System

Abstract

A system is provided.SOLUTION: A system, comprising: means for designing and publishing a protocol for optimizing production AI; means for evaluating a website or content based on the protocol; means for calculating a reward based on the evaluation; and means for paying the reward to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] While traditional reward programs emphasize ease of measurement and efficiency for their operators, they are ineffective because the website information is not optimized for generative AI. Furthermore, while the SEO market exists, optimization protocols for generative AI have not been developed, making it difficult to meet new demands. As a result, users of generative AI face the challenge of having their optimized content properly evaluated and rewarded. [Means for solving the problem]

[0005] This invention provides a means for designing and publishing a protocol to promote optimization by generative AI and accurately evaluate its effectiveness. Furthermore, a system is constructed that includes a means for evaluating websites and content based on this protocol, calculating rewards based on the evaluation results, and paying users. This allows users of generative AI to generate content according to the protocol, have the submitted content properly evaluated, and receive rewards. Specifically, the system includes a means for downloading the protocol, generating websites and content, a means for submitting the generated website and content to a server, and a means for providing generative AI content based on the protocol and generating answers.

[0006] "Generative AI" is a technology that uses artificial intelligence to automatically generate content and answers.

[0007] A "protocol" is a set of guidelines or standards established to achieve a particular purpose.

[0008] A "website" is a collection of related web pages organized to provide information and published on the Internet.

[0009] "Content" refers collectively to the information and media provided on websites and other digital platforms.

[0010] "Evaluation" is the process of measuring and judging the performance or value of an object based on specific criteria.

[0011] "Remuneration" means money or other consideration paid for specific tasks or results.

[0012] "User" means an end user or content creator who uses the system or service.

[0013] "Submission" is the act of sending specific information or data to an intended recipient. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a 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.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0028] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] This invention provides a system that generates content optimized for generative AI, appropriately evaluates it, and pays rewards. This system mainly operates in cooperation with a server, terminals, and users. The specific operations of each entity and the program processing are explained below.

[0036] Server-side processing

[0037] Protocol Design

[0038] The server designs a detailed protocol for generative AI optimization, including, for example, the placement of meta tags on web pages, the proper placement of keywords in content, and the structure of internal links. Content is created according to these standards to achieve optimization.

[0039] Protocol Publication

[0040] The designed protocol will be published on a server and can be downloaded by users or devices. The protocol will also be available via API.

[0041] Evaluation of submissions

[0042] The server receives websites and content sent by users and devices. The received content is automatically evaluated based on the protocol. Evaluation criteria include sentence structure, keyword use, meta tag settings, etc. The scores for each item are added together to calculate the final evaluation score.

[0043] Reward calculation and payment

[0044] The server calculates the reward based on the evaluation score. The higher the evaluation score, the higher the reward will be paid. After calculation, the reward will be transferred to the user's account.

[0045] Terminal side processing

[0046] Protocol Download

[0047] The device downloads the protocol from the server and configures the site generation tool or content editor based on it, and templates and guidelines that conform to this protocol are installed on the device.

[0048] Site Generation and Content Creation

[0049] The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input, such as blog posts and product description pages. The generated content undergoes internal checks based on the protocol and is corrected as needed.

[0050] Content Submission

[0051] The generated website and content are then submitted to the server, where a final check is made on the device to ensure protocol compliance.

[0052] User-side processing

[0053] Checking Protocol Compliance

[0054] Users can check whether the websites they run and the content they create comply with the protocol by using tools on their devices to automatically check.

[0055] Content Submission

[0056] Users submit websites or content that is verified as protocol compliant to the server, and once the content reaches the server, an evaluation process begins automatically.

[0057] Receiving rewards

[0058] Users receive a reward calculated based on their rating score, which is deposited into their account and they receive feedback information.

[0059] Specific examples

[0060] 1. Site Creation

[0061] Download the protocol on your device and set up the website generator.

[0062] A user inputs a blog post into a generative AI tool.

[0063] A generative AI tool generates articles based on protocols and optimizes meta tags and keyword placement.

[0064] 2. Content Submission and Evaluation

[0065] The user submits the generated article to the server.

[0066] The server receives the article and evaluates its conformance to the protocol.

[0067] The scores for each evaluation item are added together to calculate the final score.

[0068] 3. Payment of Rewards

[0069] The server calculates the reward based on the evaluation score and transfers it to the user's account.

[0070] Users receive a reward and use the feedback to optimize their next iteration.

[0071] This will create a system where optimized content created using generative AI is fairly evaluated and appropriate rewards are provided.

[0072] The processing flow will be explained below.

[0073] Server-side processing

[0074] Step 1: Design the protocol

[0075] The server designs a detailed protocol for generative AI optimization, including meta tag placement, proper keyword placement, internal link structure, etc.

[0076] Step 2: Publishing the protocol

[0077] The server publishes the designed protocol on the web in a downloadable format and also provides protocol information through an API.

[0078] Step 3: Evaluate submissions

[0079] The server receives websites and content submitted by users and devices and automatically evaluates them based on the protocol, including content structure, keyword usage, and meta tag settings.

[0080] Step 4: Calculating and paying rewards

[0081] The server calculates the reward based on the evaluation score and transfers it to the user's account.

[0082] Terminal side processing

[0083] Step 1: Download the protocol

[0084] The device downloads the protocol from the server and configures the site generation tool or content editor accordingly.

[0085] Step 2: Site Creation and Content Creation

[0086] The on-device generative AI tool generates protocol-compliant websites and content based on user input, performs internal checks based on the protocol, and makes corrections as needed.

[0087] Step 3: Submit your content

[0088] The device submits the generated website or content to the server, where a final check is made to ensure compliance with the protocol.

[0089] User-side processing

[0090] Step 1: Verify protocol compliance

[0091] Users can use the terminal to check whether the websites and content they have created comply with the protocol.

[0092] Step 2: Submit your content

[0093] Users submit websites and content to the server that is verified as conforming to the protocol.

[0094] Step 3: Receive your rewards

[0095] Users receive rewards based on the content rated by the server. Check the reward receipt notification and feedback from the server.

[0096] Specific examples

[0097] Server side

[0098] Step 1: Design the protocol

[0099] The server designs a protocol that includes rules such as "include the main keyword in the article title" and "use one H1 tag per page."

[0100] Step 2: Publishing the protocol

[0101] The server publishes the designed protocol on the web so that it can be downloaded by users and devices.

[0102] Step 3: Evaluate submissions

[0103] The server receives blog posts submitted by users and checks whether the title and H1 tag usage conform to the protocol.

[0104] Step 4: Calculating and paying rewards

[0105] The server calculates the reward based on the evaluation results and transfers it to the user's account.

[0106] Terminal side

[0107] Step 1: Download the protocol

[0108] The terminal downloads the protocol from the server and sets it in the site generation tool.

[0109] Step 2: Site Creation and Content Creation

[0110] When a user enters a blog post, the generative AI tool generates the post according to the protocol and performs internal checks on meta tags and keyword placement.

[0111] Step 3: Submit your content

[0112] Before submitting the generated blog post to the server, the terminal checks whether it complies with the protocol and then sends it to the server.

[0113] User side

[0114] Step 1: Verify protocol compliance

[0115] Users can use tools on their devices to verify that the blog posts they generate comply with the protocol.

[0116] Step 2: Submit your content

[0117] Users submit articles to the server that are verified to be protocol compliant.

[0118] Step 3: Receive your rewards

[0119] The user checks the evaluation results and reward receipt notification from the server and receives the reward.

[0120] Example 1

[0121] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0122] In conventional systems, content optimization and evaluation by generative AI are performed as independent processes, making it difficult to provide efficient feedback and calculate rewards. Furthermore, there is a lack of means to verify whether the generated content actually complies with the protocol, which creates the problem of not guaranteeing fair evaluation and reward payment. This invention aims to solve these issues and realize the generation of high-quality content and fair evaluation by providing a consistent system for optimizing and evaluating generative AI.

[0123] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0124] In this invention, the server includes a means for designing and publishing rules for optimizing generative AI, a means for evaluating digital content based on the rules, a means for calculating a reward based on the evaluation results, a means for paying the reward to end users, and a means for including sentence structure, keyword usage, and meta tag settings in the evaluation criteria for the digital content. This makes it possible to consistently optimize and evaluate content using generative AI and pay fair rewards to users.

[0125] "Generative AI" is a technology that uses artificial intelligence to automatically create digital content such as text and images.

[0126] The "Terms and Conditions" are a detailed set of rules and guidelines for the generative AI to create optimized content, including things like meta tag placement and keyword use.

[0127] "Digital content" refers to various forms of content that appear on the internet, such as websites, blog posts, and product description pages.

[0128] "Evaluation criteria" are the criteria used to determine whether digital content complies with the regulations, and specifically include sentence structure, keyword use, meta tag settings, etc.

[0129] "Reward" refers to monetary compensation paid to a user based on the evaluation results.

[0130] "User" refers to an end user who generates digital content in accordance with the Terms and Conditions and receives evaluation and rewards.

[0131] The "server" is a central management system that handles a series of processes, including the design and publication of rules, evaluation of digital content, and calculation and payment of rewards.

[0132] A "generative AI tool" is software or a program that automatically generates compliant content based on user input.

[0133] "Final confirmation of protocol compliance" is the process of finally confirming whether the generated digital content complies with the regulations.

[0134] This invention provides a system that creates digital content optimized for generative AI, appropriately evaluates it, and pays fair rewards. This system mainly operates in cooperation between a server, terminals, and users.

[0135] The server designs rules for optimizing the generative AI and publishes them on the server. These rules include, for example, rules for placing meta tags on web pages, how to properly place keywords in content, and the structure of internal links. The server hosts these rules at a specific URL or API endpoint so that users and devices can download them. Specific examples include the URL "https: / / example.com / protocols / optimization_protocol_v1.json" and the API endpoint "https: / / api.example.com / protocols / latest."

[0136] The device downloads the regulations from the server and configures the site generation tool and content editor based on them. The device incorporates templates and guidelines that comply with the regulations, allowing the generative AI tool to automatically generate content based on prompts entered by the user. For example, if a user enters the topic "Latest Trends in AI Technology," the generative AI tool will automatically place the necessary meta tags and keywords to generate an optimized article. The generated content undergoes a final check on the device for protocol compliance, and once it passes the final inspection, it is submitted to the server.

[0137] Users use a tool on their device to check whether the digital content they manage complies with the terms and conditions. Once the check is complete, the user submits the content to the server. The server receives the submitted content and automatically begins the evaluation process. Evaluation criteria include sentence structure, keyword use, and meta tag settings, and the final evaluation score is calculated by adding up the scores for each item.

[0138] The server calculates rewards based on the evaluation score and transfers them to the end user's account. The higher the evaluation score, the higher the reward paid. For example, a tiered reward system could be used, such as $100 for a score of 90 or above, and $80 for a score of 80 or above.

[0139] For example, consider the following flow:

[0140] 1. The server designs the contract and publishes it at a specific URL.

[0141] 2. The device downloads the terms and conditions and configures the generation AI tool.

[0142] 3. The user enters the prompt "Latest trends in AI technology" and checks the generated content.

[0143] 4. The generated article is submitted to the server, which evaluates it.

[0144] 5. Rewards are calculated based on the rating score and deposited into the user's account.

[0145] This allows for consistent content optimization and evaluation using generative AI, ensuring fair rewards are paid to users.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1:

[0148] The server designs the contract.

[0149] Specific actions

[0150] Input: Industry standards and SEO optimization guidelines

[0151] Data processing: Compiling web page meta tag placement rules, keyword placement methods, internal link structure, etc.

[0152] Output: Terms file (JSON or XML format)

[0153] The server designs detailed regulations based on industry standards and SEO optimization guidelines, including rules for meta tag placement on web pages, keyword placement methods, internal link structures, etc., and outputs these as a regulations file.

[0154] Step 2:

[0155] The server publishes the terms.

[0156] Specific actions

[0157] Input: Terms file

[0158] Data processing: Uploading files to the hosting server, setting up API endpoints

[0159] Output: URL or API endpoint

[0160] The server uploads the designed contract file to the hosting server and makes it available at a specific URL or API endpoint. For example, endpoints such as "https: / / example.com / protocols / optimization_protocol_v1.json" or "https: / / api.example.com / protocols / latest" are output.

[0161] Step 3:

[0162] The device downloads the terms and conditions.

[0163] Specific actions

[0164] Input: A publicly available URL or API endpoint

[0165] Data processing: Download the contract file from a URL or API endpoint

[0166] Output: A contract file in the local file system on the device.

[0167] The device downloads the contract file from the published URL or API endpoint and saves it to its local file system. For example, download it using "curl -O https: / / example.com / protocols / optimization_protocol_v1.json" on the command line.

[0168] Step 4:

[0169] The device configures the generative AI tool and generates the content.

[0170] Specific actions

[0171] Input: User prompt, downloaded terms file

[0172] Data processing: Generate compliant websites, blog posts, etc. based on prompts

[0173] Output: Generated content files (HTML, Markdown, etc.)

[0174] The user inputs a prompt into the device, and the device uses a generative AI tool to automatically generate content based on the rules. For example, if a user inputs the prompt "Latest trends in AI technology," the generative AI tool will generate an article with optimized meta tags and keyword placement.

[0175] Step 5:

[0176] The user confirms compliance.

[0177] Specific actions

[0178] Input: Generated content file, downloaded terms file

[0179] Data processing: Use standard checking tools to ensure generated content complies with standards

[0180] Output: Check result (pass / fail)

[0181] Users can use a check tool on their device to check whether the generated content complies with the regulations. The check tool automatically scans for the application of meta tags and keywords, and outputs a result of either "applied (passed)" or "not applied (failed)."

[0182] Step 6:

[0183] A user submits content to a server.

[0184] Specific actions

[0185] Input: Generated content files

[0186] Data processing: Upload content files to the server

[0187] Output: Submission confirmation message

[0188] Once the content file has been confirmed to comply with the regulations, the user submits it to the server. This is done by uploading the file to "https: / / upload.example.com". When the upload is complete, a "Submission Complete" message is displayed.

[0189] Step 7:

[0190] The server evaluates the content.

[0191] Specific actions

[0192] Input: Submitted content file, downloaded terms and conditions file

[0193] Data processing: Evaluate content based on the rules and calculate scores for each item

[0194] Output: Evaluation score, feedback

[0195] The server receives the submitted content file and initiates an automated evaluation process. Evaluation criteria include sentence structure, keyword usage, meta tag settings, etc., and the scores for each item are added together to calculate a final evaluation score. Feedback is provided along with the evaluation score.

[0196] Step 8:

[0197] The server calculates and pays the reward.

[0198] Specific actions

[0199] Input: Rating score

[0200] Data processing: Calculate the reward amount based on the evaluation score and transfer the reward to the user's account

[0201] Output: Reward transfer confirmation message

[0202] The server calculates rewards based on the evaluation score, with higher rewards paid for higher scores. For example, rewards are set in stages, such as $100 for a score of 90 or above, $80 for a score of 80 or above, etc. Once the calculated reward amount has been transferred to the user's account, a "Reward transfer completed" message is displayed.

[0203] (Application example 1)

[0204] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0205] In the generation and evaluation of content using generative AI models, there is a lack of mechanisms to generate high-quality content based on appropriate protocols and provide fair compensation according to the evaluation. This makes it difficult for creators and content providers to create content in the optimal way and receive fair compensation.

[0206] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0207] In this invention, the server includes means for designing and publishing a protocol for optimizing the generative AI, means for evaluating websites and content based on the protocol, means for calculating a reward based on the evaluation results, means for paying the reward to users, means for generating content that conforms to the protocol using a generative AI model, and means for submitting the content to the server and receiving an evaluation. This allows content created using the generative AI model to be evaluated fairly, and rewards based on the evaluation results to be appropriately paid.

[0208] A "generative AI model" refers to algorithms or software that use artificial intelligence to automatically generate content based on human-provided prompts.

[0209] A "protocol" refers to a set of rules or procedures that serve as the basis for optimizing and evaluating generative AI models.

[0210] A "prompt" is a piece of text that describes an instruction or request that is input to a generative AI model.

[0211] "Server" refers to a computer system that runs in the cloud or on-premise and runs generative AI models, publishes protocols, evaluates content and pays rewards.

[0212] "User" refers to an entity that uses a generative AI model to create content and submits that content for evaluation and reward.

[0213] "Content" refers to the text, images, video, and other creative works generated by generative AI models.

[0214] "Evaluation" refers to the process of determining how well generated content complies with the protocol and assigning it a quantitative score.

[0215] "Reward" refers to compensation such as money or points paid to a user based on the evaluation results.

[0216] This invention is a system that generates optimized content using a generative AI model and pays rewards based on the evaluation of the content. This system operates in cooperation between a server, terminals, and users.

[0217] Server-side processing

[0218] The server first designs and publishes a protocol for optimizing the generative AI. The protocol includes, for example, the placement of meta tags on web pages, the appropriate placement of keywords in content, and the structure of internal links. This protocol is published on the server and can be downloaded by users and devices via API.

[0219] The server then receives the website or content sent by the user or device. The received content is automatically evaluated based on the protocol. Evaluation criteria include sentence structure, keyword use, meta tag settings, etc., and the scores for each evaluation item are added together to calculate a final evaluation score. Finally, rewards are calculated based on the evaluation score and deposited into the user's account.

[0220] Terminal side processing

[0221] The device downloads the protocol from the server and configures the site generation tool and content editor based on it. Templates and guidelines that conform to this protocol are installed on the device. Next, a tool using a generative AI model is used to automatically generate protocol-compliant content based on user input. Examples include blog articles and product description pages. The generated content undergoes internal checks based on the protocol and is corrected as necessary. Finally, the generated content is submitted to the server. A final protocol compliance check is performed on the device before submission.

[0222] User-side processing

[0223] Users check whether the websites they manage or the content they create comply with the protocol. This is done automatically using a tool on their device. Next, they submit websites or content that have been confirmed to comply with the protocol to the server. Once the content reaches the server, the evaluation process begins automatically. Finally, users receive a reward calculated based on the evaluation score. The reward is deposited into the user's account, and they receive feedback information.

[0224] Hardware and software used

[0225] The server runs on a web server (e.g., AWS, Google Cloud), and the generative AI model used is OpenAI's GPT-3. HTTP / HTTPS is used as the communication protocol, and Python is used as the development language. On the terminal side, PCs, smartphones, head-mounted displays (HMDs), etc. are used.

[0226] Examples and prompts

[0227] For example, a user might input the following prompt sentence into a generative AI model to write a "travel blog post."

[0228] Prompt: "Write a blog post about travel."

[0229] The content generated in this way is of high quality and conforms to the protocol, and is evaluated by the server, with appropriate compensation being paid.

[0230] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0231] Step 1: Design and publish a protocol

[0232] The server designs and publishes a protocol for optimizing generative AI models. This protocol includes meta tag placement, appropriate keyword placement, and internal link structure. The server publishes the protocol via an API so that users and devices can download it. When designing the protocol, data analysis tools are used to determine the optimal placement and structure.

[0233] Input: Data and criteria for optimization

[0234] Data processing: Designing protocols using data analysis tools

[0235] Output: Protocol published

[0236] Step 2: Download the protocol

[0237] The terminal downloads the published protocol from the server, which is then used as a template or guideline to be applied to the site generation tool or content editor.

[0238] Input: Protocol exposed via API

[0239] Data processing: Apply protocols to devices as templates or guidelines

[0240] Output: Protocol download complete

[0241] Step 3: Generate content

[0242] The device receives a prompt from the user and uses a generative AI model to generate content that conforms to the protocol. The generative AI model automatically generates content in the specified format based on the input prompt. For example, it generates an article based on the prompt, "Please write a blog post about travel."

[0243] Input: User prompt text

[0244] Data processing: Content generation based on generative AI models

[0245] Output: Generated content

[0246] Step 4: Submit your content

[0247] The user submits the generated content to the server. Before submission, the terminal performs a final check to ensure that the content complies with the protocol and that there are no problems with the content.

