system

Abstract

We provide the system. [Solution] A means for the user to input topics of interest, Means for transmitting input topic information to a central processing unit, A method for obtaining real-time trend information using a public API, A means for generating content ideas based on acquired trend information and input topic information using generative artificial intelligence, A means of presenting generated content ideas to users, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern social networking services (SNS), there is a problem that it is difficult for users to obtain ideas for making their posted content go viral. With previous approaches, users had to collect trend information by themselves and plan content independently, which resulted in time and labor requirements and made it difficult to predict effective buzz. The purpose of the present invention is to solve these problems and provide a means for users to easily and efficiently obtain posting ideas that can go viral.

Means for Solving the Problems

[0005] The present invention solves the above problems by the following means. First, a user can input a topic of interest using a terminal. This topic information is transmitted from the terminal to a central processing unit (server). The server obtains real-time trend information using a public API and uses generative artificial intelligence to generate content ideas based on this trend information and the topic information entered by the user. The generated content ideas are then transmitted back to the terminal and presented to the user. Furthermore, by including means for evaluating the effectiveness of the generated content ideas, more specific and effective feedback can be provided. In addition, by collecting reaction data after posting the proposed content ideas and updating the training data of the generative artificial intelligence, the generation accuracy can be continuously improved. As a result, users can always obtain effective posting ideas that correspond to the latest trends, and it becomes possible to provide a means to increase attention on social media.

[0006] A "user" refers to an individual or group that uses the system to obtain ideas for social media posts.

[0007] "Topic information" refers to information about themes and topics that users are interested in.

[0008] A "central processing unit" refers to a server or computer used to process topic information and trend information.

[0009] A "public API" refers to a standardized interface for accessing services and databases provided by third parties.

[0010] "Trending information" refers to data such as topics, hashtags, images, and videos that are currently attracting attention on social media.

[0011] "Generative artificial intelligence" refers to artificial intelligence that generates content ideas to suggest to users based on collected topic information and trend information.

[0012] "Content ideas" refer to information that suggests specific content and formats for users to post on social media.

[0013] A "terminal" refers to a device used by a user to input topic information or receive generated content ideas through the system.

[0014] "Means for evaluating effectiveness" refers to methods and devices for measuring the success and impact of generated content ideas.

[0015] "Reaction data" refers to data related to user engagement, such as the number of likes, comments, and shares of content posted on social media.

[0016] "Training data" refers to the dataset that generative artificial intelligence uses to acquire new knowledge and skills. [Brief explanation of the drawing]

[0017] [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]It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 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 Example 2 when an 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 an emotion engine is combined.

Mode for Carrying Out the Invention

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0022] 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.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] To implement this invention, a system is constructed in which a user inputs information on topics of interest via a terminal, and a server uses generative artificial intelligence to generate content ideas that are likely to go viral based on that information.

[0039] Specifically, the following system configuration and program processing are required.

[0040] System Configuration

[0041] 1. User terminal:

[0042] It has an interface where users can input information on topics they are interested in.

[0043] It has a means of communication to receive input information and send it to the server.

[0044] It has a means of displaying content ideas received from the server.

[0045] 2. Server:

[0046] It has the ability to obtain real-time trend information through a public API.

[0047] It has the ability to generate content ideas based on topic information and trend information using generative artificial intelligence.

[0048] It has a means of communication to send the generated content ideas to the user's terminal.

[0049] It has features to evaluate the effectiveness of content ideas and collect user feedback.

[0050] It has a function to update the training data of generative artificial intelligence based on feedback.

[0051] Program processing

[0052] The following steps are necessary for a user's device to generate ideas for social media posts.

[0053] 1. Enter topic information:

[0054] The user accesses the device's interface and enters topics of interest such as "pets," "cooking," or "travel."

[0055] 2. Sending information:

[0056] The terminal sends this topic information to the server. This topic information is sent in JSON format or another appropriate data format.

[0057] 3. Obtaining trend information:

[0058] The server retrieves trending information such as currently popular hashtags, trending words, images, and videos in real time through the public APIs of social media platforms.

[0059] 4. Generating content ideas:

[0060] The server uses acquired trend information and user topic information as input data to run a generative artificial intelligence.

[0061] Based on this data, the generative artificial intelligence generates specific social media posting ideas. The proposed ideas are structured in the form of text, images, and videos.

[0062] 5. Idea presentation:

[0063] The server then sends the generated content ideas back to the user's terminal in an appropriate format, such as JSON.

[0064] The user's terminal analyzes the received data and presents it to the user in an easy-to-understand format.

[0065] Users review the suggested content ideas and post them to their own social media accounts.

[0066] Specific example

[0067] For example, if a user enters "I want to post a picture of my pet," the server will operate as follows:

[0068] 1. The server retrieves popular hashtags such as "Life with pets" and "Today's dog" from the public APIs of social media platforms.

[0069] 2. Generative artificial intelligence generates a specific idea such as, "It would be good to post a cute photo of your pet taken during your walk this morning and use the hashtag 'Today's Puppy'."

[0070] 3. The server sends this generated idea to the user's terminal, and the terminal displays it to the user.

[0071] Users post the suggested ideas to social media, and the server analyzes the reactions to those posts (number of likes, comments, etc.) to inform the generation of future ideas. This process ensures that users always have access to content ideas that are in line with current trends and have a high potential to go viral.

[0072] The following describes the processing flow.

[0073] Step 1:

[0074] The user accesses the device interface and enters topic information they want to post to social media (e.g., "pets," "cooking," "travel," etc.). The information entered by the user is sent to the server in the next step.

[0075] Step 2:

[0076] The terminal converts the topic information entered by the user into JSON format data and sends it to the server. At this point, the terminal checks the integrity of the data structure and verifies that there are no errors.

[0077] Step 3:

[0078] The server analyzes the topic information it receives and uses public APIs (e.g., Twitter API, Instagram API) to retrieve real-time trend information. This includes currently popular hashtags, trending words, images, videos, and more.

[0079] Step 4:

[0080] After the server acquires trend information, it passes the topic information and the acquired trend information as input data to a generative artificial intelligence (e.g., a natural language processing model). Based on this data, the generative artificial intelligence generates specific SNS posting ideas to suggest to the user.

[0081] Step 5:

[0082] The server receives specific posting ideas generated by a generative artificial intelligence and sends them to the user's terminal in JSON format or another appropriate data format. These ideas include relevant hashtags, text, and recommended images.

[0083] Step 6:

[0084] The device analyzes content ideas received from the server and displays them in a user-friendly format. The user reviews the displayed ideas and decides whether or not to post them on social media.

[0085] Step 7:

[0086] Users post content ideas suggested to them on social media. Users can modify the generated text and images as needed before posting.

[0087] Step 8:

[0088] The server collects reaction data to posts on social media (e.g., number of likes, comments, shares, etc.). This data collection again utilizes a public API.

[0089] Step 9:

[0090] The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This data will be used in the next idea generation process to improve the generation accuracy.

[0091] Step 10:

[0092] Users review the feedback and use it to improve future posts. They then use the system again for their next post to get content ideas based on the latest trends.

[0093] The above is the specific processing flow of the viral posting idea suggestion system using generative artificial intelligence. Through this system, users can always create effective social media posts based on the latest trend information.

[0094] (Example 1)

[0095] 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."

[0096] Traditional social media posting support systems lack the functionality to automatically generate effective content ideas for topics of user interest, making it difficult for users to create trending content with a high potential for virality. Furthermore, they lack mechanisms to evaluate the effectiveness of generated content and incorporate those evaluations into future content creation. This results in a problem where the efficiency and effectiveness of users' social media activities are not sufficiently improved.

[0097] 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.

[0098] In this invention, the server includes means for generating specific prompt sentences based on user input and passing them to a generative artificial intelligence system; means for monitoring the reactions to the user's SNS posts for a certain period and analyzing the data; and means for evaluating the effectiveness of the generated content ideas. As a result, users can easily obtain content ideas that are in line with trends and have a high potential to go viral, and by evaluating their effectiveness and reflecting it in the generation of future content, they can improve the efficiency and effectiveness of their SNS activities.

[0099] A "user terminal" is a device that allows users to input information on topics they are interested in and exchange data with the server.

[0100] A "central processing unit" is a system that receives data transmitted from user terminals, acquires trend information through public APIs, and generates content ideas using generative artificial intelligence.

[0101] A "public API" is an application programming interface that is made publicly accessible to external developers and services, and in this case, it is used to obtain real-time trend information from social media.

[0102] "Generative artificial intelligence" refers to an artificial intelligence system that receives data as input and generates content ideas using machine learning algorithms.

[0103] A "prompt" is a specific instruction or phrase given to a generative artificial intelligence system to generate content ideas.

[0104] "Content ideas" refer to specific ideas or suggestions that users can post on social media, and are composed of various forms such as text, images, and videos.

[0105] "SNS reaction data" refers to user reaction data such as the number of "likes," comments, and shares on content posted by users on social media.

[0106] "User feedback" refers to the evaluations and comments that users provide after using the generated content ideas, and it is used to improve generative artificial intelligence.

[0107] To implement this invention, it is necessary to construct a system in which a user inputs information on topics of interest via a terminal, and a server uses generative artificial intelligence to generate content ideas that are likely to go viral based on that information. The specific system configuration and program processing will be described below.

[0108] System Configuration

[0109] 1. User terminal:

[0110] It has an interface that allows users to input information on topics they are interested in.

[0111] It has a means of communication to send the input information to the server.

[0112] It has a means of displaying content ideas received from the server to the user.

[0113] 2. Server:

[0114] It has the ability to obtain real-time trend information through public APIs. In this case, APIs such as the Twitter API and Facebook Graph API can be used.

[0115] It has the function of generating content ideas based on topic information and trend information using generative artificial intelligence. Examples of generative artificial intelligence that can be used include GPT-4 (registered trademark).

[0116] It has a means of communication to send the generated content ideas to the user's terminal.

[0117] It has features to evaluate the effectiveness of content ideas and collect user feedback.

[0118] It has a function to update the training data of generative artificial intelligence based on feedback.

[0119] Program processing

[0120] User terminal

[0121] 1. The user accesses the device's interface and enters a topic of interest (e.g., "pets," "cooking," "travel," etc.).

[0122] 2. The terminal sends the entered topic information to the server in JSON format. A POST request is used for this communication.

[0123] server

[0124] 1. The server uses publicly available APIs from social media platforms to retrieve trending information in real time, such as currently popular hashtags, trending words, images, and videos. For example, it retrieves "trending topics" from the Twitter API.

[0125] 2. The server sends prompt messages to the generative AI model (e.g., GPT-4) based on the user's topic and trend information. An example of a prompt message is as follows:

[0126] User topic: Pets

[0127] Trend Information: Life with Pets, Today's Dog

[0128] Prompt: Generate posting ideas that you would like to recommend to users.

[0129] 3. Based on this data, the generative artificial intelligence generates specific SNS posting ideas. For example, it might output an idea such as, "It would be good to post a cute photo of your pet taken during your walk this morning and use the hashtag 'Today's Puppy'."

[0130] 4. The server sends the generated content idea to the user's terminal in JSON format. Specifically, it uses a POST request.

[0131] 5. The server monitors the reactions to content posted by users on social media (e.g., the number of likes and comments) for a certain period of time and analyzes that data.

[0132] 6. The server updates the training data of the generative artificial intelligence based on the collected response data, improving the quality of new ideas.

[0133] This allows users to easily obtain content ideas that are in line with trends and have a high potential to go viral, enabling them to engage in social media activities efficiently and effectively.

[0134] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0135] Step 1:

[0136] The user accesses the device's interface and enters a topic of interest.

[0137] Input: Information on topics of interest (e.g., "pets," "cooking," "travel," etc.)

[0138] Output: A screen where topic information is displayed in the input field.

[0139] In terms of the specific operation, a dedicated text box appears on the terminal's input form, and the user enters topic information into it. When the user enters "pets," that information proceeds to the next step.

[0140] Step 2:

[0141] The terminal sends the entered topic information to the server in JSON format.

[0142] Input: Topic information (e.g., "pets")

[0143] Output: Data in JSON format is sent to the server as a POST request.

[0144] Specifically, the terminal converts the input topic information into JSON format and sends it to the server using an HTTP POST request. The topic information is included in the request body.

[0145] Step 3:

[0146] The server uses publicly available APIs from social media platforms to retrieve trending information in real time.

[0147] Input: Topic information (e.g., "pets")

[0148] Output: Trend information (e.g., Life with pets, Today's dog)

[0149] Specifically, the server accesses the public API endpoint to retrieve current trend data. It makes API calls, obtains trend information in JSON format, and analyzes it.

[0150] Step 4:

[0151] The server sends prompt messages to the generating AI model based on the user's topic information and trend information.

[0152] Input: User topic information (e.g., "Pets") and trending information (e.g., Life with pets, Today's dog)

[0153] Output: Prompt message (Example: "User topic: Pets. Trending topics: Life with pets, Today's dog. Generate post ideas to recommend to the user.")

[0154] In terms of specific operation, the server combines topic information and trend information and sends a prompt message like the following to the AI ​​model (e.g., GPT-4). An example of a prompt message is as follows:

[0155] User topic: Pets

[0156] Trend Information: Life with Pets, Today's Dog

[0157] Prompt: Generate posting ideas that you would like to recommend to users.

[0158] Step 5:

[0159] The generative AI model generates specific social media posting ideas based on the prompt text.

[0160] Input: Prompt message (Example: As shown above)

[0161] Output: Content ideas (Example: "Post a cute photo of your pet taken during your morning walk and use the hashtag #TodaysDog")

[0162] In terms of its operation, the generative AI model analyzes the prompt text and generates specific posting ideas based on the topic and trends. As a result, social media posting ideas in text format are output.

[0163] Step 6:

[0164] The server sends the generated content ideas to the user's terminal in JSON format.

[0165] Input: Content idea (Example: As above)

[0166] Output: Data in JSON format is sent to the user's terminal as a POST request.

[0167] Specifically, the server converts the generated content idea into JSON format and sends it to the user's terminal using an HTTP POST request.

[0168] Step 7:

[0169] The device displays received content ideas in a format that is easy for the user to view.

[0170] Input: Content idea in JSON format (e.g., as shown above)

[0171] Output: Content ideas displayed to the user (e.g., "Post a cute photo of your pet taken during your morning walk and use the hashtag #TodaysDog")

[0172] Specifically, the device parses the received JSON data and displays content ideas on the screen in text format. Based on this information, the user posts to social media.

[0173] Step 8:

[0174] A user posts something on social media, and then the server monitors the reactions to that post for a certain period of time.

[0175] Input: User posts and social media reaction data

[0176] Output: Post reaction data (e.g., number of likes, number of comments, etc.)

[0177] Specifically, the server uses APIs and scraping tools to collect user response data to posts from social media over a certain period of time.

[0178] Step 9:

[0179] The server updates the training data for the generated AI model based on the collected reaction data.

[0180] Input: Post reaction data (e.g., number of likes, number of comments, etc.)

[0181] Output: Updated generative AI model

[0182] Specifically, the server analyzes the collected response data and adds it as feedback to the training dataset of the generating AI model, thereby improving the accuracy of idea generation in the future.

[0183] (Application Example 1)

[0184] 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."

[0185] Traditional content generation systems struggled to incorporate trending information and propose viral content. Furthermore, the lack of mechanisms to collect user feedback and optimize the generating AI made it difficult to continuously improve the effectiveness of the generated content. Therefore, there is a need for a system that reflects trending information in real time and improves the quality of content generation based on user feedback.

[0186] 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.

[0187] In this invention, the server includes means for inputting topics of interest to the user, means for transmitting the input topic information to a central processing unit, means for obtaining real-time trend information using a public API, means for generating content ideas based on the acquired trend information and the input topic information using generative artificial intelligence, means for presenting the generated content ideas to the user, and means for collecting user feedback and updating the generative artificial intelligence. This makes it possible to always provide content ideas that reflect the latest trend information and to optimize the generative artificial intelligence based on user feedback.

[0188] "A means for users to input topics they are interested in" refers to an interface or input device that allows users to input specific topics they are interested in.

[0189] "Means for transmitting input topic information to a central processing unit" refers to communication means that have the function of transferring topic information entered by a user to a server or central processing unit via a network.

[0190] "Methods for obtaining real-time trend information using public APIs" refers to functions that use publicly available application programming interfaces provided by services such as social media and news sites to obtain the latest trend information.

[0191] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to learn from large amounts of data and generate new ideas or content in response to specific inputs.

[0192] "Content ideas" refer to specific suggestions or ideas, such as text, images, and videos, that users can share on social media and other digital platforms.

[0193] "Means of presenting to the user" refers to interfaces or devices that have the functionality to display generated content ideas so that users can directly see and review them.

[0194] "Means for collecting user feedback and updating generative artificial intelligence" refers to a function that collects user reactions and evaluations, uses that data as training data for generative artificial intelligence, and improves the accuracy and performance of the model.

[0195] This invention relates to a system that takes a user's input of topics of interest and generates content ideas with a high probability of going viral based on that input. This system is implemented using a user terminal, a server, and generative artificial intelligence.

[0196] System program

[0197] A system for carrying out this invention includes the following configuration and process.

[0198] 1. Processing at the user terminal

[0199] The user terminal provides an interface for users to input topics they are interested in. When a user inputs a topic through the interface, that information is sent to the server. It also has the functionality to display content ideas received from the server.

[0200] 2. Server processing

[0201] The server receives topic information entered from the user's terminal. Using this information, the server accesses public APIs (e.g., Twitter API, Instagram Graph API) to obtain real-time trend information.

[0202] The server uses generative artificial intelligence (e.g., OpenAI's GPT model) to generate content ideas based on acquired trend information and user topic information. The generated content ideas are sent from the server to the user's terminal. Furthermore, feedback from the user is collected and used to update the training data of the generative artificial intelligence model.

[0203] 3. Details of Generative Artificial Intelligence

[0204] Generative artificial intelligence has the ability to learn from large amounts of data and generate new ideas and content in response to specific inputs. This system uses generative AI models such as OpenAI's GPT-4. The model uses acquired trend information and user topic information as prompts to generate specific content ideas.

[0205] Hardware and software to be used

[0206] Smartphones: iOS and Android® devices

[0207] SNS public API: Twitter API, Instagram Graph API

[0208] Generative artificial intelligence: OpenAI's GPT-4 model

[0209] Cloud services: Hosting servers and databases using AWS® or Google® Cloud.

[0210] Communication protocol: Data transmission using HTTP / HTTPS

[0211] Specific example

[0212] For example, if a user enters "post a photo of my pet," the server will operate as follows:

[0213] 1. The server retrieves trending information such as "Today's Dog," "Cute Animals," and "Walking Time" from publicly available APIs of social media platforms.

[0214] 2. Generative artificial intelligence generates posting ideas that are likely to go viral based on acquired trend information and user topic information. In this case, specific ideas such as "It would be good to post a cute photo of your pet taken on your walk this morning with the hashtag 'Today's Puppy'" might be suggested.

[0215] 3. The server sends the generated ideas to the user terminal, and the user terminal displays them.

[0216] 4. Users post ideas based on the suggestions on social media and input the reactions to those posts into the app. This feedback is sent to the server and used as training data for generative artificial intelligence.

[0217] Example of a prompt

[0218] Based on the following topic and trending information, generate social media post ideas that are likely to go viral.

[0219] Topic Information: Pet Photo Submissions

[0220] Trending Information: Today's Dogs, Cute Animals, Walking Time

[0221] This method allows the system to continuously optimize its generative artificial intelligence, enabling it to consistently provide users with high-quality content ideas that reflect the latest trends.

[0222] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0223] Step 1:

[0224] The user enters a topic of interest. The user enters a specific topic (e.g., "Posting photos of pets") into a text box via the smartphone application interface. This input data is then sent directly to the server in JSON format.

