system

Abstract

We provide the system. [Solution] A means for obtaining SNS posts entered by the user, The content of the retrieved posts is analyzed using a natural language processing model to determine sentiment and context, and A means of calculating a user's score based on the analysis results, A means of saving and updating the calculated score in a database, A means of checking user scores and providing incentives to users who exceed a certain threshold, A means of organizing and providing user score information to businesses, 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, and includes 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] Although SNS is an important communication means in modern society, slander, inappropriate posts, fraudulent advertisements, and invitations to dark jobs are frequently carried out. Such inappropriate behaviors not only increase the mental burden of users, but also damage the brand image of enterprises and cause risks such as customer harassment (customer harassment). In conventional countermeasures, means for identifying and preventing these inappropriate behaviors in real time were insufficient, so it has been demanded to construct a sound SNS environment.

Means for Solving the Problems

[0005] The present invention is a system that includes means for acquiring SNS posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine sentiment and context, means for calculating a user score based on the analysis results, means for storing and updating the calculated score in a database, means for checking the user's score and providing incentives to users who exceed a certain threshold, and means for organizing and providing user score information to companies. This makes it possible to monitor defamation and inappropriate behavior on SNS in real time, promote healthy communication, and improve the overall quality of SNS.

[0006] "SNS posts" refer to messages and content that users input and publish on social networking services.

[0007] A "natural language processing model" refers to algorithms and technologies for understanding and analyzing human language, making it possible to automatically determine emotions and context.

[0008] "Emotions" refer to the emotional state or intentions of the user included in the post, and are classified into positive, negative, neutral, etc.

[0009] A "score" is a numerical value used to evaluate user behavior and posted content based on analysis results, thereby quantitatively representing the healthiness of user behavior.

[0010] A "database" is a system for storing and managing information such as user scores and analysis results, and it allows for efficient data storage and retrieval.

[0011] An "incentive" is a reward or perk offered to encourage user behavior, and specifically includes things like purchase points and special offers.

[0012] "Company" refers to a legal entity or organization that provides goods or services. In this invention, a "company" is an entity that uses SNS user score information to identify low-risk customers and improve services. [Brief explanation of the drawing]

[0013] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Implementing the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention provides a system for identifying and preventing inappropriate behavior on social media in real time. This system promotes healthy communication by analyzing user posts and scoring their behavior. Specific embodiments are described below.

[0035] System Overview

[0036] This system mainly consists of the following elements:

[0037] 1. User terminal: A device used by a user to input and send posts using social networking services (SNS).

[0038] 2. Server: A central processing unit that receives SNS posts and performs analysis using natural language processing models.

[0039] 3. Database: A storage system for storing and managing analysis results and scores.

[0040] Retrieve user posts

[0041] User terminal: The user enters a message into the SNS posting form and sends the content to the server by pressing the post button. For example, consider a case where a user posts, "This product is really great!"

[0042] Submit post

[0043] User terminal: The user's entered content is sent to the server via the internet.

[0044] Analysis and scoring of submitted content

[0045] Server: Receives posts sent from terminals. The received posts are analyzed by a natural language processing model and classified as positive, negative, or neutral. The server calculates a score based on the analysis results. For example, if a post that says "This product is really great!" is judged to be positive, the server will assign this user a score of +10.

[0046] Saving and managing scores

[0047] Server: The calculated score is linked to the user ID and stored in the database. This allows each user's score to be managed as a history. For example, the score of user ID "12345" is updated from 90 to 100.

[0048] Provision of incentives

[0049] Server: Users whose scores exceed a certain threshold are offered incentives. For example, users who exceed 100 points may receive special coupons or purchase points. Furthermore, score information is compiled to provide companies with information on high-scoring users. Based on this, companies can offer special offers to low-risk customers.

[0050] Information provision for businesses

[0051] Server: Organizes and provides customer score information needed by businesses at the required time. Based on this data, businesses can implement marketing strategies that target lower-risk customer segments.

[0052] Examples

[0053] When a user posts "This product is the best!" on social media, the post is sent to the server. The server analyzes the post using a natural language processing model and determines that it expresses positive emotion. Based on the analysis, the server scores the user +10 points and saves the user's new score to the database. Since the score exceeds 100 points, the server offers this user a special coupon.

[0054] This system will promote healthy communication on social media and create a valuable social media environment for both users and businesses.

[0055] The following describes the processing flow.

[0056] Step 1:

[0057] User: Enter a message into the social media posting form.

[0058] Example: Enter the message, "This product is truly amazing!"

[0059] Step 2:

[0060] Terminal: The user presses the post button and sends the content to the server.

[0061] The submitted content is sent to the server via the internet.

[0062] Step 3:

[0063] Server: Receives posted data sent from terminals.

[0064] For example, the following JSON data arrives on the server:

[0065] json

[0066] {

[0067] "user_id": "12345",

[0068] "timestamp": "2023-10-01T10:00:00Z",

[0069] "content": "This product is truly amazing!"

[0070] }

[0071] Step 4:

[0072] Server: Sends the received posted content to a natural language processing model for text analysis.

[0073] The model performs sentiment classification (positive, negative, neutral) and contextual analysis.

[0074] Step 5:

[0075] Server: Assigns a score to posts based on the analysis results from a natural language processing model.

[0076] Example: The keyword "great" is recognized as positive, so a score of +10 is calculated.

[0077] Step 6:

[0078] Server: Associates the calculated score with the user ID, saves it to the database, and updates existing scores.

[0079] Example: The score of user ID "12345" will be updated from 90 to 100.

[0080] Step 7:

[0081] Server: Checks user scores in the database and provides incentives if the score exceeds a certain threshold.

[0082] Example: Because the score exceeded 100, the user will be given a special coupon.

[0083] Step 8:

[0084] Server: Executes the incentive distribution process and notifies the user.

[0085] Example: Send a coupon offer notification to the user via electronic message or similar means.

[0086] Step 9:

[0087] Server: Organizes user score information for enterprises and generates lists of high-rated users.

[0088] This system aggregates score data and related information from highly-rated users.

[0089] Step 10:

[0090] Server: Provides organized score information to companies and helps them identify low-risk customers.

[0091] Example: By providing companies with data indicating that "User ID: 12345 has a high rating," this data can be used for marketing strategies such as special offers.

[0092] The above outlines the specific processing steps of the program. This system can identify defamation and inappropriate behavior on social media in real time, thereby promoting healthy communication.

[0093] (Example 1)

[0094] 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."

[0095] While it is necessary to identify inappropriate behavior and posts on social media in real time and promote healthy communication, conventional systems had problems with low accuracy in analyzing posts and being unable to properly evaluate user behavior. Furthermore, there were insufficient means of providing reliable user information to companies. This made it difficult to provide appropriate incentives to promote healthy user communication and for companies to implement efficient marketing strategies.

[0096] 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.

[0097] In this invention, the server includes means for acquiring posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine sentiment and context, means for calculating and updating the user's score based on the analysis results, means for storing the calculated score in a storage device, means for checking the user's score and providing incentives to users who exceed a certain threshold, and means for organizing and providing user score information to companies. This makes it possible to identify inappropriate behavior on social media in real time, promote healthy communication, and provide appropriate incentives to users. It also makes it possible to provide companies with reliable user information and support the implementation of efficient marketing strategies.

[0098] A "user" refers to an individual or group that inputs and submits posts using social networking services (SNS).

[0099] A "post" refers to information such as text, images, videos, and links that a user shares on social media.

[0100] A "natural language processing model" refers to a machine learning algorithm or program that analyzes text data entered by a user to determine its sentiment and context.

[0101] "Analysis results" refer to information about the sentiment and context of posts obtained by a natural language processing model.

[0102] A "score" is a numerical value calculated based on the analysis of the content of a post, and it is an indicator that evaluates the healthiness of a user's behavior and statements.

[0103] A "memory device" refers to a database or storage system used to store calculated scores.

[0104] An "incentive" refers to a reward or benefit offered to users who exceed a certain score.

[0105] "Company" refers to a business organization that uses user score information to develop marketing strategies and manage risks.

[0106] Modes for carrying out the invention

[0107] This invention is a system that identifies inappropriate behavior on social networking services (SNS) in real time and promotes healthy communication. This system consists of the following hardware and software: a user terminal, a server, a database, and a natural language processing model.

[0108] 1. System Overview

[0109] This system consists of the following elements:

[0110] User device: A device used by a user to input and send posts using social networking services (e.g., smartphone, PC).

[0111] Server: A central processing unit that receives SNS posts and performs analysis using natural language processing models. Examples of software used include natural language processing models such as BERT and GPT-4 (registered trademark).

[0112] Database: A storage system for storing and managing analysis results and scores. Specific examples include MySQL (registered trademark) and PostgreSQL.

[0113] 2. Retrieve user posts

[0114] User terminal: The user enters a message into the social media posting form and presses the post button. For example, this would be the case when the user enters "This product is really great!"

[0115] 3. Submit your post

[0116] User terminal: When the post button is pressed, the content of the post is sent to the server via the internet. Specifically, the post data is sent using an HTTP request.

[0117] 4. Receiving the posted content

[0118] Server: Receives posted content via the internet for analysis. Receives the posted content contained in the body of the HTTP request.

[0119] 5. Analysis of posted content

[0120] Server: The server passes the received post content to a natural language processing model, which classifies it as positive, negative, or neutral. For example, if "This product is truly wonderful!" is judged to be positive, the natural language processing model returns that result.

[0121] 6. Score calculation and updating

[0122] Server: Based on the analysis results, the server calculates and updates the user's score. Posts containing positive emotions are given higher scores, and posts containing negative emotions are given lower scores. For example, it might calculate to add +10 points to the user's score.

[0123] 7. Saving and managing scores

[0124] Server: Stores the calculated score in the database, associating it with the user ID. Executes an SQL query to save the new score information in the database. For example, saves a new score of 100 for user ID "12345".

[0125] 8. Provision of incentives

[0126] Server: When a user's score exceeds a certain threshold, an incentive (e.g., a special coupon) is provided. A special coupon is issued to users who exceed the threshold, and a notification email is sent.

[0127] 9. Information provision for businesses

[0128] Server: Organizes and provides customer score information available to businesses as needed. Creates lists of high-rated users and sends data via API to businesses in a specific format (e.g., CSV file).

[0129] Examples

[0130] When a user posts on social media saying "This product is the best!", the following actions are taken:

[0131] 1. User's device: The user enters "This product is the best!" into the posting form of the SNS app and presses the post button.

[0132] 2. Terminal: The posted content is sent to the server as an HTTP request.

[0133] 3. Server: The received post content is passed to a natural language processing model, which determines that it expresses positive sentiment.

[0134] 4. Server: Based on the analysis results, the server assigns a score of +10 points to the user and saves the new score to the database.

[0135] 5. Server: Since the score has exceeded 100 points, issue a special coupon to the user and notify them via email.

[0136] This system promotes healthy communication on social media. Furthermore, it provides companies with reliable user information, enabling them to implement more efficient marketing strategies.

[0137] Examples of prompts for generative AI models

[0138] I want to post a positive comment on social media saying, "I like this product," but what kind of content should I include to ensure that this post is viewed as healthy communication on social media?

[0139] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0140] Step 1:

[0141] User input

[0142] User: Enter a message into the posting form of the SNS app and press the post button.

[0143] Input: Text entered by the user in the social media posting form.

[0144] Specific action: The user types "This product is truly amazing!" and clicks the submit button.

[0145] Output: The post is ready to be sent.

[0146] Step 2:

[0147] Submit post

[0148] Terminal: When the post button is pressed, the content of the post is sent to the server via the internet.

[0149] Input: The content of the post entered by the user.

[0150] Specific action: The smartphone or PC sends the message "This product is truly amazing!" to the server as an HTTP request.

[0151] Output: The submitted content arrives at the server.

[0152] Step 3:

[0153] Received the posted content

[0154] Server: Receives submitted content via the internet for analysis.

[0155] Input: HTTP request containing the content of the post sent from the terminal.

[0156] Specific action: The server receives the message "This product is really great!" which is included in the body of the HTTP request.

[0157] Output: The submitted content is ready for analysis.

[0158] Step 4:

[0159] Analysis of posted content

[0160] Server: The posted content is passed to a natural language processing model (e.g., BERT or GPT-4) to perform sentiment analysis.

[0161] Input: Received post content: "This product is truly amazing!"

[0162] Specific operation: Input the post content into a natural language processing model and obtain sentiment analysis results. For example, the model identifies it as a positive sentiment.

[0163] Output: Positive emotions are obtained as the analysis result.

[0164] Step 5:

[0165] Score calculation

[0166] Server: Calculates the user's score based on the analysis results.

[0167] Input: Analysis results from a natural language processing model (e.g., positive emotions).

[0168] Specific operation: Based on the analysis results, the user is awarded a score of +10 points. If the original score was 90, the new score will be 100.

[0169] Output: The new calculated score.

[0170] Step 6:

[0171] Save score

[0172] Server: Stores the calculated score in the database, linked to the user ID.

[0173] Input: User ID and new score (e.g., User ID "12345" and score 100).

[0174] Specific action: Execute an SQL query and save the new score to the database.

[0175] Output: Score information is saved to the database.

[0176] Step 7:

[0177] Provision of incentives

[0178] Server: Provides an incentive to the user if their score exceeds a certain threshold.

[0179] Input: Updated score (e.g., 100 points or more).

[0180] Specific action: Issue a special coupon and send a notification email to the user.

[0181] Output: Incentive information is provided to the user.

[0182] Step 8:

[0183] Providing information for businesses

[0184] Server: Organizes customer score information available to the company and provides it as needed.

[0185] Input: User score information retrieved from the database.

[0186] Specific operation: Create a list of highly-rated users and send the data to companies via an API in CSV file format.

[0187] Output: User score information organized for enterprise use.

[0188] (Application Example 1)

[0189] 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."

[0190] In today's e-commerce environment, it is crucial that user reviews and comments are a sound and reliable source of information. However, there is a lack of systems to evaluate inappropriate behavior and comments, often hindering healthy communication. Furthermore, there are insufficient effective methods for evaluating user trustworthiness, making it difficult for stores to provide appropriate incentives. This invention aims to solve these problems and provide a valuable environment for both users and stores.

[0191] 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.

[0192] In this invention, the server includes means for acquiring electronic posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine sentiment and context, means for calculating the user's score based on the analysis results, means for storing and updating the calculated score in a database, means for checking the user's score and providing incentives to users who exceed a certain threshold, means for organizing and providing user score information to companies, means for analyzing user reviews and comments in real time in an e-commerce environment, and means for providing benefits and discounts when a user's score exceeds a certain threshold. This promotes healthy communication and enables an e-commerce environment that is valuable to both users and companies.

[0193] "Electronic submissions" refer to digital content such as text messages and comments that users send over the internet.

[0194] A "natural language processing model" is a collection of algorithms and technologies used to analyze and understand human language and to provide appropriate responses and processing.

[0195] "Determining emotions and context" is the process of analyzing words and context within a text to determine whether they represent positive, negative, or neutral emotions.

[0196] "Calculating a score" is the process of quantifying the evaluation of a post based on its analyzed content.

[0197] "Saving and updating in a database" means recording the calculated score in data storage in a way that allows each user to be identified, and updating it as needed whenever new data is added.

[0198] An "incentive" is a reward or benefit given to users who meet certain criteria.

[0199] "Organizing and providing user score information to businesses" refers to the process of classifying and processing user score data in a way that is easy for businesses to use, and providing that data to businesses at the appropriate time.

[0200] An "e-commerce environment" refers to a system or platform for buying and selling goods and services over the internet.

[0201] "Analyzing reviews and comments in real time" means instantly receiving reviews and comments posted by users and analyzing their content using a natural language processing model.

[0202] "Benefits and discounts" refer to coupons, discount services, or other forms of rewards offered to users.

[0203] System Overview

[0204] The system for realizing this application retrieves electronic submissions entered by users and analyzes them using a natural language processing model. Based on the analysis results, it calculates a score and stores and updates the calculated score in a database. Furthermore, if a user's score exceeds a certain threshold, it offers benefits or discounts. For businesses, it includes a function to organize and provide user score information.

[0205] Hardware and software to use

[0206] Hardware:

[0207] User devices: Smartphones, tablets, PCs, etc.

[0208] Server: Operates as a central processing unit and is connected to the database.

[0209] software:

[0210] Natural language processing models: Natural language processing libraries such as TextBlob and Spacy.

[0211] Database: A database management system such as SQLite or PostgreSQL.

[0212] Details of data processing and calculations

[0213] 1. Obtaining electronic submissions:

[0214] When a user enters a review or comment on their device and presses the submit button, the content is sent to the server. For example, consider a case where a user posts, "This product is truly amazing!"

[0215] 2. Analysis of the posted content:

[0216] The server analyzes the received posts using a natural language processing model (such as TextBlob). It then classifies them as positive, negative, or neutral and generates a sentiment score. For example, a post saying "This product is really great!" would be judged as having a positive sentiment.

[0217] 3. Calculating and saving the score:

[0218] The server calculates a score based on the analysis results and stores / updates that score in the database, linking it to the user's ID. For example, user ID "12345" is assigned a score of +10 points, and the new score is stored in the database.

[0219] 4. Providing incentives:

[0220] The server offers rewards or discounts when a user's score exceeds a certain threshold (e.g., 100 points). For example, a special coupon might be issued when a user scores over 100 points.

[0221] 5. Organization and provision of data for businesses:

[0222] The server organizes user score information for businesses and provides that data. This allows businesses to implement marketing strategies that target lower-risk customer segments.

[0223] Specific example

[0224] When a user posts a review of a specific product on their smartphone, saying "This product is the best!", and presses the Post button, the post is sent to the server. The server uses a natural language processing model (e.g., TextBlob) to analyze the post and determines that it expresses positive sentiment. Based on this analysis, the server scores the user +10 points and saves it to the database. Since the user's score exceeds 100 points, the server sends the user a special coupon. The next action is a notification saying, "A coupon for 10% off all products is now available!"

[0225] Example of a prompt:

[0226] When a user types "This product is the best!" about a specific product on their smartphone and presses the Post button, the following message appears: "This post has been sent to the sentiment analysis module and has been scored as positive content. Since the score exceeds 100, a reward coupon has been issued. Please take the next action."

[0227] This system promotes healthy communication and creates a valuable e-commerce environment for both users and businesses.

[0228] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0229] Step 1:

[0230] Retrieving posts from user terminals

[0231] When a user enters a review or comment on their device and presses the submit button, the post is submitted. The user's device then sends this input to the server. The input data is in text format, such as reviews or comments. Specifically, if a user enters "This product is really great!", that text data is sent to the server via the internet.

[0232] Step 2:

[0233] Receiving and analyzing submitted data

[0234] The server receives posted data sent from the user's terminal. The received text data is analyzed using a natural language processing model (e.g., TextBlob or Spacy). In this analysis, the text is classified as positive, negative, or neutral, and a sentiment score is generated. For example, the text "This product is really great!" is judged as positive, and a positive score of +10 points is calculated.

[0235] Step 3:

[0236] Calculating and saving scores

[0237] The server calculates a user's score based on the analysis results. Specifically, positive posts are assigned high scores, and negative posts are assigned low scores. The calculated score is linked to the user ID, stored in the database, and updated. For example, user ID "12345" is assigned a score of +10 points, which is added to the existing score and stored in the database.

[0238] Step 4:

[0239] Verification and provision of incentives

[0240] The server monitors user scores and provides incentives when a certain threshold is exceeded. For example, if a user's score exceeds 100 points, a special coupon or discount will be offered. This will be notified to the user's device, and the coupon code or discount information will be displayed.

[0241] Step 5:

[0242] Organization and provision of data for businesses

[0243] The server organizes user score information for businesses and provides it as needed. This organized data is used by businesses to identify low-risk customer segments and implement targeted marketing strategies. For example, businesses can improve customer satisfaction by offering special offers to high-rated users.

[0244] This entire process enables the creation of an e-commerce environment that is valuable to both users and businesses.

[0245] 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.

[0246] This invention is a system for identifying and preventing inappropriate behavior on social media in real time. This system promotes healthy communication by analyzing user posts and scoring their behavior. Specific embodiments are described below.

[0247] System Overview

[0248] This system mainly consists of the following elements:

[0249] 1. User terminal: A device used by a user to input and send posts using social networking services (SNS).

[0250] 2. Server: A central processing unit that receives SNS posts and performs analysis using natural language processing models and sentiment engines.