[0248] Input: Generated content

[0249] Data processing: Checking protocol compliance

[0250] Output: The content sent to the server

[0251] Step 5: Evaluate your content

[0252] The server automatically evaluates the received content based on the protocol. Evaluation items include sentence structure, keyword use, meta tag settings, etc., and a score is assigned for each item. Finally, the scores for each item are added together to calculate an overall evaluation score.

[0253] Input: Content sent to the server

[0254] Data processing: Protocol compliance assessment and scoring

[0255] Output: Evaluation score

[0256] Step 6: Calculating and paying rewards

[0257] The server calculates rewards based on the evaluation score. The higher the evaluation score, the higher the reward. The reward is automatically transferred to the user's account.

[0258] Input: Rating score

[0259] Data processing: Reward calculation

[0260] Output: Reward deposited into user account

[0261] These processing steps enable fair evaluation of content using generative AI models and appropriate compensation payments.

[0262] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0263] This invention provides a system that combines a generative AI to generate optimized content and an emotion engine that recognizes user emotions. This system operates primarily in cooperation with a server, a terminal, and a user. The specific operations of each entity and the program processing are described below.

[0264] Server-side processing

[0265] Protocol Design

[0266] The server designs a detailed protocol for optimizing the generated AI, including proper placement of meta tags, effective use of keywords, internal link structure, etc. Content is created according to these standards to achieve optimization.

[0267] Protocol Publication

[0268] The designed protocol will be published on a server and can be downloaded by users or devices. The protocol will also be available via API.

[0269] Evaluation of submissions

[0270] The server receives websites and content sent by users and devices and automatically evaluates them based on the protocol. Evaluation criteria include sentence structure, keyword usage, meta tag settings, etc. The scores for each item are added together to calculate the final evaluation score.

[0271] Reward calculation and payment

[0272] The server calculates a reward based on the evaluation score and transfers it to the user's account.

[0273] Terminal side processing

[0274] Protocol Download

[0275] The device downloads the protocol from the server and configures the site generation tool and content editor based on it, and templates and guidelines that conform to the protocol are installed on the device.

[0276] Site Generation and Content Creation

[0277] The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input, such as blog posts and product description pages. The generated content undergoes internal checks based on the protocol and is corrected as needed.

[0278] Use of emotion engine

[0279] The device collects user emotion data using an emotion engine that recognizes the user's emotions. For example, it analyzes the user's facial expressions and voice and captures the emotion information.

[0280] Content Adjustment

[0281] The content generated by the generative AI is adjusted based on the emotional data collected by the emotion engine. For example, if the user is happy, the AI ​​will generate content that contains more positive expressions.

[0282] Content Submission

[0283] The generated website and content are then submitted to the server, where a final check is made on the device to ensure protocol compliance.

[0284] User-side processing

[0285] Checking Protocol Compliance

[0286] Users can check whether the websites they run and the content they create comply with the protocol by using tools on their devices to automatically check.

[0287] Providing emotion data

[0288] Users provide their emotional data using the camera and microphone on their device, which allows the emotion engine to accurately recognize the user's emotions.

[0289] Content Submission

[0290] Users submit websites or content that is verified as protocol compliant to the server, and once the content reaches the server, an evaluation process begins automatically.

[0291] Receiving rewards

[0292] Users receive a reward calculated based on their rating score, which is deposited into their account and they receive feedback information.

[0293] Specific examples

[0294] 1. Emotion Recognition and Site Generation

[0295] Download the protocol on your device and set up the website generator.

[0296] When a user inputs a blog post into the generation AI tool, the emotion engine recognizes the user's emotions.

[0297] A generative AI tool generates articles based on user sentiment and adjusts meta tags and keyword placement.

[0298] 2. Content Submission and Evaluation

[0299] The user submits the generated article to the server.

[0300] The server receives the article and evaluates its conformance to the protocol.

[0301] The scores for each evaluation item are added together to calculate the final score.

[0302] 3. Payment of Rewards

[0303] The server calculates the reward based on the evaluation score and transfers it to the user's account.

[0304] Users receive a reward and use the feedback to optimize their next iteration.

[0305] This allows content optimized using generative AI to be fairly evaluated, and more personalized content is provided based on the user's emotions. The entire system works together to pay appropriate rewards to users.

[0306] The processing flow will be explained below.

[0307] Server-side processing

[0308] Step 1: Design the protocol

[0309] The server designs protocols for generative AI optimization, including proper use of title tags, placement of key keywords, and how to set meta descriptions.

[0310] Step 2: Publishing the protocol

[0311] The server publishes the designed protocol on the web so that it can be downloaded by users or devices, or the protocol can be provided through an API.

[0312] Step 3: Evaluate submissions

[0313] The server receives websites and content submitted by users and devices, evaluates the received content based on the protocol, and checks the appropriateness of sentence structure, keyword usage, and meta tags.

[0314] Step 4: Calculating and paying rewards

[0315] The server calculates rewards based on the rating score. The higher the rating score, the higher the reward. The rewards are deposited into the user's account.

[0316] Terminal side processing

[0317] Step 1: Download the protocol

[0318] The terminal downloads the protocol from the server and configures the site generation tool and content editor. Templates and guidelines that conform to the protocol are installed on the terminal.

[0319] Step 2: Collecting emotion data

[0320] The device activates an emotion engine to recognize the user's emotions. It uses a camera and microphone to collect the user's facial expressions and voice, and analyzes the emotional data.

[0321] Step 3: Site Creation and Content Creation

[0322] The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input and emotional data. For example, if a user is happy, it will generate an article that uses a lot of positive language.

[0323] Step 4: Submit your content

[0324] The device submits the generated website or content to the server, which performs a final check to ensure compliance with the protocol before submitting.

[0325] User-side processing

[0326] Step 1: Log in and verify protocol compliance

[0327] Users can log in to their devices and check whether the websites and content they create comply with the protocol. Checks are performed automatically using tools on the devices.

[0328] Step 2: Provide emotion data

[0329] Users provide their emotional data using a camera or microphone, which is then analyzed by the emotion engine and recognized as emotional information.

[0330] Step 3: Review and submit content

[0331] Users can submit websites and content that are verified as protocol compliant to the server, and can even make final adjustments on their devices before submitting.

[0332] Step 4: Receive your rewards

[0333] The user receives a reward based on the score of the content evaluated by the server. Check whether the reward has been deposited into the account.

[0334] Example: Creating and rating blog posts

[0335] Server side

[0336] Step 1: Design the protocol

[0337] The server designs protocols such as "include the main keyword in the article title" and "use H1 tags only once per page."

[0338] Step 2: Publishing the protocol

[0339] The server publishes the designed protocol on the web so that it can be downloaded by users and devices.

[0340] Step 3: Evaluate submissions

[0341] It receives blog posts submitted by users and evaluates whether the title, H1 tag, keyword placement, etc. comply with the protocol.

[0342] Step 4: Calculating and paying rewards

[0343] Rewards are calculated based on the evaluation and transferred to the user's account.

[0344] Terminal side

[0345] Step 1: Download the protocol

[0346] Download protocols from the server and configure site generation tools and editors.

[0347] Step 2: Collecting emotion data

[0348] It uses a camera and microphone to analyze facial expressions and voice to recognize the user's emotions.

[0349] Step 3: Site Creation and Content Creation

[0350] A generative AI tool generates protocol-compliant articles based on sentiment data and user input. For example, if a user is feeling happy, the article will have a positive tone.

[0351] Step 4: Submit your content

[0352] The device submits the generated blog post to the server, which checks whether it complies with the protocol before submitting.

[0353] User side

[0354] Step 1: Log in and verify protocol compliance

[0355] The user logs in to the terminal and checks whether the article has been created in accordance with the protocol.

[0356] Step 2: Provide emotion data

[0357] Users provide their emotional data using a camera and microphone.

[0358] Step 3: Review and submit content

[0359] The user submits the article to the server after final review.

[0360] Step 4: Receive your rewards

[0361] Check whether the reward based on the evaluation score has been paid and use it for the next optimization.

[0362] This will enable content optimized using generative AI to be fairly evaluated, and personalized content based on user emotions will be provided, creating a system in which appropriate rewards are paid to users.

[0363] Example 2

[0364] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0365] Conventional content generation systems using generative AI were unable to incorporate user emotions, making it difficult to provide optimal content for users. Furthermore, the mechanisms for evaluating the degree to which the generated content was optimized and for paying appropriate rewards to users were inadequate. This led to issues such as a decline in user satisfaction and motivation.

[0366] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for designing and publishing a protocol for optimizing the generation AI, a means for evaluating websites and content based on the protocol, a means for calculating a reward based on the evaluation results, a means for paying the reward to the user, a means for collecting and analyzing user emotion data, and a means for adjusting the content based on the emotion data. This enables content generation that takes user emotions into consideration, fair evaluation of the content, and appropriate payment of rewards.

[0367] "Generative AI optimization" refers to methods and standards for improving the quality and search engine optimization (SEO) of content created by generative AI.

[0368] A "protocol" is a set of rules and guidelines for content creation, and refers to the standards used to ensure content quality and optimization.

[0369] "Evaluation" refers to the process of analyzing and scoring the generated content to see how well it conforms to the protocol.

[0370] "Reward" refers to the compensation paid to the generator (user) based on the evaluation results.

[0371] "User" refers to a user who generates and submits content using this system.

[0372] "Emotion data" refers to data relating to emotions acquired through the user's facial expressions, voice, etc.

[0373] "Content" refers to a collection of information created by generative AI, such as a website, article, or product description page.

[0374] This invention provides a system that combines a generative AI to generate optimized content and an emotion engine that recognizes user emotions. This system operates primarily in cooperation between a server, a terminal, and a user.

[0375] 1. Server Role

[0376] Protocol design and publication

[0377] The server designs a detailed protocol for optimizing the generative AI, including how to place meta tags, how to effectively use keywords, and the structure of internal links. The protocol is published on the server and can be downloaded to users and devices via API.

[0378] Specific examples:

[0379] The server extracts the keyword set and inserts example meta tags into the HTML template.

[0380] Content Rating and Reward Calculation

[0381] The server receives content sent by users and devices and automatically evaluates it based on protocol criteria, including sentence structure, keyword usage, meta tag settings, etc. Based on the evaluation results, a reward is calculated and deposited into the user's account.

[0382] Specific examples:

[0383] Based on the prompt sentence "I recently traveled. I would like to write a blog post about this trip. I am happy.", the generated blog post is evaluated.

[0384] 2. Role of the terminal

[0385] Downloading the protocol and setting up the AI ​​generation tool

[0386] The device downloads the protocol from the server and configures its generative AI tools and content editors based on it, and the protocol-compliant templates and guidelines are deployed on the device.

[0387] Specific examples:

[0388] The device will set up a template for creating a travel blog.

[0389] Collecting sentiment data and tailoring content

[0390] The device uses hardware such as a camera and microphone to collect user emotional data, using facial recognition and voice analysis technology. The collected emotional data is reflected in the generated content.

[0391] Specific examples:

[0392] The camera detects the user's smile and uses a lot of positive language in articles.

[0393] 3. User Roles

[0394] Verifying protocol compliance and providing emotional data

[0395] Users can use tools on their devices to check whether the websites they run and the content they create comply with the protocol. Users also use their cameras and microphones to provide emotional data, which is collected and analyzed by the devices.

[0396] Specific examples:

[0397] A user creates a blog post and enters a prompt into the terminal, such as "I would like to submit this blog post. Please rate it."

[0398] Submit content and get paid

[0399] When users submit their generated content to the server, the server will rate it and calculate a reward, which will be credited to the user's account based on the rating score.

[0400] Specific examples:

[0401] The user receives the reward with the prompt, "The evaluation is complete. I would like to receive my reward."

[0402] The above configuration enables optimal content generation using generative AI, fair evaluation, and personalization of content according to user emotions, which will improve user satisfaction and motivation and enable the provision of even higher quality content.

[0403] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0404] Step 1: Design the protocol

[0405] The server designs a detailed protocol for optimizing the generative AI, including proper placement of meta tags, effective use of keywords, and internal link structure. Specifically, the server analyzes existing SEO data and extracts the optimal keyword set. It also automatically inserts example meta tags into HTML templates. The input is the existing SEO data and predicted search queries, and the output is a protocol for optimizing the generative AI.

[0406] Step 2: Publishing and downloading the protocol

[0407] The server publishes the designed protocol on the server and allows users and devices to download it via API. Specifically, the server provides the latest version of the protocol at the API endpoint. The input is the designed protocol, and the output is the provision of protocol data to users and devices.

[0408] Step 3: Configuring the Generative AI Tool

[0409] The device configures the generative AI tool and content editor based on the protocol downloaded from the server. Specifically, the device stores the protocol rule set locally and reflects it in the initial settings of the generative AI. It also uses a template engine to generate an initial template that conforms to the protocol. The input is the downloaded protocol, and the output is the configured generative AI tool.

[0410] Step 4: Collecting emotion data

[0411] The device collects user emotional data using a camera and microphone. Specifically, the camera captures facial expressions and applies a facial recognition algorithm. The microphone also captures audio data and analyzes it with an emotion analysis algorithm. The input is the user's facial expression and audio data, and the output is analyzed emotional data.

[0412] Step 5: Generate and refine content

[0413] The device uses a generative AI tool to generate and adjust content based on the emotion data collected by the emotion engine. Specifically, the generative AI tool uses the emotion data as an input parameter, and if positive emotions are detected, it generates content that makes heavy use of positive expressions. The input is the emotion data and the user's content request, and the output is adjusted content that complies with the protocol.

[0414] Step 6: Submit and rate your content

[0415] Users submit generated content to a server, which then evaluates the submitted content based on a set of criteria. Specifically, the server parses the content and scores it based on criteria such as structure, keyword usage, and meta tag settings. The input is the submitted content, and the output is the evaluation score.

[0416] Step 7: Calculation and payment of rewards based on the evaluation results

[0417] The server calculates the reward based on the rating score and transfers it to the user's account. Specifically, the server analyzes the rating score and calculates the reward amount. The input is the rating score, and the output is the calculated reward amount and its payment.

[0418] (Application example 2)

[0419] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0420] While conventional content generation systems using generative AI models achieve protocol-based content optimization, they lack personalization based on user emotions. This results in insufficient improvement of the user experience. Furthermore, there is a demand for technology that can improve the quality of generated content by utilizing emotion recognition data. Therefore, the present invention aims to solve the above problems by providing a system that recognizes user emotions and generates and adjusts content based on them.

[0421] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0422] In this invention, the server includes a means for designing and publishing a protocol for optimizing the generative AI, a means for evaluating websites and content based on the protocol, a means for calculating rewards based on the evaluation results, a means including an emotion recognition engine for recognizing user emotions, and a means for generating content based on the emotion data. This enables the provision of personalized content based on user emotions and an optimized evaluation system.

[0423] "Generative AI" is an artificial intelligence technology that automatically generates content based on user input.

[0424] A "protocol" defines detailed guidelines and rules for optimizing content generation by generative AI.

[0425] An "emotion recognition engine" is a technology that analyzes a user's facial expressions and voice data to recognize their emotional state.

[0426] "Content" refers to information or media that users view or use, such as websites, blog posts, product description pages, etc.

[0427] The "evaluation means" is a mechanism for automatically evaluating whether the generated content complies with the protocol.

[0428] "Reward payment means" is a system that provides monetary rewards to users based on the results of their content evaluations.

[0429] "Downloading means" refers to a mechanism for acquiring the protocol on the user's terminal.

[0430] "Content generation means" refers to technology for automatically generating websites and content in accordance with protocols.

[0431] A "submission mechanism" is a mechanism for transmitting user-generated content to a server.

[0432] "Personalization" refers to providing content optimized for a specific user based on the user's emotional data and preferences.

[0433] "Internal check" is a mechanism for checking on the terminal side whether the generated content complies with the protocol.

[0434] overview

[0435] This invention is a system that combines optimization of generative AI with user emotion recognition. This system works in cooperation with the server, the terminal, and the user.

[0436] server

[0437] The server has the means to design and publish a protocol for optimizing the generative AI. The protocol includes detailed guidelines such as the appropriate placement of meta tags, effective use of keywords, and link structure. The designed protocol is published on the server and can be downloaded by users and devices. It can also be obtained via API. The server evaluates content sent by users and devices based on the protocol, calculates rewards based on the evaluation score, and deposits them into the user's account.

[0438] Terminal

[0439] The device downloads the protocol from the server and configures the site generation tool and content editor. The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input. At this time, an emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data. This allows the generated content to be adjusted based on the user's emotions. For example, if the user is happy, content containing many positive expressions will be generated. The generated content undergoes a final protocol compliance check on the device before being submitted to the server.

[0440] User

[0441] Users download the protocol using their own devices and generate websites and content. During the generation process, an emotion recognition engine analyzes the user's emotions in real time, and this data is reflected in the content generation. The user submits the generated content to the server, which evaluates it based on the protocol. Rewards are deposited into the user's account based on the evaluation results. This allows the user to use the feedback to improve their next content generation.

[0442] Hardware and software used

[0443] Server: High-performance computers, cloud computing services (e.g., AWS, Google Cloud, etc.).

[0444] Devices: Smartphones, tablets, PCs.

[0445] Software: EmotionRecognition library, ContentGenerator library, REST API.

[0446] Specific examples

[0447] Users use their smartphone camera to analyze their facial expressions, and the emotion recognition engine detects "joy." Based on this data, the AI ​​automatically generates positive news articles. For example, it can be based on a prompt such as, "Please create good news about the latest technological innovation."

[0448] The generated content is submitted to the server, where it is evaluated according to the protocol. Based on the evaluation results, rewards are calculated and paid to the user.

[0449] Prompt Sentence Examples

[0450] "Create a joy-based, breaking news story. The topic should be related to technological innovation and contain a lot of positivity."

[0451] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0452] Step 1:

[0453] The server will design a protocol for optimizing the generative AI, including detailed standards for meta tag placement, keyword use, link structure, etc. The protocol will be made available to users and devices in a downloadable format, and can also be obtained via API.

[0454] Input: Content generation optimization criteria

[0455] Output: Protocol document

[0456] Step 2:

[0457] The device downloads the protocol from the server, configures site generators and content editors based on it, and implements protocol-compliant templates and guidelines.

[0458] Input: Protocol document

[0459] Output: Configured tools to comply with the protocol

[0460] Step 3:

[0461] When a user uses a device to create a website or create content, the emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data.

[0462] Input: User facial and voice data

[0463] Output: Emotion data (emotional state information such as happiness, anger, sadness, and happiness)

[0464] Step 4:

[0465] The device uses generative AI tools to automatically generate protocol-compliant content based on the collected emotional data. Personalization is achieved using emotional data. For example, if the user is happy, positive content is generated.

[0466] Input: Emotion data, protocol criteria

[0467] Output: Generated content (e.g. blog post, product description page)

[0468] Step 5:

[0469] Device-generated content is internally checked to ensure it complies with the protocol, and if it passes this internal check, it is submitted to the server.

[0470] Input: Generated content

[0471] Output: Content that has been checked for protocol compliance

[0472] Step 6:

[0473] The server receives the submitted content and evaluates it based on the protocol. Evaluation criteria include document structure, keyword usage, meta tag settings, etc. A rating score is calculated.

[0474] Input: Submitted content, protocol criteria

[0475] Output: Evaluation score

[0476] Step 7:

[0477] The server calculates the user's reward based on the evaluation result and deposits the reward into the user's account.

[0478] Input: Rating score

[0479] Output: Reward calculation result, deposited into user account

[0480] Step 8:

[0481] The user receives the reward and checks the feedback from the server, which is used to generate the next content.

