System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to input requirements; means for a terminal to transmit inputted data to a server; means for the server to supply data to a generated AI to generate a naming scheme; means for the server to transmit the generated naming scheme to the terminal; means for the server to display the naming scheme to the user; means for the user to input feedback; means for the terminal to transmit the feedback to the server; means for the server to resupply data to the generated AI together with the feedback to generate a modified naming scheme; and means for the server to transmit the modified naming scheme to the terminal.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] In today's world, it is common for companies and individuals to spend a lot of time and effort coming up with names for new brands, products, pets, domain names, etc. Furthermore, there are individual differences in the ability to come up with creative and appealing names, making it difficult to find an appropriate name. This has led to companies and individuals seeking effective tools to efficiently generate naming ideas. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means: a system comprising: means for a user to input requirements; means for a terminal to send the input data to a server; means for the server to supply data to a generation AI and generate naming suggestions; means for the server to send the generated naming suggestions to the terminal; means for the terminal to display the naming suggestions to the user; means for a user to input feedback; means for the terminal to send the feedback to the server; means for the server to resupply the data to the generation AI together with the feedback and generate revised naming suggestions; and means for the server to send the revised naming suggestions to the terminal. This system can improve the efficiency and quality of name generation.

[0006] "Requirements" are naming-related conditions and wishes entered by the user.

[0007] A "terminal" is a device that a user uses to enter data and review generated naming suggestions.

[0008] The "server" is a central computer system that receives data sent from the terminal, supplies the data to the generation AI, and sends the generated naming suggestions to the terminal.

[0009] "Generative AI" is an artificial intelligence that automatically generates naming suggestions based on supplied data.

[0010] "Data supply" refers to the act of the server providing the requirements and concept information input to the generation AI.

[0011] "Naming suggestions" are new name candidates generated by the generation AI.

[0012] "Feedback" refers to evaluations and correction requests that users input for generated naming proposals.

[0013] "Revised naming proposals" are naming proposals regenerated by the generation AI based on user feedback. [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 relates to a system that allows users to input requirements and generates original and attractive naming suggestions using a generation AI. The basic configuration of this system consists of a terminal where the user inputs the requirements, a terminal that sends the data to a server, generates naming suggestions using a generation AI, and returns the generated naming suggestions as feedback to the user, and a server that revises and generates new naming suggestions based on the feedback.

[0036] The program processing of this system will be specifically explained below.

[0037] 1. The user enters the requirements

[0038] Users simply launch the Creative Name Genie application on their device, create a new project, and enter the concept information, keywords, and related images needed to come up with a new product name, for example.

[0039] Example: If a user wants to come up with a name for a new organic cosmetics brand, they can enter keywords like "natural," "gentle on the skin," and "beauty" and upload an image.

[0040] 2. The device sends the data to the server

[0041] The device generates a data packet containing the input requirements, keywords, and images, encrypts it, and sends it to the server, ensuring data security.

[0042] The server converts the received data into the required format and prepares it as data that can be processed by the generation AI.

[0043] 3. The server provides the data to the AI ​​to generate naming suggestions.

[0044] The server provides the supplied data as input to the AI ​​generator, which then generates naming suggestions based on that data. The AI ​​generator uses a large amount of data set it has learned to suggest original names that match the user's requirements.

[0045] Example: Generative AI uses the keywords "natural," "skin-friendly," and "beauty" to generate naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals."

[0046] 4. The server sends the generated naming proposal to the device.

[0047] The server collects the naming suggestions received from the AI ​​generator into a data packet, encrypts it, and sends it to the device, ensuring that the generated naming suggestions are delivered securely to the user's device.

[0048] The device decodes the received naming suggestions and displays them in a user interface, where the user can review them and provide feedback.

[0049] 5. User provides feedback

[0050] Users can view the displayed naming suggestions and then enter their evaluation and requests for revisions, such as "Make it a little simpler" or "Add specific keywords."

[0051] The terminal transmits this feedback back to the server.

[0052] 6. The server re-supplies the data to the AI ​​based on the feedback and generates revised naming proposals.

[0053] The server analyzes the feedback from the user and provides the feedback to the generation AI again, instructing it to generate revised naming proposals.

[0054] The AI ​​then takes new feedback into account and generates more optimized naming suggestions, such as "PureGlow Organics."

[0055] 7. The server sends the revised naming proposal to the device and displays it to the user.

[0056] The server again receives a revised naming proposal from the generation AI and sends it to the device, which again displays it on the user interface for final confirmation by the user.

[0057] This process is repeated until the user selects and finalizes the best naming suggestion. This system efficiently generates high-quality naming suggestions and allows for a process that can be tailored to the user's needs.

[0058] The processing flow will be explained below.

[0059] Step 1: User enters requirements

[0060] The user opens the device and launches the "Creative Name Genie" application.

[0061] A user creates a new project and selects a naming category (e.g., brand name, product name, pet name).

[0062] The user enters naming requirements and concept information (e.g., keywords, target market, characteristics, etc.) into text fields.

[0063] If the user has a related image, upload the image.

[0064] Step 2: The device sends the data to the server

[0065] The terminal assembles the input requirements, concept information, and images into a single data packet.

[0066] The device encrypts the collected data packets and sends them to the server using a secure protocol (e.g., HTTPS).

[0067] Step 3: The server receives the data and provides it to the generation AI.

[0068] The server decrypts the data packets received from the terminal and stores them in a database.

[0069] The server converts the stored data into the required format to match the input format of the generating AI.

[0070] The server supplies the converted data to the generation AI and sends a request to generate naming suggestions.

[0071] Step 4: Generative AI generates naming ideas

[0072] The generative AI analyzes the data provided and extracts relevant keywords and concepts.

[0073] The generative AI generates original and attractive naming ideas based on the large amount of naming data it has learned.

[0074] Evaluate the generated naming ideas and select the most appropriate candidate.

[0075] Step 5: The server sends the naming proposal to the device

[0076] The server compiles the naming suggestions received from the generation AI into a data packet.

[0077] The server encrypts the collected data packets and sends them to the terminal.

[0078] Step 6: The device displays naming suggestions to the user

[0079] The terminal decrypts the data packet received from the server.

[0080] The decrypted naming proposal is read and displayed in the user interface.

[0081] The user reviews the naming proposal and provides feedback.

[0082] Step 7: User Provides Feedback

[0083] The user enters requests and feedback (e.g., corrections, additional information) for the provided naming proposal.

[0084] The user finalizes the feedback and presses the submit button.

[0085] Step 8: The device sends feedback to the server

[0086] The terminal packages the input feedback into data packets.

[0087] The terminal encrypts the feedback data packet and sends it to the server.

[0088] Step 9: The server receives the feedback and re-feeds it to the generating AI.

[0089] The server decodes the feedback packet received from the terminal and adds it to the database.

[0090] The server converts the added feedback into a format suitable for the generated AI.

[0091] The server re-feeds the feedback to the generation AI and sends a request to generate revised naming suggestions.

[0092] Step 10: Generative AI generates revised naming ideas

[0093] The generative AI analyzes the provided feedback and implements any necessary modifications.

[0094] The generative AI generates revised naming suggestions.

[0095] Evaluate the revised naming proposals and select the most appropriate candidate.

[0096] Step 11: The server sends the revised naming proposal to the device.

[0097] The server compiles the revised naming proposals received from the generation AI into a data packet.

[0098] The server encrypts the collected data packets and sends them to the terminal.

[0099] Step 12: The device displays the revised naming proposal to the user.

[0100] The terminal again decrypts the data packets received from the server.

[0101] The decoded revised naming proposal is displayed again in the user interface.

[0102] The user confirms the revised naming proposal.

[0103] Step 13: Finalize the naming idea

[0104] The user finally selects the naming proposal they like and presses the confirm button.

[0105] The terminal assembles the selected final naming proposal into a data packet and transmits it to the server.

[0106] The server saves the final naming proposal in the database as final information, completing the project.

[0107] Example 1

[0108] 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."

[0109] Conventional naming proposal generation systems have limitations in generating original names that perfectly match user requirements. Furthermore, the process of regenerating names based on user feedback is ineffective, resulting in a large amount of manual revisions. Such systems make it difficult to increase user satisfaction, and efficient generation of naming proposals is required.

[0110] 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.

[0111] In this invention, the server includes means for converting received data into JSON format, means for supplying the data to a generative AI model to generate naming suggestions, and means for analyzing feedback and resupplying the data to the generative AI model to generate revised naming suggestions, thereby enabling user feedback to be effectively reflected and the generated naming suggestions to be revised quickly and reliably.

[0112] A "user" is a person or organization that uses the system to generate and provide feedback on naming suggestions.

[0113] "Requirements" are data such as concept information, keywords, and related images that are necessary for the user to generate naming suggestions.

[0114] "Device" refers to an electronic device used by a user to input requirements and review and provide feedback on naming proposals, including smartphones, tablets, and PCs.

[0115] A "data packet" is a format in which data to be transmitted is collected and sent securely by encryption.

[0116] "Server" is a computer system for receiving data, feeding the data to the generative AI model, generating naming suggestions, analyzing feedback, and generating revised naming suggestions.

[0117] A "generative AI model" is an artificial intelligence model that utilizes machine learning and natural language processing techniques and is used to generate original naming ideas based on user requirements.

[0118] "Feedback" refers to evaluations and correction requests that users input for generated naming proposals.

[0119] "JSON format" is a standard data format used by servers to exchange data, and is an abbreviation for JavaScript Object Notation (JSON).

[0120] "AES-256 encryption" means an algorithm used to encrypt data at a high level and is a 256-bit key length version of the Advanced Encryption Standard (AES).

[0121] This invention relates to a system that generates original and attractive naming suggestions using a generative AI model based on user input requirements. This system consists of a terminal where the user inputs the requirements, a terminal that sends the data to a server, generates naming suggestions using a generative AI model, and returns the generated naming suggestions as feedback to the user, and a server that revises and generates new naming suggestions based on the feedback.

[0122] To implement this system, the following hardware and software are required:

[0123] Hardware used

[0124] 1. User device: A smartphone, tablet, or PC that users use to enter requirements and review and provide feedback on naming ideas.

[0125] 2. Server: A cloud-based server with a powerful CPU, GPU, sufficient memory, and storage capacity, used to receive data, feed it to the generative AI model, generate naming suggestions, and generate revised naming suggestions.

[0126] Software used

[0127] 1. Terminal software: "Creative Name Genie" application, which provides an interface for users to input requirements and review and provide feedback on naming suggestions.

[0128] 2. Server software: Data management software, generative AI models (e.g., GPT-4), data conversion, encryption, data transmission, naming proposal generation, and feedback analysis.

[0129] System Overview

[0130] User operations

[0131] Users launch the Creative Name Genie application on their device and create a new project. They then enter the concept information, keywords, and related images needed to come up with a new product name. For example, if a user wants to come up with a name for a new organic cosmetics brand, they can enter keywords such as "natural," "gentle on the skin," and "beauty" and upload an image.

[0132] Server Processing

[0133] The device generates a data packet containing the user's input requirements, keywords, and images, encrypts it using AES-256, and sends it to the server. The server then converts the received data into JSON format and supplies it to a generative AI model (such as GPT-4) to generate creative naming suggestions. The generative AI model uses a large dataset to suggest the best naming suggestions for the given requirements. For example, based on the keywords "natural," "gentle on the skin," and "beauty," naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals" are generated.

[0134] Naming proposal feedback and regeneration

[0135] The generated naming suggestions are then encrypted again using AES-256 and sent to the device. The user then uses the device to review the displayed naming suggestions and enter their evaluation and correction requests. For example, they can enter requests such as "Make it a little simpler" or "Add specific keywords." The device then encrypts this feedback again using AES-256 and sends it to the server. The server analyzes the user feedback and supplies it to the generative AI model as a new dataset to generate further optimized naming suggestions. For example, a new suggestion such as "PureGlow Organics" is generated.

[0136] Prompt Sentence Examples

[0137] Examples of prompts for generative AI models include:

[0138] "Think of a name for a new organic cosmetics brand. The keywords are 'natural,' 'gentle on the skin,' and 'beauty.'"

[0139] This system can generate efficient and high-quality naming suggestions based on user requirements and adjust the optimal naming suggestions based on user feedback.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1:

[0142] A user launches the "Creative Name Genie" application on their device. They then create a new project and input concept information and keywords for the name. They can also upload related images. Keywords and images such as "natural," "gentle on the skin," and "beauty" are used as input. The output is a data packet containing these requirements, keywords, and images.

[0143] Step 2:

[0144] The terminal generates a data packet based on the requirements, keywords, and images entered by the user, encrypts it using AES-256, and sends it to the server. The data entered by the user is used as input, and the output is an encrypted data packet. Specifically, the terminal starts sending data by clicking the "Send" button.

[0145] Step 3:

[0146] The server receives the incoming data packets and decrypts them using AES-256 encryption to extract the data. The encrypted data packets are used as input, and the output is data containing decrypted requirements, keywords, and images. The server converts this data into JSON format, making it ready for the generative AI model to process.

[0147] Step 4:

[0148] The server supplies the converted data to the generative AI model to generate naming suggestions. The inputs are requirements, keywords, and images converted into JSON format. The output is the naming suggestions generated by the generative AI model. Specifically, the generative AI model generates original naming suggestions based on a large dataset.

[0149] Step 5:

[0150] The server re-encrypts the naming proposal received from the generative AI model using AES-256 and sends it to the terminal. The naming proposal from the generative AI model is used as input, and the output is a data packet of the encrypted naming proposal. The server sends this data packet to the terminal.

[0151] Step 6:

[0152] The terminal decrypts the received data packet and displays the naming proposal on the user interface. The encrypted naming proposal data packet is used as input, and the output is the decrypted naming proposal. The user checks the displayed naming proposal and is ready to enter feedback.

[0153] Step 7:

[0154] The user inputs their evaluation and correction requests for the displayed naming proposals. The input is the user's feedback, and the output is a data packet containing the evaluation and correction requests. Specifically, the user clicks the "Send Feedback" button.

[0155] Step 8:

[0156] The device encrypts the feedback with AES-256 and sends it to the server. The input is the user's feedback, and the output is the encrypted feedback data packet.

[0157] Step 9:

[0158] The server receives the feedback and decrypts it using AES-256 encryption to extract the data. The encrypted feedback is used as input, and the output is the decrypted feedback data. The server analyzes this feedback and feeds it into a generative AI model as a new dataset.

[0159] Step 10:

[0160] The server provides the analyzed feedback to the generative AI model to generate revised naming suggestions. The analyzed feedback is used as input, and the revised naming suggestions are the output. Specifically, the generative AI model reflects the feedback and generates new naming suggestions.

[0161] Step 11:

[0162] The server re-encrypts the revised naming proposal received from the generative AI model using AES-256 and sends it to the terminal. The revised naming proposal is used as input, and the output is a data packet of the encrypted revised naming proposal.

[0163] Step 12:

[0164] The terminal decrypts the received data packet and displays the revised naming proposal on the user interface. The encrypted revised naming proposal data packet is used as input, and the output is the decrypted revised naming proposal. The user reviews the revised naming proposal, and this process is repeated until the best naming proposal is determined.

[0165] (Application example 1)

[0166] 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."

[0167] A system that can effectively generate new naming suggestions is needed for autonomous vehicles. In particular, it is necessary for the user to intuitively input information via the vehicle's dashboard or voice recognition system, review the generated naming suggestions, and generate new naming suggestions based on the feedback efficiently. However, conventional methods have the problem that it is difficult for users to easily generate naming suggestions.

[0168] 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.

[0169] In this invention, the server includes: means for a user to input requirements via a voice recognition system or a touch screen; means for a vehicle terminal to encrypt and transmit the input data packet to the server; means for the server to supply data to a generative AI model and generate naming suggestions; means for the server to encrypt and transmit the generated naming suggestions to the vehicle terminal; means for the vehicle terminal to display the naming suggestions to the user; means for the user to input feedback via a voice recognition system or a touch screen; means for the vehicle terminal to encrypt and transmit the feedback to the server; means for the server to supply data together with the feedback to the generative AI model again and generate revised naming suggestions; and means for the server to encrypt and transmit the revised naming suggestions to the vehicle terminal. This makes it possible to effectively and efficiently generate naming suggestions in an environment where the user can input names intuitively, and to obtain optimal naming suggestions through a revision process.

