system

Abstract

We provide the system. [Solution] An input method for setting the ideal self-image of SNS users, A data collection means for collecting SNS-related data based on the self-image set above, A plan generation means that analyzes the collected data and generates an operational plan for social media, Output means for displaying the generated operation plan, A feedback processing means that receives feedback from the aforementioned SNS users and improves the AI ​​model, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] In modern social networking services (SNS), an individual's ability to communicate and the success of self-branding have a great impact on online and offline human relationships. However, there is a lack of specific methods and guidelines for effective self-branding, and it is particularly difficult for beginners or users who lack confidence in their branding strategies to achieve it. Furthermore, it is not easy to quickly respond to the continuously changing SNS trends. To solve these problems, an efficient support system is required.

Means for Solving the Problems

[0005] This invention provides a system that supports SNS (Social Networking Services) users in managing their social media accounts based on their ideal self-image, which they input. Specifically, it receives goal settings from users using an input means and collects relevant data from multiple SNS platforms using a data collection means. Subsequently, a plan generation means uses natural language processing and machine learning algorithms to analyze the collected data and generate an SNS management plan. This plan is presented to the user by an output means, enabling the user to effectively engage in self-branding based on it. Furthermore, a feedback processing means improves the AI ​​model based on user feedback, allowing for the continuous provision of optimized advice.

[0006] "SNS users" refer to individuals or groups who use social networking services to send and receive information.

[0007] An "ideal self-image" refers to the goals and visions of how you want to present yourself to others through social media.

[0008] An "input method" refers to an interface that allows SNS users to provide data such as text and numbers to the system.

[0009] "Data collection means" refers to a function or process that automatically acquires necessary information from multiple social networking services (SNS) platforms.

[0010] A "plan generation tool" is a function that automatically builds strategies and plans for effective self-branding based on collected data.

[0011] "Output method" refers to an interface for visually or audibly presenting generated plans and information to SNS users.

[0012] A "feedback processing mechanism" refers to the process of receiving responses and evaluations from users and using them to improve the system's performance and algorithms.

[0013] "Natural language processing" refers to the technology used by computers to understand, interpret, and generate natural human language.

[0014] A "machine learning algorithm" is a technique or method that uses data to train a computer to perform a specific task. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The system according to the present invention provides support for SNS users to effectively engage in self-branding, and is equipped with a variety of functions as follows:

[0037] First, users can access the system using their device and input their ideal self-image and goals. This is done through a dedicated input interface. If a user sets a goal such as "I want to become an influential SNS user related to cooking," the entered data is sent to the server.

[0038] The server is equipped with means to collect social media-related data that corresponds to its objectives. For example, it searches for hashtags and trends related to a specific cooking genre and retrieves relevant posts from social media platforms. The collected data is analyzed by machine learning algorithms to extract successful posting patterns and follower acquisition trends.

[0039] Next, the server generates an operational plan based on the analyzed data. This plan includes specific advice on what content to post, when to post, and which target audience to approach, and this information is displayed on the user's device through an output method. For example, a cooking-related social media user might be presented with specific suggestions such as, "Introduce new recipes twice a week and promote interaction with a specific cooking community."

[0040] Furthermore, after users engage in social media activities based on the system's suggestions, they can send feedback about the results to the server via their device. The server analyzes this feedback and updates the AI ​​model to provide more accurate advice in the future.

[0041] As described above, the system of the present invention comprises input means, data collection means, plan generation means, output means, and feedback processing means, and provides continuous and systematic support for self-branding for SNS users.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user logs into their device and accesses the system. An interface is displayed on the device for entering a specific self-image and goals, where the user enters specific goals such as "I want to increase my follower count" or "I want to become a cooking influencer."

[0045] Step 2:

[0046] The terminal sends the entered target to the server. This data is encrypted before transmission, and the server prepares the received data for analysis.

[0047] Step 3:

[0048] The server collects relevant data from multiple social media platforms via SNS APIs. For example, it queries and retrieves trending hashtags related to cooking and posts from popular accounts.

[0049] Step 4:

[0050] The server analyzes the acquired data using machine learning algorithms. Specifically, it uses natural language processing to analyze patterns in successful posts and identify factors that contribute to gaining followers.

[0051] Step 5:

[0052] Based on the analysis results, the server generates an optimal social media management plan for the user. This plan includes recommended post content, frequency, hashtags to use, and target audience.

[0053] Step 6:

[0054] The server sends the generated operational plan to the terminal. The terminal receives it and presents the information in a user-friendly format. The user can then refer to this plan while engaging in activities on social media.

[0055] Step 7:

[0056] Users input feedback on the results of their SNS operations from their devices and send it to the server. This feedback includes information about the success of the activities and the effectiveness of the plan.

[0057] Step 8:

[0058] The server analyzes the feedback it receives and updates the AI ​​system. This improves the accuracy of future advice.

[0059] (Example 1)

[0060] 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."

[0061] In modern society, it is crucial for information users to establish their own brand and effectively disseminate information aligned with their objectives. However, developing concrete and objective plans for effective information dissemination is difficult for information users, and individually collecting and analyzing data from various sources is inefficient. A solution to this problem is needed.

[0062] 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.

[0063] In this invention, the server includes a device for inputting the information user's goals, a device for collecting relevant information based on the goals, and a device for analyzing the collected information using a machine learning algorithm. This enables information users to effectively and efficiently establish their own brand and generate operational plans and formulate highly accurate strategies for disseminating information appropriate to their objectives.

[0064] "Information users" refer to individuals or organizations that use a system to input data in order to achieve their own goals.

[0065] A "device for inputting goals" refers to hardware or software that provides an interface for information users to input their desired objectives or ideal state into a system.

[0066] A "device for collecting relevant information" refers to hardware or software that has the function of gathering data related to a goal set by the information user from multiple sources.

[0067] "Devices that analyze data using machine learning algorithms" refer to hardware and software that apply learning models to collected data and analyze patterns and trends in that data.

[0068] A "device for generating operational plans" refers to hardware or software used to formulate specific action plans that information users should implement based on analysis results.

[0069] A "device for updating generative models" refers to hardware or software that has functions to improve the accuracy and usefulness of generative models based on user reactions and feedback.

[0070] This invention is a system for information users to build an effective personal brand. The system basically functions through users, terminals, and servers.

[0071] The user first uses a device to access an interface for setting goals. Here, the user can enter a specific goal, such as "I want to become an influential information provider related to cooking." The device then sends this input data to the server.

[0072] The server has the capability to collect relevant information. This capability allows it to gather topics and trends related to specific fields from the internet. Furthermore, the collected data is analyzed using machine learning algorithms such as TENSORFLOW® or PyTorch. The purpose of the analysis is to learn patterns from successful posts and identify trends in gaining followers.

[0073] Based on the analysis results, the server generates an operational plan for the information user. This plan includes detailed advice on specific content to post, frequency, and target audience to approach. This information is sent to the user's device and displayed on the screen.

[0074] As a concrete example, the plan includes guidelines such as "Post recipes focusing on seasonal ingredients three times a week and promote discussion in cooking forums." Additionally, one of the prompts to be input into the generative AI model is, "What content strategy should be adopted to become an influential information provider regarding cooking?"

[0075] Based on the proposed operational plan, users execute information dissemination and send the results as feedback to the server via their terminal. The server uses this feedback to update the AI ​​model and improve the accuracy of the advice it generates. This cycle allows information users to continuously improve their personal brand and build an influence that aligns with their objectives.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user uses the terminal's input interface to enter their goal. At this point, the goal might be, for example, "to become an influential information provider related to cooking." The terminal then sends this input data to the server.

[0079] Step 2:

[0080] Based on the received target, the server collects relevant social media information from its database. A Web API is used for data collection, targeting hashtags related to cooking genres and currently popular trends. The obtained data is stored as primary data for target identification.

[0081] Step 3:

[0082] The server analyzes the collected data using machine learning algorithms. Specifically, it uses the Python TensorFlow library to extract successful patterns from the collected posts. The input data includes the content of the posts and the number of followers, and the output is successful patterns and characteristics of popular posts.

[0083] Step 4:

[0084] The server generates an operational plan based on the analysis results. This plan includes what kind of content to post and how often, which time slots are most effective, and which user segments to target. The plan generation method uses an AI model to create new suggestions that fit the patterns obtained from the analysis results.

[0085] Step 5:

[0086] The server sends the generated operational plan to the terminal and displays it on the terminal's screen. Here, the user can see the specific action plan that has been proposed. An example of a proposal might be, "Share seasonal recipes in the cooking forum three times a week."

[0087] Step 6:

[0088] Users disseminate information based on their plan and observe the subsequent reactions. They send the results and their impressions as feedback to the server via their device. This feedback is used to improve the system's next analysis.

[0089] Step 7:

[0090] The server analyzes the feedback it receives and updates its AI model to improve the accuracy of future advice. Specifically, it re-introduces the data based on the feedback into a machine learning process to adapt to new successful patterns. This iterative process allows users to continuously improve their personal brand.

[0091] (Application Example 1)

[0092] 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."

[0093] In recent years, the use of information platforms has expanded, and the individual brand power of users has become increasingly important. However, SNS users and online shop owners often struggle to find effective ways to disseminate information that suit their personality and goals. Therefore, there is a need for effective means to support self-branding on information platforms.

[0094] 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.

[0095] In this invention, the server includes an input means for setting the ideal self-image of an SNS user, a data collection means for collecting relevant data from multiple information platforms, and a plan generation means for analyzing the collected data and generating an operational plan on the information platform. This enables information providers to effectively enhance their individuality and achieve optimal self-branding.

[0096] An "information platform" refers to any online service that users utilize to collect and disseminate information.

[0097] "Data collection means" refers to the function of gathering relevant information from an information platform and acquiring specific data based on the user's goals.

[0098] "Plan generation method" refers to a function that analyzes collected data and proposes an operational method that suits the user.

[0099] "Feedback processing means" refers to a function that receives feedback from users and uses it to improve the system.

[0100] An "AI model" refers to a program that uses machine learning algorithms to analyze input data and generate the optimal output.

[0101] An "operational plan" refers to a specific plan formulated to effectively carry out activities on an information platform.

[0102] "Self-branding" refers to the process of clarifying one's personal characteristics and values, and presenting oneself in an attractive way to others.

[0103] The system that realizes this invention allows SNS users to set an ideal self-image and effectively supports self-branding based on that image. The server collects relevant data from multiple information platforms using data collection means based on the goals and self-image received from the user's terminal. The collected data is analyzed using machine learning algorithms (e.g., TensorFlow) by plan generation means on the server to generate an optimal operational plan for the user.

[0104] The generated operational plan is displayed on the terminal, providing specific advice for information providers to effectively express their individuality. Users then engage in activities on the information platform based on the system's suggestions via the terminal. The results of these activities are sent to the server through a feedback processing mechanism, and the AI ​​model is continuously improved based on this feedback. This improves the accuracy of the user's future planning.

[0105] As a concrete example, suppose a seller of handmade accessories sets the goal of "making their brand known to more people." In this case, the server analyzes trends and popular visual themes related to accessories and suggests photos and captions for the user to post. An example of a prompt might be, "Please suggest a posting plan to make your handmade accessory brand more appealing on social media."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user uses a terminal to input their ideal self-image and goals. The data received through the input interface is sent from the terminal to the server. In this step, the user sets specific goals, and this information is used as the basis for data processing.

[0109] Step 2:

[0110] The server activates data collection mechanisms based on the received target data. It retrieves relevant trends, hashtags, and user interest-related posting data from multiple information platforms to prepare for initial analysis. Here, relevant information that matches the target is aggregated.

[0111] Step 3:

[0112] The server plan generation method involves analyzing collected data using a machine learning algorithm (TensorFlow). This analysis extracts success trends and follower acquisition patterns on the information platform, and then formulates an operational plan for the next steps. As a result of the analysis, a specific posting strategy is generated.

[0113] Step 4:

[0114] The generated operational plan is sent to the device and displayed to the user. The plan includes optimal timing for information dissemination, content of posts, and methods for approaching the target audience. Based on this specific advice, users can develop an activity plan for their actual information platform.

[0115] Step 5:

[0116] After the user executes the plan, the results and feedback are sent from the terminal to the server. The feedback processing mechanism collects the user's responses and stores them as data. This information is used to improve the system's AI model.