[0482] Input: Feedback information, reward

[0483] Output: Reference for next content generation

[0484] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0485] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0486] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0487] [Second embodiment]

[0488] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0489] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0490] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0491] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0492] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0493] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0494] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0495] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0496] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0497] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0498] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0499] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0500] This invention provides a system that generates content optimized for generative AI, appropriately evaluates it, and pays rewards. This system mainly operates in cooperation with a server, terminals, and users. The specific operations of each entity and the program processing are explained below.

[0501] Server-side processing

[0502] Protocol Design

[0503] The server designs a detailed protocol for generative AI optimization, including, for example, the placement of meta tags on web pages, the proper placement of keywords in content, and the structure of internal links. Content is created according to these standards to achieve optimization.

[0504] Protocol Publication

[0505] The designed protocol will be published on a server and can be downloaded by users or devices. The protocol will also be available via API.

[0506] Evaluation of submissions

[0507] The server receives websites and content sent by users and devices. The received content is automatically evaluated based on the protocol. Evaluation criteria include sentence structure, keyword use, meta tag settings, etc. The scores for each item are added together to calculate the final evaluation score.

[0508] Reward calculation and payment

[0509] The server calculates the reward based on the evaluation score. The higher the evaluation score, the higher the reward will be paid. After calculation, the reward will be transferred to the user's account.

[0510] Terminal side processing

[0511] Protocol Download

[0512] The device downloads the protocol from the server and configures the site generation tool or content editor based on it, and templates and guidelines that conform to this protocol are installed on the device.

[0513] Site Generation and Content Creation

[0514] The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input, such as blog posts and product description pages. The generated content undergoes internal checks based on the protocol and is corrected as needed.

[0515] Content Submission

[0516] The generated website and content are then submitted to the server, where a final check is made on the device to ensure protocol compliance.

[0517] User-side processing

[0518] Checking Protocol Compliance

[0519] Users can check whether the websites they run and the content they create comply with the protocol by using tools on their devices to automatically check.

[0520] Content Submission

[0521] Users submit websites or content that is verified as protocol compliant to the server, and once the content reaches the server, an evaluation process begins automatically.

[0522] Receiving rewards

[0523] Users receive a reward calculated based on their rating score, which is deposited into their account and they receive feedback information.

[0524] Specific examples

[0525] 1. Site Creation

[0526] Download the protocol on your device and set up the website generator.

[0527] A user inputs a blog post into a generative AI tool.

[0528] A generative AI tool generates articles based on protocols and optimizes meta tags and keyword placement.

[0529] 2. Content Submission and Evaluation

[0530] The user submits the generated article to the server.

[0531] The server receives the article and evaluates its conformance to the protocol.

[0532] The scores for each evaluation item are added together to calculate the final score.

[0533] 3. Payment of Rewards

[0534] The server calculates the reward based on the evaluation score and transfers it to the user's account.

[0535] Users receive a reward and use the feedback to optimize their next iteration.

[0536] This will create a system where optimized content created using generative AI is fairly evaluated and appropriate rewards are provided.

[0537] The processing flow will be explained below.

[0538] Server-side processing

[0539] Step 1: Design the protocol

[0540] The server designs a detailed protocol for generative AI optimization, including meta tag placement, proper keyword placement, internal link structure, etc.

[0541] Step 2: Publishing the protocol

[0542] The server publishes the designed protocol on the web in a downloadable format and also provides protocol information through an API.

[0543] Step 3: Evaluate submissions

[0544] The server receives websites and content submitted by users and devices and automatically evaluates them based on the protocol, including content structure, keyword usage, and meta tag settings.

[0545] Step 4: Calculating and paying rewards

[0546] The server calculates the reward based on the evaluation score and transfers it to the user's account.

[0547] Terminal side processing

[0548] Step 1: Download the protocol

[0549] The device downloads the protocol from the server and configures the site generation tool or content editor accordingly.

[0550] Step 2: Site Creation and Content Creation

[0551] The on-device generative AI tool generates protocol-compliant websites and content based on user input, performs internal checks based on the protocol, and makes corrections as needed.

[0552] Step 3: Submit your content

[0553] The device submits the generated website or content to the server, where a final check is made to ensure compliance with the protocol.

[0554] User-side processing

[0555] Step 1: Verify protocol compliance

[0556] Users can use the terminal to check whether the websites and content they have created comply with the protocol.

[0557] Step 2: Submit your content

[0558] Users submit websites and content to the server that is verified as conforming to the protocol.

[0559] Step 3: Receive your rewards

[0560] Users receive rewards based on the content rated by the server. Check the reward receipt notification and feedback from the server.

[0561] Specific examples

[0562] Server side

[0563] Step 1: Design the protocol

[0564] The server designs a protocol that includes rules such as "include the main keyword in the article title" and "use one H1 tag per page."

[0565] Step 2: Publishing the protocol

[0566] The server publishes the designed protocol on the web so that it can be downloaded by users and devices.

[0567] Step 3: Evaluate submissions

[0568] The server receives blog posts submitted by users and checks whether the title and H1 tag usage conform to the protocol.

[0569] Step 4: Calculating and paying rewards

[0570] The server calculates the reward based on the evaluation results and transfers it to the user's account.

[0571] Terminal side

[0572] Step 1: Download the protocol

[0573] The terminal downloads the protocol from the server and sets it in the site generation tool.

[0574] Step 2: Site Creation and Content Creation

[0575] When a user enters a blog post, the generative AI tool generates the post according to the protocol and performs internal checks on meta tags and keyword placement.

[0576] Step 3: Submit your content

[0577] Before submitting the generated blog post to the server, the terminal checks whether it complies with the protocol and then sends it to the server.

[0578] User side

[0579] Step 1: Verify protocol compliance

[0580] Users can use tools on their devices to verify that the blog posts they generate comply with the protocol.

[0581] Step 2: Submit your content

[0582] Users submit articles to the server that are verified to be protocol compliant.

[0583] Step 3: Receive your rewards

[0584] The user checks the evaluation results and reward receipt notification from the server and receives the reward.

[0585] Example 1

[0586] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0587] In conventional systems, content optimization and evaluation by generative AI are performed as independent processes, making it difficult to provide efficient feedback and calculate rewards. Furthermore, there is a lack of means to verify whether the generated content actually complies with the protocol, which creates the problem of not guaranteeing fair evaluation and reward payment. This invention aims to solve these issues and realize the generation of high-quality content and fair evaluation by providing a consistent system for optimizing and evaluating generative AI.

[0588] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0589] In this invention, the server includes a means for designing and publishing rules for optimizing generative AI, a means for evaluating digital content based on the rules, a means for calculating a reward based on the evaluation results, a means for paying the reward to end users, and a means for including sentence structure, keyword usage, and meta tag settings in the evaluation criteria for the digital content. This makes it possible to consistently optimize and evaluate content using generative AI and pay fair rewards to users.

[0590] "Generative AI" is a technology that uses artificial intelligence to automatically create digital content such as text and images.

[0591] The "Terms and Conditions" are a detailed set of rules and guidelines for the generative AI to create optimized content, including things like meta tag placement and keyword use.

[0592] "Digital content" refers to various forms of content that appear on the internet, such as websites, blog posts, and product description pages.

[0593] "Evaluation criteria" are the criteria used to determine whether digital content complies with the regulations, and specifically include sentence structure, keyword use, meta tag settings, etc.

[0594] "Reward" refers to monetary compensation paid to a user based on the evaluation results.

[0595] "User" refers to an end user who generates digital content in accordance with the Terms and Conditions and receives evaluation and rewards.

[0596] The "server" is a central management system that handles a series of processes, including the design and publication of rules, evaluation of digital content, and calculation and payment of rewards.

[0597] A "generative AI tool" is software or a program that automatically generates compliant content based on user input.

[0598] "Final confirmation of protocol compliance" is the process of finally confirming whether the generated digital content complies with the regulations.

[0599] This invention provides a system that creates digital content optimized for generative AI, appropriately evaluates it, and pays fair rewards. This system mainly operates in cooperation between a server, terminals, and users.

[0600] The server designs rules for optimizing the generative AI and publishes them on the server. These rules include, for example, rules for placing meta tags on web pages, how to properly place keywords in content, and the structure of internal links. The server hosts these rules at a specific URL or API endpoint so that users and devices can download them. Specific examples include the URL "https: / / example.com / protocols / optimization_protocol_v1.json" and the API endpoint "https: / / api.example.com / protocols / latest."

[0601] The device downloads the regulations from the server and configures the site generation tool and content editor based on them. The device incorporates templates and guidelines that comply with the regulations, allowing the generative AI tool to automatically generate content based on prompts entered by the user. For example, if a user enters the topic "Latest Trends in AI Technology," the generative AI tool will automatically place the necessary meta tags and keywords to generate an optimized article. The generated content undergoes a final check on the device for protocol compliance, and once it passes the final inspection, it is submitted to the server.

[0602] Users use a tool on their device to check whether the digital content they manage complies with the terms and conditions. Once the check is complete, the user submits the content to the server. The server receives the submitted content and automatically begins the evaluation process. Evaluation criteria include sentence structure, keyword use, and meta tag settings, and the final evaluation score is calculated by adding up the scores for each item.

[0603] The server calculates rewards based on the evaluation score and transfers them to the end user's account. The higher the evaluation score, the higher the reward paid. For example, a tiered reward system could be used, such as $100 for a score of 90 or above, and $80 for a score of 80 or above.

[0604] For example, consider the following flow:

[0605] 1. The server designs the contract and publishes it at a specific URL.

[0606] 2. The device downloads the terms and conditions and configures the generation AI tool.

[0607] 3. The user enters the prompt "Latest trends in AI technology" and checks the generated content.

[0608] 4. The generated article is submitted to the server, which evaluates it.

[0609] 5. Rewards are calculated based on the rating score and deposited into the user's account.

[0610] This allows for consistent content optimization and evaluation using generative AI, ensuring fair rewards are paid to users.

[0611] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0612] Step 1:

[0613] The server designs the contract.

[0614] Specific actions

[0615] Input: Industry standards and SEO optimization guidelines

[0616] Data processing: Compiling web page meta tag placement rules, keyword placement methods, internal link structure, etc.

[0617] Output: Terms file (JSON or XML format)

[0618] The server designs detailed regulations based on industry standards and SEO optimization guidelines, including rules for meta tag placement on web pages, keyword placement methods, internal link structures, etc., and outputs these as a regulations file.

[0619] Step 2:

[0620] The server publishes the terms.

[0621] Specific actions

[0622] Input: Terms file

[0623] Data processing: Uploading files to the hosting server, setting up API endpoints

[0624] Output: URL or API endpoint

[0625] The server uploads the designed contract file to the hosting server and makes it available at a specific URL or API endpoint. For example, endpoints such as "https: / / example.com / protocols / optimization_protocol_v1.json" or "https: / / api.example.com / protocols / latest" are output.

[0626] Step 3:

[0627] The device downloads the terms and conditions.

[0628] Specific actions

[0629] Input: A publicly available URL or API endpoint

[0630] Data processing: Download the contract file from a URL or API endpoint

[0631] Output: A contract file in the local file system on the device.

[0632] The device downloads the contract file from the published URL or API endpoint and saves it to its local file system. For example, download it using "curl -O https: / / example.com / protocols / optimization_protocol_v1.json" on the command line.

[0633] Step 4:

[0634] The device configures the generative AI tool and generates the content.

[0635] Specific actions

[0636] Input: User prompt, downloaded terms file

[0637] Data processing: Generate compliant websites, blog posts, etc. based on prompts

[0638] Output: Generated content files (HTML, Markdown, etc.)

[0639] The user inputs a prompt into the device, and the device uses a generative AI tool to automatically generate content based on the rules. For example, if a user inputs the prompt "Latest trends in AI technology," the generative AI tool will generate an article with optimized meta tags and keyword placement.

[0640] Step 5:

[0641] The user confirms compliance.

[0642] Specific actions

[0643] Input: Generated content file, downloaded terms file

[0644] Data processing: Use standard checking tools to ensure generated content complies with standards

[0645] Output: Check result (pass / fail)

[0646] Users can use a check tool on their device to check whether the generated content complies with the regulations. The check tool automatically scans for the application of meta tags and keywords, and outputs a result of either "applied (passed)" or "not applied (failed)."

[0647] Step 6:

[0648] A user submits content to a server.

[0649] Specific actions

[0650] Input: Generated content files

[0651] Data processing: Upload content files to the server

[0652] Output: Submission confirmation message

[0653] Once the content file has been confirmed to comply with the regulations, the user submits it to the server. This is done by uploading the file to "https: / / upload.example.com". When the upload is complete, a "Submission Complete" message is displayed.

[0654] Step 7:

[0655] The server evaluates the content.

[0656] Specific actions

[0657] Input: Submitted content file, downloaded terms and conditions file

[0658] Data processing: Evaluate content based on the rules and calculate scores for each item

[0659] Output: Evaluation score, feedback

[0660] The server receives the submitted content file and initiates an automated evaluation process. Evaluation criteria include sentence structure, keyword usage, meta tag settings, etc., and the scores for each item are added together to calculate a final evaluation score. Feedback is provided along with the evaluation score.

[0661] Step 8:

[0662] The server calculates and pays the reward.

[0663] Specific actions

[0664] Input: Rating score

[0665] Data processing: Calculate the reward amount based on the evaluation score and transfer the reward to the user's account

[0666] Output: Reward transfer confirmation message

[0667] The server calculates rewards based on the evaluation score, with higher rewards paid for higher scores. For example, rewards are set in stages, such as $100 for a score of 90 or above, $80 for a score of 80 or above, etc. Once the calculated reward amount has been transferred to the user's account, a "Reward transfer completed" message is displayed.

[0668] (Application example 1)

[0669] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0670] In the generation and evaluation of content using generative AI models, there is a lack of mechanisms to generate high-quality content based on appropriate protocols and provide fair compensation according to the evaluation. This makes it difficult for creators and content providers to create content in the optimal way and receive fair compensation.

[0671] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0672] In this invention, the server includes means for designing and publishing a protocol for optimizing the generative AI, means for evaluating websites and content based on the protocol, means for calculating a reward based on the evaluation results, means for paying the reward to users, means for generating content that conforms to the protocol using a generative AI model, and means for submitting the content to the server and receiving an evaluation. This allows content created using the generative AI model to be evaluated fairly, and rewards based on the evaluation results to be appropriately paid.

[0673] A "generative AI model" refers to algorithms or software that use artificial intelligence to automatically generate content based on human-provided prompts.

[0674] A "protocol" refers to a set of rules or procedures that serve as the basis for optimizing and evaluating generative AI models.

[0675] A "prompt" is a piece of text that describes an instruction or request that is input to a generative AI model.

[0676] "Server" refers to a computer system that runs in the cloud or on-premise and runs generative AI models, publishes protocols, evaluates content and pays rewards.

[0677] "User" refers to an entity that uses a generative AI model to create content and submits that content for evaluation and reward.

[0678] "Content" refers to the text, images, video, and other creative works generated by generative AI models.

[0679] "Evaluation" refers to the process of determining how well generated content complies with the protocol and assigning it a quantitative score.

[0680] "Reward" refers to compensation such as money or points paid to a user based on the evaluation results.

[0681] This invention is a system that generates optimized content using a generative AI model and pays rewards based on the evaluation of the content. This system operates in cooperation between a server, terminals, and users.

[0682] Server-side processing

[0683] The server first designs and publishes a protocol for optimizing the generative AI. The protocol includes, for example, the placement of meta tags on web pages, the appropriate placement of keywords in content, and the structure of internal links. This protocol is published on the server and can be downloaded by users and devices via API.

[0684] The server then receives the website or content sent by the user or device. The received content is automatically evaluated based on the protocol. Evaluation criteria include sentence structure, keyword use, meta tag settings, etc., and the scores for each evaluation item are added together to calculate a final evaluation score. Finally, rewards are calculated based on the evaluation score and deposited into the user's account.

[0685] Terminal side processing

[0686] The device downloads the protocol from the server and configures the site generation tool and content editor based on it. Templates and guidelines that conform to this protocol are installed on the device. Next, a tool using a generative AI model is used to automatically generate protocol-compliant content based on user input. Examples include blog articles and product description pages. The generated content undergoes internal checks based on the protocol and is corrected as necessary. Finally, the generated content is submitted to the server. A final protocol compliance check is performed on the device before submission.

[0687] User-side processing

[0688] Users check whether the websites they manage or the content they create comply with the protocol. This is done automatically using a tool on their device. Next, they submit websites or content that have been confirmed to comply with the protocol to the server. Once the content reaches the server, the evaluation process begins automatically. Finally, users receive a reward calculated based on the evaluation score. The reward is deposited into the user's account, and they receive feedback information.

[0689] Hardware and software used

[0690] The server runs on a web server (e.g., AWS, Google Cloud), and the generative AI model used is OpenAI's GPT-3. HTTP / HTTPS is used as the communication protocol, and Python is used as the development language. On the terminal side, PCs, smartphones, head-mounted displays (HMDs), etc. are used.

[0691] Examples and prompts

[0692] For example, a user might input the following prompt sentence into a generative AI model to write a "travel blog post."

[0693] Prompt: "Write a blog post about travel."

[0694] The content generated in this way is of high quality and conforms to the protocol, and is evaluated by the server, with appropriate compensation being paid.

[0695] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0696] Step 1: Design and publish a protocol

[0697] The server designs and publishes a protocol for optimizing generative AI models. This protocol includes meta tag placement, appropriate keyword placement, and internal link structure. The server publishes the protocol via an API so that users and devices can download it. When designing the protocol, data analysis tools are used to determine the optimal placement and structure.

[0698] Input: Data and criteria for optimization

[0699] Data processing: Designing protocols using data analysis tools

[0700] Output: Protocol published

[0701] Step 2: Download the protocol

[0702] The terminal downloads the published protocol from the server, which is then used as a template or guideline to be applied to the site generation tool or content editor.

[0703] Input: Protocol exposed via API

[0704] Data processing: Apply protocols to devices as templates or guidelines

[0705] Output: Protocol download complete

[0706] Step 3: Generate content

[0707] The device receives a prompt from the user and uses a generative AI model to generate content that conforms to the protocol. The generative AI model automatically generates content in the specified format based on the input prompt. For example, it generates an article based on the prompt, "Please write a blog post about travel."

[0708] Input: User prompt text

[0709] Data processing: Content generation based on generative AI models

[0710] Output: Generated content

[0711] Step 4: Submit your content

[0712] The user submits the generated content to the server. Before submission, the terminal performs a final check to ensure that the content complies with the protocol and that there are no problems with the content.

[0713] Input: Generated content

[0714] Data processing: Checking protocol compliance

[0715] Output: The content sent to the server

[0716] Step 5: Evaluate your content

[0717] The server automatically evaluates the received content based on the protocol. Evaluation items include sentence structure, keyword use, meta tag settings, etc., and a score is assigned for each item. Finally, the scores for each item are added together to calculate an overall evaluation score.

[0718] Input: Content sent to the server

[0719] Data processing: Protocol compliance assessment and scoring

[0720] Output: Evaluation score

[0721] Step 6: Calculating and paying rewards

[0722] The server calculates rewards based on the evaluation score. The higher the evaluation score, the higher the reward. The reward is automatically transferred to the user's account.

[0723] Input: Rating score

[0724] Data processing: Reward calculation

[0725] Output: Reward deposited into user account

[0726] These processing steps enable fair evaluation of content using generative AI models and appropriate compensation payments.

[0727] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0728] This invention provides a system that combines a generative AI to generate optimized content and an emotion engine that recognizes user emotions. This system operates primarily in cooperation with a server, a terminal, and a user. The specific operations of each entity and the program processing are described below.

[0729] Server-side processing

[0730] Protocol Design

[0731] The server designs a detailed protocol for optimizing the generated AI, including proper placement of meta tags, effective use of keywords, internal link structure, etc. Content is created according to these standards to achieve optimization.

[0732] Protocol Publication

[0733] The designed protocol will be published on a server and can be downloaded by users or devices. The protocol will also be available via API.