[0170] "Means for users to input requirements via a voice recognition system or touch screen" refers to a device or function that allows a vehicle user to input requirements related to naming of the vehicle through voice commands or by operating a touch screen on the dashboard.

[0171] "Means for encrypting and transmitting data packets input by a vehicle terminal to a server" refers to a computer system within a vehicle that forms information input by a user into a data packet, encrypts it, and securely transmits it to a remote server.

[0172] "Means by which the server supplies data to the generative AI model and generates naming suggestions" refers to the function or process by which the server provides data to the generative AI model and the AI ​​automatically generates naming suggestions based on that data.

[0173] "Means for the server to encrypt and transmit the generated naming proposals to the vehicle's terminal" refers to the process of assembling the generated naming proposals into data packets, encrypting them, and securely transmitting them to the terminal in the vehicle.

[0174] The "means for the vehicle terminal to display the naming suggestions to the user" refers to a device or function that displays the generated naming suggestions on the dashboard or touch screen of the vehicle.

[0175] "Means for users to input feedback via a voice recognition system or touch screen" refers to a device or function that allows users to input their opinions or requests for corrections to the generated naming proposals via voice commands or a touch screen.

[0176] The "means for the vehicle terminal to encrypt the feedback and transmit it to the server" refers to an in-vehicle computer system that assembles the user's feedback into data packets, encrypts them, and transmits them securely to the server.

[0177] "Means for the server to again provide data to the generative AI model together with feedback to generate revised naming proposals" refers to the process in which the server provides data to the generative AI model based on user feedback and generates revised naming proposals.

[0178] The "means for the server to encrypt and transmit the revised naming proposal to the vehicle's terminal" refers to the process of assembling the generated revised naming proposal into a data packet, encrypting it, and securely transmitting it to the terminal in the vehicle.

[0179] The present invention relates to a system that allows users to input requirements via a voice recognition system or touch screen, and generates original and attractive naming ideas using a generative AI model. The basic components of this system include a vehicle terminal, a cloud server, a generative AI model, and an interface for incorporating user feedback.

[0180] First, the user uses the touchscreen or voice recognition system on the vehicle's dashboard to input the concept information and keywords needed for the naming proposal. For example, they can enter keywords such as "innovation," "future," or "eco," and upload related images. This data is then formed into a data packet by the vehicle's terminal and sent to the cloud server using TLS / SSL encryption.

[0181] The cloud server then analyzes the received data and provides it to a generative AI model (such as OpenAI GPT-4). The generative AI model uses a large dataset to generate appropriate naming suggestions based on the input requirements. For example, using the keywords "innovation," "future," and "eco," it can generate naming suggestions such as "EcoFuture Drive" and "Green Innovate Rover."

[0182] The generated naming suggestions are then packaged again into a data packet, encrypted, and sent to the vehicle's terminal, which decodes the suggestions and displays them on the dashboard or touchscreen. The user can review the displayed naming suggestions and enter feedback, such as requests to make them simpler or to add specific keywords.

[0183] The user's feedback is again generated as a data packet from the vehicle's terminal, encrypted, and sent to the server. The cloud server then inputs the feedback into the generative AI model again, generating revised naming suggestions. This process is repeated until a naming suggestion that satisfies the user is generated.

[0184] For example, if the user enters the following prompt:

[0185] Generate naming ideas for your vehicle.

[0186] Keywords: Innovation, Future, Eco

[0187] Concept: Eco-friendly autonomous driving technology

[0188] Related images: (Ecosystem image URL)

[0189] Based on this prompt, the system generates naming suggestions such as "EcoFuture Drive" or "Green Innovate Rover." Further revisions are suggested based on the user's feedback, allowing the user to find the optimal name.

[0190] The hardware requires an on-board computer system, a voice recognition microphone, and a touchscreen, while the software requires a generative AI model (OpenAI GPT-4) hosted on a cloud server, a data encryption and data packet generation module, and an in-vehicle user interface application.

[0191] This allows users to input information intuitively, efficiently generate naming suggestions based on the generative AI model, and obtain the optimal naming suggestions through a revision process.

[0192] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0193] Step 1:

[0194] The device accepts user input. Specifically, the user uses a voice recognition system or touchscreen to input keywords, concepts, and related images related to the proposed name. The input data is then formed into a data packet within the device. Keywords such as "innovation," "future," and "eco" and related images are inserted as input. This data packet is then ready to be fed to the generative AI model.

[0195] Step 2:

[0196] The device encrypts the generated data packet. TLS / SSL encryption is used to enhance data security. The device then sends this encrypted data packet to the cloud server. If the data transmission is successful, the device waits for a response.

[0197] Step 3:

[0198] The server receives the data sent from the device and decrypts the data packets. During this process, the server analyzes the received data and converts it into a format that can be fed to the generative AI model. The converted data becomes, for example, a prompt sentence: "Generate a name proposal for a vehicle. Keywords: innovation, future, eco-concept: environmentally friendly autonomous driving technology." This prompt sentence is passed to the generative AI model.

[0199] Step 4:

[0200] The server inputs a prompt to the generative AI model, which then generates naming suggestions. The generative AI model then creates naming suggestions based on the prompt and returns the results to the server. Specifically, naming suggestions such as "EcoFuture Drive" and "Green Innovate Rover" are generated.

[0201] Step 5:

[0202] The server assembles the generated naming proposals into a data packet and encrypts it again. The encrypted data packet is sent to the terminal. When the server completes the transmission, it waits for a response.

[0203] Step 6:

[0204] The device receives and decrypts the data packets sent by the server, converts them into a format that is displayed in the user interface, and displays them as naming suggestions on the dashboard or touchscreen, where the user can review them and provide feedback.

[0205] Step 7:

[0206] The user provides feedback on the proposed name, such as "Make it simpler" or "Add specific keywords" through a voice recognition system or touch screen. This feedback is then sent to the device as a data packet.

[0207] Step 8:

[0208] The device encrypts the feedback data packet. TLS / SSL encryption is also used to ensure security. The device then sends the encrypted feedback data packet to the cloud server. Once the transmission is complete, the device waits for a response.

[0209] Step 9:

[0210] The server receives the feedback data and decrypts it. It then analyzes the received feedback and converts it into a format that can be fed to the generative AI model. Based on the feedback, it inputs a prompt sentence into the generative AI model again to generate new naming suggestions.

[0211] Step 10:

[0212] The server generates revised naming proposals based on the generative AI model and assembles them into data packets, which are then re-encrypted and sent to the vehicle's terminal. This process is repeated until a revised naming proposal is sent to the terminal.

[0213] This allows the user to efficiently generate naming suggestions in an intuitive input environment, and to obtain the optimal naming suggestion through a revision process.

[0214] 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.

[0215] This invention relates to a system that generates original and attractive naming suggestions using a generative AI and an emotion engine after a user inputs requirements. The basic configuration of this system consists of a terminal where the user inputs requirements, a means for sending the data to a server, generating naming suggestions using generative AI, presenting the generated naming suggestions to the user and collecting feedback, and a server that modifies and generates naming suggestions based on the feedback and emotion data.

[0216] Furthermore, in the present invention, by incorporating an emotion engine that recognizes the user's emotions, it is possible to provide naming suggestions that further increase user satisfaction.

[0217] The program processing using this system and emotion engine will be specifically explained below.

[0218] 1. The user enters the requirements

[0219] The user launches the Creative Name Genie application on their device, creates a new project, selects a naming category (e.g., brand name, product name, pet name), and enters the necessary concept information, keywords, and related images.

[0220] Example: If a user wants to come up with a name for a new organic cosmetics brand, they can enter keywords like "natural," "gentle on the skin," and "beauty" and upload an image.

[0221] 2. The device sends the data to the server

[0222] The device generates a data packet containing the input requirements, keywords, and images, encrypts it, and sends it to the server, ensuring data security.

[0223] The server converts the received data into the required format and prepares it as data that can be processed by the generative AI and emotion engine.

[0224] 3. The emotion engine recognizes the user's emotions

[0225] The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received from the device in real time to recognize the user's emotional state (e.g., joy, surprise, stress, etc.).

[0226] Example: If the user smiles while typing, the emotion engine detects the user's positive emotion.

[0227] 4. The server supplies the data to the AI ​​to generate naming suggestions.

[0228] The server provides the input data and emotional data to the AI ​​generator, instructing it to generate naming suggestions. The AI ​​then generates original naming suggestions based on the information provided.

[0229] Example: Generative AI generates naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals" by taking into account keywords such as "natural," "skin-friendly," and "beauty" as well as positive emotional data from users.

[0230] 5. The server sends the generated naming proposal to the device.

[0231] The server collects the naming suggestions received from the AI ​​generator into a data packet, encrypts it, and sends it to the device, ensuring that the generated naming suggestions are delivered securely to the user's device.

[0232] The device decodes the received naming suggestions and displays them in a user interface, where the user can review them and provide feedback.

[0233] 6. User provides feedback

[0234] The user can then rate or request revisions to the displayed naming ideas. Furthermore, the emotion engine continuously analyzes the user's facial expressions and voice during this process.

[0235] For example, you can input requests such as "Make it a little simpler" or "Add specific keywords," and the emotion engine will collect emotional data at that time.

[0236] 7. The device sends feedback to the server

[0237] The terminal assembles the input feedback and emotion data into a data packet, encrypts it, and transmits it to the server.

[0238] The server analyzes the received data and provides feedback and emotion data to the generative AI.

[0239] 8. The server re-supplies the data to the AI ​​based on the emotion data and generates revised naming suggestions.

[0240] The server issues a command to the AI ​​generator based on the feedback and emotional data to generate revised naming suggestions. The AI ​​generator takes the new feedback and emotional data into account and generates optimized naming suggestions.

[0241] For example, a new suggestion could be generated such as "PureGlow Organics."

[0242] 9. The server sends the revised naming proposal to the device and presents it to the user.

[0243] The server receives a revised naming proposal from the AI ​​again and sends it to the device, which displays it again on the user interface for final confirmation by the user.

[0244] This process is repeated until the user selects and finalizes the optimal naming suggestion.This system generates efficient and high-quality naming suggestions that reflect the user's emotional state, thereby improving user satisfaction.

[0245] The processing flow will be explained below.

[0246] Step 1:

[0247] A user launches the "Creative Name Genie" application on their device. They create a new project and select a naming category (e.g., brand name, product name, pet name). Next, the user enters naming requirements and concept information (e.g., keywords, target market, characteristics, etc.) into text fields and uploads related images, if available.

[0248] Step 2:

[0249] The device combines the input requirements, concept information, and images into a single data packet, which the device encrypts and sends to the server using a secure protocol (e.g., HTTPS).

[0250] Step 3:

[0251] The server decrypts the data packets received from the device and stores them in a database. The server then converts the stored data into the required format and prepares it as data that can be processed by the generative AI and emotion engine.

[0252] Step 4:

[0253] The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received from the device in real time to recognize the user's emotional state (e.g., joy, surprise, stress, etc.). For example, if the user smiles while typing, the emotion engine detects the user's positive emotion.

[0254] Step 5:

[0255] The server provides the input data along with emotional data to the AI ​​and instructs it to generate naming suggestions. The AI ​​generates original naming suggestions based on the information provided. For example, the AI ​​might consider keywords such as "natural," "skin-friendly," and "beauty" along with the user's positive emotional data to generate naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals."

[0256] Step 6:

[0257] The server collects the naming suggestions received from the AI ​​generator into a data packet, encrypts it, and sends it to the device. The device decrypts the received naming suggestions and displays them on the user interface. The user can review these suggestions and provide feedback.

[0258] Step 7:

[0259] The user can then rate the displayed naming proposals and input any corrections they wish to make. Furthermore, the emotion engine continuously analyzes the user's facial expressions and voice during this process. For example, if a request is made such as "I'd like it to be a little simpler" or "I'd like a specific keyword added," the emotion engine will also collect emotional data at that time.

[0260] Step 8:

[0261] The device assembles the input feedback and emotion data into a data packet, encrypts it, and sends it to the server, which analyzes the received data and provides the feedback and emotion data to the generative AI.

[0262] Step 9:

[0263] The server then issues a new command to the AI ​​based on the feedback and emotional data, instructing it to generate a revised naming proposal. The AI ​​then takes the new feedback and emotional data into account and generates an optimized naming proposal, such as "PureGlow Organics."

[0264] Step 10:

[0265] The server receives a revised naming proposal from the AI ​​again and sends it to the device. The device displays the revised naming proposal again on the user interface. The user reviews the revised naming proposal and again judges whether it is good or bad.

[0266] Step 11:

[0267] The user finally selects their favorite naming plan and presses the confirm button. The device assembles the final naming plan into a data packet and sends it to the server. The server saves the final naming plan in the database as final information, and the project is completed.

[0268] Example 2

[0269] 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."

[0270] Conventional naming generation systems generate naming suggestions based solely on user requirements and feedback, which means they are unable to fully reflect the user's emotions and intentions. This can lead to a decline in the quality of the generated naming suggestions and user satisfaction. Furthermore, even in the process of incorporating user feedback to generate revised naming suggestions, emotional data is not taken into account, making it difficult to generate suggestions that reflect the user's true intentions.

[0271] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0272] In this invention, the server includes: means for a user to input requirements; means for a terminal to transmit the input data to the server; means for the server to supply data to a generative AI model and generate naming suggestions; means for the server to transmit the generated naming suggestions to the terminal; means for the terminal to display the naming suggestions to the user; means for a user to input feedback; means for the terminal to transmit the feedback to the server; means for the server to resupply data to the generative AI model together with the feedback and generate revised naming suggestions; means for the server to transmit the revised naming suggestions to the terminal; means for the terminal to use an emotion engine that recognizes the user's emotions and transmit user emotion data to the server; and means for the server to supply data to the generative AI model based on the emotion data and adjust the generated naming suggestions. This enables the generation of high-quality naming suggestions that reflect the user's emotional state and true intentions.

[0273] The "means for inputting requirements" is a system component that allows a user to input their own wishes and necessary conditions.

[0274] The "means for transmitting data to a server" is a system component for transmitting information input by a user from a terminal to a server.

[0275] The "means for supplying data to the generative AI model" is a system component that allows the server to pass the user's input data and emotion data to the generative AI model and generate naming suggestions.

[0276] A "means for generating naming suggestions" is a system component for using a generative AI model to create new naming suggestions based on supplied data.

[0277] The "means for transmitting the generated naming proposal" is a system component for transmitting the generated naming proposal from the server to the terminal.

[0278] The "means for displaying naming suggestions to the user" is a system component for displaying the naming suggestions received by the terminal on the user interface.

[0279] The "means for inputting feedback" is a system component that allows the user to input evaluations and requests for corrections to the displayed naming proposals.

[0280] The "means for transmitting feedback to the server" is a system component for transmitting feedback data including the user's evaluation and correction requests from the terminal to the server.

[0281] The "means for using the emotion engine and transmitting user emotion data" is a system component that enables the terminal to recognize the user's emotional state and transmit the data to the server.

[0282] The "means for supplying data to the generative AI model based on emotional data and adjusting the generated naming suggestions" is a system component that enables the server to supply data to the generative AI model taking emotional data into consideration and optimize the generated naming suggestions.

[0283] The "means for generating revised naming suggestions" is a system component for generating revised naming suggestions using a generative AI model based on the input feedback and sentiment data.

[0284] This invention is a system that generates original and attractive naming suggestions using a generative AI model and an emotion engine after a user inputs requirements.The basic configuration of this system consists of a terminal where the user inputs requirements, a means for sending the data to a server, generating naming suggestions using a generative AI model, presenting the generated naming suggestions to the user and collecting feedback, and a server that modifies and generates naming suggestions based on the feedback and emotion data.

[0285] Furthermore, by incorporating an emotion engine that recognizes the user's emotions, the present invention can provide naming suggestions that further increase user satisfaction. Below, we will specifically explain the program processing using this system and emotion engine.

[0286] A user launches an application such as "Creative Name Genie" on their device and creates a new project. Here, the user selects a naming category (e.g., brand name, product name, pet name) and enters the necessary concept information, keywords, and related images. For example, when thinking of a name for a new organic cosmetics brand, a user enters keywords such as "natural," "gentle on the skin," and "beauty," and uploads an image of the skin care products.