[0251] 3. Database: A storage system for storing and managing analysis results and scores.

[0252] Retrieve user posts

[0253] User terminal: The user enters a message into the SNS posting input form and sends the content to the server by pressing the post button. For example, consider a case where a user posts, "This product is really great!"

[0254] Submit post

[0255] User terminal: The user's entered content is sent to the server via the internet.

[0256] Analysis and scoring of submitted content

[0257] Server: Receives posts sent from terminals. The received posts are analyzed by a natural language processing model and sentiment engine.

[0258] The natural language processing model analyzes the context and keywords of the text and performs sentiment classification (positive, negative, neutral) based on the content of the post.

[0259] The emotion engine further analyzes the emotions contained in user posts, determining their intensity and type. For example, posts with strong positive emotions are given high scores, while posts with strong negative emotions are given low scores.

[0260] Saving and managing scores

[0261] Server: The calculated score is linked to the user ID and stored in the database. This allows each user's score to be managed as a history. For example, the score of user ID "12345" is updated from 90 to 100.

[0262] Provision of incentives

[0263] Server: Users whose scores exceed a certain threshold are offered incentives. For example, users who exceed 100 points may receive special coupons or purchase points. Furthermore, score information is compiled to provide companies with information on high-scoring users. Based on this, companies can offer special offers to low-risk customers.

[0264] Information provision for businesses

[0265] Server: Organizes and provides customer score information needed by businesses at the required time. Based on this data, businesses can implement marketing strategies that target lower-risk customer segments.

[0266] Examples

[0267] When a user posts "This product is the best!" on social media, the post is sent to the server. The server analyzes the post using a natural language processing model and sentiment engine and determines that it expresses positive emotion. Based on the analysis, the server scores the user +10 points and saves the user's new score to the database. Since the score exceeds 100 points, the server offers this user a special coupon.

[0268] Furthermore, if a user posts something like, "This service is terrible!", the emotion engine detects a strong negative emotion. Based on this information, the server scores the user -10 points and also evaluates the impact the negative post has on other users.

[0269] This system promotes healthy communication on the SNS and realizes an SNS environment that is valuable to users and enterprises. By using the emotion engine, it is possible to analyze the emotions of users in detail and perform more accurate scoring.

[0270] The following describes the process flow.

[0271] Step 1:

[0272] User: The user inputs a message into the SNS post input form.

[0273] Example: Input a message such as "This product is really wonderful!"

[0274] Step 2:

[0275] Terminal: The user presses the post button and sends the post content to the server.

[0276] ? The post content is sent to the server via the Internet.

[0277] Step 3:

[0278] Server: The server receives the post data sent from the terminal.

[0279] As an example, JSON data like the following arrives at the server:

[0280] json

[0281] {

[0282] "user_id": "12345",

[0283] "timestamp": "2T10:00:00Z",

[0284] "content": "This product is really wonderful!" It should be noted that there seems to be an error in the "timestamp" value in the original text you provided. The correct format should be something like "2023-10-01T10:00:00Z" instead of "2T10:00:00Z". I have translated it as it is in the provided text but this might need to be corrected in the original source.

[0285] }

[0286] Step 4:

[0287] Server: Send the received post content to the natural language processing model for text analysis.

[0288] The model analyzes the context and keywords of the text and performs sentiment classification (positive, negative, neutral).

[0289] Example: The keyword "wonderful" is recognized as positive.

[0290] Step 5:

[0291] Server: Send the analysis results from the natural language processing model to the sentiment engine.

[0292] The sentiment engine receives the analysis results and determines the intensity and type of sentiment.

[0293] Step 6:

[0294] Server: Assign a score to the post based on the output of the sentiment engine.

[0295] For example, assign +10 to a post with strong positive sentiment and -10 to a post with strong negative sentiment.

[0296] Example: A post saying "This product is really wonderful!" is judged as positive and a score of +10 is calculated.

[0297] Step 7:

[0298] Server: Save the calculated score in the database associated with the user ID and update the existing score.

[0299] Example: The score of user ID "12345" is updated from 90 to 100.

[0300] Step 8:

[0301] Server: Check the user scores in the database and provide incentives if the score exceeds a certain threshold.

[0302] Example: Since the score exceeds 100, a special coupon is given to the user.

[0303] Step 9:

[0304] Server: Execute the process of providing incentives and notify the user.

[0305] Example: Send a notification of coupon provision to the user via an electronic message or the like.

[0306] Step 10:

[0307] Server: Organize the user score information for enterprises and generate a list of highly evaluated users.

[0308] Aggregate the score data and related information of highly evaluated users.

[0309] Step 11:

[0310] Server: Provide the organized score information to enterprises and assist in identifying low-risk customers.

[0311] Example: By providing data such as "User ID: 12345 is highly evaluated" to enterprises, it can be used in marketing strategies such as special offers.

[0312] The above are the specific processing steps of the program including the emotion engine. With this system, slander and inappropriate behaviors on SNS can be identified in real time, and healthy communication can be promoted.

[0313] (Example 2)

[0314] 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".

[0315] The increase in inappropriate behavior and harmful posts on social media is hindering healthy communication. This is leading to decreased user satisfaction and increased marketing risks for businesses. Existing systems do not adequately analyze and score content, making it difficult to identify and prevent inappropriate behavior in real time. Therefore, there is a need for a new system that can analyze content in detail and promote healthy communication.

[0316] 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.

[0317] In this invention, the server includes means for acquiring SNS posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine emotions and context, means for performing a detailed analysis using an emotion engine based on the analysis results to determine the intensity and type of emotion, means for calculating a user score based on the analysis results, means for storing and updating the calculated score in a database, means for checking the user's score and providing incentives to users who exceed a certain threshold, and means for organizing and providing user score information to companies. This makes it possible to promote healthy communication on SNS in real time and realize an SNS environment that is valuable to both users and companies.

[0318] A "user" is someone who uses a system to post content via social networking services (SNS).

[0319] A "user terminal" is a device used by a user to input and send posts using social networking services (SNS).

[0320] A "server" is a central processing unit that receives, analyzes, and scores social media posts.

[0321] A "database" is a storage system used to store and manage analysis results and scores.

[0322] A "natural language processing model" is a general term for algorithms that analyze the context and keywords of text and classify sentiment (positive, negative, neutral) based on the content of a post.

[0323] An "emotion engine" is a processing system that analyzes the emotions contained in a user's post in detail and determines the intensity and type of those emotions.

[0324] A "score" is a numerical value calculated based on the content of a post, and it serves as a criterion for evaluating a user's behavior and emotions.

[0325] An "incentive" is a reward or benefit given when a user's score exceeds a certain threshold.

[0326] A "company" is a legal entity or organization that implements marketing strategies based on user score information.

[0327] "Analysis results" refer to the sentiment classification and detailed sentiment analysis output of the posted content obtained through a natural language processing model and sentiment engine.

[0328] A "threshold" is a threshold value used to provide an incentive when a score reaches a certain standard.

[0329] This invention is a system for identifying and preventing inappropriate behavior on social media in real time. This system promotes healthy communication by analyzing user posts and scoring their behavior. A specific embodiment of this system is described below.

[0330] System Overview

[0331] This system mainly consists of the following elements:

[0332] 1. User terminal: A device used by users to input and send posts using social networking services (SNS). User terminals include smartphones, tablets, and personal computers.

[0333] 2. Server: This is a central processing unit that receives SNS posts and performs analysis using natural language processing models and sentiment engines.

[0334] 3. Database: A storage system for storing and managing analysis results and scores.

[0335] Retrieve user posts

[0336] User terminal:

[0337] The user enters a message into the social media posting form and presses the post button. This saves the posted content on the user's device. For example, if the user enters "This product is truly amazing!", this content proceeds to the next step.

[0338] Submit post

[0339] User terminal:

[0340] This involves calling the API of a social networking service (SNS) application and sending the posted content to the server via the internet.

[0341] Analysis and scoring of submitted content

[0342] server:

[0343] The server receives the posted content sent from the user's terminal. The received posted content is parsed in the following steps:

[0344] 1. Natural Language Processing Models (NLP Models):

[0345] The server analyzes the context and keywords of the text and classifies the sentiment of the post (positive, negative, neutral). Specifically, it uses an NLP model to understand the meaning of the text data and assign appropriate sentiment labels.

[0346] 2. Emotional Engine:

[0347] The system analyzes the emotions contained in posts in detail, determining their intensity and type. For example, posts with strong positive emotions are given high scores, while posts with strong negative emotions are given low scores.

[0348] Saving and managing scores

[0349] server:

[0350] The calculated score is linked to the user ID and stored in the database. This allows each user's score to be managed as a history. For example, the score of user ID "12345" is updated from 90 to 100 based on their posts.

[0351] Provision of incentives

[0352] server:

[0353] Incentives are offered to users whose scores exceed a certain threshold. For example, users who score over 100 points may receive special coupons or purchase points. Furthermore, score information is compiled to provide companies with information on high-scoring users. Based on this, companies can offer special offers to low-risk customers.

[0354] Information provision for businesses

[0355] server:

[0356] We organize the customer score information that companies need and provide it at the right time. Based on this data, companies can implement marketing strategies that target lower-risk customer segments.

[0357] Examples

[0358] For example, if a user posts "This product is the best!" on social media, that content is sent from the user's device to the server. The server analyzes the post using a natural language processing model and an emotion engine and determines that it expresses a positive emotion. Based on the analysis, the server scores the user +10 points and saves the new score to the database. Since the score exceeds 100 points, the server offers this user a special coupon.

[0359] Example of a prompt

[0360] A user posted on social media, "I had so much fun today!" This system analyzes the post using a natural language processing model and sentiment engine, detects positive emotions, and scores the user +10 points. Save the new score to the database, and issue a coupon when the score exceeds 100 points.

[0361] This system promotes healthy communication on social media in real time, creating a valuable social media environment for both users and businesses. By using an emotion engine, it is possible to analyze users' emotions in detail and perform more accurate scoring.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] Retrieve user posts

[0365] User terminal:

[0366] The user enters a message into the social media posting form and presses the post button. This causes the user's message to be stored on the device as input data.

[0367] input:

[0368] A text message entered by the user (e.g., "This product is really great!").

[0369] output:

[0370] The retained post content is ready to be sent to the next processing step.

[0371] Specific actions:

[0372] The user opens a social networking app on their smartphone or PC.

[0373] Enter "This product is truly amazing!" into the submission form.

[0374] Click the submit button.

[0375] Step 2:

[0376] Submit post

[0377] User terminal:

[0378] When the submit button is pressed, the user's entered content is sent to the server via the internet.

[0379] input:

[0380] Post content stored on the device.

[0381] output:

[0382] The server receives the data via the internet.

[0383] Specific actions:

[0384] The user's device calls the SNS application's API and sends the content to be posted as a parameter.

[0385] The data reaches the server via the internet.

[0386] Step 3:

[0387] Analysis and scoring of submitted content

[0388] server:

[0389] The received posts are analyzed using a natural language processing (NLP) model, and then further analyzed in detail using an emotion engine.

[0390] input:

[0391] Content received via the internet (e.g., "This product is truly amazing!").

[0392] output:

[0393] A score based on the results of emotion classification (positive, negative, neutral) and the intensity and type of emotion.

[0394] Specific actions:

[0395] The server saves the received post content to a buffer.

[0396] Using natural language processing (NLP) models, we analyze the context and keywords of text to classify sentiment.

[0397] Example: A post saying "This product is truly amazing!" is judged as positive.

[0398] The emotion engine further analyzes the emotion classification results to determine the intensity and type of emotion.

[0399] Example: A high score was given due to strong positive emotions.

[0400] Step 4:

[0401] Saving and managing scores

[0402] server:

[0403] The calculated score is linked to the user ID and saved in the database.

[0404] input:

[0405] The score analyzed by the emotion engine (e.g., +10 points) and the user ID.

[0406] output:

[0407] The updated score is saved in the database.

[0408] Specific actions:

[0409] The server sets the user ID and score.

[0410] The set score is saved to the database.

[0411] Example: Update the score of user ID "12345" from 90 to 100.

[0412] Step 5:

[0413] Provision of incentives

[0414] server:

[0415] Incentives are provided to users whose scores exceed a certain threshold.

[0416] input:

[0417] Updated user score and threshold information.

[0418] output:

[0419] Offering incentives (e.g., special coupons or purchase points).

[0420] Specific actions:

[0421] The server periodically checks the database to identify users whose scores exceed a threshold.

[0422] This involves executing the routine for providing incentives and handling the procedures for issuing coupons and points.

[0423] Example: Issue a special coupon to users who score over 100 points.

[0424] Step 6:

[0425] Information provision for businesses

[0426] server:

[0427] We organize customer score information requested by companies and provide it at the necessary time.

[0428] input:

[0429] Organized user score information.

[0430] output:

[0431] Customer score information provided to companies.

[0432] Specific actions:

[0433] Receive requests from companies and filter and organize user score information.

[0434] Convert the data to the required format (e.g., CSV file) and send it to the company.

[0435] In this way, this system promotes healthy communication on social media in real time, providing a valuable environment for both users and businesses.

[0436] (Application Example 2)

[0437] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0438] Conventional social media post analysis systems only analyze text-based posts, making it difficult to effectively grasp customer emotions and satisfaction levels in real time during customer service interactions. Furthermore, the lack of means to provide feedback on improving customer service quality has limited improvements in customer service operations. In this situation, customer satisfaction cannot be adequately improved, making it urgent to improve the efficiency of service delivery and enhance customer satisfaction through the use of customer service robots and other technologies in physical stores.

[0439] 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.

[0440] In this invention, the server includes means for acquiring SNS posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine emotions and context, means for calculating a user score based on the analysis results, means for storing and updating the calculated score in a database, means for checking the user's score and providing incentives to users who exceed a certain threshold, means for organizing and providing user score information to companies, means for converting speech to text in real time using speech recognition, and means for identifying low-risk customers and providing feedback to improve customer service quality. This enables real-time evaluation of customer service quality in physical stores, and by combining speech recognition and emotion analysis, it becomes possible to measure customer satisfaction with greater accuracy and improve customer service quality.

[0441] "Methods for obtaining user-submitted SNS posts" refers to systems that collect data such as text and images posted by users on social media.

[0442] "A means of analyzing the content of acquired posts using a natural language processing model to determine emotions and context" refers to a system that analyzes collected SNS post data using natural language processing technology and extracts emotions and context from the text.

[0443] "A means of calculating user scores based on analysis results" refers to a system that calculates an evaluation score for each user's post based on analyzed sentiment and contextual information.

[0444] "Means for saving and updating calculated scores in a database" refers to a system that records calculated evaluation scores in a digital storage system and updates them as needed.

[0445] "A means of checking user scores and providing incentives to users who exceed a certain threshold" refers to a system that monitors user evaluation scores and provides benefits or rewards to users who meet specified criteria.

[0446] "A means of organizing and providing user score information to businesses" refers to a system that compiles and provides user evaluation score information in a format that businesses can use.

[0447] "A means of converting speech to text in real time using speech recognition" refers to a system that uses speech recognition technology to convert speech data acquired in real time into text information.

[0448] "A means of identifying low-risk customers and providing feedback to improve service quality" refers to a system that identifies low-risk customers based on an analysis of customer emotions and satisfaction, and provides feedback to service staff and systems on areas for improvement.

[0449] This invention is a system that identifies and prevents inappropriate behavior on social media in real time. The system promotes healthy communication by analyzing user posts and scoring their behavior. Furthermore, based on examples of customer service robot applications in physical stores, it aims to improve customer satisfaction in real time and enhance the quality of customer service.

[0450] System Overview

[0451] This system mainly consists of the following elements:

[0452] 1. User terminal: A device used by users to input and send posts using social networking services (SNS). It also has voice recognition capabilities that allow for voice input.

[0453] 2. Server: A central processing unit that receives SNS posts and audio data and performs analysis using natural language processing models and sentiment engines. It calculates a score based on the analysis results and stores it in a database.

[0454] 3. Database: A storage system for storing and managing analysis results and scores.

[0455] 4. Interface: Includes a user interface for organizing and providing score information to businesses.

[0456] Hardware and software to be used

[0457] This system uses the following hardware and software:

[0458] Hardware: Microphone, speaker, user device (smartphone or tablet), and internet connection.

[0459] Software: Python, speech recognition library (Google® Speech-to-Text), natural language processing library (Hugging Face Transformers), sentiment analysis library (TextBlob, VaderSentiment), database (SQLite)

[0460] Data processing and data calculation

[0461] 1. Speech recognition:

[0462] The user's device uses a microphone to capture the customer's conversation. The captured audio data is converted into text data using the Google Speech-to-Text API. This allows the customer service robot to convert interactions with customers into text in real time.

[0463] 2. Natural language processing and sentiment analysis:

[0464] The server analyzes this text data using a natural language processing model (Hugging Face's Transformers). It extracts context and keywords and performs sentiment classification (positive, negative, neutral). Then, it uses sentiment engines (TextBlob, VaderSentiment) to perform a detailed analysis of the customer's emotions.

[0465] 3. Scoring:

[0466] Based on the analysis results, the server calculates a sentiment score for each user. Posts with strong positive emotions are assigned high scores, while posts with strong negative emotions are assigned low scores.

[0467] 4. Data storage and updates:

[0468] The calculated scores are stored in a database and updated as needed. This allows for the management of each user's sentiment score history.

[0469] 5. Providing incentives and organizing company information:

[0470] When a user's score exceeds a certain threshold, they will be offered rewards (coupons or special offers). Furthermore, companies will be provided with compiled score information of low-risk customers.

[0471] Specific example

[0472] For example, consider a customer service scenario in a physical store. If a customer says, "The service at this cafe is truly amazing!", this audio is converted to text in real time. This text is sent to a server and a natural language processing model determines that it represents a positive emotion. After detailed analysis by the emotion engine, a satisfaction score for this customer is calculated and stored in a database. If the score is high, a coupon is offered as an incentive. The company then uses this data to implement special offers for low-risk customers.

[0473] Example of a prompt

[0474] "Please design a new customer service robot application. This application will perform real-time speech recognition of customer conversations and use natural language processing and sentiment analysis to score customer satisfaction. Furthermore, if a certain score is reached, the customer will receive coupons or other benefits."

[0475] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0476] Step 1:

[0477] Processing details: Acquisition of voice input

[0478] Specific operation: The user's terminal uses a microphone to capture conversations during customer service. The audio data spoken by the user is captured in real time.

[0479] Input: User's voice data

[0480] Output: Captured audio data

[0481] Step 2:

[0482] Processing details: Text conversion of audio data

[0483] Specific operation: The captured audio data is converted into text data using the Google Speech-to-Text API.

[0484] Input: Captured audio data

[0485] Output: Converted text data

[0486] Step 3:

[0487] Processing details: Sending text data

[0488] Specific operation: The user's terminal sends the converted text data to the server.

[0489] Input: Converted text data

[0490] Output: Text data sent to the server

[0491] Step 4:

[0492] Processing details: Analysis of text data

[0493] Specific operation: The server analyzes the received text data using a natural language processing model (e.g., Hugging Face's Transformers). It extracts the context and emotions of the text and performs emotion classification (positive, negative, neutral).

[0494] Input: Text data sent to the server

[0495] Output: Emotion classification result

[0496] Step 5:

[0497] Processing details: Detailed analysis of emotions

[0498] Specific operation: The server uses the emotion engine (TextBlob, VaderSentiment) to further analyze the intensity and type of emotion.

[0499] Input: Sentiment classification result

[0500] Output: Emotion analysis results

[0501] Step 6:

[0502] Processing details: Calculation of user score

[0503] Specific operation: The server calculates a customer satisfaction score based on the sentiment analysis results. Positive emotions are assigned high scores, and negative emotions are assigned low scores.

[0504] Input: Sentiment analysis results

[0505] Output: User score

[0506] Step 7:

[0507] Processing details: Saving and updating scores

[0508] Specific operation: The server saves the calculated score to the database and updates the existing score as needed.

[0509] Input: User score

[0510] Output: Score data stored in the database

[0511] Step 8:

[0512] Processing details: Provision of incentives

[0513] Specific operation: The server checks the user score and provides the customer with a reward (such as a coupon) if the score exceeds a certain threshold.

[0514] Input: Score data stored in the database

[0515] Output: Benefits offered to customers

[0516] Step 9:

[0517] Processing details: Organization and provision of information for businesses.

[0518] Specific operation: The server organizes user score information for the company and provides information on highly-rated users.