[0117] Step 6:

[0118] The server updates its AI model based on accumulated feedback data. By analyzing the feedback results, it incorporates new insights necessary for future plan development, enabling it to provide more effective advice and operational plans.

[0119] 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.

[0120] The system according to the present invention supports SNS users in achieving their ideal self-image and, by combining it with an emotion engine, provides optimal advice tailored to the user's emotions. This system is configured in a multifaceted way through the user, terminal, and server, and specific embodiments are as follows.

[0121] Users access the system through their devices and input their goals and ideal self-image on social media. During this process, the user's input is analyzed by an emotion engine, which identifies their emotions. For example, if a user sends a comment stating, "I feel anxious because my number of followers on social media isn't increasing," the emotion engine recognizes this anxious emotion.

[0122] The device sends this data to a server, which collects SNS-related data from multiple platforms while taking emotions into consideration. The collected data is analyzed using machine learning algorithms and natural language processing to generate an SNS operation plan that is sensitive to the user's emotions. This operation plan is adjusted according to the user's psychological state and includes things like "posting during times when positive comments are expected" or "displaying messages to calm emotions."

[0123] The operational plan generated by the server is sent to the terminal, which then presents it to the user in a visually easy-to-understand format. Based on these plans, users can then conduct more effective and considerate social media activities.

[0124] Furthermore, users can send feedback from their devices to the server regarding the results of their social media activities. The server uses this feedback to continuously improve the AI ​​model and provide more appropriate, emotion-responsive advice for future interactions. This feedback system allows the emotion engine to adapt to users' long-term emotional changes and provide support optimized for each individual user.

[0125] Thus, the system of the present invention contributes to the user's goal achievement while also providing emotional support, enabling more intimate and effective assistance in self-branding.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] Users access the system using their devices and input their ideal self-image and goals on social media. Information about the user's feelings and motivations is also entered at this stage. At this point, users can include specific emotional statements, such as "I'm feeling anxious about the recent stagnation in my follower growth."

[0129] Step 2:

[0130] The device sends data entered by the user to an emotion engine, which analyzes the user's emotional state. The emotion engine uses natural language processing technology to identify emotions such as anxiety, tension, and anticipation from the entered text.

[0131] Step 3:

[0132] The device sends identified sentiment data and the user's goal settings to the server. This initiates the server's process of collecting SNS-related data from multiple social media platforms, while taking sentiment information into consideration.

[0133] Step 4:

[0134] The server analyzes the acquired data using machine learning algorithms. This identifies patterns of success stories related to the user's emotional state and builds the foundation for an operational plan. At this stage, advice tailored to the emotional state is considered, and the plan is adjusted to emphasize positive feedback as needed.

[0135] Step 5:

[0136] Based on the analysis results, the server generates an SNS management plan tailored to the user's emotions. This plan includes content that matches the emotional state, posting timing, and appropriate content tone. For example, it may suggest techniques to reduce stress levels and posting strategies based on those techniques.

[0137] Step 6:

[0138] The generated operational plan is sent from the server to the terminal. The terminal receives it and displays it to the user in a visually easy-to-understand format. The user can use this plan as a reference to carry out SNS activities while maintaining a favorable emotional state.

[0139] Step 7:

[0140] After a user engages in social media activity, feedback regarding the results of that activity and any new emotional states is sent from the device to the server. This feedback may include specific numerical data and the user's impressions.

[0141] Step 8:

[0142] The server analyzes the feedback and updates the AI ​​model. This process allows the system to provide more accurate advice that better reflects the user's emotional state in subsequent interactions.

[0143] (Example 2)

[0144] 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".

[0145] For SNS users, receiving emotionally resonant advice while supporting their goal achievement is crucial. However, traditional systems struggled to provide appropriate operational plans based on emotions, hindering the efficient use of SNS. This resulted in users not receiving appropriate feedback and their approach to achieving their goals becoming inefficient.

[0146] 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.

[0147] In this invention, the server includes an information input means for inputting information related to the goals and ideals of SNS users, an emotion analysis means for analyzing the input information and identifying emotions, and an information collection means for collecting relevant information from multiple data sources based on the identified emotions. This makes it possible to provide advice based on the user's emotions.

[0148] "SNS users" refers to individuals or groups who use social networking services.

[0149] "Information related to goals and ideals" refers to specific data or text related to the goals that SNS users want to achieve or the ideal self-image they aspire to.

[0150] An "information input means" is an interface or device that allows SNS users to input information about their goals and ideals into the system.

[0151] "Emotion analysis means" refers to software or algorithms that analyze input information and identify the user's emotions.

[0152] "Information gathering means" refers to a process or device for obtaining relevant information from multiple data sources based on analyzed emotions.

[0153] An "algorithm" is a set of procedures or computational methods defined to solve a specific problem.

[0154] A "plan generation method" is a system or process that uses collected information to create an operational plan to optimize activities on social media.

[0155] An "information output means" is an interface or device for presenting the generated operational plan to the user.

[0156] "Evaluation processing means" refers to technology that receives and analyzes the results or feedback from SNS users to improve the system's performance.

[0157] The system necessary to implement this invention consists of three main elements: a server, a terminal, and a user.

[0158] On the device, SNS users input information about their goals, ideals, and emotions through a dedicated user interface. For example, a user might input, "I want to gain more followers on SNS." This information is collected as text data and processed on the device in preparation for sentiment analysis.

[0159] Sentiment analysis is performed on the server. Text data sent from the terminal is processed by the server's sentiment analysis engine. This processing utilizes natural language processing techniques, specifically libraries such as spaCy and NLTK. Through this analysis, the server identifies the user's emotions and classifies them into emotional categories such as anxiety and joy.

[0160] The server also performs multifaceted data collection. Based on the analyzed sentiment, it collects relevant information from multiple data sources (e.g., SNS APIs). The collected data is processed by machine learning algorithms (e.g., scikit-learn and TensorFlow). This generates an SNS management plan tailored to the user's emotional state.

[0161] The generated operational plan is presented to the user via the terminal. This presentation is done through an interactive graphical user interface, displaying information in a format that is easy for the user to understand. For example, graphs and charts visualize recommendations regarding effective posting times and content.

[0162] In the feedback process, users can provide feedback on the results of their SNS activities via their devices afterward. This feedback data is analyzed by the server and used as training material for the generated AI model, which is then used to improve the accuracy of future operational plans.

[0163] A specific example of a prompt would be, "Analyze the user's emotions and create a plan to alleviate anxiety in their social media activities."

[0164] This system allows social media users to receive advice based on scientifically supported data, and to get closer to achieving their goals while receiving emotional support.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] Users input information about their social media goals and ideal self-image into the device. Specifically, they enter text data through the user interface and press the "Send" button. This input data is used as basic information to understand the user's emotional state.

[0168] Step 2:

[0169] The terminal sends the input text data to the server for sentiment analysis. The server receives this data and passes it to the sentiment analysis engine. During this process, the text data is analyzed using natural language processing techniques to classify the user's emotions into categories such as "anxiety" and "joy." The analyzed sentiment information is then generated as output.

[0170] Step 3:

[0171] The server collects relevant information from multiple data sources based on the analyzed sentiment. Specifically, it obtains relevant post data and engagement data through the APIs of social media platforms. The collected data serves as the basis for generating social media management plans that respond to the user's sentiment.

[0172] Step 4:

[0173] The server processes the collected data using machine learning algorithms to generate an operational plan. Specifically, it uses libraries such as scikit-learn and TensorFlow to analyze the data and generate a plan that proposes the optimal posting schedule and content. This plan is designed to be sensitive to the user's emotions.

[0174] Step 5:

[0175] The generated operational plan is sent from the server to the terminal, which then visually presents this information on a graphical user interface. Specifically, charts and graphs are used to clearly show the information to the user. This output allows the user to take practical action.

[0176] Step 6:

[0177] Users conduct SNS activities based on the proposed operational plan and input feedback on the results into their devices. This allows the system to reflect the user's activity results and provides information that will help improve future operational plans.

[0178] Step 7:

[0179] The device sends the input feedback to the server, which then analyzes this data. The feedback is used as training material for the generated AI model, improving the accuracy of future advice. This allows for support that is more tailored to the user's emotions.

[0180] (Application Example 2)

[0181] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0182] For modern social media users, effectively establishing their personal brand and realizing their ideal self-image is a crucial challenge. However, with increasing activity on social media, determining the appropriate timing and content for posts, and effectively engaging in self-branding while minimizing the user's mental burden, is not easy. This invention aims to solve these problems and enable social media users to efficiently achieve their ideal self-image in a way that resonates with their own emotions.

[0183] 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.

[0184] In this invention, the server includes an input means for setting the ideal self-image of the SNS user, an information gathering means, an emotion analysis means, and a policy generation means for providing emotionally resonant advice. This makes it possible to suggest suitable posting times and content for SNS users and to formulate an optimal SNS operation policy that takes emotional aspects into consideration.

[0185] An "input method" is an interface that allows SNS users to input their ideal self-image and goals into the system.

[0186] "Information gathering means" refers to a device or system that acquires relevant data from SNS platforms and provides information necessary for SNS users to realize their self-image.

[0187] A "policy generation means" is a process or device that, based on collected data and analysis results, plans for SNS users to formulate an optimal posting strategy.

[0188] "Output means" refers to a device that presents generated operational policies and suggestions regarding posts to SNS users visually or audibly.

[0189] A "feedback processing method" is a process or device for collecting opinions and activity results from SNS users and using them to improve an AI model.

[0190] "Emotional analysis means" refers to a process or system for analyzing the emotions expressed in the input of social media users and providing support and advice tailored to those emotions.

[0191] The system implementing this invention functions as a tool for SNS users to set and realize their ideal self-image using smartphones or personal computers. First, the user inputs their goals and ideal self-image through the terminal's input interface. At this time, an emotion analysis system analyzes the user's emotions in real time and generates advice that is appropriate to the user's psychological state.

[0192] The server uses an information gathering module to collect relevant information from multiple social networking services (SNS) platforms and processes it with machine learning algorithms. The software used includes pandas and scikit-learn for data analysis, and Hugging Face Transformers for natural language processing. This generates posting guidelines optimized for the emotions and goals of SNS users. The generated guidelines are presented to the user through the terminal's output module, offering specific posting times and content suggestions.

[0193] Furthermore, the results of the user's SNS activities are sent from the device to the server as feedback. This feedback is used to improve the AI ​​model and help in formulating strategies for future use. Through this process, the system can respond to the user's long-term emotional changes and continuously provide more appropriate support.

[0194] For example, an influencer might send a prompt to the system saying, "I need a strategy to reach 1,000 followers. I'm feeling anxious because the response to my recent posts has been poor." Based on this information, the system provides specific advice such as "Post new content during times when many viewers are online." In this way, a system is built that provides social media users with concrete means to achieve their ideal self-image while receiving emotional support.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] Users input their ideal self-image and desired goals in text format through the terminal's input interface. This input includes specific numerical and behavioral targets that the user wants to achieve. The entered information is sent from the terminal to the server for sentiment analysis.

[0198] Step 2:

[0199] The server analyzes the user's emotional state in real time using an emotion analysis model based on the received user input information. This analysis applies an emotion recognition algorithm using natural language processing technology to quantify emotions such as anxiety, anticipation, and impatience. The analysis results are returned to the terminal as specific emotion tags.

[0200] Step 3:

[0201] The server activates an information gathering module and collects relevant public data from multiple social networking platforms. This data includes posting times, engagement rates, and popular hashtags. The collected information is stored in a database and used in the next analysis step.

[0202] Step 4:

[0203] The server uses a data analysis engine to integrate acquired information from social media platforms with user sentiment data to generate an optimized posting strategy. This process utilizes machine learning algorithms. Specifically, it builds a predictive model based on similar social media examples to predict the most effective posting times and content for each user. The generated strategy is then sent to the user's device.

[0204] Step 5:

[0205] The device presents the generated posting strategy to the user through a visual interface. The user reviews the proposed strategy and, if necessary, posts to social media. Here, the device displays specific posting suggestions and graphs of posting times on the screen.