[0287] The terminal assembles these input data into data packets, encrypts them, and sends them to the server. The security of this data is maintained using TLS (Transport Layer Security).

[0288] The server converts the received data into the required format and prepares it as data that can be processed by the generative AI model and emotion engine. The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received in real time from the device to recognize the user's emotional state (e.g., joy, surprise, stress, etc.). For example, if the user smiles while typing, the emotion engine detects that positive emotion.

[0289] Next, the server supplies the input data and emotional data to a generative AI model and instructs it to generate naming suggestions. As an example of a generative AI model, GPT-3 can be used. The generative AI model generates original naming suggestions based on the information provided. As a specific example, by combining the keywords "natural," "skin-friendly," and "beauty" with the user's positive emotional data, naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals" are generated.

[0290] The generated naming suggestions are packaged in encrypted data packets from the server and sent to the device. The device decrypts the packets and displays them on the user interface. The user can then review the displayed naming suggestions and enter their evaluations or requests for revisions. The emotion engine continuously analyzes the user's facial expressions and voice while they are entering feedback, and generates emotion data.

[0291] The feedback and emotion data entered by the user is sent from the device to a server, which analyzes the data and feeds it to a generative AI model. The generative AI model generates revised naming suggestions based on the new feedback and emotion data. For example, a new suggestion such as "PureGlow Organics" is generated.

[0292] The regenerated naming suggestions are sent from the server to the device and redisplayed on the user interface, allowing the user to repeat this process until the best naming suggestion is selected and finalized.

[0293] This system generates efficient and high-quality naming suggestions that reflect the user's emotional state, thereby improving user satisfaction.

[0294] "Think of a name for a new organic cosmetics brand. Keywords: natural, gentle, beauty."

[0295] Examples include:

[0296] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0297] Program processing steps

[0298] Step 1:

[0299] User enters requirements

[0300] What it does: A user launches the Creative Name Genie application on their device, creates a new project, selects a naming category (e.g., brand name, product name, pet name), and enters the required concept information, keywords, and related images.

[0301] Input: Category, concept information, keywords, related images

[0302] Output: Generate input data (categories, concept information, keywords, images)

[0303] Step 2:

[0304] The device sends the entered data to the server.

[0305] Specific operation: The device assembles the requirements, keywords, and images entered by the user into a data packet, encrypts it, and sends it to the server.

[0306] Input: User input data (categories, concept information, keywords, images)

[0307] Data processing: Packetization and encryption of input data (using TLS)

[0308] Output: Encrypted data packet

[0309] Step 3:

[0310] The server prepares the input data

[0311] What it does: The server decrypts the encrypted data it receives and converts it into a format that can be processed by the generative AI model and emotion engine.

[0312] Input: Encrypted data packet

[0313] Data processing: Data interpretation and format conversion

[0314] Output: Data converted into a processable format

[0315] Step 4:

[0316] Emotion engine recognizes user emotions

[0317] Specific operation: The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received in real time from the device to recognize the user's emotional state.

[0318] Input: Real-time user data (facial expressions, voice, typing speed, gestures)

[0319] Data Computation: Emotional state analysis and emotional data generation

[0320] Output: Emotion data

[0321] Step 5:

[0322] The server supplies data to the generative AI model to generate naming suggestions.

[0323] Specific operation: The server provides input data and emotion data to a generative AI model (e.g., GPT-3) and instructs it to generate naming suggestions. The generative AI model generates naming suggestions based on the provided information.

[0324] Input: Input data, emotion data converted into a processable format

[0325] Data Computation: Data computation using generative AI models

[0326] Output: Generated naming ideas

[0327] Step 6:

[0328] The server sends the generated naming proposal to the device.

[0329] How it works: The server compiles the naming ideas obtained from the generative AI model into a data packet, encrypts it, and sends it to the device, which then decrypts it and displays it on the user interface.

[0330] Input: Generated naming ideas

[0331] Data processing: Packetization and encryption of naming proposals, decryption after receiving

[0332] Output: Naming proposal displayed in the user interface

[0333] Step 7:

[0334] User enters feedback

[0335] Specific operation: The user inputs their evaluation and correction requests for the displayed naming proposals. The emotion engine analyzes their facial expressions and voices and generates emotion data.

[0336] Input: Evaluation of naming proposals, requests for revisions, user facial expressions and voice

[0337] Data computation: Emotional state analysis, emotional data generation

[0338] Output: Feedback data, emotion data

[0339] Step 8:

[0340] The device sends feedback to the server

[0341] Specific operation: The device assembles the input feedback and emotion data into a data packet, encrypts it, and sends it to the server.

[0342] Input: Feedback data, emotion data

[0343] Data processing: Packetization and encryption of feedback and emotion data

[0344] Output: Encrypted data packet

[0345] Step 9:

[0346] The server re-feeds the data to the generative AI model to generate revised naming proposals.

[0347] Specific operation: The server provides feedback and emotion data to the generative AI model and instructs it to generate revised naming suggestions. The generative AI model generates optimized naming suggestions based on the new feedback and emotion data.

[0348] Input: Feedback and emotion data

[0349] Data computation: Recomputing data using generative AI models

[0350] Output: Revised naming proposal

[0351] Step 10:

[0352] The server sends the revised naming proposal to the device and presents it to the user.

[0353] How it works: The server assembles revised naming suggestions from the generative AI model into a data packet, encrypts it, and sends it to the device. The device decrypts it and displays it again on the user interface. This process can be repeated until the user selects and finalizes the best naming suggestion.

[0354] Input: Revised naming proposal

[0355] Data processing: Packetization and encryption of the revised naming proposal, and decryption after receiving it

[0356] Output: Revised naming proposal displayed in the user interface

[0357] (Application example 2)

[0358] 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."

[0359] Conventional naming suggestion generation systems have difficulty generating naming suggestions that take user emotions into account, and have not been able to sufficiently increase user satisfaction. In addition, the process of revising generated naming suggestions based on feedback is cumbersome, making them difficult to use for users.

[0360] 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.

[0361] In this invention, the server includes a means for analyzing the user's emotions using an emotion engine and supplying the analysis results to the generation AI to generate revised naming suggestions, and a revision process for regenerating based on the user's feedback and emotion data. This makes it possible to generate original and attractive naming suggestions that take the user's emotions into consideration, thereby improving user satisfaction.

[0362] A "user" is someone who uses the system to generate naming suggestions and provide feedback.

[0363] "Requirements" are conditions or information that a user inputs into the system, such as keywords or categories.

[0364] A "terminal" is a device operated by a user, which inputs requirements, transmits data, displays naming suggestions, etc.

[0365] A "server" is a device that receives data sent from a terminal, processes and generates the data using generative AI or an emotion engine, and returns the results to the terminal.

[0366] "Generative AI" refers to artificial intelligence that generates naming ideas based on given data.

[0367] "Naming suggestions" are names or candidate names generated by the generation AI.

[0368] An "emotion engine" is a technology that analyzes user emotions and reflects the results in generating naming suggestions.

[0369] "Feedback" refers to the user's evaluation of the generated naming proposals and requests for corrections.

[0370] A "data packet" is a unit of digital information that contains user-entered requirements and feedback.

[0371] "Analysis results" refers to the data obtained after the emotion engine analyzes the user's emotions.

[0372] "Revised naming proposals" are naming proposals regenerated by the generation AI, taking into account user feedback and emotional data.

[0373] A "process" is a series of tasks that indicate the overall procedure or processing flow.

[0374] The present invention relates to a system that allows a user to input requirements and generates original and attractive naming ideas using a generative AI and an emotion engine. Specific embodiments are described below.

[0375] 1. User operations

[0376] Users launch a dedicated application on their device and create a new project. At that time, they select a naming category (e.g., brand name, product name, etc.) and enter the necessary concept information, keywords, and related images. For example, if they want to think of a name for a new organic cosmetics brand, they can enter keywords such as "natural," "gentle on the skin," and "beauty" and upload an image.

[0377] 2. Data transmission

[0378] The device generates data packets from the input requirements, keywords, images, etc., and encrypts the data before sending it to the server, ensuring data security.

[0379] 3. Emotion Data Analysis

[0380] When data arrives at the server, the emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. in real time to recognize the user's emotional state (happiness, surprise, stress, etc.). For example, if the user smiles while typing, the emotion engine will detect that positive emotion.

[0381] 4. Naming Idea Generation

[0382] The server provides the received input data and emotional data to the generation AI, instructing it to generate naming suggestions. The generation AI generates original naming suggestions based on the information provided. For example, by taking into account the keywords "natural," "skin-friendly," and "beauty" and the user's positive emotional data, it generates naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals."

[0383] 5. Submit a naming proposal

[0384] The server packages the generated naming suggestions into a data packet, encrypts it, and sends it to the device. The device then decrypts the received naming suggestions and displays them on a user interface. The user can review these suggestions and provide feedback.

[0385] 6. Processing Feedback

[0386] Users can then evaluate the generated naming proposals and input their feedback and requests for revisions, while their facial expressions and voices are continuously analyzed by the emotion engine. For example, users can input requests such as "Make it a little simpler" or "Add specific keywords." This feedback and emotion data is collected and sent back to the server from the device.

[0387] 7. Generate revised naming ideas

[0388] The server analyzes the received feedback and sentiment data and feeds it into a generative AI to generate revised naming suggestions, such as "PureGlow Organics."

[0389] 8. Final Check

[0390] The terminal receives the revised naming proposals sent again from the server, and the user finally checks them and selects and confirms the most suitable naming proposal.

[0391] Hardware / Software used

[0392] Servers and Terminals

[0393] Emotion Engine and Generative AI Models

[0394] Dedicated application

[0395] Prompt Sentence Examples

[0396] "Generate a name for a new organic cosmetics brand. Keywords are 'natural,' 'gentle on the skin,' and 'beauty.'"

[0397] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0398] Step 1:

[0399] User enters requirements

[0400] Input: The user launches a dedicated application on the device and enters the naming category, necessary concept information, keywords, and related images.

[0401] Specific operation: The user selects the category "Brand Name" and enters keywords such as "natural," "gentle on the skin," and "beauty" along with an image.

[0402] Output: Data entered into the terminal is generated.

[0403] Step 2:

[0404] The device sends the entered data to the server.

[0405] Input: The data generated in step 1.

[0406] Specific operation: The device creates a data packet, encrypts it, and sends it to the server.

[0407] Output: The data packet arrives at the server.

[0408] Step 3:

[0409] The server uses an emotion engine to analyze the user's emotions.

[0410] Input: Data packets arriving at the server and real-time data such as the user's facial expressions, voice, typing speed, and gestures.

[0411] Specific operation: The server's emotion engine analyzes this data and recognizes the user's emotional state (happiness, surprise, stress, etc.).

[0412] Output: Analysis results about the user's emotional state.

[0413] Step 4:

[0414] The server provides data to the AI ​​to generate naming suggestions.

[0415] Input: Input data and emotion data.

[0416] Specific operation: The server provides these data to the generation AI and instructs it to generate naming suggestions. The generation AI generates naming suggestions based on the provided information.

[0417] Output: Generated naming ideas (e.g. "PureSkin Botanicals" or "EcoGlam Naturals").

[0418] Step 5:

[0419] The server sends the generated naming proposal to the device.

[0420] Input: Generated naming ideas.

[0421] Specific operation: The server assembles the naming proposals into a data packet, encrypts it, and sends it to the terminal.

[0422] Output: Data packets arrive at the terminal.

[0423] Step 6:

[0424] The device displays naming suggestions to the user

[0425] Input: The received data packet.

[0426] Specific operation: The terminal decodes the data packet and displays naming suggestions on the user interface.

[0427] Output: The naming proposal is displayed on the terminal screen.

[0428] Step 7:

[0429] User enters feedback

[0430] Input: The displayed naming suggestion.

[0431] Specific operation: The user inputs their evaluation and requests for revisions to the naming proposals, and facial expressions and voice data are also analyzed by the emotion engine.

[0432] Output: Feedback and emotion data.

[0433] Step 8:

[0434] The device sends feedback to the server

[0435] Input: Feedback and emotion data.

[0436] Specific operation: The device assembles this data into a data packet, encrypts it, and sends it to the server.

[0437] Output: The data packet arrives at the server.

[0438] Step 9:

[0439] The server re-feeds the feedback and sentiment data and generates revised naming proposals.

[0440] Input: Feedback and emotion data.

[0441] Specific actions: The server analyzes this data and feeds it back to the generation AI, instructing it to generate revised naming proposals.

[0442] Output: A newly generated revised naming proposal (e.g., "PureGlow Organics").

[0443] Step 10:

[0444] The server sends the revised naming proposal to the device and presents it to the user.

[0445] Input: The newly generated revised naming proposal.

[0446] Specific operation: The server assembles the revised naming proposal into a data packet, encrypts it, and sends it to the device, which decrypts the data packet and displays it on the user interface.

[0447] Output: The revised naming proposal is displayed on the terminal screen.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] [Second embodiment]

[0452] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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."

[0464] This invention relates to a system that allows users to input requirements and generates original and attractive naming suggestions using a generation AI. The basic configuration of this system consists of a terminal where the user inputs the requirements, a terminal that sends the data to a server, generates naming suggestions using a generation AI, and returns the generated naming suggestions as feedback to the user, and a server that revises and generates new naming suggestions based on the feedback.

[0465] The program processing of this system will be specifically explained below.

[0466] 1. The user enters the requirements

[0467] Users simply launch the Creative Name Genie application on their device, create a new project, and enter the concept information, keywords, and related images needed to come up with a new product name, for example.

[0468] Example: If a user wants to come up with a name for a new organic cosmetics brand, they can enter keywords like "natural," "gentle on the skin," and "beauty" and upload an image.

[0469] 2. The device sends the data to the server

[0470] The device generates a data packet containing the input requirements, keywords, and images, encrypts it, and sends it to the server, ensuring data security.

[0471] The server converts the received data into the required format and prepares it as data that can be processed by the generation AI.

[0472] 3. The server provides the data to the AI ​​to generate naming suggestions.

[0473] The server provides the supplied data as input to the AI ​​generator, which then generates naming suggestions based on that data. The AI ​​generator uses a large amount of data set it has learned to suggest original names that match the user's requirements.

[0474] Example: Generative AI uses the keywords "natural," "skin-friendly," and "beauty" to generate naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals."

[0475] 4. The server sends the generated naming proposal to the device.

[0476] The server collects the naming suggestions received from the AI ​​generator into a data packet, encrypts it, and sends it to the device, ensuring that the generated naming suggestions are delivered securely to the user's device.

[0477] The device decodes the received naming suggestions and displays them in a user interface, where the user can review them and provide feedback.

[0478] 5. User provides feedback

[0479] Users can view the displayed naming suggestions and then enter their evaluation and requests for revisions, such as "Make it a little simpler" or "Add specific keywords."

[0480] The terminal transmits this feedback back to the server.

[0481] 6. The server re-supplies the data to the AI ​​based on the feedback and generates revised naming proposals.

[0482] The server analyzes the feedback from the user and provides the feedback to the generation AI again, instructing it to generate revised naming proposals.

[0483] The AI ​​then takes new feedback into account and generates more optimized naming suggestions, such as "PureGlow Organics."

[0484] 7. The server sends the revised naming proposal to the device and displays it to the user.

[0485] The server again receives a revised naming proposal from the generation AI and sends it to the device, which again displays it on the user interface for final confirmation by the user.

[0486] This process is repeated until the user selects and finalizes the best naming suggestion. This system efficiently generates high-quality naming suggestions and allows for a process that can be tailored to the user's needs.

[0487] The processing flow will be explained below.

[0488] Step 1: User enters requirements

[0489] The user opens the device and launches the "Creative Name Genie" application.

[0490] A user creates a new project and selects a naming category (e.g., brand name, product name, pet name).

[0491] The user enters naming requirements and concept information (e.g., keywords, target market, characteristics, etc.) into text fields.

[0492] If the user has a related image, upload the image.

[0493] Step 2: The device sends the data to the server

[0494] The terminal assembles the input requirements, concept information, and images into a single data packet.

[0495] The device encrypts the collected data packets and sends them to the server using a secure protocol (e.g., HTTPS).

[0496] Step 3: The server receives the data and provides it to the generation AI.

[0497] The server decrypts the data packets received from the terminal and stores them in a database.