[0519] Input: Score data stored in the database

[0520] Output: Organized user score information

[0521] 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.

[0522] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0523] 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.

[0524] [Second Embodiment]

[0525] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0526] 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.

[0527] 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).

[0528] 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.

[0529] 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.

[0530] 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).

[0531] 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.

[0532] 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.

[0533] 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.

[0534] 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.

[0535] 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.

[0536] 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".

[0537] This invention provides a system for identifying and preventing inappropriate behavior on social media in real time. This system promotes healthy communication by analyzing user posts and scoring their behavior. Specific embodiments are described below.

[0538] System Overview

[0539] This system mainly consists of the following elements:

[0540] 1. User terminal: A device used by a user to input and send posts using social networking services (SNS).

[0541] 2. Server: A central processing unit that receives SNS posts and performs analysis using natural language processing models.

[0542] 3. Database: A storage system for storing and managing analysis results and scores.

[0543] Retrieve user posts

[0544] User terminal: The user enters a message into the SNS posting form and sends the content to the server by pressing the post button. For example, consider a case where a user posts, "This product is really great!"

[0545] Submit post

[0546] User terminal: The user's entered content is sent to the server via the internet.

[0547] Analysis and scoring of submitted content

[0548] Server: Receives posts sent from terminals. The received posts are analyzed by a natural language processing model and classified as positive, negative, or neutral. The server calculates a score based on the analysis results. For example, if a post that says "This product is really great!" is judged to be positive, the server will assign this user a score of +10.

[0549] Saving and managing scores

[0550] Server: The calculated score is linked to the user ID and stored in the database. This allows each user's score to be managed as a history. For example, the score of user ID "12345" is updated from 90 to 100.

[0551] Provision of incentives

[0552] Server: Users whose scores exceed a certain threshold are offered incentives. For example, users who exceed 100 points may receive special coupons or purchase points. Furthermore, score information is compiled to provide companies with information on high-scoring users. Based on this, companies can offer special offers to low-risk customers.

[0553] Information provision for businesses

[0554] Server: Organizes and provides customer score information needed by businesses at the required time. Based on this data, businesses can implement marketing strategies that target lower-risk customer segments.

[0555] Examples

[0556] When a user posts "This product is the best!" on social media, the post is sent to the server. The server analyzes the post using a natural language processing model and determines that it expresses positive emotion. Based on the analysis, the server scores the user +10 points and saves the user's new score to the database. Since the score exceeds 100 points, the server offers this user a special coupon.

[0557] This system will promote healthy communication on social media and create a valuable social media environment for both users and businesses.

[0558] The following describes the processing flow.

[0559] Step 1:

[0560] User: Enter a message into the social media posting form.

[0561] Example: Enter the message, "This product is truly amazing!"

[0562] Step 2:

[0563] Terminal: The user presses the post button and sends the content to the server.

[0564] The submitted content is sent to the server via the internet.

[0565] Step 3:

[0566] Server: Receives posted data sent from terminals.

[0567] For example, the following JSON data arrives on the server:

[0568] json

[0569] {

[0570] "user_id": "12345",

[0571] "timestamp": "2023-10-01T10:00:00Z",

[0572] "content": "This product is truly amazing!"

[0573] }

[0574] Step 4:

[0575] Server: Sends the received posted content to a natural language processing model for text analysis.

[0576] The model performs sentiment classification (positive, negative, neutral) and contextual analysis.

[0577] Step 5:

[0578] Server: Assigns a score to posts based on the analysis results from a natural language processing model.

[0579] Example: The keyword "great" is recognized as positive, so a score of +10 is calculated.

[0580] Step 6:

[0581] Server: Associates the calculated score with the user ID, saves it to the database, and updates existing scores.

[0582] Example: The score of user ID "12345" will be updated from 90 to 100.

[0583] Step 7:

[0584] Server: Checks user scores in the database and provides incentives if the score exceeds a certain threshold.

[0585] Example: Because the score exceeded 100, the user will be given a special coupon.

[0586] Step 8:

[0587] Server: Executes the incentive distribution process and notifies the user.

[0588] Example: Send a coupon offer notification to the user via electronic message or similar means.

[0589] Step 9:

[0590] Server: Organizes user score information for enterprises and generates lists of high-rated users.

[0591] This system aggregates score data and related information from highly-rated users.

[0592] Step 10:

[0593] Server: Provides organized score information to companies and helps them identify low-risk customers.

[0594] Example: By providing companies with data indicating that "User ID: 12345 has a high rating," this data can be used for marketing strategies such as special offers.

[0595] The above outlines the specific processing steps of the program. This system can identify defamation and inappropriate behavior on social media in real time, thereby promoting healthy communication.

[0596] (Example 1)

[0597] 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."

[0598] While it is necessary to identify inappropriate behavior and posts on social media in real time and promote healthy communication, conventional systems had problems with low accuracy in analyzing posts and being unable to properly evaluate user behavior. Furthermore, there were insufficient means of providing reliable user information to companies. This made it difficult to provide appropriate incentives to promote healthy user communication and for companies to implement efficient marketing strategies.

[0599] 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.

[0600] In this invention, the server includes means for acquiring posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine sentiment and context, means for calculating and updating the user's score based on the analysis results, means for storing the calculated score in a storage device, means for checking the user's score and providing incentives to users who exceed a certain threshold, and means for organizing and providing user score information to companies. This makes it possible to identify inappropriate behavior on social media in real time, promote healthy communication, and provide appropriate incentives to users. It also makes it possible to provide companies with reliable user information and support the implementation of efficient marketing strategies.

[0601] A "user" refers to an individual or group that inputs and submits posts using social networking services (SNS).

[0602] A "post" refers to information such as text, images, videos, and links that a user shares on social media.

[0603] A "natural language processing model" refers to a machine learning algorithm or program that analyzes text data entered by a user to determine its sentiment and context.

[0604] "Analysis results" refer to information about the sentiment and context of posts obtained by a natural language processing model.

[0605] A "score" is a numerical value calculated based on the analysis of the content of a post, and it is an indicator that evaluates the healthiness of a user's behavior and statements.

[0606] A "memory device" refers to a database or storage system used to store calculated scores.

[0607] An "incentive" refers to a reward or benefit offered to users who exceed a certain score.

[0608] "Company" refers to a business organization that uses user score information to develop marketing strategies and manage risks.

[0609] Modes for carrying out the invention

[0610] This invention is a system that identifies inappropriate behavior on social networking services (SNS) in real time and promotes healthy communication. This system consists of the following hardware and software: a user terminal, a server, a database, and a natural language processing model.

[0611] 1. System Overview

[0612] This system consists of the following elements:

[0613] User device: A device used by a user to input and send posts using social networking services (e.g., smartphone, PC).

[0614] Server: A central processing unit that receives SNS posts and performs analysis using natural language processing models. Examples of software used include natural language processing models such as BERT and GPT-4.

[0615] Database: A storage system for storing and managing analysis results and scores. Specific examples include MySQL and PostgreSQL.

[0616] 2. Retrieve user posts

[0617] User terminal: The user enters a message into the social media posting form and presses the post button. For example, this would be the case when the user enters "This product is really great!"

[0618] 3. Submit your post

[0619] User terminal: When the post button is pressed, the content of the post is sent to the server via the internet. Specifically, the post data is sent using an HTTP request.

[0620] 4. Receiving the posted content

[0621] Server: Receives posted content via the internet for analysis. Receives the posted content contained in the body of the HTTP request.

[0622] 5. Analysis of posted content

[0623] Server: The server passes the received post content to a natural language processing model, which classifies it as positive, negative, or neutral. For example, if "This product is truly wonderful!" is judged to be positive, the natural language processing model returns that result.

[0624] 6. Score calculation and updating

[0625] Server: Based on the analysis results, the server calculates and updates the user's score. Posts containing positive emotions are given higher scores, and posts containing negative emotions are given lower scores. For example, it might calculate to add +10 points to the user's score.

[0626] 7. Saving and managing scores

[0627] Server: Stores the calculated score in the database, associating it with the user ID. Executes an SQL query to save the new score information in the database. For example, saves a new score of 100 for user ID "12345".

[0628] 8. Provision of incentives

[0629] Server: When a user's score exceeds a certain threshold, an incentive (e.g., a special coupon) is provided. A special coupon is issued to users who exceed the threshold, and a notification email is sent.

[0630] 9. Information provision for businesses

[0631] Server: Organizes and provides customer score information available to businesses as needed. Creates lists of high-rated users and sends data via API to businesses in a specific format (e.g., CSV file).

[0632] Examples

[0633] When a user posts on social media saying "This product is the best!", the following actions are taken:

[0634] 1. User's device: The user enters "This product is the best!" into the posting form of the SNS app and presses the post button.

[0635] 2. Terminal: The posted content is sent to the server as an HTTP request.

[0636] 3. Server: The received post content is passed to a natural language processing model, which determines that it expresses positive sentiment.

[0637] 4. Server: Based on the analysis results, the server assigns a score of +10 points to the user and saves the new score to the database.

[0638] 5. Server: Since the score has exceeded 100 points, issue a special coupon to the user and notify them via email.

[0639] This system promotes healthy communication on social media. Furthermore, it provides companies with reliable user information, enabling them to implement more efficient marketing strategies.

[0640] Examples of prompts for generative AI models

[0641] I want to post a positive comment on social media saying, "I like this product," but what kind of content should I include to ensure that this post is viewed as healthy communication on social media?

[0642] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0643] Step 1:

[0644] User input

[0645] User: Enter a message into the posting form of the SNS app and press the post button.

[0646] Input: Text entered by the user in the social media posting form.

[0647] Specific action: The user types "This product is truly amazing!" and clicks the submit button.

[0648] Output: The post is ready to be sent.

[0649] Step 2:

[0650] Submit post

[0651] Terminal: When the post button is pressed, the content of the post is sent to the server via the internet.

[0652] Input: The content of the post entered by the user.

[0653] Specific action: The smartphone or PC sends the message "This product is truly amazing!" to the server as an HTTP request.

[0654] Output: The submitted content arrives at the server.

[0655] Step 3:

[0656] Received the posted content

[0657] Server: Receives submitted content via the internet for analysis.

[0658] Input: HTTP request containing the content of the post sent from the terminal.

[0659] Specific action: The server receives the message "This product is really great!" which is included in the body of the HTTP request.

[0660] Output: The submitted content is ready for analysis.

[0661] Step 4:

[0662] Analysis of posted content

[0663] Server: The posted content is passed to a natural language processing model (e.g., BERT or GPT-4) to perform sentiment analysis.

[0664] Input: Received post content: "This product is truly amazing!"

[0665] Specific operation: Input the post content into a natural language processing model and obtain sentiment analysis results. For example, the model identifies it as a positive sentiment.

[0666] Output: Positive emotions are obtained as the analysis result.

[0667] Step 5:

[0668] Score calculation

[0669] Server: Calculates the user's score based on the analysis results.

[0670] Input: Analysis results from a natural language processing model (e.g., positive emotions).

[0671] Specific operation: Based on the analysis results, the user is awarded a score of +10 points. If the original score was 90, the new score will be 100.

[0672] Output: The new calculated score.

[0673] Step 6:

[0674] Save score

[0675] Server: Stores the calculated score in the database, linked to the user ID.

[0676] Input: User ID and new score (e.g., User ID "12345" and score 100).

[0677] Specific action: Execute an SQL query and save the new score to the database.

[0678] Output: Score information is saved to the database.

[0679] Step 7:

[0680] Provision of incentives

[0681] Server: Provides an incentive to the user if their score exceeds a certain threshold.

[0682] Input: Updated score (e.g., 100 points or more).

[0683] Specific action: Issue a special coupon and send a notification email to the user.

[0684] Output: Incentive information is provided to the user.

[0685] Step 8:

[0686] Providing information for businesses

[0687] Server: Organizes customer score information available to the company and provides it as needed.

[0688] Input: User score information retrieved from the database.

[0689] Specific operation: Create a list of highly-rated users and send the data to companies via an API in CSV file format.

[0690] Output: User score information organized for enterprise use.

[0691] (Application Example 1)

[0692] 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."

[0693] In today's e-commerce environment, it is crucial that user reviews and comments are a sound and reliable source of information. However, there is a lack of systems to evaluate inappropriate behavior and comments, often hindering healthy communication. Furthermore, there are insufficient effective methods for evaluating user trustworthiness, making it difficult for stores to provide appropriate incentives. This invention aims to solve these problems and provide a valuable environment for both users and stores.

[0694] 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.

[0695] In this invention, the server includes means for acquiring electronic posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine sentiment and context, means for calculating the user's score based on the analysis results, means for storing and updating the calculated score in a database, means for checking the user's score and providing incentives to users who exceed a certain threshold, means for organizing and providing user score information to companies, means for analyzing user reviews and comments in real time in an e-commerce environment, and means for providing benefits and discounts when a user's score exceeds a certain threshold. This promotes healthy communication and enables an e-commerce environment that is valuable to both users and companies.

[0696] "Electronic submissions" refer to digital content such as text messages and comments that users send over the internet.

[0697] A "natural language processing model" is a collection of algorithms and technologies used to analyze and understand human language and to provide appropriate responses and processing.

[0698] "Determining emotions and context" is the process of analyzing words and context within a text to determine whether they represent positive, negative, or neutral emotions.

[0699] "Calculating a score" is the process of quantifying the evaluation of a post based on its analyzed content.

[0700] "Saving and updating in a database" means recording the calculated score in data storage in a way that allows each user to be identified, and updating it as needed whenever new data is added.

[0701] An "incentive" is a reward or benefit given to users who meet certain criteria.

[0702] "Organizing and providing user score information to businesses" refers to the process of classifying and processing user score data in a way that is easy for businesses to use, and providing that data to businesses at the appropriate time.

[0703] An "e-commerce environment" refers to a system or platform for buying and selling goods and services over the internet.

[0704] "Analyzing reviews and comments in real time" means instantly receiving reviews and comments posted by users and analyzing their content using a natural language processing model.

[0705] "Benefits and discounts" refer to coupons, discount services, or other forms of rewards offered to users.

[0706] System Overview

[0707] The system for realizing this application retrieves electronic submissions entered by users and analyzes them using a natural language processing model. Based on the analysis results, it calculates a score and stores and updates the calculated score in a database. Furthermore, if a user's score exceeds a certain threshold, it offers benefits or discounts. For businesses, it includes a function to organize and provide user score information.

[0708] Hardware and software to use

[0709] Hardware:

[0710] User devices: Smartphones, tablets, PCs, etc.

[0711] Server: Operates as a central processing unit and is connected to the database.

[0712] software:

[0713] Natural language processing models: Natural language processing libraries such as TextBlob and Spacy.

[0714] Database: A database management system such as SQLite or PostgreSQL.

[0715] Details of data processing and calculations

[0716] 1. Obtaining electronic submissions:

[0717] When a user enters a review or comment on their device and presses the submit button, the content is sent to the server. For example, consider a case where a user posts, "This product is truly amazing!"

[0718] 2. Analysis of the posted content:

[0719] The server analyzes the received posts using a natural language processing model (such as TextBlob). It then classifies them as positive, negative, or neutral and generates a sentiment score. For example, a post saying "This product is really great!" would be judged as having a positive sentiment.

[0720] 3. Calculating and saving the score:

[0721] The server calculates a score based on the analysis results and stores / updates that score in the database, linking it to the user's ID. For example, user ID "12345" is assigned a score of +10 points, and the new score is stored in the database.

[0722] 4. Providing incentives:

[0723] The server offers rewards or discounts when a user's score exceeds a certain threshold (e.g., 100 points). For example, a special coupon might be issued when a user scores over 100 points.

[0724] 5. Organization and provision of data for businesses:

[0725] The server organizes user score information for businesses and provides that data. This allows businesses to implement marketing strategies that target lower-risk customer segments.

[0726] Specific example

[0727] When a user posts a review of a specific product on their smartphone, saying "This product is the best!", and presses the Post button, the post is sent to the server. The server uses a natural language processing model (e.g., TextBlob) to analyze the post and determines that it expresses positive sentiment. Based on this analysis, the server scores the user +10 points and saves it to the database. Since the user's score exceeds 100 points, the server sends the user a special coupon. The next action is a notification saying, "A coupon for 10% off all products is now available!"

[0728] Example of a prompt:

[0729] When a user types "This product is the best!" about a specific product on their smartphone and presses the Post button, the following message appears: "This post has been sent to the sentiment analysis module and has been scored as positive content. Since the score exceeds 100, a reward coupon has been issued. Please take the next action."

[0730] This system promotes healthy communication and creates a valuable e-commerce environment for both users and businesses.

[0731] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0732] Step 1:

[0733] Retrieving posts from user terminals

[0734] When a user enters a review or comment on their device and presses the submit button, the post is submitted. The user's device then sends this input to the server. The input data is in text format, such as reviews or comments. Specifically, if a user enters "This product is really great!", that text data is sent to the server via the internet.

[0735] Step 2:

[0736] Receiving and analyzing submitted data

[0737] The server receives posted data sent from the user's terminal. The received text data is analyzed using a natural language processing model (e.g., TextBlob or Spacy). In this analysis, the text is classified as positive, negative, or neutral, and a sentiment score is generated. For example, the text "This product is really great!" is judged as positive, and a positive score of +10 points is calculated.

[0738] Step 3:

[0739] Calculating and saving scores

[0740] The server calculates a user's score based on the analysis results. Specifically, positive posts are assigned high scores, and negative posts are assigned low scores. The calculated score is linked to the user ID, stored in the database, and updated. For example, user ID "12345" is assigned a score of +10 points, which is added to the existing score and stored in the database.

[0741] Step 4:

[0742] Verification and provision of incentives

[0743] The server monitors user scores and provides incentives when a certain threshold is exceeded. For example, if a user's score exceeds 100 points, a special coupon or discount will be offered. This will be notified to the user's device, and the coupon code or discount information will be displayed.

[0744] Step 5:

[0745] Organization and provision of data for businesses

[0746] The server organizes user score information for businesses and provides it as needed. This organized data is used by businesses to identify low-risk customer segments and implement targeted marketing strategies. For example, businesses can improve customer satisfaction by offering special offers to high-rated users.

[0747] This entire process enables the creation of an e-commerce environment that is valuable to both users and businesses.

[0748] 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.

[0749] This invention is a system for identifying and preventing inappropriate behavior on social media in real time. This system promotes healthy communication by analyzing user posts and scoring their behavior. Specific embodiments are described below.

[0750] System Overview

[0751] This system mainly consists of the following elements:

[0752] 1. User terminal: A device used by a user to input and send posts using social networking services (SNS).

[0753] 2. Server: A central processing unit that receives SNS posts and performs analysis using natural language processing models and sentiment engines.

[0754] 3. Database: A storage system for storing and managing analysis results and scores.

[0755] Retrieve user posts

[0756] User terminal: The user enters a message into the SNS posting input form and sends the content to the server by pressing the post button. For example, consider a case where a user posts, "This product is really great!"

[0757] Submit post

[0758] User terminal: The user's entered content is sent to the server via the internet.

[0759] Analysis and scoring of submitted content

[0760] Server: Receives posts sent from terminals. The received posts are analyzed by a natural language processing model and sentiment engine.

[0761] The natural language processing model analyzes the context and keywords of the text and performs sentiment classification (positive, negative, neutral) based on the content of the post.

[0762] The emotion engine further analyzes the emotions contained in user posts, determining their intensity and type. For example, posts with strong positive emotions are given high scores, while posts with strong negative emotions are given low scores.

[0763] Saving and managing scores

[0764] Server: The calculated score is linked to the user ID and stored in the database. This allows each user's score to be managed as a history. For example, the score of user ID "12345" is updated from 90 to 100.

[0765] Provision of incentives

[0766] Server: Users whose scores exceed a certain threshold are offered incentives. For example, users who exceed 100 points may receive special coupons or purchase points. Furthermore, score information is compiled to provide companies with information on high-scoring users. Based on this, companies can offer special offers to low-risk customers.

[0767] Information provision for businesses

[0768] Server: Organizes and provides customer score information needed by businesses at the required time. Based on this data, businesses can implement marketing strategies that target lower-risk customer segments.

[0769] Examples

[0770] When a user posts "This product is the best!" on social media, the post is sent to the server. The server analyzes the post using a natural language processing model and sentiment engine and determines that it expresses positive emotion. Based on the analysis, the server scores the user +10 points and saves the user's new score to the database. Since the score exceeds 100 points, the server offers this user a special coupon.