[0734] Evaluation of submissions

[0735] The server receives websites and content sent by users and devices and automatically evaluates them based on the protocol. Evaluation criteria include sentence structure, keyword usage, meta tag settings, etc. The scores for each item are added together to calculate the final evaluation score.

[0736] Reward calculation and payment

[0737] The server calculates a reward based on the evaluation score and transfers it to the user's account.

[0738] Terminal side processing

[0739] Protocol Download

[0740] The device downloads the protocol from the server and configures the site generation tool and content editor based on it, and templates and guidelines that conform to the protocol are installed on the device.

[0741] Site Generation and Content Creation

[0742] The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input, such as blog posts and product description pages. The generated content undergoes internal checks based on the protocol and is corrected as needed.

[0743] Use of emotion engine

[0744] The device collects user emotion data using an emotion engine that recognizes the user's emotions. For example, it analyzes the user's facial expressions and voice and captures the emotion information.

[0745] Content Adjustment

[0746] The content generated by the generative AI is adjusted based on the emotional data collected by the emotion engine. For example, if the user is happy, the AI ​​will generate content that contains more positive expressions.

[0747] Content Submission

[0748] The generated website and content are then submitted to the server, where a final check is made on the device to ensure protocol compliance.

[0749] User-side processing

[0750] Checking Protocol Compliance

[0751] Users can check whether the websites they run and the content they create comply with the protocol by using tools on their devices to automatically check.

[0752] Providing emotion data

[0753] Users provide their emotional data using the camera and microphone on their device, which allows the emotion engine to accurately recognize the user's emotions.

[0754] Content Submission

[0755] Users submit websites or content that is verified as protocol compliant to the server, and once the content reaches the server, an evaluation process begins automatically.

[0756] Receiving rewards

[0757] Users receive a reward calculated based on their rating score, which is deposited into their account and they receive feedback information.

[0758] Specific examples

[0759] 1. Emotion Recognition and Site Generation

[0760] Download the protocol on your device and set up the website generator.

[0761] When a user inputs a blog post into the generation AI tool, the emotion engine recognizes the user's emotions.

[0762] A generative AI tool generates articles based on user sentiment and adjusts meta tags and keyword placement.

[0763] 2. Content Submission and Evaluation

[0764] The user submits the generated article to the server.

[0765] The server receives the article and evaluates its conformance to the protocol.

[0766] The scores for each evaluation item are added together to calculate the final score.

[0767] 3. Payment of Rewards

[0768] The server calculates the reward based on the evaluation score and transfers it to the user's account.

[0769] Users receive a reward and use the feedback to optimize their next iteration.

[0770] This allows content optimized using generative AI to be fairly evaluated, and more personalized content is provided based on the user's emotions. The entire system works together to pay appropriate rewards to users.

[0771] The processing flow will be explained below.

[0772] Server-side processing

[0773] Step 1: Design the protocol

[0774] The server designs protocols for generative AI optimization, including proper use of title tags, placement of key keywords, and how to set meta descriptions.

[0775] Step 2: Publishing the protocol

[0776] The server publishes the designed protocol on the web so that it can be downloaded by users or devices, or the protocol can be provided through an API.

[0777] Step 3: Evaluate submissions

[0778] The server receives websites and content submitted by users and devices, evaluates the received content based on the protocol, and checks the appropriateness of sentence structure, keyword usage, and meta tags.

[0779] Step 4: Calculating and paying rewards

[0780] The server calculates rewards based on the rating score. The higher the rating score, the higher the reward. The rewards are deposited into the user's account.

[0781] Terminal side processing

[0782] Step 1: Download the protocol

[0783] The terminal downloads the protocol from the server and configures the site generation tool and content editor. Templates and guidelines that conform to the protocol are installed on the terminal.

[0784] Step 2: Collecting emotion data

[0785] The device activates an emotion engine to recognize the user's emotions. It uses a camera and microphone to collect the user's facial expressions and voice, and analyzes the emotional data.

[0786] Step 3: Site Creation and Content Creation

[0787] The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input and emotional data. For example, if a user is happy, it will generate an article that uses a lot of positive language.

[0788] Step 4: Submit your content

[0789] The device submits the generated website or content to the server, which performs a final check to ensure compliance with the protocol before submitting.

[0790] User-side processing

[0791] Step 1: Log in and verify protocol compliance

[0792] Users can log in to their devices and check whether the websites and content they create comply with the protocol. Checks are performed automatically using tools on the devices.

[0793] Step 2: Provide emotion data

[0794] Users provide their emotional data using a camera or microphone, which is then analyzed by the emotion engine and recognized as emotional information.

[0795] Step 3: Review and submit content

[0796] Users can submit websites and content that are verified as protocol compliant to the server, and can even make final adjustments on their devices before submitting.

[0797] Step 4: Receive your rewards

[0798] The user receives a reward based on the score of the content evaluated by the server. Check whether the reward has been deposited into the account.

[0799] Example: Creating and rating blog posts

[0800] Server side

[0801] Step 1: Design the protocol

[0802] The server designs protocols such as "include the main keyword in the article title" and "use H1 tags only once per page."

[0803] Step 2: Publishing the protocol

[0804] The server publishes the designed protocol on the web so that it can be downloaded by users and devices.

[0805] Step 3: Evaluate submissions

[0806] It receives blog posts submitted by users and evaluates whether the title, H1 tag, keyword placement, etc. comply with the protocol.

[0807] Step 4: Calculating and paying rewards

[0808] Rewards are calculated based on the evaluation and transferred to the user's account.

[0809] Terminal side

[0810] Step 1: Download the protocol

[0811] Download protocols from the server and configure site generation tools and editors.

[0812] Step 2: Collecting emotion data

[0813] It uses a camera and microphone to analyze facial expressions and voice to recognize the user's emotions.

[0814] Step 3: Site Creation and Content Creation

[0815] A generative AI tool generates protocol-compliant articles based on sentiment data and user input. For example, if a user is feeling happy, the article will have a positive tone.

[0816] Step 4: Submit your content

[0817] The device submits the generated blog post to the server, which checks whether it complies with the protocol before submitting.

[0818] User side

[0819] Step 1: Log in and verify protocol compliance

[0820] The user logs in to the terminal and checks whether the article has been created in accordance with the protocol.

[0821] Step 2: Provide emotion data

[0822] Users provide their emotional data using a camera and microphone.

[0823] Step 3: Review and submit content

[0824] The user submits the article to the server after final review.

[0825] Step 4: Receive your rewards

[0826] Check whether the reward based on the evaluation score has been paid and use it for the next optimization.

[0827] This will enable content optimized using generative AI to be fairly evaluated, and personalized content based on user emotions will be provided, creating a system in which appropriate rewards are paid to users.

[0828] Example 2

[0829] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0830] Conventional content generation systems using generative AI were unable to incorporate user emotions, making it difficult to provide optimal content for users. Furthermore, the mechanisms for evaluating the degree to which the generated content was optimized and for paying appropriate rewards to users were inadequate. This led to issues such as a decline in user satisfaction and motivation.

[0831] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for designing and publishing a protocol for optimizing the generation AI, a means for evaluating websites and content based on the protocol, a means for calculating a reward based on the evaluation results, a means for paying the reward to the user, a means for collecting and analyzing user emotion data, and a means for adjusting the content based on the emotion data. This enables content generation that takes user emotions into consideration, fair evaluation of the content, and appropriate payment of rewards.

[0832] "Generative AI optimization" refers to methods and standards for improving the quality and search engine optimization (SEO) of content created by generative AI.

[0833] A "protocol" is a set of rules and guidelines for content creation, and refers to the standards used to ensure content quality and optimization.

[0834] "Evaluation" refers to the process of analyzing and scoring the generated content to see how well it conforms to the protocol.

[0835] "Reward" refers to the compensation paid to the generator (user) based on the evaluation results.

[0836] "User" refers to a user who generates and submits content using this system.

[0837] "Emotion data" refers to data relating to emotions acquired through the user's facial expressions, voice, etc.

[0838] "Content" refers to a collection of information created by generative AI, such as a website, article, or product description page.

[0839] This invention provides a system that combines a generative AI to generate optimized content and an emotion engine that recognizes user emotions. This system operates primarily in cooperation between a server, a terminal, and a user.

[0840] 1. Server Role

[0841] Protocol design and publication

[0842] The server designs a detailed protocol for optimizing the generative AI, including how to place meta tags, how to effectively use keywords, and the structure of internal links. The protocol is published on the server and can be downloaded to users and devices via API.

[0843] Specific examples:

[0844] The server extracts the keyword set and inserts example meta tags into the HTML template.

[0845] Content Rating and Reward Calculation

[0846] The server receives content sent by users and devices and automatically evaluates it based on protocol criteria, including sentence structure, keyword usage, meta tag settings, etc. Based on the evaluation results, a reward is calculated and deposited into the user's account.

[0847] Specific examples:

[0848] Based on the prompt sentence "I recently traveled. I would like to write a blog post about this trip. I am happy.", the generated blog post is evaluated.

[0849] 2. Role of the terminal

[0850] Downloading the protocol and setting up the AI ​​generation tool

[0851] The device downloads the protocol from the server and configures its generative AI tools and content editors based on it, and the protocol-compliant templates and guidelines are deployed on the device.

[0852] Specific examples:

[0853] The device will set up a template for creating a travel blog.

[0854] Collecting sentiment data and tailoring content

[0855] The device uses hardware such as a camera and microphone to collect user emotional data, using facial recognition and voice analysis technology. The collected emotional data is reflected in the generated content.

[0856] Specific examples:

[0857] The camera detects the user's smile and uses a lot of positive language in articles.

[0858] 3. User Roles

[0859] Verifying protocol compliance and providing emotional data

[0860] Users can use tools on their devices to check whether the websites they run and the content they create comply with the protocol. Users also use their cameras and microphones to provide emotional data, which is collected and analyzed by the devices.

[0861] Specific examples:

[0862] A user creates a blog post and enters a prompt into the terminal, such as "I would like to submit this blog post. Please rate it."

[0863] Submit content and get paid

[0864] When users submit their generated content to the server, the server will rate it and calculate a reward, which will be credited to the user's account based on the rating score.

[0865] Specific examples:

[0866] The user receives the reward with the prompt, "The evaluation is complete. I would like to receive my reward."

[0867] The above configuration enables optimal content generation using generative AI, fair evaluation, and personalization of content according to user emotions, which will improve user satisfaction and motivation and enable the provision of even higher quality content.

[0868] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0869] Step 1: Design the protocol

[0870] The server designs a detailed protocol for optimizing the generative AI, including proper placement of meta tags, effective use of keywords, and internal link structure. Specifically, the server analyzes existing SEO data and extracts the optimal keyword set. It also automatically inserts example meta tags into HTML templates. The input is the existing SEO data and predicted search queries, and the output is a protocol for optimizing the generative AI.

[0871] Step 2: Publishing and downloading the protocol

[0872] The server publishes the designed protocol on the server and allows users and devices to download it via API. Specifically, the server provides the latest version of the protocol at the API endpoint. The input is the designed protocol, and the output is the provision of protocol data to users and devices.

[0873] Step 3: Configuring the Generative AI Tool

[0874] The device configures the generative AI tool and content editor based on the protocol downloaded from the server. Specifically, the device stores the protocol rule set locally and reflects it in the initial settings of the generative AI. It also uses a template engine to generate an initial template that conforms to the protocol. The input is the downloaded protocol, and the output is the configured generative AI tool.

[0875] Step 4: Collecting emotion data

[0876] The device collects user emotional data using a camera and microphone. Specifically, the camera captures facial expressions and applies a facial recognition algorithm. The microphone also captures audio data and analyzes it with an emotion analysis algorithm. The input is the user's facial expression and audio data, and the output is analyzed emotional data.

[0877] Step 5: Generate and refine content

[0878] The device uses a generative AI tool to generate and adjust content based on the emotion data collected by the emotion engine. Specifically, the generative AI tool uses the emotion data as an input parameter, and if positive emotions are detected, it generates content that makes heavy use of positive expressions. The input is the emotion data and the user's content request, and the output is adjusted content that complies with the protocol.

[0879] Step 6: Submit and rate your content

[0880] Users submit generated content to a server, which then evaluates the submitted content based on a set of criteria. Specifically, the server parses the content and scores it based on criteria such as structure, keyword usage, and meta tag settings. The input is the submitted content, and the output is the evaluation score.

[0881] Step 7: Calculation and payment of rewards based on the evaluation results

[0882] The server calculates the reward based on the rating score and transfers it to the user's account. Specifically, the server analyzes the rating score and calculates the reward amount. The input is the rating score, and the output is the calculated reward amount and its payment.

[0883] (Application example 2)

[0884] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0885] While conventional content generation systems using generative AI models achieve protocol-based content optimization, they lack personalization based on user emotions. This results in insufficient improvement of the user experience. Furthermore, there is a demand for technology that can improve the quality of generated content by utilizing emotion recognition data. Therefore, the present invention aims to solve the above problems by providing a system that recognizes user emotions and generates and adjusts content based on them.

[0886] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0887] In this invention, the server includes a means for designing and publishing a protocol for optimizing the generative AI, a means for evaluating websites and content based on the protocol, a means for calculating rewards based on the evaluation results, a means including an emotion recognition engine for recognizing user emotions, and a means for generating content based on the emotion data. This enables the provision of personalized content based on user emotions and an optimized evaluation system.

[0888] "Generative AI" is an artificial intelligence technology that automatically generates content based on user input.

[0889] A "protocol" defines detailed guidelines and rules for optimizing content generation by generative AI.

[0890] An "emotion recognition engine" is a technology that analyzes a user's facial expressions and voice data to recognize their emotional state.

[0891] "Content" refers to information or media that users view or use, such as websites, blog posts, product description pages, etc.

[0892] The "evaluation means" is a mechanism for automatically evaluating whether the generated content complies with the protocol.

[0893] "Reward payment means" is a system that provides monetary rewards to users based on the results of their content evaluations.

[0894] "Downloading means" refers to a mechanism for acquiring the protocol on the user's terminal.

[0895] "Content generation means" refers to technology for automatically generating websites and content in accordance with protocols.

[0896] A "submission mechanism" is a mechanism for transmitting user-generated content to a server.

[0897] "Personalization" refers to providing content optimized for a specific user based on the user's emotional data and preferences.

[0898] "Internal check" is a mechanism for checking on the terminal side whether the generated content complies with the protocol.

[0899] overview

[0900] This invention is a system that combines optimization of generative AI with user emotion recognition. This system works in cooperation with the server, the terminal, and the user.

[0901] server

[0902] The server has the means to design and publish a protocol for optimizing the generative AI. The protocol includes detailed guidelines such as the appropriate placement of meta tags, effective use of keywords, and link structure. The designed protocol is published on the server and can be downloaded by users and devices. It can also be obtained via API. The server evaluates content sent by users and devices based on the protocol, calculates rewards based on the evaluation score, and deposits them into the user's account.

[0903] Terminal

[0904] The device downloads the protocol from the server and configures the site generation tool and content editor. The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input. At this time, an emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data. This allows the generated content to be adjusted based on the user's emotions. For example, if the user is happy, content containing many positive expressions will be generated. The generated content undergoes a final protocol compliance check on the device before being submitted to the server.

[0905] User

[0906] Users download the protocol using their own devices and generate websites and content. During the generation process, an emotion recognition engine analyzes the user's emotions in real time, and this data is reflected in the content generation. The user submits the generated content to the server, which evaluates it based on the protocol. Rewards are deposited into the user's account based on the evaluation results. This allows the user to use the feedback to improve their next content generation.

[0907] Hardware and software used

[0908] Server: High-performance computers, cloud computing services (e.g., AWS, Google Cloud, etc.).

[0909] Devices: Smartphones, tablets, PCs.

[0910] Software: EmotionRecognition library, ContentGenerator library, REST API.

[0911] Specific examples

[0912] Users use their smartphone camera to analyze their facial expressions, and the emotion recognition engine detects "joy." Based on this data, the AI ​​automatically generates positive news articles. For example, it can be based on a prompt such as, "Please create good news about the latest technological innovation."

[0913] The generated content is submitted to the server, where it is evaluated according to the protocol. Based on the evaluation results, rewards are calculated and paid to the user.

[0914] Prompt Sentence Examples

[0915] "Create a joy-based, breaking news story. The topic should be related to technological innovation and contain a lot of positivity."

[0916] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0917] Step 1:

[0918] The server will design a protocol for optimizing the generative AI, including detailed standards for meta tag placement, keyword use, link structure, etc. The protocol will be made available to users and devices in a downloadable format, and can also be obtained via API.

[0919] Input: Content generation optimization criteria

[0920] Output: Protocol document

[0921] Step 2:

[0922] The device downloads the protocol from the server, configures site generators and content editors based on it, and implements protocol-compliant templates and guidelines.

[0923] Input: Protocol document

[0924] Output: Configured tools to comply with the protocol

[0925] Step 3:

[0926] When a user uses a device to create a website or create content, the emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data.

[0927] Input: User facial and voice data

[0928] Output: Emotion data (emotional state information such as happiness, anger, sadness, and happiness)

[0929] Step 4:

[0930] The device uses generative AI tools to automatically generate protocol-compliant content based on the collected emotional data. Personalization is achieved using emotional data. For example, if the user is happy, positive content is generated.

[0931] Input: Emotion data, protocol criteria

[0932] Output: Generated content (e.g. blog post, product description page)

[0933] Step 5:

[0934] Device-generated content is internally checked to ensure it complies with the protocol, and if it passes this internal check, it is submitted to the server.

[0935] Input: Generated content

[0936] Output: Content that has been checked for protocol compliance

[0937] Step 6:

[0938] The server receives the submitted content and evaluates it based on the protocol. Evaluation criteria include document structure, keyword usage, meta tag settings, etc. A rating score is calculated.

[0939] Input: Submitted content, protocol criteria

[0940] Output: Evaluation score

[0941] Step 7:

[0942] The server calculates the user's reward based on the evaluation result and deposits the reward into the user's account.

[0943] Input: Rating score

[0944] Output: Reward calculation result, deposited into user account

[0945] Step 8:

[0946] The user receives the reward and checks the feedback from the server, which is used to generate the next content.

[0947] Input: Feedback information, reward

[0948] Output: Reference for next content generation

[0949] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0950] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0951] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0952] [Third embodiment]

[0953] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0954] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0955] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0956] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0957] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0958] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0959] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0960] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0961] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0962] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0963] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0964] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0965] This invention provides a system that generates content optimized for generative AI, appropriately evaluates it, and pays rewards. This system mainly operates in cooperation with a server, terminals, and users. The specific operations of each entity and the program processing are explained below.

[0966] Server-side processing

[0967] Protocol Design

[0968] The server designs a detailed protocol for generative AI optimization, including, for example, the placement of meta tags on web pages, the proper placement of keywords in content, and the structure of internal links. Content is created according to these standards to achieve optimization.

[0969] Protocol Publication

[0970] The designed protocol will be published on a server and can be downloaded by users or devices. The protocol will also be available via API.

[0971] Evaluation of submissions

[0972] The server receives websites and content sent by users and devices. The received content is automatically evaluated based on the protocol. Evaluation criteria include sentence structure, keyword use, meta tag settings, etc. The scores for each item are added together to calculate the final evaluation score.

[0973] Reward calculation and payment

[0974] The server calculates the reward based on the evaluation score. The higher the evaluation score, the higher the reward will be paid. After calculation, the reward will be transferred to the user's account.

[0975] Terminal side processing

[0976] Protocol Download

[0977] The device downloads the protocol from the server and configures the site generation tool or content editor based on it, and templates and guidelines that conform to this protocol are installed on the device.

[0978] Site Generation and Content Creation

[0979] The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input, such as blog posts and product description pages. The generated content undergoes internal checks based on the protocol and is corrected as needed.

[0980] Content Submission

[0981] The generated website and content are then submitted to the server, where a final check is made on the device to ensure protocol compliance.