[0498] The server converts the stored data into the required format to match the input format of the generating AI.

[0499] The server supplies the converted data to the generation AI and sends a request to generate naming suggestions.

[0500] Step 4: Generative AI generates naming ideas

[0501] The generative AI analyzes the data provided and extracts relevant keywords and concepts.

[0502] The generative AI generates original and attractive naming ideas based on the large amount of naming data it has learned.

[0503] Evaluate the generated naming ideas and select the most appropriate candidate.

[0504] Step 5: The server sends the naming proposal to the device

[0505] The server compiles the naming suggestions received from the generation AI into a data packet.

[0506] The server encrypts the collected data packets and sends them to the terminal.

[0507] Step 6: The device displays naming suggestions to the user

[0508] The terminal decrypts the data packet received from the server.

[0509] The decrypted naming proposal is read and displayed in the user interface.

[0510] The user reviews the naming proposal and provides feedback.

[0511] Step 7: User Provides Feedback

[0512] The user enters requests and feedback (e.g., corrections, additional information) for the provided naming proposal.

[0513] The user finalizes the feedback and presses the submit button.

[0514] Step 8: The device sends feedback to the server

[0515] The terminal packages the input feedback into data packets.

[0516] The terminal encrypts the feedback data packet and sends it to the server.

[0517] Step 9: The server receives the feedback and re-feeds it to the generating AI.

[0518] The server decodes the feedback packet received from the terminal and adds it to the database.

[0519] The server converts the added feedback into a format suitable for the generated AI.

[0520] The server re-feeds the feedback to the generation AI and sends a request to generate revised naming suggestions.

[0521] Step 10: Generative AI generates revised naming ideas

[0522] The generative AI analyzes the provided feedback and implements any necessary modifications.

[0523] The generative AI generates revised naming suggestions.

[0524] Evaluate the revised naming proposals and select the most appropriate candidate.

[0525] Step 11: The server sends the revised naming proposal to the device.

[0526] The server compiles the revised naming proposals received from the generation AI into a data packet.

[0527] The server encrypts the collected data packets and sends them to the terminal.

[0528] Step 12: The device displays the revised naming proposal to the user.

[0529] The terminal again decrypts the data packets received from the server.

[0530] The decoded revised naming proposal is displayed again in the user interface.

[0531] The user confirms the revised naming proposal.

[0532] Step 13: Finalize the naming idea

[0533] The user finally selects the naming proposal they like and presses the confirm button.

[0534] The terminal assembles the selected final naming proposal into a data packet and transmits it to the server.

[0535] The server saves the final naming proposal in the database as final information, completing the project.

[0536] Example 1

[0537] 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."

[0538] Conventional naming proposal generation systems have limitations in generating original names that perfectly match user requirements. Furthermore, the process of regenerating names based on user feedback is ineffective, resulting in a large amount of manual revisions. Such systems make it difficult to increase user satisfaction, and efficient generation of naming proposals is required.

[0539] 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.

[0540] In this invention, the server includes means for converting received data into JSON format, means for supplying the data to a generative AI model to generate naming suggestions, and means for analyzing feedback and resupplying the data to the generative AI model to generate revised naming suggestions, thereby enabling user feedback to be effectively reflected and the generated naming suggestions to be revised quickly and reliably.

[0541] A "user" is a person or organization that uses the system to generate and provide feedback on naming suggestions.

[0542] "Requirements" are data such as concept information, keywords, and related images that are necessary for the user to generate naming suggestions.

[0543] "Device" refers to an electronic device used by a user to input requirements and review and provide feedback on naming proposals, including smartphones, tablets, and PCs.

[0544] A "data packet" is a format in which data to be transmitted is collected and sent securely by encryption.

[0545] "Server" is a computer system for receiving data, feeding the data to the generative AI model, generating naming suggestions, analyzing feedback, and generating revised naming suggestions.

[0546] A "generative AI model" is an artificial intelligence model that utilizes machine learning and natural language processing techniques and is used to generate original naming ideas based on user requirements.

[0547] "Feedback" refers to evaluations and correction requests that users input for generated naming proposals.

[0548] "JSON format" is a standard data format used by servers to exchange data, and is an abbreviation for JavaScript Object Notation (JSON).

[0549] "AES-256 encryption" means an algorithm used to encrypt data at a high level and is a 256-bit key length version of the Advanced Encryption Standard (AES).

[0550] This invention relates to a system that generates original and attractive naming suggestions using a generative AI model based on user input requirements. This system consists of a terminal where the user inputs the requirements, a terminal that sends the data to a server, generates naming suggestions using a generative AI model, and returns the generated naming suggestions as feedback to the user, and a server that revises and generates new naming suggestions based on the feedback.

[0551] To implement this system, the following hardware and software are required:

[0552] Hardware used

[0553] 1. User device: A smartphone, tablet, or PC that users use to enter requirements and review and provide feedback on naming ideas.

[0554] 2. Server: A cloud-based server with a powerful CPU, GPU, sufficient memory, and storage capacity, used to receive data, feed it to the generative AI model, generate naming suggestions, and generate revised naming suggestions.

[0555] Software used

[0556] 1. Terminal software: "Creative Name Genie" application, which provides an interface for users to input requirements and review and provide feedback on naming suggestions.

[0557] 2. Server software: Data management software, generative AI models (e.g., GPT-4), data conversion, encryption, data transmission, naming proposal generation, and feedback analysis.

[0558] System Overview

[0559] User operations

[0560] Users launch the Creative Name Genie application on their device and create a new project. They then enter the concept information, keywords, and related images needed to come up with a new product name. For example, if a user wants to come up with a name for a new organic cosmetics brand, they can enter keywords such as "natural," "gentle on the skin," and "beauty" and upload an image.

[0561] Server Processing

[0562] The device generates a data packet containing the user's input requirements, keywords, and images, encrypts it using AES-256, and sends it to the server. The server then converts the received data into JSON format and supplies it to a generative AI model (such as GPT-4) to generate creative naming suggestions. The generative AI model uses a large dataset to suggest the best naming suggestions for the given requirements. For example, based on the keywords "natural," "gentle on the skin," and "beauty," naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals" are generated.

[0563] Naming proposal feedback and regeneration

[0564] The generated naming suggestions are then encrypted again using AES-256 and sent to the device. The user then uses the device to review the displayed naming suggestions and enter their evaluation and correction requests. For example, they can enter requests such as "Make it a little simpler" or "Add specific keywords." The device then encrypts this feedback again using AES-256 and sends it to the server. The server analyzes the user feedback and supplies it to the generative AI model as a new dataset to generate further optimized naming suggestions. For example, a new suggestion such as "PureGlow Organics" is generated.

[0565] Prompt Sentence Examples

[0566] Examples of prompts for generative AI models include:

[0567] "Think of a name for a new organic cosmetics brand. The keywords are 'natural,' 'gentle on the skin,' and 'beauty.'"

[0568] This system can generate efficient and high-quality naming suggestions based on user requirements and adjust the optimal naming suggestions based on user feedback.

[0569] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0570] Step 1:

[0571] A user launches the "Creative Name Genie" application on their device. They then create a new project and input concept information and keywords for the name. They can also upload related images. Keywords and images such as "natural," "gentle on the skin," and "beauty" are used as input. The output is a data packet containing these requirements, keywords, and images.

[0572] Step 2:

[0573] The terminal generates a data packet based on the requirements, keywords, and images entered by the user, encrypts it using AES-256, and sends it to the server. The data entered by the user is used as input, and the output is an encrypted data packet. Specifically, the terminal starts sending data by clicking the "Send" button.

[0574] Step 3:

[0575] The server receives the incoming data packets and decrypts them using AES-256 encryption to extract the data. The encrypted data packets are used as input, and the output is data containing decrypted requirements, keywords, and images. The server converts this data into JSON format, making it ready for the generative AI model to process.

[0576] Step 4:

[0577] The server supplies the converted data to the generative AI model to generate naming suggestions. The inputs are requirements, keywords, and images converted into JSON format. The output is the naming suggestions generated by the generative AI model. Specifically, the generative AI model generates original naming suggestions based on a large dataset.

[0578] Step 5:

[0579] The server re-encrypts the naming proposal received from the generative AI model using AES-256 and sends it to the terminal. The naming proposal from the generative AI model is used as input, and the output is a data packet of the encrypted naming proposal. The server sends this data packet to the terminal.

[0580] Step 6:

[0581] The terminal decrypts the received data packet and displays the naming proposal on the user interface. The encrypted naming proposal data packet is used as input, and the output is the decrypted naming proposal. The user checks the displayed naming proposal and is ready to enter feedback.

[0582] Step 7:

[0583] The user inputs their evaluation and correction requests for the displayed naming proposals. The input is the user's feedback, and the output is a data packet containing the evaluation and correction requests. Specifically, the user clicks the "Send Feedback" button.

[0584] Step 8:

[0585] The device encrypts the feedback with AES-256 and sends it to the server. The input is the user's feedback, and the output is the encrypted feedback data packet.

[0586] Step 9:

[0587] The server receives the feedback and decrypts it using AES-256 encryption to extract the data. The encrypted feedback is used as input, and the output is the decrypted feedback data. The server analyzes this feedback and feeds it into a generative AI model as a new dataset.

[0588] Step 10:

[0589] The server provides the analyzed feedback to the generative AI model to generate revised naming suggestions. The analyzed feedback is used as input, and the revised naming suggestions are the output. Specifically, the generative AI model reflects the feedback and generates new naming suggestions.

[0590] Step 11:

[0591] The server re-encrypts the revised naming proposal received from the generative AI model using AES-256 and sends it to the terminal. The revised naming proposal is used as input, and the output is a data packet of the encrypted revised naming proposal.

[0592] Step 12:

[0593] The terminal decrypts the received data packet and displays the revised naming proposal on the user interface. The encrypted revised naming proposal data packet is used as input, and the output is the decrypted revised naming proposal. The user reviews the revised naming proposal, and this process is repeated until the best naming proposal is determined.

[0594] (Application example 1)

[0595] 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."

[0596] A system that can effectively generate new naming suggestions is needed for autonomous vehicles. In particular, it is necessary for the user to intuitively input information via the vehicle's dashboard or voice recognition system, review the generated naming suggestions, and generate new naming suggestions based on the feedback efficiently. However, conventional methods have the problem that it is difficult for users to easily generate naming suggestions.

[0597] 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.

[0598] In this invention, the server includes: means for a user to input requirements via a voice recognition system or a touch screen; means for a vehicle terminal to encrypt and transmit the input data packet to the server; means for the server to supply data to a generative AI model and generate naming suggestions; means for the server to encrypt and transmit the generated naming suggestions to the vehicle terminal; means for the vehicle terminal to display the naming suggestions to the user; means for the user to input feedback via a voice recognition system or a touch screen; means for the vehicle terminal to encrypt and transmit the feedback to the server; means for the server to supply data together with the feedback to the generative AI model again and generate revised naming suggestions; and means for the server to encrypt and transmit the revised naming suggestions to the vehicle terminal. This makes it possible to effectively and efficiently generate naming suggestions in an environment where the user can input names intuitively, and to obtain optimal naming suggestions through a revision process.

[0599] "Means for users to input requirements via a voice recognition system or touch screen" refers to a device or function that allows a vehicle user to input requirements related to naming of the vehicle through voice commands or by operating a touch screen on the dashboard.

[0600] "Means for encrypting and transmitting data packets input by a vehicle terminal to a server" refers to a computer system within a vehicle that forms information input by a user into a data packet, encrypts it, and securely transmits it to a remote server.

[0601] "Means by which the server supplies data to the generative AI model and generates naming suggestions" refers to the function or process by which the server provides data to the generative AI model and the AI ​​automatically generates naming suggestions based on that data.

[0602] "Means for the server to encrypt and transmit the generated naming proposals to the vehicle's terminal" refers to the process of assembling the generated naming proposals into data packets, encrypting them, and securely transmitting them to the terminal in the vehicle.

[0603] The "means for the vehicle terminal to display the naming suggestions to the user" refers to a device or function that displays the generated naming suggestions on the dashboard or touch screen of the vehicle.

[0604] "Means for users to input feedback via a voice recognition system or touch screen" refers to a device or function that allows users to input their opinions or requests for corrections to the generated naming proposals via voice commands or a touch screen.

[0605] The "means for the vehicle terminal to encrypt the feedback and transmit it to the server" refers to an in-vehicle computer system that assembles the user's feedback into data packets, encrypts them, and transmits them securely to the server.

[0606] "Means for the server to again provide data to the generative AI model together with feedback to generate revised naming proposals" refers to the process in which the server provides data to the generative AI model based on user feedback and generates revised naming proposals.

[0607] The "means for the server to encrypt and transmit the revised naming proposal to the vehicle's terminal" refers to the process of assembling the generated revised naming proposal into a data packet, encrypting it, and securely transmitting it to the terminal in the vehicle.

[0608] The present invention relates to a system that allows users to input requirements via a voice recognition system or touch screen, and generates original and attractive naming ideas using a generative AI model. The basic components of this system include a vehicle terminal, a cloud server, a generative AI model, and an interface for incorporating user feedback.

[0609] First, the user uses the touchscreen or voice recognition system on the vehicle's dashboard to input the concept information and keywords needed for the naming proposal. For example, they can enter keywords such as "innovation," "future," or "eco," and upload related images. This data is then formed into a data packet by the vehicle's terminal and sent to the cloud server using TLS / SSL encryption.

[0610] The cloud server then analyzes the received data and provides it to a generative AI model (such as OpenAI GPT-4). The generative AI model uses a large dataset to generate appropriate naming suggestions based on the input requirements. For example, using the keywords "innovation," "future," and "eco," it can generate naming suggestions such as "EcoFuture Drive" and "Green Innovate Rover."

[0611] The generated naming suggestions are then packaged again into a data packet, encrypted, and sent to the vehicle's terminal, which decodes the suggestions and displays them on the dashboard or touchscreen. The user can review the displayed naming suggestions and enter feedback, such as requests to make them simpler or to add specific keywords.

[0612] The user's feedback is again generated as a data packet from the vehicle's terminal, encrypted, and sent to the server. The cloud server then inputs the feedback into the generative AI model again, generating revised naming suggestions. This process is repeated until a naming suggestion that satisfies the user is generated.

[0613] For example, if the user enters the following prompt:

[0614] Generate naming ideas for your vehicle.

[0615] Keywords: Innovation, Future, Eco

[0616] Concept: Eco-friendly autonomous driving technology

[0617] Related images: (Ecosystem image URL)

[0618] Based on this prompt, the system generates naming suggestions such as "EcoFuture Drive" or "Green Innovate Rover." Further revisions are suggested based on the user's feedback, allowing the user to find the optimal name.

[0619] The hardware requires an on-board computer system, a voice recognition microphone, and a touchscreen, while the software requires a generative AI model (OpenAI GPT-4) hosted on a cloud server, a data encryption and data packet generation module, and an in-vehicle user interface application.

[0620] This allows users to input information intuitively, efficiently generate naming suggestions based on the generative AI model, and obtain the optimal naming suggestions through a revision process.

[0621] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0622] Step 1:

[0623] The device accepts user input. Specifically, the user uses a voice recognition system or touchscreen to input keywords, concepts, and related images related to the proposed name. The input data is then formed into a data packet within the device. Keywords such as "innovation," "future," and "eco" and related images are inserted as input. This data packet is then ready to be fed to the generative AI model.

[0624] Step 2:

[0625] The device encrypts the generated data packet. TLS / SSL encryption is used to enhance data security. The device then sends this encrypted data packet to the cloud server. If the data transmission is successful, the device waits for a response.

[0626] Step 3:

[0627] The server receives the data sent from the device and decrypts the data packets. During this process, the server analyzes the received data and converts it into a format that can be fed to the generative AI model. The converted data becomes, for example, a prompt sentence: "Generate a name proposal for a vehicle. Keywords: innovation, future, eco-concept: environmentally friendly autonomous driving technology." This prompt sentence is passed to the generative AI model.

[0628] Step 4:

[0629] The server inputs a prompt to the generative AI model, which then generates naming suggestions. The generative AI model then creates naming suggestions based on the prompt and returns the results to the server. Specifically, naming suggestions such as "EcoFuture Drive" and "Green Innovate Rover" are generated.