[0771] Furthermore, if a user posts something like, "This service is terrible!", the emotion engine detects a strong negative emotion. Based on this information, the server scores the user -10 points and also evaluates the impact the negative post has on other users.

[0772] This system promotes healthy communication on social media, creating a valuable social media environment for both users and businesses. By using an emotion engine, it is possible to analyze users' emotions in detail and perform more accurate scoring.

[0773] The following describes the processing flow.

[0774] Step 1:

[0775] User: Enter a message into the social media posting form.

[0776] Example: Enter the message, "This product is truly amazing!"

[0777] Step 2:

[0778] Terminal: The user presses the post button and sends the content to the server.

[0779] The submitted content is sent to the server via the internet.

[0780] Step 3:

[0781] Server: Receives posted data sent from terminals.

[0782] For example, the following JSON data arrives on the server:

[0783] json

[0784] {

[0785] "user_id": "12345",

[0786] "timestamp": "2023-10-01T10:00:00Z",

[0787] "content": "This product is truly amazing!"

[0788] }

[0789] Step 4:

[0790] Server: Sends the received posted content to a natural language processing model for text analysis.

[0791] The model analyzes the context and keywords of the text and performs sentiment classification (positive, negative, neutral).

[0792] Example: The keyword "wonderful" is perceived as positive.

[0793] Step 5:

[0794] Server: Sends the analysis results from the natural language processing model to the emotion engine.

[0795] The emotion engine receives the analysis results and determines the intensity and type of emotion.

[0796] Step 6:

[0797] Server: Assigns a score to posts based on the output of the sentiment engine.

[0798] For example, posts with strong positive emotions will be given a +10, and posts with strong negative emotions will be given a -10.

[0799] For example, a post that says "This product is truly amazing!" will be judged as positive and will receive a score of +10.

[0800] Step 7:

[0801] Server: Associates the calculated score with the user ID, saves it to the database, and updates existing scores.

[0802] Example: The score of user ID "12345" will be updated from 90 to 100.

[0803] Step 8:

[0804] Server: Checks user scores in the database and provides incentives if the score exceeds a certain threshold.

[0805] Example: Because the score exceeded 100, the user will be given a special coupon.

[0806] Step 9:

[0807] Server: Executes the incentive distribution process and notifies the user.

[0808] Example: Send a coupon offer notification to the user via electronic message or similar means.

[0809] Step 10:

[0810] Server: Organizes user score information for enterprises and generates lists of high-rated users.

[0811] This system aggregates score data and related information from highly-rated users.

[0812] Step 11:

[0813] Server: Provides organized score information to companies and helps them identify low-risk customers.

[0814] Example: By providing companies with data indicating that "User ID: 12345 has a high rating," this data can be used for marketing strategies such as special offers.

[0815] The above outlines the specific processing steps of the program, including the emotion engine. This system can identify defamation and inappropriate behavior on social media in real time, thereby promoting healthy communication.

[0816] (Example 2)

[0817] 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".

[0818] The increase in inappropriate behavior and harmful posts on social media is hindering healthy communication. This is leading to decreased user satisfaction and increased marketing risks for businesses. Existing systems do not adequately analyze and score content, making it difficult to identify and prevent inappropriate behavior in real time. Therefore, there is a need for a new system that can analyze content in detail and promote healthy communication.

[0819] 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.

[0820] In this invention, the server includes means for acquiring SNS posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine emotions and context, means for performing a detailed analysis using an emotion engine based on the analysis results to determine the intensity and type of emotion, means for calculating a user score based on the analysis results, means for storing and updating the calculated score in a database, means for checking the user's score and providing incentives to users who exceed a certain threshold, and means for organizing and providing user score information to companies. This makes it possible to promote healthy communication on SNS in real time and realize an SNS environment that is valuable to both users and companies.

[0821] A "user" is someone who uses a system to post content via social networking services (SNS).

[0822] A "user terminal" is a device used by a user to input and send posts using social networking services (SNS).

[0823] A "server" is a central processing unit that receives, analyzes, and scores social media posts.

[0824] A "database" is a storage system used to store and manage analysis results and scores.

[0825] A "natural language processing model" is a general term for algorithms that analyze the context and keywords of text and classify sentiment (positive, negative, neutral) based on the content of a post.

[0826] An "emotion engine" is a processing system that analyzes the emotions contained in a user's post in detail and determines the intensity and type of those emotions.

[0827] A "score" is a numerical value calculated based on the content of a post, and it serves as a criterion for evaluating a user's behavior and emotions.

[0828] An "incentive" is a reward or benefit given when a user's score exceeds a certain threshold.

[0829] A "company" is a legal entity or organization that implements marketing strategies based on user score information.

[0830] "Analysis results" refer to the sentiment classification and detailed sentiment analysis output of the posted content obtained through a natural language processing model and sentiment engine.

[0831] A "threshold" is a threshold value used to provide an incentive when a score reaches a certain standard.

[0832] This invention is a system for identifying and preventing inappropriate behavior on social media in real time. This system promotes healthy communication by analyzing user posts and scoring their behavior. A specific embodiment of this system is described below.

[0833] System Overview

[0834] This system mainly consists of the following elements:

[0835] 1. User terminal: A device used by users to input and send posts using social networking services (SNS). User terminals include smartphones, tablets, and personal computers.

[0836] 2. Server: This is a central processing unit that receives SNS posts and performs analysis using natural language processing models and sentiment engines.

[0837] 3. Database: A storage system for storing and managing analysis results and scores.

[0838] Retrieve user posts

[0839] User terminal:

[0840] The user enters a message into the social media posting form and presses the post button. This saves the posted content on the user's device. For example, if the user enters "This product is truly amazing!", this content proceeds to the next step.

[0841] Submit post

[0842] User terminal:

[0843] This involves calling the API of a social networking service (SNS) application and sending the posted content to the server via the internet.

[0844] Analysis and scoring of submitted content

[0845] server:

[0846] The server receives the posted content sent from the user's terminal. The received posted content is parsed in the following steps:

[0847] 1. Natural Language Processing Models (NLP Models):

[0848] The server analyzes the context and keywords of the text and classifies the sentiment of the post (positive, negative, neutral). Specifically, it uses an NLP model to understand the meaning of the text data and assign appropriate sentiment labels.

[0849] 2. Emotional Engine:

[0850] The system analyzes the emotions contained in posts in detail, determining their intensity and type. For example, posts with strong positive emotions are given high scores, while posts with strong negative emotions are given low scores.

[0851] Saving and managing scores

[0852] server:

[0853] The calculated score is linked to the user ID and stored in the database. This allows each user's score to be managed as a history. For example, the score of user ID "12345" is updated from 90 to 100 based on their posts.

[0854] Provision of incentives

[0855] server:

[0856] Incentives are offered to users whose scores exceed a certain threshold. For example, users who score over 100 points may receive special coupons or purchase points. Furthermore, score information is compiled to provide companies with information on high-scoring users. Based on this, companies can offer special offers to low-risk customers.

[0857] Information provision for businesses

[0858] server:

[0859] We organize the customer score information that companies need and provide it at the right time. Based on this data, companies can implement marketing strategies that target lower-risk customer segments.

[0860] Examples

[0861] For example, if a user posts "This product is the best!" on social media, that content is sent from the user's device to the server. The server analyzes the post using a natural language processing model and an emotion engine and determines that it expresses a positive emotion. Based on the analysis, the server scores the user +10 points and saves the new score to the database. Since the score exceeds 100 points, the server offers this user a special coupon.

[0862] Example of a prompt

[0863] A user posted on social media, "I had so much fun today!" This system analyzes the post using a natural language processing model and sentiment engine, detects positive emotions, and scores the user +10 points. Save the new score to the database, and issue a coupon when the score exceeds 100 points.

[0864] This system promotes healthy communication on social media in real time, creating a valuable social media environment for both users and businesses. By using an emotion engine, it is possible to analyze users' emotions in detail and perform more accurate scoring.

[0865] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0866] Step 1:

[0867] Retrieve user posts

[0868] User terminal:

[0869] The user enters a message into the social media posting form and presses the post button. This causes the user's message to be stored on the device as input data.

[0870] input:

[0871] A text message entered by the user (e.g., "This product is really great!").

[0872] output:

[0873] The retained post content is ready to be sent to the next processing step.

[0874] Specific actions:

[0875] The user opens a social networking app on their smartphone or PC.

[0876] Enter "This product is truly amazing!" into the submission form.

[0877] Click the submit button.

[0878] Step 2:

[0879] Submit post

[0880] User terminal:

[0881] When the submit button is pressed, the user's entered content is sent to the server via the internet.

[0882] input:

[0883] Post content stored on the device.

[0884] output:

[0885] The server receives the data via the internet.

[0886] Specific actions:

[0887] The user's device calls the SNS application's API and sends the content to be posted as a parameter.

[0888] The data reaches the server via the internet.

[0889] Step 3:

[0890] Analysis and scoring of submitted content

[0891] server:

[0892] The received posts are analyzed using a natural language processing (NLP) model, and then further analyzed in detail using an emotion engine.

[0893] input:

[0894] Content received via the internet (e.g., "This product is truly amazing!").

[0895] output:

[0896] A score based on the results of emotion classification (positive, negative, neutral) and the intensity and type of emotion.

[0897] Specific actions:

[0898] The server saves the received post content to a buffer.

[0899] Using natural language processing (NLP) models, we analyze the context and keywords of text to classify sentiment.

[0900] Example: A post saying "This product is truly amazing!" is judged as positive.

[0901] The emotion engine further analyzes the emotion classification results to determine the intensity and type of emotion.

[0902] Example: A high score was given due to strong positive emotions.

[0903] Step 4:

[0904] Saving and managing scores

[0905] server:

[0906] The calculated score is linked to the user ID and saved in the database.

[0907] input:

[0908] The score analyzed by the emotion engine (e.g., +10 points) and the user ID.

[0909] output:

[0910] The updated score is saved in the database.

[0911] Specific actions:

[0912] The server sets the user ID and score.

[0913] The set score is saved to the database.

[0914] Example: Update the score of user ID "12345" from 90 to 100.

[0915] Step 5:

[0916] Provision of incentives

[0917] server:

[0918] Incentives are provided to users whose scores exceed a certain threshold.

[0919] input:

[0920] Updated user score and threshold information.

[0921] output:

[0922] Offering incentives (e.g., special coupons or purchase points).

[0923] Specific actions:

[0924] The server periodically checks the database to identify users whose scores exceed a threshold.

[0925] This involves executing the routine for providing incentives and handling the procedures for issuing coupons and points.

[0926] Example: Issue a special coupon to users who score over 100 points.

[0927] Step 6:

[0928] Information provision for businesses

[0929] server:

[0930] We organize customer score information requested by companies and provide it at the necessary time.

[0931] input:

[0932] Organized user score information.

[0933] output:

[0934] Customer score information provided to companies.

[0935] Specific actions:

[0936] Receive requests from companies and filter and organize user score information.

[0937] Convert the data to the required format (e.g., CSV file) and send it to the company.

[0938] In this way, this system promotes healthy communication on social media in real time, providing a valuable environment for both users and businesses.

[0939] (Application Example 2)

[0940] 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."

[0941] Conventional social media post analysis systems only analyze text-based posts, making it difficult to effectively grasp customer emotions and satisfaction levels in real time during customer service interactions. Furthermore, the lack of means to provide feedback on improving customer service quality has limited improvements in customer service operations. In this situation, customer satisfaction cannot be adequately improved, making it urgent to improve the efficiency of service delivery and enhance customer satisfaction through the use of customer service robots and other technologies in physical stores.

[0942] 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.

[0943] In this invention, the server includes means for acquiring SNS posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine emotions and context, means for calculating a user score based on the analysis results, means for storing and updating the calculated score in a database, means for checking the user's score and providing incentives to users who exceed a certain threshold, means for organizing and providing user score information to companies, means for converting speech to text in real time using speech recognition, and means for identifying low-risk customers and providing feedback to improve customer service quality. This enables real-time evaluation of customer service quality in physical stores, and by combining speech recognition and emotion analysis, it becomes possible to measure customer satisfaction with greater accuracy and improve customer service quality.

[0944] "Methods for obtaining user-submitted SNS posts" refers to systems that collect data such as text and images posted by users on social media.

[0945] "A means of analyzing the content of acquired posts using a natural language processing model to determine emotions and context" refers to a system that analyzes collected SNS post data using natural language processing technology and extracts emotions and context from the text.

[0946] "A means of calculating user scores based on analysis results" refers to a system that calculates an evaluation score for each user's post based on analyzed sentiment and contextual information.

[0947] "Means for saving and updating calculated scores in a database" refers to a system that records calculated evaluation scores in a digital storage system and updates them as needed.

[0948] "A means of checking user scores and providing incentives to users who exceed a certain threshold" refers to a system that monitors user evaluation scores and provides benefits or rewards to users who meet specified criteria.

[0949] "A means of organizing and providing user score information to businesses" refers to a system that compiles and provides user evaluation score information in a format that businesses can use.

[0950] "A means of converting speech to text in real time using speech recognition" refers to a system that uses speech recognition technology to convert speech data acquired in real time into text information.

[0951] "A means of identifying low-risk customers and providing feedback to improve service quality" refers to a system that identifies low-risk customers based on an analysis of customer emotions and satisfaction, and provides feedback to service staff and systems on areas for improvement.

[0952] This invention is a system that identifies and prevents inappropriate behavior on social media in real time. The system promotes healthy communication by analyzing user posts and scoring their behavior. Furthermore, based on examples of customer service robot applications in physical stores, it aims to improve customer satisfaction in real time and enhance the quality of customer service.

[0953] System Overview

[0954] This system mainly consists of the following elements:

[0955] 1. User terminal: A device used by users to input and send posts using social networking services (SNS). It also has voice recognition capabilities that allow for voice input.

[0956] 2. Server: A central processing unit that receives SNS posts and audio data and performs analysis using natural language processing models and sentiment engines. It calculates a score based on the analysis results and stores it in a database.

[0957] 3. Database: A storage system for storing and managing analysis results and scores.

[0958] 4. Interface: Includes a user interface for organizing and providing score information to businesses.

[0959] Hardware and software to be used

[0960] This system uses the following hardware and software:

[0961] Hardware: Microphone, speaker, user device (smartphone or tablet), and internet connection.

[0962] Software: Python, speech recognition library (Google Speech-to-Text), natural language processing library (Hugging Face Transformers), sentiment analysis library (TextBlob, VaderSentiment), database (SQLite)

[0963] Data processing and data calculation

[0964] 1. Speech recognition:

[0965] The user's device uses a microphone to capture the customer's conversation. The captured audio data is converted into text data using the Google Speech-to-Text API. This allows the customer service robot to convert interactions with customers into text in real time.

[0966] 2. Natural language processing and sentiment analysis:

[0967] The server analyzes this text data using a natural language processing model (Hugging Face's Transformers). It extracts context and keywords and performs sentiment classification (positive, negative, neutral). Then, it uses sentiment engines (TextBlob, VaderSentiment) to perform a detailed analysis of the customer's emotions.

[0968] 3. Scoring:

[0969] Based on the analysis results, the server calculates a sentiment score for each user. Posts with strong positive emotions are assigned high scores, while posts with strong negative emotions are assigned low scores.

[0970] 4. Data storage and updates:

[0971] The calculated scores are stored in a database and updated as needed. This allows for the management of each user's sentiment score history.

[0972] 5. Providing incentives and organizing company information:

[0973] When a user's score exceeds a certain threshold, they will be offered rewards (coupons or special offers). Furthermore, companies will be provided with compiled score information of low-risk customers.

[0974] Specific example

[0975] For example, consider a customer service scenario in a physical store. If a customer says, "The service at this cafe is truly amazing!", this audio is converted to text in real time. This text is sent to a server and a natural language processing model determines that it represents a positive emotion. After detailed analysis by the emotion engine, a satisfaction score for this customer is calculated and stored in a database. If the score is high, a coupon is offered as an incentive. The company then uses this data to implement special offers for low-risk customers.

[0976] Example of a prompt

[0977] "Please design a new customer service robot application. This application will perform real-time speech recognition of customer conversations and use natural language processing and sentiment analysis to score customer satisfaction. Furthermore, if a certain score is reached, the customer will receive coupons or other benefits."

[0978] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0979] Step 1:

[0980] Processing details: Acquisition of voice input

[0981] Specific operation: The user's terminal uses a microphone to capture conversations during customer service. The audio data spoken by the user is captured in real time.

[0982] Input: User's voice data

[0983] Output: Captured audio data

[0984] Step 2:

[0985] Processing details: Text conversion of audio data

[0986] Specific operation: The captured audio data is converted into text data using the Google Speech-to-Text API.

[0987] Input: Captured audio data

[0988] Output: Converted text data

[0989] Step 3:

[0990] Processing details: Sending text data

[0991] Specific operation: The user's terminal sends the converted text data to the server.

[0992] Input: Converted text data

[0993] Output: Text data sent to the server

[0994] Step 4:

[0995] Processing details: Analysis of text data

[0996] Specific operation: The server analyzes the received text data using a natural language processing model (e.g., Hugging Face's Transformers). It extracts the context and emotions of the text and performs emotion classification (positive, negative, neutral).

[0997] Input: Text data sent to the server

[0998] Output: Emotion classification result

[0999] Step 5:

[1000] Processing details: Detailed analysis of emotions

[1001] Specific operation: The server uses the emotion engine (TextBlob, VaderSentiment) to further analyze the intensity and type of emotion.

[1002] Input: Sentiment classification result

[1003] Output: Emotion analysis results

[1004] Step 6:

[1005] Processing details: Calculation of user score

[1006] Specific operation: The server calculates a customer satisfaction score based on the sentiment analysis results. Positive emotions are assigned high scores, and negative emotions are assigned low scores.

[1007] Input: Sentiment analysis results

[1008] Output: User score

[1009] Step 7:

[1010] Processing details: Saving and updating scores

[1011] Specific operation: The server saves the calculated score to the database and updates the existing score as needed.

[1012] Input: User score

[1013] Output: Score data stored in the database

[1014] Step 8:

[1015] Processing details: Provision of incentives

[1016] Specific operation: The server checks the user score and provides the customer with a reward (such as a coupon) if the score exceeds a certain threshold.

[1017] Input: Score data stored in the database

[1018] Output: Benefits offered to customers

[1019] Step 9:

[1020] Processing details: Organization and provision of information for businesses.

[1021] Specific operation: The server organizes user score information for the company and provides information on highly-rated users.

[1022] Input: Score data stored in the database

[1023] Output: Organized user score information

[1024] 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.

[1025] 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.

[1026] 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.

[1027] [Third Embodiment]

[1028] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1029] 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.

[1030] 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).

[1031] 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.

[1032] 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.

[1033] 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).

[1034] 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.

[1035] 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.

[1036] 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.

[1037] 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.

[1038] 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.

[1039] 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".

[1040] This invention provides a system for identifying and preventing inappropriate behavior on social media in real time. This system promotes healthy communication by analyzing user posts and scoring their behavior. Specific embodiments are described below.

[1041] System Overview

[1042] This system mainly consists of the following elements:

[1043] 1. User terminal: A device used by a user to input and send posts using social networking services (SNS).

[1044] 2. Server: A central processing unit that receives SNS posts and performs analysis using natural language processing models.

[1045] 3. Database: A storage system for storing and managing analysis results and scores.

[1046] Retrieve user posts

[1047] User terminal: The user enters a message into the SNS posting form and sends the content to the server by pressing the post button. For example, consider a case where a user posts, "This product is really great!"

[1048] Submit post

[1049] User terminal: The user's entered content is sent to the server via the internet.

[1050] Analysis and scoring of submitted content

[1051] Server: Receives posts sent from terminals. The received posts are analyzed by a natural language processing model and classified as positive, negative, or neutral. The server calculates a score based on the analysis results. For example, if a post that says "This product is really great!" is judged to be positive, the server will assign this user a score of +10.

[1052] Saving and managing scores

[1053] Server: The calculated score is linked to the user ID and stored in the database. This allows each user's score to be managed as a history. For example, the score of user ID "12345" is updated from 90 to 100.

[1054] Provision of incentives

[1055] Server: Users whose scores exceed a certain threshold are offered incentives. For example, users who exceed 100 points may receive special coupons or purchase points. Furthermore, score information is compiled to provide companies with information on high-scoring users. Based on this, companies can offer special offers to low-risk customers.