[0982] User-side processing

[0983] Checking Protocol Compliance

[0984] Users can check whether the websites they run and the content they create comply with the protocol by using tools on their devices to automatically check.

[0985] Content Submission

[0986] Users submit websites or content that is verified as protocol compliant to the server, and once the content reaches the server, an evaluation process begins automatically.

[0987] Receiving rewards

[0988] Users receive a reward calculated based on their rating score, which is deposited into their account and they receive feedback information.

[0989] Specific examples

[0990] 1. Site Creation

[0991] Download the protocol on your device and set up the website generator.

[0992] A user inputs a blog post into a generative AI tool.

[0993] A generative AI tool generates articles based on protocols and optimizes meta tags and keyword placement.

[0994] 2. Content Submission and Evaluation

[0995] The user submits the generated article to the server.

[0996] The server receives the article and evaluates its conformance to the protocol.

[0997] The scores for each evaluation item are added together to calculate the final score.

[0998] 3. Payment of Rewards

[0999] The server calculates the reward based on the evaluation score and transfers it to the user's account.

[1000] Users receive a reward and use the feedback to optimize their next iteration.

[1001] This will create a system where optimized content created using generative AI is fairly evaluated and appropriate rewards are provided.

[1002] The processing flow will be explained below.

[1003] Server-side processing

[1004] Step 1: Design the protocol

[1005] The server designs a detailed protocol for generative AI optimization, including meta tag placement, proper keyword placement, internal link structure, etc.

[1006] Step 2: Publishing the protocol

[1007] The server publishes the designed protocol on the web in a downloadable format and also provides protocol information through an API.

[1008] Step 3: Evaluate submissions

[1009] The server receives websites and content submitted by users and devices and automatically evaluates them based on the protocol, including content structure, keyword usage, and meta tag settings.

[1010] Step 4: Calculating and paying rewards

[1011] The server calculates the reward based on the evaluation score and transfers it to the user's account.

[1012] Terminal side processing

[1013] Step 1: Download the protocol

[1014] The device downloads the protocol from the server and configures the site generation tool or content editor accordingly.

[1015] Step 2: Site Creation and Content Creation

[1016] The on-device generative AI tool generates protocol-compliant websites and content based on user input, performs internal checks based on the protocol, and makes corrections as needed.

[1017] Step 3: Submit your content

[1018] The device submits the generated website or content to the server, where a final check is made to ensure compliance with the protocol.

[1019] User-side processing

[1020] Step 1: Verify protocol compliance

[1021] Users can use the terminal to check whether the websites and content they have created comply with the protocol.

[1022] Step 2: Submit your content

[1023] Users submit websites and content to the server that is verified as conforming to the protocol.

[1024] Step 3: Receive your rewards

[1025] Users receive rewards based on the content rated by the server. Check the reward receipt notification and feedback from the server.

[1026] Specific examples

[1027] Server side

[1028] Step 1: Design the protocol

[1029] The server designs a protocol that includes rules such as "include the main keyword in the article title" and "use one H1 tag per page."

[1030] Step 2: Publishing the protocol

[1031] The server publishes the designed protocol on the web so that it can be downloaded by users and devices.

[1032] Step 3: Evaluate submissions

[1033] The server receives blog posts submitted by users and checks whether the title and H1 tag usage conform to the protocol.

[1034] Step 4: Calculating and paying rewards

[1035] The server calculates the reward based on the evaluation results and transfers it to the user's account.

[1036] Terminal side

[1037] Step 1: Download the protocol

[1038] The terminal downloads the protocol from the server and sets it in the site generation tool.

[1039] Step 2: Site Creation and Content Creation

[1040] When a user enters a blog post, the generative AI tool generates the post according to the protocol and performs internal checks on meta tags and keyword placement.

[1041] Step 3: Submit your content

[1042] Before submitting the generated blog post to the server, the terminal checks whether it complies with the protocol and then sends it to the server.

[1043] User side

[1044] Step 1: Verify protocol compliance

[1045] Users can use tools on their devices to verify that the blog posts they generate comply with the protocol.

[1046] Step 2: Submit your content

[1047] Users submit articles to the server that are verified to be protocol compliant.

[1048] Step 3: Receive your rewards

[1049] The user checks the evaluation results and reward receipt notification from the server and receives the reward.

[1050] Example 1

[1051] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1052] In conventional systems, content optimization and evaluation by generative AI are performed as independent processes, making it difficult to provide efficient feedback and calculate rewards. Furthermore, there is a lack of means to verify whether the generated content actually complies with the protocol, which creates the problem of not guaranteeing fair evaluation and reward payment. This invention aims to solve these issues and realize the generation of high-quality content and fair evaluation by providing a consistent system for optimizing and evaluating generative AI.

[1053] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1054] In this invention, the server includes a means for designing and publishing rules for optimizing generative AI, a means for evaluating digital content based on the rules, a means for calculating a reward based on the evaluation results, a means for paying the reward to end users, and a means for including sentence structure, keyword usage, and meta tag settings in the evaluation criteria for the digital content. This makes it possible to consistently optimize and evaluate content using generative AI and pay fair rewards to users.

[1055] "Generative AI" is a technology that uses artificial intelligence to automatically create digital content such as text and images.

[1056] The "Terms and Conditions" are a detailed set of rules and guidelines for the generative AI to create optimized content, including things like meta tag placement and keyword use.

[1057] "Digital content" refers to various forms of content that appear on the internet, such as websites, blog posts, and product description pages.

[1058] "Evaluation criteria" are the criteria used to determine whether digital content complies with the regulations, and specifically include sentence structure, keyword use, meta tag settings, etc.

[1059] "Reward" refers to monetary compensation paid to a user based on the evaluation results.

[1060] "User" refers to an end user who generates digital content in accordance with the Terms and Conditions and receives evaluation and rewards.

[1061] The "server" is a central management system that handles a series of processes, including the design and publication of rules, evaluation of digital content, and calculation and payment of rewards.

[1062] A "generative AI tool" is software or a program that automatically generates compliant content based on user input.

[1063] "Final confirmation of protocol compliance" is the process of finally confirming whether the generated digital content complies with the regulations.

[1064] This invention provides a system that creates digital content optimized for generative AI, appropriately evaluates it, and pays fair rewards. This system mainly operates in cooperation between a server, terminals, and users.

[1065] The server designs rules for optimizing the generative AI and publishes them on the server. These rules include, for example, rules for placing meta tags on web pages, how to properly place keywords in content, and the structure of internal links. The server hosts these rules at a specific URL or API endpoint so that users and devices can download them. Specific examples include the URL "https: / / example.com / protocols / optimization_protocol_v1.json" and the API endpoint "https: / / api.example.com / protocols / latest."

[1066] The device downloads the regulations from the server and configures the site generation tool and content editor based on them. The device incorporates templates and guidelines that comply with the regulations, allowing the generative AI tool to automatically generate content based on prompts entered by the user. For example, if a user enters the topic "Latest Trends in AI Technology," the generative AI tool will automatically place the necessary meta tags and keywords to generate an optimized article. The generated content undergoes a final check on the device for protocol compliance, and once it passes the final inspection, it is submitted to the server.

[1067] Users use a tool on their device to check whether the digital content they manage complies with the terms and conditions. Once the check is complete, the user submits the content to the server. The server receives the submitted content and automatically begins the evaluation process. Evaluation criteria include sentence structure, keyword use, and meta tag settings, and the final evaluation score is calculated by adding up the scores for each item.

[1068] The server calculates rewards based on the evaluation score and transfers them to the end user's account. The higher the evaluation score, the higher the reward paid. For example, a tiered reward system could be used, such as $100 for a score of 90 or above, and $80 for a score of 80 or above.

[1069] For example, consider the following flow:

[1070] 1. The server designs the contract and publishes it at a specific URL.

[1071] 2. The device downloads the terms and conditions and configures the generation AI tool.

[1072] 3. The user enters the prompt "Latest trends in AI technology" and checks the generated content.

[1073] 4. The generated article is submitted to the server, which evaluates it.

[1074] 5. Rewards are calculated based on the rating score and deposited into the user's account.

[1075] This allows for consistent content optimization and evaluation using generative AI, ensuring fair rewards are paid to users.

[1076] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1077] Step 1:

[1078] The server designs the contract.

[1079] Specific actions

[1080] Input: Industry standards and SEO optimization guidelines

[1081] Data processing: Compiling web page meta tag placement rules, keyword placement methods, internal link structure, etc.

[1082] Output: Terms file (JSON or XML format)

[1083] The server designs detailed regulations based on industry standards and SEO optimization guidelines, including rules for meta tag placement on web pages, keyword placement methods, internal link structures, etc., and outputs these as a regulations file.

[1084] Step 2:

[1085] The server publishes the terms.

[1086] Specific actions

[1087] Input: Terms file

[1088] Data processing: Uploading files to the hosting server, setting up API endpoints

[1089] Output: URL or API endpoint

[1090] The server uploads the designed contract file to the hosting server and makes it available at a specific URL or API endpoint. For example, endpoints such as "https: / / example.com / protocols / optimization_protocol_v1.json" or "https: / / api.example.com / protocols / latest" are output.

[1091] Step 3:

[1092] The device downloads the terms and conditions.

[1093] Specific actions

[1094] Input: A publicly available URL or API endpoint

[1095] Data processing: Download the contract file from a URL or API endpoint

[1096] Output: A contract file in the local file system on the device.

[1097] The device downloads the contract file from the published URL or API endpoint and saves it to its local file system. For example, download it using "curl -O https: / / example.com / protocols / optimization_protocol_v1.json" on the command line.

[1098] Step 4:

[1099] The device configures the generative AI tool and generates the content.

[1100] Specific actions

[1101] Input: User prompt, downloaded terms file

[1102] Data processing: Generate compliant websites, blog posts, etc. based on prompts

[1103] Output: Generated content files (HTML, Markdown, etc.)

[1104] The user inputs a prompt into the device, and the device uses a generative AI tool to automatically generate content based on the rules. For example, if a user inputs the prompt "Latest trends in AI technology," the generative AI tool will generate an article with optimized meta tags and keyword placement.

[1105] Step 5:

[1106] The user confirms compliance.

[1107] Specific actions

[1108] Input: Generated content file, downloaded terms file

[1109] Data processing: Use standard checking tools to ensure generated content complies with standards

[1110] Output: Check result (pass / fail)

[1111] Users can use a check tool on their device to check whether the generated content complies with the regulations. The check tool automatically scans for the application of meta tags and keywords, and outputs a result of either "applied (passed)" or "not applied (failed)."

[1112] Step 6:

[1113] A user submits content to a server.

[1114] Specific actions

[1115] Input: Generated content files

[1116] Data processing: Upload content files to the server

[1117] Output: Submission confirmation message

[1118] Once the content file has been confirmed to comply with the regulations, the user submits it to the server. This is done by uploading the file to "https: / / upload.example.com". When the upload is complete, a "Submission Complete" message is displayed.

[1119] Step 7:

[1120] The server evaluates the content.

[1121] Specific actions

[1122] Input: Submitted content file, downloaded terms and conditions file

[1123] Data processing: Evaluate content based on the rules and calculate scores for each item

[1124] Output: Evaluation score, feedback

[1125] The server receives the submitted content file and initiates an automated evaluation process. Evaluation criteria include sentence structure, keyword usage, meta tag settings, etc., and the scores for each item are added together to calculate a final evaluation score. Feedback is provided along with the evaluation score.

[1126] Step 8:

[1127] The server calculates and pays the reward.

[1128] Specific actions

[1129] Input: Rating score

[1130] Data processing: Calculate the reward amount based on the evaluation score and transfer the reward to the user's account

[1131] Output: Reward transfer confirmation message

[1132] The server calculates rewards based on the evaluation score, with higher rewards paid for higher scores. For example, rewards are set in stages, such as $100 for a score of 90 or above, $80 for a score of 80 or above, etc. Once the calculated reward amount has been transferred to the user's account, a "Reward transfer completed" message is displayed.

[1133] (Application example 1)

[1134] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1135] In the generation and evaluation of content using generative AI models, there is a lack of mechanisms to generate high-quality content based on appropriate protocols and provide fair compensation according to the evaluation. This makes it difficult for creators and content providers to create content in the optimal way and receive fair compensation.

[1136] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1137] In this invention, the server includes means for designing and publishing a protocol for optimizing the generative AI, means for evaluating websites and content based on the protocol, means for calculating a reward based on the evaluation results, means for paying the reward to users, means for generating content that conforms to the protocol using a generative AI model, and means for submitting the content to the server and receiving an evaluation. This allows content created using the generative AI model to be evaluated fairly, and rewards based on the evaluation results to be appropriately paid.

[1138] A "generative AI model" refers to algorithms or software that use artificial intelligence to automatically generate content based on human-provided prompts.

[1139] A "protocol" refers to a set of rules or procedures that serve as the basis for optimizing and evaluating generative AI models.

[1140] A "prompt" is a piece of text that describes an instruction or request that is input to a generative AI model.

[1141] "Server" refers to a computer system that runs in the cloud or on-premise and runs generative AI models, publishes protocols, evaluates content and pays rewards.

[1142] "User" refers to an entity that uses a generative AI model to create content and submits that content for evaluation and reward.

[1143] "Content" refers to the text, images, video, and other creative works generated by generative AI models.

[1144] "Evaluation" refers to the process of determining how well generated content complies with the protocol and assigning it a quantitative score.

[1145] "Reward" refers to compensation such as money or points paid to a user based on the evaluation results.

[1146] This invention is a system that generates optimized content using a generative AI model and pays rewards based on the evaluation of the content. This system operates in cooperation between a server, terminals, and users.

[1147] Server-side processing

[1148] The server first designs and publishes a protocol for optimizing the generative AI. The protocol includes, for example, the placement of meta tags on web pages, the appropriate placement of keywords in content, and the structure of internal links. This protocol is published on the server and can be downloaded by users and devices via API.

[1149] The server then receives the website or content sent by the user or device. The received content is automatically evaluated based on the protocol. Evaluation criteria include sentence structure, keyword use, meta tag settings, etc., and the scores for each evaluation item are added together to calculate a final evaluation score. Finally, rewards are calculated based on the evaluation score and deposited into the user's account.

[1150] Terminal side processing

[1151] The device downloads the protocol from the server and configures the site generation tool and content editor based on it. Templates and guidelines that conform to this protocol are installed on the device. Next, a tool using a generative AI model is used to automatically generate protocol-compliant content based on user input. Examples include blog articles and product description pages. The generated content undergoes internal checks based on the protocol and is corrected as necessary. Finally, the generated content is submitted to the server. A final protocol compliance check is performed on the device before submission.

[1152] User-side processing

[1153] Users check whether the websites they manage or the content they create comply with the protocol. This is done automatically using a tool on their device. Next, they submit websites or content that have been confirmed to comply with the protocol to the server. Once the content reaches the server, the evaluation process begins automatically. Finally, users receive a reward calculated based on the evaluation score. The reward is deposited into the user's account, and they receive feedback information.

[1154] Hardware and software used

[1155] The server runs on a web server (e.g., AWS, Google Cloud), and the generative AI model used is OpenAI's GPT-3. HTTP / HTTPS is used as the communication protocol, and Python is used as the development language. On the terminal side, PCs, smartphones, head-mounted displays (HMDs), etc. are used.

[1156] Examples and prompts

[1157] For example, a user might input the following prompt sentence into a generative AI model to write a "travel blog post."

[1158] Prompt: "Write a blog post about travel."

[1159] The content generated in this way is of high quality and conforms to the protocol, and is evaluated by the server, with appropriate compensation being paid.

[1160] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1161] Step 1: Design and publish a protocol

[1162] The server designs and publishes a protocol for optimizing generative AI models. This protocol includes meta tag placement, appropriate keyword placement, and internal link structure. The server publishes the protocol via an API so that users and devices can download it. When designing the protocol, data analysis tools are used to determine the optimal placement and structure.

[1163] Input: Data and criteria for optimization

[1164] Data processing: Designing protocols using data analysis tools

[1165] Output: Protocol published

[1166] Step 2: Download the protocol

[1167] The terminal downloads the published protocol from the server, which is then used as a template or guideline to be applied to the site generation tool or content editor.

[1168] Input: Protocol exposed via API

[1169] Data processing: Apply protocols to devices as templates or guidelines

[1170] Output: Protocol download complete

[1171] Step 3: Generate content

[1172] The device receives a prompt from the user and uses a generative AI model to generate content that conforms to the protocol. The generative AI model automatically generates content in the specified format based on the input prompt. For example, it generates an article based on the prompt, "Please write a blog post about travel."

[1173] Input: User prompt text

[1174] Data processing: Content generation based on generative AI models

[1175] Output: Generated content

[1176] Step 4: Submit your content

[1177] The user submits the generated content to the server. Before submission, the terminal performs a final check to ensure that the content complies with the protocol and that there are no problems with the content.

[1178] Input: Generated content

[1179] Data processing: Checking protocol compliance

[1180] Output: The content sent to the server

[1181] Step 5: Evaluate your content

[1182] The server automatically evaluates the received content based on the protocol. Evaluation items include sentence structure, keyword use, meta tag settings, etc., and a score is assigned for each item. Finally, the scores for each item are added together to calculate an overall evaluation score.

[1183] Input: Content sent to the server

[1184] Data processing: Protocol compliance assessment and scoring

[1185] Output: Evaluation score

[1186] Step 6: Calculating and paying rewards

[1187] The server calculates rewards based on the evaluation score. The higher the evaluation score, the higher the reward. The reward is automatically transferred to the user's account.

[1188] Input: Rating score

[1189] Data processing: Reward calculation

[1190] Output: Reward deposited into user account

[1191] These processing steps enable fair evaluation of content using generative AI models and appropriate compensation payments.

[1192] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1193] This invention provides a system that combines a generative AI to generate optimized content and an emotion engine that recognizes user emotions. This system operates primarily in cooperation with a server, a terminal, and a user. The specific operations of each entity and the program processing are described below.

[1194] Server-side processing

[1195] Protocol Design

[1196] The server designs a detailed protocol for optimizing the generated AI, including proper placement of meta tags, effective use of keywords, internal link structure, etc. Content is created according to these standards to achieve optimization.

[1197] Protocol Publication

[1198] The designed protocol will be published on a server and can be downloaded by users or devices. The protocol will also be available via API.

[1199] Evaluation of submissions

[1200] The server receives websites and content sent by users and devices and automatically evaluates them based on the protocol. Evaluation criteria include sentence structure, keyword usage, meta tag settings, etc. The scores for each item are added together to calculate the final evaluation score.

[1201] Reward calculation and payment

[1202] The server calculates a reward based on the evaluation score and transfers it to the user's account.

[1203] Terminal side processing

[1204] Protocol Download

[1205] The device downloads the protocol from the server and configures the site generation tool and content editor based on it, and templates and guidelines that conform to the protocol are installed on the device.

[1206] Site Generation and Content Creation

[1207] The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input, such as blog posts and product description pages. The generated content undergoes internal checks based on the protocol and is corrected as needed.

[1208] Use of emotion engine

[1209] The device collects user emotion data using an emotion engine that recognizes the user's emotions. For example, it analyzes the user's facial expressions and voice and captures the emotion information.

[1210] Content Adjustment

[1211] The content generated by the generative AI is adjusted based on the emotional data collected by the emotion engine. For example, if the user is happy, the AI ​​will generate content that contains more positive expressions.

[1212] Content Submission

[1213] The generated website and content are then submitted to the server, where a final check is made on the device to ensure protocol compliance.

[1214] User-side processing

[1215] Checking Protocol Compliance

[1216] Users can check whether the websites they run and the content they create comply with the protocol by using tools on their devices to automatically check.

[1217] Providing emotion data

[1218] Users provide their emotional data using the camera and microphone on their device, which allows the emotion engine to accurately recognize the user's emotions.

[1219] Content Submission

[1220] Users submit websites or content that is verified as protocol compliant to the server, and once the content reaches the server, an evaluation process begins automatically.