[0206] Step 6:

[0207] Users send feedback from their devices to the server based on the results of their social media activities. This feedback includes the actual number of responses, details of engagement, and the user's own impressions. The server uses this feedback to improve the AI ​​model and incorporate it into future strategies.

[0208] Step 7:

[0209] The server adjusts the generated AI model based on the collected feedback. Here, the machine learning retraining process is automatically initiated, involving data refinement and tuning to improve the model's accuracy. The adjusted model is then ready to provide more accurate advice with subsequent user input.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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".

[0226] The system according to the present invention provides support for SNS users to effectively engage in self-branding, and is equipped with a variety of functions as follows:

[0227] First, users can access the system using their device and input their ideal self-image and goals. This is done through a dedicated input interface. If a user sets a goal such as "I want to become an influential SNS user related to cooking," the entered data is sent to the server.

[0228] The server is equipped with means to collect social media-related data that corresponds to its objectives. For example, it searches for hashtags and trends related to a specific cooking genre and retrieves relevant posts from social media platforms. The collected data is analyzed by machine learning algorithms to extract successful posting patterns and follower acquisition trends.

[0229] Next, the server generates an operational plan based on the analyzed data. This plan includes specific advice on what content to post, when to post, and which target audience to approach, and this information is displayed on the user's device through an output method. For example, a cooking-related social media user might be presented with specific suggestions such as, "Introduce new recipes twice a week and promote interaction with a specific cooking community."

[0230] Furthermore, after users engage in social media activities based on the system's suggestions, they can send feedback about the results to the server via their device. The server analyzes this feedback and updates the AI ​​model to provide more accurate advice in the future.

[0231] As described above, the system of the present invention comprises input means, data collection means, plan generation means, output means, and feedback processing means, and provides continuous and systematic support for self-branding for SNS users.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The user logs into their device and accesses the system. An interface is displayed on the device for entering a specific self-image and goals, where the user enters specific goals such as "I want to increase my follower count" or "I want to become a cooking influencer."

[0235] Step 2:

[0236] The terminal sends the entered target to the server. This data is encrypted before transmission, and the server prepares the received data for analysis.

[0237] Step 3:

[0238] The server collects relevant data from multiple social media platforms via SNS APIs. For example, it queries and retrieves trending hashtags related to cooking and posts from popular accounts.

[0239] Step 4:

[0240] The server analyzes the acquired data using machine learning algorithms. Specifically, it uses natural language processing to analyze patterns in successful posts and identify factors that contribute to gaining followers.

[0241] Step 5:

[0242] Based on the analysis results, the server generates an optimal social media management plan for the user. This plan includes recommended post content, frequency, hashtags to use, and target audience.

[0243] Step 6:

[0244] The server sends the generated operational plan to the terminal. The terminal receives it and presents the information in a user-friendly format. The user can then refer to this plan while engaging in activities on social media.

[0245] Step 7:

[0246] Users input feedback on the results of their SNS operations from their devices and send it to the server. This feedback includes information about the success of the activities and the effectiveness of the plan.

[0247] Step 8:

[0248] The server analyzes the feedback it receives and updates the AI ​​system. This improves the accuracy of future advice.

[0249] (Example 1)

[0250] 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."

[0251] In modern society, it is crucial for information users to establish their own brand and effectively disseminate information aligned with their objectives. However, developing concrete and objective plans for effective information dissemination is difficult for information users, and individually collecting and analyzing data from various sources is inefficient. A solution to this problem is needed.

[0252] 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.

[0253] In this invention, the server includes a device for inputting the information user's goals, a device for collecting relevant information based on the goals, and a device for analyzing the collected information using a machine learning algorithm. This enables information users to effectively and efficiently establish their own brand and generate operational plans and formulate highly accurate strategies for disseminating information appropriate to their objectives.

[0254] "Information users" refer to individuals or organizations that use a system to input data in order to achieve their own goals.

[0255] A "device for inputting goals" refers to hardware or software that provides an interface for information users to input their desired objectives or ideal state into a system.

[0256] A "device for collecting relevant information" refers to hardware or software that has the function of gathering data related to a goal set by the information user from multiple sources.

[0257] "Devices that analyze data using machine learning algorithms" refer to hardware and software that apply learning models to collected data and analyze patterns and trends in that data.

[0258] A "device for generating operational plans" refers to hardware or software used to formulate specific action plans that information users should implement based on analysis results.

[0259] A "device for updating generative models" refers to hardware or software that has functions to improve the accuracy and usefulness of generative models based on user reactions and feedback.

[0260] This invention is a system for information users to build an effective personal brand. The system basically functions through users, terminals, and servers.

[0261] The user first uses a device to access an interface for setting goals. Here, the user can enter a specific goal, such as "I want to become an influential information provider related to cooking." The device then sends this input data to the server.

[0262] The server has the capability to collect relevant information. This capability allows it to gather topics and trends related to a specific field from the internet. Furthermore, the collected data is analyzed using machine learning algorithms such as TensorFlow or PyTorch. The purpose of the analysis is to learn patterns from successful posts and identify trends in gaining followers.

[0263] Based on the analysis results, the server generates an operational plan for the information user. This plan includes detailed advice on specific content to post, frequency, and target audience to approach. This information is sent to the user's device and displayed on the screen.

[0264] As a concrete example, the plan includes guidelines such as "Post recipes focusing on seasonal ingredients three times a week and promote discussion in cooking forums." Additionally, one of the prompts to be input into the generative AI model is, "What content strategy should be adopted to become an influential information provider regarding cooking?"

[0265] Based on the proposed operational plan, users execute information dissemination and send the results as feedback to the server via their terminal. The server uses this feedback to update the AI ​​model and improve the accuracy of the advice it generates. This cycle allows information users to continuously improve their personal brand and build an influence that aligns with their objectives.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The user uses the terminal's input interface to enter their goal. At this point, the goal might be, for example, "to become an influential information provider related to cooking." The terminal then sends this input data to the server.

[0269] Step 2:

[0270] Based on the received target, the server collects relevant social media information from its database. A Web API is used for data collection, targeting hashtags related to cooking genres and currently popular trends. The obtained data is stored as primary data for target identification.

[0271] Step 3:

[0272] The server analyzes the collected data using machine learning algorithms. Specifically, it uses the Python TensorFlow library to extract successful patterns from the collected posts. The input data includes the content of the posts and the number of followers, and the output is successful patterns and characteristics of popular posts.

[0273] Step 4:

[0274] The server generates an operational plan based on the analysis results. This plan includes what kind of content to post and how often, which time slots are most effective, and which user segments to target. The plan generation method uses an AI model to create new suggestions that fit the patterns obtained from the analysis results.

[0275] Step 5:

[0276] The server sends the generated operational plan to the terminal and displays it on the terminal's screen. Here, the user can see the specific action plan that has been proposed. An example of a proposal might be, "Share seasonal recipes in the cooking forum three times a week."

[0277] Step 6:

[0278] Users disseminate information based on their plan and observe the subsequent reactions. They send the results and their impressions as feedback to the server via their device. This feedback is used to improve the system's next analysis.

[0279] Step 7:

[0280] The server analyzes the received feedback and updates the AI model to improve the accuracy of subsequent advice. Specifically, the data based on the feedback is subjected to the machine learning process again to adapt to new successful patterns. Through this repetition, it becomes possible for users to continuously improve their self-brands.

[0281] (Application Example 1)

[0282] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0283] In recent years, the use of information platforms has expanded, and the brand power of individual users has come to be regarded as important. However, SNS users and online store owners often cannot find an effective information dissemination method according to their own personalities and goals. Therefore, an effective means to support self-branding on information platforms is required.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0285] In this invention, the server includes an input means for setting an ideal self-image of an SNS user, a data collection means for collecting related data from a plurality of information platforms, and a plan generation means for analyzing the collected data and generating an operation plan on the information platform. Thereby, the information sender can effectively strengthen their own personality, and optimal self-branding becomes possible.

[0286] "Information platform" refers to all online services used by users to collect and disseminate information.

[0287] "Data collection means" refers to the function of collecting related information from information platforms and acquiring specific data based on the goals of users.

[0288] "Plan generation method" refers to a function that analyzes collected data and proposes an operational method that suits the user.

[0289] "Feedback processing means" refers to a function that receives feedback from users and uses it to improve the system.

[0290] An "AI model" refers to a program that uses machine learning algorithms to analyze input data and generate the optimal output.

[0291] An "operational plan" refers to a specific plan formulated to effectively carry out activities on an information platform.

[0292] "Self-branding" refers to the process of clarifying one's personal characteristics and values, and presenting oneself in an attractive way to others.

[0293] The system that realizes this invention allows SNS users to set an ideal self-image and effectively supports self-branding based on that image. The server collects relevant data from multiple information platforms using data collection means based on the goals and self-image received from the user's terminal. The collected data is analyzed using machine learning algorithms (e.g., TensorFlow) by plan generation means on the server to generate an optimal operational plan for the user.

[0294] The generated operational plan is displayed on the terminal, providing specific advice for information providers to effectively express their individuality. Users then engage in activities on the information platform based on the system's suggestions via the terminal. The results of these activities are sent to the server through a feedback processing mechanism, and the AI ​​model is continuously improved based on this feedback. This improves the accuracy of the user's future planning.

[0295] As a concrete example, suppose a seller of handmade accessories sets the goal of "making their brand known to more people." In this case, the server analyzes trends and popular visual themes related to accessories and suggests photos and captions for the user to post. An example of a prompt might be, "Please suggest a posting plan to make your handmade accessory brand more appealing on social media."

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The user uses a terminal to input their ideal self-image and goals. The data received through the input interface is sent from the terminal to the server. In this step, the user sets specific goals, and this information is used as the basis for data processing.

[0299] Step 2:

[0300] The server activates data collection mechanisms based on the received target data. It retrieves relevant trends, hashtags, and user interest-related posting data from multiple information platforms to prepare for initial analysis. Here, relevant information that matches the target is aggregated.

[0301] Step 3:

[0302] The server plan generation method involves analyzing collected data using a machine learning algorithm (TensorFlow). This analysis extracts success trends and follower acquisition patterns on the information platform, and then formulates an operational plan for the next steps. As a result of the analysis, a specific posting strategy is generated.

[0303] Step 4:

[0304] The generated operation plan is transmitted to the terminal and displayed to the user. The plan includes the optimal timing of information transmission, the content of posts, the approach method to the target layer, etc. Based on this specific advice, the user can make an activity plan on the actual information platform.

[0305] Step 5:

[0306] After the user executes the plan, the results and feedback are sent from the terminal to the server. The feedback processing means collects the user's reactions and stores them as data. This information is used to improve the system's AI model.

[0307] Step 6:

[0308] The server updates the generated AI model based on the accumulated feedback data. By analyzing the feedback results, new insights necessary for formulating subsequent plans can be incorporated, enabling the provision of more effective advice and operation plans.

[0309] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion identification model 59 and perform specific processing using the user's emotions.

[0310] The system according to the present invention supports SNS users in achieving an ideal self-image, and further provides optimal advice according to the user's emotions by combining an emotion engine. This system is configured in a multi-faceted manner through the user, terminal, and server, and the specific embodiments are as follows.

[0311] Users access the system through their devices and input their goals and ideal self-image on social media. During this process, the user's input is analyzed by an emotion engine, which identifies their emotions. For example, if a user sends a comment stating, "I feel anxious because my number of followers on social media isn't increasing," the emotion engine recognizes this anxious emotion.

[0312] The device sends this data to a server, which collects SNS-related data from multiple platforms while taking emotions into consideration. The collected data is analyzed using machine learning algorithms and natural language processing to generate an SNS operation plan that is sensitive to the user's emotions. This operation plan is adjusted according to the user's psychological state and includes things like "posting during times when positive comments are expected" or "displaying messages to calm emotions."

[0313] The operational plan generated by the server is sent to the terminal, which then presents it to the user in a visually easy-to-understand format. Based on these plans, users can then conduct more effective and considerate social media activities.

[0314] Furthermore, users can send feedback from their devices to the server regarding the results of their social media activities. The server uses this feedback to continuously improve the AI ​​model and provide more appropriate, emotion-responsive advice for future interactions. This feedback system allows the emotion engine to adapt to users' long-term emotional changes and provide support optimized for each individual user.