[0630] Step 5:

[0631] The server assembles the generated naming proposals into a data packet and encrypts it again. The encrypted data packet is sent to the terminal. When the server completes the transmission, it waits for a response.

[0632] Step 6:

[0633] The device receives and decrypts the data packets sent by the server, converts them into a format that is displayed in the user interface, and displays them as naming suggestions on the dashboard or touchscreen, where the user can review them and provide feedback.

[0634] Step 7:

[0635] The user provides feedback on the proposed name, such as "Make it simpler" or "Add specific keywords" through a voice recognition system or touch screen. This feedback is then sent to the device as a data packet.

[0636] Step 8:

[0637] The device encrypts the feedback data packet. TLS / SSL encryption is also used to ensure security. The device then sends the encrypted feedback data packet to the cloud server. Once the transmission is complete, the device waits for a response.

[0638] Step 9:

[0639] The server receives the feedback data and decrypts it. It then analyzes the received feedback and converts it into a format that can be fed to the generative AI model. Based on the feedback, it inputs a prompt sentence into the generative AI model again to generate new naming suggestions.

[0640] Step 10:

[0641] The server generates revised naming proposals based on the generative AI model and assembles them into data packets, which are then re-encrypted and sent to the vehicle's terminal. This process is repeated until a revised naming proposal is sent to the terminal.

[0642] This allows the user to efficiently generate naming suggestions in an intuitive input environment, and to obtain the optimal naming suggestion through a revision process.

[0643] 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.

[0644] This invention relates to a system that generates original and attractive naming suggestions using a generative AI and an emotion engine after a user inputs requirements. The basic configuration of this system consists of a terminal where the user inputs requirements, a means for sending the data to a server, generating naming suggestions using generative AI, presenting the generated naming suggestions to the user and collecting feedback, and a server that modifies and generates naming suggestions based on the feedback and emotion data.

[0645] Furthermore, in the present invention, by incorporating an emotion engine that recognizes the user's emotions, it is possible to provide naming suggestions that further increase user satisfaction.

[0646] The program processing using this system and emotion engine will be specifically explained below.

[0647] 1. The user enters the requirements

[0648] The user launches the Creative Name Genie application on their device, creates a new project, selects a naming category (e.g., brand name, product name, pet name), and enters the necessary concept information, keywords, and related images.

[0649] Example: If a user wants to come up with a name for a new organic cosmetics brand, they can enter keywords like "natural," "gentle on the skin," and "beauty" and upload an image.

[0650] 2. The device sends the data to the server

[0651] The device generates a data packet containing the input requirements, keywords, and images, encrypts it, and sends it to the server, ensuring data security.

[0652] The server converts the received data into the required format and prepares it as data that can be processed by the generative AI and emotion engine.

[0653] 3. The emotion engine recognizes the user's emotions

[0654] The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received from the device in real time to recognize the user's emotional state (e.g., joy, surprise, stress, etc.).

[0655] Example: If the user smiles while typing, the emotion engine detects the user's positive emotion.

[0656] 4. The server supplies the data to the AI ​​to generate naming suggestions.

[0657] The server provides the input data and emotional data to the AI ​​generator, instructing it to generate naming suggestions. The AI ​​then generates original naming suggestions based on the information provided.

[0658] Example: Generative AI generates naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals" by taking into account keywords such as "natural," "skin-friendly," and "beauty" as well as positive emotional data from users.

[0659] 5. The server sends the generated naming proposal to the device.

[0660] The server collects the naming suggestions received from the AI ​​generator into a data packet, encrypts it, and sends it to the device, ensuring that the generated naming suggestions are delivered securely to the user's device.

[0661] The device decodes the received naming suggestions and displays them in a user interface, where the user can review them and provide feedback.

[0662] 6. User provides feedback

[0663] The user can then rate or request revisions to the displayed naming ideas. Furthermore, the emotion engine continuously analyzes the user's facial expressions and voice during this process.

[0664] For example, you can input requests such as "Make it a little simpler" or "Add specific keywords," and the emotion engine will collect emotional data at that time.

[0665] 7. The device sends feedback to the server

[0666] The terminal assembles the input feedback and emotion data into a data packet, encrypts it, and transmits it to the server.

[0667] The server analyzes the received data and provides feedback and emotion data to the generative AI.

[0668] 8. The server re-supplies the data to the AI ​​based on the emotion data and generates revised naming suggestions.

[0669] The server issues a command to the AI ​​generator based on the feedback and emotional data to generate revised naming suggestions. The AI ​​generator takes the new feedback and emotional data into account and generates optimized naming suggestions.

[0670] For example, a new suggestion could be generated such as "PureGlow Organics."

[0671] 9. The server sends the revised naming proposal to the device and presents it to the user.

[0672] The server receives a revised naming proposal from the AI ​​again and sends it to the device, which displays it again on the user interface for final confirmation by the user.

[0673] This process is repeated until the user selects and finalizes the optimal naming suggestion.This system generates efficient and high-quality naming suggestions that reflect the user's emotional state, thereby improving user satisfaction.

[0674] The processing flow will be explained below.

[0675] Step 1:

[0676] A user launches the "Creative Name Genie" application on their device. They create a new project and select a naming category (e.g., brand name, product name, pet name). Next, the user enters naming requirements and concept information (e.g., keywords, target market, characteristics, etc.) into text fields and uploads related images, if available.

[0677] Step 2:

[0678] The device combines the input requirements, concept information, and images into a single data packet, which the device encrypts and sends to the server using a secure protocol (e.g., HTTPS).

[0679] Step 3:

[0680] The server decrypts the data packets received from the device and stores them in a database. The server then converts the stored data into the required format and prepares it as data that can be processed by the generative AI and emotion engine.

[0681] Step 4:

[0682] The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received from the device in real time to recognize the user's emotional state (e.g., joy, surprise, stress, etc.). For example, if the user smiles while typing, the emotion engine detects the user's positive emotion.

[0683] Step 5:

[0684] The server provides the input data along with emotional data to the AI ​​and instructs it to generate naming suggestions. The AI ​​generates original naming suggestions based on the information provided. For example, the AI ​​might consider keywords such as "natural," "skin-friendly," and "beauty" along with the user's positive emotional data to generate naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals."

[0685] Step 6:

[0686] The server collects the naming suggestions received from the AI ​​generator into a data packet, encrypts it, and sends it to the device. The device decrypts the received naming suggestions and displays them on the user interface. The user can review these suggestions and provide feedback.

[0687] Step 7:

[0688] The user can then rate the displayed naming proposals and input any corrections they wish to make. Furthermore, the emotion engine continuously analyzes the user's facial expressions and voice during this process. For example, if a request is made such as "I'd like it to be a little simpler" or "I'd like a specific keyword added," the emotion engine will also collect emotional data at that time.

[0689] Step 8:

[0690] The device assembles the input feedback and emotion data into a data packet, encrypts it, and sends it to the server, which analyzes the received data and provides the feedback and emotion data to the generative AI.

[0691] Step 9:

[0692] The server then issues a new command to the AI ​​based on the feedback and emotional data, instructing it to generate a revised naming proposal. The AI ​​then takes the new feedback and emotional data into account and generates an optimized naming proposal, such as "PureGlow Organics."

[0693] Step 10:

[0694] The server receives a revised naming proposal from the AI ​​again and sends it to the device. The device displays the revised naming proposal again on the user interface. The user reviews the revised naming proposal and again judges whether it is good or bad.

[0695] Step 11:

[0696] The user finally selects their favorite naming plan and presses the confirm button. The device assembles the final naming plan into a data packet and sends it to the server. The server saves the final naming plan in the database as final information, and the project is completed.

[0697] Example 2

[0698] 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."

[0699] Conventional naming generation systems generate naming suggestions based solely on user requirements and feedback, which means they are unable to fully reflect the user's emotions and intentions. This can lead to a decline in the quality of the generated naming suggestions and user satisfaction. Furthermore, even in the process of incorporating user feedback to generate revised naming suggestions, emotional data is not taken into account, making it difficult to generate suggestions that reflect the user's true intentions.

[0700] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0701] In this invention, the server includes: means for a user to input requirements; means for a terminal to transmit the input data to the server; means for the server to supply data to a generative AI model and generate naming suggestions; means for the server to transmit the generated naming suggestions to the terminal; means for the terminal to display the naming suggestions to the user; means for a user to input feedback; means for the terminal to transmit the feedback to the server; means for the server to resupply data to the generative AI model together with the feedback and generate revised naming suggestions; means for the server to transmit the revised naming suggestions to the terminal; means for the terminal to use an emotion engine that recognizes the user's emotions and transmit user emotion data to the server; and means for the server to supply data to the generative AI model based on the emotion data and adjust the generated naming suggestions. This enables the generation of high-quality naming suggestions that reflect the user's emotional state and true intentions.

[0702] The "means for inputting requirements" is a system component that allows a user to input their own wishes and necessary conditions.

[0703] The "means for transmitting data to a server" is a system component for transmitting information input by a user from a terminal to a server.

[0704] The "means for supplying data to the generative AI model" is a system component that allows the server to pass the user's input data and emotion data to the generative AI model and generate naming suggestions.

[0705] A "means for generating naming suggestions" is a system component for using a generative AI model to create new naming suggestions based on supplied data.

[0706] The "means for transmitting the generated naming proposal" is a system component for transmitting the generated naming proposal from the server to the terminal.

[0707] The "means for displaying naming suggestions to the user" is a system component for displaying the naming suggestions received by the terminal on the user interface.

[0708] The "means for inputting feedback" is a system component that allows the user to input evaluations and requests for corrections to the displayed naming proposals.

[0709] The "means for transmitting feedback to the server" is a system component for transmitting feedback data including the user's evaluation and correction requests from the terminal to the server.

[0710] The "means for using the emotion engine and transmitting user emotion data" is a system component that enables the terminal to recognize the user's emotional state and transmit the data to the server.

[0711] The "means for supplying data to the generative AI model based on emotional data and adjusting the generated naming suggestions" is a system component that enables the server to supply data to the generative AI model taking emotional data into consideration and optimize the generated naming suggestions.

[0712] The "means for generating revised naming suggestions" is a system component for generating revised naming suggestions using a generative AI model based on the input feedback and sentiment data.

[0713] This invention is a system that generates original and attractive naming suggestions using a generative AI model and an emotion engine after a user inputs requirements.The basic configuration of this system consists of a terminal where the user inputs requirements, a means for sending the data to a server, generating naming suggestions using a generative AI model, presenting the generated naming suggestions to the user and collecting feedback, and a server that modifies and generates naming suggestions based on the feedback and emotion data.

[0714] Furthermore, by incorporating an emotion engine that recognizes the user's emotions, the present invention can provide naming suggestions that further increase user satisfaction. Below, we will specifically explain the program processing using this system and emotion engine.

[0715] A user launches an application such as "Creative Name Genie" on their device and creates a new project. Here, the user selects a naming category (e.g., brand name, product name, pet name) and enters the necessary concept information, keywords, and related images. For example, when thinking of a name for a new organic cosmetics brand, a user enters keywords such as "natural," "gentle on the skin," and "beauty," and uploads an image of the skin care products.

[0716] The terminal assembles these input data into data packets, encrypts them, and sends them to the server. The security of this data is maintained using TLS (Transport Layer Security).

[0717] The server converts the received data into the required format and prepares it as data that can be processed by the generative AI model and emotion engine. The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received in real time from the device to recognize the user's emotional state (e.g., joy, surprise, stress, etc.). For example, if the user smiles while typing, the emotion engine detects that positive emotion.

[0718] Next, the server supplies the input data and emotional data to a generative AI model and instructs it to generate naming suggestions. As an example of a generative AI model, GPT-3 can be used. The generative AI model generates original naming suggestions based on the information provided. As a specific example, by combining the keywords "natural," "skin-friendly," and "beauty" with the user's positive emotional data, naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals" are generated.

[0719] The generated naming suggestions are packaged in encrypted data packets from the server and sent to the device. The device decrypts the packets and displays them on the user interface. The user can then review the displayed naming suggestions and enter their evaluations or requests for revisions. The emotion engine continuously analyzes the user's facial expressions and voice while they are entering feedback, and generates emotion data.

[0720] The feedback and emotion data entered by the user is sent from the device to a server, which analyzes the data and feeds it to a generative AI model. The generative AI model generates revised naming suggestions based on the new feedback and emotion data. For example, a new suggestion such as "PureGlow Organics" is generated.

[0721] The regenerated naming suggestions are sent from the server to the device and redisplayed on the user interface, allowing the user to repeat this process until the best naming suggestion is selected and finalized.

[0722] This system generates efficient and high-quality naming suggestions that reflect the user's emotional state, thereby improving user satisfaction.

[0723] "Think of a name for a new organic cosmetics brand. Keywords: natural, gentle, beauty."

[0724] Examples include:

[0725] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0726] Program processing steps

[0727] Step 1:

[0728] User enters requirements

[0729] What it does: A user launches the Creative Name Genie application on their device, creates a new project, selects a naming category (e.g., brand name, product name, pet name), and enters the required concept information, keywords, and related images.

[0730] Input: Category, concept information, keywords, related images

[0731] Output: Generate input data (categories, concept information, keywords, images)

[0732] Step 2:

[0733] The device sends the entered data to the server.

[0734] Specific operation: The device assembles the requirements, keywords, and images entered by the user into a data packet, encrypts it, and sends it to the server.

[0735] Input: User input data (categories, concept information, keywords, images)

[0736] Data processing: Packetization and encryption of input data (using TLS)

[0737] Output: Encrypted data packet

[0738] Step 3:

[0739] The server prepares the input data

[0740] What it does: The server decrypts the encrypted data it receives and converts it into a format that can be processed by the generative AI model and emotion engine.

[0741] Input: Encrypted data packet

[0742] Data processing: Data interpretation and format conversion

[0743] Output: Data converted into a processable format

[0744] Step 4:

[0745] Emotion engine recognizes user emotions

[0746] Specific operation: The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received in real time from the device to recognize the user's emotional state.

[0747] Input: Real-time user data (facial expressions, voice, typing speed, gestures)

[0748] Data Computation: Emotional state analysis and emotional data generation

[0749] Output: Emotion data

[0750] Step 5:

[0751] The server supplies data to the generative AI model to generate naming suggestions.

[0752] Specific operation: The server provides input data and emotion data to a generative AI model (e.g., GPT-3) and instructs it to generate naming suggestions. The generative AI model generates naming suggestions based on the provided information.

[0753] Input: Input data, emotion data converted into a processable format

[0754] Data Computation: Data computation using generative AI models

[0755] Output: Generated naming ideas

[0756] Step 6:

[0757] The server sends the generated naming proposal to the device.

[0758] How it works: The server compiles the naming ideas obtained from the generative AI model into a data packet, encrypts it, and sends it to the device, which then decrypts it and displays it on the user interface.

[0759] Input: Generated naming ideas

[0760] Data processing: Packetization and encryption of naming proposals, decryption after receiving

[0761] Output: Naming proposal displayed in the user interface

[0762] Step 7:

[0763] User enters feedback

[0764] Specific operation: The user inputs their evaluation and correction requests for the displayed naming proposals. The emotion engine analyzes their facial expressions and voices and generates emotion data.

[0765] Input: Evaluation of naming proposals, requests for revisions, user facial expressions and voice

[0766] Data computation: Emotional state analysis, emotional data generation

[0767] Output: Feedback data, emotion data

[0768] Step 8:

[0769] The device sends feedback to the server

[0770] Specific operation: The device assembles the input feedback and emotion data into a data packet, encrypts it, and sends it to the server.

[0771] Input: Feedback data, emotion data

[0772] Data processing: Packetization and encryption of feedback and emotion data

[0773] Output: Encrypted data packet

[0774] Step 9:

[0775] The server re-feeds the data to the generative AI model to generate revised naming proposals.

[0776] Specific operation: The server provides feedback and emotion data to the generative AI model and instructs it to generate revised naming suggestions. The generative AI model generates optimized naming suggestions based on the new feedback and emotion data.

[0777] Input: Feedback and emotion data

[0778] Data computation: Recomputing data using generative AI models

[0779] Output: Revised naming proposal

[0780] Step 10:

[0781] The server sends the revised naming proposal to the device and presents it to the user.

[0782] How it works: The server assembles revised naming suggestions from the generative AI model into a data packet, encrypts it, and sends it to the device. The device decrypts it and displays it again on the user interface. This process can be repeated until the user selects and finalizes the best naming suggestion.