[1221] Receiving rewards

[1222] Users receive a reward calculated based on their rating score, which is deposited into their account and they receive feedback information.

[1223] Specific examples

[1224] 1. Emotion Recognition and Site Generation

[1225] Download the protocol on your device and set up the website generator.

[1226] When a user inputs a blog post into the generation AI tool, the emotion engine recognizes the user's emotions.

[1227] A generative AI tool generates articles based on user sentiment and adjusts meta tags and keyword placement.

[1228] 2. Content Submission and Evaluation

[1229] The user submits the generated article to the server.

[1230] The server receives the article and evaluates its conformance to the protocol.

[1231] The scores for each evaluation item are added together to calculate the final score.

[1232] 3. Payment of Rewards

[1233] The server calculates the reward based on the evaluation score and transfers it to the user's account.

[1234] Users receive a reward and use the feedback to optimize their next iteration.

[1235] This allows content optimized using generative AI to be fairly evaluated, and more personalized content is provided based on the user's emotions. The entire system works together to pay appropriate rewards to users.

[1236] The processing flow will be explained below.

[1237] Server-side processing

[1238] Step 1: Design the protocol

[1239] The server designs protocols for generative AI optimization, including proper use of title tags, placement of key keywords, and how to set meta descriptions.

[1240] Step 2: Publishing the protocol

[1241] The server publishes the designed protocol on the web so that it can be downloaded by users or devices, or the protocol can be provided through an API.

[1242] Step 3: Evaluate submissions

[1243] The server receives websites and content submitted by users and devices, evaluates the received content based on the protocol, and checks the appropriateness of sentence structure, keyword usage, and meta tags.

[1244] Step 4: Calculating and paying rewards

[1245] The server calculates rewards based on the rating score. The higher the rating score, the higher the reward. The rewards are deposited into the user's account.

[1246] Terminal side processing

[1247] Step 1: Download the protocol

[1248] The terminal downloads the protocol from the server and configures the site generation tool and content editor. Templates and guidelines that conform to the protocol are installed on the terminal.

[1249] Step 2: Collecting emotion data

[1250] The device activates an emotion engine to recognize the user's emotions. It uses a camera and microphone to collect the user's facial expressions and voice, and analyzes the emotional data.

[1251] Step 3: Site Creation and Content Creation

[1252] The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input and emotional data. For example, if a user is happy, it will generate an article that uses a lot of positive language.

[1253] Step 4: Submit your content

[1254] The device submits the generated website or content to the server, which performs a final check to ensure compliance with the protocol before submitting.

[1255] User-side processing

[1256] Step 1: Log in and verify protocol compliance

[1257] Users can log in to their devices and check whether the websites and content they create comply with the protocol. Checks are performed automatically using tools on the devices.

[1258] Step 2: Provide emotion data

[1259] Users provide their emotional data using a camera or microphone, which is then analyzed by the emotion engine and recognized as emotional information.

[1260] Step 3: Review and submit content

[1261] Users can submit websites and content that are verified as protocol compliant to the server, and can even make final adjustments on their devices before submitting.

[1262] Step 4: Receive your rewards

[1263] The user receives a reward based on the score of the content evaluated by the server. Check whether the reward has been deposited into the account.

[1264] Example: Creating and rating blog posts

[1265] Server side

[1266] Step 1: Design the protocol

[1267] The server designs protocols such as "include the main keyword in the article title" and "use H1 tags only once per page."

[1268] Step 2: Publishing the protocol

[1269] The server publishes the designed protocol on the web so that it can be downloaded by users and devices.

[1270] Step 3: Evaluate submissions

[1271] It receives blog posts submitted by users and evaluates whether the title, H1 tag, keyword placement, etc. comply with the protocol.

[1272] Step 4: Calculating and paying rewards

[1273] Rewards are calculated based on the evaluation and transferred to the user's account.

[1274] Terminal side

[1275] Step 1: Download the protocol

[1276] Download protocols from the server and configure site generation tools and editors.

[1277] Step 2: Collecting emotion data

[1278] It uses a camera and microphone to analyze facial expressions and voice to recognize the user's emotions.

[1279] Step 3: Site Creation and Content Creation

[1280] A generative AI tool generates protocol-compliant articles based on sentiment data and user input. For example, if a user is feeling happy, the article will have a positive tone.

[1281] Step 4: Submit your content

[1282] The device submits the generated blog post to the server, which checks whether it complies with the protocol before submitting.

[1283] User side

[1284] Step 1: Log in and verify protocol compliance

[1285] The user logs in to the terminal and checks whether the article has been created in accordance with the protocol.

[1286] Step 2: Provide emotion data

[1287] Users provide their emotional data using a camera and microphone.

[1288] Step 3: Review and submit content

[1289] The user submits the article to the server after final review.

[1290] Step 4: Receive your rewards

[1291] Check whether the reward based on the evaluation score has been paid and use it for the next optimization.

[1292] This will enable content optimized using generative AI to be fairly evaluated, and personalized content based on user emotions will be provided, creating a system in which appropriate rewards are paid to users.

[1293] Example 2

[1294] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1295] Conventional content generation systems using generative AI were unable to incorporate user emotions, making it difficult to provide optimal content for users. Furthermore, the mechanisms for evaluating the degree to which the generated content was optimized and for paying appropriate rewards to users were inadequate. This led to issues such as a decline in user satisfaction and motivation.

[1296] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for designing and publishing a protocol for optimizing the generation AI, a means for evaluating websites and content based on the protocol, a means for calculating a reward based on the evaluation results, a means for paying the reward to the user, a means for collecting and analyzing user emotion data, and a means for adjusting the content based on the emotion data. This enables content generation that takes user emotions into consideration, fair evaluation of the content, and appropriate payment of rewards.

[1297] "Generative AI optimization" refers to methods and standards for improving the quality and search engine optimization (SEO) of content created by generative AI.

[1298] A "protocol" is a set of rules and guidelines for content creation, and refers to the standards used to ensure content quality and optimization.

[1299] "Evaluation" refers to the process of analyzing and scoring the generated content to see how well it conforms to the protocol.

[1300] "Reward" refers to the compensation paid to the generator (user) based on the evaluation results.

[1301] "User" refers to a user who generates and submits content using this system.

[1302] "Emotion data" refers to data relating to emotions acquired through the user's facial expressions, voice, etc.

[1303] "Content" refers to a collection of information created by generative AI, such as a website, article, or product description page.

[1304] This invention provides a system that combines a generative AI to generate optimized content and an emotion engine that recognizes user emotions. This system operates primarily in cooperation between a server, a terminal, and a user.

[1305] 1. Server Role

[1306] Protocol design and publication

[1307] The server designs a detailed protocol for optimizing the generative AI, including how to place meta tags, how to effectively use keywords, and the structure of internal links. The protocol is published on the server and can be downloaded to users and devices via API.

[1308] Specific examples:

[1309] The server extracts the keyword set and inserts example meta tags into the HTML template.

[1310] Content Rating and Reward Calculation

[1311] The server receives content sent by users and devices and automatically evaluates it based on protocol criteria, including sentence structure, keyword usage, meta tag settings, etc. Based on the evaluation results, a reward is calculated and deposited into the user's account.

[1312] Specific examples:

[1313] Based on the prompt sentence "I recently traveled. I would like to write a blog post about this trip. I am happy.", the generated blog post is evaluated.

[1314] 2. Role of the terminal

[1315] Downloading the protocol and setting up the AI ​​generation tool

[1316] The device downloads the protocol from the server and configures its generative AI tools and content editors based on it, and the protocol-compliant templates and guidelines are deployed on the device.

[1317] Specific examples:

[1318] The device will set up a template for creating a travel blog.

[1319] Collecting sentiment data and tailoring content

[1320] The device uses hardware such as a camera and microphone to collect user emotional data, using facial recognition and voice analysis technology. The collected emotional data is reflected in the generated content.

[1321] Specific examples:

[1322] The camera detects the user's smile and uses a lot of positive language in articles.

[1323] 3. User Roles

[1324] Verifying protocol compliance and providing emotional data

[1325] Users can use tools on their devices to check whether the websites they run and the content they create comply with the protocol. Users also use their cameras and microphones to provide emotional data, which is collected and analyzed by the devices.

[1326] Specific examples:

[1327] A user creates a blog post and enters a prompt into the terminal, such as "I would like to submit this blog post. Please rate it."

[1328] Submit content and get paid

[1329] When users submit their generated content to the server, the server will rate it and calculate a reward, which will be credited to the user's account based on the rating score.

[1330] Specific examples:

[1331] The user receives the reward with the prompt, "The evaluation is complete. I would like to receive my reward."

[1332] The above configuration enables optimal content generation using generative AI, fair evaluation, and personalization of content according to user emotions, which will improve user satisfaction and motivation and enable the provision of even higher quality content.

[1333] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1334] Step 1: Design the protocol

[1335] The server designs a detailed protocol for optimizing the generative AI, including proper placement of meta tags, effective use of keywords, and internal link structure. Specifically, the server analyzes existing SEO data and extracts the optimal keyword set. It also automatically inserts example meta tags into HTML templates. The input is the existing SEO data and predicted search queries, and the output is a protocol for optimizing the generative AI.

[1336] Step 2: Publishing and downloading the protocol

[1337] The server publishes the designed protocol on the server and allows users and devices to download it via API. Specifically, the server provides the latest version of the protocol at the API endpoint. The input is the designed protocol, and the output is the provision of protocol data to users and devices.

[1338] Step 3: Configuring the Generative AI Tool

[1339] The device configures the generative AI tool and content editor based on the protocol downloaded from the server. Specifically, the device stores the protocol rule set locally and reflects it in the initial settings of the generative AI. It also uses a template engine to generate an initial template that conforms to the protocol. The input is the downloaded protocol, and the output is the configured generative AI tool.

[1340] Step 4: Collecting emotion data

[1341] The device collects user emotional data using a camera and microphone. Specifically, the camera captures facial expressions and applies a facial recognition algorithm. The microphone also captures audio data and analyzes it with an emotion analysis algorithm. The input is the user's facial expression and audio data, and the output is analyzed emotional data.

[1342] Step 5: Generate and refine content

[1343] The device uses a generative AI tool to generate and adjust content based on the emotion data collected by the emotion engine. Specifically, the generative AI tool uses the emotion data as an input parameter, and if positive emotions are detected, it generates content that makes heavy use of positive expressions. The input is the emotion data and the user's content request, and the output is adjusted content that complies with the protocol.

[1344] Step 6: Submit and rate your content

[1345] Users submit generated content to a server, which then evaluates the submitted content based on a set of criteria. Specifically, the server parses the content and scores it based on criteria such as structure, keyword usage, and meta tag settings. The input is the submitted content, and the output is the evaluation score.

[1346] Step 7: Calculation and payment of rewards based on the evaluation results

[1347] The server calculates the reward based on the rating score and transfers it to the user's account. Specifically, the server analyzes the rating score and calculates the reward amount. The input is the rating score, and the output is the calculated reward amount and its payment.

[1348] (Application example 2)

[1349] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1350] While conventional content generation systems using generative AI models achieve protocol-based content optimization, they lack personalization based on user emotions. This results in insufficient improvement of the user experience. Furthermore, there is a demand for technology that can improve the quality of generated content by utilizing emotion recognition data. Therefore, the present invention aims to solve the above problems by providing a system that recognizes user emotions and generates and adjusts content based on them.

[1351] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1352] In this invention, the server includes a means for designing and publishing a protocol for optimizing the generative AI, a means for evaluating websites and content based on the protocol, a means for calculating rewards based on the evaluation results, a means including an emotion recognition engine for recognizing user emotions, and a means for generating content based on the emotion data. This enables the provision of personalized content based on user emotions and an optimized evaluation system.

[1353] "Generative AI" is an artificial intelligence technology that automatically generates content based on user input.

[1354] A "protocol" defines detailed guidelines and rules for optimizing content generation by generative AI.

[1355] An "emotion recognition engine" is a technology that analyzes a user's facial expressions and voice data to recognize their emotional state.

[1356] "Content" refers to information or media that users view or use, such as websites, blog posts, product description pages, etc.

[1357] The "evaluation means" is a mechanism for automatically evaluating whether the generated content complies with the protocol.

[1358] "Reward payment means" is a system that provides monetary rewards to users based on the results of their content evaluations.

[1359] "Downloading means" refers to a mechanism for acquiring the protocol on the user's terminal.

[1360] "Content generation means" refers to technology for automatically generating websites and content in accordance with protocols.

[1361] A "submission mechanism" is a mechanism for transmitting user-generated content to a server.

[1362] "Personalization" refers to providing content optimized for a specific user based on the user's emotional data and preferences.

[1363] "Internal check" is a mechanism for checking on the terminal side whether the generated content complies with the protocol.

[1364] overview

[1365] This invention is a system that combines optimization of generative AI with user emotion recognition. This system works in cooperation with the server, the terminal, and the user.

[1366] server

[1367] The server has the means to design and publish a protocol for optimizing the generative AI. The protocol includes detailed guidelines such as the appropriate placement of meta tags, effective use of keywords, and link structure. The designed protocol is published on the server and can be downloaded by users and devices. It can also be obtained via API. The server evaluates content sent by users and devices based on the protocol, calculates rewards based on the evaluation score, and deposits them into the user's account.

[1368] Terminal

[1369] The device downloads the protocol from the server and configures the site generation tool and content editor. The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input. At this time, an emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data. This allows the generated content to be adjusted based on the user's emotions. For example, if the user is happy, content containing many positive expressions will be generated. The generated content undergoes a final protocol compliance check on the device before being submitted to the server.

[1370] User

[1371] Users download the protocol using their own devices and generate websites and content. During the generation process, an emotion recognition engine analyzes the user's emotions in real time, and this data is reflected in the content generation. The user submits the generated content to the server, which evaluates it based on the protocol. Rewards are deposited into the user's account based on the evaluation results. This allows the user to use the feedback to improve their next content generation.

[1372] Hardware and software used

[1373] Server: High-performance computers, cloud computing services (e.g., AWS, Google Cloud, etc.).

[1374] Devices: Smartphones, tablets, PCs.

[1375] Software: EmotionRecognition library, ContentGenerator library, REST API.

[1376] Specific examples

[1377] Users use their smartphone camera to analyze their facial expressions, and the emotion recognition engine detects "joy." Based on this data, the AI ​​automatically generates positive news articles. For example, it can be based on a prompt such as, "Please create good news about the latest technological innovation."

[1378] The generated content is submitted to the server, where it is evaluated according to the protocol. Based on the evaluation results, rewards are calculated and paid to the user.

[1379] Prompt Sentence Examples

[1380] "Create a joy-based, breaking news story. The topic should be related to technological innovation and contain a lot of positivity."

[1381] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1382] Step 1:

[1383] The server will design a protocol for optimizing the generative AI, including detailed standards for meta tag placement, keyword use, link structure, etc. The protocol will be made available to users and devices in a downloadable format, and can also be obtained via API.

[1384] Input: Content generation optimization criteria

[1385] Output: Protocol document

[1386] Step 2:

[1387] The device downloads the protocol from the server, configures site generators and content editors based on it, and implements protocol-compliant templates and guidelines.

[1388] Input: Protocol document

[1389] Output: Configured tools to comply with the protocol

[1390] Step 3:

[1391] When a user uses a device to create a website or create content, the emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data.

[1392] Input: User facial and voice data

[1393] Output: Emotion data (emotional state information such as happiness, anger, sadness, and happiness)

[1394] Step 4:

[1395] The device uses generative AI tools to automatically generate protocol-compliant content based on the collected emotional data. Personalization is achieved using emotional data. For example, if the user is happy, positive content is generated.

[1396] Input: Emotion data, protocol criteria

[1397] Output: Generated content (e.g. blog post, product description page)

[1398] Step 5:

[1399] Device-generated content is internally checked to ensure it complies with the protocol, and if it passes this internal check, it is submitted to the server.

[1400] Input: Generated content

[1401] Output: Content that has been checked for protocol compliance

[1402] Step 6:

[1403] The server receives the submitted content and evaluates it based on the protocol. Evaluation criteria include document structure, keyword usage, meta tag settings, etc. A rating score is calculated.

[1404] Input: Submitted content, protocol criteria

[1405] Output: Evaluation score

[1406] Step 7:

[1407] The server calculates the user's reward based on the evaluation result and deposits the reward into the user's account.

[1408] Input: Rating score

[1409] Output: Reward calculation result, deposited into user account

[1410] Step 8:

[1411] The user receives the reward and checks the feedback from the server, which is used to generate the next content.

[1412] Input: Feedback information, reward

[1413] Output: Reference for next content generation

[1414] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1415] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1416] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1417] [Fourth embodiment]

[1418] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1419] 7, a 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.

[1420] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1421] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1422] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1423] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1424] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1425] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1426] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1427] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1428] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1429] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1430] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1431] This invention provides a system that generates content optimized for generative AI, appropriately evaluates it, and pays rewards. This system mainly operates in cooperation with a server, terminals, and users. The specific operations of each entity and the program processing are explained below.

[1432] Server-side processing

[1433] Protocol Design

[1434] The server designs a detailed protocol for generative AI optimization, including, for example, the placement of meta tags on web pages, the proper placement of keywords in content, and the structure of internal links. Content is created according to these standards to achieve optimization.

[1435] Protocol Publication

[1436] The designed protocol will be published on a server and can be downloaded by users or devices. The protocol will also be available via API.

[1437] Evaluation of submissions

[1438] The server receives websites and content sent by users and devices. The received content is automatically evaluated based on the protocol. Evaluation criteria include sentence structure, keyword use, meta tag settings, etc. The scores for each item are added together to calculate the final evaluation score.

[1439] Reward calculation and payment

[1440] The server calculates the reward based on the evaluation score. The higher the evaluation score, the higher the reward will be paid. After calculation, the reward will be transferred to the user's account.

[1441] Terminal side processing

[1442] Protocol Download

[1443] The device downloads the protocol from the server and configures the site generation tool or content editor based on it, and templates and guidelines that conform to this protocol are installed on the device.

[1444] Site Generation and Content Creation

[1445] The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input, such as blog posts and product description pages. The generated content undergoes internal checks based on the protocol and is corrected as needed.

[1446] Content Submission

[1447] The generated website and content are then submitted to the server, where a final check is made on the device to ensure protocol compliance.

[1448] User-side processing

[1449] Checking Protocol Compliance

[1450] Users can check whether the websites they run and the content they create comply with the protocol by using tools on their devices to automatically check.

[1451] Content Submission

[1452] Users submit websites or content that is verified as protocol compliant to the server, and once the content reaches the server, an evaluation process begins automatically.

[1453] Receiving rewards

[1454] Users receive a reward calculated based on their rating score, which is deposited into their account and they receive feedback information.

[1455] Specific examples

[1456] 1. Site Creation

[1457] Download the protocol on your device and set up the website generator.

[1458] A user inputs a blog post into a generative AI tool.

[1459] A generative AI tool generates articles based on protocols and optimizes meta tags and keyword placement.

[1460] 2. Content Submission and Evaluation

[1461] The user submits the generated article to the server.

[1462] The server receives the article and evaluates its conformance to the protocol.

[1463] The scores for each evaluation item are added together to calculate the final score.

[1464] 3. Payment of Rewards

[1465] The server calculates the reward based on the evaluation score and transfers it to the user's account.

[1466] Users receive a reward and use the feedback to optimize their next iteration.

[1467] This will create a system where optimized content created using generative AI is fairly evaluated and appropriate rewards are provided.

[1468] The processing flow will be explained below.

[1469] Server-side processing

[1470] Step 1: Design the protocol

[1471] The server designs a detailed protocol for generative AI optimization, including meta tag placement, proper keyword placement, internal link structure, etc.

[1472] Step 2: Publishing the protocol

[1473] The server publishes the designed protocol on the web in a downloadable format and also provides protocol information through an API.