[0225] Step 2:

[0226] The server receives the input topic information. The server receives the topic information sent from the user terminal and temporarily stores this information in its internal database. The input is "Pet photo submission," and the output is the related data stored internally.

[0227] Step 3:

[0228] This system uses public APIs to retrieve real-time trend information. The server calls public APIs of social media platforms (e.g., Twitter API, Instagram Graph API) to obtain the latest trend information (e.g., "Today's Dog," "Cute Animals," "Walking Time"). The input is an API call request, and the output is real-time trend information.

[0229] Step 4:

[0230] This system uses generative artificial intelligence to generate content ideas. The server uses acquired trend information and user-entered topic information as input data. It inputs prompts into a generative AI model (e.g., OpenAI's GPT-4) to generate specific SNS posting ideas. The input consists of pairs of topic and trend information, and the output is the generated content idea.

[0231] Examples of specific prompt messages:

[0232] Based on the following topic and trending information, generate social media post ideas that are likely to go viral.

[0233] Topic Information: Pet Photo Submissions

[0234] Trending Information: Today's Dogs, Cute Animals, Walking Time

[0235] Step 5:

[0236] The generated content ideas are presented to the user. The server converts the generated ideas back into JSON format and sends them to the user's terminal. The user's terminal parses the received JSON data and presents it to the user in an easy-to-read format. The input is the generated content idea data, and the output is the specific content idea displayed on the user's terminal screen.

[0237] Step 6:

[0238] Users review the suggested content ideas and post them on social media. Based on the presented content ideas, users post on their own social media accounts. At this stage, users can also customize their posts.

[0239] Step 7:

[0240] The system collects reaction data after a post is made. Users input reactions to their posts (e.g., number of likes, comments, shares, etc.) into the app. The user's device sends this feedback data to the server in JSON format. The input is the user's feedback data, and the output is a feedback record stored on the server.

[0241] Step 8:

[0242] The training data for the generative artificial intelligence is updated. Based on the collected feedback data, the server updates the training dataset for the generative AI to aid in future idea generation. This feedback loop continuously improves the generated content ideas. The input is the feedback data, and the output is the updated AI model. This entire process improves the overall performance and accuracy of the system.

[0243] 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.

[0244] To implement this invention, a system is provided that allows users to input information on topics they are interested in, and then combines an emotion engine and generative artificial intelligence to generate ideas for social media posts.

[0245] System Configuration

[0246] 1. User terminal:

[0247] It has an interface where users can input information on topics they are interested in.

[0248] It features an emotion engine that recognizes emotions from the user's voice and text.

[0249] It has a means of communication that sends input information and recognized emotional states to a server.

[0250] It has a means of displaying content ideas from the server to the user.

[0251] 2. Server:

[0252] It has the ability to obtain real-time trend information through a public API.

[0253] The emotion engine recognizes the user's emotional state and topic information, which are then passed as input data to the generative artificial intelligence.

[0254] The generative artificial intelligence has the ability to generate content ideas based on trend information and emotional states.

[0255] It has a means of communication to send the generated content ideas to the user's terminal.

[0256] It has features to evaluate the effectiveness of content ideas and collect user feedback.

[0257] It has a function to update the training data of generative artificial intelligence based on feedback.

[0258] Program processing

[0259] The system provides content ideas with a high probability of going viral through the following processing steps.

[0260] 1. Enter topic information:

[0261] The user accesses the device's interface and enters topic information for a social media post.

[0262] 2. Recognition of emotions:

[0263] When a user is typing or using voice input, the emotion engine built into the device recognizes the user's emotional state from the text or voice. For example, if a user expresses emotions such as "happy" or "sad," the emotion engine analyzes that.

[0264] 3. Sending information:

[0265] The device converts topic information and recognized emotional states into JSON data and sends it to the server.

[0266] 4. Obtaining trend information:

[0267] The server uses the public APIs of social media platforms to retrieve current trending information. This includes data such as popular hashtags, trending words, images, and videos.

[0268] 5. Generating content ideas:

[0269] The server provides the generative artificial intelligence (AI) with topic information, emotional state, and acquired trend information as input data. Based on this data, the AI ​​generates content ideas that match the user's emotions.

[0270] 6. Idea presentation:

[0271] The server then sends the generated content ideas back to the user's terminal in an appropriate format, such as JSON.

[0272] The system analyzes the data received by the user's device and displays it in a user-friendly format. For example, if the emotion is recognized as "happy," ideas containing bright and positive expressions will be presented.

[0273] 7. Posting on social media:

[0274] The user checks the displayed content ideas and posts them on the SNS. The text and images can also be customized as needed.

[0275] 8. Collection of Reaction Data for Posts:

[0276] The server collects the reaction data of the posts on the SNS (e.g., number of likes, number of comments, number of shares, etc.).

[0277] 9. Learning of Generative AI:

[0278] The server analyzes the reaction data it has collected and saves it as learning data for the generative AI. This data is used to improve the generation accuracy when generating ideas next time.

[0279] Specific Example

[0280] For example, when the user inputs "I want to post a photo of my pet" and the emotion engine on the terminal recognizes the user's emotional state as "happy", the server operates as follows.

[0281] 1. The server obtains popular hashtags such as "Life with Pets" and "Today's Dog" from the public API of the SNS.

[0282] 2. The generative AI generates a specific idea such as "Let's post a cute photo of the pet taken during this morning's walk together with the hashtag 'Today's Dog'. Share the happy moment and have fun with others!"

[0283] 3. The server sends this generated idea to the user terminal, and the terminal displays it to the user.

[0284] Based on the proposed idea, the user posts on the SNS, and the server analyzes the reaction of the post and reflects it in the next idea generation. By using the emotion engine, it becomes possible to provide more effective content ideas that fit the user's emotions.

[0285] The processing flow will be described below.

[0286] Step 1:

[0287] The user accesses the interface of the terminal and enters topic information (e.g., "pet", "cooking", "travel", etc.) to be posted on the SNS. The operation is performed using an input form or a voice input function.

[0288] Step 2:

[0289] When the user is inputting or entering topic information by voice, the emotion engine installed on the terminal recognizes the user's emotional state (e.g., "happy", "sad", "excited", etc.) from the text or voice.

[0290] Step 3:

[0291] The emotion engine converts the recognized user's emotional state into data in JSON format together with the topic information. The terminal sends this data to the server.

[0292] Step 4:

[0293] The server analyzes the received topic information and the user's emotional state, and uses the public API of the SNS (e.g., Twitter API, Instagram API, etc.) to obtain real-time trend information. What is obtained is popular hashtags, trend words, images, videos, etc.

[0294] Step 5:

[0295] The server passes the obtained trend information, the topic information and the emotional state sent from the user to the generative artificial intelligence as input data. The generative artificial intelligence analyzes based on these data and generates specific SNS posting ideas according to the user's emotional state.

[0296] Step 6:

[0297] The server receives specific posting ideas generated by a generative artificial intelligence and sends them to the user's terminal in an appropriate format (e.g., JSON). These ideas include relevant hashtags, text, and recommended images.

[0298] Step 7:

[0299] The device analyzes the content ideas it receives and displays them in a user-friendly format. For example, if the emotion is recognized as "happy," ideas containing bright and positive expressions will be presented.

[0300] Step 8:

[0301] Users can review the displayed content ideas and post them to their own social media accounts. They can also customize the text and images as needed.

[0302] Step 9:

[0303] After a user posts on social media, the server uses a public API to collect reaction data to that post (e.g., number of likes, comments, shares, etc.).

[0304] Step 10:

[0305] The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This data is then used to improve the generation accuracy in the next idea generation cycle.

[0306] For example, when the user inputs "I want to post a photo of my pet" and the emotion engine recognizes it as "fun", the server retrieves popular hashtags such as "Life with Pets" or "Today's Pomeranian" from the public API of the SNS. Then, the generative artificial intelligence generates a specific idea like "Let's post a cute photo of my pet taken during this morning's walk together with the hashtag 'Today's Pomeranian'. Share the fun moment and enjoy it with others!". The server sends this idea to the user terminal, and the user posts it on the SNS. Through this series of processes, the user can obtain effective content ideas that fit their emotional state.

[0307] (Example 2)

[0308] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0309] In SNS posting, there are problems that it is difficult for users to generate attractive and trendy content. Also, it is an issue to provide content ideas that fit the user's emotions. Furthermore, there is a need for a method to analyze the reaction of the generated content and improve the quality of the next idea generation.

[0310] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0311] In this invention, the server includes means for inputting topics of interest to the user, means for transmitting the input topic information to a central processing unit, means for recognizing the user's emotional state from input or voice input, means for obtaining real-time trend information using a public API, means for generating content ideas using generative artificial intelligence based on the acquired trend information, the input topic information, and the recognized emotional state, means for presenting the generated content ideas to the user, means for evaluating the effectiveness of the generated content ideas, and means for collecting reaction data after posting the proposed content ideas to update the learning data of the generative artificial intelligence. This makes it possible to generate attractive content ideas based on the user's emotions and trends.

[0312] "Topic information" refers to information about themes and topics that users are interested in for their social media posts.

[0313] A "central processing unit" is a computer device that receives data within a system, analyzes it, and processes it in cooperation with other devices.

[0314] "Emotional state" refers to the emotional state (for example, happy, sad, excited, etc.) recognized from the user's input or voice.

[0315] A "public API" is an official interface provided for integrating with external applications and services, serving as a means of retrieving and manipulating data.

[0316] "Trending information" refers to data such as hashtags, keywords, images, and videos that are currently popular on social media and the internet.

[0317] "Generative artificial intelligence" refers to artificial intelligence models that generate new content and ideas based on collected data and input information.

[0318] "Content ideas" refer to suggestions regarding the specific content and format of social media posts that users can actually use to create their own posts.

[0319] "Evaluating effectiveness" is the process of assessing how successful the generated content ideas were on social media (e.g., number of likes, comments, shares).

[0320] "Response data" refers to data on user feedback (e.g., likes, comments, shares, etc.) on social media posts.

[0321] "Updating the learning data" is the process of retraining the generative artificial intelligence based on the collected response data to improve the accuracy of content idea generation in the future.

[0322] As an embodiment of this invention, a system is provided that allows a user to input information on topics of interest and generates ideas for social media posts by combining an emotion engine and generative artificial intelligence. This system is configured using a user terminal and a server.

[0323] User terminal

[0324] The user terminal has the following features:

[0325] Topic Information Input Interface: Includes text boxes and voice input functions for users to input information on topics of interest.

[0326] Emotion Engine: Recognizes emotions from the user's voice or text. For example, if a user says, "I'm in a happy mood today," the emotion "happy" is recognized.

[0327] Communication method: The input topic information and recognized emotional state are converted into JSON format data and sent to the server.

[0328] Displaying content ideas: Analyzes content ideas received from the server and displays them in a user-friendly format.

[0329] server

[0330] The server has the following features:

[0331] Method for obtaining trend information: Real-time trend information is obtained using public APIs of social media platforms. This includes popular hashtags, trending words, and related images and videos.

[0332] Generative Artificial Intelligence: Generates content ideas based on the user's emotional state recognized by the emotion engine, along with topic and trend information. This process is executed using a generative AI model.

[0333] Communication method: The generated content idea is converted to JSON format and sent to the user's terminal.

[0334] Feedback Collection and Learning: Collect reaction data (e.g., number of likes, comments, shares, etc.) to user-submitted social media content and update it as training data for generative artificial intelligence.

[0335] Specific example

[0336] For example, if a user types "I want to post a picture of my pet" and the device's emotion engine recognizes the emotion "happy," the following steps are executed: The server uses the public APIs of social media to retrieve popular hashtags such as "life with pets" and "dog of the day." Based on this information, the generative AI generates a specific idea such as, "Let's post a cute picture of your pet taken on this morning's walk, along with the hashtag 'dog of the day.' Share the fun moment and enjoy it with others!" The server sends this information to the user's device, which then presents it to the user. The user makes the post, and the server analyzes the reaction to help with future generation.

[0337] Example of a prompt

[0338] Enter "User wants to post a photo of their pet" and, if a positive emotion is detected, generate recommended social media posting ideas. The current trending hashtags are "Today's Dog" and "Life with Pets".

[0339] In this way, the system generates content ideas with a high potential to go viral based on user emotions and trend information, and supports effective posting on social media.

[0340] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0341] Program processing flow

[0342] Step 1: Enter topic information

[0343] Specific actions:

[0344] The user accesses their device and enters "I want to post a picture of my pet" as the topic for their SNS post.

[0345] Input: User topic information.

[0346] Output: Topic information is recognized by the terminal.

[0347] Step 2: Recognizing Emotions

[0348] Specific actions:

[0349] When a user is typing or using voice input, the device's built-in emotion engine recognizes emotions such as "happy." For example, if the user says, "I'm in a happy mood today," the voice is captured and analyzed.

[0350] Input: User voice or text input.

[0351] Output: Recognized emotion (e.g., "happy").

[0352] Step 3: Submit information

[0353] Specific actions:

[0354] The topic information and recognized emotional state are converted into JSON format data, which the device then sends to the server over the network.

[0355] Input: Topic information and recognized emotional state.

[0356] Output: Data in JSON format is sent to the server.

[0357] Step 4: Obtaining trend information

[0358] Specific actions:

[0359] The server uses public APIs from social networking services to retrieve trending information such as "Life with Pets" and "Today's Dog." It accesses the APIs and saves the necessary data to an internal database.

[0360] Input: Request to access the SNS public API.

[0361] Output: Acquired trend information.

[0362] Step 5: Generating Content Ideas

[0363] Specific actions:

[0364] The server provides topic information, sentiment, and trend information as input data to the generative artificial intelligence (AI). The AI ​​model processes this information and generates specific content ideas that fit the user's sentiment. For example, it might generate an idea like, "Let's post a cute photo of our pet that we took on our walk this morning, along with the hashtag #TodaysDog."

[0365] Input: Topic information, sentiment status, trend information.

[0366] Output: Generated content ideas.

[0367] Step 6: Presenting Ideas

[0368] Specific actions:

[0369] The generated content ideas are converted into JSON format, and the server sends this to the user's terminal. The terminal parses the received data and displays it in a user-friendly format.

[0370] Input: Generated content idea.

[0371] Output: Content ideas to be displayed on the user's terminal.

[0372] Step 7: Posting on social media

[0373] Specific actions:

[0374] Users review the displayed content ideas, customize the text and images as needed, and post them on social media.

[0375] Input: A suggested content idea.

[0376] Output: Post to social media.

[0377] Step 8: Collecting post response data

[0378] Specific actions:

[0379] The server uses SNS APIs to collect reaction data to posts (e.g., number of likes, comments, shares, etc.).

[0380] Input: Request to access the SNS API.

[0381] Output: Collected reaction data.

[0382] Step 9: Updating the training data for generative artificial intelligence.

[0383] Specific actions:

[0384] The server analyzes the collected reaction data and updates the training data for the generative artificial intelligence. This improves the accuracy of the next generation.

[0385] Input: Collected response data.

[0386] Output: Updated training data for generative artificial intelligence.

[0387] (Application Example 2)

[0388] 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".

[0389] Traditional content generation systems did not adequately consider user emotions and trend information, making it difficult to generate content ideas that would generate higher engagement. Furthermore, advertising and marketing require the generation of effective advertising ideas that reflect user emotional states and are based on real-time trend information. Additionally, the lack of mechanisms to evaluate the effectiveness of generated content and feed that feedback back as learning data has made continuous improvement of generative artificial intelligence difficult.

[0390] 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. In this invention, the server includes means for transmitting topic information including emotional state to a central processing device, means for acquiring real-time trend information using a public API, and means for generating content ideas based on the acquired trend information, emotional state, and input topic information using generative artificial intelligence. This enables highly engaging advertising and marketing by generating content ideas that reflect the user's emotional state and real-time trend information.

[0391] A "user" is an individual or group that uses the system to generate content ideas.

[0392] A "topic" is information about subjects or themes that users are interested in.

[0393] "Emotional state" refers to information that indicates the user's current psychological state or mood, and is recognized by the emotion engine.

[0394] A "central processing unit" is a server that processes input topic information and sentiment states, and generates content ideas based on that information.

[0395] A "public API" is an application programming interface that is made publicly available to allow external access to specific functions or data.

[0396] "Real-time trend information" refers to topics and data that are attracting attention in society and the market at the present time.

[0397] "Generative artificial intelligence" is an artificial intelligence technology that generates new content ideas based on input data.

[0398] A "content idea" is a concrete concept of content that users can use on social media, in advertisements, etc.

[0399] "Presentation" refers to visually or audibly informing the user of a generated content idea.

[0400] This invention is a system that generates content ideas that reflect real-time trend information based on the user's interests and emotional state. To achieve this, the following program and hardware configuration are used.

[0401] Hardware and software to use

[0402] 1. Smartphone: A device in which the user inputs topic information through an interface and recognizes the emotional state using an emotion engine.

[0403] 2. Server: This is a central processing unit that receives and processes topic information and emotional states, and generates content ideas using generative artificial intelligence.

[0404] 3. Emotion Engine: Utilizes the Google Cloud Natural Language API to analyze user text and voice data and recognize their emotional state.

[0405] 4. Generative Artificial Intelligence: Using OpenAI GPT-3 (registered trademark), new content ideas are generated based on input topics, sentiment states, and trend data.

[0406] 5. Public APIs: Real-time trend information is obtained using the Twitter API and Facebook Graph API.

[0407] System operation

[0408] Input and Recognition

[0409] The user enters topics of interest via text or voice on a smartphone app. The emotion engine analyzes this input data and recognizes the user's current emotional state (e.g., "excited"). The topic information and emotional state are then sent to the server in JSON format.

[0410] Acquisition of trend data

[0411] The server uses public APIs (Twitter API, Facebook Graph API) to retrieve real-time trend information. This includes popular hashtags and trending words.

[0412] Content Idea Generation

[0413] The server's generative artificial intelligence (OpenAI GPT-3) generates effective content ideas that match the user's emotions based on acquired topic information, emotional states, and trend information. For example, if the emotion is "excited," the generated ideas will include positive and impactful expressions.

[0414] Presentation and Feedback

[0415] The generated content ideas are sent back to the smartphone in JSON format and displayed visually to the user. The user then posts to social media based on the suggested ideas. The server collects reaction data to these posts (e.g., number of likes, comments, shares, etc.) and uses this as training data for generative artificial intelligence.

[0416] Examples of specific cases and prompt statements

[0417] For example, if a user enters "new product campaign" and the emotion engine recognizes the emotion "excited," an example of a prompt to the generative AI would be as follows:

[0418] Users are excited about the topic of "new product campaigns." Popular hashtags include "new product," "campaign," and "exciting new product." Based on this information, please generate positive and impactful advertising ideas.

[0419] The advertising ideas generated in this way can take the form of specific content proposals, such as, "Let's liven up the campaign by posting photos of the new product with a hashtag like 'Exciting New Product'!"

[0420] As described above, the present invention enables highly engaging advertising and marketing by generating content ideas that reflect the user's emotional state and real-time trend information.

[0421] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0422] Step 1: The user opens the app on their smartphone and enters information on a topic of interest (e.g., "New Product Campaign") via text or voice. The entered topic information is sent to the emotion engine.

[0423] Step 2: The emotion engine (Google Cloud Natural Language API) analyzes the input text and audio data to recognize the user's emotional state (e.g., "excited"). This process outputs the emotional state as the analysis result.

[0424] Step 3: The device converts the recognized emotional state and topic information into JSON format data and sends it to the server. The input is topic information and emotional state, and the output is JSON data that integrates these.

[0425] Step 4: The server parses the received JSON data and extracts topic information and sentiment status. Simultaneously, it retrieves real-time trend information using public APIs (Twitter API, Facebook Graph API). These API calls output popular hashtags and trending words.