[0783] Input: Revised naming proposal

[0784] Data processing: Packetization and encryption of the revised naming proposal, and decryption after receiving it

[0785] Output: Revised naming proposal displayed in the user interface

[0786] (Application example 2)

[0787] 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."

[0788] Conventional naming suggestion generation systems have difficulty generating naming suggestions that take user emotions into account, and have not been able to sufficiently increase user satisfaction. In addition, the process of revising generated naming suggestions based on feedback is cumbersome, making them difficult to use for users.

[0789] 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.

[0790] In this invention, the server includes a means for analyzing the user's emotions using an emotion engine and supplying the analysis results to the generation AI to generate revised naming suggestions, and a revision process for regenerating based on the user's feedback and emotion data. This makes it possible to generate original and attractive naming suggestions that take the user's emotions into consideration, thereby improving user satisfaction.

[0791] A "user" is someone who uses the system to generate naming suggestions and provide feedback.

[0792] "Requirements" are conditions or information that a user inputs into the system, such as keywords or categories.

[0793] A "terminal" is a device operated by a user, which inputs requirements, transmits data, displays naming suggestions, etc.

[0794] A "server" is a device that receives data sent from a terminal, processes and generates the data using generative AI or an emotion engine, and returns the results to the terminal.

[0795] "Generative AI" refers to artificial intelligence that generates naming ideas based on given data.

[0796] "Naming suggestions" are names or candidate names generated by the generation AI.

[0797] An "emotion engine" is a technology that analyzes user emotions and reflects the results in generating naming suggestions.

[0798] "Feedback" refers to the user's evaluation of the generated naming proposals and requests for corrections.

[0799] A "data packet" is a unit of digital information that contains user-entered requirements and feedback.

[0800] "Analysis results" refers to the data obtained after the emotion engine analyzes the user's emotions.

[0801] "Revised naming proposals" are naming proposals regenerated by the generation AI, taking into account user feedback and emotional data.

[0802] A "process" is a series of tasks that indicate the overall procedure or processing flow.

[0803] The present invention relates to a system that allows a user to input requirements and generates original and attractive naming ideas using a generative AI and an emotion engine. Specific embodiments are described below.

[0804] 1. User operations

[0805] Users launch a dedicated application on their device and create a new project. At that time, they select a naming category (e.g., brand name, product name, etc.) and enter the necessary concept information, keywords, and related images. For example, if they want to think of a name for a new organic cosmetics brand, they can enter keywords such as "natural," "gentle on the skin," and "beauty" and upload an image.

[0806] 2. Data transmission

[0807] The device generates data packets from the input requirements, keywords, images, etc., and encrypts the data before sending it to the server, ensuring data security.

[0808] 3. Emotion Data Analysis

[0809] When data arrives at the server, the emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. in real time to recognize the user's emotional state (happiness, surprise, stress, etc.). For example, if the user smiles while typing, the emotion engine will detect that positive emotion.

[0810] 4. Naming Idea Generation

[0811] The server provides the received input data and emotional data to the generation AI, instructing it to generate naming suggestions. The generation AI generates original naming suggestions based on the information provided. For example, by taking into account the keywords "natural," "skin-friendly," and "beauty" and the user's positive emotional data, it generates naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals."

[0812] 5. Submit a naming proposal

[0813] The server packages the generated naming suggestions into a data packet, encrypts it, and sends it to the device. The device then decrypts the received naming suggestions and displays them on a user interface. The user can review these suggestions and provide feedback.

[0814] 6. Processing Feedback

[0815] Users can then evaluate the generated naming proposals and input their feedback and requests for revisions, while their facial expressions and voices are continuously analyzed by the emotion engine. For example, users can input requests such as "Make it a little simpler" or "Add specific keywords." This feedback and emotion data is collected and sent back to the server from the device.

[0816] 7. Generate revised naming ideas

[0817] The server analyzes the received feedback and sentiment data and feeds it into a generative AI to generate revised naming suggestions, such as "PureGlow Organics."

[0818] 8. Final Check

[0819] The terminal receives the revised naming proposals sent again from the server, and the user finally checks them and selects and confirms the most suitable naming proposal.

[0820] Hardware / Software used

[0821] Servers and Terminals

[0822] Emotion Engine and Generative AI Models

[0823] Dedicated application

[0824] Prompt Sentence Examples

[0825] "Generate a name for a new organic cosmetics brand. Keywords are 'natural,' 'gentle on the skin,' and 'beauty.'"

[0826] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0827] Step 1:

[0828] User enters requirements

[0829] Input: The user launches a dedicated application on the device and enters the naming category, necessary concept information, keywords, and related images.

[0830] Specific operation: The user selects the category "Brand Name" and enters keywords such as "natural," "gentle on the skin," and "beauty" along with an image.

[0831] Output: Data entered into the terminal is generated.

[0832] Step 2:

[0833] The device sends the entered data to the server.

[0834] Input: The data generated in step 1.

[0835] Specific operation: The device creates a data packet, encrypts it, and sends it to the server.

[0836] Output: The data packet arrives at the server.

[0837] Step 3:

[0838] The server uses an emotion engine to analyze the user's emotions.

[0839] Input: Data packets arriving at the server and real-time data such as the user's facial expressions, voice, typing speed, and gestures.

[0840] Specific operation: The server's emotion engine analyzes this data and recognizes the user's emotional state (happiness, surprise, stress, etc.).

[0841] Output: Analysis results about the user's emotional state.

[0842] Step 4:

[0843] The server provides data to the AI ​​to generate naming suggestions.

[0844] Input: Input data and emotion data.

[0845] Specific operation: The server provides these data to the generation AI and instructs it to generate naming suggestions. The generation AI generates naming suggestions based on the provided information.

[0846] Output: Generated naming ideas (e.g. "PureSkin Botanicals" or "EcoGlam Naturals").

[0847] Step 5:

[0848] The server sends the generated naming proposal to the device.

[0849] Input: Generated naming ideas.

[0850] Specific operation: The server assembles the naming proposals into a data packet, encrypts it, and sends it to the terminal.

[0851] Output: Data packets arrive at the terminal.

[0852] Step 6:

[0853] The device displays naming suggestions to the user

[0854] Input: The received data packet.

[0855] Specific operation: The terminal decodes the data packet and displays naming suggestions on the user interface.

[0856] Output: The naming proposal is displayed on the terminal screen.

[0857] Step 7:

[0858] User enters feedback

[0859] Input: The displayed naming suggestion.

[0860] Specific operation: The user inputs their evaluation and requests for revisions to the naming proposals, and facial expressions and voice data are also analyzed by the emotion engine.

[0861] Output: Feedback and emotion data.

[0862] Step 8:

[0863] The device sends feedback to the server

[0864] Input: Feedback and emotion data.

[0865] Specific operation: The device assembles this data into a data packet, encrypts it, and sends it to the server.

[0866] Output: The data packet arrives at the server.

[0867] Step 9:

[0868] The server re-feeds the feedback and sentiment data and generates revised naming proposals.

[0869] Input: Feedback and emotion data.

[0870] Specific actions: The server analyzes this data and feeds it back to the generation AI, instructing it to generate revised naming proposals.

[0871] Output: A newly generated revised naming proposal (e.g., "PureGlow Organics").

[0872] Step 10:

[0873] The server sends the revised naming proposal to the device and presents it to the user.

[0874] Input: The newly generated revised naming proposal.

[0875] Specific operation: The server assembles the revised naming proposal into a data packet, encrypts it, and sends it to the device, which decrypts the data packet and displays it on the user interface.

[0876] Output: The revised naming proposal is displayed on the terminal screen.

[0877] 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.

[0878] 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.

[0879] 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.

[0880] [Third embodiment]

[0881] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0882] 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.

[0883] 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).

[0884] 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.

[0885] 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.

[0886] 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).

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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."

[0893] This invention relates to a system that allows users to input requirements and generates original and attractive naming suggestions using a generation AI. The basic configuration of this system consists of a terminal where the user inputs the requirements, a terminal that sends the data to a server, generates naming suggestions using a generation AI, and returns the generated naming suggestions as feedback to the user, and a server that revises and generates new naming suggestions based on the feedback.

[0894] The program processing of this system will be specifically explained below.

[0895] 1. The user enters the requirements

[0896] Users simply launch the Creative Name Genie application on their device, create a new project, and enter the concept information, keywords, and related images needed to come up with a new product name, for example.

[0897] Example: If a user wants to come up with a name for a new organic cosmetics brand, they can enter keywords like "natural," "gentle on the skin," and "beauty" and upload an image.

[0898] 2. The device sends the data to the server

[0899] The device generates a data packet containing the input requirements, keywords, and images, encrypts it, and sends it to the server, ensuring data security.

[0900] The server converts the received data into the required format and prepares it as data that can be processed by the generation AI.

[0901] 3. The server provides the data to the AI ​​to generate naming suggestions.

[0902] The server provides the supplied data as input to the AI ​​generator, which then generates naming suggestions based on that data. The AI ​​generator uses a large amount of data set it has learned to suggest original names that match the user's requirements.

[0903] Example: Generative AI uses the keywords "natural," "skin-friendly," and "beauty" to generate naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals."

[0904] 4. The server sends the generated naming proposal to the device.

[0905] The server collects the naming suggestions received from the AI ​​generator into a data packet, encrypts it, and sends it to the device, ensuring that the generated naming suggestions are delivered securely to the user's device.

[0906] The device decodes the received naming suggestions and displays them in a user interface, where the user can review them and provide feedback.

[0907] 5. User provides feedback

[0908] Users can view the displayed naming suggestions and then enter their evaluation and requests for revisions, such as "Make it a little simpler" or "Add specific keywords."

[0909] The terminal transmits this feedback back to the server.

[0910] 6. The server re-supplies the data to the AI ​​based on the feedback and generates revised naming proposals.

[0911] The server analyzes the feedback from the user and provides the feedback to the generation AI again, instructing it to generate revised naming proposals.

[0912] The AI ​​then takes new feedback into account and generates more optimized naming suggestions, such as "PureGlow Organics."

[0913] 7. The server sends the revised naming proposal to the device and displays it to the user.

[0914] The server again receives a revised naming proposal from the generation AI and sends it to the device, which again displays it on the user interface for final confirmation by the user.

[0915] This process is repeated until the user selects and finalizes the best naming suggestion. This system efficiently generates high-quality naming suggestions and allows for a process that can be tailored to the user's needs.

[0916] The processing flow will be explained below.

[0917] Step 1: User enters requirements

[0918] The user opens the device and launches the "Creative Name Genie" application.

[0919] A user creates a new project and selects a naming category (e.g., brand name, product name, pet name).

[0920] The user enters naming requirements and concept information (e.g., keywords, target market, characteristics, etc.) into text fields.

[0921] If the user has a related image, upload the image.

[0922] Step 2: The device sends the data to the server

[0923] The terminal assembles the input requirements, concept information, and images into a single data packet.

[0924] The device encrypts the collected data packets and sends them to the server using a secure protocol (e.g., HTTPS).

[0925] Step 3: The server receives the data and provides it to the generation AI.

[0926] The server decrypts the data packets received from the terminal and stores them in a database.

[0927] The server converts the stored data into the required format to match the input format of the generating AI.

[0928] The server supplies the converted data to the generation AI and sends a request to generate naming suggestions.

[0929] Step 4: Generative AI generates naming ideas

[0930] The generative AI analyzes the data provided and extracts relevant keywords and concepts.

[0931] The generative AI generates original and attractive naming ideas based on the large amount of naming data it has learned.

[0932] Evaluate the generated naming ideas and select the most appropriate candidate.

[0933] Step 5: The server sends the naming proposal to the device

[0934] The server compiles the naming suggestions received from the generation AI into a data packet.

[0935] The server encrypts the collected data packets and sends them to the terminal.

[0936] Step 6: The device displays naming suggestions to the user

[0937] The terminal decrypts the data packet received from the server.

[0938] The decrypted naming proposal is read and displayed in the user interface.

[0939] The user reviews the naming proposal and provides feedback.

[0940] Step 7: User Provides Feedback

[0941] The user enters requests and feedback (e.g., corrections, additional information) for the provided naming proposal.

[0942] The user finalizes the feedback and presses the submit button.

[0943] Step 8: The device sends feedback to the server

[0944] The terminal packages the input feedback into data packets.

[0945] The terminal encrypts the feedback data packet and sends it to the server.

[0946] Step 9: The server receives the feedback and re-feeds it to the generating AI.

[0947] The server decodes the feedback packet received from the terminal and adds it to the database.

[0948] The server converts the added feedback into a format suitable for the generated AI.

[0949] The server re-feeds the feedback to the generation AI and sends a request to generate revised naming suggestions.

[0950] Step 10: Generative AI generates revised naming ideas

[0951] The generative AI analyzes the provided feedback and implements any necessary modifications.

[0952] The generative AI generates revised naming suggestions.

[0953] Evaluate the revised naming proposals and select the most appropriate candidate.

[0954] Step 11: The server sends the revised naming proposal to the device.

[0955] The server compiles the revised naming proposals received from the generation AI into a data packet.

[0956] The server encrypts the collected data packets and sends them to the terminal.

[0957] Step 12: The device displays the revised naming proposal to the user.

[0958] The terminal again decrypts the data packets received from the server.

[0959] The decoded revised naming proposal is displayed again in the user interface.

[0960] The user confirms the revised naming proposal.

[0961] Step 13: Finalize the naming idea

[0962] The user finally selects the naming proposal they like and presses the confirm button.

[0963] The terminal assembles the selected final naming proposal into a data packet and transmits it to the server.

[0964] The server saves the final naming proposal in the database as final information, completing the project.

[0965] Example 1

[0966] 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."

[0967] Conventional naming proposal generation systems have limitations in generating original names that perfectly match user requirements. Furthermore, the process of regenerating names based on user feedback is ineffective, resulting in a large amount of manual revisions. Such systems make it difficult to increase user satisfaction, and efficient generation of naming proposals is required.

[0968] 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.

[0969] In this invention, the server includes means for converting received data into JSON format, means for supplying the data to a generative AI model to generate naming suggestions, and means for analyzing feedback and resupplying the data to the generative AI model to generate revised naming suggestions, thereby enabling user feedback to be effectively reflected and the generated naming suggestions to be revised quickly and reliably.

[0970] A "user" is a person or organization that uses the system to generate and provide feedback on naming suggestions.

[0971] "Requirements" are data such as concept information, keywords, and related images that are necessary for the user to generate naming suggestions.

[0972] "Device" refers to an electronic device used by a user to input requirements and review and provide feedback on naming proposals, including smartphones, tablets, and PCs.

[0973] A "data packet" is a format in which data to be transmitted is collected and sent securely by encryption.

[0974] "Server" is a computer system for receiving data, feeding the data to the generative AI model, generating naming suggestions, analyzing feedback, and generating revised naming suggestions.

[0975] A "generative AI model" is an artificial intelligence model that utilizes machine learning and natural language processing techniques and is used to generate original naming ideas based on user requirements.

[0976] "Feedback" refers to evaluations and correction requests that users input for generated naming proposals.

[0977] "JSON format" is a standard data format used by servers to exchange data, and is an abbreviation for JavaScript Object Notation (JSON).

[0978] "AES-256 encryption" means an algorithm used to encrypt data at a high level and is a 256-bit key length version of the Advanced Encryption Standard (AES).

[0979] This invention relates to a system that generates original and attractive naming suggestions using a generative AI model based on user input requirements. This system consists of a terminal where the user inputs the requirements, a terminal that sends the data to a server, generates naming suggestions using a generative AI model, and returns the generated naming suggestions as feedback to the user, and a server that revises and generates new naming suggestions based on the feedback.

[0980] To implement this system, the following hardware and software are required:

[0981] Hardware used

[0982] 1. User device: A smartphone, tablet, or PC that users use to enter requirements and review and provide feedback on naming ideas.

[0983] 2. Server: A cloud-based server with a powerful CPU, GPU, sufficient memory, and storage capacity, used to receive data, feed it to the generative AI model, generate naming suggestions, and generate revised naming suggestions.

[0984] Software used

[0985] 1. Terminal software: "Creative Name Genie" application, which provides an interface for users to input requirements and review and provide feedback on naming suggestions.