[0315] Thus, the system of the present invention contributes to the user's goal achievement while also providing emotional support, enabling more intimate and effective assistance in self-branding.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] Users access the system using their devices and input their ideal self-image and goals on social media. Information about the user's feelings and motivations is also entered at this stage. At this point, users can include specific emotional statements, such as "I'm feeling anxious about the recent stagnation in my follower growth."

[0319] Step 2:

[0320] The device sends data entered by the user to an emotion engine, which analyzes the user's emotional state. The emotion engine uses natural language processing technology to identify emotions such as anxiety, tension, and anticipation from the entered text.

[0321] Step 3:

[0322] The device sends identified sentiment data and the user's goal settings to the server. This initiates the server's process of collecting SNS-related data from multiple social media platforms, while taking sentiment information into consideration.

[0323] Step 4:

[0324] The server analyzes the acquired data using machine learning algorithms. This identifies patterns of success stories related to the user's emotional state and builds the foundation for an operational plan. At this stage, advice tailored to the emotional state is considered, and the plan is adjusted to emphasize positive feedback as needed.

[0325] Step 5:

[0326] Based on the analysis results, the server generates an SNS management plan tailored to the user's emotions. This plan includes content that matches the emotional state, posting timing, and appropriate content tone. For example, it may suggest techniques to reduce stress levels and posting strategies based on those techniques.

[0327] Step 6:

[0328] The generated operational plan is sent from the server to the terminal. The terminal receives it and displays it to the user in a visually easy-to-understand format. The user can use this plan as a reference to carry out SNS activities while maintaining a favorable emotional state.

[0329] Step 7:

[0330] After a user engages in social media activity, feedback regarding the results of that activity and any new emotional states is sent from the device to the server. This feedback may include specific numerical data and the user's impressions.

[0331] Step 8:

[0332] The server analyzes the feedback and updates the AI ​​model. This process allows the system to provide more accurate advice that better reflects the user's emotional state in subsequent interactions.

[0333] (Example 2)

[0334] 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".

[0335] For SNS users, receiving emotionally resonant advice while supporting their goal achievement is crucial. However, traditional systems struggled to provide appropriate operational plans based on emotions, hindering the efficient use of SNS. This resulted in users not receiving appropriate feedback and their approach to achieving their goals becoming inefficient.

[0336] 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.

[0337] In this invention, the server includes an information input means for inputting information related to the goals and ideals of SNS users, an emotion analysis means for analyzing the input information and identifying emotions, and an information collection means for collecting relevant information from multiple data sources based on the identified emotions. This makes it possible to provide advice based on the user's emotions.

[0338] "SNS users" refers to individuals or groups who use social networking services.

[0339] "Information related to goals and ideals" refers to specific data or text related to the goals that SNS users want to achieve or the ideal self-image they aspire to.

[0340] An "information input means" is an interface or device that allows SNS users to input information about their goals and ideals into the system.

[0341] "Emotion analysis means" refers to software or algorithms that analyze input information and identify the user's emotions.

[0342] "Information gathering means" refers to a process or device for obtaining relevant information from multiple data sources based on analyzed emotions.

[0343] An "algorithm" is a set of procedures or computational methods defined to solve a specific problem.

[0344] A "plan generation method" is a system or process that uses collected information to create an operational plan to optimize activities on social media.

[0345] An "information output means" is an interface or device for presenting the generated operational plan to the user.

[0346] "Evaluation processing means" refers to technology that receives and analyzes the results or feedback from SNS users to improve the system's performance.

[0347] The system necessary to implement this invention consists of three main elements: a server, a terminal, and a user.

[0348] On the device, SNS users input information about their goals, ideals, and emotions through a dedicated user interface. For example, a user might input, "I want to gain more followers on SNS." This information is collected as text data and processed on the device in preparation for sentiment analysis.

[0349] Sentiment analysis is performed on the server. Text data sent from the terminal is processed by the server's sentiment analysis engine. This processing utilizes natural language processing techniques, specifically libraries such as spaCy and NLTK. Through this analysis, the server identifies the user's emotions and classifies them into emotional categories such as anxiety and joy.

[0350] The server also performs multifaceted data collection. Based on the analyzed sentiment, it collects relevant information from multiple data sources (e.g., SNS APIs). The collected data is processed by machine learning algorithms (e.g., scikit-learn and TensorFlow). This generates an SNS management plan tailored to the user's emotional state.

[0351] The generated operational plan is presented to the user via the terminal. This presentation is done through an interactive graphical user interface, displaying information in a format that is easy for the user to understand. For example, graphs and charts visualize recommendations regarding effective posting times and content.

[0352] In the feedback process, users can provide feedback on the results of their SNS activities via their devices afterward. This feedback data is analyzed by a server and used as training material for the generated AI model, which is then used to improve the accuracy of future operational plans.

[0353] A specific example of a prompt would be, "Analyze the user's emotions and create a plan to alleviate anxiety in their social media activities."

[0354] This system allows social media users to receive advice based on scientifically supported data, and to get closer to achieving their goals while receiving emotional support.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] Users input information about their social media goals and ideal self-image into the device. Specifically, they enter text data through the user interface and press the "Send" button. This input data is used as basic information to understand the user's emotional state.

[0358] Step 2:

[0359] The terminal sends the input text data to the server for sentiment analysis. The server receives this data and passes it to the sentiment analysis engine. During this process, the text data is analyzed using natural language processing technology to classify the user's emotions into categories such as "anxiety" and "joy." The analyzed sentiment information is then generated as output.

[0360] Step 3:

[0361] The server collects relevant information from multiple data sources based on the analyzed sentiment. Specifically, it obtains relevant post data and engagement data through the APIs of social media platforms. The collected data serves as the basis for generating social media management plans that respond to the user's sentiment.

[0362] Step 4:

[0363] The server processes the collected data using machine learning algorithms to generate an operational plan. Specifically, it uses libraries such as scikit-learn and TensorFlow to analyze the data and generate a plan that proposes the optimal posting schedule and content. This plan is designed to be sensitive to the user's emotions.

[0364] Step 5:

[0365] The generated operational plan is sent from the server to the terminal, which then visually presents this information on a graphical user interface. Specifically, charts and graphs are used to clearly show the information to the user. This output allows the user to take practical action.

[0366] Step 6:

[0367] Users conduct SNS activities based on the proposed operational plan and input feedback on the results into their devices. This allows the system to reflect the user's activity results and provides information that will help improve future operational plans.

[0368] Step 7:

[0369] The device sends the input feedback to the server, which then analyzes this data. The feedback is used as training material for the generated AI model, improving the accuracy of future advice. This allows for support that is more tailored to the user's emotions.

[0370] (Application Example 2)

[0371] 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."

[0372] For modern social media users, effectively establishing their personal brand and realizing their ideal self-image is a crucial challenge. However, with increasing activity on social media, determining the appropriate timing and content for posts, and effectively engaging in self-branding while minimizing the user's mental burden, is not easy. This invention aims to solve these problems and enable social media users to efficiently achieve their ideal self-image in a way that resonates with their own emotions.

[0373] 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.

[0374] In this invention, the server includes an input means for setting the ideal self-image of the SNS user, an information gathering means, an emotion analysis means, and a policy generation means for providing emotionally resonant advice. This makes it possible to suggest suitable posting times and content for SNS users and to formulate an optimal SNS operation policy that takes emotional aspects into consideration.

[0375] An "input method" is an interface that allows SNS users to input their ideal self-image and goals into the system.

[0376] "Information gathering means" refers to a device or system that acquires relevant data from SNS platforms and provides information necessary for SNS users to realize their self-image.

[0377] A "policy generation means" is a process or device that, based on collected data and analysis results, plans for SNS users to formulate an optimal posting strategy.

[0378] "Output means" refers to a device that presents generated operational policies and suggestions regarding posts to SNS users visually or audibly.

[0379] A "feedback processing method" is a process or device for collecting opinions and activity results from SNS users and using them to improve an AI model.

[0380] "Emotional analysis means" refers to a process or system for analyzing the emotions expressed in the input of social media users and providing support and advice tailored to those emotions.

[0381] The system implementing this invention functions as a tool for SNS users to set and realize their ideal self-image using smartphones or personal computers. First, the user inputs their goals and ideal self-image through the terminal's input interface. At this time, an emotion analysis system analyzes the user's emotions in real time and generates advice that is appropriate to the user's psychological state.

[0382] The server uses an information gathering module to collect relevant information from multiple social networking services (SNS) platforms and processes it with machine learning algorithms. The software used includes pandas and scikit-learn for data analysis, and Hugging Face Transformers for natural language processing. This generates posting guidelines optimized for the emotions and goals of SNS users. The generated guidelines are presented to the user through the terminal's output module, offering specific posting times and content suggestions.

[0383] Furthermore, the results of the user's SNS activities are sent from the device to the server as feedback. This feedback is used to improve the AI ​​model and help in formulating strategies for future use. Through this process, the system can respond to the user's long-term emotional changes and continuously provide more appropriate support.

[0384] For example, an influencer might send a prompt to the system saying, "I need a strategy to reach 1,000 followers. I'm feeling anxious because the response to my recent posts has been poor." Based on this information, the system provides specific advice such as "Post new content during times when many viewers are online." In this way, a system is built that provides social media users with concrete means to achieve their ideal self-image while receiving emotional support.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] Users input their ideal self-image and desired goals in text format through the terminal's input interface. This input includes specific numerical and behavioral targets that the user wants to achieve. The entered information is sent from the terminal to the server for sentiment analysis.

[0388] Step 2:

[0389] The server analyzes the user's emotional state in real time using an emotion analysis model based on the received user input information. This analysis applies an emotion recognition algorithm using natural language processing technology to quantify emotions such as anxiety, anticipation, and impatience. The analysis results are returned to the terminal as specific emotion tags.

[0390] Step 3:

[0391] The server activates an information gathering module and collects relevant public data from multiple social networking platforms. This data includes posting times, engagement rates, and popular hashtags. The collected information is stored in a database and used in the next analysis step.

[0392] Step 4:

[0393] The server uses a data analysis engine to integrate acquired information from social media platforms with user sentiment data to generate an optimized posting strategy. This process utilizes machine learning algorithms. Specifically, it builds a predictive model based on similar social media examples to predict the most effective posting times and content for each user. The generated strategy is then sent to the user's device.

[0394] Step 5:

[0395] The device presents the generated posting strategy to the user through a visual interface. The user reviews the proposed strategy and, if necessary, posts to social media. Here, the device displays specific posting suggestions and graphs of posting times on the screen.

[0396] Step 6:

[0397] Users send feedback from their devices to the server based on the results of their social media activities. This feedback includes the actual number of responses, details of engagement, and the user's own impressions. The server uses this feedback to improve the AI ​​model and incorporate it into future strategies.

[0398] Step 7:

[0399] The server adjusts the generated AI model based on the collected feedback. Here, the machine learning retraining process is automatically initiated, involving data refinement and tuning to improve the model's accuracy. The adjusted model is then ready to provide more accurate advice with subsequent user input.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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).

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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".

[0416] The system according to the present invention provides support for SNS users to effectively engage in self-branding, and is equipped with a variety of functions as follows:

[0417] First, users can access the system using their device and input their ideal self-image and goals. This is done through a dedicated input interface. If a user sets a goal such as "I want to become an influential SNS user related to cooking," the entered data is sent to the server.

[0418] The server is equipped with means to collect social media-related data that corresponds to its objectives. For example, it searches for hashtags and trends related to a specific cooking genre and retrieves relevant posts from social media platforms. The collected data is analyzed by machine learning algorithms to extract successful posting patterns and follower acquisition trends.

[0419] Next, the server generates an operational plan based on the analyzed data. This plan includes specific advice on what content to post, when to post, and which target audience to approach, and this information is displayed on the user's device through an output method. For example, a cooking-related social media user might be presented with specific suggestions such as, "Introduce new recipes twice a week and promote interaction with a specific cooking community."

[0420] Furthermore, after users engage in social media activities based on the system's suggestions, they can send feedback about the results to the server via their device. The server analyzes this feedback and updates the AI ​​model to provide more accurate advice in the future.