[0426] Step 5: The server passes the acquired topic information, sentiment status, and trend information as input data to the generative artificial intelligence (OpenAI GPT-3) to generate content ideas. The input consists of topic information, sentiment status, and trend information, and the generated content ideas are output.

[0427] Step 6: Convert the generated content idea to JSON format and send it back to the terminal. The input is the generated content idea, and the output is data in JSON format.

[0428] Step 7: Visually present the content ideas received by the device to the user. For example, a positive advertising idea that evokes the emotion of "excitement" is displayed on the user's smartphone screen.

[0429] Step 8: The user reviews the suggested content idea, customizes it as needed, and posts it to social media. After posting, the server collects reaction data (e.g., number of likes, comments, shares).

[0430] Step 9: The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This improves the accuracy of content idea generation in the future.

[0431] Through the specific actions of each step, the present invention enables the efficient generation of highly engaging content ideas that reflect the user's emotional state and trend information.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] [Second Embodiment]

[0436] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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".

[0448] To implement this invention, a system is constructed in which a user inputs information on topics of interest via a terminal, and a server uses generative artificial intelligence to generate content ideas that are likely to go viral based on that information.

[0449] Specifically, the following system configuration and program processing are required.

[0450] System Configuration

[0451] 1. User terminal:

[0452] It has an interface where users can input information on topics they are interested in.

[0453] It has a means of communication to receive input information and send it to the server.

[0454] It has a means of displaying content ideas received from the server.

[0455] 2. Server:

[0456] It has the ability to obtain real-time trend information through a public API.

[0457] It has the ability to generate content ideas based on topic information and trend information using generative artificial intelligence.

[0458] It has a means of communication to send the generated content ideas to the user's terminal.

[0459] It has features to evaluate the effectiveness of content ideas and collect user feedback.

[0460] It has a function to update the training data of generative artificial intelligence based on feedback.

[0461] Program processing

[0462] The following steps are necessary for a user's device to generate ideas for social media posts.

[0463] 1. Enter topic information:

[0464] The user accesses the device's interface and enters topics of interest such as "pets," "cooking," or "travel."

[0465] 2. Sending information:

[0466] The terminal sends this topic information to the server. This topic information is sent in JSON format or another appropriate data format.

[0467] 3. Obtaining trend information:

[0468] The server retrieves trending information such as currently popular hashtags, trending words, images, and videos in real time through the public APIs of social media platforms.

[0469] 4. Generating content ideas:

[0470] The server uses acquired trend information and user topic information as input data to run a generative artificial intelligence.

[0471] Based on this data, the generative artificial intelligence generates specific social media posting ideas. The proposed ideas are structured in the form of text, images, and videos.

[0472] 5. Idea presentation:

[0473] The server then sends the generated content ideas back to the user's terminal in an appropriate format, such as JSON.

[0474] The user's terminal analyzes the received data and presents it to the user in an easy-to-understand format.

[0475] Users review the suggested content ideas and post them to their own social media accounts.

[0476] Specific example

[0477] For example, if a user enters "I want to post a picture of my pet," the server will operate as follows:

[0478] 1. The server retrieves popular hashtags such as "Life with pets" and "Today's dog" from the public APIs of social media platforms.

[0479] 2. Generative artificial intelligence generates a specific idea such as, "It would be good to post a cute photo of your pet taken during your walk this morning and use the hashtag 'Today's Puppy'."

[0480] 3. The server sends this generated idea to the user's terminal, and the terminal displays it to the user.

[0481] Users post the suggested ideas to social media, and the server analyzes the reactions to those posts (number of likes, comments, etc.) to inform the generation of future ideas. This process ensures that users always have access to content ideas that are in line with current trends and have a high potential to go viral.

[0482] The following describes the processing flow.

[0483] Step 1:

[0484] The user accesses the device interface and enters topic information they want to post to social media (e.g., "pets," "cooking," "travel," etc.). The information entered by the user is sent to the server in the next step.

[0485] Step 2:

[0486] The terminal converts the topic information entered by the user into JSON format data and sends it to the server. At this point, the terminal checks the integrity of the data structure and verifies that there are no errors.

[0487] Step 3:

[0488] The server analyzes the topic information it receives and uses public APIs (e.g., Twitter API, Instagram API) to retrieve real-time trend information. This includes currently popular hashtags, trending words, images, videos, and more.

[0489] Step 4:

[0490] After the server acquires trend information, it passes the topic information and the acquired trend information as input data to a generative artificial intelligence (e.g., a natural language processing model). Based on this data, the generative artificial intelligence generates specific SNS posting ideas to suggest to the user.

[0491] Step 5:

[0492] The server receives specific posting ideas generated by a generative artificial intelligence and sends them to the user's terminal in JSON format or another appropriate data format. These ideas include relevant hashtags, text, and recommended images.

[0493] Step 6:

[0494] The device analyzes content ideas received from the server and displays them in a user-friendly format. The user reviews the displayed ideas and decides whether or not to post them on social media.

[0495] Step 7:

[0496] Users post content ideas suggested to them on social media. Users can modify the generated text and images as needed before posting.

[0497] Step 8:

[0498] The server collects reaction data to posts on social media (e.g., number of likes, comments, shares, etc.). This data collection again utilizes a public API.

[0499] Step 9:

[0500] The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This data will be used in the next idea generation process to improve the generation accuracy.

[0501] Step 10:

[0502] Users review the feedback and use it to improve future posts. They then use the system again for their next post to get content ideas based on the latest trends.

[0503] The above is the specific processing flow of the viral posting idea suggestion system using generative artificial intelligence. Through this system, users can always create effective social media posts based on the latest trend information.

[0504] (Example 1)

[0505] 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."

[0506] Traditional social media posting support systems lack the functionality to automatically generate effective content ideas for topics of user interest, making it difficult for users to create trending content with a high potential for virality. Furthermore, they lack mechanisms to evaluate the effectiveness of generated content and incorporate those evaluations into future content creation. This results in a problem where the efficiency and effectiveness of users' social media activities are not sufficiently improved.

[0507] 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.

[0508] In this invention, the server includes means for generating specific prompt sentences based on user input and passing them to a generative artificial intelligence system; means for monitoring the reactions to the user's SNS posts for a certain period and analyzing the data; and means for evaluating the effectiveness of the generated content ideas. As a result, users can easily obtain content ideas that are in line with trends and have a high potential to go viral, and by evaluating their effectiveness and reflecting it in the generation of future content, they can improve the efficiency and effectiveness of their SNS activities.

[0509] A "user terminal" is a device that allows users to input information on topics they are interested in and exchange data with the server.

[0510] A "central processing unit" is a system that receives data transmitted from user terminals, acquires trend information through public APIs, and generates content ideas using generative artificial intelligence.

[0511] A "public API" is an application programming interface that is made publicly accessible to external developers and services, and in this case, it is used to obtain real-time trend information from social media.

[0512] "Generative artificial intelligence" refers to an artificial intelligence system that receives data as input and generates content ideas using machine learning algorithms.

[0513] A "prompt" is a specific instruction or phrase given to a generative artificial intelligence system to generate content ideas.

[0514] "Content ideas" refer to specific ideas or suggestions that users can post on social media, and are composed of various forms such as text, images, and videos.

[0515] "SNS reaction data" refers to user reaction data such as the number of "likes," comments, and shares on content posted by users on social media.

[0516] "User feedback" refers to the evaluations and comments that users provide after using the generated content ideas, and it is used to improve generative artificial intelligence.

[0517] To implement this invention, it is necessary to construct a system in which a user inputs information on topics of interest via a terminal, and a server uses generative artificial intelligence to generate content ideas that are likely to go viral based on that information. The specific system configuration and program processing will be described below.

[0518] System Configuration

[0519] 1. User terminal:

[0520] It has an interface that allows users to input information on topics they are interested in.

[0521] It has a means of communication to send the input information to the server.

[0522] It has a means of displaying content ideas received from the server to the user.

[0523] 2. Server:

[0524] It has the ability to obtain real-time trend information through public APIs. In this case, APIs such as the Twitter API and Facebook Graph API can be used.

[0525] It has the function of generating content ideas based on topic information and trend information using generative artificial intelligence. GPT-4 and other similar generative AI can be used.

[0526] It has a means of communication to send the generated content ideas to the user's terminal.

[0527] It has features to evaluate the effectiveness of content ideas and collect user feedback.

[0528] It has a function to update the training data of generative artificial intelligence based on feedback.

[0529] Program processing

[0530] User terminal

[0531] 1. The user accesses the device's interface and enters a topic of interest (e.g., "pets," "cooking," "travel," etc.).

[0532] 2. The terminal sends the entered topic information to the server in JSON format. A POST request is used for this communication.

[0533] server

[0534] 1. The server uses publicly available APIs from social media platforms to retrieve trending information in real time, such as currently popular hashtags, trending words, images, and videos. For example, it retrieves "trending topics" from the Twitter API.

[0535] 2. The server sends prompt messages to the generative AI model (e.g., GPT-4) based on the user's topic and trend information. An example of a prompt message is as follows:

[0536] User topic: Pets

[0537] Trend Information: Life with Pets, Today's Dog

[0538] Prompt: Generate posting ideas that you would like to recommend to users.

[0539] 3. Based on this data, the generative artificial intelligence generates specific SNS posting ideas. For example, it might output an idea such as, "It would be good to post a cute photo of your pet taken during your walk this morning and use the hashtag 'Today's Puppy'."

[0540] 4. The server sends the generated content idea to the user's terminal in JSON format. Specifically, it uses a POST request.

[0541] 5. The server monitors the reactions to content posted by users on social media (e.g., the number of likes and comments) for a certain period of time and analyzes that data.

[0542] 6. The server updates the training data of the generative artificial intelligence based on the collected response data, improving the quality of new ideas.

[0543] This allows users to easily obtain content ideas that are in line with trends and have a high potential to go viral, enabling them to engage in social media activities efficiently and effectively.

[0544] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0545] Step 1:

[0546] The user accesses the device's interface and enters a topic of interest.

[0547] Input: Information on topics of interest (e.g., "pets," "cooking," "travel," etc.)

[0548] Output: A screen where topic information is displayed in the input field.

[0549] In terms of the specific operation, a dedicated text box appears on the terminal's input form, and the user enters topic information into it. When the user enters "pets," that information proceeds to the next step.

[0550] Step 2:

[0551] The terminal sends the entered topic information to the server in JSON format.

[0552] Input: Topic information (e.g., "pets")

[0553] Output: Data in JSON format is sent to the server as a POST request.

[0554] Specifically, the terminal converts the input topic information into JSON format and sends it to the server using an HTTP POST request. The topic information is included in the request body.

[0555] Step 3:

[0556] The server uses publicly available APIs from social media platforms to retrieve trending information in real time.

[0557] Input: Topic information (e.g., "pets")

[0558] Output: Trend information (e.g., Life with pets, Today's dog)

[0559] Specifically, the server accesses the public API endpoint to retrieve current trend data. It makes API calls, obtains trend information in JSON format, and analyzes it.

[0560] Step 4:

[0561] The server sends prompt messages to the generating AI model based on the user's topic information and trend information.

[0562] Input: User topic information (e.g., "Pets") and trending information (e.g., Life with pets, Today's dog)

[0563] Output: Prompt message (Example: "User topic: Pets. Trending topics: Life with pets, Today's dog. Generate post ideas to recommend to the user.")

[0564] In terms of specific operation, the server combines topic information and trend information and sends a prompt message like the following to the AI ​​model (e.g., GPT-4). An example of a prompt message is as follows:

[0565] User topic: Pets

[0566] Trend Information: Life with Pets, Today's Dog

[0567] Prompt: Generate posting ideas that you would like to recommend to users.

[0568] Step 5:

[0569] The generative AI model generates specific social media posting ideas based on the prompt text.

[0570] Input: Prompt message (Example: As shown above)

[0571] Output: Content ideas (Example: "Post a cute photo of your pet taken during your morning walk and use the hashtag #TodaysDog")

[0572] In terms of its operation, the generative AI model analyzes the prompt text and generates specific posting ideas based on the topic and trends. As a result, social media posting ideas in text format are output.

[0573] Step 6:

[0574] The server sends the generated content ideas to the user's terminal in JSON format.

[0575] Input: Content idea (Example: As above)

[0576] Output: Data in JSON format is sent to the user's terminal as a POST request.

[0577] Specifically, the server converts the generated content idea into JSON format and sends it to the user's terminal using an HTTP POST request.

[0578] Step 7:

[0579] The device displays received content ideas in a format that is easy for the user to view.

[0580] Input: Content idea in JSON format (e.g., as shown above)

[0581] Output: Content ideas displayed to the user (e.g., "Post a cute photo of your pet taken during your morning walk and use the hashtag #TodaysDog")

[0582] Specifically, the device parses the received JSON data and displays content ideas on the screen in text format. Based on this information, the user posts to social media.

[0583] Step 8:

[0584] A user posts something on social media, and then the server monitors the reactions to that post for a certain period of time.

[0585] Input: User posts and social media reaction data

[0586] Output: Post reaction data (e.g., number of likes, number of comments, etc.)

[0587] Specifically, the server uses APIs and scraping tools to collect user response data to posts from social media over a certain period of time.

[0588] Step 9:

[0589] The server updates the training data for the generated AI model based on the collected reaction data.

[0590] Input: Post reaction data (e.g., number of likes, number of comments, etc.)

[0591] Output: Updated generative AI model

[0592] Specifically, the server analyzes the collected response data and adds it as feedback to the training dataset of the generating AI model, thereby improving the accuracy of idea generation in the future.

[0593] (Application Example 1)

[0594] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0595] Traditional content generation systems struggled to incorporate trending information and propose viral content. Furthermore, the lack of mechanisms to collect user feedback and optimize the generating AI made it difficult to continuously improve the effectiveness of the generated content. Therefore, there is a need for a system that reflects trending information in real time and improves the quality of content generation based on user feedback.

[0596] 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.

[0597] In this invention, the server includes means for inputting topics of interest to the user, means for transmitting the input topic information to a central processing unit, means for obtaining real-time trend information using a public API, means for generating content ideas based on the acquired trend information and the input topic information using generative artificial intelligence, means for presenting the generated content ideas to the user, and means for collecting user feedback and updating the generative artificial intelligence. This makes it possible to always provide content ideas that reflect the latest trend information and to optimize the generative artificial intelligence based on user feedback.

[0598] "A means for users to input topics they are interested in" refers to an interface or input device that allows users to input specific topics they are interested in.

[0599] "Means for transmitting input topic information to a central processing unit" refers to communication means that have the function of transferring topic information entered by a user to a server or central processing unit via a network.

[0600] "Methods for obtaining real-time trend information using public APIs" refers to functions that use publicly available application programming interfaces provided by services such as social media and news sites to obtain the latest trend information.

[0601] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to learn from large amounts of data and generate new ideas or content in response to specific inputs.

[0602] "Content ideas" refer to specific suggestions or ideas, such as text, images, and videos, that users can share on social media and other digital platforms.

[0603] "Means of presenting to the user" refers to interfaces or devices that have the functionality to display generated content ideas so that users can directly see and review them.

[0604] "Means for collecting user feedback and updating generative artificial intelligence" refers to a function that collects user reactions and evaluations, uses that data as training data for generative artificial intelligence, and improves the accuracy and performance of the model.

[0605] This invention relates to a system that takes a user's input of topics of interest and generates content ideas with a high probability of going viral based on that input. This system is implemented using a user terminal, a server, and generative artificial intelligence.

[0606] System program

[0607] A system for carrying out this invention includes the following configuration and process.

[0608] 1. Processing at the user terminal

[0609] The user terminal provides an interface for users to input topics they are interested in. When a user inputs a topic through the interface, that information is sent to the server. It also has the functionality to display content ideas received from the server.

[0610] 2. Server processing

[0611] The server receives topic information entered from the user's terminal. Using this information, the server accesses public APIs (e.g., Twitter API, Instagram Graph API) to obtain real-time trend information.

[0612] The server uses generative artificial intelligence (e.g., OpenAI's GPT model) to generate content ideas based on acquired trend information and user topic information. The generated content ideas are sent from the server to the user's terminal. Furthermore, feedback from the user is collected and used to update the training data of the generative artificial intelligence model.

[0613] 3. Details of Generative Artificial Intelligence

[0614] Generative artificial intelligence has the ability to learn from large amounts of data and generate new ideas and content in response to specific inputs. This system uses generative AI models such as OpenAI's GPT-4. The model uses acquired trend information and user topic information as prompts to generate specific content ideas.

[0615] Hardware and software to be used

[0616] Smartphones: iOS and Android devices

[0617] SNS public API: Twitter API, Instagram Graph API

[0618] Generative artificial intelligence: OpenAI's GPT-4 model

[0619] Cloud services: Use AWS or Google Cloud to host servers and databases.

[0620] Communication protocol: Data transmission using HTTP / HTTPS

[0621] Specific example

[0622] For example, if a user enters "post a photo of my pet," the server will operate as follows:

[0623] 1. The server retrieves trending information such as "Today's Dog," "Cute Animals," and "Walking Time" from publicly available APIs of social media platforms.

[0624] 2. Generative artificial intelligence generates posting ideas that are likely to go viral based on acquired trend information and user topic information. In this case, specific ideas such as "It would be good to post a cute photo of your pet taken on your walk this morning with the hashtag 'Today's Puppy'" might be suggested.

[0625] 3. The server sends the generated ideas to the user terminal, and the user terminal displays them.

[0626] 4. Users post ideas based on the suggestions on social media and input the reactions to those posts into the app. This feedback is sent to the server and used as training data for generative artificial intelligence.

[0627] Example of a prompt

[0628] Based on the following topic and trending information, generate social media post ideas that are likely to go viral.

[0629] Topic Information: Pet Photo Submissions

[0630] Trending Information: Today's Dogs, Cute Animals, Walking Time

[0631] This method allows the system to continuously optimize its generative artificial intelligence, enabling it to consistently provide users with high-quality content ideas that reflect the latest trends.

[0632] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0633] Step 1:

[0634] The user enters a topic of interest. The user enters a specific topic (e.g., "Posting photos of pets") into a text box via the smartphone application interface. This input data is then sent directly to the server in JSON format.

[0635] Step 2:

[0636] The server receives the input topic information. The server receives the topic information sent from the user terminal and temporarily stores this information in its internal database. The input is "Pet photo submission," and the output is the related data stored internally.

[0637] Step 3:

[0638] This system uses public APIs to retrieve real-time trend information. The server calls public APIs of social media platforms (e.g., Twitter API, Instagram Graph API) to obtain the latest trend information (e.g., "Today's Dog," "Cute Animals," "Walking Time"). The input is an API call request, and the output is real-time trend information.

[0639] Step 4:

[0640] This system uses generative artificial intelligence to generate content ideas. The server uses acquired trend information and user-entered topic information as input data. It inputs prompts into a generative AI model (e.g., OpenAI's GPT-4) to generate specific SNS posting ideas. The input consists of pairs of topic and trend information, and the output is the generated content idea.

[0641] Examples of specific prompt messages:

[0642] Based on the following topic and trending information, generate social media post ideas that are likely to go viral.

[0643] Topic Information: Pet Photo Submissions

[0644] Trending Information: Today's Dogs, Cute Animals, Walking Time

[0645] Step 5:

[0646] The generated content ideas are presented to the user. The server converts the generated ideas back into JSON format and sends them to the user's terminal. The user's terminal parses the received JSON data and presents it to the user in an easy-to-read format. The input is the generated content idea data, and the output is the specific content idea displayed on the user's terminal screen.

[0647] Step 6:

[0648] Users review the suggested content ideas and post them on social media. Based on the presented content ideas, users post on their own social media accounts. At this stage, users can also customize their posts.

[0649] Step 7:

[0650] The system collects reaction data after a post is made. Users input reactions to their posts (e.g., number of likes, comments, shares, etc.) into the app. The user's device sends this feedback data to the server in JSON format. The input is the user's feedback data, and the output is a feedback record stored on the server.