[1474] Step 3: Evaluate submissions

[1475] The server receives websites and content submitted by users and devices and automatically evaluates them based on the protocol, including content structure, keyword usage, and meta tag settings.

[1476] Step 4: Calculating and paying rewards

[1477] The server calculates the reward based on the evaluation score and transfers it to the user's account.

[1478] Terminal side processing

[1479] Step 1: Download the protocol

[1480] The device downloads the protocol from the server and configures the site generation tool or content editor accordingly.

[1481] Step 2: Site Creation and Content Creation

[1482] The on-device generative AI tool generates protocol-compliant websites and content based on user input, performs internal checks based on the protocol, and makes corrections as needed.

[1483] Step 3: Submit your content

[1484] The device submits the generated website or content to the server, where a final check is made to ensure compliance with the protocol.

[1485] User-side processing

[1486] Step 1: Verify protocol compliance

[1487] Users can use the terminal to check whether the websites and content they have created comply with the protocol.

[1488] Step 2: Submit your content

[1489] Users submit websites and content to the server that is verified as conforming to the protocol.

[1490] Step 3: Receive your rewards

[1491] Users receive rewards based on the content rated by the server. Check the reward receipt notification and feedback from the server.

[1492] Specific examples

[1493] Server side

[1494] Step 1: Design the protocol

[1495] The server designs a protocol that includes rules such as "include the main keyword in the article title" and "use one H1 tag per page."

[1496] Step 2: Publishing the protocol

[1497] The server publishes the designed protocol on the web so that it can be downloaded by users and devices.

[1498] Step 3: Evaluate submissions

[1499] The server receives blog posts submitted by users and checks whether the title and H1 tag usage conform to the protocol.

[1500] Step 4: Calculating and paying rewards

[1501] The server calculates the reward based on the evaluation results and transfers it to the user's account.

[1502] Terminal side

[1503] Step 1: Download the protocol

[1504] The terminal downloads the protocol from the server and sets it in the site generation tool.

[1505] Step 2: Site Creation and Content Creation

[1506] When a user enters a blog post, the generative AI tool generates the post according to the protocol and performs internal checks on meta tags and keyword placement.

[1507] Step 3: Submit your content

[1508] Before submitting the generated blog post to the server, the terminal checks whether it complies with the protocol and then sends it to the server.

[1509] User side

[1510] Step 1: Verify protocol compliance

[1511] Users can use tools on their devices to verify that the blog posts they generate comply with the protocol.

[1512] Step 2: Submit your content

[1513] Users submit articles to the server that are verified to be protocol compliant.

[1514] Step 3: Receive your rewards

[1515] The user checks the evaluation results and reward receipt notification from the server and receives the reward.

[1516] Example 1

[1517] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1518] In conventional systems, content optimization and evaluation by generative AI are performed as independent processes, making it difficult to provide efficient feedback and calculate rewards. Furthermore, there is a lack of means to verify whether the generated content actually complies with the protocol, which creates the problem of not guaranteeing fair evaluation and reward payment. This invention aims to solve these issues and realize the generation of high-quality content and fair evaluation by providing a consistent system for optimizing and evaluating generative AI.

[1519] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1520] In this invention, the server includes a means for designing and publishing rules for optimizing generative AI, a means for evaluating digital content based on the rules, a means for calculating a reward based on the evaluation results, a means for paying the reward to end users, and a means for including sentence structure, keyword usage, and meta tag settings in the evaluation criteria for the digital content. This makes it possible to consistently optimize and evaluate content using generative AI and pay fair rewards to users.

[1521] "Generative AI" is a technology that uses artificial intelligence to automatically create digital content such as text and images.

[1522] The "Terms and Conditions" are a detailed set of rules and guidelines for the generative AI to create optimized content, including things like meta tag placement and keyword use.

[1523] "Digital content" refers to various forms of content that appear on the internet, such as websites, blog posts, and product description pages.

[1524] "Evaluation criteria" are the criteria used to determine whether digital content complies with the regulations, and specifically include sentence structure, keyword use, meta tag settings, etc.

[1525] "Reward" refers to monetary compensation paid to a user based on the evaluation results.

[1526] "User" refers to an end user who generates digital content in accordance with the Terms and Conditions and receives evaluation and rewards.

[1527] The "server" is a central management system that handles a series of processes, including the design and publication of rules, evaluation of digital content, and calculation and payment of rewards.

[1528] A "generative AI tool" is software or a program that automatically generates compliant content based on user input.

[1529] "Final confirmation of protocol compliance" is the process of finally confirming whether the generated digital content complies with the regulations.

[1530] This invention provides a system that creates digital content optimized for generative AI, appropriately evaluates it, and pays fair rewards. This system mainly operates in cooperation between a server, terminals, and users.

[1531] The server designs rules for optimizing the generative AI and publishes them on the server. These rules include, for example, rules for placing meta tags on web pages, how to properly place keywords in content, and the structure of internal links. The server hosts these rules at a specific URL or API endpoint so that users and devices can download them. Specific examples include the URL "https: / / example.com / protocols / optimization_protocol_v1.json" and the API endpoint "https: / / api.example.com / protocols / latest."

[1532] The device downloads the regulations from the server and configures the site generation tool and content editor based on them. The device incorporates templates and guidelines that comply with the regulations, allowing the generative AI tool to automatically generate content based on prompts entered by the user. For example, if a user enters the topic "Latest Trends in AI Technology," the generative AI tool will automatically place the necessary meta tags and keywords to generate an optimized article. The generated content undergoes a final check on the device for protocol compliance, and once it passes the final inspection, it is submitted to the server.

[1533] Users use a tool on their device to check whether the digital content they manage complies with the terms and conditions. Once the check is complete, the user submits the content to the server. The server receives the submitted content and automatically begins the evaluation process. Evaluation criteria include sentence structure, keyword use, and meta tag settings, and the final evaluation score is calculated by adding up the scores for each item.

[1534] The server calculates rewards based on the evaluation score and transfers them to the end user's account. The higher the evaluation score, the higher the reward paid. For example, a tiered reward system could be used, such as $100 for a score of 90 or above, and $80 for a score of 80 or above.

[1535] For example, consider the following flow:

[1536] 1. The server designs the contract and publishes it at a specific URL.

[1537] 2. The device downloads the terms and conditions and configures the generation AI tool.

[1538] 3. The user enters the prompt "Latest trends in AI technology" and checks the generated content.

[1539] 4. The generated article is submitted to the server, which evaluates it.

[1540] 5. Rewards are calculated based on the rating score and deposited into the user's account.

[1541] This allows for consistent content optimization and evaluation using generative AI, ensuring fair rewards are paid to users.

[1542] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1543] Step 1:

[1544] The server designs the contract.

[1545] Specific actions

[1546] Input: Industry standards and SEO optimization guidelines

[1547] Data processing: Compiling web page meta tag placement rules, keyword placement methods, internal link structure, etc.

[1548] Output: Terms file (JSON or XML format)

[1549] The server designs detailed regulations based on industry standards and SEO optimization guidelines, including rules for meta tag placement on web pages, keyword placement methods, internal link structures, etc., and outputs these as a regulations file.

[1550] Step 2:

[1551] The server publishes the terms.

[1552] Specific actions

[1553] Input: Terms file

[1554] Data processing: Uploading files to the hosting server, setting up API endpoints

[1555] Output: URL or API endpoint

[1556] The server uploads the designed contract file to the hosting server and makes it available at a specific URL or API endpoint. For example, endpoints such as "https: / / example.com / protocols / optimization_protocol_v1.json" or "https: / / api.example.com / protocols / latest" are output.

[1557] Step 3:

[1558] The device downloads the terms and conditions.

[1559] Specific actions

[1560] Input: A publicly available URL or API endpoint

[1561] Data processing: Download the contract file from a URL or API endpoint

[1562] Output: A contract file in the local file system on the device.

[1563] The device downloads the contract file from the published URL or API endpoint and saves it to its local file system. For example, download it using "curl -O https: / / example.com / protocols / optimization_protocol_v1.json" on the command line.

[1564] Step 4:

[1565] The device configures the generative AI tool and generates the content.

[1566] Specific actions

[1567] Input: User prompt, downloaded terms file

[1568] Data processing: Generate compliant websites, blog posts, etc. based on prompts

[1569] Output: Generated content files (HTML, Markdown, etc.)

[1570] The user inputs a prompt into the device, and the device uses a generative AI tool to automatically generate content based on the rules. For example, if a user inputs the prompt "Latest trends in AI technology," the generative AI tool will generate an article with optimized meta tags and keyword placement.

[1571] Step 5:

[1572] The user confirms compliance.

[1573] Specific actions

[1574] Input: Generated content file, downloaded terms file

[1575] Data processing: Use standard checking tools to ensure generated content complies with standards

[1576] Output: Check result (pass / fail)

[1577] Users can use a check tool on their device to check whether the generated content complies with the regulations. The check tool automatically scans for the application of meta tags and keywords, and outputs a result of either "applied (passed)" or "not applied (failed)."

[1578] Step 6:

[1579] A user submits content to a server.

[1580] Specific actions

[1581] Input: Generated content files

[1582] Data processing: Upload content files to the server

[1583] Output: Submission confirmation message

[1584] Once the content file has been confirmed to comply with the regulations, the user submits it to the server. This is done by uploading the file to "https: / / upload.example.com". When the upload is complete, a "Submission Complete" message is displayed.

[1585] Step 7:

[1586] The server evaluates the content.

[1587] Specific actions

[1588] Input: Submitted content file, downloaded terms and conditions file

[1589] Data processing: Evaluate content based on the rules and calculate scores for each item

[1590] Output: Evaluation score, feedback

[1591] The server receives the submitted content file and initiates an automated evaluation process. Evaluation criteria include sentence structure, keyword usage, meta tag settings, etc., and the scores for each item are added together to calculate a final evaluation score. Feedback is provided along with the evaluation score.

[1592] Step 8:

[1593] The server calculates and pays the reward.

[1594] Specific actions

[1595] Input: Rating score

[1596] Data processing: Calculate the reward amount based on the evaluation score and transfer the reward to the user's account

[1597] Output: Reward transfer confirmation message

[1598] The server calculates rewards based on the evaluation score, with higher rewards paid for higher scores. For example, rewards are set in stages, such as $100 for a score of 90 or above, $80 for a score of 80 or above, etc. Once the calculated reward amount has been transferred to the user's account, a "Reward transfer completed" message is displayed.

[1599] (Application example 1)

[1600] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1601] In the generation and evaluation of content using generative AI models, there is a lack of mechanisms to generate high-quality content based on appropriate protocols and provide fair compensation according to the evaluation. This makes it difficult for creators and content providers to create content in the optimal way and receive fair compensation.

[1602] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1603] In this invention, the server includes means for designing and publishing a protocol for optimizing the generative AI, means for evaluating websites and content based on the protocol, means for calculating a reward based on the evaluation results, means for paying the reward to users, means for generating content that conforms to the protocol using a generative AI model, and means for submitting the content to the server and receiving an evaluation. This allows content created using the generative AI model to be evaluated fairly, and rewards based on the evaluation results to be appropriately paid.

[1604] A "generative AI model" refers to algorithms or software that use artificial intelligence to automatically generate content based on human-provided prompts.

[1605] A "protocol" refers to a set of rules or procedures that serve as the basis for optimizing and evaluating generative AI models.

[1606] A "prompt" is a piece of text that describes an instruction or request that is input to a generative AI model.

[1607] "Server" refers to a computer system that runs in the cloud or on-premise and runs generative AI models, publishes protocols, evaluates content and pays rewards.

[1608] "User" refers to an entity that uses a generative AI model to create content and submits that content for evaluation and reward.

[1609] "Content" refers to the text, images, video, and other creative works generated by generative AI models.

[1610] "Evaluation" refers to the process of determining how well generated content complies with the protocol and assigning it a quantitative score.

[1611] "Reward" refers to compensation such as money or points paid to a user based on the evaluation results.

[1612] This invention is a system that generates optimized content using a generative AI model and pays rewards based on the evaluation of the content. This system operates in cooperation between a server, terminals, and users.

[1613] Server-side processing

[1614] The server first designs and publishes a protocol for optimizing the generative AI. The protocol includes, for example, the placement of meta tags on web pages, the appropriate placement of keywords in content, and the structure of internal links. This protocol is published on the server and can be downloaded by users and devices via API.

[1615] The server then receives the website or content sent by the user or device. The received content is automatically evaluated based on the protocol. Evaluation criteria include sentence structure, keyword use, meta tag settings, etc., and the scores for each evaluation item are added together to calculate a final evaluation score. Finally, rewards are calculated based on the evaluation score and deposited into the user's account.

[1616] Terminal side processing

[1617] The device downloads the protocol from the server and configures the site generation tool and content editor based on it. Templates and guidelines that conform to this protocol are installed on the device. Next, a tool using a generative AI model is used to automatically generate protocol-compliant content based on user input. Examples include blog articles and product description pages. The generated content undergoes internal checks based on the protocol and is corrected as necessary. Finally, the generated content is submitted to the server. A final protocol compliance check is performed on the device before submission.

[1618] User-side processing

[1619] Users check whether the websites they manage or the content they create comply with the protocol. This is done automatically using a tool on their device. Next, they submit websites or content that have been confirmed to comply with the protocol to the server. Once the content reaches the server, the evaluation process begins automatically. Finally, users receive a reward calculated based on the evaluation score. The reward is deposited into the user's account, and they receive feedback information.

[1620] Hardware and software used

[1621] The server runs on a web server (e.g., AWS, Google Cloud), and the generative AI model used is OpenAI's GPT-3. HTTP / HTTPS is used as the communication protocol, and Python is used as the development language. On the terminal side, PCs, smartphones, head-mounted displays (HMDs), etc. are used.

[1622] Examples and prompts

[1623] For example, a user might input the following prompt sentence into a generative AI model to write a "travel blog post."

[1624] Prompt: "Write a blog post about travel."

[1625] The content generated in this way is of high quality and conforms to the protocol, and is evaluated by the server, with appropriate compensation being paid.

[1626] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1627] Step 1: Design and publish a protocol

[1628] The server designs and publishes a protocol for optimizing generative AI models. This protocol includes meta tag placement, appropriate keyword placement, and internal link structure. The server publishes the protocol via an API so that users and devices can download it. When designing the protocol, data analysis tools are used to determine the optimal placement and structure.

[1629] Input: Data and criteria for optimization

[1630] Data processing: Designing protocols using data analysis tools

[1631] Output: Protocol published

[1632] Step 2: Download the protocol

[1633] The terminal downloads the published protocol from the server, which is then used as a template or guideline to be applied to the site generation tool or content editor.

[1634] Input: Protocol exposed via API

[1635] Data processing: Apply protocols to devices as templates or guidelines

[1636] Output: Protocol download complete

[1637] Step 3: Generate content

[1638] The device receives a prompt from the user and uses a generative AI model to generate content that conforms to the protocol. The generative AI model automatically generates content in the specified format based on the input prompt. For example, it generates an article based on the prompt, "Please write a blog post about travel."

[1639] Input: User prompt text

[1640] Data processing: Content generation based on generative AI models

[1641] Output: Generated content

[1642] Step 4: Submit your content

[1643] The user submits the generated content to the server. Before submission, the terminal performs a final check to ensure that the content complies with the protocol and that there are no problems with the content.

[1644] Input: Generated content

[1645] Data processing: Checking protocol compliance

[1646] Output: The content sent to the server

[1647] Step 5: Evaluate your content

[1648] The server automatically evaluates the received content based on the protocol. Evaluation items include sentence structure, keyword use, meta tag settings, etc., and a score is assigned for each item. Finally, the scores for each item are added together to calculate an overall evaluation score.

[1649] Input: Content sent to the server

[1650] Data processing: Protocol compliance assessment and scoring

[1651] Output: Evaluation score

[1652] Step 6: Calculating and paying rewards

[1653] The server calculates rewards based on the evaluation score. The higher the evaluation score, the higher the reward. The reward is automatically transferred to the user's account.

[1654] Input: Rating score

[1655] Data processing: Reward calculation

[1656] Output: Reward deposited into user account

[1657] These processing steps enable fair evaluation of content using generative AI models and appropriate compensation payments.

[1658] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1659] This invention provides a system that combines a generative AI to generate optimized content and an emotion engine that recognizes user emotions. This system operates primarily in cooperation with a server, a terminal, and a user. The specific operations of each entity and the program processing are described below.

[1660] Server-side processing

[1661] Protocol Design

[1662] The server designs a detailed protocol for optimizing the generated AI, including proper placement of meta tags, effective use of keywords, internal link structure, etc. Content is created according to these standards to achieve optimization.

[1663] Protocol Publication

[1664] The designed protocol will be published on a server and can be downloaded by users or devices. The protocol will also be available via API.

[1665] Evaluation of submissions

[1666] The server receives websites and content sent by users and devices and automatically evaluates them based on the protocol. Evaluation criteria include sentence structure, keyword usage, meta tag settings, etc. The scores for each item are added together to calculate the final evaluation score.

[1667] Reward calculation and payment

[1668] The server calculates a reward based on the evaluation score and transfers it to the user's account.

[1669] Terminal side processing

[1670] Protocol Download

[1671] The device downloads the protocol from the server and configures the site generation tool and content editor based on it, and templates and guidelines that conform to the protocol are installed on the device.

[1672] Site Generation and Content Creation

[1673] The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input, such as blog posts and product description pages. The generated content undergoes internal checks based on the protocol and is corrected as needed.

[1674] Use of emotion engine

[1675] The device collects user emotion data using an emotion engine that recognizes the user's emotions. For example, it analyzes the user's facial expressions and voice and captures the emotion information.

[1676] Content Adjustment

[1677] The content generated by the generative AI is adjusted based on the emotional data collected by the emotion engine. For example, if the user is happy, the AI ​​will generate content that contains more positive expressions.

[1678] Content Submission

[1679] The generated website and content are then submitted to the server, where a final check is made on the device to ensure protocol compliance.

[1680] User-side processing

[1681] Checking Protocol Compliance

[1682] Users can check whether the websites they run and the content they create comply with the protocol by using tools on their devices to automatically check.

[1683] Providing emotion data

[1684] Users provide their emotional data using the camera and microphone on their device, which allows the emotion engine to accurately recognize the user's emotions.

[1685] Content Submission

[1686] Users submit websites or content that is verified as protocol compliant to the server, and once the content reaches the server, an evaluation process begins automatically.

[1687] Receiving rewards

[1688] Users receive a reward calculated based on their rating score, which is deposited into their account and they receive feedback information.

[1689] Specific examples

[1690] 1. Emotion Recognition and Site Generation

[1691] Download the protocol on your device and set up the website generator.

[1692] When a user inputs a blog post into the generation AI tool, the emotion engine recognizes the user's emotions.

[1693] A generative AI tool generates articles based on user sentiment and adjusts meta tags and keyword placement.

[1694] 2. Content Submission and Evaluation

[1695] The user submits the generated article to the server.

[1696] The server receives the article and evaluates its conformance to the protocol.

[1697] The scores for each evaluation item are added together to calculate the final score.

[1698] 3. Payment of Rewards

[1699] The server calculates the reward based on the evaluation score and transfers it to the user's account.

[1700] Users receive a reward and use the feedback to optimize their next iteration.

[1701] This allows content optimized using generative AI to be fairly evaluated, and more personalized content is provided based on the user's emotions. The entire system works together to pay appropriate rewards to users.

[1702] The processing flow will be explained below.

[1703] Server-side processing

[1704] Step 1: Design the protocol

[1705] The server designs protocols for generative AI optimization, including proper use of title tags, placement of key keywords, and how to set meta descriptions.

[1706] Step 2: Publishing the protocol

[1707] The server publishes the designed protocol on the web so that it can be downloaded by users or devices, or the protocol can be provided through an API.

[1708] Step 3: Evaluate submissions

[1709] The server receives websites and content submitted by users and devices, evaluates the received content based on the protocol, and checks the appropriateness of sentence structure, keyword usage, and meta tags.

