Application Example 1"

[0792] (Claim 1)

[0793] A means of identifying information sharing infrastructure in a specific region by analyzing data collected from information and communication networks in various countries,

[0794] A means of translating product information into multiple languages ​​using translation tools and disseminating it on an information sharing platform in a specific region,

[0795] A means of monitoring responses to transmitted information, acquiring relevant response information, and responding accordingly.

[0796] An analytical means for analyzing acquired reaction information and measuring the effectiveness of information dissemination,

[0797] A means of presenting multilingual promotional information based on purchase history and interests,

[0798] A means of visualizing the presented information in the user's preferred language and making it widely shareable within a specific region,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the translation means uses natural language processing technology to translate into a target language.

[0802] (Claim 3)

[0803] The system according to claim 1, further comprising means for illustrating and displaying the results of measuring the effectiveness of information dissemination.

[0804] "Example 2 of combining an emotion engine"

[0805] (Claim 1)

[0806] A means of identifying information sharing infrastructure in a specific region by analyzing data collected from information and communication networks in various countries,

[0807] A means of translating product information into multiple languages ​​using translation tools and disseminating it on an information sharing platform in a specific region,

[0808] A means of monitoring responses to transmitted information, acquiring relevant response information, and responding accordingly.

[0809] A means of classifying user emotions by analyzing acquired reaction information using emotion analysis technology,

[0810] A method for measuring the effectiveness of information dissemination using sentiment analysis results and proposing strategies using a generative AI model,

[0811] A system that includes this.

[0812] (Claim 2)

[0813] The system according to claim 1, wherein the translation means uses language processing technology to translate into a target language.

[0814] (Claim 3)

[0815] The system according to claim 1, further comprising means for visualizing and displaying the results of measuring the effectiveness of information dissemination.

[0816] "Application example 2 when combining with an emotional engine"

[0817] (Claim 1)

[0818] A means of identifying information sharing platforms in a specific region by analyzing data collected from information and communication networks in various countries,

[0819] A means of translating product information into multiple languages ​​using translation tools and disseminating it on an information sharing platform in a specific region,

[0820] A means of monitoring responses to transmitted information, acquiring relevant response information, and responding accordingly.

[0821] A means for analyzing acquired response information, measuring the effectiveness of information dissemination, and generating improvement proposals based on response trends in each region,

[0822] A means to evaluate the effectiveness of advertising campaigns in real time and optimize promotional activities in a specified area,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, wherein the translation means uses natural language processing technology to translate into a target language.

[0826] (Claim 3)

[0827] The system according to claim 1, further comprising means for visualizing and displaying the results of measuring the effectiveness of information dissemination and visually presenting improvement suggestions for the target area of ​​the advertising campaign. [Explanation of Symbols]

[0828] 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 of identifying information sharing infrastructure in a specific region by analyzing data collected from information and communication networks in various countries, A means of translating product information into multiple languages ​​using translation tools and disseminating it on an information sharing platform in a specific region, A means of monitoring responses to transmitted information, acquiring relevant response information, and responding accordingly. An analytical means for analyzing acquired reaction information and measuring the effectiveness of information dissemination, A means of presenting multilingual promotional information based on purchase history and interests, A means of visualizing the presented information in the user's preferred language and making it widely shareable within a specific region, A system that includes this.

2. The system according to claim 1, wherein the translation means uses natural language processing technology to translate into a target language.

3. The system according to claim 1, further comprising means for illustrating and displaying the results of measuring the effectiveness of information dissemination.

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