[0651] Step 8:

[0652] The training data for the generative artificial intelligence is updated. Based on the collected feedback data, the server updates the training dataset for the generative AI to aid in future idea generation. This feedback loop continuously improves the generated content ideas. The input is the feedback data, and the output is the updated AI model. This entire process improves the overall performance and accuracy of the system.

[0653] 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.

[0654] To implement this invention, a system is provided that allows users to input information on topics they are interested in, and then combines an emotion engine and generative artificial intelligence to generate ideas for social media posts.

[0655] System Configuration

[0656] 1. User terminal:

[0657] It has an interface where users can input information on topics they are interested in.

[0658] It features an emotion engine that recognizes emotions from the user's voice and text.

[0659] It has a means of communication that sends input information and recognized emotional states to a server.

[0660] It has a means of displaying content ideas from the server to the user.

[0661] 2. Server:

[0662] It has the ability to obtain real-time trend information through a public API.

[0663] The emotion engine recognizes the user's emotional state and topic information, which are then passed as input data to the generative artificial intelligence.

[0664] The generative artificial intelligence has the ability to generate content ideas based on trend information and emotional states.

[0665] It has a means of communication to send the generated content ideas to the user's terminal.

[0666] It has features to evaluate the effectiveness of content ideas and collect user feedback.

[0667] It has a function to update the training data of generative artificial intelligence based on feedback.

[0668] Program processing

[0669] The system provides content ideas with a high probability of going viral through the following processing steps.

[0670] 1. Enter topic information:

[0671] The user accesses the device's interface and enters topic information for a social media post.

[0672] 2. Recognition of emotions:

[0673] When a user is typing or using voice input, the emotion engine built into the device recognizes the user's emotional state from the text or voice. For example, if a user expresses emotions such as "happy" or "sad," the emotion engine analyzes that.

[0674] 3. Sending information:

[0675] The device converts topic information and recognized emotional states into JSON data and sends it to the server.

[0676] 4. Obtaining trend information:

[0677] The server uses the public APIs of social media platforms to retrieve current trending information. This includes data such as popular hashtags, trending words, images, and videos.

[0678] 5. Generating content ideas:

[0679] The server provides the generative artificial intelligence (AI) with topic information, emotional state, and acquired trend information as input data. Based on this data, the AI ​​generates content ideas that match the user's emotions.

[0680] 6. Idea presentation:

[0681] The server then sends the generated content ideas back to the user's terminal in an appropriate format, such as JSON.

[0682] The system analyzes the data received by the user's device and displays it in a user-friendly format. For example, if the emotion is recognized as "happy," ideas containing bright and positive expressions will be presented.

[0683] 7. Posting on social media:

[0684] Users can review the displayed content ideas and post them to social media. They can also customize the text and images as needed.

[0685] 8. Collection of reaction data to posts:

[0686] The server collects reaction data to posts on social media (e.g., number of likes, comments, shares, etc.).

[0687] 9. Learning of Generative Artificial Intelligence:

[0688] The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This data is then used to improve the generation accuracy in the next idea generation cycle.

[0689] Specific example

[0690] For example, if a user types "I want to post a picture of my pet" and the device's emotion engine recognizes the user's emotional state as "happy," the server will operate as follows:

[0691] 1. The server retrieves popular hashtags such as "Life with pets" and "Today's dog" from the public APIs of social media platforms.

[0692] 2. The generative artificial intelligence generates a specific idea such as, "Let's post a cute photo of your pet taken during this morning's walk, along with the hashtag 'Today's Puppy.' Let's share fun moments and have fun with others!"

[0693] 3. The server sends this generated idea to the user's terminal, and the terminal displays it to the user.

[0694] Users post the suggested ideas on social media, and the server analyzes the reactions to those posts and incorporates the findings into future idea generation. By using an emotion engine, it becomes possible to provide more effective content ideas that resonate with users' emotions.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] The user accesses the device's interface and enters topic information they want to post to social media (e.g., "pets," "cooking," "travel," etc.). They can use input forms or voice input functions to do so.

[0698] Step 2:

[0699] As the user types or voices topic information, the device's built-in emotion engine recognizes the user's emotional state (e.g., "happy," "sad," "excited," etc.) from the text or voice.

[0700] Step 3:

[0701] The emotion engine recognizes the user's emotional state and converts it into JSON data along with topic information. The device then sends this data to the server.

[0702] Step 4:

[0703] The server analyzes the topic information received and the user's sentiment state, and uses public SNS APIs (e.g., Twitter API, Instagram API, etc.) to obtain real-time trend information. This information includes popular hashtags, trending words, images, videos, and more.

[0704] Step 5:

[0705] The server receives trend information, along with topic information and emotional states submitted by users, as input data for a generative artificial intelligence (AI). The AI ​​then analyzes this data and generates specific SNS posting ideas tailored to the user's emotional state.

[0706] Step 6:

[0707] The server receives specific posting ideas generated by a generative artificial intelligence and sends them to the user's terminal in an appropriate format (e.g., JSON). These ideas include relevant hashtags, text, and recommended images.

[0708] Step 7:

[0709] The device analyzes the content ideas it receives and displays them in a user-friendly format. For example, if the emotion is recognized as "happy," ideas containing bright and positive expressions will be presented.

[0710] Step 8:

[0711] Users can review the displayed content ideas and post them to their own social media accounts. They can also customize the text and images as needed.

[0712] Step 9:

[0713] After a user posts on social media, the server uses a public API to collect reaction data to that post (e.g., number of likes, comments, shares, etc.).

[0714] Step 10:

[0715] The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This data is then used to improve the generation accuracy in the next idea generation cycle.

[0716] For example, if a user inputs "I want to post a picture of my pet" and the emotion engine recognizes this as "fun," the server retrieves popular hashtags such as "life with pets" and "dog of the day" from the public APIs of social media. Then, the generative AI generates a specific idea such as, "Let's post a cute picture of your pet that you took on your walk this morning, along with the hashtag 'dog of the day.' Share this fun moment and have fun with others!" The server sends this idea to the user's device, and the user posts it to social media. Through this series of processes, the user can obtain effective content ideas that fit their emotional state.

[0717] (Example 2)

[0718] 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".

[0719] In social media posting, users face challenges in generating engaging and trending content. Furthermore, providing content ideas that resonate with users' emotions is a significant challenge. Additionally, there's a need for methods to analyze the response to generated content and improve the quality of future idea generation.

[0720] 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.

[0721] In this invention, the server includes means for inputting topics of interest to the user, means for transmitting the input topic information to a central processing unit, means for recognizing the user's emotional state from input or voice input, means for obtaining real-time trend information using a public API, means for generating content ideas using generative artificial intelligence based on the acquired trend information, the input topic information, and the recognized emotional state, means for presenting the generated content ideas to the user, means for evaluating the effectiveness of the generated content ideas, and means for collecting reaction data after posting the proposed content ideas to update the learning data of the generative artificial intelligence. This makes it possible to generate attractive content ideas based on the user's emotions and trends.

[0722] "Topic information" refers to information about themes and topics that users are interested in for their social media posts.

[0723] A "central processing unit" is a computer device that receives data within a system, analyzes it, and processes it in cooperation with other devices.

[0724] "Emotional state" refers to the emotional state (for example, happy, sad, excited, etc.) recognized from the user's input or voice.

[0725] A "public API" is an official interface provided for integrating with external applications and services, serving as a means of retrieving and manipulating data.

[0726] "Trending information" refers to data such as hashtags, keywords, images, and videos that are currently popular on social media and the internet.

[0727] "Generative artificial intelligence" refers to artificial intelligence models that generate new content and ideas based on collected data and input information.

[0728] "Content ideas" refer to suggestions regarding the specific content and format of social media posts that users can actually use to create their own posts.

[0729] "Evaluating effectiveness" is the process of assessing how successful the generated content ideas were on social media (e.g., number of likes, comments, shares).

[0730] "Response data" refers to data on user feedback (e.g., likes, comments, shares, etc.) on social media posts.

[0731] "Updating the learning data" is the process of retraining the generative artificial intelligence based on the collected response data to improve the accuracy of content idea generation in the future.

[0732] As an embodiment of this invention, a system is provided that allows a user to input information on topics of interest and generates ideas for social media posts by combining an emotion engine and generative artificial intelligence. This system is configured using a user terminal and a server.

[0733] User terminal

[0734] The user terminal has the following features:

[0735] Topic Information Input Interface: Includes text boxes and voice input functions for users to input information on topics of interest.

[0736] Emotion Engine: Recognizes emotions from the user's voice or text. For example, if a user says, "I'm in a happy mood today," the emotion "happy" is recognized.

[0737] Communication method: The input topic information and recognized emotional state are converted into JSON format data and sent to the server.

[0738] Displaying content ideas: Analyzes content ideas received from the server and displays them in a user-friendly format.

[0739] server

[0740] The server has the following features:

[0741] Method for obtaining trend information: Real-time trend information is obtained using public APIs of social media platforms. This includes popular hashtags, trending words, and related images and videos.

[0742] Generative Artificial Intelligence: Generates content ideas based on the user's emotional state recognized by the emotion engine, along with topic and trend information. This process is executed using a generative AI model.

[0743] Communication method: The generated content idea is converted to JSON format and sent to the user's terminal.

[0744] Feedback Collection and Learning: Collect reaction data (e.g., number of likes, comments, shares, etc.) to user-submitted social media content and update it as training data for generative artificial intelligence.

[0745] Specific example

[0746] For example, if a user types "I want to post a picture of my pet" and the device's emotion engine recognizes the emotion "happy," the following steps are executed: The server uses the public APIs of social media to retrieve popular hashtags such as "life with pets" and "dog of the day." Based on this information, the generative AI generates a specific idea such as, "Let's post a cute picture of your pet taken on this morning's walk, along with the hashtag 'dog of the day.' Share the fun moment and enjoy it with others!" The server sends this information to the user's device, which then presents it to the user. The user makes the post, and the server analyzes the reaction to help with future generation.

[0747] Example of a prompt

[0748] Enter "User wants to post a photo of their pet" and, if a positive emotion is detected, generate recommended social media posting ideas. The current trending hashtags are "Today's Dog" and "Life with Pets".

[0749] In this way, the system generates content ideas with a high potential to go viral based on user emotions and trend information, and supports effective posting on social media.

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Program processing flow

[0752] Step 1: Enter topic information

[0753] Specific actions:

[0754] The user accesses their device and enters "I want to post a picture of my pet" as the topic for their SNS post.

[0755] Input: User topic information.

[0756] Output: Topic information is recognized by the terminal.

[0757] Step 2: Recognizing Emotions

[0758] Specific actions:

[0759] When a user is typing or using voice input, the device's built-in emotion engine recognizes emotions such as "happy." For example, if the user says, "I'm in a happy mood today," the voice is captured and analyzed.

[0760] Input: User voice or text input.

[0761] Output: Recognized emotion (e.g., "happy").

[0762] Step 3: Submit information

[0763] Specific actions:

[0764] The topic information and recognized emotional state are converted into JSON format data, which the device then sends to the server over the network.

[0765] Input: Topic information and recognized emotional state.

[0766] Output: Data in JSON format is sent to the server.

[0767] Step 4: Obtaining trend information

[0768] Specific actions:

[0769] The server uses public APIs from social networking services to retrieve trending information such as "Life with Pets" and "Today's Dog." It accesses the APIs and saves the necessary data to an internal database.

[0770] Input: Request to access the SNS public API.

[0771] Output: Acquired trend information.

[0772] Step 5: Generating Content Ideas

[0773] Specific actions:

[0774] The server provides topic information, sentiment, and trend information as input data to the generative artificial intelligence (AI). The AI ​​model processes this information and generates specific content ideas that fit the user's sentiment. For example, it might generate an idea like, "Let's post a cute photo of our pet that we took on our walk this morning, along with the hashtag #TodaysDog."

[0775] Input: Topic information, sentiment status, trend information.

[0776] Output: Generated content ideas.

[0777] Step 6: Presenting Ideas

[0778] Specific actions:

[0779] The generated content ideas are converted into JSON format, and the server sends this to the user's terminal. The terminal parses the received data and displays it in a user-friendly format.

[0780] Input: Generated content idea.

[0781] Output: Content ideas to be displayed on the user's terminal.

[0782] Step 7: Posting on social media

[0783] Specific actions:

[0784] Users review the displayed content ideas, customize the text and images as needed, and post them on social media.

[0785] Input: A suggested content idea.

[0786] Output: Post to social media.

[0787] Step 8: Collecting post response data

[0788] Specific actions:

[0789] The server uses SNS APIs to collect reaction data to posts (e.g., number of likes, comments, shares, etc.).

[0790] Input: Request to access the SNS API.

[0791] Output: Collected reaction data.

[0792] Step 9: Updating the training data for generative artificial intelligence.

[0793] Specific actions:

[0794] The server analyzes the collected reaction data and updates the training data for the generative artificial intelligence. This improves the accuracy of the next generation.

[0795] Input: Collected response data.

[0796] Output: Updated training data for generative artificial intelligence.

[0797] (Application Example 2)

[0798] 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."

[0799] Traditional content generation systems did not adequately consider user emotions and trend information, making it difficult to generate content ideas that would generate higher engagement. Furthermore, advertising and marketing require the generation of effective advertising ideas that reflect user emotional states and are based on real-time trend information. Additionally, the lack of mechanisms to evaluate the effectiveness of generated content and feed that feedback back as learning data has made continuous improvement of generative artificial intelligence difficult.

[0800] 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. In this invention, the server includes means for transmitting topic information including emotional state to a central processing device, means for acquiring real-time trend information using a public API, and means for generating content ideas based on the acquired trend information, emotional state, and input topic information using generative artificial intelligence. This enables highly engaging advertising and marketing by generating content ideas that reflect the user's emotional state and real-time trend information.

[0801] A "user" is an individual or group that uses the system to generate content ideas.

[0802] A "topic" is information about subjects or themes that users are interested in.

[0803] "Emotional state" refers to information that indicates the user's current psychological state or mood, and is recognized by the emotion engine.

[0804] A "central processing unit" is a server that processes input topic information and sentiment states, and generates content ideas based on that information.

[0805] A "public API" is an application programming interface that is made publicly available to allow external access to specific functions or data.

[0806] "Real-time trend information" refers to topics and data that are attracting attention in society and the market at the present time.

[0807] "Generative artificial intelligence" is an artificial intelligence technology that generates new content ideas based on input data.

[0808] A "content idea" is a concrete concept of content that users can use on social media, in advertisements, etc.

[0809] "Presentation" refers to visually or audibly informing the user of a generated content idea.

[0810] This invention is a system that generates content ideas that reflect real-time trend information based on the user's interests and emotional state. To achieve this, the following program and hardware configuration are used.

[0811] Hardware and software to use

[0812] 1. Smartphone: A device in which the user inputs topic information through an interface and recognizes the emotional state using an emotion engine.

[0813] 2. Server: This is a central processing unit that receives and processes topic information and emotional states, and generates content ideas using generative artificial intelligence.

[0814] 3. Emotion Engine: Utilizes the Google Cloud Natural Language API to analyze user text and voice data and recognize their emotional state.

[0815] 4. Generative Artificial Intelligence: Using OpenAI GPT-3, new content ideas are generated based on input topics, sentiment states, and trend data.

[0816] 5. Public APIs: Real-time trend information is obtained using the Twitter API and Facebook Graph API.

[0817] System operation

[0818] Input and Recognition

[0819] The user enters topics of interest via text or voice on a smartphone app. The emotion engine analyzes this input data and recognizes the user's current emotional state (e.g., "excited"). The topic information and emotional state are then sent to the server in JSON format.

[0820] Acquisition of trend data

[0821] The server uses public APIs (Twitter API, Facebook Graph API) to retrieve real-time trend information. This includes popular hashtags and trending words.

[0822] Content Idea Generation

[0823] The server's generative artificial intelligence (OpenAI GPT-3) generates effective content ideas that match the user's emotions based on acquired topic information, emotional states, and trend information. For example, if the emotion is "excited," the generated ideas will include positive and impactful expressions.

[0824] Presentation and Feedback

[0825] The generated content ideas are sent back to the smartphone in JSON format and displayed visually to the user. The user then posts to social media based on the suggested ideas. The server collects reaction data to these posts (e.g., number of likes, comments, shares, etc.) and uses this as training data for generative artificial intelligence.

[0826] Examples of specific cases and prompt statements

[0827] For example, if a user enters "new product campaign" and the emotion engine recognizes the emotion "excited," an example of a prompt to the generative AI would be as follows:

[0828] Users are excited about the topic of "new product campaigns." Popular hashtags include "new product," "campaign," and "exciting new product." Based on this information, please generate positive and impactful advertising ideas.

[0829] The advertising ideas generated in this way can take the form of specific content proposals, such as, "Let's liven up the campaign by posting photos of the new product with a hashtag like 'Exciting New Product'!"

[0830] As described above, the present invention enables highly engaging advertising and marketing by generating content ideas that reflect the user's emotional state and real-time trend information.

[0831] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0832] Step 1: The user opens the app on their smartphone and enters information on a topic of interest (e.g., "New Product Campaign") via text or voice. The entered topic information is sent to the emotion engine.

[0833] Step 2: The emotion engine (Google Cloud Natural Language API) analyzes the input text and audio data to recognize the user's emotional state (e.g., "excited"). This process outputs the emotional state as the analysis result.

[0834] Step 3: The device converts the recognized emotional state and topic information into JSON format data and sends it to the server. The input is topic information and emotional state, and the output is JSON data that integrates these.

[0835] Step 4: The server parses the received JSON data and extracts topic information and sentiment status. Simultaneously, it retrieves real-time trend information using public APIs (Twitter API, Facebook Graph API). These API calls output popular hashtags and trending words.

[0836] Step 5: The server passes the acquired topic information, sentiment status, and trend information as input data to the generative artificial intelligence (OpenAI GPT-3) to generate content ideas. The input consists of topic information, sentiment status, and trend information, and the generated content ideas are output.

[0837] Step 6: Convert the generated content idea to JSON format and send it back to the terminal. The input is the generated content idea, and the output is data in JSON format.

[0838] Step 7: Visually present the content ideas received by the device to the user. For example, a positive advertising idea that evokes the emotion of "excitement" is displayed on the user's smartphone screen.

[0839] Step 8: The user reviews the suggested content idea, customizes it as needed, and posts it to social media. After posting, the server collects reaction data (e.g., number of likes, comments, shares).

[0840] Step 9: The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This improves the accuracy of content idea generation in the future.

[0841] Through the specific actions of each step, the present invention enables the efficient generation of highly engaging content ideas that reflect the user's emotional state and trend information.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] [Third Embodiment]

[0846] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0847] 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.

[0848] 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).

[0849] 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.

[0850] 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.

[0851] 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).

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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".

[0858] To implement this invention, a system is constructed in which a user inputs information on topics of interest via a terminal, and a server uses generative artificial intelligence to generate content ideas that are likely to go viral based on that information.

[0859] Specifically, the following system configuration and program processing are required.

[0860] System Configuration

[0861] 1. User terminal:

[0862] It has an interface where users can input information on topics they are interested in.

[0863] It has a means of communication to receive input information and send it to the server.

[0864] It has a means of displaying content ideas received from the server.

[0865] 2. Server:

[0866] It has the ability to obtain real-time trend information through a public API.

[0867] It has the ability to generate content ideas based on topic information and trend information using generative artificial intelligence.

[0868] It has a means of communication to send the generated content ideas to the user's terminal.

[0869] It has features to evaluate the effectiveness of content ideas and collect user feedback.

[0870] It has a function to update the training data of generative artificial intelligence based on feedback.

[0871] Program processing

[0872] The following steps are necessary for a user's device to generate ideas for social media posts.

[0873] 1. Enter topic information:

[0874] The user accesses the device's interface and enters topics of interest such as "pets," "cooking," or "travel."