[1056] Information provision for businesses

[1057] Server: Organizes and provides customer score information needed by businesses at the required time. Based on this data, businesses can implement marketing strategies that target lower-risk customer segments.

[1058] Examples

[1059] When a user posts "This product is the best!" on social media, the post is sent to the server. The server analyzes the post using a natural language processing model and determines that it expresses positive emotion. Based on the analysis, the server scores the user +10 points and saves the user's new score to the database. Since the score exceeds 100 points, the server offers this user a special coupon.

[1060] This system will promote healthy communication on social media and create a valuable social media environment for both users and businesses.

[1061] The following describes the processing flow.

[1062] Step 1:

[1063] User: Enter a message into the social media posting form.

[1064] Example: Enter the message, "This product is truly amazing!"

[1065] Step 2:

[1066] Terminal: The user presses the post button and sends the content to the server.

[1067] The submitted content is sent to the server via the internet.

[1068] Step 3:

[1069] Server: Receives posted data sent from terminals.

[1070] For example, the following JSON data arrives on the server:

[1071] json

[1072] {

[1073] "user_id": "12345",

[1074] "timestamp": "2023-10-01T10:00:00Z",

[1075] "content": "This product is truly amazing!"

[1076] }

[1077] Step 4:

[1078] Server: Sends the received posted content to a natural language processing model for text analysis.

[1079] The model performs sentiment classification (positive, negative, neutral) and contextual analysis.

[1080] Step 5:

[1081] Server: Assigns a score to posts based on the analysis results from a natural language processing model.

[1082] Example: The keyword "great" is recognized as positive, so a score of +10 is calculated.

[1083] Step 6:

[1084] Server: Associates the calculated score with the user ID, saves it to the database, and updates existing scores.

[1085] Example: The score of user ID "12345" will be updated from 90 to 100.

[1086] Step 7:

[1087] Server: Checks user scores in the database and provides incentives if the score exceeds a certain threshold.

[1088] Example: Because the score exceeded 100, the user will be given a special coupon.

[1089] Step 8:

[1090] Server: Executes the incentive distribution process and notifies the user.

[1091] Example: Send a coupon offer notification to the user via electronic message or similar means.

[1092] Step 9:

[1093] Server: Organizes user score information for enterprises and generates lists of high-rated users.

[1094] This system aggregates score data and related information from highly-rated users.

[1095] Step 10:

[1096] Server: Provides organized score information to companies and helps them identify low-risk customers.

[1097] Example: By providing companies with data indicating that "User ID: 12345 has a high rating," this data can be used for marketing strategies such as special offers.

[1098] The above outlines the specific processing steps of the program. This system can identify defamation and inappropriate behavior on social media in real time, thereby promoting healthy communication.

[1099] (Example 1)

[1100] 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."

[1101] While it is necessary to identify inappropriate behavior and posts on social media in real time and promote healthy communication, conventional systems had problems with low accuracy in analyzing posts and being unable to properly evaluate user behavior. Furthermore, there were insufficient means of providing reliable user information to companies. This made it difficult to provide appropriate incentives to promote healthy user communication and for companies to implement efficient marketing strategies.

[1102] 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.

[1103] In this invention, the server includes means for acquiring posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine sentiment and context, means for calculating and updating the user's score based on the analysis results, means for storing the calculated score in a storage device, means for checking the user's score and providing incentives to users who exceed a certain threshold, and means for organizing and providing user score information to companies. This makes it possible to identify inappropriate behavior on social media in real time, promote healthy communication, and provide appropriate incentives to users. It also makes it possible to provide companies with reliable user information and support the implementation of efficient marketing strategies.

[1104] A "user" refers to an individual or group that inputs and submits posts using social networking services (SNS).

[1105] A "post" refers to information such as text, images, videos, and links that a user shares on social media.

[1106] A "natural language processing model" refers to a machine learning algorithm or program that analyzes text data entered by a user to determine its sentiment and context.

[1107] "Analysis results" refer to information about the sentiment and context of posts obtained by a natural language processing model.

[1108] A "score" is a numerical value calculated based on the analysis of the content of a post, and it is an indicator that evaluates the healthiness of a user's behavior and statements.

[1109] A "memory device" refers to a database or storage system used to store calculated scores.

[1110] An "incentive" refers to a reward or benefit offered to users who exceed a certain score.

[1111] "Company" refers to a business organization that uses user score information to develop marketing strategies and manage risks.

[1112] Modes for carrying out the invention

[1113] This invention is a system that identifies inappropriate behavior on social networking services (SNS) in real time and promotes healthy communication. This system consists of the following hardware and software: a user terminal, a server, a database, and a natural language processing model.

[1114] 1. System Overview

[1115] This system consists of the following elements:

[1116] User device: A device used by a user to input and send posts using social networking services (e.g., smartphone, PC).

[1117] Server: A central processing unit that receives SNS posts and performs analysis using natural language processing models. Examples of software used include natural language processing models such as BERT and GPT-4.

[1118] Database: A storage system for storing and managing analysis results and scores. Specific examples include MySQL and PostgreSQL.

[1119] 2. Retrieve user posts

[1120] User terminal: The user enters a message into the social media posting form and presses the post button. For example, this would be the case when the user enters "This product is really great!"

[1121] 3. Submit your post

[1122] User terminal: When the post button is pressed, the content of the post is sent to the server via the internet. Specifically, the post data is sent using an HTTP request.

[1123] 4. Receiving the posted content

[1124] Server: Receives posted content via the internet for analysis. Receives the posted content contained in the body of the HTTP request.

[1125] 5. Analysis of posted content

[1126] Server: The server passes the received post content to a natural language processing model, which classifies it as positive, negative, or neutral. For example, if "This product is truly wonderful!" is judged to be positive, the natural language processing model returns that result.

[1127] 6. Score calculation and updating

[1128] Server: Based on the analysis results, the server calculates and updates the user's score. Posts containing positive emotions are given higher scores, and posts containing negative emotions are given lower scores. For example, it might calculate to add +10 points to the user's score.

[1129] 7. Saving and managing scores

[1130] Server: Stores the calculated score in the database, associating it with the user ID. Executes an SQL query to save the new score information in the database. For example, saves a new score of 100 for user ID "12345".

[1131] 8. Provision of incentives

[1132] Server: When a user's score exceeds a certain threshold, an incentive (e.g., a special coupon) is provided. A special coupon is issued to users who exceed the threshold, and a notification email is sent.

[1133] 9. Information provision for businesses

[1134] Server: Organizes and provides customer score information available to businesses as needed. Creates lists of high-rated users and sends data via API to businesses in a specific format (e.g., CSV file).

[1135] Examples

[1136] When a user posts on social media saying "This product is the best!", the following actions are taken:

[1137] 1. User's device: The user enters "This product is the best!" into the posting form of the SNS app and presses the post button.

[1138] 2. Terminal: The posted content is sent to the server as an HTTP request.

[1139] 3. Server: The received post content is passed to a natural language processing model, which determines that it expresses positive sentiment.

[1140] 4. Server: Based on the analysis results, the server assigns a score of +10 points to the user and saves the new score to the database.

[1141] 5. Server: Since the score has exceeded 100 points, issue a special coupon to the user and notify them via email.

[1142] This system promotes healthy communication on social media. Furthermore, it provides companies with reliable user information, enabling them to implement more efficient marketing strategies.

[1143] Examples of prompts for generative AI models

[1144] I want to post a positive comment on social media saying, "I like this product," but what kind of content should I include to ensure that this post is viewed as healthy communication on social media?

[1145] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1146] Step 1:

[1147] User input

[1148] User: Enter a message into the posting form of the SNS app and press the post button.

[1149] Input: Text entered by the user in the social media posting form.

[1150] Specific action: The user types "This product is truly amazing!" and clicks the submit button.

[1151] Output: The post is ready to be sent.

[1152] Step 2:

[1153] Submit post

[1154] Terminal: When the post button is pressed, the content of the post is sent to the server via the internet.

[1155] Input: The content of the post entered by the user.

[1156] Specific action: The smartphone or PC sends the message "This product is truly amazing!" to the server as an HTTP request.

[1157] Output: The submitted content arrives at the server.

[1158] Step 3:

[1159] Received the posted content

[1160] Server: Receives submitted content via the internet for analysis.

[1161] Input: HTTP request containing the content of the post sent from the terminal.

[1162] Specific action: The server receives the message "This product is really great!" which is included in the body of the HTTP request.

[1163] Output: The submitted content is ready for analysis.

[1164] Step 4:

[1165] Analysis of posted content

[1166] Server: The posted content is passed to a natural language processing model (e.g., BERT or GPT-4) to perform sentiment analysis.

[1167] Input: Received post content: "This product is truly amazing!"

[1168] Specific operation: Input the post content into a natural language processing model and obtain sentiment analysis results. For example, the model identifies it as a positive sentiment.

[1169] Output: Positive emotions are obtained as the analysis result.

[1170] Step 5:

[1171] Score calculation

[1172] Server: Calculates the user's score based on the analysis results.

[1173] Input: Analysis results from a natural language processing model (e.g., positive emotions).

[1174] Specific operation: Based on the analysis results, the user is awarded a score of +10 points. If the original score was 90, the new score will be 100.

[1175] Output: The new calculated score.

[1176] Step 6:

[1177] Save score

[1178] Server: Stores the calculated score in the database, linked to the user ID.

[1179] Input: User ID and new score (e.g., User ID "12345" and score 100).

[1180] Specific action: Execute an SQL query and save the new score to the database.

[1181] Output: Score information is saved to the database.

[1182] Step 7:

[1183] Provision of incentives

[1184] Server: Provides an incentive to the user if their score exceeds a certain threshold.

[1185] Input: Updated score (e.g., 100 points or more).

[1186] Specific action: Issue a special coupon and send a notification email to the user.

[1187] Output: Incentive information is provided to the user.

[1188] Step 8:

[1189] Providing information for businesses

[1190] Server: Organizes customer score information available to the company and provides it as needed.

[1191] Input: User score information retrieved from the database.

[1192] Specific operation: Create a list of highly-rated users and send the data to companies via an API in CSV file format.

[1193] Output: User score information organized for enterprise use.

[1194] (Application Example 1)

[1195] 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."

[1196] In today's e-commerce environment, it is crucial that user reviews and comments are a sound and reliable source of information. However, there is a lack of systems to evaluate inappropriate behavior and comments, often hindering healthy communication. Furthermore, there are insufficient effective methods for evaluating user trustworthiness, making it difficult for stores to provide appropriate incentives. This invention aims to solve these problems and provide a valuable environment for both users and stores.

[1197] 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.

[1198] In this invention, the server includes means for acquiring electronic posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine sentiment and context, means for calculating the user's score based on the analysis results, means for storing and updating the calculated score in a database, means for checking the user's score and providing incentives to users who exceed a certain threshold, means for organizing and providing user score information to companies, means for analyzing user reviews and comments in real time in an e-commerce environment, and means for providing benefits and discounts when a user's score exceeds a certain threshold. This promotes healthy communication and enables an e-commerce environment that is valuable to both users and companies.

[1199] "Electronic submissions" refer to digital content such as text messages and comments that users send over the internet.

[1200] A "natural language processing model" is a collection of algorithms and technologies used to analyze and understand human language and to provide appropriate responses and processing.

[1201] "Determining emotions and context" is the process of analyzing words and context within a text to determine whether they represent positive, negative, or neutral emotions.

[1202] "Calculating a score" is the process of quantifying the evaluation of a post based on its analyzed content.

[1203] "Saving and updating in a database" means recording the calculated score in data storage in a way that allows each user to be identified, and updating it as needed whenever new data is added.

[1204] An "incentive" is a reward or benefit given to users who meet certain criteria.

[1205] "Organizing and providing user score information to businesses" refers to the process of classifying and processing user score data in a way that is easy for businesses to use, and providing that data to businesses at the appropriate time.

[1206] An "e-commerce environment" refers to a system or platform for buying and selling goods and services over the internet.

[1207] "Analyzing reviews and comments in real time" means instantly receiving reviews and comments posted by users and analyzing their content using a natural language processing model.

[1208] "Benefits and discounts" refer to coupons, discount services, or other forms of rewards offered to users.

[1209] System Overview

[1210] The system for realizing this application retrieves electronic submissions entered by users and analyzes them using a natural language processing model. Based on the analysis results, it calculates a score and stores and updates the calculated score in a database. Furthermore, if a user's score exceeds a certain threshold, it offers benefits or discounts. For businesses, it includes a function to organize and provide user score information.

[1211] Hardware and software to use

[1212] Hardware:

[1213] User devices: Smartphones, tablets, PCs, etc.

[1214] Server: Operates as a central processing unit and is connected to the database.

[1215] software:

[1216] Natural language processing models: Natural language processing libraries such as TextBlob and Spacy.

[1217] Database: A database management system such as SQLite or PostgreSQL.

[1218] Details of data processing and calculations

[1219] 1. Obtaining electronic submissions:

[1220] When a user enters a review or comment on their device and presses the submit button, the content is sent to the server. For example, consider a case where a user posts, "This product is truly amazing!"

[1221] 2. Analysis of the posted content:

[1222] The server analyzes the received posts using a natural language processing model (such as TextBlob). It then classifies them as positive, negative, or neutral and generates a sentiment score. For example, a post saying "This product is really great!" would be judged as having a positive sentiment.

[1223] 3. Calculating and saving the score:

[1224] The server calculates a score based on the analysis results and stores / updates that score in the database, linking it to the user's ID. For example, user ID "12345" is assigned a score of +10 points, and the new score is stored in the database.

[1225] 4. Providing incentives:

[1226] The server offers rewards or discounts when a user's score exceeds a certain threshold (e.g., 100 points). For example, a special coupon might be issued when a user scores over 100 points.

[1227] 5. Organization and provision of data for businesses:

[1228] The server organizes user score information for businesses and provides that data. This allows businesses to implement marketing strategies that target lower-risk customer segments.

[1229] Specific example

[1230] When a user posts a review of a specific product on their smartphone, saying "This product is the best!", and presses the Post button, the post is sent to the server. The server uses a natural language processing model (e.g., TextBlob) to analyze the post and determines that it expresses positive sentiment. Based on this analysis, the server scores the user +10 points and saves it to the database. Since the user's score exceeds 100 points, the server sends the user a special coupon. The next action is a notification saying, "A coupon for 10% off all products is now available!"

[1231] Example of a prompt:

[1232] When a user types "This product is the best!" about a specific product on their smartphone and presses the Post button, the following message appears: "This post has been sent to the sentiment analysis module and has been scored as positive content. Since the score exceeds 100, a reward coupon has been issued. Please take the next action."

[1233] This system promotes healthy communication and creates a valuable e-commerce environment for both users and businesses.

[1234] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1235] Step 1:

[1236] Retrieving posts from user terminals

[1237] When a user enters a review or comment on their device and presses the submit button, the post is submitted. The user's device then sends this input to the server. The input data is in text format, such as reviews or comments. Specifically, if a user enters "This product is really great!", that text data is sent to the server via the internet.

[1238] Step 2:

[1239] Receiving and analyzing submitted data

[1240] The server receives posted data sent from the user's terminal. The received text data is analyzed using a natural language processing model (e.g., TextBlob or Spacy). In this analysis, the text is classified as positive, negative, or neutral, and a sentiment score is generated. For example, the text "This product is really great!" is judged as positive, and a positive score of +10 points is calculated.

[1241] Step 3:

[1242] Calculating and saving scores

[1243] The server calculates a user's score based on the analysis results. Specifically, positive posts are assigned high scores, and negative posts are assigned low scores. The calculated score is linked to the user ID, stored in the database, and updated. For example, user ID "12345" is assigned a score of +10 points, which is added to the existing score and stored in the database.

[1244] Step 4:

[1245] Verification and provision of incentives

[1246] The server monitors user scores and provides incentives when a certain threshold is exceeded. For example, if a user's score exceeds 100 points, a special coupon or discount will be offered. This will be notified to the user's device, and the coupon code or discount information will be displayed.

[1247] Step 5:

[1248] Organization and provision of data for businesses

[1249] The server organizes user score information for businesses and provides it as needed. This organized data is used by businesses to identify low-risk customer segments and implement targeted marketing strategies. For example, businesses can improve customer satisfaction by offering special offers to high-rated users.

[1250] This entire process enables the creation of an e-commerce environment that is valuable to both users and businesses.

[1251] 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.

[1252] This invention is a system for identifying and preventing inappropriate behavior on social media in real time. This system promotes healthy communication by analyzing user posts and scoring their behavior. Specific embodiments are described below.

[1253] System Overview

[1254] This system mainly consists of the following elements:

[1255] 1. User terminal: A device used by a user to input and send posts using social networking services (SNS).

[1256] 2. Server: A central processing unit that receives SNS posts and performs analysis using natural language processing models and sentiment engines.

[1257] 3. Database: A storage system for storing and managing analysis results and scores.

[1258] Retrieve user posts

[1259] User terminal: The user enters a message into the SNS posting input form and sends the content to the server by pressing the post button. For example, consider a case where a user posts, "This product is really great!"

[1260] Submit post

[1261] User terminal: The user's entered content is sent to the server via the internet.

[1262] Analysis and scoring of submitted content

[1263] Server: Receives posts sent from terminals. The received posts are analyzed by a natural language processing model and sentiment engine.

[1264] The natural language processing model analyzes the context and keywords of the text and performs sentiment classification (positive, negative, neutral) based on the content of the post.

[1265] The emotion engine further analyzes the emotions contained in user posts, determining their intensity and type. For example, posts with strong positive emotions are given high scores, while posts with strong negative emotions are given low scores.

[1266] Saving and managing scores

[1267] Server: The calculated score is linked to the user ID and stored in the database. This allows each user's score to be managed as a history. For example, the score of user ID "12345" is updated from 90 to 100.

[1268] Provision of incentives

[1269] Server: Users whose scores exceed a certain threshold are offered incentives. For example, users who exceed 100 points may receive special coupons or purchase points. Furthermore, score information is compiled to provide companies with information on high-scoring users. Based on this, companies can offer special offers to low-risk customers.

[1270] Information provision for businesses

[1271] Server: Organizes and provides customer score information needed by businesses at the required time. Based on this data, businesses can implement marketing strategies that target lower-risk customer segments.

[1272] Examples

[1273] When a user posts "This product is the best!" on social media, the post is sent to the server. The server analyzes the post using a natural language processing model and sentiment engine and determines that it expresses positive emotion. Based on the analysis, the server scores the user +10 points and saves the user's new score to the database. Since the score exceeds 100 points, the server offers this user a special coupon.

[1274] Furthermore, if a user posts something like, "This service is terrible!", the emotion engine detects a strong negative emotion. Based on this information, the server scores the user -10 points and also evaluates the impact the negative post has on other users.

[1275] This system promotes healthy communication on social media, creating a valuable social media environment for both users and businesses. By using an emotion engine, it is possible to analyze users' emotions in detail and perform more accurate scoring.

[1276] The following describes the processing flow.

[1277] Step 1:

[1278] User: Enter a message into the social media posting form.

[1279] Example: Enter the message, "This product is truly amazing!"

[1280] Step 2:

[1281] Terminal: The user presses the post button and sends the content to the server.

[1282] The submitted content is sent to the server via the internet.

[1283] Step 3:

[1284] Server: Receives posted data sent from terminals.

[1285] For example, the following JSON data arrives on the server:

[1286] json

[1287] {

[1288] "user_id": "12345",

[1289] "timestamp": "2023-10-01T10:00:00Z",

[1290] "content": "This product is truly amazing!"

[1291] }

[1292] Step 4:

[1293] Server: Sends the received posted content to a natural language processing model for text analysis.

[1294] The model analyzes the context and keywords of the text and performs sentiment classification (positive, negative, neutral).

[1295] Example: The keyword "wonderful" is perceived as positive.

[1296] Step 5:

[1297] Server: Sends the analysis results from the natural language processing model to the emotion engine.

[1298] The emotion engine receives the analysis results and determines the intensity and type of emotion.

[1299] Step 6:

[1300] Server: Assigns a score to posts based on the output of the sentiment engine.

[1301] For example, posts with strong positive emotions will be given a +10, and posts with strong negative emotions will be given a -10.

[1302] For example, a post that says "This product is truly amazing!" will be judged as positive and will receive a score of +10.

[1303] Step 7:

[1304] Server: Associates the calculated score with the user ID, saves it to the database, and updates existing scores.

[1305] Example: The score of user ID "12345" will be updated from 90 to 100.

[1306] Step 8:

[1307] Server: Checks user scores in the database and provides incentives if the score exceeds a certain threshold.