[0421] As described above, the system of the present invention comprises input means, data collection means, plan generation means, output means, and feedback processing means, and provides continuous and systematic support for self-branding for SNS users.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] The user logs into their device and accesses the system. An interface is displayed on the device for entering a specific self-image and goals, where the user enters specific goals such as "I want to increase my follower count" or "I want to become a cooking influencer."

[0425] Step 2:

[0426] The terminal sends the entered target to the server. This data is encrypted before transmission, and the server prepares the received data for analysis.

[0427] Step 3:

[0428] The server collects relevant data from multiple social media platforms via SNS APIs. For example, it queries and retrieves trending hashtags related to cooking and posts from popular accounts.

[0429] Step 4:

[0430] The server analyzes the acquired data using machine learning algorithms. Specifically, it uses natural language processing to analyze patterns in successful posts and identify factors that contribute to gaining followers.

[0431] Step 5:

[0432] Based on the analysis results, the server generates an optimal social media management plan for the user. This plan includes recommended post content, frequency, hashtags to use, and target audience.

[0433] Step 6:

[0434] The server sends the generated operational plan to the terminal. The terminal receives it and presents the information in a user-friendly format. The user can then refer to this plan while engaging in activities on social media.

[0435] Step 7:

[0436] Users input feedback on the results of their SNS operations from their devices and send it to the server. This feedback includes information about the success of the activities and the effectiveness of the plan.

[0437] Step 8:

[0438] The server analyzes the feedback it receives and updates the AI ​​system. This improves the accuracy of future advice.

[0439] (Example 1)

[0440] 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."

[0441] In modern society, it is crucial for information users to establish their own brand and effectively disseminate information aligned with their objectives. However, developing concrete and objective plans for effective information dissemination is difficult for information users, and individually collecting and analyzing data from various sources is inefficient. A solution to this problem is needed.

[0442] 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.

[0443] In this invention, the server includes a device for inputting the information user's goals, a device for collecting relevant information based on the goals, and a device for analyzing the collected information using a machine learning algorithm. This enables information users to effectively and efficiently establish their own brand and generate operational plans and formulate highly accurate strategies for disseminating information appropriate to their objectives.

[0444] "Information users" refer to individuals or organizations that use a system to input data in order to achieve their own goals.

[0445] A "device for inputting goals" refers to hardware or software that provides an interface for information users to input their desired objectives or ideal state into a system.

[0446] A "device for collecting relevant information" refers to hardware or software that has the function of gathering data related to a goal set by the information user from multiple sources.

[0447] "Devices that analyze data using machine learning algorithms" refer to hardware and software that apply learning models to collected data and analyze patterns and trends in that data.

[0448] A "device for generating operational plans" refers to hardware or software used to formulate specific action plans that information users should implement based on analysis results.

[0449] A "device for updating generative models" refers to hardware or software that has functions to improve the accuracy and usefulness of generative models based on user reactions and feedback.

[0450] This invention is a system for information users to build an effective personal brand. The system basically functions through users, terminals, and servers.

[0451] The user first uses a device to access an interface for setting goals. Here, the user can enter a specific goal, such as "I want to become an influential information provider related to cooking." The device then sends this input data to the server.

[0452] The server has the capability to collect relevant information. This capability allows it to gather topics and trends related to a specific field from the internet. Furthermore, the collected data is analyzed using machine learning algorithms such as TensorFlow or PyTorch. The purpose of the analysis is to learn patterns from successful posts and identify trends in gaining followers.

[0453] Based on the analysis results, the server generates an operational plan for the information user. This plan includes detailed advice on specific content to post, frequency, and target audience to approach. This information is sent to the user's device and displayed on the screen.

[0454] As a concrete example, the plan includes guidelines such as "Post recipes focusing on seasonal ingredients three times a week and promote discussion in cooking forums." Additionally, one of the prompts to be input into the generative AI model is, "What content strategy should be adopted to become an influential information provider regarding cooking?"

[0455] Based on the proposed operational plan, users execute information dissemination and send the results as feedback to the server via their terminal. The server uses this feedback to update the AI ​​model and improve the accuracy of the advice it generates. This cycle allows information users to continuously improve their personal brand and build an influence that aligns with their objectives.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The user uses the terminal's input interface to enter their goal. At this point, the goal might be, for example, "to become an influential information provider related to cooking." The terminal then sends this input data to the server.

[0459] Step 2:

[0460] Based on the received target, the server collects relevant social media information from its database. A Web API is used for data collection, targeting hashtags related to cooking genres and currently popular trends. The obtained data is stored as primary data for target identification.

[0461] Step 3:

[0462] The server analyzes the collected data using machine learning algorithms. Specifically, it uses the Python TensorFlow library to extract successful patterns from the collected posts. The input data includes the content of the posts and the number of followers, and the output is successful patterns and characteristics of popular posts.

[0463] Step 4:

[0464] The server generates an operational plan based on the analysis results. This plan includes what kind of content to post and how often, which time slots are most effective, and which user segments to target. The plan generation method uses an AI model to create new suggestions that fit the patterns obtained from the analysis results.

[0465] Step 5:

[0466] The server sends the generated operational plan to the terminal and displays it on the terminal's screen. Here, the user can see the specific action plan that has been proposed. An example of a proposal might be, "Share seasonal recipes in the cooking forum three times a week."

[0467] Step 6:

[0468] Users disseminate information based on their plan and observe the subsequent reactions. They send the results and their impressions as feedback to the server via their device. This feedback is used to improve the system's next analysis.

[0469] Step 7:

[0470] The server analyzes the feedback it receives and updates its AI model to improve the accuracy of future advice. Specifically, it re-introduces the data based on the feedback into a machine learning process to adapt to new successful patterns. This iterative process allows users to continuously improve their personal brand.

[0471] (Application Example 1)

[0472] 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."

[0473] In recent years, the use of information platforms has expanded, and the individual brand power of users has become increasingly important. However, SNS users and online shop owners often struggle to find effective ways to disseminate information that suit their personality and goals. Therefore, there is a need for effective means to support self-branding on information platforms.

[0474] 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.

[0475] In this invention, the server includes an input means for setting the ideal self-image of an SNS user, a data collection means for collecting relevant data from multiple information platforms, and a plan generation means for analyzing the collected data and generating an operational plan on the information platform. This enables information providers to effectively enhance their individuality and achieve optimal self-branding.

[0476] An "information platform" refers to any online service that users utilize to collect and disseminate information.

[0477] "Data collection means" refers to the function of gathering relevant information from an information platform and acquiring specific data based on the user's goals.

[0478] "Plan generation method" refers to a function that analyzes collected data and proposes an operational method that suits the user.

[0479] "Feedback processing means" refers to a function that receives feedback from users and uses it to improve the system.

[0480] An "AI model" refers to a program that uses machine learning algorithms to analyze input data and generate the optimal output.

[0481] An "operational plan" refers to a specific plan formulated to effectively carry out activities on an information platform.

[0482] "Self-branding" refers to the process of clarifying one's personal characteristics and values, and presenting oneself in an attractive way to others.

[0483] The system that realizes this invention allows SNS users to set an ideal self-image and effectively supports self-branding based on that image. The server collects relevant data from multiple information platforms using data collection means based on the goals and self-image received from the user's terminal. The collected data is analyzed using machine learning algorithms (e.g., TensorFlow) by plan generation means on the server to generate an optimal operational plan for the user.

[0484] The generated operational plan is displayed on the terminal, providing specific advice for information providers to effectively express their individuality. Users then engage in activities on the information platform based on the system's suggestions via the terminal. The results of these activities are sent to the server through a feedback processing mechanism, and the AI ​​model is continuously improved based on this feedback. This improves the accuracy of the user's future planning.

[0485] As a concrete example, suppose a seller of handmade accessories sets the goal of "making their brand known to more people." In this case, the server analyzes trends and popular visual themes related to accessories and suggests photos and captions for the user to post. An example of a prompt might be, "Please suggest a posting plan to make your handmade accessory brand more appealing on social media."

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The user uses a terminal to input their ideal self-image and goals. The data received through the input interface is sent from the terminal to the server. In this step, the user sets specific goals, and this information is used as the basis for data processing.

[0489] Step 2:

[0490] The server activates data collection mechanisms based on the received target data. It retrieves relevant trends, hashtags, and user interest-related posting data from multiple information platforms to prepare for initial analysis. Here, relevant information that matches the target is aggregated.

[0491] Step 3:

[0492] The server plan generation method involves analyzing collected data using a machine learning algorithm (TensorFlow). This analysis extracts success trends and follower acquisition patterns on the information platform, and then formulates an operational plan for the next steps. As a result of the analysis, a specific posting strategy is generated.

[0493] Step 4:

[0494] The generated operational plan is sent to the device and displayed to the user. The plan includes optimal timing for information dissemination, content of posts, and methods for approaching the target audience. Based on this specific advice, users can develop an activity plan for their actual information platform.

[0495] Step 5:

[0496] After the user executes the plan, the results and feedback are sent from the terminal to the server. The feedback processing mechanism collects the user's responses and stores them as data. This information is used to improve the system's AI model.

[0497] Step 6:

[0498] The server updates its AI model based on accumulated feedback data. By analyzing the feedback results, it incorporates new insights necessary for future plan development, enabling it to provide more effective advice and operational plans.

[0499] 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.

[0500] The system according to the present invention supports SNS users in achieving their ideal self-image and, by combining it with an emotion engine, provides optimal advice tailored to the user's emotions. This system is configured in a multifaceted way through the user, terminal, and server, and specific embodiments are as follows.

[0501] Users access the system through their devices and input their goals and ideal self-image on social media. During this process, the user's input is analyzed by an emotion engine, which identifies their emotions. For example, if a user sends a comment stating, "I feel anxious because my number of followers on social media isn't increasing," the emotion engine recognizes this anxious emotion.

[0502] The device sends this data to a server, which collects SNS-related data from multiple platforms while taking emotions into consideration. The collected data is analyzed using machine learning algorithms and natural language processing to generate an SNS operation plan that is sensitive to the user's emotions. This operation plan is adjusted according to the user's psychological state and includes things like "posting during times when positive comments are expected" or "displaying messages to calm emotions."

[0503] The operational plan generated by the server is sent to the terminal, which then presents it to the user in a visually easy-to-understand format. Based on these plans, users can then conduct more effective and considerate social media activities.

[0504] Furthermore, users can send feedback from their devices to the server regarding the results of their social media activities. The server uses this feedback to continuously improve the AI ​​model and provide more appropriate, emotion-responsive advice for future interactions. This feedback system allows the emotion engine to adapt to users' long-term emotional changes and provide support optimized for each individual user.

[0505] Thus, the system of the present invention contributes to the user's goal achievement while also providing emotional support, enabling more intimate and effective assistance in self-branding.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] Users access the system using their devices and input their ideal self-image and goals on social media. Information about the user's feelings and motivations is also entered at this stage. At this point, users can include specific emotional statements, such as "I'm feeling anxious about the recent stagnation in my follower growth."

[0509] Step 2:

[0510] The device sends data entered by the user to an emotion engine, which analyzes the user's emotional state. The emotion engine uses natural language processing technology to identify emotions such as anxiety, tension, and anticipation from the entered text.

[0511] Step 3:

[0512] The device sends identified sentiment data and the user's goal settings to the server. This initiates the server's process of collecting SNS-related data from multiple social media platforms, while taking sentiment information into consideration.

[0513] Step 4:

[0514] The server analyzes the acquired data using machine learning algorithms. This identifies patterns of success stories related to the user's emotional state and builds the foundation for an operational plan. At this stage, advice tailored to the emotional state is considered, and the plan is adjusted to emphasize positive feedback as needed.

[0515] Step 5:

[0516] Based on the analysis results, the server generates an SNS management plan tailored to the user's emotions. This plan includes content that matches the emotional state, posting timing, and appropriate content tone. For example, it may suggest techniques to reduce stress levels and posting strategies based on those techniques.

[0517] Step 6:

[0518] The generated operational plan is sent from the server to the terminal. The terminal receives it and displays it to the user in a visually easy-to-understand format. The user can use this plan as a reference to carry out SNS activities while maintaining a favorable emotional state.

[0519] Step 7:

[0520] After a user engages in social media activity, feedback regarding the results of that activity and any new emotional states is sent from the device to the server. This feedback may include specific numerical data and the user's impressions.