[0875] 2. Sending information:

[0876] The terminal sends this topic information to the server. This topic information is sent in JSON format or another appropriate data format.

[0877] 3. Obtaining trend information:

[0878] The server retrieves trending information such as currently popular hashtags, trending words, images, and videos in real time through the public APIs of social media platforms.

[0879] 4. Generating content ideas:

[0880] The server uses acquired trend information and user topic information as input data to run a generative artificial intelligence.

[0881] Based on this data, the generative artificial intelligence generates specific social media posting ideas. The proposed ideas are structured in the form of text, images, and videos.

[0882] 5. Idea presentation:

[0883] The server then sends the generated content ideas back to the user's terminal in an appropriate format, such as JSON.

[0884] The user's terminal analyzes the received data and presents it to the user in an easy-to-understand format.

[0885] Users review the suggested content ideas and post them to their own social media accounts.

[0886] Specific example

[0887] For example, if a user enters "I want to post a picture of my pet," the server will operate as follows:

[0888] 1. The server retrieves popular hashtags such as "Life with pets" and "Today's dog" from the public APIs of social media platforms.

[0889] 2. Generative artificial intelligence generates a specific idea such as, "It would be good to post a cute photo of your pet taken during your walk this morning and use the hashtag 'Today's Puppy'."

[0890] 3. The server sends this generated idea to the user's terminal, and the terminal displays it to the user.

[0891] Users post the suggested ideas to social media, and the server analyzes the reactions to those posts (number of likes, comments, etc.) to inform the generation of future ideas. This process ensures that users always have access to content ideas that are in line with current trends and have a high potential to go viral.

[0892] The following describes the processing flow.

[0893] Step 1:

[0894] The user accesses the device interface and enters topic information they want to post to social media (e.g., "pets," "cooking," "travel," etc.). The information entered by the user is sent to the server in the next step.

[0895] Step 2:

[0896] The terminal converts the topic information entered by the user into JSON format data and sends it to the server. At this point, the terminal checks the integrity of the data structure and verifies that there are no errors.

[0897] Step 3:

[0898] The server analyzes the topic information it receives and uses public APIs (e.g., Twitter API, Instagram API) to retrieve real-time trend information. This includes currently popular hashtags, trending words, images, videos, and more.

[0899] Step 4:

[0900] After the server acquires trend information, it passes the topic information and the acquired trend information as input data to a generative artificial intelligence (e.g., a natural language processing model). Based on this data, the generative artificial intelligence generates specific SNS posting ideas to suggest to the user.

[0901] Step 5:

[0902] The server receives specific posting ideas generated by a generative artificial intelligence and sends them to the user's terminal in JSON format or another appropriate data format. These ideas include relevant hashtags, text, and recommended images.

[0903] Step 6:

[0904] The device analyzes content ideas received from the server and displays them in a user-friendly format. The user reviews the displayed ideas and decides whether or not to post them on social media.

[0905] Step 7:

[0906] Users post content ideas suggested to them on social media. Users can modify the generated text and images as needed before posting.

[0907] Step 8:

[0908] The server collects reaction data to posts on social media (e.g., number of likes, comments, shares, etc.). This data collection again utilizes a public API.

[0909] Step 9:

[0910] The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This data will be used in the next idea generation process to improve the generation accuracy.

[0911] Step 10:

[0912] Users review the feedback and use it to improve future posts. They then use the system again for their next post to get content ideas based on the latest trends.

[0913] The above is the specific processing flow of the viral posting idea suggestion system using generative artificial intelligence. Through this system, users can always create effective social media posts based on the latest trend information.

[0914] (Example 1)

[0915] 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."

[0916] Traditional social media posting support systems lack the functionality to automatically generate effective content ideas for topics of user interest, making it difficult for users to create trending content with a high potential for virality. Furthermore, they lack mechanisms to evaluate the effectiveness of generated content and incorporate those evaluations into future content creation. This results in a problem where the efficiency and effectiveness of users' social media activities are not sufficiently improved.

[0917] 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.

[0918] In this invention, the server includes means for generating specific prompt sentences based on user input and passing them to a generative artificial intelligence system; means for monitoring the reactions to the user's SNS posts for a certain period and analyzing the data; and means for evaluating the effectiveness of the generated content ideas. As a result, users can easily obtain content ideas that are in line with trends and have a high potential to go viral, and by evaluating their effectiveness and reflecting it in the generation of future content, they can improve the efficiency and effectiveness of their SNS activities.

[0919] A "user terminal" is a device that allows users to input information on topics they are interested in and exchange data with the server.

[0920] A "central processing unit" is a system that receives data transmitted from user terminals, acquires trend information through public APIs, and generates content ideas using generative artificial intelligence.

[0921] A "public API" is an application programming interface that is made publicly accessible to external developers and services, and in this case, it is used to obtain real-time trend information from social media.

[0922] "Generative artificial intelligence" refers to an artificial intelligence system that receives data as input and generates content ideas using machine learning algorithms.

[0923] A "prompt" is a specific instruction or phrase given to a generative artificial intelligence system to generate content ideas.

[0924] "Content ideas" refer to specific ideas or suggestions that users can post on social media, and are composed of various forms such as text, images, and videos.

[0925] "SNS reaction data" refers to user reaction data such as the number of "likes," comments, and shares on content posted by users on social media.

[0926] "User feedback" refers to the evaluations and comments that users provide after using the generated content ideas, and it is used to improve generative artificial intelligence.

[0927] To implement this invention, it is necessary to construct a system in which a user inputs information on topics of interest via a terminal, and a server uses generative artificial intelligence to generate content ideas that are likely to go viral based on that information. The specific system configuration and program processing will be described below.

[0928] System Configuration

[0929] 1. User terminal:

[0930] It has an interface that allows users to input information on topics they are interested in.

[0931] It has a means of communication to send the input information to the server.

[0932] It has a means of displaying content ideas received from the server to the user.

[0933] 2. Server:

[0934] It has the ability to obtain real-time trend information through public APIs. In this case, APIs such as the Twitter API and Facebook Graph API can be used.

[0935] It has the function of generating content ideas based on topic information and trend information using generative artificial intelligence. GPT-4 and other similar generative AI can be used.

[0936] It has a means of communication to send the generated content ideas to the user's terminal.

[0937] It has features to evaluate the effectiveness of content ideas and collect user feedback.

[0938] It has a function to update the training data of generative artificial intelligence based on feedback.

[0939] Program processing

[0940] User terminal

[0941] 1. The user accesses the device's interface and enters a topic of interest (e.g., "pets," "cooking," "travel," etc.).

[0942] 2. The terminal sends the entered topic information to the server in JSON format. A POST request is used for this communication.

[0943] server

[0944] 1. The server uses publicly available APIs from social media platforms to retrieve trending information in real time, such as currently popular hashtags, trending words, images, and videos. For example, it retrieves "trending topics" from the Twitter API.

[0945] 2. The server sends prompt messages to the generative AI model (e.g., GPT-4) based on the user's topic and trend information. An example of a prompt message is as follows:

[0946] User topic: Pets

[0947] Trend Information: Life with Pets, Today's Dog

[0948] Prompt: Generate posting ideas that you would like to recommend to users.

[0949] 3. Based on this data, the generative artificial intelligence generates specific SNS posting ideas. For example, it might output an idea such as, "It would be good to post a cute photo of your pet taken during your walk this morning and use the hashtag 'Today's Puppy'."

[0950] 4. The server sends the generated content idea to the user's terminal in JSON format. Specifically, it uses a POST request.

[0951] 5. The server monitors the reactions to content posted by users on social media (e.g., the number of likes and comments) for a certain period of time and analyzes that data.

[0952] 6. The server updates the training data of the generative artificial intelligence based on the collected response data, improving the quality of new ideas.

[0953] This allows users to easily obtain content ideas that are in line with trends and have a high potential to go viral, enabling them to engage in social media activities efficiently and effectively.

[0954] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0955] Step 1:

[0956] The user accesses the device's interface and enters a topic of interest.

[0957] Input: Information on topics of interest (e.g., "pets," "cooking," "travel," etc.)

[0958] Output: A screen where topic information is displayed in the input field.

[0959] In terms of the specific operation, a dedicated text box appears on the terminal's input form, and the user enters topic information into it. When the user enters "pets," that information proceeds to the next step.

[0960] Step 2:

[0961] The terminal sends the entered topic information to the server in JSON format.

[0962] Input: Topic information (e.g., "pets")

[0963] Output: Data in JSON format is sent to the server as a POST request.

[0964] Specifically, the terminal converts the input topic information into JSON format and sends it to the server using an HTTP POST request. The topic information is included in the request body.

[0965] Step 3:

[0966] The server uses publicly available APIs from social media platforms to retrieve trending information in real time.

[0967] Input: Topic information (e.g., "pets")

[0968] Output: Trend information (e.g., Life with pets, Today's dog)

[0969] Specifically, the server accesses the public API endpoint to retrieve current trend data. It makes API calls, obtains trend information in JSON format, and analyzes it.

[0970] Step 4:

[0971] The server sends prompt messages to the generating AI model based on the user's topic information and trend information.

[0972] Input: User topic information (e.g., "Pets") and trending information (e.g., Life with pets, Today's dog)

[0973] Output: Prompt message (Example: "User topic: Pets. Trending topics: Life with pets, Today's dog. Generate post ideas to recommend to the user.")

[0974] In terms of specific operation, the server combines topic information and trend information and sends a prompt message like the following to the AI ​​model (e.g., GPT-4). An example of a prompt message is as follows:

[0975] User topic: Pets

[0976] Trend Information: Life with Pets, Today's Dog

[0977] Prompt: Generate posting ideas that you would like to recommend to users.

[0978] Step 5:

[0979] The generative AI model generates specific social media posting ideas based on the prompt text.

[0980] Input: Prompt message (Example: As shown above)

[0981] Output: Content ideas (Example: "Post a cute photo of your pet taken during your morning walk and use the hashtag #TodaysDog")

[0982] In terms of its operation, the generative AI model analyzes the prompt text and generates specific posting ideas based on the topic and trends. As a result, social media posting ideas in text format are output.

[0983] Step 6:

[0984] The server sends the generated content ideas to the user's terminal in JSON format.

[0985] Input: Content idea (Example: As above)

[0986] Output: Data in JSON format is sent to the user's terminal as a POST request.

[0987] Specifically, the server converts the generated content idea into JSON format and sends it to the user's terminal using an HTTP POST request.

[0988] Step 7:

[0989] The device displays received content ideas in a format that is easy for the user to view.

[0990] Input: Content idea in JSON format (e.g., as shown above)

[0991] Output: Content ideas displayed to the user (e.g., "Post a cute photo of your pet taken during your morning walk and use the hashtag #TodaysDog")

[0992] Specifically, the device parses the received JSON data and displays content ideas on the screen in text format. Based on this information, the user posts to social media.

[0993] Step 8:

[0994] A user posts something on social media, and then the server monitors the reactions to that post for a certain period of time.

[0995] Input: User posts and social media reaction data

[0996] Output: Post reaction data (e.g., number of likes, number of comments, etc.)

[0997] Specifically, the server uses APIs and scraping tools to collect user response data to posts from social media over a certain period of time.

[0998] Step 9:

[0999] The server updates the training data for the generated AI model based on the collected reaction data.

[1000] Input: Post reaction data (e.g., number of likes, number of comments, etc.)

[1001] Output: Updated generative AI model

[1002] Specifically, the server analyzes the collected response data and adds it as feedback to the training dataset of the generating AI model, thereby improving the accuracy of idea generation in the future.

[1003] (Application Example 1)

[1004] 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."

[1005] Traditional content generation systems struggled to incorporate trending information and propose viral content. Furthermore, the lack of mechanisms to collect user feedback and optimize the generating AI made it difficult to continuously improve the effectiveness of the generated content. Therefore, there is a need for a system that reflects trending information in real time and improves the quality of content generation based on user feedback.

[1006] 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.

[1007] In this invention, the server includes means for inputting topics of interest to the user, means for transmitting the input topic information to a central processing unit, means for obtaining real-time trend information using a public API, means for generating content ideas based on the acquired trend information and the input topic information using generative artificial intelligence, means for presenting the generated content ideas to the user, and means for collecting user feedback and updating the generative artificial intelligence. This makes it possible to always provide content ideas that reflect the latest trend information and to optimize the generative artificial intelligence based on user feedback.

[1008] "A means for users to input topics they are interested in" refers to an interface or input device that allows users to input specific topics they are interested in.

[1009] "Means for transmitting input topic information to a central processing unit" refers to communication means that have the function of transferring topic information entered by a user to a server or central processing unit via a network.

[1010] "Methods for obtaining real-time trend information using public APIs" refers to functions that use publicly available application programming interfaces provided by services such as social media and news sites to obtain the latest trend information.

[1011] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to learn from large amounts of data and generate new ideas or content in response to specific inputs.

[1012] "Content ideas" refer to specific suggestions or ideas, such as text, images, and videos, that users can share on social media and other digital platforms.

[1013] "Means of presenting to the user" refers to interfaces or devices that have the functionality to display generated content ideas so that users can directly see and review them.

[1014] "Means for collecting user feedback and updating generative artificial intelligence" refers to a function that collects user reactions and evaluations, uses that data as training data for generative artificial intelligence, and improves the accuracy and performance of the model.

[1015] This invention relates to a system that takes a user's input of topics of interest and generates content ideas with a high probability of going viral based on that input. This system is implemented using a user terminal, a server, and generative artificial intelligence.

[1016] System program

[1017] A system for carrying out this invention includes the following configuration and process.

[1018] 1. Processing at the user terminal

[1019] The user terminal provides an interface for users to input topics they are interested in. When a user inputs a topic through the interface, that information is sent to the server. It also has the functionality to display content ideas received from the server.

[1020] 2. Server processing

[1021] The server receives topic information entered from the user's terminal. Using this information, the server accesses public APIs (e.g., Twitter API, Instagram Graph API) to obtain real-time trend information.

[1022] The server uses generative artificial intelligence (e.g., OpenAI's GPT model) to generate content ideas based on acquired trend information and user topic information. The generated content ideas are sent from the server to the user's terminal. Furthermore, feedback from the user is collected and used to update the training data of the generative artificial intelligence model.

[1023] 3. Details of Generative Artificial Intelligence

[1024] Generative artificial intelligence has the ability to learn from large amounts of data and generate new ideas and content in response to specific inputs. This system uses generative AI models such as OpenAI's GPT-4. The model uses acquired trend information and user topic information as prompts to generate specific content ideas.

[1025] Hardware and software to be used

[1026] Smartphones: iOS and Android devices

[1027] SNS public API: Twitter API, Instagram Graph API

[1028] Generative artificial intelligence: OpenAI's GPT-4 model

[1029] Cloud services: Use AWS or Google Cloud to host servers and databases.

[1030] Communication protocol: Data transmission using HTTP / HTTPS

[1031] Specific example

[1032] For example, if a user enters "post a photo of my pet," the server will operate as follows:

[1033] 1. The server retrieves trending information such as "Today's Dog," "Cute Animals," and "Walking Time" from publicly available APIs of social media platforms.

[1034] 2. Generative artificial intelligence generates posting ideas that are likely to go viral based on acquired trend information and user topic information. In this case, specific ideas such as "It would be good to post a cute photo of your pet taken on your walk this morning with the hashtag 'Today's Puppy'" might be suggested.

[1035] 3. The server sends the generated ideas to the user terminal, and the user terminal displays them.

[1036] 4. Users post ideas based on the suggestions on social media and input the reactions to those posts into the app. This feedback is sent to the server and used as training data for generative artificial intelligence.

[1037] Example of a prompt

[1038] Based on the following topic and trending information, generate social media post ideas that are likely to go viral.

[1039] Topic Information: Pet Photo Submissions

[1040] Trending Information: Today's Dogs, Cute Animals, Walking Time

[1041] This method allows the system to continuously optimize its generative artificial intelligence, enabling it to consistently provide users with high-quality content ideas that reflect the latest trends.

[1042] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1043] Step 1:

[1044] The user enters a topic of interest. The user enters a specific topic (e.g., "Posting photos of pets") into a text box via the smartphone application interface. This input data is then sent directly to the server in JSON format.

[1045] Step 2:

[1046] The server receives the input topic information. The server receives the topic information sent from the user terminal and temporarily stores this information in its internal database. The input is "Pet photo submission," and the output is the related data stored internally.

[1047] Step 3:

[1048] This system uses public APIs to retrieve real-time trend information. The server calls public APIs of social media platforms (e.g., Twitter API, Instagram Graph API) to obtain the latest trend information (e.g., "Today's Dog," "Cute Animals," "Walking Time"). The input is an API call request, and the output is real-time trend information.

[1049] Step 4:

[1050] This system uses generative artificial intelligence to generate content ideas. The server uses acquired trend information and user-entered topic information as input data. It inputs prompts into a generative AI model (e.g., OpenAI's GPT-4) to generate specific SNS posting ideas. The input consists of pairs of topic and trend information, and the output is the generated content idea.

[1051] Examples of specific prompt messages:

[1052] Based on the following topic and trending information, generate social media post ideas that are likely to go viral.

[1053] Topic Information: Pet Photo Submissions

[1054] Trending Information: Today's Dogs, Cute Animals, Walking Time

[1055] Step 5:

[1056] The generated content ideas are presented to the user. The server converts the generated ideas back into JSON format and sends them to the user's terminal. The user's terminal parses the received JSON data and presents it to the user in an easy-to-read format. The input is the generated content idea data, and the output is the specific content idea displayed on the user's terminal screen.

[1057] Step 6:

[1058] Users review the suggested content ideas and post them on social media. Based on the presented content ideas, users post on their own social media accounts. At this stage, users can also customize their posts.

[1059] Step 7:

[1060] The system collects reaction data after a post is made. Users input reactions to their posts (e.g., number of likes, comments, shares, etc.) into the app. The user's device sends this feedback data to the server in JSON format. The input is the user's feedback data, and the output is a feedback record stored on the server.

[1061] Step 8:

[1062] The training data for the generative artificial intelligence is updated. Based on the collected feedback data, the server updates the training dataset for the generative AI to aid in future idea generation. This feedback loop continuously improves the generated content ideas. The input is the feedback data, and the output is the updated AI model. This entire process improves the overall performance and accuracy of the system.

[1063] 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.

[1064] To implement this invention, a system is provided that allows users to input information on topics they are interested in, and then combines an emotion engine and generative artificial intelligence to generate ideas for social media posts.

[1065] System Configuration

[1066] 1. User terminal:

[1067] It has an interface where users can input information on topics they are interested in.

[1068] It features an emotion engine that recognizes emotions from the user's voice and text.

[1069] It has a means of communication that sends input information and recognized emotional states to a server.

[1070] It has a means of displaying content ideas from the server to the user.

[1071] 2. Server:

[1072] It has the ability to obtain real-time trend information through a public API.

[1073] The emotion engine recognizes the user's emotional state and topic information, which are then passed as input data to the generative artificial intelligence.

[1074] The generative artificial intelligence has the ability to generate content ideas based on trend information and emotional states.

[1075] It has a means of communication to send the generated content ideas to the user's terminal.

[1076] It has features to evaluate the effectiveness of content ideas and collect user feedback.

[1077] It has a function to update the training data of generative artificial intelligence based on feedback.

[1078] Program processing

[1079] The system provides content ideas with a high probability of going viral through the following processing steps.

[1080] 1. Enter topic information:

[1081] The user accesses the device's interface and enters topic information for a social media post.

[1082] 2. Recognition of emotions:

[1083] When a user is typing or using voice input, the emotion engine built into the device recognizes the user's emotional state from the text or voice. For example, if a user expresses emotions such as "happy" or "sad," the emotion engine analyzes that.

[1084] 3. Sending information:

[1085] The device converts topic information and recognized emotional states into JSON data and sends it to the server.