[1710] Step 4: Calculating and paying rewards

[1711] The server calculates rewards based on the rating score. The higher the rating score, the higher the reward. The rewards are deposited into the user's account.

[1712] Terminal side processing

[1713] Step 1: Download the protocol

[1714] The terminal downloads the protocol from the server and configures the site generation tool and content editor. Templates and guidelines that conform to the protocol are installed on the terminal.

[1715] Step 2: Collecting emotion data

[1716] The device activates an emotion engine to recognize the user's emotions. It uses a camera and microphone to collect the user's facial expressions and voice, and analyzes the emotional data.

[1717] Step 3: Site Creation and Content Creation

[1718] The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input and emotional data. For example, if a user is happy, it will generate an article that uses a lot of positive language.

[1719] Step 4: Submit your content

[1720] The device submits the generated website or content to the server, which performs a final check to ensure compliance with the protocol before submitting.

[1721] User-side processing

[1722] Step 1: Log in and verify protocol compliance

[1723] Users can log in to their devices and check whether the websites and content they create comply with the protocol. Checks are performed automatically using tools on the devices.

[1724] Step 2: Provide emotion data

[1725] Users provide their emotional data using a camera or microphone, which is then analyzed by the emotion engine and recognized as emotional information.

[1726] Step 3: Review and submit content

[1727] Users can submit websites and content that are verified as protocol compliant to the server, and can even make final adjustments on their devices before submitting.

[1728] Step 4: Receive your rewards

[1729] The user receives a reward based on the score of the content evaluated by the server. Check whether the reward has been deposited into the account.

[1730] Example: Creating and rating blog posts

[1731] Server side

[1732] Step 1: Design the protocol

[1733] The server designs protocols such as "include the main keyword in the article title" and "use H1 tags only once per page."

[1734] Step 2: Publishing the protocol

[1735] The server publishes the designed protocol on the web so that it can be downloaded by users and devices.

[1736] Step 3: Evaluate submissions

[1737] It receives blog posts submitted by users and evaluates whether the title, H1 tag, keyword placement, etc. comply with the protocol.

[1738] Step 4: Calculating and paying rewards

[1739] Rewards are calculated based on the evaluation and transferred to the user's account.

[1740] Terminal side

[1741] Step 1: Download the protocol

[1742] Download protocols from the server and configure site generation tools and editors.

[1743] Step 2: Collecting emotion data

[1744] It uses a camera and microphone to analyze facial expressions and voice to recognize the user's emotions.

[1745] Step 3: Site Creation and Content Creation

[1746] A generative AI tool generates protocol-compliant articles based on sentiment data and user input. For example, if a user is feeling happy, the article will have a positive tone.

[1747] Step 4: Submit your content

[1748] The device submits the generated blog post to the server, which checks whether it complies with the protocol before submitting.

[1749] User side

[1750] Step 1: Log in and verify protocol compliance

[1751] The user logs in to the terminal and checks whether the article has been created in accordance with the protocol.

[1752] Step 2: Provide emotion data

[1753] Users provide their emotional data using a camera and microphone.

[1754] Step 3: Review and submit content

[1755] The user submits the article to the server after final review.

[1756] Step 4: Receive your rewards

[1757] Check whether the reward based on the evaluation score has been paid and use it for the next optimization.

[1758] This will enable content optimized using generative AI to be fairly evaluated, and personalized content based on user emotions will be provided, creating a system in which appropriate rewards are paid to users.

[1759] Example 2

[1760] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1761] Conventional content generation systems using generative AI were unable to incorporate user emotions, making it difficult to provide optimal content for users. Furthermore, the mechanisms for evaluating the degree to which the generated content was optimized and for paying appropriate rewards to users were inadequate. This led to issues such as a decline in user satisfaction and motivation.

[1762] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for designing and publishing a protocol for optimizing the generation AI, a means for evaluating websites and content based on the protocol, a means for calculating a reward based on the evaluation results, a means for paying the reward to the user, a means for collecting and analyzing user emotion data, and a means for adjusting the content based on the emotion data. This enables content generation that takes user emotions into consideration, fair evaluation of the content, and appropriate payment of rewards.

[1763] "Generative AI optimization" refers to methods and standards for improving the quality and search engine optimization (SEO) of content created by generative AI.

[1764] A "protocol" is a set of rules and guidelines for content creation, and refers to the standards used to ensure content quality and optimization.

[1765] "Evaluation" refers to the process of analyzing and scoring the generated content to see how well it conforms to the protocol.

[1766] "Reward" refers to the compensation paid to the generator (user) based on the evaluation results.

[1767] "User" refers to a user who generates and submits content using this system.

[1768] "Emotion data" refers to data relating to emotions acquired through the user's facial expressions, voice, etc.

[1769] "Content" refers to a collection of information created by generative AI, such as a website, article, or product description page.

[1770] This invention provides a system that combines a generative AI to generate optimized content and an emotion engine that recognizes user emotions. This system operates primarily in cooperation between a server, a terminal, and a user.

[1771] 1. Server Role

[1772] Protocol design and publication

[1773] The server designs a detailed protocol for optimizing the generative AI, including how to place meta tags, how to effectively use keywords, and the structure of internal links. The protocol is published on the server and can be downloaded to users and devices via API.

[1774] Specific examples:

[1775] The server extracts the keyword set and inserts example meta tags into the HTML template.

[1776] Content Rating and Reward Calculation

[1777] The server receives content sent by users and devices and automatically evaluates it based on protocol criteria, including sentence structure, keyword usage, meta tag settings, etc. Based on the evaluation results, a reward is calculated and deposited into the user's account.

[1778] Specific examples:

[1779] Based on the prompt sentence "I recently traveled. I would like to write a blog post about this trip. I am happy.", the generated blog post is evaluated.

[1780] 2. Role of the terminal

[1781] Downloading the protocol and setting up the AI ​​generation tool

[1782] The device downloads the protocol from the server and configures its generative AI tools and content editors based on it, and the protocol-compliant templates and guidelines are deployed on the device.

[1783] Specific examples:

[1784] The device will set up a template for creating a travel blog.

[1785] Collecting sentiment data and tailoring content

[1786] The device uses hardware such as a camera and microphone to collect user emotional data, using facial recognition and voice analysis technology. The collected emotional data is reflected in the generated content.

[1787] Specific examples:

[1788] The camera detects the user's smile and uses a lot of positive language in articles.

[1789] 3. User Roles

[1790] Verifying protocol compliance and providing emotional data

[1791] Users can use tools on their devices to check whether the websites they run and the content they create comply with the protocol. Users also use their cameras and microphones to provide emotional data, which is collected and analyzed by the devices.

[1792] Specific examples:

[1793] A user creates a blog post and enters a prompt into the terminal, such as "I would like to submit this blog post. Please rate it."

[1794] Submit content and get paid

[1795] When users submit their generated content to the server, the server will rate it and calculate a reward, which will be credited to the user's account based on the rating score.

[1796] Specific examples:

[1797] The user receives the reward with the prompt, "The evaluation is complete. I would like to receive my reward."

[1798] The above configuration enables optimal content generation using generative AI, fair evaluation, and personalization of content according to user emotions, which will improve user satisfaction and motivation and enable the provision of even higher quality content.

[1799] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1800] Step 1: Design the protocol

[1801] The server designs a detailed protocol for optimizing the generative AI, including proper placement of meta tags, effective use of keywords, and internal link structure. Specifically, the server analyzes existing SEO data and extracts the optimal keyword set. It also automatically inserts example meta tags into HTML templates. The input is the existing SEO data and predicted search queries, and the output is a protocol for optimizing the generative AI.

[1802] Step 2: Publishing and downloading the protocol

[1803] The server publishes the designed protocol on the server and allows users and devices to download it via API. Specifically, the server provides the latest version of the protocol at the API endpoint. The input is the designed protocol, and the output is the provision of protocol data to users and devices.

[1804] Step 3: Configuring the Generative AI Tool

[1805] The device configures the generative AI tool and content editor based on the protocol downloaded from the server. Specifically, the device stores the protocol rule set locally and reflects it in the initial settings of the generative AI. It also uses a template engine to generate an initial template that conforms to the protocol. The input is the downloaded protocol, and the output is the configured generative AI tool.

[1806] Step 4: Collecting emotion data

[1807] The device collects user emotional data using a camera and microphone. Specifically, the camera captures facial expressions and applies a facial recognition algorithm. The microphone also captures audio data and analyzes it with an emotion analysis algorithm. The input is the user's facial expression and audio data, and the output is analyzed emotional data.

[1808] Step 5: Generate and refine content

[1809] The device uses a generative AI tool to generate and adjust content based on the emotion data collected by the emotion engine. Specifically, the generative AI tool uses the emotion data as an input parameter, and if positive emotions are detected, it generates content that makes heavy use of positive expressions. The input is the emotion data and the user's content request, and the output is adjusted content that complies with the protocol.

[1810] Step 6: Submit and rate your content

[1811] Users submit generated content to a server, which then evaluates the submitted content based on a set of criteria. Specifically, the server parses the content and scores it based on criteria such as structure, keyword usage, and meta tag settings. The input is the submitted content, and the output is the evaluation score.

[1812] Step 7: Calculation and payment of rewards based on the evaluation results

[1813] The server calculates the reward based on the rating score and transfers it to the user's account. Specifically, the server analyzes the rating score and calculates the reward amount. The input is the rating score, and the output is the calculated reward amount and its payment.

[1814] (Application example 2)

[1815] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1816] While conventional content generation systems using generative AI models achieve protocol-based content optimization, they lack personalization based on user emotions. This results in insufficient improvement of the user experience. Furthermore, there is a demand for technology that can improve the quality of generated content by utilizing emotion recognition data. Therefore, the present invention aims to solve the above problems by providing a system that recognizes user emotions and generates and adjusts content based on them.

[1817] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1818] In this invention, the server includes a means for designing and publishing a protocol for optimizing the generative AI, a means for evaluating websites and content based on the protocol, a means for calculating rewards based on the evaluation results, a means including an emotion recognition engine for recognizing user emotions, and a means for generating content based on the emotion data. This enables the provision of personalized content based on user emotions and an optimized evaluation system.

[1819] "Generative AI" is an artificial intelligence technology that automatically generates content based on user input.

[1820] A "protocol" defines detailed guidelines and rules for optimizing content generation by generative AI.

[1821] An "emotion recognition engine" is a technology that analyzes a user's facial expressions and voice data to recognize their emotional state.

[1822] "Content" refers to information or media that users view or use, such as websites, blog posts, product description pages, etc.

[1823] The "evaluation means" is a mechanism for automatically evaluating whether the generated content complies with the protocol.

[1824] "Reward payment means" is a system that provides monetary rewards to users based on the results of their content evaluations.

[1825] "Downloading means" refers to a mechanism for acquiring the protocol on the user's terminal.

[1826] "Content generation means" refers to technology for automatically generating websites and content in accordance with protocols.

[1827] A "submission mechanism" is a mechanism for transmitting user-generated content to a server.

[1828] "Personalization" refers to providing content optimized for a specific user based on the user's emotional data and preferences.

[1829] "Internal check" is a mechanism for checking on the terminal side whether the generated content complies with the protocol.

[1830] overview

[1831] This invention is a system that combines optimization of generative AI with user emotion recognition. This system works in cooperation with the server, the terminal, and the user.

[1832] server

[1833] The server has the means to design and publish a protocol for optimizing the generative AI. The protocol includes detailed guidelines such as the appropriate placement of meta tags, effective use of keywords, and link structure. The designed protocol is published on the server and can be downloaded by users and devices. It can also be obtained via API. The server evaluates content sent by users and devices based on the protocol, calculates rewards based on the evaluation score, and deposits them into the user's account.

[1834] Terminal

[1835] The device downloads the protocol from the server and configures the site generation tool and content editor. The on-device generative AI tool automatically generates protocol-compliant websites and content based on user input. At this time, an emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data. This allows the generated content to be adjusted based on the user's emotions. For example, if the user is happy, content containing many positive expressions will be generated. The generated content undergoes a final protocol compliance check on the device before being submitted to the server.

[1836] User

[1837] Users download the protocol using their own devices and generate websites and content. During the generation process, an emotion recognition engine analyzes the user's emotions in real time, and this data is reflected in the content generation. The user submits the generated content to the server, which evaluates it based on the protocol. Rewards are deposited into the user's account based on the evaluation results. This allows the user to use the feedback to improve their next content generation.

[1838] Hardware and software used

[1839] Server: High-performance computers, cloud computing services (e.g., AWS, Google Cloud, etc.).

[1840] Devices: Smartphones, tablets, PCs.

[1841] Software: EmotionRecognition library, ContentGenerator library, REST API.

[1842] Specific examples

[1843] Users use their smartphone camera to analyze their facial expressions, and the emotion recognition engine detects "joy." Based on this data, the AI ​​automatically generates positive news articles. For example, it can be based on a prompt such as, "Please create good news about the latest technological innovation."

[1844] The generated content is submitted to the server, where it is evaluated according to the protocol. Based on the evaluation results, rewards are calculated and paid to the user.

[1845] Prompt Sentence Examples

[1846] "Create a joy-based, breaking news story. The topic should be related to technological innovation and contain a lot of positivity."

[1847] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1848] Step 1:

[1849] The server will design a protocol for optimizing the generative AI, including detailed standards for meta tag placement, keyword use, link structure, etc. The protocol will be made available to users and devices in a downloadable format, and can also be obtained via API.

[1850] Input: Content generation optimization criteria

[1851] Output: Protocol document

[1852] Step 2:

[1853] The device downloads the protocol from the server, configures site generators and content editors based on it, and implements protocol-compliant templates and guidelines.

[1854] Input: Protocol document

[1855] Output: Configured tools to comply with the protocol

[1856] Step 3:

[1857] When a user uses a device to create a website or create content, the emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data.

[1858] Input: User facial and voice data

[1859] Output: Emotion data (emotional state information such as happiness, anger, sadness, and happiness)

[1860] Step 4:

[1861] The device uses generative AI tools to automatically generate protocol-compliant content based on the collected emotional data. Personalization is achieved using emotional data. For example, if the user is happy, positive content is generated.

[1862] Input: Emotion data, protocol criteria

[1863] Output: Generated content (e.g. blog post, product description page)

[1864] Step 5:

[1865] Device-generated content is internally checked to ensure it complies with the protocol, and if it passes this internal check, it is submitted to the server.

[1866] Input: Generated content

[1867] Output: Content that has been checked for protocol compliance

[1868] Step 6:

[1869] The server receives the submitted content and evaluates it based on the protocol. Evaluation criteria include document structure, keyword usage, meta tag settings, etc. A rating score is calculated.

[1870] Input: Submitted content, protocol criteria

[1871] Output: Evaluation score

[1872] Step 7:

[1873] The server calculates the user's reward based on the evaluation result and deposits the reward into the user's account.

[1874] Input: Rating score

[1875] Output: Reward calculation result, deposited into user account

[1876] Step 8:

[1877] The user receives the reward and checks the feedback from the server, which is used to generate the next content.

[1878] Input: Feedback information, reward

[1879] Output: Reference for next content generation

[1880] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1881] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1882] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1883] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1884] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1885] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1886] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1887] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1888] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1889] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1890] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1891] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1892] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1893] 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.

[1894] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1895] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1896] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1897] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1898] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1899] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1900] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1901] The following is further disclosed regarding the above embodiment.

[1902] (Claim 1)

[1903] A means to design and publish protocols for optimizing generative AI;

[1904] means for evaluating websites and content based on said protocols;

[1905] means for calculating a reward based on the evaluation result;

[1906] means for paying the reward to the user;

[1907] A system including:

[1908] (Claim 2)

[1909] means for downloading said protocol and generating a website or content;

[1910] means for submitting the generated website and content;

[1911] The system of claim 1 further comprising:

[1912] (Claim 3)

[1913] means for providing generated AI content based on said protocol;

[1914] means for generating a response in a format conforming to said protocol;

[1915] The system of claim 1 further comprising:

[1916] "Example 1"

[1917] (Claim 1)

[1918] A means to design and publish rules for optimizing generative AI;

[1919] means for evaluating digital content based on said rules;

[1920] means for calculating a reward based on the evaluation result;

[1921] means for paying said remuneration to end users;

[1922] A means for including sentence structure, keyword usage, and meta tag settings in the evaluation criteria of the digital content;

[1923] A system including:

[1924] (Claim 2)

[1925] means for downloading the terms and generating digital content;

[1926] means for submitting the generated digital content;

[1927] means for performing a final check of the generated digital content to ensure that it complies with a protocol;

[1928] The system of claim 1 further comprising:

[1929] (Claim 3)

[1930] A means for providing generated AI content based on the rules;

[1931] means for generating a response in a format conforming to said convention;

[1932] means for optimizing content in accordance with said regulations;

[1933] The system of claim 1 further comprising:

[1934] "Application Example 1"

[1935] (Claim 1)

[1936] A means to design and publish protocols for optimizing generative AI;

[1937] means for evaluating websites and content based on said protocols;

[1938] means for calculating a reward based on the evaluation result;

[1939] means for paying the reward to the user;

[1940] a means for generating protocol-compliant content using a generative AI model;

[1941] means for submitting said content to a server for evaluation;

[1942] A system including:

[1943] (Claim 2)

[1944] means for downloading said protocol and generating a website or content;

[1945] means for submitting the generated website and content;

[1946] A means for generating content by inputting a prompt sentence into the generative AI model;

[1947] means for submitting the generated content to a server for evaluation;

[1948] The system of claim 1 further comprising:

[1949] (Claim 3)

[1950] means for providing generated AI content based on said protocol;

[1951] means for generating a response in a format conforming to said protocol;

[1952] means for setting a prompt sentence for the generative AI model;

[1953] The system of claim 1 further comprising:

[1954] "Example 2: Combining Emotion Engines"

[1955] (Claim 1)

[1956] A means to design and publish protocols for optimizing generative AI;

[1957] means for evaluating websites and content based on said protocols;

[1958] means for calculating a reward based on the evaluation result;

[1959] means for paying the reward to the user;

[1960] means for collecting and analyzing user emotion data;

[1961] means for adjusting content based on the emotion data;

[1962] A system including:

[1963] (Claim 2)

[1964] means for downloading said protocol and generating a website or content;

[1965] means for submitting the generated website and content;

[1966] The system of claim 1 further comprising:

[1967] (Claim 3)

[1968] means for providing generated AI content based on said protocol;

[1969] means for generating a response in a format conforming to said protocol;

[1970] The system of claim 1 further comprising:

[1971] "Application example 2 when combining emotion engines"

[1972] (Claim 1)

[1973] A means to design and publish protocols for optimizing generative AI;

[1974] means for evaluating websites and content based on said protocols;

[1975] means for calculating a reward based on the evaluation result;

[1976] means for paying the reward to the user;

[1977] means including an emotion recognition engine for recognizing an emotion of a user;

[1978] means for generating content based on the emotion data;

[1979] A system including:

[1980] (Claim 2)

[1981] means for downloading said protocol and generating a website or content;

[1982] means for submitting the generated website and content;

[1983] means for adjusting the generated content based on the user's emotions;

[1984] 10. The system of claim 1.

[1985] (Claim 3)

[1986] means for providing generated AI content based on said protocol;

[1987] means for generating a response in a format conforming to said protocol;

[1988] means for generating and delivering personalized content based on the emotional data;

[1989] 10. The system of claim 1. [Explanation of symbols]

[1990] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means to design and publish protocols for optimizing generative AI; means for evaluating websites and content based on said protocols; means for calculating a reward based on the evaluation result; means for paying the reward to the user; A system including:

2. means for downloading said protocol and generating a website or content; means for submitting the generated website and content; The system of claim 1 further comprising:

3. means for providing generated AI content based on the protocol; means for generating a response in a format conforming to said protocol; The system of claim 1 further comprising:

Images