[1308] Example: Because the score exceeded 100, the user will be given a special coupon.

[1309] Step 9:

[1310] Server: Executes the incentive distribution process and notifies the user.

[1311] Example: Send a coupon offer notification to the user via electronic message or similar means.

[1312] Step 10:

[1313] Server: Organizes user score information for enterprises and generates lists of high-rated users.

[1314] This system aggregates score data and related information from highly-rated users.

[1315] Step 11:

[1316] Server: Provides organized score information to companies and helps them identify low-risk customers.

[1317] Example: By providing companies with data indicating that "User ID: 12345 has a high rating," this data can be used for marketing strategies such as special offers.

[1318] The above outlines the specific processing steps of the program, including the emotion engine. This system can identify defamation and inappropriate behavior on social media in real time, thereby promoting healthy communication.

[1319] (Example 2)

[1320] 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."

[1321] The increase in inappropriate behavior and harmful posts on social media is hindering healthy communication. This is leading to decreased user satisfaction and increased marketing risks for businesses. Existing systems do not adequately analyze and score content, making it difficult to identify and prevent inappropriate behavior in real time. Therefore, there is a need for a new system that can analyze content in detail and promote healthy communication.

[1322] 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.

[1323] In this invention, the server includes means for acquiring SNS posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine emotions and context, means for performing a detailed analysis using an emotion engine based on the analysis results to determine the intensity and type of emotion, means for calculating a user score based on the analysis results, means for storing and updating the calculated score in a database, means for checking the user's score and providing incentives to users who exceed a certain threshold, and means for organizing and providing user score information to companies. This makes it possible to promote healthy communication on SNS in real time and realize an SNS environment that is valuable to both users and companies.

[1324] A "user" is someone who uses a system to post content via social networking services (SNS).

[1325] A "user terminal" is a device used by a user to input and send posts using social networking services (SNS).

[1326] A "server" is a central processing unit that receives, analyzes, and scores social media posts.

[1327] A "database" is a storage system used to store and manage analysis results and scores.

[1328] A "natural language processing model" is a general term for algorithms that analyze the context and keywords of text and classify sentiment (positive, negative, neutral) based on the content of a post.

[1329] An "emotion engine" is a processing system that analyzes the emotions contained in a user's post in detail and determines the intensity and type of those emotions.

[1330] A "score" is a numerical value calculated based on the content of a post, and it serves as a criterion for evaluating a user's behavior and emotions.

[1331] An "incentive" is a reward or benefit given when a user's score exceeds a certain threshold.

[1332] A "company" is a legal entity or organization that implements marketing strategies based on user score information.

[1333] "Analysis results" refer to the sentiment classification and detailed sentiment analysis output of the posted content obtained through a natural language processing model and sentiment engine.

[1334] A "threshold" is a threshold value used to provide an incentive when a score reaches a certain standard.

[1335] This invention is a system for identifying and preventing inappropriate behavior on social media in real time. This system promotes healthy communication by analyzing user posts and scoring their behavior. A specific embodiment of this system is described below.

[1336] System Overview

[1337] This system mainly consists of the following elements:

[1338] 1. User terminal: A device used by users to input and send posts using social networking services (SNS). User terminals include smartphones, tablets, and personal computers.

[1339] 2. Server: This is a central processing unit that receives SNS posts and performs analysis using natural language processing models and sentiment engines.

[1340] 3. Database: A storage system for storing and managing analysis results and scores.

[1341] Retrieve user posts

[1342] User terminal:

[1343] The user enters a message into the social media posting form and presses the post button. This saves the posted content on the user's device. For example, if the user enters "This product is truly amazing!", this content proceeds to the next step.

[1344] Submit post

[1345] User terminal:

[1346] This involves calling the API of a social networking service (SNS) application and sending the posted content to the server via the internet.

[1347] Analysis and scoring of submitted content

[1348] server:

[1349] The server receives the posted content sent from the user's terminal. The received posted content is parsed in the following steps:

[1350] 1. Natural Language Processing Models (NLP Models):

[1351] The server analyzes the context and keywords of the text and classifies the sentiment of the post (positive, negative, neutral). Specifically, it uses an NLP model to understand the meaning of the text data and assign appropriate sentiment labels.

[1352] 2. Emotional Engine:

[1353] The system analyzes the emotions contained in posts in detail, determining their intensity and type. For example, posts with strong positive emotions are given high scores, while posts with strong negative emotions are given low scores.

[1354] Saving and managing scores

[1355] server:

[1356] The calculated score is linked to the user ID and stored in the database. This allows each user's score to be managed as a history. For example, the score of user ID "12345" is updated from 90 to 100 based on their posts.

[1357] Provision of incentives

[1358] server:

[1359] Incentives are offered to users whose scores exceed a certain threshold. For example, users who score over 100 points may receive special coupons or purchase points. Furthermore, score information is compiled to provide companies with information on high-scoring users. Based on this, companies can offer special offers to low-risk customers.

[1360] Information provision for businesses

[1361] server:

[1362] We organize the customer score information that companies need and provide it at the right time. Based on this data, companies can implement marketing strategies that target lower-risk customer segments.

[1363] Examples

[1364] For example, if a user posts "This product is the best!" on social media, that content is sent from the user's device to the server. The server analyzes the post using a natural language processing model and an emotion engine and determines that it expresses a positive emotion. Based on the analysis, the server scores the user +10 points and saves the new score to the database. Since the score exceeds 100 points, the server offers this user a special coupon.

[1365] Example of a prompt

[1366] A user posted on social media, "I had so much fun today!" This system analyzes the post using a natural language processing model and sentiment engine, detects positive emotions, and scores the user +10 points. Save the new score to the database, and issue a coupon when the score exceeds 100 points.

[1367] This system promotes healthy communication on social media in real time, creating a valuable social media environment for both users and businesses. By using an emotion engine, it is possible to analyze users' emotions in detail and perform more accurate scoring.

[1368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1369] Step 1:

[1370] Retrieve user posts

[1371] User terminal:

[1372] The user enters a message into the social media posting form and presses the post button. This causes the user's message to be stored on the device as input data.

[1373] input:

[1374] A text message entered by the user (e.g., "This product is really great!").

[1375] output:

[1376] The retained post content is ready to be sent to the next processing step.

[1377] Specific actions:

[1378] The user opens a social networking app on their smartphone or PC.

[1379] Enter "This product is truly amazing!" into the submission form.

[1380] Click the submit button.

[1381] Step 2:

[1382] Submit post

[1383] User terminal:

[1384] When the submit button is pressed, the user's entered content is sent to the server via the internet.

[1385] input:

[1386] Post content stored on the device.

[1387] output:

[1388] The server receives the data via the internet.

[1389] Specific actions:

[1390] The user's device calls the SNS application's API and sends the content to be posted as a parameter.

[1391] The data reaches the server via the internet.

[1392] Step 3:

[1393] Analysis and scoring of submitted content

[1394] server:

[1395] The received posts are analyzed using a natural language processing (NLP) model, and then further analyzed in detail using an emotion engine.

[1396] input:

[1397] Content received via the internet (e.g., "This product is truly amazing!").

[1398] output:

[1399] A score based on the results of emotion classification (positive, negative, neutral) and the intensity and type of emotion.

[1400] Specific actions:

[1401] The server saves the received post content to a buffer.

[1402] Using natural language processing (NLP) models, we analyze the context and keywords of text to classify sentiment.

[1403] Example: A post saying "This product is truly amazing!" is judged as positive.

[1404] The emotion engine further analyzes the emotion classification results to determine the intensity and type of emotion.

[1405] Example: A high score was given due to strong positive emotions.

[1406] Step 4:

[1407] Saving and managing scores

[1408] server:

[1409] The calculated score is linked to the user ID and saved in the database.

[1410] input:

[1411] The score analyzed by the emotion engine (e.g., +10 points) and the user ID.

[1412] output:

[1413] The updated score is saved in the database.

[1414] Specific actions:

[1415] The server sets the user ID and score.

[1416] The set score is saved to the database.

[1417] Example: Update the score of user ID "12345" from 90 to 100.

[1418] Step 5:

[1419] Provision of incentives

[1420] server:

[1421] Incentives are provided to users whose scores exceed a certain threshold.

[1422] input:

[1423] Updated user score and threshold information.

[1424] output:

[1425] Offering incentives (e.g., special coupons or purchase points).

[1426] Specific actions:

[1427] The server periodically checks the database to identify users whose scores exceed a threshold.

[1428] This involves executing the routine for providing incentives and handling the procedures for issuing coupons and points.

[1429] Example: Issue a special coupon to users who score over 100 points.

[1430] Step 6:

[1431] Information provision for businesses

[1432] server:

[1433] We organize customer score information requested by companies and provide it at the necessary time.

[1434] input:

[1435] Organized user score information.

[1436] output:

[1437] Customer score information provided to companies.

[1438] Specific actions:

[1439] Receive requests from companies and filter and organize user score information.

[1440] Convert the data to the required format (e.g., CSV file) and send it to the company.

[1441] In this way, this system promotes healthy communication on social media in real time, providing a valuable environment for both users and businesses.

[1442] (Application Example 2)

[1443] 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."

[1444] Conventional social media post analysis systems only analyze text-based posts, making it difficult to effectively grasp customer emotions and satisfaction levels in real time during customer service interactions. Furthermore, the lack of means to provide feedback on improving customer service quality has limited improvements in customer service operations. In this situation, customer satisfaction cannot be adequately improved, making it urgent to improve the efficiency of service delivery and enhance customer satisfaction through the use of customer service robots and other technologies in physical stores.

[1445] 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.

[1446] In this invention, the server includes means for acquiring SNS posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine emotions and context, means for calculating a user score based on the analysis results, means for storing and updating the calculated score in a database, means for checking the user's score and providing incentives to users who exceed a certain threshold, means for organizing and providing user score information to companies, means for converting speech to text in real time using speech recognition, and means for identifying low-risk customers and providing feedback to improve customer service quality. This enables real-time evaluation of customer service quality in physical stores, and by combining speech recognition and emotion analysis, it becomes possible to measure customer satisfaction with greater accuracy and improve customer service quality.

[1447] "Methods for obtaining user-submitted SNS posts" refers to systems that collect data such as text and images posted by users on social media.

[1448] "A means of analyzing the content of acquired posts using a natural language processing model to determine emotions and context" refers to a system that analyzes collected SNS post data using natural language processing technology and extracts emotions and context from the text.

[1449] "A means of calculating user scores based on analysis results" refers to a system that calculates an evaluation score for each user's post based on analyzed sentiment and contextual information.

[1450] "Means for saving and updating calculated scores in a database" refers to a system that records calculated evaluation scores in a digital storage system and updates them as needed.

[1451] "A means of checking user scores and providing incentives to users who exceed a certain threshold" refers to a system that monitors user evaluation scores and provides benefits or rewards to users who meet specified criteria.

[1452] "A means of organizing and providing user score information to businesses" refers to a system that compiles and provides user evaluation score information in a format that businesses can use.

[1453] "A means of converting speech to text in real time using speech recognition" refers to a system that uses speech recognition technology to convert speech data acquired in real time into text information.

[1454] "A means of identifying low-risk customers and providing feedback to improve service quality" refers to a system that identifies low-risk customers based on an analysis of customer emotions and satisfaction, and provides feedback to service staff and systems on areas for improvement.

[1455] This invention is a system that identifies and prevents inappropriate behavior on social media in real time. The system promotes healthy communication by analyzing user posts and scoring their behavior. Furthermore, based on examples of customer service robot applications in physical stores, it aims to improve customer satisfaction in real time and enhance the quality of customer service.

[1456] System Overview

[1457] This system mainly consists of the following elements:

[1458] 1. User terminal: A device used by users to input and send posts using social networking services (SNS). It also has voice recognition capabilities that allow for voice input.

[1459] 2. Server: A central processing unit that receives SNS posts and audio data and performs analysis using natural language processing models and sentiment engines. It calculates a score based on the analysis results and stores it in a database.

[1460] 3. Database: A storage system for storing and managing analysis results and scores.

[1461] 4. Interface: Includes a user interface for organizing and providing score information to businesses.

[1462] Hardware and software to be used

[1463] This system uses the following hardware and software:

[1464] Hardware: Microphone, speaker, user device (smartphone or tablet), and internet connection.

[1465] Software: Python, speech recognition library (Google Speech-to-Text), natural language processing library (Hugging Face Transformers), sentiment analysis library (TextBlob, VaderSentiment), database (SQLite)

[1466] Data processing and data calculation

[1467] 1. Speech recognition:

[1468] The user's device uses a microphone to capture the customer's conversation. The captured audio data is converted into text data using the Google Speech-to-Text API. This allows the customer service robot to convert interactions with customers into text in real time.

[1469] 2. Natural language processing and sentiment analysis:

[1470] The server analyzes this text data using a natural language processing model (Hugging Face's Transformers). It extracts context and keywords and performs sentiment classification (positive, negative, neutral). Then, it uses sentiment engines (TextBlob, VaderSentiment) to perform a detailed analysis of the customer's emotions.

[1471] 3. Scoring:

[1472] Based on the analysis results, the server calculates a sentiment score for each user. Posts with strong positive emotions are assigned high scores, while posts with strong negative emotions are assigned low scores.

[1473] 4. Data storage and updates:

[1474] The calculated scores are stored in a database and updated as needed. This allows for the management of each user's sentiment score history.

[1475] 5. Providing incentives and organizing company information:

[1476] When a user's score exceeds a certain threshold, they will be offered rewards (coupons or special offers). Furthermore, companies will be provided with compiled score information of low-risk customers.

[1477] Specific example

[1478] For example, consider a customer service scenario in a physical store. If a customer says, "The service at this cafe is truly amazing!", this audio is converted to text in real time. This text is sent to a server and a natural language processing model determines that it represents a positive emotion. After detailed analysis by the emotion engine, a satisfaction score for this customer is calculated and stored in a database. If the score is high, a coupon is offered as an incentive. The company then uses this data to implement special offers for low-risk customers.

[1479] Example of a prompt

[1480] "Please design a new customer service robot application. This application will perform real-time speech recognition of customer conversations and use natural language processing and sentiment analysis to score customer satisfaction. Furthermore, if a certain score is reached, the customer will receive coupons or other benefits."

[1481] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1482] Step 1:

[1483] Processing details: Acquisition of voice input

[1484] Specific operation: The user's terminal uses a microphone to capture conversations during customer service. The audio data spoken by the user is captured in real time.

[1485] Input: User's voice data

[1486] Output: Captured audio data

[1487] Step 2:

[1488] Processing details: Text conversion of audio data

[1489] Specific operation: The captured audio data is converted into text data using the Google Speech-to-Text API.

[1490] Input: Captured audio data

[1491] Output: Converted text data

[1492] Step 3:

[1493] Processing details: Sending text data

[1494] Specific operation: The user's terminal sends the converted text data to the server.

[1495] Input: Converted text data

[1496] Output: Text data sent to the server

[1497] Step 4:

[1498] Processing details: Analysis of text data

[1499] Specific operation: The server analyzes the received text data using a natural language processing model (e.g., Hugging Face's Transformers). It extracts the context and emotions of the text and performs emotion classification (positive, negative, neutral).

[1500] Input: Text data sent to the server

[1501] Output: Emotion classification result

[1502] Step 5:

[1503] Processing details: Detailed analysis of emotions

[1504] Specific operation: The server uses the emotion engine (TextBlob, VaderSentiment) to further analyze the intensity and type of emotion.

[1505] Input: Sentiment classification result

[1506] Output: Emotion analysis results

[1507] Step 6:

[1508] Processing details: Calculation of user score

[1509] Specific operation: The server calculates a customer satisfaction score based on the sentiment analysis results. Positive emotions are assigned high scores, and negative emotions are assigned low scores.

[1510] Input: Sentiment analysis results

[1511] Output: User score

[1512] Step 7:

[1513] Processing details: Saving and updating scores

[1514] Specific operation: The server saves the calculated score to the database and updates the existing score as needed.

[1515] Input: User score

[1516] Output: Score data stored in the database

[1517] Step 8:

[1518] Processing details: Provision of incentives

[1519] Specific operation: The server checks the user score and provides the customer with a reward (such as a coupon) if the score exceeds a certain threshold.

[1520] Input: Score data stored in the database

[1521] Output: Benefits offered to customers

[1522] Step 9:

[1523] Processing details: Organization and provision of information for businesses.

[1524] Specific operation: The server organizes user score information for the company and provides information on highly-rated users.

[1525] Input: Score data stored in the database

[1526] Output: Organized user score information

[1527] 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.

[1528] 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.

[1529] 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.

[1530] [Fourth Embodiment]

[1531] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1532] 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.

[1533] 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).

[1534] 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.

[1535] 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.

[1536] 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).

[1537] 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.

[1538] 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.

[1539] 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.

[1540] 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.

[1541] 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.

[1542] 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.

[1543] 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".

[1544] This invention provides a system for identifying and preventing inappropriate behavior on social media in real time. This system promotes healthy communication by analyzing user posts and scoring their behavior. Specific embodiments are described below.

[1545] System Overview

[1546] This system mainly consists of the following elements:

[1547] 1. User terminal: A device used by a user to input and send posts using social networking services (SNS).

[1548] 2. Server: A central processing unit that receives SNS posts and performs analysis using natural language processing models.

[1549] 3. Database: A storage system for storing and managing analysis results and scores.

[1550] Retrieve user posts

[1551] User terminal: The user enters a message into the SNS posting form and sends the content to the server by pressing the post button. For example, consider a case where a user posts, "This product is really great!"

[1552] Submit post

[1553] User terminal: The user's entered content is sent to the server via the internet.

[1554] Analysis and scoring of submitted content

[1555] Server: Receives posts sent from terminals. The received posts are analyzed by a natural language processing model and classified as positive, negative, or neutral. The server calculates a score based on the analysis results. For example, if a post that says "This product is really great!" is judged to be positive, the server will assign this user a score of +10.

[1556] Saving and managing scores

[1557] Server: The calculated score is linked to the user ID and stored in the database. This allows each user's score to be managed as a history. For example, the score of user ID "12345" is updated from 90 to 100.

[1558] Provision of incentives

[1559] Server: Users whose scores exceed a certain threshold are offered incentives. For example, users who exceed 100 points may receive special coupons or purchase points. Furthermore, score information is compiled to provide companies with information on high-scoring users. Based on this, companies can offer special offers to low-risk customers.

[1560] Information provision for businesses

[1561] Server: Organizes and provides customer score information needed by businesses at the required time. Based on this data, businesses can implement marketing strategies that target lower-risk customer segments.

[1562] Examples

[1563] When a user posts "This product is the best!" on social media, the post is sent to the server. The server analyzes the post using a natural language processing model and determines that it expresses positive emotion. Based on the analysis, the server scores the user +10 points and saves the user's new score to the database. Since the score exceeds 100 points, the server offers this user a special coupon.

[1564] This system will promote healthy communication on social media and create a valuable social media environment for both users and businesses.

[1565] The following describes the processing flow.

[1566] Step 1:

[1567] User: Enter a message into the social media posting form.

[1568] Example: Enter the message, "This product is truly amazing!"

[1569] Step 2:

[1570] Terminal: The user presses the post button and sends the content to the server.

[1571] The submitted content is sent to the server via the internet.

[1572] Step 3:

[1573] Server: Receives posted data sent from terminals.

[1574] For example, the following JSON data arrives on the server:

[1575] json

[1576] {

[1577] "user_id": "12345",

[1578] "timestamp": "2023-10-01T10:00:00Z",

[1579] "content": "This product is truly amazing!"

[1580] }

[1581] Step 4:

[1582] Server: Sends the received posted content to a natural language processing model for text analysis.

[1583] The model performs sentiment classification (positive, negative, neutral) and contextual analysis.

[1584] Step 5:

[1585] Server: Assigns a score to posts based on the analysis results from a natural language processing model.

[1586] Example: The keyword "great" is recognized as positive, so a score of +10 is calculated.

[1587] Step 6:

[1588] Server: Associates the calculated score with the user ID, saves it to the database, and updates existing scores.

[1589] Example: The score of user ID "12345" will be updated from 90 to 100.

[1590] Step 7:

[1591] Server: Checks user scores in the database and provides incentives if the score exceeds a certain threshold.

[1592] Example: Because the score exceeded 100, the user will be given a special coupon.

[1593] Step 8:

[1594] Server: Executes the incentive distribution process and notifies the user.