[1086] 4. Obtaining trend information:

[1087] The server uses the public APIs of social media platforms to retrieve current trending information. This includes data such as popular hashtags, trending words, images, and videos.

[1088] 5. Generating content ideas:

[1089] The server provides the generative artificial intelligence (AI) with topic information, emotional state, and acquired trend information as input data. Based on this data, the AI ​​generates content ideas that match the user's emotions.

[1090] 6. Idea presentation:

[1091] The server then sends the generated content ideas back to the user's terminal in an appropriate format, such as JSON.

[1092] The system analyzes the data received by the user's device and displays it in a user-friendly format. For example, if the emotion is recognized as "happy," ideas containing bright and positive expressions will be presented.

[1093] 7. Posting on social media:

[1094] Users can review the displayed content ideas and post them to social media. They can also customize the text and images as needed.

[1095] 8. Collection of reaction data to posts:

[1096] The server collects reaction data to posts on social media (e.g., number of likes, comments, shares, etc.).

[1097] 9. Learning of Generative Artificial Intelligence:

[1098] The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This data is then used to improve the generation accuracy in the next idea generation cycle.

[1099] Specific example

[1100] For example, if a user types "I want to post a picture of my pet" and the device's emotion engine recognizes the user's emotional state as "happy," the server will operate as follows:

[1101] 1. The server retrieves popular hashtags such as "Life with pets" and "Today's dog" from the public APIs of social media platforms.

[1102] 2. The generative artificial intelligence generates a specific idea such as, "Let's post a cute photo of your pet taken during this morning's walk, along with the hashtag 'Today's Puppy.' Let's share fun moments and have fun with others!"

[1103] 3. The server sends this generated idea to the user's terminal, and the terminal displays it to the user.

[1104] Users post the suggested ideas on social media, and the server analyzes the reactions to those posts and incorporates the findings into future idea generation. By using an emotion engine, it becomes possible to provide more effective content ideas that resonate with users' emotions.

[1105] The following describes the processing flow.

[1106] Step 1:

[1107] The user accesses the device's interface and enters topic information they want to post to social media (e.g., "pets," "cooking," "travel," etc.). They can use input forms or voice input functions to do so.

[1108] Step 2:

[1109] As the user types or voices topic information, the device's built-in emotion engine recognizes the user's emotional state (e.g., "happy," "sad," "excited," etc.) from the text or voice.

[1110] Step 3:

[1111] The emotion engine recognizes the user's emotional state and converts it into JSON data along with topic information. The device then sends this data to the server.

[1112] Step 4:

[1113] The server analyzes the topic information received and the user's sentiment state, and uses public SNS APIs (e.g., Twitter API, Instagram API, etc.) to obtain real-time trend information. This information includes popular hashtags, trending words, images, videos, and more.

[1114] Step 5:

[1115] The server receives trend information, along with topic information and emotional states submitted by users, as input data for a generative artificial intelligence (AI). The AI ​​then analyzes this data and generates specific SNS posting ideas tailored to the user's emotional state.

[1116] Step 6:

[1117] The server receives specific posting ideas generated by a generative artificial intelligence and sends them to the user's terminal in an appropriate format (e.g., JSON). These ideas include relevant hashtags, text, and recommended images.

[1118] Step 7:

[1119] The device analyzes the content ideas it receives and displays them in a user-friendly format. For example, if the emotion is recognized as "happy," ideas containing bright and positive expressions will be presented.

[1120] Step 8:

[1121] Users can review the displayed content ideas and post them to their own social media accounts. They can also customize the text and images as needed.

[1122] Step 9:

[1123] After a user posts on social media, the server uses a public API to collect reaction data to that post (e.g., number of likes, comments, shares, etc.).

[1124] Step 10:

[1125] The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This data is then used to improve the generation accuracy in the next idea generation cycle.

[1126] For example, if a user inputs "I want to post a picture of my pet" and the emotion engine recognizes this as "fun," the server retrieves popular hashtags such as "life with pets" and "dog of the day" from the public APIs of social media. Then, the generative AI generates a specific idea such as, "Let's post a cute picture of your pet that you took on your walk this morning, along with the hashtag 'dog of the day.' Share this fun moment and have fun with others!" The server sends this idea to the user's device, and the user posts it to social media. Through this series of processes, the user can obtain effective content ideas that fit their emotional state.

[1127] (Example 2)

[1128] 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."

[1129] In social media posting, users face challenges in generating engaging and trending content. Furthermore, providing content ideas that resonate with users' emotions is a significant challenge. Additionally, there's a need for methods to analyze the response to generated content and improve the quality of future idea generation.

[1130] 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.

[1131] In this invention, the server includes means for inputting topics of interest to the user, means for transmitting the input topic information to a central processing unit, means for recognizing the user's emotional state from input or voice input, means for obtaining real-time trend information using a public API, means for generating content ideas using generative artificial intelligence based on the acquired trend information, the input topic information, and the recognized emotional state, means for presenting the generated content ideas to the user, means for evaluating the effectiveness of the generated content ideas, and means for collecting reaction data after posting the proposed content ideas to update the learning data of the generative artificial intelligence. This makes it possible to generate attractive content ideas based on the user's emotions and trends.

[1132] "Topic information" refers to information about themes and topics that users are interested in for their social media posts.

[1133] A "central processing unit" is a computer device that receives data within a system, analyzes it, and processes it in cooperation with other devices.

[1134] "Emotional state" refers to the emotional state (for example, happy, sad, excited, etc.) recognized from the user's input or voice.

[1135] A "public API" is an official interface provided for integrating with external applications and services, serving as a means of retrieving and manipulating data.

[1136] "Trending information" refers to data such as hashtags, keywords, images, and videos that are currently popular on social media and the internet.

[1137] "Generative artificial intelligence" refers to artificial intelligence models that generate new content and ideas based on collected data and input information.

[1138] "Content ideas" refer to suggestions regarding the specific content and format of social media posts that users can actually use to create their own posts.

[1139] "Evaluating effectiveness" is the process of assessing how successful the generated content ideas were on social media (e.g., number of likes, comments, shares).

[1140] "Response data" refers to data on user feedback (e.g., likes, comments, shares, etc.) on social media posts.

[1141] "Updating the learning data" is the process of retraining the generative artificial intelligence based on the collected response data to improve the accuracy of content idea generation in the future.

[1142] As an embodiment of this invention, a system is provided that allows a user to input information on topics of interest and generates ideas for social media posts by combining an emotion engine and generative artificial intelligence. This system is configured using a user terminal and a server.

[1143] User terminal

[1144] The user terminal has the following features:

[1145] Topic Information Input Interface: Includes text boxes and voice input functions for users to input information on topics of interest.

[1146] Emotion Engine: Recognizes emotions from the user's voice or text. For example, if a user says, "I'm in a happy mood today," the emotion "happy" is recognized.

[1147] Communication method: The input topic information and recognized emotional state are converted into JSON format data and sent to the server.

[1148] Displaying content ideas: Analyzes content ideas received from the server and displays them in a user-friendly format.

[1149] server

[1150] The server has the following features:

[1151] Method for obtaining trend information: Real-time trend information is obtained using public APIs of social media platforms. This includes popular hashtags, trending words, and related images and videos.

[1152] Generative Artificial Intelligence: Generates content ideas based on the user's emotional state recognized by the emotion engine, along with topic and trend information. This process is executed using a generative AI model.

[1153] Communication method: The generated content idea is converted to JSON format and sent to the user's terminal.

[1154] Feedback Collection and Learning: Collect reaction data (e.g., number of likes, comments, shares, etc.) to user-submitted social media content and update it as training data for generative artificial intelligence.

[1155] Specific example

[1156] For example, if a user types "I want to post a picture of my pet" and the device's emotion engine recognizes the emotion "happy," the following steps are executed: The server uses the public APIs of social media to retrieve popular hashtags such as "life with pets" and "dog of the day." Based on this information, the generative AI generates a specific idea such as, "Let's post a cute picture of your pet taken on this morning's walk, along with the hashtag 'dog of the day.' Share the fun moment and enjoy it with others!" The server sends this information to the user's device, which then presents it to the user. The user makes the post, and the server analyzes the reaction to help with future generation.

[1157] Example of a prompt

[1158] Enter "User wants to post a photo of their pet" and, if a positive emotion is detected, generate recommended social media posting ideas. The current trending hashtags are "Today's Dog" and "Life with Pets".

[1159] In this way, the system generates content ideas with a high potential to go viral based on user emotions and trend information, and supports effective posting on social media.

[1160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1161] Program processing flow

[1162] Step 1: Enter topic information

[1163] Specific actions:

[1164] The user accesses their device and enters "I want to post a picture of my pet" as the topic for their SNS post.

[1165] Input: User topic information.

[1166] Output: Topic information is recognized by the terminal.

[1167] Step 2: Recognizing Emotions

[1168] Specific actions:

[1169] When a user is typing or using voice input, the device's built-in emotion engine recognizes emotions such as "happy." For example, if the user says, "I'm in a happy mood today," the voice is captured and analyzed.

[1170] Input: User voice or text input.

[1171] Output: Recognized emotion (e.g., "happy").

[1172] Step 3: Submit information

[1173] Specific actions:

[1174] The topic information and recognized emotional state are converted into JSON format data, which the device then sends to the server over the network.

[1175] Input: Topic information and recognized emotional state.

[1176] Output: Data in JSON format is sent to the server.

[1177] Step 4: Obtaining trend information

[1178] Specific actions:

[1179] The server uses public APIs from social networking services to retrieve trending information such as "Life with Pets" and "Today's Dog." It accesses the APIs and saves the necessary data to an internal database.

[1180] Input: Request to access the SNS public API.

[1181] Output: Acquired trend information.

[1182] Step 5: Generating Content Ideas

[1183] Specific actions:

[1184] The server provides topic information, sentiment, and trend information as input data to the generative artificial intelligence (AI). The AI ​​model processes this information and generates specific content ideas that fit the user's sentiment. For example, it might generate an idea like, "Let's post a cute photo of our pet that we took on our walk this morning, along with the hashtag #TodaysDog."

[1185] Input: Topic information, sentiment status, trend information.

[1186] Output: Generated content ideas.

[1187] Step 6: Presenting Ideas

[1188] Specific actions:

[1189] The generated content ideas are converted into JSON format, and the server sends this to the user's terminal. The terminal parses the received data and displays it in a user-friendly format.

[1190] Input: Generated content idea.

[1191] Output: Content ideas to be displayed on the user's terminal.

[1192] Step 7: Posting on social media

[1193] Specific actions:

[1194] Users review the displayed content ideas, customize the text and images as needed, and post them on social media.

[1195] Input: A suggested content idea.

[1196] Output: Post to social media.

[1197] Step 8: Collecting post response data

[1198] Specific actions:

[1199] The server uses SNS APIs to collect reaction data to posts (e.g., number of likes, comments, shares, etc.).

[1200] Input: Request to access the SNS API.

[1201] Output: Collected reaction data.

[1202] Step 9: Updating the training data for generative artificial intelligence.

[1203] Specific actions:

[1204] The server analyzes the collected reaction data and updates the training data for the generative artificial intelligence. This improves the accuracy of the next generation.

[1205] Input: Collected response data.

[1206] Output: Updated training data for generative artificial intelligence.

[1207] (Application Example 2)

[1208] 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."

[1209] Traditional content generation systems did not adequately consider user emotions and trend information, making it difficult to generate content ideas that would generate higher engagement. Furthermore, advertising and marketing require the generation of effective advertising ideas that reflect user emotional states and are based on real-time trend information. Additionally, the lack of mechanisms to evaluate the effectiveness of generated content and feed that feedback back as learning data has made continuous improvement of generative artificial intelligence difficult.

[1210] 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. In this invention, the server includes means for transmitting topic information including emotional state to a central processing device, means for acquiring real-time trend information using a public API, and means for generating content ideas based on the acquired trend information, emotional state, and input topic information using generative artificial intelligence. This enables highly engaging advertising and marketing by generating content ideas that reflect the user's emotional state and real-time trend information.

[1211] A "user" is an individual or group that uses the system to generate content ideas.

[1212] A "topic" is information about subjects or themes that users are interested in.

[1213] "Emotional state" refers to information that indicates the user's current psychological state or mood, and is recognized by the emotion engine.

[1214] A "central processing unit" is a server that processes input topic information and sentiment states, and generates content ideas based on that information.

[1215] A "public API" is an application programming interface that is made publicly available to allow external access to specific functions or data.

[1216] "Real-time trend information" refers to topics and data that are attracting attention in society and the market at the present time.

[1217] "Generative artificial intelligence" is an artificial intelligence technology that generates new content ideas based on input data.

[1218] A "content idea" is a concrete concept of content that users can use on social media, in advertisements, etc.

[1219] "Presentation" refers to visually or audibly informing the user of a generated content idea.

[1220] This invention is a system that generates content ideas that reflect real-time trend information based on the user's interests and emotional state. To achieve this, the following program and hardware configuration are used.

[1221] Hardware and software to use

[1222] 1. Smartphone: A device in which the user inputs topic information through an interface and recognizes the emotional state using an emotion engine.

[1223] 2. Server: This is a central processing unit that receives and processes topic information and emotional states, and generates content ideas using generative artificial intelligence.

[1224] 3. Emotion Engine: Utilizes the Google Cloud Natural Language API to analyze user text and voice data and recognize their emotional state.

[1225] 4. Generative Artificial Intelligence: Using OpenAI GPT-3, new content ideas are generated based on input topics, sentiment states, and trend data.

[1226] 5. Public APIs: Real-time trend information is obtained using the Twitter API and Facebook Graph API.

[1227] System operation

[1228] Input and Recognition

[1229] The user enters topics of interest via text or voice on a smartphone app. The emotion engine analyzes this input data and recognizes the user's current emotional state (e.g., "excited"). The topic information and emotional state are then sent to the server in JSON format.

[1230] Acquisition of trend data

[1231] The server uses public APIs (Twitter API, Facebook Graph API) to retrieve real-time trend information. This includes popular hashtags and trending words.

[1232] Content Idea Generation

[1233] The server's generative artificial intelligence (OpenAI GPT-3) generates effective content ideas that match the user's emotions based on acquired topic information, emotional states, and trend information. For example, if the emotion is "excited," the generated ideas will include positive and impactful expressions.

[1234] Presentation and Feedback

[1235] The generated content ideas are sent back to the smartphone in JSON format and displayed visually to the user. The user then posts to social media based on the suggested ideas. The server collects reaction data to these posts (e.g., number of likes, comments, shares, etc.) and uses this as training data for generative artificial intelligence.

[1236] Examples of specific cases and prompt statements

[1237] For example, if a user enters "new product campaign" and the emotion engine recognizes the emotion "excited," an example of a prompt to the generative AI would be as follows:

[1238] Users are excited about the topic of "new product campaigns." Popular hashtags include "new product," "campaign," and "exciting new product." Based on this information, please generate positive and impactful advertising ideas.

[1239] The advertising ideas generated in this way can take the form of specific content proposals, such as, "Let's liven up the campaign by posting photos of the new product with a hashtag like 'Exciting New Product'!"

[1240] As described above, the present invention enables highly engaging advertising and marketing by generating content ideas that reflect the user's emotional state and real-time trend information.

[1241] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1242] Step 1: The user opens the app on their smartphone and enters information on a topic of interest (e.g., "New Product Campaign") via text or voice. The entered topic information is sent to the emotion engine.

[1243] Step 2: The emotion engine (Google Cloud Natural Language API) analyzes the input text and audio data to recognize the user's emotional state (e.g., "excited"). This process outputs the emotional state as the analysis result.

[1244] Step 3: The device converts the recognized emotional state and topic information into JSON format data and sends it to the server. The input is topic information and emotional state, and the output is JSON data that integrates these.

[1245] Step 4: The server parses the received JSON data and extracts topic information and sentiment status. Simultaneously, it retrieves real-time trend information using public APIs (Twitter API, Facebook Graph API). These API calls output popular hashtags and trending words.

[1246] Step 5: The server passes the acquired topic information, sentiment status, and trend information as input data to the generative artificial intelligence (OpenAI GPT-3) to generate content ideas. The input consists of topic information, sentiment status, and trend information, and the generated content ideas are output.

[1247] Step 6: Convert the generated content idea to JSON format and send it back to the terminal. The input is the generated content idea, and the output is data in JSON format.

[1248] Step 7: Visually present the content ideas received by the device to the user. For example, a positive advertising idea that evokes the emotion of "excitement" is displayed on the user's smartphone screen.

[1249] Step 8: The user reviews the suggested content idea, customizes it as needed, and posts it to social media. After posting, the server collects reaction data (e.g., number of likes, comments, shares).

[1250] Step 9: The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This improves the accuracy of content idea generation in the future.

[1251] Through the specific actions of each step, the present invention enables the efficient generation of highly engaging content ideas that reflect the user's emotional state and trend information.

[1252] 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.

[1253] 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.

[1254] 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.

[1255] [Fourth Embodiment]

[1256] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1257] 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.

[1258] 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).

[1259] 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.

[1260] 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.

[1261] 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).

[1262] 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.

[1263] 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.

[1264] 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.

[1265] 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.

[1266] 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.

[1267] 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.

[1268] 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".

[1269] To implement this invention, a system is constructed in which a user inputs information on topics of interest via a terminal, and a server uses generative artificial intelligence to generate content ideas that are likely to go viral based on that information.

[1270] Specifically, the following system configuration and program processing are required.

[1271] System Configuration

[1272] 1. User terminal:

[1273] It has an interface where users can input information on topics they are interested in.

[1274] It has a means of communication to receive input information and send it to the server.

[1275] It has a means of displaying content ideas received from the server.

[1276] 2. Server:

[1277] It has the ability to obtain real-time trend information through a public API.

[1278] It has the ability to generate content ideas based on topic information and trend information using generative artificial intelligence.

[1279] It has a means of communication to send the generated content ideas to the user's terminal.

[1280] It has features to evaluate the effectiveness of content ideas and collect user feedback.

[1281] It has a function to update the training data of generative artificial intelligence based on feedback.

[1282] Program processing

[1283] The following steps are necessary for a user's device to generate ideas for social media posts.

[1284] 1. Enter topic information:

[1285] The user accesses the device's interface and enters topics of interest such as "pets," "cooking," or "travel."

[1286] 2. Sending information:

[1287] The terminal sends this topic information to the server. This topic information is sent in JSON format or another appropriate data format.

[1288] 3. Obtaining trend information:

[1289] The server retrieves trending information such as currently popular hashtags, trending words, images, and videos in real time through the public APIs of social media platforms.

[1290] 4. Generating content ideas:

[1291] The server uses acquired trend information and user topic information as input data to run a generative artificial intelligence.

[1292] Based on this data, the generative artificial intelligence generates specific social media posting ideas. The proposed ideas are structured in the form of text, images, and videos.

[1293] 5. Idea presentation:

[1294] The server then sends the generated content ideas back to the user's terminal in an appropriate format, such as JSON.

[1295] The user's terminal analyzes the received data and presents it to the user in an easy-to-understand format.

[1296] Users review the suggested content ideas and post them to their own social media accounts.

[1297] Specific example

[1298] For example, if a user enters "I want to post a picture of my pet," the server will operate as follows:

[1299] 1. The server retrieves popular hashtags such as "Life with pets" and "Today's dog" from the public APIs of social media platforms.

[1300] 2. Generative artificial intelligence generates a specific idea such as, "It would be good to post a cute photo of your pet taken during your walk this morning and use the hashtag 'Today's Puppy'."

[1301] 3. The server sends this generated idea to the user's terminal, and the terminal displays it to the user.

[1302] Users post the suggested ideas to social media, and the server analyzes the reactions to those posts (number of likes, comments, etc.) to inform the generation of future ideas. This process ensures that users always have access to content ideas that are in line with current trends and have a high potential to go viral.

[1303] The following describes the processing flow.