[0521] Step 8:

[0522] The server analyzes the feedback and updates the AI ​​model. This process allows the system to provide more accurate advice that better reflects the user's emotional state in subsequent interactions.

[0523] (Example 2)

[0524] 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."

[0525] For SNS users, receiving emotionally resonant advice while supporting their goal achievement is crucial. However, traditional systems struggled to provide appropriate operational plans based on emotions, hindering the efficient use of SNS. This resulted in users not receiving appropriate feedback and their approach to achieving their goals becoming inefficient.

[0526] 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.

[0527] In this invention, the server includes an information input means for inputting information related to the goals and ideals of SNS users, an emotion analysis means for analyzing the input information and identifying emotions, and an information collection means for collecting relevant information from multiple data sources based on the identified emotions. This makes it possible to provide advice based on the user's emotions.

[0528] "SNS users" refers to individuals or groups who use social networking services.

[0529] "Information related to goals and ideals" refers to specific data or text related to the goals that SNS users want to achieve or the ideal self-image they aspire to.

[0530] An "information input means" is an interface or device that allows SNS users to input information about their goals and ideals into the system.

[0531] "Emotion analysis means" refers to software or algorithms that analyze input information and identify the user's emotions.

[0532] "Information gathering means" refers to a process or device for obtaining relevant information from multiple data sources based on analyzed emotions.

[0533] An "algorithm" is a set of procedures or computational methods defined to solve a specific problem.

[0534] A "plan generation method" is a system or process that uses collected information to create an operational plan to optimize activities on social media.

[0535] An "information output means" is an interface or device for presenting the generated operational plan to the user.

[0536] "Evaluation processing means" refers to technology that receives and analyzes the results or feedback from SNS users to improve the system's performance.

[0537] The system necessary to implement this invention consists of three main elements: a server, a terminal, and a user.

[0538] On the device, SNS users input information about their goals, ideals, and emotions through a dedicated user interface. For example, a user might input, "I want to gain more followers on SNS." This information is collected as text data and processed on the device in preparation for sentiment analysis.

[0539] Sentiment analysis is performed on the server. Text data sent from the terminal is processed by the server's sentiment analysis engine. This processing utilizes natural language processing techniques, specifically libraries such as spaCy and NLTK. Through this analysis, the server identifies the user's emotions and classifies them into emotional categories such as anxiety and joy.

[0540] The server also performs multifaceted data collection. Based on the analyzed sentiment, it collects relevant information from multiple data sources (e.g., SNS APIs). The collected data is processed by machine learning algorithms (e.g., scikit-learn and TensorFlow). This generates an SNS management plan tailored to the user's emotional state.

[0541] The generated operational plan is presented to the user via the terminal. This presentation is done through an interactive graphical user interface, displaying information in a format that is easy for the user to understand. For example, graphs and charts visualize recommendations regarding effective posting times and content.

[0542] In the feedback process, users can provide feedback on the results of their SNS activities via their devices afterward. This feedback data is analyzed by the server and used as training material for the generated AI model, which is then used to improve the accuracy of future operational plans.

[0543] A specific example of a prompt would be, "Analyze the user's emotions and create a plan to alleviate anxiety in their social media activities."

[0544] This system allows social media users to receive advice based on scientifically supported data, and to get closer to achieving their goals while receiving emotional support.

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] Users input information about their social media goals and ideal self-image into the device. Specifically, they enter text data through the user interface and press the "Send" button. This input data is used as basic information to understand the user's emotional state.

[0548] Step 2:

[0549] The terminal sends the input text data to the server for sentiment analysis. The server receives this data and passes it to the sentiment analysis engine. During this process, the text data is analyzed using natural language processing techniques to classify the user's emotions into categories such as "anxiety" and "joy." The analyzed sentiment information is then generated as output.

[0550] Step 3:

[0551] The server collects relevant information from multiple data sources based on the analyzed sentiment. Specifically, it obtains relevant post data and engagement data through the APIs of social media platforms. The collected data serves as the basis for generating social media management plans that respond to the user's sentiment.

[0552] Step 4:

[0553] The server processes the collected data using machine learning algorithms to generate an operational plan. Specifically, it uses libraries such as scikit-learn and TensorFlow to analyze the data and generate a plan that proposes the optimal posting schedule and content. This plan is designed to be sensitive to the user's emotions.

[0554] Step 5:

[0555] The generated operational plan is sent from the server to the terminal, which then visually presents this information on a graphical user interface. Specifically, charts and graphs are used to clearly show the information to the user. This output allows the user to take practical action.

[0556] Step 6:

[0557] Users conduct SNS activities based on the proposed operational plan and input feedback on the results into their devices. This allows the system to reflect the user's activity results and provides information that will help improve future operational plans.

[0558] Step 7:

[0559] The device sends the input feedback to the server, which then analyzes this data. The feedback is used as training material for the generated AI model, improving the accuracy of future advice. This allows for support that is more tailored to the user's emotions.

[0560] (Application Example 2)

[0561] 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."

[0562] For modern social media users, effectively establishing their personal brand and realizing their ideal self-image is a crucial challenge. However, with increasing activity on social media, determining the appropriate timing and content for posts, and effectively engaging in self-branding while minimizing the user's mental burden, is not easy. This invention aims to solve these problems and enable social media users to efficiently achieve their ideal self-image in a way that resonates with their own emotions.

[0563] 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.

[0564] In this invention, the server includes an input means for setting the ideal self-image of the SNS user, an information gathering means, an emotion analysis means, and a policy generation means for providing emotionally resonant advice. This makes it possible to suggest suitable posting times and content for SNS users and to formulate an optimal SNS operation policy that takes emotional aspects into consideration.

[0565] An "input method" is an interface that allows SNS users to input their ideal self-image and goals into the system.

[0566] "Information gathering means" refers to a device or system that acquires relevant data from SNS platforms and provides information necessary for SNS users to realize their self-image.

[0567] A "policy generation means" is a process or device that, based on collected data and analysis results, plans for SNS users to formulate an optimal posting strategy.

[0568] "Output means" refers to a device that presents generated operational policies and suggestions regarding posts to SNS users visually or audibly.

[0569] A "feedback processing method" is a process or device for collecting opinions and activity results from SNS users and using them to improve an AI model.

[0570] "Emotional analysis means" refers to a process or system for analyzing the emotions expressed in the input of social media users and providing support and advice tailored to those emotions.

[0571] The system implementing this invention functions as a tool for SNS users to set and realize their ideal self-image using smartphones or personal computers. First, the user inputs their goals and ideal self-image through the terminal's input interface. At this time, an emotion analysis system analyzes the user's emotions in real time and generates advice that is appropriate to the user's psychological state.

[0572] The server uses an information gathering module to collect relevant information from multiple social networking services (SNS) platforms and processes it with machine learning algorithms. The software used includes pandas and scikit-learn for data analysis, and Hugging Face Transformers for natural language processing. This generates posting guidelines optimized for the emotions and goals of SNS users. The generated guidelines are presented to the user through the terminal's output module, offering specific posting times and content suggestions.

[0573] Furthermore, the results of the user's SNS activities are sent from the device to the server as feedback. This feedback is used to improve the AI ​​model and help in formulating strategies for future use. Through this process, the system can respond to the user's long-term emotional changes and continuously provide more appropriate support.

[0574] For example, an influencer might send a prompt to the system saying, "I need a strategy to reach 1,000 followers. I'm feeling anxious because the response to my recent posts has been poor." Based on this information, the system provides specific advice such as "Post new content during times when many viewers are online." In this way, a system is built that provides social media users with concrete means to achieve their ideal self-image while receiving emotional support.

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] Users input their ideal self-image and desired goals in text format through the terminal's input interface. This input includes specific numerical and behavioral targets that the user wants to achieve. The entered information is sent from the terminal to the server for sentiment analysis.

[0578] Step 2:

[0579] The server analyzes the user's emotional state in real time using an emotion analysis model based on the received user input information. This analysis applies an emotion recognition algorithm using natural language processing technology to quantify emotions such as anxiety, anticipation, and impatience. The analysis results are returned to the terminal as specific emotion tags.

[0580] Step 3:

[0581] The server activates an information gathering module and collects relevant public data from multiple social networking platforms. This data includes posting times, engagement rates, and popular hashtags. The collected information is stored in a database and used in the next analysis step.

[0582] Step 4:

[0583] The server uses a data analysis engine to integrate acquired information from social media platforms with user sentiment data to generate an optimized posting strategy. This process utilizes machine learning algorithms. Specifically, it builds a predictive model based on similar social media examples to predict the most effective posting times and content for each user. The generated strategy is then sent to the user's device.

[0584] Step 5:

[0585] The device presents the generated posting strategy to the user through a visual interface. The user reviews the proposed strategy and, if necessary, posts to social media. Here, the device displays specific posting suggestions and graphs of posting times on the screen.

[0586] Step 6:

[0587] Users send feedback from their devices to the server based on the results of their social media activities. This feedback includes the actual number of responses, details of engagement, and the user's own impressions. The server uses this feedback to improve the AI ​​model and incorporate it into future strategies.

[0588] Step 7:

[0589] The server adjusts the generated AI model based on the collected feedback. Here, the machine learning retraining process is automatically initiated, involving data refinement and tuning to improve the model's accuracy. The adjusted model is then ready to provide more accurate advice with subsequent user input.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] [Fourth Embodiment]

[0594] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0595] 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.

[0596] 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).

[0597] 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.

[0598] 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.

[0599] 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).

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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".

[0607] The system according to the present invention provides support for SNS users to effectively engage in self-branding, and is equipped with a variety of functions as follows:

[0608] First, users can access the system using their device and input their ideal self-image and goals. This is done through a dedicated input interface. If a user sets a goal such as "I want to become an influential SNS user related to cooking," the entered data is sent to the server.

[0609] The server is equipped with means to collect social media-related data that corresponds to its objectives. For example, it searches for hashtags and trends related to a specific cooking genre and retrieves relevant posts from social media platforms. The collected data is analyzed by machine learning algorithms to extract successful posting patterns and follower acquisition trends.

[0610] Next, the server generates an operational plan based on the analyzed data. This plan includes specific advice on what content to post, when to post, and which target audience to approach, and this information is displayed on the user's device through an output method. For example, a cooking-related social media user might be presented with specific suggestions such as, "Introduce new recipes twice a week and promote interaction with a specific cooking community."

[0611] Furthermore, after users engage in social media activities based on the system's suggestions, they can send feedback about the results to the server via their device. The server analyzes this feedback and updates the AI ​​model to provide more accurate advice in the future.

[0612] As described above, the system of the present invention comprises input means, data collection means, plan generation means, output means, and feedback processing means, and provides continuous and systematic support for self-branding for SNS users.

[0613] The following describes the processing flow.

[0614] Step 1:

[0615] The user logs into their device and accesses the system. An interface is displayed on the device for entering a specific self-image and goals, where the user enters specific goals such as "I want to increase my follower count" or "I want to become a cooking influencer."

[0616] Step 2:

[0617] The terminal sends the entered target to the server. This data is encrypted before transmission, and the server prepares the received data for analysis.

[0618] Step 3:

[0619] The server collects relevant data from multiple social media platforms via SNS APIs. For example, it queries and retrieves trending hashtags related to cooking and posts from popular accounts.

[0620] Step 4:

[0621] The server analyzes the acquired data using machine learning algorithms. Specifically, it uses natural language processing to analyze patterns in successful posts and identify factors that contribute to gaining followers.

[0622] Step 5:

[0623] Based on the analysis results, the server generates an optimal social media management plan for the user. This plan includes recommended post content, frequency, hashtags to use, and target audience.

[0624] Step 6:

[0625] The server sends the generated operational plan to the terminal. The terminal receives it and presents the information in a user-friendly format. The user can then refer to this plan while engaging in activities on social media.

[0626] Step 7:

[0627] Users input feedback on the results of their SNS operations from their devices and send it to the server. This feedback includes information about the success of the activities and the effectiveness of the plan.

[0628] Step 8:

[0629] The server analyzes the feedback it receives and updates the AI ​​system. This improves the accuracy of future advice.

[0630] (Example 1)

[0631] 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".

[0632] In modern society, it is crucial for information users to establish their own brand and effectively disseminate information aligned with their objectives. However, developing concrete and objective plans for effective information dissemination is difficult for information users, and individually collecting and analyzing data from various sources is inefficient. A solution to this problem is needed.