[1595] Example: Send a coupon offer notification to the user via electronic message or similar means.

[1596] Step 9:

[1597] Server: Organizes user score information for enterprises and generates lists of high-rated users.

[1598] This system aggregates score data and related information from highly-rated users.

[1599] Step 10:

[1600] Server: Provides organized score information to companies and helps them identify low-risk customers.

[1601] Example: By providing companies with data indicating that "User ID: 12345 has a high rating," this data can be used for marketing strategies such as special offers.

[1602] The above outlines the specific processing steps of the program. This system can identify defamation and inappropriate behavior on social media in real time, thereby promoting healthy communication.

[1603] (Example 1)

[1604] 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".

[1605] While it is necessary to identify inappropriate behavior and posts on social media in real time and promote healthy communication, conventional systems had problems with low accuracy in analyzing posts and being unable to properly evaluate user behavior. Furthermore, there were insufficient means of providing reliable user information to companies. This made it difficult to provide appropriate incentives to promote healthy user communication and for companies to implement efficient marketing strategies.

[1606] 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.

[1607] In this invention, the server includes means for acquiring posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine sentiment and context, means for calculating and updating the user's score based on the analysis results, means for storing the calculated score in a storage device, means for checking the user's score and providing incentives to users who exceed a certain threshold, and means for organizing and providing user score information to companies. This makes it possible to identify inappropriate behavior on social media in real time, promote healthy communication, and provide appropriate incentives to users. It also makes it possible to provide companies with reliable user information and support the implementation of efficient marketing strategies.

[1608] A "user" refers to an individual or group that inputs and submits posts using social networking services (SNS).

[1609] A "post" refers to information such as text, images, videos, and links that a user shares on social media.

[1610] A "natural language processing model" refers to a machine learning algorithm or program that analyzes text data entered by a user to determine its sentiment and context.

[1611] "Analysis results" refer to information about the sentiment and context of posts obtained by a natural language processing model.

[1612] A "score" is a numerical value calculated based on the analysis of the content of a post, and it is an indicator that evaluates the healthiness of a user's behavior and statements.

[1613] A "memory device" refers to a database or storage system used to store calculated scores.

[1614] An "incentive" refers to a reward or benefit offered to users who exceed a certain score.

[1615] "Company" refers to a business organization that uses user score information to develop marketing strategies and manage risks.

[1616] Modes for carrying out the invention

[1617] This invention is a system that identifies inappropriate behavior on social networking services (SNS) in real time and promotes healthy communication. This system consists of the following hardware and software: a user terminal, a server, a database, and a natural language processing model.

[1618] 1. System Overview

[1619] This system consists of the following elements:

[1620] User device: A device used by a user to input and send posts using social networking services (e.g., smartphone, PC).

[1621] Server: A central processing unit that receives SNS posts and performs analysis using natural language processing models. Examples of software used include natural language processing models such as BERT and GPT-4.

[1622] Database: A storage system for storing and managing analysis results and scores. Specific examples include MySQL and PostgreSQL.

[1623] 2. Retrieve user posts

[1624] User terminal: The user enters a message into the social media posting form and presses the post button. For example, this would be the case when the user enters "This product is really great!"

[1625] 3. Submit your post

[1626] User terminal: When the post button is pressed, the content of the post is sent to the server via the internet. Specifically, the post data is sent using an HTTP request.

[1627] 4. Receiving the posted content

[1628] Server: Receives posted content via the internet for analysis. Receives the posted content contained in the body of the HTTP request.

[1629] 5. Analysis of posted content

[1630] Server: The server passes the received post content to a natural language processing model, which classifies it as positive, negative, or neutral. For example, if "This product is truly wonderful!" is judged to be positive, the natural language processing model returns that result.

[1631] 6. Score calculation and updating

[1632] Server: Based on the analysis results, the server calculates and updates the user's score. Posts containing positive emotions are given higher scores, and posts containing negative emotions are given lower scores. For example, it might calculate to add +10 points to the user's score.

[1633] 7. Saving and managing scores

[1634] Server: Stores the calculated score in the database, associating it with the user ID. Executes an SQL query to save the new score information in the database. For example, saves a new score of 100 for user ID "12345".

[1635] 8. Provision of incentives

[1636] Server: When a user's score exceeds a certain threshold, an incentive (e.g., a special coupon) is provided. A special coupon is issued to users who exceed the threshold, and a notification email is sent.

[1637] 9. Information provision for businesses

[1638] Server: Organizes and provides customer score information available to businesses as needed. Creates lists of high-rated users and sends data via API to businesses in a specific format (e.g., CSV file).

[1639] Examples

[1640] When a user posts on social media saying "This product is the best!", the following actions are taken:

[1641] 1. User's device: The user enters "This product is the best!" into the posting form of the SNS app and presses the post button.

[1642] 2. Terminal: The posted content is sent to the server as an HTTP request.

[1643] 3. Server: The received post content is passed to a natural language processing model, which determines that it expresses positive sentiment.

[1644] 4. Server: Based on the analysis results, the server assigns a score of +10 points to the user and saves the new score to the database.

[1645] 5. Server: Since the score has exceeded 100 points, issue a special coupon to the user and notify them via email.

[1646] This system promotes healthy communication on social media. Furthermore, it provides companies with reliable user information, enabling them to implement more efficient marketing strategies.

[1647] Examples of prompts for generative AI models

[1648] I want to post a positive comment on social media saying, "I like this product," but what kind of content should I include to ensure that this post is viewed as healthy communication on social media?

[1649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1650] Step 1:

[1651] User input

[1652] User: Enter a message into the posting form of the SNS app and press the post button.

[1653] Input: Text entered by the user in the social media posting form.

[1654] Specific action: The user types "This product is truly amazing!" and clicks the submit button.

[1655] Output: The post is ready to be sent.

[1656] Step 2:

[1657] Submit post

[1658] Terminal: When the post button is pressed, the content of the post is sent to the server via the internet.

[1659] Input: The content of the post entered by the user.

[1660] Specific action: The smartphone or PC sends the message "This product is truly amazing!" to the server as an HTTP request.

[1661] Output: The submitted content arrives at the server.

[1662] Step 3:

[1663] Received the posted content

[1664] Server: Receives submitted content via the internet for analysis.

[1665] Input: HTTP request containing the content of the post sent from the terminal.

[1666] Specific action: The server receives the message "This product is really great!" which is included in the body of the HTTP request.

[1667] Output: The submitted content is ready for analysis.

[1668] Step 4:

[1669] Analysis of posted content

[1670] Server: The posted content is passed to a natural language processing model (e.g., BERT or GPT-4) to perform sentiment analysis.

[1671] Input: Received post content: "This product is truly amazing!"

[1672] Specific operation: Input the post content into a natural language processing model and obtain sentiment analysis results. For example, the model identifies it as a positive sentiment.

[1673] Output: Positive emotions are obtained as the analysis result.

[1674] Step 5:

[1675] Score calculation

[1676] Server: Calculates the user's score based on the analysis results.

[1677] Input: Analysis results from a natural language processing model (e.g., positive emotions).

[1678] Specific operation: Based on the analysis results, the user is awarded a score of +10 points. If the original score was 90, the new score will be 100.

[1679] Output: The new calculated score.

[1680] Step 6:

[1681] Save score

[1682] Server: Stores the calculated score in the database, linked to the user ID.

[1683] Input: User ID and new score (e.g., User ID "12345" and score 100).

[1684] Specific action: Execute an SQL query and save the new score to the database.

[1685] Output: Score information is saved to the database.

[1686] Step 7:

[1687] Provision of incentives

[1688] Server: Provides an incentive to the user if their score exceeds a certain threshold.

[1689] Input: Updated score (e.g., 100 points or more).

[1690] Specific action: Issue a special coupon and send a notification email to the user.

[1691] Output: Incentive information is provided to the user.

[1692] Step 8:

[1693] Providing information for businesses

[1694] Server: Organizes customer score information available to the company and provides it as needed.

[1695] Input: User score information retrieved from the database.

[1696] Specific operation: Create a list of highly-rated users and send the data to companies via an API in CSV file format.

[1697] Output: User score information organized for enterprise use.

[1698] (Application Example 1)

[1699] 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".

[1700] In today's e-commerce environment, it is crucial that user reviews and comments are a sound and reliable source of information. However, there is a lack of systems to evaluate inappropriate behavior and comments, often hindering healthy communication. Furthermore, there are insufficient effective methods for evaluating user trustworthiness, making it difficult for stores to provide appropriate incentives. This invention aims to solve these problems and provide a valuable environment for both users and stores.

[1701] 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.

[1702] In this invention, the server includes means for acquiring electronic posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine sentiment and context, means for calculating the user's score based on the analysis results, means for storing and updating the calculated score in a database, means for checking the user's score and providing incentives to users who exceed a certain threshold, means for organizing and providing user score information to companies, means for analyzing user reviews and comments in real time in an e-commerce environment, and means for providing benefits and discounts when a user's score exceeds a certain threshold. This promotes healthy communication and enables an e-commerce environment that is valuable to both users and companies.

[1703] "Electronic submissions" refer to digital content such as text messages and comments that users send over the internet.

[1704] A "natural language processing model" is a collection of algorithms and technologies used to analyze and understand human language and to provide appropriate responses and processing.

[1705] "Determining emotions and context" is the process of analyzing words and context within a text to determine whether they represent positive, negative, or neutral emotions.

[1706] "Calculating a score" is the process of quantifying the evaluation of a post based on its analyzed content.

[1707] "Saving and updating in a database" means recording the calculated score in data storage in a way that allows each user to be identified, and updating it as needed whenever new data is added.

[1708] An "incentive" is a reward or benefit given to users who meet certain criteria.

[1709] "Organizing and providing user score information to businesses" refers to the process of classifying and processing user score data in a way that is easy for businesses to use, and providing that data to businesses at the appropriate time.

[1710] An "e-commerce environment" refers to a system or platform for buying and selling goods and services over the internet.

[1711] "Analyzing reviews and comments in real time" means instantly receiving reviews and comments posted by users and analyzing their content using a natural language processing model.

[1712] "Benefits and discounts" refer to coupons, discount services, or other forms of rewards offered to users.

[1713] System Overview

[1714] The system for realizing this application retrieves electronic submissions entered by users and analyzes them using a natural language processing model. Based on the analysis results, it calculates a score and stores and updates the calculated score in a database. Furthermore, if a user's score exceeds a certain threshold, it offers benefits or discounts. For businesses, it includes a function to organize and provide user score information.

[1715] Hardware and software to use

[1716] Hardware:

[1717] User devices: Smartphones, tablets, PCs, etc.

[1718] Server: Operates as a central processing unit and is connected to the database.

[1719] software:

[1720] Natural language processing models: Natural language processing libraries such as TextBlob and Spacy.

[1721] Database: A database management system such as SQLite or PostgreSQL.

[1722] Details of data processing and calculations

[1723] 1. Obtaining electronic submissions:

[1724] When a user enters a review or comment on their device and presses the submit button, the content is sent to the server. For example, consider a case where a user posts, "This product is truly amazing!"

[1725] 2. Analysis of the posted content:

[1726] The server analyzes the received posts using a natural language processing model (such as TextBlob). It then classifies them as positive, negative, or neutral and generates a sentiment score. For example, a post saying "This product is really great!" would be judged as having a positive sentiment.

[1727] 3. Calculating and saving the score:

[1728] The server calculates a score based on the analysis results and stores / updates that score in the database, linking it to the user's ID. For example, user ID "12345" is assigned a score of +10 points, and the new score is stored in the database.

[1729] 4. Providing incentives:

[1730] The server offers rewards or discounts when a user's score exceeds a certain threshold (e.g., 100 points). For example, a special coupon might be issued when a user scores over 100 points.

[1731] 5. Organization and provision of data for businesses:

[1732] The server organizes user score information for businesses and provides that data. This allows businesses to implement marketing strategies that target lower-risk customer segments.

[1733] Specific example

[1734] When a user posts a review of a specific product on their smartphone, saying "This product is the best!", and presses the Post button, the post is sent to the server. The server uses a natural language processing model (e.g., TextBlob) to analyze the post and determines that it expresses positive sentiment. Based on this analysis, the server scores the user +10 points and saves it to the database. Since the user's score exceeds 100 points, the server sends the user a special coupon. The next action is a notification saying, "A coupon for 10% off all products is now available!"

[1735] Example of a prompt:

[1736] When a user types "This product is the best!" about a specific product on their smartphone and presses the Post button, the following message appears: "This post has been sent to the sentiment analysis module and has been scored as positive content. Since the score exceeds 100, a reward coupon has been issued. Please take the next action."

[1737] This system promotes healthy communication and creates a valuable e-commerce environment for both users and businesses.

[1738] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1739] Step 1:

[1740] Retrieving posts from user terminals

[1741] When a user enters a review or comment on their device and presses the submit button, the post is submitted. The user's device then sends this input to the server. The input data is in text format, such as reviews or comments. Specifically, if a user enters "This product is really great!", that text data is sent to the server via the internet.

[1742] Step 2:

[1743] Receiving and analyzing submitted data

[1744] The server receives posted data sent from the user's terminal. The received text data is analyzed using a natural language processing model (e.g., TextBlob or Spacy). In this analysis, the text is classified as positive, negative, or neutral, and a sentiment score is generated. For example, the text "This product is really great!" is judged as positive, and a positive score of +10 points is calculated.

[1745] Step 3:

[1746] Calculating and saving scores

[1747] The server calculates a user's score based on the analysis results. Specifically, positive posts are assigned high scores, and negative posts are assigned low scores. The calculated score is linked to the user ID, stored in the database, and updated. For example, user ID "12345" is assigned a score of +10 points, which is added to the existing score and stored in the database.

[1748] Step 4:

[1749] Verification and provision of incentives

[1750] The server monitors user scores and provides incentives when a certain threshold is exceeded. For example, if a user's score exceeds 100 points, a special coupon or discount will be offered. This will be notified to the user's device, and the coupon code or discount information will be displayed.

[1751] Step 5:

[1752] Organization and provision of data for businesses

[1753] The server organizes user score information for businesses and provides it as needed. This organized data is used by businesses to identify low-risk customer segments and implement targeted marketing strategies. For example, businesses can improve customer satisfaction by offering special offers to high-rated users.

[1754] This entire process enables the creation of an e-commerce environment that is valuable to both users and businesses.

[1755] 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.

[1756] This invention is a system for identifying and preventing inappropriate behavior on social media in real time. This system promotes healthy communication by analyzing user posts and scoring their behavior. Specific embodiments are described below.

[1757] System Overview

[1758] This system mainly consists of the following elements:

[1759] 1. User terminal: A device used by a user to input and send posts using social networking services (SNS).

[1760] 2. Server: A central processing unit that receives SNS posts and performs analysis using natural language processing models and sentiment engines.

[1761] 3. Database: A storage system for storing and managing analysis results and scores.

[1762] Retrieve user posts

[1763] User terminal: The user enters a message into the SNS posting input form and sends the content to the server by pressing the post button. For example, consider a case where a user posts, "This product is really great!"

[1764] Submit post

[1765] User terminal: The user's entered content is sent to the server via the internet.

[1766] Analysis and scoring of submitted content

[1767] Server: Receives posts sent from terminals. The received posts are analyzed by a natural language processing model and sentiment engine.

[1768] The natural language processing model analyzes the context and keywords of the text and performs sentiment classification (positive, negative, neutral) based on the content of the post.

[1769] The emotion engine further analyzes the emotions contained in user posts, determining their intensity and type. For example, posts with strong positive emotions are given high scores, while posts with strong negative emotions are given low scores.

[1770] Saving and managing scores

[1771] Server: The calculated score is linked to the user ID and stored in the database. This allows each user's score to be managed as a history. For example, the score of user ID "12345" is updated from 90 to 100.

[1772] Provision of incentives

[1773] Server: Users whose scores exceed a certain threshold are offered incentives. For example, users who exceed 100 points may receive special coupons or purchase points. Furthermore, score information is compiled to provide companies with information on high-scoring users. Based on this, companies can offer special offers to low-risk customers.

[1774] Information provision for businesses

[1775] Server: Organizes and provides customer score information needed by businesses at the required time. Based on this data, businesses can implement marketing strategies that target lower-risk customer segments.

[1776] Examples

[1777] When a user posts "This product is the best!" on social media, the post is sent to the server. The server analyzes the post using a natural language processing model and sentiment engine and determines that it expresses positive emotion. Based on the analysis, the server scores the user +10 points and saves the user's new score to the database. Since the score exceeds 100 points, the server offers this user a special coupon.

[1778] Furthermore, if a user posts something like, "This service is terrible!", the emotion engine detects a strong negative emotion. Based on this information, the server scores the user -10 points and also evaluates the impact the negative post has on other users.

[1779] This system promotes healthy communication on social media, creating a valuable social media environment for both users and businesses. By using an emotion engine, it is possible to analyze users' emotions in detail and perform more accurate scoring.

[1780] The following describes the processing flow.

[1781] Step 1:

[1782] User: Enter a message into the social media posting form.

[1783] Example: Enter the message, "This product is truly amazing!"

[1784] Step 2:

[1785] Terminal: The user presses the post button and sends the content to the server.

[1786] The submitted content is sent to the server via the internet.

[1787] Step 3:

[1788] Server: Receives posted data sent from terminals.

[1789] For example, the following JSON data arrives on the server:

[1790] json

[1791] {

[1792] "user_id": "12345",

[1793] "timestamp": "2023-10-01T10:00:00Z",

[1794] "content": "This product is truly amazing!"

[1795] }

[1796] Step 4:

[1797] Server: Sends the received posted content to a natural language processing model for text analysis.

[1798] The model analyzes the context and keywords of the text and performs sentiment classification (positive, negative, neutral).

[1799] Example: The keyword "wonderful" is perceived as positive.

[1800] Step 5:

[1801] Server: Sends the analysis results from the natural language processing model to the emotion engine.

[1802] The emotion engine receives the analysis results and determines the intensity and type of emotion.

[1803] Step 6:

[1804] Server: Assigns a score to posts based on the output of the sentiment engine.

[1805] For example, posts with strong positive emotions will be given a +10, and posts with strong negative emotions will be given a -10.

[1806] For example, a post that says "This product is truly amazing!" will be judged as positive and will receive a score of +10.

[1807] Step 7:

[1808] Server: Associates the calculated score with the user ID, saves it to the database, and updates existing scores.

[1809] Example: The score of user ID "12345" will be updated from 90 to 100.

[1810] Step 8:

[1811] Server: Checks user scores in the database and provides incentives if the score exceeds a certain threshold.

[1812] Example: Because the score exceeded 100, the user will be given a special coupon.

[1813] Step 9:

[1814] Server: Executes the incentive distribution process and notifies the user.

[1815] Example: Send a coupon offer notification to the user via electronic message or similar means.

[1816] Step 10:

[1817] Server: Organizes user score information for enterprises and generates lists of high-rated users.

[1818] This system aggregates score data and related information from highly-rated users.

[1819] Step 11:

[1820] Server: Provides organized score information to companies and helps them identify low-risk customers.

[1821] Example: By providing companies with data indicating that "User ID: 12345 has a high rating," this data can be used for marketing strategies such as special offers.

[1822] The above outlines the specific processing steps of the program, including the emotion engine. This system can identify defamation and inappropriate behavior on social media in real time, thereby promoting healthy communication.

[1823] (Example 2)

[1824] 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".

[1825] The increase in inappropriate behavior and harmful posts on social media is hindering healthy communication. This is leading to decreased user satisfaction and increased marketing risks for businesses. Existing systems do not adequately analyze and score content, making it difficult to identify and prevent inappropriate behavior in real time. Therefore, there is a need for a new system that can analyze content in detail and promote healthy communication.

[1826] 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.

[1827] In this invention, the server includes means for acquiring SNS posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine emotions and context, means for performing a detailed analysis using an emotion engine based on the analysis results to determine the intensity and type of emotion, means for calculating a user score based on the analysis results, means for storing and updating the calculated score in a database, means for checking the user's score and providing incentives to users who exceed a certain threshold, and means for organizing and providing user score information to companies. This makes it possible to promote healthy communication on SNS in real time and realize an SNS environment that is valuable to both users and companies.

[1828] A "user" is someone who uses a system to post content via social networking services (SNS).

[1829] A "user terminal" is a device used by a user to input and send posts using social networking services (SNS).

[1830] A "server" is a central processing unit that receives, analyzes, and scores social media posts.

[1831] A "database" is a storage system used to store and manage analysis results and scores.