[1304] Step 1:

[1305] The user accesses the device interface and enters topic information they want to post to social media (e.g., "pets," "cooking," "travel," etc.). The information entered by the user is sent to the server in the next step.

[1306] Step 2:

[1307] The terminal converts the topic information entered by the user into JSON format data and sends it to the server. At this point, the terminal checks the integrity of the data structure and verifies that there are no errors.

[1308] Step 3:

[1309] The server analyzes the topic information it receives and uses public APIs (e.g., Twitter API, Instagram API) to retrieve real-time trend information. This includes currently popular hashtags, trending words, images, videos, and more.

[1310] Step 4:

[1311] After the server acquires trend information, it passes the topic information and the acquired trend information as input data to a generative artificial intelligence (e.g., a natural language processing model). Based on this data, the generative artificial intelligence generates specific SNS posting ideas to suggest to the user.

[1312] Step 5:

[1313] The server receives specific posting ideas generated by a generative artificial intelligence and sends them to the user's terminal in JSON format or another appropriate data format. These ideas include relevant hashtags, text, and recommended images.

[1314] Step 6:

[1315] The device analyzes content ideas received from the server and displays them in a user-friendly format. The user reviews the displayed ideas and decides whether or not to post them on social media.

[1316] Step 7:

[1317] Users post content ideas suggested to them on social media. Users can modify the generated text and images as needed before posting.

[1318] Step 8:

[1319] The server collects reaction data to posts on social media (e.g., number of likes, comments, shares, etc.). This data collection again utilizes a public API.

[1320] Step 9:

[1321] The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This data will be used in the next idea generation process to improve the generation accuracy.

[1322] Step 10:

[1323] Users review the feedback and use it to improve future posts. They then use the system again for their next post to get content ideas based on the latest trends.

[1324] The above is the specific processing flow of the viral posting idea suggestion system using generative artificial intelligence. Through this system, users can always create effective social media posts based on the latest trend information.

[1325] (Example 1)

[1326] 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".

[1327] Traditional social media posting support systems lack the functionality to automatically generate effective content ideas for topics of user interest, making it difficult for users to create trending content with a high potential for virality. Furthermore, they lack mechanisms to evaluate the effectiveness of generated content and incorporate those evaluations into future content creation. This results in a problem where the efficiency and effectiveness of users' social media activities are not sufficiently improved.

[1328] 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.

[1329] In this invention, the server includes means for generating specific prompt sentences based on user input and passing them to a generative artificial intelligence system; means for monitoring the reactions to the user's SNS posts for a certain period and analyzing the data; and means for evaluating the effectiveness of the generated content ideas. As a result, users can easily obtain content ideas that are in line with trends and have a high potential to go viral, and by evaluating their effectiveness and reflecting it in the generation of future content, they can improve the efficiency and effectiveness of their SNS activities.

[1330] A "user terminal" is a device that allows users to input information on topics they are interested in and exchange data with the server.

[1331] A "central processing unit" is a system that receives data transmitted from user terminals, acquires trend information through public APIs, and generates content ideas using generative artificial intelligence.

[1332] A "public API" is an application programming interface that is made publicly accessible to external developers and services, and in this case, it is used to obtain real-time trend information from social media.

[1333] "Generative artificial intelligence" refers to an artificial intelligence system that receives data as input and generates content ideas using machine learning algorithms.

[1334] A "prompt" is a specific instruction or phrase given to a generative artificial intelligence system to generate content ideas.

[1335] "Content ideas" refer to specific ideas or suggestions that users can post on social media, and are composed of various forms such as text, images, and videos.

[1336] "SNS reaction data" refers to user reaction data such as the number of "likes," comments, and shares on content posted by users on social media.

[1337] "User feedback" refers to the evaluations and comments that users provide after using the generated content ideas, and it is used to improve generative artificial intelligence.

[1338] To implement this invention, it is necessary to construct a system in which a user inputs information on topics of interest via a terminal, and a server uses generative artificial intelligence to generate content ideas that are likely to go viral based on that information. The specific system configuration and program processing will be described below.

[1339] System Configuration

[1340] 1. User terminal:

[1341] It has an interface that allows users to input information on topics they are interested in.

[1342] It has a means of communication to send the input information to the server.

[1343] It has a means of displaying content ideas received from the server to the user.

[1344] 2. Server:

[1345] It has the ability to obtain real-time trend information through public APIs. In this case, APIs such as the Twitter API and Facebook Graph API can be used.

[1346] It has the function of generating content ideas based on topic information and trend information using generative artificial intelligence. GPT-4 and other similar generative AI can be used.

[1347] It has a means of communication to send the generated content ideas to the user's terminal.

[1348] It has features to evaluate the effectiveness of content ideas and collect user feedback.

[1349] It has a function to update the training data of generative artificial intelligence based on feedback.

[1350] Program processing

[1351] User terminal

[1352] 1. The user accesses the device's interface and enters a topic of interest (e.g., "pets," "cooking," "travel," etc.).

[1353] 2. The terminal sends the entered topic information to the server in JSON format. A POST request is used for this communication.

[1354] server

[1355] 1. The server uses publicly available APIs from social media platforms to retrieve trending information in real time, such as currently popular hashtags, trending words, images, and videos. For example, it retrieves "trending topics" from the Twitter API.

[1356] 2. The server sends prompt messages to the generative AI model (e.g., GPT-4) based on the user's topic and trend information. An example of a prompt message is as follows:

[1357] User topic: Pets

[1358] Trend Information: Life with Pets, Today's Dog

[1359] Prompt: Generate posting ideas that you would like to recommend to users.

[1360] 3. Based on this data, the generative artificial intelligence generates specific SNS posting ideas. For example, it might output an idea such as, "It would be good to post a cute photo of your pet taken during your walk this morning and use the hashtag 'Today's Puppy'."

[1361] 4. The server sends the generated content idea to the user's terminal in JSON format. Specifically, it uses a POST request.

[1362] 5. The server monitors the reactions to content posted by users on social media (e.g., the number of likes and comments) for a certain period of time and analyzes that data.

[1363] 6. The server updates the training data of the generative artificial intelligence based on the collected response data, improving the quality of new ideas.

[1364] This allows users to easily obtain content ideas that are in line with trends and have a high potential to go viral, enabling them to engage in social media activities efficiently and effectively.

[1365] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1366] Step 1:

[1367] The user accesses the device's interface and enters a topic of interest.

[1368] Input: Information on topics of interest (e.g., "pets," "cooking," "travel," etc.)

[1369] Output: A screen where topic information is displayed in the input field.

[1370] In terms of the specific operation, a dedicated text box appears on the terminal's input form, and the user enters topic information into it. When the user enters "pets," that information proceeds to the next step.

[1371] Step 2:

[1372] The terminal sends the entered topic information to the server in JSON format.

[1373] Input: Topic information (e.g., "pets")

[1374] Output: Data in JSON format is sent to the server as a POST request.

[1375] Specifically, the terminal converts the input topic information into JSON format and sends it to the server using an HTTP POST request. The topic information is included in the request body.

[1376] Step 3:

[1377] The server uses publicly available APIs from social media platforms to retrieve trending information in real time.

[1378] Input: Topic information (e.g., "pets")

[1379] Output: Trend information (e.g., Life with pets, Today's dog)

[1380] Specifically, the server accesses the public API endpoint to retrieve current trend data. It makes API calls, obtains trend information in JSON format, and analyzes it.

[1381] Step 4:

[1382] The server sends prompt messages to the generating AI model based on the user's topic information and trend information.

[1383] Input: User topic information (e.g., "Pets") and trending information (e.g., Life with pets, Today's dog)

[1384] Output: Prompt message (Example: "User topic: Pets. Trending topics: Life with pets, Today's dog. Generate post ideas to recommend to the user.")

[1385] In terms of specific operation, the server combines topic information and trend information and sends a prompt message like the following to the AI ​​model (e.g., GPT-4). An example of a prompt message is as follows:

[1386] User topic: Pets

[1387] Trend Information: Life with Pets, Today's Dog

[1388] Prompt: Generate posting ideas that you would like to recommend to users.

[1389] Step 5:

[1390] The generative AI model generates specific social media posting ideas based on the prompt text.

[1391] Input: Prompt message (Example: As shown above)

[1392] Output: Content ideas (Example: "Post a cute photo of your pet taken during your morning walk and use the hashtag #TodaysDog")

[1393] In terms of its operation, the generative AI model analyzes the prompt text and generates specific posting ideas based on the topic and trends. As a result, social media posting ideas in text format are output.

[1394] Step 6:

[1395] The server sends the generated content ideas to the user's terminal in JSON format.

[1396] Input: Content idea (Example: As above)

[1397] Output: Data in JSON format is sent to the user's terminal as a POST request.

[1398] Specifically, the server converts the generated content idea into JSON format and sends it to the user's terminal using an HTTP POST request.

[1399] Step 7:

[1400] The device displays received content ideas in a format that is easy for the user to view.

[1401] Input: Content idea in JSON format (e.g., as shown above)

[1402] Output: Content ideas displayed to the user (e.g., "Post a cute photo of your pet taken during your morning walk and use the hashtag #TodaysDog")

[1403] Specifically, the device parses the received JSON data and displays content ideas on the screen in text format. Based on this information, the user posts to social media.

[1404] Step 8:

[1405] A user posts something on social media, and then the server monitors the reactions to that post for a certain period of time.

[1406] Input: User posts and social media reaction data

[1407] Output: Post reaction data (e.g., number of likes, number of comments, etc.)

[1408] Specifically, the server uses APIs and scraping tools to collect user response data to posts from social media over a certain period of time.

[1409] Step 9:

[1410] The server updates the training data for the generated AI model based on the collected reaction data.

[1411] Input: Post reaction data (e.g., number of likes, number of comments, etc.)

[1412] Output: Updated generative AI model

[1413] Specifically, the server analyzes the collected response data and adds it as feedback to the training dataset of the generating AI model, thereby improving the accuracy of idea generation in the future.

[1414] (Application Example 1)

[1415] 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".

[1416] Traditional content generation systems struggled to incorporate trending information and propose viral content. Furthermore, the lack of mechanisms to collect user feedback and optimize the generating AI made it difficult to continuously improve the effectiveness of the generated content. Therefore, there is a need for a system that reflects trending information in real time and improves the quality of content generation based on user feedback.

[1417] 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.

[1418] In this invention, the server includes means for inputting topics of interest to the user, means for transmitting the input topic information to a central processing unit, means for obtaining real-time trend information using a public API, means for generating content ideas based on the acquired trend information and the input topic information using generative artificial intelligence, means for presenting the generated content ideas to the user, and means for collecting user feedback and updating the generative artificial intelligence. This makes it possible to always provide content ideas that reflect the latest trend information and to optimize the generative artificial intelligence based on user feedback.

[1419] "A means for users to input topics they are interested in" refers to an interface or input device that allows users to input specific topics they are interested in.

[1420] "Means for transmitting input topic information to a central processing unit" refers to communication means that have the function of transferring topic information entered by a user to a server or central processing unit via a network.

[1421] "Methods for obtaining real-time trend information using public APIs" refers to functions that use publicly available application programming interfaces provided by services such as social media and news sites to obtain the latest trend information.

[1422] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to learn from large amounts of data and generate new ideas or content in response to specific inputs.

[1423] "Content ideas" refer to specific suggestions or ideas, such as text, images, and videos, that users can share on social media and other digital platforms.

[1424] "Means of presenting to the user" refers to interfaces or devices that have the functionality to display generated content ideas so that users can directly see and review them.

[1425] "Means for collecting user feedback and updating generative artificial intelligence" refers to a function that collects user reactions and evaluations, uses that data as training data for generative artificial intelligence, and improves the accuracy and performance of the model.

[1426] This invention relates to a system that takes a user's input of topics of interest and generates content ideas with a high probability of going viral based on that input. This system is implemented using a user terminal, a server, and generative artificial intelligence.

[1427] System program

[1428] A system for carrying out this invention includes the following configuration and process.

[1429] 1. Processing at the user terminal

[1430] The user terminal provides an interface for users to input topics they are interested in. When a user inputs a topic through the interface, that information is sent to the server. It also has the functionality to display content ideas received from the server.

[1431] 2. Server processing

[1432] The server receives topic information entered from the user's terminal. Using this information, the server accesses public APIs (e.g., Twitter API, Instagram Graph API) to obtain real-time trend information.

[1433] The server uses generative artificial intelligence (e.g., OpenAI's GPT model) to generate content ideas based on acquired trend information and user topic information. The generated content ideas are sent from the server to the user's terminal. Furthermore, feedback from the user is collected and used to update the training data of the generative artificial intelligence model.

[1434] 3. Details of Generative Artificial Intelligence

[1435] Generative artificial intelligence has the ability to learn from large amounts of data and generate new ideas and content in response to specific inputs. This system uses generative AI models such as OpenAI's GPT-4. The model uses acquired trend information and user topic information as prompts to generate specific content ideas.

[1436] Hardware and software to be used

[1437] Smartphones: iOS and Android devices

[1438] SNS public API: Twitter API, Instagram Graph API

[1439] Generative artificial intelligence: OpenAI's GPT-4 model

[1440] Cloud services: Use AWS or Google Cloud to host servers and databases.

[1441] Communication protocol: Data transmission using HTTP / HTTPS

[1442] Specific example

[1443] For example, if a user enters "post a photo of my pet," the server will operate as follows:

[1444] 1. The server retrieves trending information such as "Today's Dog," "Cute Animals," and "Walking Time" from publicly available APIs of social media platforms.

[1445] 2. Generative artificial intelligence generates posting ideas that are likely to go viral based on acquired trend information and user topic information. In this case, specific ideas such as "It would be good to post a cute photo of your pet taken on your walk this morning with the hashtag 'Today's Puppy'" might be suggested.

[1446] 3. The server sends the generated ideas to the user terminal, and the user terminal displays them.

[1447] 4. Users post ideas based on the suggestions on social media and input the reactions to those posts into the app. This feedback is sent to the server and used as training data for generative artificial intelligence.

[1448] Example of a prompt

[1449] Based on the following topic and trending information, generate social media post ideas that are likely to go viral.

[1450] Topic Information: Pet Photo Submissions

[1451] Trending Information: Today's Dogs, Cute Animals, Walking Time

[1452] This method allows the system to continuously optimize its generative artificial intelligence, enabling it to consistently provide users with high-quality content ideas that reflect the latest trends.

[1453] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1454] Step 1:

[1455] The user enters a topic of interest. The user enters a specific topic (e.g., "Posting photos of pets") into a text box via the smartphone application interface. This input data is then sent directly to the server in JSON format.

[1456] Step 2:

[1457] The server receives the input topic information. The server receives the topic information sent from the user terminal and temporarily stores this information in its internal database. The input is "Pet photo submission," and the output is the related data stored internally.

[1458] Step 3:

[1459] This system uses public APIs to retrieve real-time trend information. The server calls public APIs of social media platforms (e.g., Twitter API, Instagram Graph API) to obtain the latest trend information (e.g., "Today's Dog," "Cute Animals," "Walking Time"). The input is an API call request, and the output is real-time trend information.

[1460] Step 4:

[1461] This system uses generative artificial intelligence to generate content ideas. The server uses acquired trend information and user-entered topic information as input data. It inputs prompts into a generative AI model (e.g., OpenAI's GPT-4) to generate specific SNS posting ideas. The input consists of pairs of topic and trend information, and the output is the generated content idea.

[1462] Examples of specific prompt messages:

[1463] Based on the following topic and trending information, generate social media post ideas that are likely to go viral.

[1464] Topic Information: Pet Photo Submissions

[1465] Trending Information: Today's Dogs, Cute Animals, Walking Time

[1466] Step 5:

[1467] The generated content ideas are presented to the user. The server converts the generated ideas back into JSON format and sends them to the user's terminal. The user's terminal parses the received JSON data and presents it to the user in an easy-to-read format. The input is the generated content idea data, and the output is the specific content idea displayed on the user's terminal screen.

[1468] Step 6:

[1469] Users review the suggested content ideas and post them on social media. Based on the presented content ideas, users post on their own social media accounts. At this stage, users can also customize their posts.

[1470] Step 7:

[1471] The system collects reaction data after a post is made. Users input reactions to their posts (e.g., number of likes, comments, shares, etc.) into the app. The user's device sends this feedback data to the server in JSON format. The input is the user's feedback data, and the output is a feedback record stored on the server.

[1472] Step 8:

[1473] The training data for the generative artificial intelligence is updated. Based on the collected feedback data, the server updates the training dataset for the generative AI to aid in future idea generation. This feedback loop continuously improves the generated content ideas. The input is the feedback data, and the output is the updated AI model. This entire process improves the overall performance and accuracy of the system.

[1474] 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.

[1475] To implement this invention, a system is provided that allows users to input information on topics they are interested in, and then combines an emotion engine and generative artificial intelligence to generate ideas for social media posts.

[1476] System Configuration

[1477] 1. User terminal:

[1478] It has an interface where users can input information on topics they are interested in.

[1479] It features an emotion engine that recognizes emotions from the user's voice and text.

[1480] It has a means of communication that sends input information and recognized emotional states to a server.

[1481] It has a means of displaying content ideas from the server to the user.

[1482] 2. Server:

[1483] It has the ability to obtain real-time trend information through a public API.

[1484] The emotion engine recognizes the user's emotional state and topic information, which are then passed as input data to the generative artificial intelligence.

[1485] The generative artificial intelligence has the ability to generate content ideas based on trend information and emotional states.

[1486] It has a means of communication to send the generated content ideas to the user's terminal.

[1487] It has features to evaluate the effectiveness of content ideas and collect user feedback.

[1488] It has a function to update the training data of generative artificial intelligence based on feedback.

[1489] Program processing

[1490] The system provides content ideas with a high probability of going viral through the following processing steps.

[1491] 1. Enter topic information:

[1492] The user accesses the device's interface and enters topic information for a social media post.

[1493] 2. Recognition of emotions:

[1494] When a user is typing or using voice input, the emotion engine built into the device recognizes the user's emotional state from the text or voice. For example, if a user expresses emotions such as "happy" or "sad," the emotion engine analyzes that.

[1495] 3. Sending information:

[1496] The device converts topic information and recognized emotional states into JSON data and sends it to the server.

[1497] 4. Obtaining trend information:

[1498] The server uses the public APIs of social media platforms to retrieve current trending information. This includes data such as popular hashtags, trending words, images, and videos.

[1499] 5. Generating content ideas:

[1500] The server provides the generative artificial intelligence (AI) with topic information, emotional state, and acquired trend information as input data. Based on this data, the AI ​​generates content ideas that match the user's emotions.

[1501] 6. Idea presentation:

[1502] The server then sends the generated content ideas back to the user's terminal in an appropriate format, such as JSON.

[1503] The system analyzes the data received by the user's device and displays it in a user-friendly format. For example, if the emotion is recognized as "happy," ideas containing bright and positive expressions will be presented.

[1504] 7. Posting on social media:

[1505] Users can review the displayed content ideas and post them to social media. They can also customize the text and images as needed.

[1506] 8. Collection of reaction data to posts:

[1507] The server collects reaction data to posts on social media (e.g., number of likes, comments, shares, etc.).

[1508] 9. Learning of Generative Artificial Intelligence:

[1509] The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This data is then used to improve the generation accuracy in the next idea generation cycle.

[1510] Specific example

[1511] For example, if a user types "I want to post a picture of my pet" and the device's emotion engine recognizes the user's emotional state as "happy," the server will operate as follows:

[1512] 1. The server retrieves popular hashtags such as "Life with pets" and "Today's dog" from the public APIs of social media platforms.

[1513] 2. The generative artificial intelligence generates a specific idea such as, "Let's post a cute photo of your pet taken during this morning's walk, along with the hashtag 'Today's Puppy.' Let's share fun moments and have fun with others!"

[1514] 3. The server sends this generated idea to the user's terminal, and the terminal displays it to the user.