[0633] 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.

[0634] In this invention, the server includes a device for inputting the information user's goals, a device for collecting relevant information based on the goals, and a device for analyzing the collected information using a machine learning algorithm. This enables information users to effectively and efficiently establish their own brand and generate operational plans and formulate highly accurate strategies for disseminating information appropriate to their objectives.

[0635] "Information users" refer to individuals or organizations that use a system to input data in order to achieve their own goals.

[0636] A "device for inputting goals" refers to hardware or software that provides an interface for information users to input their desired objectives or ideal state into a system.

[0637] A "device for collecting relevant information" refers to hardware or software that has the function of gathering data related to a goal set by the information user from multiple sources.

[0638] "Devices that analyze data using machine learning algorithms" refer to hardware and software that apply learning models to collected data and analyze patterns and trends in that data.

[0639] A "device for generating operational plans" refers to hardware or software used to formulate specific action plans that information users should implement based on analysis results.

[0640] A "device for updating generative models" refers to hardware or software that has functions to improve the accuracy and usefulness of generative models based on user reactions and feedback.

[0641] This invention is a system for information users to build an effective personal brand. The system basically functions through users, terminals, and servers.

[0642] The user first uses a device to access an interface for setting goals. Here, the user can enter a specific goal, such as "I want to become an influential information provider related to cooking." The device then sends this input data to the server.

[0643] The server has the capability to collect relevant information. This capability allows it to gather topics and trends related to a specific field from the internet. Furthermore, the collected data is analyzed using machine learning algorithms such as TensorFlow or PyTorch. The purpose of the analysis is to learn patterns from successful posts and identify trends in gaining followers.

[0644] Based on the analysis results, the server generates an operational plan for the information user. This plan includes detailed advice on specific content to post, frequency, and target audience to approach. This information is sent to the user's device and displayed on the screen.

[0645] As a concrete example, the plan includes guidelines such as "Post recipes focusing on seasonal ingredients three times a week and promote discussion in cooking forums." Additionally, one of the prompts to be input into the generative AI model is, "What content strategy should be adopted to become an influential information provider regarding cooking?"

[0646] Based on the proposed operational plan, users execute information dissemination and send the results as feedback to the server via their terminal. The server uses this feedback to update the AI ​​model and improve the accuracy of the advice it generates. This cycle allows information users to continuously improve their personal brand and build an influence that aligns with their objectives.

[0647] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0648] Step 1:

[0649] The user uses the terminal's input interface to enter their goal. At this point, the goal might be, for example, "to become an influential information provider related to cooking." The terminal then sends this input data to the server.

[0650] Step 2:

[0651] Based on the received target, the server collects relevant social media information from its database. A Web API is used for data collection, targeting hashtags related to cooking genres and currently popular trends. The obtained data is stored as primary data for target identification.

[0652] Step 3:

[0653] The server analyzes the collected data using machine learning algorithms. Specifically, it uses the Python TensorFlow library to extract successful patterns from the collected posts. The input data includes the content of the posts and the number of followers, and the output is successful patterns and characteristics of popular posts.

[0654] Step 4:

[0655] The server generates an operational plan based on the analysis results. This plan includes what kind of content to post and how often, which time slots are most effective, and which user segments to target. The plan generation method uses an AI model to create new suggestions that fit the patterns obtained from the analysis results.

[0656] Step 5:

[0657] The server sends the generated operational plan to the terminal and displays it on the terminal's screen. Here, the user can see the specific action plan that has been proposed. An example of a proposal might be, "Share seasonal recipes in the cooking forum three times a week."

[0658] Step 6:

[0659] Users disseminate information based on their plan and observe the subsequent reactions. They send the results and their impressions as feedback to the server via their device. This feedback is used to improve the system's next analysis.

[0660] Step 7:

[0661] The server analyzes the feedback it receives and updates its AI model to improve the accuracy of future advice. Specifically, it re-introduces the data based on the feedback into a machine learning process to adapt to new successful patterns. This iterative process allows users to continuously improve their personal brand.

[0662] (Application Example 1)

[0663] 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".

[0664] In recent years, the use of information platforms has expanded, and the individual brand power of users has become increasingly important. However, SNS users and online shop owners often struggle to find effective ways to disseminate information that suit their personality and goals. Therefore, there is a need for effective means to support self-branding on information platforms.

[0665] 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.

[0666] In this invention, the server includes an input means for setting the ideal self-image of an SNS user, a data collection means for collecting relevant data from multiple information platforms, and a plan generation means for analyzing the collected data and generating an operational plan on the information platform. This enables information providers to effectively enhance their individuality and achieve optimal self-branding.

[0667] An "information platform" refers to any online service that users utilize to collect and disseminate information.

[0668] "Data collection means" refers to the function of gathering relevant information from an information platform and acquiring specific data based on the user's goals.

[0669] "Plan generation method" refers to a function that analyzes collected data and proposes an operational method that suits the user.

[0670] "Feedback processing means" refers to a function that receives feedback from users and uses it to improve the system.

[0671] An "AI model" refers to a program that uses machine learning algorithms to analyze input data and generate the optimal output.

[0672] An "operational plan" refers to a specific plan formulated to effectively carry out activities on an information platform.

[0673] "Self-branding" refers to the process of clarifying one's personal characteristics and values, and presenting oneself in an attractive way to others.

[0674] The system that realizes this invention allows SNS users to set an ideal self-image and effectively supports self-branding based on that image. The server collects relevant data from multiple information platforms using data collection means based on the goals and self-image received from the user's terminal. The collected data is analyzed using machine learning algorithms (e.g., TensorFlow) by plan generation means on the server to generate an optimal operational plan for the user.

[0675] The generated operational plan is displayed on the terminal, providing specific advice for information providers to effectively express their individuality. Users then engage in activities on the information platform based on the system's suggestions via the terminal. The results of these activities are sent to the server through a feedback processing mechanism, and the AI ​​model is continuously improved based on this feedback. This improves the accuracy of the user's future planning.

[0676] As a concrete example, suppose a seller of handmade accessories sets the goal of "making their brand known to more people." In this case, the server analyzes trends and popular visual themes related to accessories and suggests photos and captions for the user to post. An example of a prompt might be, "Please suggest a posting plan to make your handmade accessory brand more appealing on social media."

[0677] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0678] Step 1:

[0679] The user uses a terminal to input their ideal self-image and goals. The data received through the input interface is sent from the terminal to the server. In this step, the user sets specific goals, and this information is used as the basis for data processing.

[0680] Step 2:

[0681] The server activates data collection mechanisms based on the received target data. It retrieves relevant trends, hashtags, and user interest-related posting data from multiple information platforms to prepare for initial analysis. Here, relevant information that matches the target is aggregated.

[0682] Step 3:

[0683] The server plan generation method involves analyzing collected data using a machine learning algorithm (TensorFlow). This analysis extracts success trends and follower acquisition patterns on the information platform, and then formulates an operational plan for the next steps. As a result of the analysis, a specific posting strategy is generated.

[0684] Step 4:

[0685] The generated operational plan is sent to the device and displayed to the user. The plan includes optimal timing for information dissemination, content of posts, and methods for approaching the target audience. Based on this specific advice, users can develop an activity plan for their actual information platform.

[0686] Step 5:

[0687] After the user executes the plan, the results and feedback are sent from the terminal to the server. The feedback processing mechanism collects the user's responses and stores them as data. This information is used to improve the system's AI model.

[0688] Step 6:

[0689] The server updates its AI model based on accumulated feedback data. By analyzing the feedback results, it incorporates new insights necessary for future plan development, enabling it to provide more effective advice and operational plans.

[0690] 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.

[0691] The system according to the present invention supports SNS users in achieving their ideal self-image and, by combining it with an emotion engine, provides optimal advice tailored to the user's emotions. This system is configured in a multifaceted way through the user, terminal, and server, and specific embodiments are as follows.

[0692] Users access the system through their devices and input their goals and ideal self-image on social media. During this process, the user's input is analyzed by an emotion engine, which identifies their emotions. For example, if a user sends a comment stating, "I feel anxious because my number of followers on social media isn't increasing," the emotion engine recognizes this anxious emotion.

[0693] The device sends this data to a server, which collects SNS-related data from multiple platforms while taking emotions into consideration. The collected data is analyzed using machine learning algorithms and natural language processing to generate an SNS operation plan that is sensitive to the user's emotions. This operation plan is adjusted according to the user's psychological state and includes things like "posting during times when positive comments are expected" or "displaying messages to calm emotions."

[0694] The operational plan generated by the server is sent to the terminal, which then presents it to the user in a visually easy-to-understand format. Based on these plans, users can then conduct more effective and considerate social media activities.

[0695] Furthermore, users can send feedback from their devices to the server regarding the results of their social media activities. The server uses this feedback to continuously improve the AI ​​model and provide more appropriate, emotion-responsive advice for future interactions. This feedback system allows the emotion engine to adapt to users' long-term emotional changes and provide support optimized for each individual user.

[0696] Thus, the system of the present invention contributes to the user's goal achievement while also providing emotional support, enabling more intimate and effective assistance in self-branding.

[0697] The following describes the processing flow.

[0698] Step 1:

[0699] Users access the system using their devices and input their ideal self-image and goals on social media. Information about the user's feelings and motivations is also entered at this stage. At this point, users can include specific emotional statements, such as "I'm feeling anxious about the recent stagnation in my follower growth."

[0700] Step 2:

[0701] The device sends data entered by the user to an emotion engine, which analyzes the user's emotional state. The emotion engine uses natural language processing technology to identify emotions such as anxiety, tension, and anticipation from the entered text.

[0702] Step 3:

[0703] The device sends identified sentiment data and the user's goal settings to the server. This initiates the server's process of collecting SNS-related data from multiple social media platforms, while taking sentiment information into consideration.

[0704] Step 4:

[0705] The server analyzes the acquired data using machine learning algorithms. This identifies patterns of success stories related to the user's emotional state and builds the foundation for an operational plan. At this stage, advice tailored to the emotional state is considered, and the plan is adjusted to emphasize positive feedback as needed.

[0706] Step 5:

[0707] Based on the analysis results, the server generates an SNS management plan tailored to the user's emotions. This plan includes content that matches the emotional state, posting timing, and appropriate content tone. For example, it may suggest techniques to reduce stress levels and posting strategies based on those techniques.

[0708] Step 6:

[0709] The generated operational plan is sent from the server to the terminal. The terminal receives it and displays it to the user in a visually easy-to-understand format. The user can use this plan as a reference to carry out SNS activities while maintaining a favorable emotional state.

[0710] Step 7:

[0711] After a user engages in social media activity, feedback regarding the results of that activity and any new emotional states is sent from the device to the server. This feedback may include specific numerical data and the user's impressions.

[0712] Step 8:

[0713] The server analyzes the feedback and updates the AI ​​model. This process allows the system to provide more accurate advice that better reflects the user's emotional state in subsequent interactions.

[0714] (Example 2)

[0715] 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".

[0716] For SNS users, receiving emotionally resonant advice while supporting their goal achievement is crucial. However, traditional systems struggled to provide appropriate operational plans based on emotions, hindering the efficient use of SNS. This resulted in users not receiving appropriate feedback and their approach to achieving their goals becoming inefficient.

[0717] 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.

[0718] In this invention, the server includes an information input means for inputting information related to the goals and ideals of SNS users, an emotion analysis means for analyzing the input information and identifying emotions, and an information collection means for collecting relevant information from multiple data sources based on the identified emotions. This makes it possible to provide advice based on the user's emotions.

[0719] "SNS users" refers to individuals or groups who use social networking services.

[0720] "Information related to goals and ideals" refers to specific data or text related to the goals that SNS users want to achieve or the ideal self-image they aspire to.

[0721] An "information input means" is an interface or device that allows SNS users to input information about their goals and ideals into the system.

[0722] "Emotion analysis means" refers to software or algorithms that analyze input information and identify the user's emotions.

[0723] "Information gathering means" refers to a process or device for obtaining relevant information from multiple data sources based on analyzed emotions.

[0724] An "algorithm" is a set of procedures or computational methods defined to solve a specific problem.