[0986] 2. Server software: Data management software, generative AI models (e.g., GPT-4), data conversion, encryption, data transmission, naming proposal generation, and feedback analysis.

[0987] System Overview

[0988] User operations

[0989] Users launch the Creative Name Genie application on their device and create a new project. They then enter the concept information, keywords, and related images needed to come up with a new product name. For example, if a user wants to come up with a name for a new organic cosmetics brand, they can enter keywords such as "natural," "gentle on the skin," and "beauty" and upload an image.

[0990] Server Processing

[0991] The device generates a data packet containing the user's input requirements, keywords, and images, encrypts it using AES-256, and sends it to the server. The server then converts the received data into JSON format and supplies it to a generative AI model (such as GPT-4) to generate creative naming suggestions. The generative AI model uses a large dataset to suggest the best naming suggestions for the given requirements. For example, based on the keywords "natural," "gentle on the skin," and "beauty," naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals" are generated.

[0992] Naming proposal feedback and regeneration

[0993] The generated naming suggestions are then encrypted again using AES-256 and sent to the device. The user then uses the device to review the displayed naming suggestions and enter their evaluation and correction requests. For example, they can enter requests such as "Make it a little simpler" or "Add specific keywords." The device then encrypts this feedback again using AES-256 and sends it to the server. The server analyzes the user feedback and supplies it to the generative AI model as a new dataset to generate further optimized naming suggestions. For example, a new suggestion such as "PureGlow Organics" is generated.

[0994] Prompt Sentence Examples

[0995] Examples of prompts for generative AI models include:

[0996] "Think of a name for a new organic cosmetics brand. The keywords are 'natural,' 'gentle on the skin,' and 'beauty.'"

[0997] This system can generate efficient and high-quality naming suggestions based on user requirements and adjust the optimal naming suggestions based on user feedback.

[0998] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0999] Step 1:

[1000] A user launches the "Creative Name Genie" application on their device. They then create a new project and input concept information and keywords for the name. They can also upload related images. Keywords and images such as "natural," "gentle on the skin," and "beauty" are used as input. The output is a data packet containing these requirements, keywords, and images.

[1001] Step 2:

[1002] The terminal generates a data packet based on the requirements, keywords, and images entered by the user, encrypts it using AES-256, and sends it to the server. The data entered by the user is used as input, and the output is an encrypted data packet. Specifically, the terminal starts sending data by clicking the "Send" button.

[1003] Step 3:

[1004] The server receives the incoming data packets and decrypts them using AES-256 encryption to extract the data. The encrypted data packets are used as input, and the output is data containing decrypted requirements, keywords, and images. The server converts this data into JSON format, making it ready for the generative AI model to process.

[1005] Step 4:

[1006] The server supplies the converted data to the generative AI model to generate naming suggestions. The inputs are requirements, keywords, and images converted into JSON format. The output is the naming suggestions generated by the generative AI model. Specifically, the generative AI model generates original naming suggestions based on a large dataset.

[1007] Step 5:

[1008] The server re-encrypts the naming proposal received from the generative AI model using AES-256 and sends it to the terminal. The naming proposal from the generative AI model is used as input, and the output is a data packet of the encrypted naming proposal. The server sends this data packet to the terminal.

[1009] Step 6:

[1010] The terminal decrypts the received data packet and displays the naming proposal on the user interface. The encrypted naming proposal data packet is used as input, and the output is the decrypted naming proposal. The user checks the displayed naming proposal and is ready to enter feedback.

[1011] Step 7:

[1012] The user inputs their evaluation and correction requests for the displayed naming proposals. The input is the user's feedback, and the output is a data packet containing the evaluation and correction requests. Specifically, the user clicks the "Send Feedback" button.

[1013] Step 8:

[1014] The device encrypts the feedback with AES-256 and sends it to the server. The input is the user's feedback, and the output is the encrypted feedback data packet.

[1015] Step 9:

[1016] The server receives the feedback and decrypts it using AES-256 encryption to extract the data. The encrypted feedback is used as input, and the output is the decrypted feedback data. The server analyzes this feedback and feeds it into a generative AI model as a new dataset.

[1017] Step 10:

[1018] The server provides the analyzed feedback to the generative AI model to generate revised naming suggestions. The analyzed feedback is used as input, and the revised naming suggestions are the output. Specifically, the generative AI model reflects the feedback and generates new naming suggestions.

[1019] Step 11:

[1020] The server re-encrypts the revised naming proposal received from the generative AI model using AES-256 and sends it to the terminal. The revised naming proposal is used as input, and the output is a data packet of the encrypted revised naming proposal.

[1021] Step 12:

[1022] The terminal decrypts the received data packet and displays the revised naming proposal on the user interface. The encrypted revised naming proposal data packet is used as input, and the output is the decrypted revised naming proposal. The user reviews the revised naming proposal, and this process is repeated until the best naming proposal is determined.

[1023] (Application example 1)

[1024] 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."

[1025] A system that can effectively generate new naming suggestions is needed for autonomous vehicles. In particular, it is necessary for the user to intuitively input information via the vehicle's dashboard or voice recognition system, review the generated naming suggestions, and generate new naming suggestions based on the feedback efficiently. However, conventional methods have the problem that it is difficult for users to easily generate naming suggestions.

[1026] 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.

[1027] In this invention, the server includes: means for a user to input requirements via a voice recognition system or a touch screen; means for a vehicle terminal to encrypt and transmit the input data packet to the server; means for the server to supply data to a generative AI model and generate naming suggestions; means for the server to encrypt and transmit the generated naming suggestions to the vehicle terminal; means for the vehicle terminal to display the naming suggestions to the user; means for the user to input feedback via a voice recognition system or a touch screen; means for the vehicle terminal to encrypt and transmit the feedback to the server; means for the server to supply data together with the feedback to the generative AI model again and generate revised naming suggestions; and means for the server to encrypt and transmit the revised naming suggestions to the vehicle terminal. This makes it possible to effectively and efficiently generate naming suggestions in an environment where the user can input names intuitively, and to obtain optimal naming suggestions through a revision process.

[1028] "Means for users to input requirements via a voice recognition system or touch screen" refers to a device or function that allows a vehicle user to input requirements related to naming of the vehicle through voice commands or by operating a touch screen on the dashboard.

[1029] "Means for encrypting and transmitting data packets input by a vehicle terminal to a server" refers to a computer system within a vehicle that forms information input by a user into a data packet, encrypts it, and securely transmits it to a remote server.

[1030] "Means by which the server supplies data to the generative AI model and generates naming suggestions" refers to the function or process by which the server provides data to the generative AI model and the AI ​​automatically generates naming suggestions based on that data.

[1031] "Means for the server to encrypt and transmit the generated naming proposals to the vehicle's terminal" refers to the process of assembling the generated naming proposals into data packets, encrypting them, and securely transmitting them to the terminal in the vehicle.

[1032] The "means for the vehicle terminal to display the naming suggestions to the user" refers to a device or function that displays the generated naming suggestions on the dashboard or touch screen of the vehicle.

[1033] "Means for users to input feedback via a voice recognition system or touch screen" refers to a device or function that allows users to input their opinions or requests for corrections to the generated naming proposals via voice commands or a touch screen.

[1034] The "means for the vehicle terminal to encrypt the feedback and transmit it to the server" refers to an in-vehicle computer system that assembles the user's feedback into data packets, encrypts them, and transmits them securely to the server.

[1035] "Means for the server to again provide data to the generative AI model together with feedback to generate revised naming proposals" refers to the process in which the server provides data to the generative AI model based on user feedback and generates revised naming proposals.

[1036] The "means for the server to encrypt and transmit the revised naming proposal to the vehicle's terminal" refers to the process of assembling the generated revised naming proposal into a data packet, encrypting it, and securely transmitting it to the terminal in the vehicle.

[1037] The present invention relates to a system that allows users to input requirements via a voice recognition system or touch screen, and generates original and attractive naming ideas using a generative AI model. The basic components of this system include a vehicle terminal, a cloud server, a generative AI model, and an interface for incorporating user feedback.

[1038] First, the user uses the touchscreen or voice recognition system on the vehicle's dashboard to input the concept information and keywords needed for the naming proposal. For example, they can enter keywords such as "innovation," "future," or "eco," and upload related images. This data is then formed into a data packet by the vehicle's terminal and sent to the cloud server using TLS / SSL encryption.

[1039] The cloud server then analyzes the received data and provides it to a generative AI model (such as OpenAI GPT-4). The generative AI model uses a large dataset to generate appropriate naming suggestions based on the input requirements. For example, using the keywords "innovation," "future," and "eco," it can generate naming suggestions such as "EcoFuture Drive" and "Green Innovate Rover."

[1040] The generated naming suggestions are then packaged again into a data packet, encrypted, and sent to the vehicle's terminal, which decodes the suggestions and displays them on the dashboard or touchscreen. The user can review the displayed naming suggestions and enter feedback, such as requests to make them simpler or to add specific keywords.

[1041] The user's feedback is again generated as a data packet from the vehicle's terminal, encrypted, and sent to the server. The cloud server then inputs the feedback into the generative AI model again, generating revised naming suggestions. This process is repeated until a naming suggestion that satisfies the user is generated.

[1042] For example, if the user enters the following prompt:

[1043] Generate naming ideas for your vehicle.

[1044] Keywords: Innovation, Future, Eco

[1045] Concept: Eco-friendly autonomous driving technology

[1046] Related images: (Ecosystem image URL)

[1047] Based on this prompt, the system generates naming suggestions such as "EcoFuture Drive" or "Green Innovate Rover." Further revisions are suggested based on the user's feedback, allowing the user to find the optimal name.

[1048] The hardware requires an on-board computer system, a voice recognition microphone, and a touchscreen, while the software requires a generative AI model (OpenAI GPT-4) hosted on a cloud server, a data encryption and data packet generation module, and an in-vehicle user interface application.

[1049] This allows users to input information intuitively, efficiently generate naming suggestions based on the generative AI model, and obtain the optimal naming suggestions through a revision process.

[1050] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1051] Step 1:

[1052] The device accepts user input. Specifically, the user uses a voice recognition system or touchscreen to input keywords, concepts, and related images related to the proposed name. The input data is then formed into a data packet within the device. Keywords such as "innovation," "future," and "eco" and related images are inserted as input. This data packet is then ready to be fed to the generative AI model.

[1053] Step 2:

[1054] The device encrypts the generated data packet. TLS / SSL encryption is used to enhance data security. The device then sends this encrypted data packet to the cloud server. If the data transmission is successful, the device waits for a response.

[1055] Step 3:

[1056] The server receives the data sent from the device and decrypts the data packets. During this process, the server analyzes the received data and converts it into a format that can be fed to the generative AI model. The converted data becomes, for example, a prompt sentence: "Generate a name proposal for a vehicle. Keywords: innovation, future, eco-concept: environmentally friendly autonomous driving technology." This prompt sentence is passed to the generative AI model.

[1057] Step 4:

[1058] The server inputs a prompt to the generative AI model, which then generates naming suggestions. The generative AI model then creates naming suggestions based on the prompt and returns the results to the server. Specifically, naming suggestions such as "EcoFuture Drive" and "Green Innovate Rover" are generated.

[1059] Step 5:

[1060] The server assembles the generated naming proposals into a data packet and encrypts it again. The encrypted data packet is sent to the terminal. When the server completes the transmission, it waits for a response.

[1061] Step 6:

[1062] The device receives and decrypts the data packets sent by the server, converts them into a format that is displayed in the user interface, and displays them as naming suggestions on the dashboard or touchscreen, where the user can review them and provide feedback.

[1063] Step 7:

[1064] The user provides feedback on the proposed name, such as "Make it simpler" or "Add specific keywords" through a voice recognition system or touch screen. This feedback is then sent to the device as a data packet.

[1065] Step 8:

[1066] The device encrypts the feedback data packet. TLS / SSL encryption is also used to ensure security. The device then sends the encrypted feedback data packet to the cloud server. Once the transmission is complete, the device waits for a response.

[1067] Step 9:

[1068] The server receives the feedback data and decrypts it. It then analyzes the received feedback and converts it into a format that can be fed to the generative AI model. Based on the feedback, it inputs a prompt sentence into the generative AI model again to generate new naming suggestions.

[1069] Step 10:

[1070] The server generates revised naming proposals based on the generative AI model and assembles them into data packets, which are then re-encrypted and sent to the vehicle's terminal. This process is repeated until a revised naming proposal is sent to the terminal.

[1071] This allows the user to efficiently generate naming suggestions in an intuitive input environment, and to obtain the optimal naming suggestion through a revision process.

[1072] 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.

[1073] This invention relates to a system that generates original and attractive naming suggestions using a generative AI and an emotion engine after a user inputs requirements. The basic configuration of this system consists of a terminal where the user inputs requirements, a means for sending the data to a server, generating naming suggestions using generative AI, presenting the generated naming suggestions to the user and collecting feedback, and a server that modifies and generates naming suggestions based on the feedback and emotion data.

[1074] Furthermore, in the present invention, by incorporating an emotion engine that recognizes the user's emotions, it is possible to provide naming suggestions that further increase user satisfaction.

[1075] The program processing using this system and emotion engine will be specifically explained below.

[1076] 1. The user enters the requirements

[1077] The user launches the Creative Name Genie application on their device, creates a new project, selects a naming category (e.g., brand name, product name, pet name), and enters the necessary concept information, keywords, and related images.

[1078] Example: If a user wants to come up with a name for a new organic cosmetics brand, they can enter keywords like "natural," "gentle on the skin," and "beauty" and upload an image.

[1079] 2. The device sends the data to the server

[1080] The device generates a data packet containing the input requirements, keywords, and images, encrypts it, and sends it to the server, ensuring data security.

[1081] The server converts the received data into the required format and prepares it as data that can be processed by the generative AI and emotion engine.

[1082] 3. The emotion engine recognizes the user's emotions

[1083] The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received from the device in real time to recognize the user's emotional state (e.g., joy, surprise, stress, etc.).

[1084] Example: If the user smiles while typing, the emotion engine detects the user's positive emotion.

[1085] 4. The server supplies the data to the AI ​​to generate naming suggestions.

[1086] The server provides the input data and emotional data to the AI ​​generator, instructing it to generate naming suggestions. The AI ​​then generates original naming suggestions based on the information provided.

[1087] Example: Generative AI generates naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals" by taking into account keywords such as "natural," "skin-friendly," and "beauty" as well as positive emotional data from users.

[1088] 5. The server sends the generated naming proposal to the device.

[1089] The server collects the naming suggestions received from the AI ​​generator into a data packet, encrypts it, and sends it to the device, ensuring that the generated naming suggestions are delivered securely to the user's device.

[1090] The device decodes the received naming suggestions and displays them in a user interface, where the user can review them and provide feedback.

[1091] 6. User provides feedback

[1092] The user can then rate or request revisions to the displayed naming ideas. Furthermore, the emotion engine continuously analyzes the user's facial expressions and voice during this process.

[1093] For example, you can input requests such as "Make it a little simpler" or "Add specific keywords," and the emotion engine will collect emotional data at that time.

[1094] 7. The device sends feedback to the server

[1095] The terminal assembles the input feedback and emotion data into a data packet, encrypts it, and transmits it to the server.

[1096] The server analyzes the received data and provides feedback and emotion data to the generative AI.

[1097] 8. The server re-supplies the data to the AI ​​based on the emotion data and generates revised naming suggestions.

[1098] The server issues a command to the AI ​​generator based on the feedback and emotional data to generate revised naming suggestions. The AI ​​generator takes the new feedback and emotional data into account and generates optimized naming suggestions.

[1099] For example, a new suggestion could be generated such as "PureGlow Organics."

[1100] 9. The server sends the revised naming proposal to the device and presents it to the user.

[1101] The server receives a revised naming proposal from the AI ​​again and sends it to the device, which displays it again on the user interface for final confirmation by the user.

[1102] This process is repeated until the user selects and finalizes the optimal naming suggestion.This system generates efficient and high-quality naming suggestions that reflect the user's emotional state, thereby improving user satisfaction.

[1103] The processing flow will be explained below.

[1104] Step 1:

[1105] A user launches the "Creative Name Genie" application on their device. They create a new project and select a naming category (e.g., brand name, product name, pet name). Next, the user enters naming requirements and concept information (e.g., keywords, target market, characteristics, etc.) into text fields and uploads related images, if available.