[1832] A "natural language processing model" is a general term for algorithms that analyze the context and keywords of text and classify sentiment (positive, negative, neutral) based on the content of a post.

[1833] An "emotion engine" is a processing system that analyzes the emotions contained in a user's post in detail and determines the intensity and type of those emotions.

[1834] A "score" is a numerical value calculated based on the content of a post, and it serves as a criterion for evaluating a user's behavior and emotions.

[1835] An "incentive" is a reward or benefit given when a user's score exceeds a certain threshold.

[1836] A "company" is a legal entity or organization that implements marketing strategies based on user score information.

[1837] "Analysis results" refer to the sentiment classification and detailed sentiment analysis output of the posted content obtained through a natural language processing model and sentiment engine.

[1838] A "threshold" is a threshold value used to provide an incentive when a score reaches a certain standard.

[1839] This invention is a system for identifying and preventing inappropriate behavior on social media in real time. This system promotes healthy communication by analyzing user posts and scoring their behavior. A specific embodiment of this system is described below.

[1840] System Overview

[1841] This system mainly consists of the following elements:

[1842] 1. User terminal: A device used by users to input and send posts using social networking services (SNS). User terminals include smartphones, tablets, and personal computers.

[1843] 2. Server: This is a central processing unit that receives SNS posts and performs analysis using natural language processing models and sentiment engines.

[1844] 3. Database: A storage system for storing and managing analysis results and scores.

[1845] Retrieve user posts

[1846] User terminal:

[1847] The user enters a message into the social media posting form and presses the post button. This saves the posted content on the user's device. For example, if the user enters "This product is truly amazing!", this content proceeds to the next step.

[1848] Submit post

[1849] User terminal:

[1850] This involves calling the API of a social networking service (SNS) application and sending the posted content to the server via the internet.

[1851] Analysis and scoring of submitted content

[1852] server:

[1853] The server receives the posted content sent from the user's terminal. The received posted content is parsed in the following steps:

[1854] 1. Natural Language Processing Models (NLP Models):

[1855] The server analyzes the context and keywords of the text and classifies the sentiment of the post (positive, negative, neutral). Specifically, it uses an NLP model to understand the meaning of the text data and assign appropriate sentiment labels.

[1856] 2. Emotional Engine:

[1857] The system analyzes the emotions contained in posts in detail, determining their intensity and type. For example, posts with strong positive emotions are given high scores, while posts with strong negative emotions are given low scores.

[1858] Saving and managing scores

[1859] server:

[1860] The calculated score is linked to the user ID and stored in the database. This allows each user's score to be managed as a history. For example, the score of user ID "12345" is updated from 90 to 100 based on their posts.

[1861] Provision of incentives

[1862] server:

[1863] Incentives are offered to users whose scores exceed a certain threshold. For example, users who score over 100 points may receive special coupons or purchase points. Furthermore, score information is compiled to provide companies with information on high-scoring users. Based on this, companies can offer special offers to low-risk customers.

[1864] Information provision for businesses

[1865] server:

[1866] We organize the customer score information that companies need and provide it at the right time. Based on this data, companies can implement marketing strategies that target lower-risk customer segments.

[1867] Examples

[1868] For example, if a user posts "This product is the best!" on social media, that content is sent from the user's device to the server. The server analyzes the post using a natural language processing model and an emotion engine and determines that it expresses a positive emotion. Based on the analysis, the server scores the user +10 points and saves the new score to the database. Since the score exceeds 100 points, the server offers this user a special coupon.

[1869] Example of a prompt

[1870] A user posted on social media, "I had so much fun today!" This system analyzes the post using a natural language processing model and sentiment engine, detects positive emotions, and scores the user +10 points. Save the new score to the database, and issue a coupon when the score exceeds 100 points.

[1871] This system promotes healthy communication on social media in real time, creating a valuable social media environment for both users and businesses. By using an emotion engine, it is possible to analyze users' emotions in detail and perform more accurate scoring.

[1872] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1873] Step 1:

[1874] Retrieve user posts

[1875] User terminal:

[1876] The user enters a message into the social media posting form and presses the post button. This causes the user's message to be stored on the device as input data.

[1877] input:

[1878] A text message entered by the user (e.g., "This product is really great!").

[1879] output:

[1880] The retained post content is ready to be sent to the next processing step.

[1881] Specific actions:

[1882] The user opens a social networking app on their smartphone or PC.

[1883] Enter "This product is truly amazing!" into the submission form.

[1884] Click the submit button.

[1885] Step 2:

[1886] Submit post

[1887] User terminal:

[1888] When the submit button is pressed, the user's entered content is sent to the server via the internet.

[1889] input:

[1890] Post content stored on the device.

[1891] output:

[1892] The server receives the data via the internet.

[1893] Specific actions:

[1894] The user's device calls the SNS application's API and sends the content to be posted as a parameter.

[1895] The data reaches the server via the internet.

[1896] Step 3:

[1897] Analysis and scoring of submitted content

[1898] server:

[1899] The received posts are analyzed using a natural language processing (NLP) model, and then further analyzed in detail using an emotion engine.

[1900] input:

[1901] Content received via the internet (e.g., "This product is truly amazing!").

[1902] output:

[1903] A score based on the results of emotion classification (positive, negative, neutral) and the intensity and type of emotion.

[1904] Specific actions:

[1905] The server saves the received post content to a buffer.

[1906] Using natural language processing (NLP) models, we analyze the context and keywords of text to classify sentiment.

[1907] Example: A post saying "This product is truly amazing!" is judged as positive.

[1908] The emotion engine further analyzes the emotion classification results to determine the intensity and type of emotion.

[1909] Example: A high score was given due to strong positive emotions.

[1910] Step 4:

[1911] Saving and managing scores

[1912] server:

[1913] The calculated score is linked to the user ID and saved in the database.

[1914] input:

[1915] The score analyzed by the emotion engine (e.g., +10 points) and the user ID.

[1916] output:

[1917] The updated score is saved in the database.

[1918] Specific actions:

[1919] The server sets the user ID and score.

[1920] The set score is saved to the database.

[1921] Example: Update the score of user ID "12345" from 90 to 100.

[1922] Step 5:

[1923] Provision of incentives

[1924] server:

[1925] Incentives are provided to users whose scores exceed a certain threshold.

[1926] input:

[1927] Updated user score and threshold information.

[1928] output:

[1929] Offering incentives (e.g., special coupons or purchase points).

[1930] Specific actions:

[1931] The server periodically checks the database to identify users whose scores exceed a threshold.

[1932] This involves executing the routine for providing incentives and handling the procedures for issuing coupons and points.

[1933] Example: Issue a special coupon to users who score over 100 points.

[1934] Step 6:

[1935] Information provision for businesses

[1936] server:

[1937] We organize customer score information requested by companies and provide it at the necessary time.

[1938] input:

[1939] Organized user score information.

[1940] output:

[1941] Customer score information provided to companies.

[1942] Specific actions:

[1943] Receive requests from companies and filter and organize user score information.

[1944] Convert the data to the required format (e.g., CSV file) and send it to the company.

[1945] In this way, this system promotes healthy communication on social media in real time, providing a valuable environment for both users and businesses.

[1946] (Application Example 2)

[1947] 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".

[1948] Conventional social media post analysis systems only analyze text-based posts, making it difficult to effectively grasp customer emotions and satisfaction levels in real time during customer service interactions. Furthermore, the lack of means to provide feedback on improving customer service quality has limited improvements in customer service operations. In this situation, customer satisfaction cannot be adequately improved, making it urgent to improve the efficiency of service delivery and enhance customer satisfaction through the use of customer service robots and other technologies in physical stores.

[1949] 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.

[1950] In this invention, the server includes means for acquiring SNS posts entered by users, means for analyzing the content of the acquired posts using a natural language processing model to determine emotions and context, means for calculating a user score based on the analysis results, means for storing and updating the calculated score in a database, means for checking the user's score and providing incentives to users who exceed a certain threshold, means for organizing and providing user score information to companies, means for converting speech to text in real time using speech recognition, and means for identifying low-risk customers and providing feedback to improve customer service quality. This enables real-time evaluation of customer service quality in physical stores, and by combining speech recognition and emotion analysis, it becomes possible to measure customer satisfaction with greater accuracy and improve customer service quality.

[1951] "Methods for obtaining user-submitted SNS posts" refers to systems that collect data such as text and images posted by users on social media.

[1952] "A means of analyzing the content of acquired posts using a natural language processing model to determine emotions and context" refers to a system that analyzes collected SNS post data using natural language processing technology and extracts emotions and context from the text.

[1953] "A means of calculating user scores based on analysis results" refers to a system that calculates an evaluation score for each user's post based on analyzed sentiment and contextual information.

[1954] "Means for saving and updating calculated scores in a database" refers to a system that records calculated evaluation scores in a digital storage system and updates them as needed.

[1955] "A means of checking user scores and providing incentives to users who exceed a certain threshold" refers to a system that monitors user evaluation scores and provides benefits or rewards to users who meet specified criteria.

[1956] "A means of organizing and providing user score information to businesses" refers to a system that compiles and provides user evaluation score information in a format that businesses can use.

[1957] "A means of converting speech to text in real time using speech recognition" refers to a system that uses speech recognition technology to convert speech data acquired in real time into text information.

[1958] "A means of identifying low-risk customers and providing feedback to improve service quality" refers to a system that identifies low-risk customers based on an analysis of customer emotions and satisfaction, and provides feedback to service staff and systems on areas for improvement.

[1959] This invention is a system that identifies and prevents inappropriate behavior on social media in real time. The system promotes healthy communication by analyzing user posts and scoring their behavior. Furthermore, based on examples of customer service robot applications in physical stores, it aims to improve customer satisfaction in real time and enhance the quality of customer service.

[1960] System Overview

[1961] This system mainly consists of the following elements:

[1962] 1. User terminal: A device used by users to input and send posts using social networking services (SNS). It also has voice recognition capabilities that allow for voice input.

[1963] 2. Server: A central processing unit that receives SNS posts and audio data and performs analysis using natural language processing models and sentiment engines. It calculates a score based on the analysis results and stores it in a database.

[1964] 3. Database: A storage system for storing and managing analysis results and scores.

[1965] 4. Interface: Includes a user interface for organizing and providing score information to businesses.

[1966] Hardware and software to be used

[1967] This system uses the following hardware and software:

[1968] Hardware: Microphone, speaker, user device (smartphone or tablet), and internet connection.

[1969] Software: Python, speech recognition library (Google Speech-to-Text), natural language processing library (Hugging Face Transformers), sentiment analysis library (TextBlob, VaderSentiment), database (SQLite)

[1970] Data processing and data calculation

[1971] 1. Speech recognition:

[1972] The user's device uses a microphone to capture the customer's conversation. The captured audio data is converted into text data using the Google Speech-to-Text API. This allows the customer service robot to convert interactions with customers into text in real time.

[1973] 2. Natural language processing and sentiment analysis:

[1974] The server analyzes this text data using a natural language processing model (Hugging Face's Transformers). It extracts context and keywords and performs sentiment classification (positive, negative, neutral). Then, it uses sentiment engines (TextBlob, VaderSentiment) to perform a detailed analysis of the customer's emotions.

[1975] 3. Scoring:

[1976] Based on the analysis results, the server calculates a sentiment score for each user. Posts with strong positive emotions are assigned high scores, while posts with strong negative emotions are assigned low scores.

[1977] 4. Data storage and updates:

[1978] The calculated scores are stored in a database and updated as needed. This allows for the management of each user's sentiment score history.

[1979] 5. Providing incentives and organizing company information:

[1980] When a user's score exceeds a certain threshold, they will be offered rewards (coupons or special offers). Furthermore, companies will be provided with compiled score information of low-risk customers.

[1981] Specific example

[1982] For example, consider a customer service scenario in a physical store. If a customer says, "The service at this cafe is truly amazing!", this audio is converted to text in real time. This text is sent to a server and a natural language processing model determines that it represents a positive emotion. After detailed analysis by the emotion engine, a satisfaction score for this customer is calculated and stored in a database. If the score is high, a coupon is offered as an incentive. The company then uses this data to implement special offers for low-risk customers.

[1983] Example of a prompt

[1984] "Please design a new customer service robot application. This application will perform real-time speech recognition of customer conversations and use natural language processing and sentiment analysis to score customer satisfaction. Furthermore, if a certain score is reached, the customer will receive coupons or other benefits."

[1985] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1986] Step 1:

[1987] Processing details: Acquisition of voice input

[1988] Specific operation: The user's terminal uses a microphone to capture conversations during customer service. The audio data spoken by the user is captured in real time.

[1989] Input: User's voice data

[1990] Output: Captured audio data

[1991] Step 2:

[1992] Processing details: Text conversion of audio data

[1993] Specific operation: The captured audio data is converted into text data using the Google Speech-to-Text API.

[1994] Input: Captured audio data

[1995] Output: Converted text data

[1996] Step 3:

[1997] Processing details: Sending text data

[1998] Specific operation: The user's terminal sends the converted text data to the server.

[1999] Input: Converted text data

[2000] Output: Text data sent to the server

[2001] Step 4:

[2002] Processing details: Analysis of text data

[2003] Specific operation: The server analyzes the received text data using a natural language processing model (e.g., Hugging Face's Transformers). It extracts the context and emotions of the text and performs emotion classification (positive, negative, neutral).

[2004] Input: Text data sent to the server

[2005] Output: Emotion classification result

[2006] Step 5:

[2007] Processing details: Detailed analysis of emotions

[2008] Specific operation: The server uses the emotion engine (TextBlob, VaderSentiment) to further analyze the intensity and type of emotion.

[2009] Input: Sentiment classification result

[2010] Output: Emotion analysis results

[2011] Step 6:

[2012] Processing details: Calculation of user score

[2013] Specific operation: The server calculates a customer satisfaction score based on the sentiment analysis results. Positive emotions are assigned high scores, and negative emotions are assigned low scores.

[2014] Input: Sentiment analysis results

[2015] Output: User score

[2016] Step 7:

[2017] Processing details: Saving and updating scores

[2018] Specific operation: The server saves the calculated score to the database and updates the existing score as needed.

[2019] Input: User score

[2020] Output: Score data stored in the database

[2021] Step 8:

[2022] Processing details: Provision of incentives

[2023] Specific operation: The server checks the user score and provides the customer with a reward (such as a coupon) if the score exceeds a certain threshold.

[2024] Input: Score data stored in the database

[2025] Output: Benefits offered to customers

[2026] Step 9:

[2027] Processing details: Organization and provision of information for businesses.

[2028] Specific operation: The server organizes user score information for the company and provides information on highly-rated users.

[2029] Input: Score data stored in the database

[2030] Output: Organized user score information

[2031] 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.

[2032] 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.

[2033] 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.

[2034] 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.

[2035] 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.

[2036] 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.

[2037] 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.

[2038] 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.

[2039] 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."

[2040] 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.

[2041] 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.

[2042] 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.

[2043] 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.

[2044] 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.

[2045] 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.

[2046] 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.

[2047] 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.

[2048] 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.

[2049] 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.

[2050] 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.

[2051] 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 as being incorporated by reference.

[2052] The following is further disclosed regarding the embodiments described above.

[2053] (Claim 1)

[2054] A means of obtaining SNS posts entered by users,

[2055] The content of the retrieved posts is analyzed using a natural language processing model to determine sentiment and context, and

[2056] A means of calculating a user's score based on the analysis results,

[2057] A means of saving and updating the calculated score in a database,

[2058] A means of checking user scores and providing incentives to users who exceed a certain threshold,

[2059] A means of organizing and providing user score information to businesses,

[2060] A system that includes this.

[2061] (Claim 2)

[2062] The system according to claim 1, further comprising means for calculating a score based on the analysis results of a user's social media posts, assigning a high score to posts containing positive emotions and a low score to posts containing negative emotions.

[2063] (Claim 3)

[2064] The system according to claim 1, further comprising means for narrowing down user score information provided to a company to highly rated users and identifying low-risk customers.

[2065] "Example 1"

[2066] (Claim 1)

[2067] A means of retrieving posts entered by users,

[2068] The content of the retrieved posts is analyzed using a natural language processing model to determine sentiment and context, and

[2069] A means of calculating and updating the user's score based on the analysis results,

[2070] A means for storing the calculated score in a storage device,

[2071] A means of checking user scores and providing incentives to users who exceed a certain threshold,

[2072] A means of organizing and providing user score information to businesses,

[2073] A system that includes this.

[2074] (Claim 2)

[2075] The system according to claim 1, further comprising means for assigning a high score to posts containing positive emotions and a low score to posts containing negative emotions when calculating a score based on the analysis results.

[2076] (Claim 3)

[2077] The system according to claim 1, further comprising means for narrowing down user score information provided to a company to highly rated users and identifying low-risk customers.

[2078] "Application Example 1"

[2079] (Claim 1)

[2080] A means of obtaining electronic submissions entered by users,

[2081] The content of the retrieved posts is analyzed using a natural language processing model to determine sentiment and context, and

[2082] A means of calculating a user's score based on the analysis results,

[2083] A means of saving and updating the calculated score in a database,

[2084] A means of checking user scores and providing incentives to users who exceed a certain threshold,

[2085] A means of organizing and providing user score information to businesses,

[2086] A means of analyzing user reviews and comments in real time in an e-commerce environment,

[2087] A means of providing benefits or discounts when a user's score exceeds a certain threshold,

[2088] A system that includes this.

[2089] (Claim 2)

[2090] The system according to claim 1, further comprising means for assigning a high score to posts containing positive emotions and a low score to posts containing negative emotions.

[2091] (Claim 3)

[2092] The system according to claim 1, further comprising means for narrowing down user score information provided to a company to highly rated users and identifying low-risk customers.

[2093] "Example 2 of combining an emotion engine"

[2094] (Claim 1)

[2095] A means of obtaining SNS posts entered by users,

[2096] The content of the retrieved posts is analyzed using a natural language processing model to determine sentiment and context, and

[2097] A means of calculating a user's score based on the analysis results,

[2098] A means of saving and updating the calculated score in a database,

[2099] A means of checking user scores and providing incentives to users who exceed a certain threshold,

[2100] A means of organizing and providing user score information to businesses,

[2101] A method for analyzing the content of posts in detail using an emotion engine to determine the intensity and type of emotion,

[2102] A method for scoring based on the analysis results,

[2103] A system that includes this.

[2104] (Claim 2)

[2105] The system according to claim 1, further comprising means for calculating a score based on the analysis results of a user's social media posts, assigning a high score to posts containing positive emotions and a low score to posts containing negative emotions.

[2106] (Claim 3)

[2107] The system according to claim 1, further comprising means for narrowing down user score information provided to a company to highly rated users and identifying low-risk customers.

[2108] "Application example 2 when combining with an emotional engine"

[2109] (Claim 1)

[2110] A means of obtaining SNS posts entered by users,

[2111] The content of the retrieved posts is analyzed using a natural language processing model to determine sentiment and context, and

[2112] A means of calculating a user's score based on the analysis results,

[2113] A means of saving and updating the calculated score in a database,

[2114] A means of checking user scores and providing incentives to users who exceed a certain threshold,

[2115] A means of organizing and providing user score information to businesses,

[2116] A method for converting speech to text in real time using speech recognition,

[2117] A means to identify low-risk customers and provide feedback to improve service quality,

[2118] A system that includes this.

[2119] (Claim 2)

[2120] The system according to claim 1, further comprising means for assigning a high score to posts or conversations containing positive emotions and a low score to posts or conversations containing negative emotions when calculating a score based on the results of analyzing a user's SNS posts and when calculating a score based on the results of speech recognition.

[2121] (Claim 3)

[2122] The system according to claim 1, further comprising means for narrowing down user score information provided to a company to highly rated users and identifying low-risk customers, and means for evaluating customer service quality in real time and providing feedback. [Explanation of Symbols]

[2123] 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 obtaining SNS posts entered by users, The content of the retrieved posts is analyzed using a natural language processing model to determine sentiment and context, and A means of calculating a user's score based on the analysis results, A means of saving and updating the calculated score in a database, A means of checking user scores and providing incentives to users who exceed a certain threshold, A means of organizing and providing user score information to businesses, A system that includes this.

2. The system according to claim 1, further comprising means for calculating a score based on the analysis results of a user's SNS posts, assigning a high score to posts containing positive emotions and a low score to posts containing negative emotions.

3. The system according to claim 1, further comprising means for narrowing down user score information provided to a company to highly rated users and identifying low-risk customers.

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