[0725] A "plan generation method" is a system or process that uses collected information to create an operational plan to optimize activities on social media.

[0726] An "information output means" is an interface or device for presenting the generated operational plan to the user.

[0727] "Evaluation processing means" refers to technology that receives and analyzes the results or feedback from SNS users to improve the system's performance.

[0728] The system necessary to implement this invention consists of three main elements: a server, a terminal, and a user.

[0729] On the device, SNS users input information about their goals, ideals, and emotions through a dedicated user interface. For example, a user might input, "I want to gain more followers on SNS." This information is collected as text data and processed on the device in preparation for sentiment analysis.

[0730] Sentiment analysis is performed on the server. Text data sent from the terminal is processed by the server's sentiment analysis engine. This processing utilizes natural language processing techniques, specifically libraries such as spaCy and NLTK. Through this analysis, the server identifies the user's emotions and classifies them into emotional categories such as anxiety and joy.

[0731] The server also performs multifaceted data collection. Based on the analyzed sentiment, it collects relevant information from multiple data sources (e.g., SNS APIs). The collected data is processed by machine learning algorithms (e.g., scikit-learn and TensorFlow). This generates an SNS management plan tailored to the user's emotional state.

[0732] The generated operational plan is presented to the user via the terminal. This presentation is done through an interactive graphical user interface, displaying information in a format that is easy for the user to understand. For example, graphs and charts visualize recommendations regarding effective posting times and content.

[0733] In the feedback process, users can provide feedback on the results of their SNS activities via their devices afterward. This feedback data is analyzed by the server and used as training material for the generated AI model, which is then used to improve the accuracy of future operational plans.

[0734] A specific example of a prompt would be, "Analyze the user's emotions and create a plan to alleviate anxiety in their social media activities."

[0735] This system allows social media users to receive advice based on scientifically supported data, and to get closer to achieving their goals while receiving emotional support.

[0736] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0737] Step 1:

[0738] Users input information about their social media goals and ideal self-image into the device. Specifically, they enter text data through the user interface and press the "Send" button. This input data is used as basic information to understand the user's emotional state.

[0739] Step 2:

[0740] The terminal sends the input text data to the server for sentiment analysis. The server receives this data and passes it to the sentiment analysis engine. During this process, the text data is analyzed using natural language processing techniques to classify the user's emotions into categories such as "anxiety" and "joy." The analyzed sentiment information is then generated as output.

[0741] Step 3:

[0742] The server collects relevant information from multiple data sources based on the analyzed sentiment. Specifically, it obtains relevant post data and engagement data through the APIs of social media platforms. The collected data serves as the basis for generating social media management plans that respond to the user's sentiment.

[0743] Step 4:

[0744] The server processes the collected data using machine learning algorithms to generate an operational plan. Specifically, it uses libraries such as scikit-learn and TensorFlow to analyze the data and generate a plan that proposes the optimal posting schedule and content. This plan is designed to be sensitive to the user's emotions.

[0745] Step 5:

[0746] The generated operational plan is sent from the server to the terminal, which then visually presents this information on a graphical user interface. Specifically, charts and graphs are used to clearly show the information to the user. This output allows the user to take practical action.

[0747] Step 6:

[0748] Users conduct SNS activities based on the proposed operational plan and input feedback on the results into their devices. This allows the system to reflect the user's activity results and provides information that will help improve future operational plans.

[0749] Step 7:

[0750] The device sends the input feedback to the server, which then analyzes this data. The feedback is used as training material for the generated AI model, improving the accuracy of future advice. This allows for support that is more tailored to the user's emotions.

[0751] (Application Example 2)

[0752] 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".

[0753] For modern social media users, effectively establishing their personal brand and realizing their ideal self-image is a crucial challenge. However, with increasing activity on social media, determining the appropriate timing and content for posts, and effectively engaging in self-branding while minimizing the user's mental burden, is not easy. This invention aims to solve these problems and enable social media users to efficiently achieve their ideal self-image in a way that resonates with their own emotions.

[0754] 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.

[0755] In this invention, the server includes an input means for setting the ideal self-image of the SNS user, an information gathering means, an emotion analysis means, and a policy generation means for providing emotionally resonant advice. This makes it possible to suggest suitable posting times and content for SNS users and to formulate an optimal SNS operation policy that takes emotional aspects into consideration.

[0756] An "input method" is an interface that allows SNS users to input their ideal self-image and goals into the system.

[0757] "Information gathering means" refers to a device or system that acquires relevant data from SNS platforms and provides information necessary for SNS users to realize their self-image.

[0758] A "policy generation means" is a process or device that, based on collected data and analysis results, plans for SNS users to formulate an optimal posting strategy.

[0759] "Output means" refers to a device that presents generated operational policies and suggestions regarding posts to SNS users visually or audibly.

[0760] A "feedback processing method" is a process or device for collecting opinions and activity results from SNS users and using them to improve an AI model.

[0761] "Emotional analysis means" refers to a process or system for analyzing the emotions expressed in the input of social media users and providing support and advice tailored to those emotions.

[0762] The system implementing this invention functions as a tool for SNS users to set and realize their ideal self-image using smartphones or personal computers. First, the user inputs their goals and ideal self-image through the terminal's input interface. At this time, an emotion analysis system analyzes the user's emotions in real time and generates advice that is appropriate to the user's psychological state.

[0763] The server uses an information gathering module to collect relevant information from multiple social networking services (SNS) platforms and processes it with machine learning algorithms. The software used includes pandas and scikit-learn for data analysis, and Hugging Face Transformers for natural language processing. This generates posting guidelines optimized for the emotions and goals of SNS users. The generated guidelines are presented to the user through the terminal's output module, offering specific posting times and content suggestions.

[0764] Furthermore, the results of the user's SNS activities are sent from the device to the server as feedback. This feedback is used to improve the AI ​​model and help in formulating strategies for future use. Through this process, the system can respond to the user's long-term emotional changes and continuously provide more appropriate support.

[0765] For example, an influencer might send a prompt to the system saying, "I need a strategy to reach 1,000 followers. I'm feeling anxious because the response to my recent posts has been poor." Based on this information, the system provides specific advice such as "Post new content during times when many viewers are online." In this way, a system is built that provides social media users with concrete means to achieve their ideal self-image while receiving emotional support.

[0766] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0767] Step 1:

[0768] Users input their ideal self-image and desired goals in text format through the terminal's input interface. This input includes specific numerical and behavioral targets that the user wants to achieve. The entered information is sent from the terminal to the server for sentiment analysis.

[0769] Step 2:

[0770] The server analyzes the user's emotional state in real time using an emotion analysis model based on the received user input information. This analysis applies an emotion recognition algorithm using natural language processing technology to quantify emotions such as anxiety, anticipation, and impatience. The analysis results are returned to the terminal as specific emotion tags.

[0771] Step 3:

[0772] The server activates an information gathering module and collects relevant public data from multiple social networking platforms. This data includes posting times, engagement rates, and popular hashtags. The collected information is stored in a database and used in the next analysis step.

[0773] Step 4:

[0774] The server uses a data analysis engine to integrate acquired information from social media platforms with user sentiment data to generate an optimized posting strategy. This process utilizes machine learning algorithms. Specifically, it builds a predictive model based on similar social media examples to predict the most effective posting times and content for each user. The generated strategy is then sent to the user's device.

[0775] Step 5:

[0776] The device presents the generated posting strategy to the user through a visual interface. The user reviews the proposed strategy and, if necessary, posts to social media. Here, the device displays specific posting suggestions and graphs of posting times on the screen.

[0777] Step 6:

[0778] Users send feedback from their devices to the server based on the results of their social media activities. This feedback includes the actual number of responses, details of engagement, and the user's own impressions. The server uses this feedback to improve the AI ​​model and incorporate it into future strategies.

[0779] Step 7:

[0780] The server adjusts the generated AI model based on the collected feedback. Here, the machine learning retraining process is automatically initiated, involving data refinement and tuning to improve the model's accuracy. The adjusted model is then ready to provide more accurate advice with subsequent user input.

[0781] 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.

[0782] 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.

[0783] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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."

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] The following is further disclosed regarding the embodiments described above.

[0803] (Claim 1)

[0804] An input method for setting the ideal self-image of SNS users,

[0805] A data collection means for collecting SNS-related data based on the self-image set above,

[0806] A plan generation means that analyzes the collected data and generates an operational plan for social media,

[0807] Output means for displaying the generated operation plan,

[0808] A feedback processing means that receives feedback from the aforementioned SNS users and improves the AI ​​model,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, wherein the data collection means is a means for acquiring data from multiple SNS platforms.

[0812] (Claim 3)

[0813] The system according to claim 1, wherein the plan generation means is a means of using natural language processing and machine learning algorithms.

[0814] "Example 1"

[0815] (Claim 1)

[0816] A device for inputting the information user's goals,

[0817] A device for collecting relevant information based on the aforementioned objective,

[0818] A device for analyzing the collected information using a machine learning algorithm,

[0819] A device that generates an operational plan based on the aforementioned analysis results,

[0820] A device for displaying the generated operational plan,

[0821] A device that receives feedback from the aforementioned information user and updates the generation model,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, which is a device for obtaining the aforementioned related information from multiple sources.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the device for generating the operational plan is a device that uses language processing technology and a learning algorithm.

[0827] "Application Example 1"

[0828] (Claim 1)

[0829] An input method for setting the ideal self-image of SNS users,

[0830] A data collection means that collects relevant data from multiple information platforms based on the self-image set above,

[0831] A plan generation means that analyzes the collected data and generates an operational plan on the information platform,

[0832] An output means that displays the generated operational plan and proposes methods to enhance the individuality of the information provider,

[0833] A feedback processing means that receives feedback from users of the aforementioned information platform, improves the AI ​​model, and optimizes plans for future use.

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, wherein the data collection means is a means for acquiring data from multiple information platforms, and collects data that takes into account product information and user characteristics.

[0837] (Claim 3)

[0838] The system according to claim 1, wherein the plan generation means is a means for generating a strategy to support the user's brand building using natural language processing and machine learning algorithms.

[0839] "Example 2 of combining an emotion engine"

[0840] (Claim 1)

[0841] An information input method for entering information related to the goals and ideals of SNS users,

[0842] An emotion analysis means that analyzes the input information and identifies emotions,

[0843] Information gathering means that collect relevant information from multiple data sources based on identified emotions,

[0844] A plan generation means that generates an operational plan using an algorithm based on the collected information,

[0845] Information output means for visualizing and presenting the generated operational plan,

[0846] An evaluation processing means that receives implementation results from the aforementioned SNS users and continuously learns from them,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, wherein the information gathering means is a means for acquiring data from multiple information sources.

[0850] (Claim 3)

[0851] The system according to claim 1, wherein the plan generation means is a means that uses language processing technology and a learning algorithm.

[0852] "Application example 2 when combining with an emotional engine"

[0853] (Claim 1)

[0854] An input method for setting the ideal self-image of SNS users,

[0855] Based on the self-image set above, an information gathering means collects SNS-related data,

[0856] A policy generation means that analyzes the collected information and generates an optimized SNS operation policy from multiple SNS platforms,

[0857] An output means that displays the generated operational policy and suggests posting times and content suitable for SNS users,

[0858] A feedback processing means that receives feedback from the aforementioned SNS users and improves the AI ​​model,

[0859] A sentiment analysis tool that uses sentiment analysis to provide advice that is tailored to the emotions of SNS users,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, wherein the information gathering means is a means of acquiring information from a large number of social networking platforms and supporting effective self-branding.

[0863] (Claim 3)

[0864] The system according to claim 1, wherein the policy generation means is a means for formulating an optimal posting strategy for the goals of SNS users using natural language processing and machine learning algorithms. [Explanation of Symbols]

[0865] 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. An input method for setting the ideal self-image of SNS users, A data collection means for collecting SNS-related data based on the self-image set above, A plan generation means that analyzes the collected data and generates an operational plan for social media, Output means for displaying the generated operation plan, A feedback processing means that receives feedback from the aforementioned SNS users and improves the AI ​​model, A system that includes this.

2. The system according to claim 1, wherein the data collection means is a means for acquiring data from multiple SNS platforms.

3. The system according to claim 1, wherein the plan generation means is a means of using natural language processing and machine learning algorithms.

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