[1515] Users post the suggested ideas on social media, and the server analyzes the reactions to those posts and incorporates the findings into future idea generation. By using an emotion engine, it becomes possible to provide more effective content ideas that resonate with users' emotions.

[1516] The following describes the processing flow.

[1517] Step 1:

[1518] The user accesses the device's interface and enters topic information they want to post to social media (e.g., "pets," "cooking," "travel," etc.). They can use input forms or voice input functions to do so.

[1519] Step 2:

[1520] As the user types or voices topic information, the device's built-in emotion engine recognizes the user's emotional state (e.g., "happy," "sad," "excited," etc.) from the text or voice.

[1521] Step 3:

[1522] The emotion engine recognizes the user's emotional state and converts it into JSON data along with topic information. The device then sends this data to the server.

[1523] Step 4:

[1524] The server analyzes the topic information received and the user's sentiment state, and uses public SNS APIs (e.g., Twitter API, Instagram API, etc.) to obtain real-time trend information. This information includes popular hashtags, trending words, images, videos, and more.

[1525] Step 5:

[1526] The server receives trend information, along with topic information and emotional states submitted by users, as input data for a generative artificial intelligence (AI). The AI ​​then analyzes this data and generates specific SNS posting ideas tailored to the user's emotional state.

[1527] Step 6:

[1528] The server receives specific posting ideas generated by a generative artificial intelligence and sends them to the user's terminal in an appropriate format (e.g., JSON). These ideas include relevant hashtags, text, and recommended images.

[1529] Step 7:

[1530] The device analyzes the content ideas it receives and displays them in a user-friendly format. For example, if the emotion is recognized as "happy," ideas containing bright and positive expressions will be presented.

[1531] Step 8:

[1532] Users can review the displayed content ideas and post them to their own social media accounts. They can also customize the text and images as needed.

[1533] Step 9:

[1534] After a user posts on social media, the server uses a public API to collect reaction data to that post (e.g., number of likes, comments, shares, etc.).

[1535] Step 10:

[1536] The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This data is then used to improve the generation accuracy in the next idea generation cycle.

[1537] For example, if a user inputs "I want to post a picture of my pet" and the emotion engine recognizes this as "fun," the server retrieves popular hashtags such as "life with pets" and "dog of the day" from the public APIs of social media. Then, the generative AI generates a specific idea such as, "Let's post a cute picture of your pet that you took on your walk this morning, along with the hashtag 'dog of the day.' Share this fun moment and have fun with others!" The server sends this idea to the user's device, and the user posts it to social media. Through this series of processes, the user can obtain effective content ideas that fit their emotional state.

[1538] (Example 2)

[1539] 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".

[1540] In social media posting, users face challenges in generating engaging and trending content. Furthermore, providing content ideas that resonate with users' emotions is a significant challenge. Additionally, there's a need for methods to analyze the response to generated content and improve the quality of future idea generation.

[1541] 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.

[1542] In this invention, the server includes means for inputting topics of interest to the user, means for transmitting the input topic information to a central processing unit, means for recognizing the user's emotional state from input or voice input, means for obtaining real-time trend information using a public API, means for generating content ideas using generative artificial intelligence based on the acquired trend information, the input topic information, and the recognized emotional state, means for presenting the generated content ideas to the user, means for evaluating the effectiveness of the generated content ideas, and means for collecting reaction data after posting the proposed content ideas to update the learning data of the generative artificial intelligence. This makes it possible to generate attractive content ideas based on the user's emotions and trends.

[1543] "Topic information" refers to information about themes and topics that users are interested in for their social media posts.

[1544] A "central processing unit" is a computer device that receives data within a system, analyzes it, and processes it in cooperation with other devices.

[1545] "Emotional state" refers to the emotional state (for example, happy, sad, excited, etc.) recognized from the user's input or voice.

[1546] A "public API" is an official interface provided for integrating with external applications and services, serving as a means of retrieving and manipulating data.

[1547] "Trending information" refers to data such as hashtags, keywords, images, and videos that are currently popular on social media and the internet.

[1548] "Generative artificial intelligence" refers to artificial intelligence models that generate new content and ideas based on collected data and input information.

[1549] "Content ideas" refer to suggestions regarding the specific content and format of social media posts that users can actually use to create their own posts.

[1550] "Evaluating effectiveness" is the process of assessing how successful the generated content ideas were on social media (e.g., number of likes, comments, shares).

[1551] "Response data" refers to data on user feedback (e.g., likes, comments, shares, etc.) on social media posts.

[1552] "Updating the learning data" is the process of retraining the generative artificial intelligence based on the collected response data to improve the accuracy of content idea generation in the future.

[1553] As an embodiment of this invention, a system is provided that allows a user to input information on topics of interest and generates ideas for social media posts by combining an emotion engine and generative artificial intelligence. This system is configured using a user terminal and a server.

[1554] User terminal

[1555] The user terminal has the following features:

[1556] Topic Information Input Interface: Includes text boxes and voice input functions for users to input information on topics of interest.

[1557] Emotion Engine: Recognizes emotions from the user's voice or text. For example, if a user says, "I'm in a happy mood today," the emotion "happy" is recognized.

[1558] Communication method: The input topic information and recognized emotional state are converted into JSON format data and sent to the server.

[1559] Displaying content ideas: Analyzes content ideas received from the server and displays them in a user-friendly format.

[1560] server

[1561] The server has the following features:

[1562] Method for obtaining trend information: Real-time trend information is obtained using public APIs of social media platforms. This includes popular hashtags, trending words, and related images and videos.

[1563] Generative Artificial Intelligence: Generates content ideas based on the user's emotional state recognized by the emotion engine, along with topic and trend information. This process is executed using a generative AI model.

[1564] Communication method: The generated content idea is converted to JSON format and sent to the user's terminal.

[1565] Feedback Collection and Learning: Collect reaction data (e.g., number of likes, comments, shares, etc.) to user-submitted social media content and update it as training data for generative artificial intelligence.

[1566] Specific example

[1567] For example, if a user types "I want to post a picture of my pet" and the device's emotion engine recognizes the emotion "happy," the following steps are executed: The server uses the public APIs of social media to retrieve popular hashtags such as "life with pets" and "dog of the day." Based on this information, the generative AI generates a specific idea such as, "Let's post a cute picture of your pet taken on this morning's walk, along with the hashtag 'dog of the day.' Share the fun moment and enjoy it with others!" The server sends this information to the user's device, which then presents it to the user. The user makes the post, and the server analyzes the reaction to help with future generation.

[1568] Example of a prompt

[1569] Enter "User wants to post a photo of their pet" and, if a positive emotion is detected, generate recommended social media posting ideas. The current trending hashtags are "Today's Dog" and "Life with Pets".

[1570] In this way, the system generates content ideas with a high potential to go viral based on user emotions and trend information, and supports effective posting on social media.

[1571] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1572] Program processing flow

[1573] Step 1: Enter topic information

[1574] Specific actions:

[1575] The user accesses their device and enters "I want to post a picture of my pet" as the topic for their SNS post.

[1576] Input: User topic information.

[1577] Output: Topic information is recognized by the terminal.

[1578] Step 2: Recognizing Emotions

[1579] Specific actions:

[1580] When a user is typing or using voice input, the device's built-in emotion engine recognizes emotions such as "happy." For example, if the user says, "I'm in a happy mood today," the voice is captured and analyzed.

[1581] Input: User voice or text input.

[1582] Output: Recognized emotion (e.g., "happy").

[1583] Step 3: Submit information

[1584] Specific actions:

[1585] The topic information and recognized emotional state are converted into JSON format data, which the device then sends to the server over the network.

[1586] Input: Topic information and recognized emotional state.

[1587] Output: Data in JSON format is sent to the server.

[1588] Step 4: Obtaining trend information

[1589] Specific actions:

[1590] The server uses public APIs from social networking services to retrieve trending information such as "Life with Pets" and "Today's Dog." It accesses the APIs and saves the necessary data to an internal database.

[1591] Input: Request to access the SNS public API.

[1592] Output: Acquired trend information.

[1593] Step 5: Generating Content Ideas

[1594] Specific actions:

[1595] The server provides topic information, sentiment, and trend information as input data to the generative artificial intelligence (AI). The AI ​​model processes this information and generates specific content ideas that fit the user's sentiment. For example, it might generate an idea like, "Let's post a cute photo of our pet that we took on our walk this morning, along with the hashtag #TodaysDog."

[1596] Input: Topic information, sentiment status, trend information.

[1597] Output: Generated content ideas.

[1598] Step 6: Presenting Ideas

[1599] Specific actions:

[1600] The generated content ideas are converted into JSON format, and the server sends this to the user's terminal. The terminal parses the received data and displays it in a user-friendly format.

[1601] Input: Generated content idea.

[1602] Output: Content ideas to be displayed on the user's terminal.

[1603] Step 7: Posting on social media

[1604] Specific actions:

[1605] Users review the displayed content ideas, customize the text and images as needed, and post them on social media.

[1606] Input: A suggested content idea.

[1607] Output: Post to social media.

[1608] Step 8: Collecting post response data

[1609] Specific actions:

[1610] The server uses SNS APIs to collect reaction data to posts (e.g., number of likes, comments, shares, etc.).

[1611] Input: Request to access the SNS API.

[1612] Output: Collected reaction data.

[1613] Step 9: Updating the training data for generative artificial intelligence.

[1614] Specific actions:

[1615] The server analyzes the collected reaction data and updates the training data for the generative artificial intelligence. This improves the accuracy of the next generation.

[1616] Input: Collected response data.

[1617] Output: Updated training data for generative artificial intelligence.

[1618] (Application Example 2)

[1619] 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".

[1620] Traditional content generation systems did not adequately consider user emotions and trend information, making it difficult to generate content ideas that would generate higher engagement. Furthermore, advertising and marketing require the generation of effective advertising ideas that reflect user emotional states and are based on real-time trend information. Additionally, the lack of mechanisms to evaluate the effectiveness of generated content and feed that feedback back as learning data has made continuous improvement of generative artificial intelligence difficult.

[1621] 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. In this invention, the server includes means for transmitting topic information including emotional state to a central processing device, means for acquiring real-time trend information using a public API, and means for generating content ideas based on the acquired trend information, emotional state, and input topic information using generative artificial intelligence. This enables highly engaging advertising and marketing by generating content ideas that reflect the user's emotional state and real-time trend information.

[1622] A "user" is an individual or group that uses the system to generate content ideas.

[1623] A "topic" is information about subjects or themes that users are interested in.

[1624] "Emotional state" refers to information that indicates the user's current psychological state or mood, and is recognized by the emotion engine.

[1625] A "central processing unit" is a server that processes input topic information and sentiment states, and generates content ideas based on that information.

[1626] A "public API" is an application programming interface that is made publicly available to allow external access to specific functions or data.

[1627] "Real-time trend information" refers to topics and data that are attracting attention in society and the market at the present time.

[1628] "Generative artificial intelligence" is an artificial intelligence technology that generates new content ideas based on input data.

[1629] A "content idea" is a concrete concept of content that users can use on social media, in advertisements, etc.

[1630] "Presentation" refers to visually or audibly informing the user of a generated content idea.

[1631] This invention is a system that generates content ideas that reflect real-time trend information based on the user's interests and emotional state. To achieve this, the following program and hardware configuration are used.

[1632] Hardware and software to use

[1633] 1. Smartphone: A device in which the user inputs topic information through an interface and recognizes the emotional state using an emotion engine.

[1634] 2. Server: This is a central processing unit that receives and processes topic information and emotional states, and generates content ideas using generative artificial intelligence.

[1635] 3. Emotion Engine: Utilizes the Google Cloud Natural Language API to analyze user text and voice data and recognize their emotional state.

[1636] 4. Generative Artificial Intelligence: Using OpenAI GPT-3, new content ideas are generated based on input topics, sentiment states, and trend data.

[1637] 5. Public APIs: Real-time trend information is obtained using the Twitter API and Facebook Graph API.

[1638] System operation

[1639] Input and Recognition

[1640] The user enters topics of interest via text or voice on a smartphone app. The emotion engine analyzes this input data and recognizes the user's current emotional state (e.g., "excited"). The topic information and emotional state are then sent to the server in JSON format.

[1641] Acquisition of trend data

[1642] The server uses public APIs (Twitter API, Facebook Graph API) to retrieve real-time trend information. This includes popular hashtags and trending words.

[1643] Content Idea Generation

[1644] The server's generative artificial intelligence (OpenAI GPT-3) generates effective content ideas that match the user's emotions based on acquired topic information, emotional states, and trend information. For example, if the emotion is "excited," the generated ideas will include positive and impactful expressions.

[1645] Presentation and Feedback

[1646] The generated content ideas are sent back to the smartphone in JSON format and displayed visually to the user. The user then posts to social media based on the suggested ideas. The server collects reaction data to these posts (e.g., number of likes, comments, shares, etc.) and uses this as training data for generative artificial intelligence.

[1647] Examples of specific cases and prompt statements

[1648] For example, if a user enters "new product campaign" and the emotion engine recognizes the emotion "excited," an example of a prompt to the generative AI would be as follows:

[1649] Users are excited about the topic of "new product campaigns." Popular hashtags include "new product," "campaign," and "exciting new product." Based on this information, please generate positive and impactful advertising ideas.

[1650] The advertising ideas generated in this way can take the form of specific content proposals, such as, "Let's liven up the campaign by posting photos of the new product with a hashtag like 'Exciting New Product'!"

[1651] As described above, the present invention enables highly engaging advertising and marketing by generating content ideas that reflect the user's emotional state and real-time trend information.

[1652] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1653] Step 1: The user opens the app on their smartphone and enters information on a topic of interest (e.g., "New Product Campaign") via text or voice. The entered topic information is sent to the emotion engine.

[1654] Step 2: The emotion engine (Google Cloud Natural Language API) analyzes the input text and audio data to recognize the user's emotional state (e.g., "excited"). This process outputs the emotional state as the analysis result.

[1655] Step 3: The device converts the recognized emotional state and topic information into JSON format data and sends it to the server. The input is topic information and emotional state, and the output is JSON data that integrates these.

[1656] Step 4: The server parses the received JSON data and extracts topic information and sentiment status. Simultaneously, it retrieves real-time trend information using public APIs (Twitter API, Facebook Graph API). These API calls output popular hashtags and trending words.

[1657] Step 5: The server passes the acquired topic information, sentiment status, and trend information as input data to the generative artificial intelligence (OpenAI GPT-3) to generate content ideas. The input consists of topic information, sentiment status, and trend information, and the generated content ideas are output.

[1658] Step 6: Convert the generated content idea to JSON format and send it back to the terminal. The input is the generated content idea, and the output is data in JSON format.

[1659] Step 7: Visually present the content ideas received by the device to the user. For example, a positive advertising idea that evokes the emotion of "excitement" is displayed on the user's smartphone screen.

[1660] Step 8: The user reviews the suggested content idea, customizes it as needed, and posts it to social media. After posting, the server collects reaction data (e.g., number of likes, comments, shares).

[1661] Step 9: The server analyzes the collected reaction data and saves it as training data for the generative artificial intelligence. This improves the accuracy of content idea generation in the future.

[1662] Through the specific actions of each step, the present invention enables the efficient generation of highly engaging content ideas that reflect the user's emotional state and trend information.

[1663] 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.

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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.

[1669] 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.

[1670] 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.

[1671] 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."

[1672] 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.

[1673] 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.

[1674] 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.

[1675] 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.

[1676] 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.

[1677] 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.

[1678] 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.

[1679] 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.

[1680] 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.

[1681] 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.

[1682] 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.

[1683] 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.

[1684] The following is further disclosed regarding the embodiments described above.

[1685] (Claim 1)

[1686] A means for users to input topics they are interested in,

[1687] Means for transmitting input topic information to a central processing unit,

[1688] A method for obtaining real-time trend information using a public API,

[1689] A means for generating content ideas based on acquired trend information and input topic information using generative artificial intelligence,

[1690] A means of presenting generated content ideas to users,

[1691] A system that includes this.

[1692] (Claim 2)

[1693] The system according to claim 1, further comprising means for evaluating the effectiveness of the generated content ideas.

[1694] (Claim 3)

[1695] The system according to claim 1, further comprising means for collecting response data after the posting of proposed content ideas to update the training data of a generative artificial intelligence.

[1696] "Example 1"

[1697] (Claim 1)

[1698] A means for users to input topics they are interested in,

[1699] Means for transmitting input topic information to a central processing unit,

[1700] A method for obtaining real-time trend information using a public API,

[1701] A means for generating content ideas based on acquired trend information and input topic information using generative artificial intelligence,

[1702] A means of presenting generated content ideas to users,

[1703] A means of generating specific prompt sentences based on user input and passing them to a generative artificial intelligence,

[1704] A method for monitoring user reactions to social media posts over a certain period and analyzing that data,

[1705] A system that includes this.

[1706] (Claim 2)

[1707] The system according to claim 1, further comprising means for evaluating the effectiveness of the generated content ideas.

[1708] (Claim 3)

[1709] The system according to claim 1, further comprising means for collecting response data after the posting of proposed content ideas to update the training data of a generative artificial intelligence.

[1710] "Application Example 1"

[1711] (Claim 1)

[1712] A means for users to input topics they are interested in,

[1713] Means for transmitting input topic information to a central processing unit,

[1714] A method for obtaining real-time trend information using a public API,

[1715] A means for generating content ideas based on acquired trend information and input topic information using generative artificial intelligence,

[1716] A means of presenting generated content ideas to users,

[1717] A means of collecting user feedback and updating the generative artificial intelligence,

[1718] A system that includes this.

[1719] (Claim 2)

[1720] The system according to claim 1, further comprising means for evaluating the effectiveness of the generated content ideas.

[1721] (Claim 3)

[1722] The system according to claim 1, further comprising means for collecting response data after the posting of proposed content ideas to update the training data of a generative artificial intelligence.

[1723] "Example 2 of combining an emotion engine"

[1724] (Claim 1)

[1725] A means for users to input topics they are interested in,

[1726] Means for transmitting input topic information to a central processing unit,

[1727] A means of recognizing the user's emotional state from input or voice input,

[1728] A method for obtaining real-time trend information using a public API,

[1729] A means for generating content ideas using generative artificial intelligence, based on acquired trend information, input topic information, and recognized emotional states,

[1730] A means of presenting generated content ideas to users,

[1731] A system that includes this.

[1732] (Claim 2)

[1733] The system according to claim 1, further comprising means for evaluating the effectiveness of the generated content ideas.

[1734] (Claim 3)

[1735] The system according to claim 1, further comprising means for collecting response data after the posting of proposed content ideas to update the training data of a generative artificial intelligence.

[1736] "Application example 2 of combining emotional engines"

[1737] (Claim 1)

[1738] A means for users to input topics they are interested in,

[1739] Means for transmitting input topic information and emotional state to a central processing unit,

[1740] A method for obtaining real-time trend information using a public API,

[1741] A means for generating content ideas based on acquired trend information, emotional state, and input topic information using generative artificial intelligence,

[1742] A means of presenting generated content ideas to users,

[1743] A system that includes this.

[1744] (Claim 2)

[1745] The system according to claim 1, further comprising means for evaluating the effectiveness of the generated content ideas.

[1746] (Claim 3)

[1747] The system according to claim 1, further comprising means for collecting response data after the posting of proposed content ideas to update the training data of a generative artificial intelligence. [Explanation of symbols]

[1748] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to input topics they are interested in, Means for transmitting input topic information to a central processing unit, A method for obtaining real-time trend information using a public API, A means for generating content ideas based on acquired trend information and input topic information using generative artificial intelligence, A means of presenting generated content ideas to users, A system that includes this.

2. The system according to claim 1, further comprising means for evaluating the effectiveness of the generated content ideas.

3. The system according to claim 1, further comprising means for collecting response data after the submission of proposed content ideas and updating the training data of a generative artificial intelligence.

Images