[1106] Step 2:

[1107] The device combines the input requirements, concept information, and images into a single data packet, which the device encrypts and sends to the server using a secure protocol (e.g., HTTPS).

[1108] Step 3:

[1109] The server decrypts the data packets received from the device and stores them in a database. The server then converts the stored data into the required format and prepares it as data that can be processed by the generative AI and emotion engine.

[1110] Step 4:

[1111] The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received from the device in real time to recognize the user's emotional state (e.g., joy, surprise, stress, etc.). For example, if the user smiles while typing, the emotion engine detects the user's positive emotion.

[1112] Step 5:

[1113] The server provides the input data along with emotional data to the AI ​​and instructs it to generate naming suggestions. The AI ​​generates original naming suggestions based on the information provided. For example, the AI ​​might consider keywords such as "natural," "skin-friendly," and "beauty" along with the user's positive emotional data to generate naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals."

[1114] Step 6:

[1115] The server collects the naming suggestions received from the AI ​​generator into a data packet, encrypts it, and sends it to the device. The device decrypts the received naming suggestions and displays them on the user interface. The user can review these suggestions and provide feedback.

[1116] Step 7:

[1117] The user can then rate the displayed naming proposals and input any corrections they wish to make. Furthermore, the emotion engine continuously analyzes the user's facial expressions and voice during this process. For example, if a request is made such as "I'd like it to be a little simpler" or "I'd like a specific keyword added," the emotion engine will also collect emotional data at that time.

[1118] Step 8:

[1119] The device assembles the input feedback and emotion data into a data packet, encrypts it, and sends it to the server, which analyzes the received data and provides the feedback and emotion data to the generative AI.

[1120] Step 9:

[1121] The server then issues a new command to the AI ​​based on the feedback and emotional data, instructing it to generate a revised naming proposal. The AI ​​then takes the new feedback and emotional data into account and generates an optimized naming proposal, such as "PureGlow Organics."

[1122] Step 10:

[1123] The server receives a revised naming proposal from the AI ​​again and sends it to the device. The device displays the revised naming proposal again on the user interface. The user reviews the revised naming proposal and again judges whether it is good or bad.

[1124] Step 11:

[1125] The user finally selects their favorite naming plan and presses the confirm button. The device assembles the final naming plan into a data packet and sends it to the server. The server saves the final naming plan in the database as final information, and the project is completed.

[1126] Example 2

[1127] 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."

[1128] Conventional naming generation systems generate naming suggestions based solely on user requirements and feedback, which means they are unable to fully reflect the user's emotions and intentions. This can lead to a decline in the quality of the generated naming suggestions and user satisfaction. Furthermore, even in the process of incorporating user feedback to generate revised naming suggestions, emotional data is not taken into account, making it difficult to generate suggestions that reflect the user's true intentions.

[1129] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1130] In this invention, the server includes: means for a user to input requirements; means for a terminal to transmit the input data to the server; means for the server to supply data to a generative AI model and generate naming suggestions; means for the server to transmit the generated naming suggestions to the terminal; means for the terminal to display the naming suggestions to the user; means for a user to input feedback; means for the terminal to transmit the feedback to the server; means for the server to resupply data to the generative AI model together with the feedback and generate revised naming suggestions; means for the server to transmit the revised naming suggestions to the terminal; means for the terminal to use an emotion engine that recognizes the user's emotions and transmit user emotion data to the server; and means for the server to supply data to the generative AI model based on the emotion data and adjust the generated naming suggestions. This enables the generation of high-quality naming suggestions that reflect the user's emotional state and true intentions.

[1131] The "means for inputting requirements" is a system component that allows a user to input their own wishes and necessary conditions.

[1132] The "means for transmitting data to a server" is a system component for transmitting information input by a user from a terminal to a server.

[1133] The "means for supplying data to the generative AI model" is a system component that allows the server to pass the user's input data and emotion data to the generative AI model and generate naming suggestions.

[1134] A "means for generating naming suggestions" is a system component for using a generative AI model to create new naming suggestions based on supplied data.

[1135] The "means for transmitting the generated naming proposal" is a system component for transmitting the generated naming proposal from the server to the terminal.

[1136] The "means for displaying naming suggestions to the user" is a system component for displaying the naming suggestions received by the terminal on the user interface.

[1137] The "means for inputting feedback" is a system component that allows the user to input evaluations and requests for corrections to the displayed naming proposals.

[1138] The "means for transmitting feedback to the server" is a system component for transmitting feedback data including the user's evaluation and correction requests from the terminal to the server.

[1139] The "means for using the emotion engine and transmitting user emotion data" is a system component that enables the terminal to recognize the user's emotional state and transmit the data to the server.

[1140] The "means for supplying data to the generative AI model based on emotional data and adjusting the generated naming suggestions" is a system component that enables the server to supply data to the generative AI model taking emotional data into consideration and optimize the generated naming suggestions.

[1141] The "means for generating revised naming suggestions" is a system component for generating revised naming suggestions using a generative AI model based on the input feedback and sentiment data.

[1142] This invention is a system that generates original and attractive naming suggestions using a generative AI model and an emotion engine after a user inputs requirements.The basic configuration of this system consists of a terminal where the user inputs requirements, a means for sending the data to a server, generating naming suggestions using a generative AI model, presenting the generated naming suggestions to the user and collecting feedback, and a server that modifies and generates naming suggestions based on the feedback and emotion data.

[1143] Furthermore, by incorporating an emotion engine that recognizes the user's emotions, the present invention can provide naming suggestions that further increase user satisfaction. Below, we will specifically explain the program processing using this system and emotion engine.

[1144] A user launches an application such as "Creative Name Genie" on their device and creates a new project. Here, the user selects a naming category (e.g., brand name, product name, pet name) and enters the necessary concept information, keywords, and related images. For example, when thinking of a name for a new organic cosmetics brand, a user enters keywords such as "natural," "gentle on the skin," and "beauty," and uploads an image of the skin care products.

[1145] The terminal assembles these input data into data packets, encrypts them, and sends them to the server. The security of this data is maintained using TLS (Transport Layer Security).

[1146] The server converts the received data into the required format and prepares it as data that can be processed by the generative AI model and emotion engine. The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received in real time from the device to recognize the user's emotional state (e.g., joy, surprise, stress, etc.). For example, if the user smiles while typing, the emotion engine detects that positive emotion.

[1147] Next, the server supplies the input data and emotional data to a generative AI model and instructs it to generate naming suggestions. As an example of a generative AI model, GPT-3 can be used. The generative AI model generates original naming suggestions based on the information provided. As a specific example, by combining the keywords "natural," "skin-friendly," and "beauty" with the user's positive emotional data, naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals" are generated.

[1148] The generated naming suggestions are packaged in encrypted data packets from the server and sent to the device. The device decrypts the packets and displays them on the user interface. The user can then review the displayed naming suggestions and enter their evaluations or requests for revisions. The emotion engine continuously analyzes the user's facial expressions and voice while they are entering feedback, and generates emotion data.

[1149] The feedback and emotion data entered by the user is sent from the device to a server, which analyzes the data and feeds it to a generative AI model. The generative AI model generates revised naming suggestions based on the new feedback and emotion data. For example, a new suggestion such as "PureGlow Organics" is generated.

[1150] The regenerated naming suggestions are sent from the server to the device and redisplayed on the user interface, allowing the user to repeat this process until the best naming suggestion is selected and finalized.

[1151] This system generates efficient and high-quality naming suggestions that reflect the user's emotional state, thereby improving user satisfaction.

[1152] "Think of a name for a new organic cosmetics brand. Keywords: natural, gentle, beauty."

[1153] Examples include:

[1154] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1155] Program processing steps

[1156] Step 1:

[1157] User enters requirements

[1158] What it does: A user launches the Creative Name Genie application on their device, creates a new project, selects a naming category (e.g., brand name, product name, pet name), and enters the required concept information, keywords, and related images.

[1159] Input: Category, concept information, keywords, related images

[1160] Output: Generate input data (categories, concept information, keywords, images)

[1161] Step 2:

[1162] The device sends the entered data to the server.

[1163] Specific operation: The device assembles the requirements, keywords, and images entered by the user into a data packet, encrypts it, and sends it to the server.

[1164] Input: User input data (categories, concept information, keywords, images)

[1165] Data processing: Packetization and encryption of input data (using TLS)

[1166] Output: Encrypted data packet

[1167] Step 3:

[1168] The server prepares the input data

[1169] What it does: The server decrypts the encrypted data it receives and converts it into a format that can be processed by the generative AI model and emotion engine.

[1170] Input: Encrypted data packet

[1171] Data processing: Data interpretation and format conversion

[1172] Output: Data converted into a processable format

[1173] Step 4:

[1174] Emotion engine recognizes user emotions

[1175] Specific operation: The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received in real time from the device to recognize the user's emotional state.

[1176] Input: Real-time user data (facial expressions, voice, typing speed, gestures)

[1177] Data Computation: Emotional state analysis and emotional data generation

[1178] Output: Emotion data

[1179] Step 5:

[1180] The server supplies data to the generative AI model to generate naming suggestions.

[1181] Specific operation: The server provides input data and emotion data to a generative AI model (e.g., GPT-3) and instructs it to generate naming suggestions. The generative AI model generates naming suggestions based on the provided information.

[1182] Input: Input data, emotion data converted into a processable format

[1183] Data Computation: Data computation using generative AI models

[1184] Output: Generated naming ideas

[1185] Step 6:

[1186] The server sends the generated naming proposal to the device.

[1187] How it works: The server compiles the naming ideas obtained from the generative AI model into a data packet, encrypts it, and sends it to the device, which then decrypts it and displays it on the user interface.

[1188] Input: Generated naming ideas

[1189] Data processing: Packetization and encryption of naming proposals, decryption after receiving

[1190] Output: Naming proposal displayed in the user interface

[1191] Step 7:

[1192] User enters feedback

[1193] Specific operation: The user inputs their evaluation and correction requests for the displayed naming proposals. The emotion engine analyzes their facial expressions and voices and generates emotion data.

[1194] Input: Evaluation of naming proposals, requests for revisions, user facial expressions and voice

[1195] Data computation: Emotional state analysis, emotional data generation

[1196] Output: Feedback data, emotion data

[1197] Step 8:

[1198] The device sends feedback to the server

[1199] Specific operation: The device assembles the input feedback and emotion data into a data packet, encrypts it, and sends it to the server.

[1200] Input: Feedback data, emotion data

[1201] Data processing: Packetization and encryption of feedback and emotion data

[1202] Output: Encrypted data packet

[1203] Step 9:

[1204] The server re-feeds the data to the generative AI model to generate revised naming proposals.

[1205] Specific operation: The server provides feedback and emotion data to the generative AI model and instructs it to generate revised naming suggestions. The generative AI model generates optimized naming suggestions based on the new feedback and emotion data.

[1206] Input: Feedback and emotion data

[1207] Data computation: Recomputing data using generative AI models

[1208] Output: Revised naming proposal

[1209] Step 10:

[1210] The server sends the revised naming proposal to the device and presents it to the user.

[1211] How it works: The server assembles revised naming suggestions from the generative AI model into a data packet, encrypts it, and sends it to the device. The device decrypts it and displays it again on the user interface. This process can be repeated until the user selects and finalizes the best naming suggestion.

[1212] Input: Revised naming proposal

[1213] Data processing: Packetization and encryption of the revised naming proposal, and decryption after receiving it

[1214] Output: Revised naming proposal displayed in the user interface

[1215] (Application example 2)

[1216] 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."

[1217] Conventional naming suggestion generation systems have difficulty generating naming suggestions that take user emotions into account, and have not been able to sufficiently increase user satisfaction. In addition, the process of revising generated naming suggestions based on feedback is cumbersome, making them difficult to use for users.

[1218] 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.

[1219] In this invention, the server includes a means for analyzing the user's emotions using an emotion engine and supplying the analysis results to the generation AI to generate revised naming suggestions, and a revision process for regenerating based on the user's feedback and emotion data. This makes it possible to generate original and attractive naming suggestions that take the user's emotions into consideration, thereby improving user satisfaction.

[1220] A "user" is someone who uses the system to generate naming suggestions and provide feedback.

[1221] "Requirements" are conditions or information that a user inputs into the system, such as keywords or categories.

[1222] A "terminal" is a device operated by a user, which inputs requirements, transmits data, displays naming suggestions, etc.

[1223] A "server" is a device that receives data sent from a terminal, processes and generates the data using generative AI or an emotion engine, and returns the results to the terminal.

[1224] "Generative AI" refers to artificial intelligence that generates naming ideas based on given data.

[1225] "Naming suggestions" are names or candidate names generated by the generation AI.

[1226] An "emotion engine" is a technology that analyzes user emotions and reflects the results in generating naming suggestions.

[1227] "Feedback" refers to the user's evaluation of the generated naming proposals and requests for corrections.

[1228] A "data packet" is a unit of digital information that contains user-entered requirements and feedback.

[1229] "Analysis results" refers to the data obtained after the emotion engine analyzes the user's emotions.

[1230] "Revised naming proposals" are naming proposals regenerated by the generation AI, taking into account user feedback and emotional data.

[1231] A "process" is a series of tasks that indicate the overall procedure or processing flow.

[1232] The present invention relates to a system that allows a user to input requirements and generates original and attractive naming ideas using a generative AI and an emotion engine. Specific embodiments are described below.

[1233] 1. User operations

[1234] Users launch a dedicated application on their device and create a new project. At that time, they select a naming category (e.g., brand name, product name, etc.) and enter the necessary concept information, keywords, and related images. For example, if they want to think of a name for a new organic cosmetics brand, they can enter keywords such as "natural," "gentle on the skin," and "beauty" and upload an image.

[1235] 2. Data transmission

[1236] The device generates data packets from the input requirements, keywords, images, etc., and encrypts the data before sending it to the server, ensuring data security.

[1237] 3. Emotion Data Analysis

[1238] When data arrives at the server, the emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. in real time to recognize the user's emotional state (happiness, surprise, stress, etc.). For example, if the user smiles while typing, the emotion engine will detect that positive emotion.

[1239] 4. Naming Idea Generation

[1240] The server provides the received input data and emotional data to the generation AI, instructing it to generate naming suggestions. The generation AI generates original naming suggestions based on the information provided. For example, by taking into account the keywords "natural," "skin-friendly," and "beauty" and the user's positive emotional data, it generates naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals."

[1241] 5. Submit a naming proposal

[1242] The server packages the generated naming suggestions into a data packet, encrypts it, and sends it to the device. The device then decrypts the received naming suggestions and displays them on a user interface. The user can review these suggestions and provide feedback.

[1243] 6. Processing Feedback

[1244] Users can then evaluate the generated naming proposals and input their feedback and requests for revisions, while their facial expressions and voices are continuously analyzed by the emotion engine. For example, users can input requests such as "Make it a little simpler" or "Add specific keywords." This feedback and emotion data is collected and sent back to the server from the device.

[1245] 7. Generate revised naming ideas

[1246] The server analyzes the received feedback and sentiment data and feeds it into a generative AI to generate revised naming suggestions, such as "PureGlow Organics."

[1247] 8. Final Check

[1248] The terminal receives the revised naming proposals sent again from the server, and the user finally checks them and selects and confirms the most suitable naming proposal.

[1249] Hardware / Software used

[1250] Servers and Terminals

[1251] Emotion Engine and Generative AI Models

[1252] Dedicated application

[1253] Prompt Sentence Examples

[1254] "Generate a name for a new organic cosmetics brand. Keywords are 'natural,' 'gentle on the skin,' and 'beauty.'"

[1255] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1256] Step 1:

[1257] User enters requirements

[1258] Input: The user launches a dedicated application on the device and enters the naming category, necessary concept information, keywords, and related images.

[1259] Specific operation: The user selects the category "Brand Name" and enters keywords such as "natural," "gentle on the skin," and "beauty" along with an image.

[1260] Output: Data entered into the terminal is generated.

[1261] Step 2:

[1262] The device sends the entered data to the server.

[1263] Input: The data generated in step 1.

[1264] Specific operation: The device creates a data packet, encrypts it, and sends it to the server.

[1265] Output: The data packet arrives at the server.

[1266] Step 3:

[1267] The server uses an emotion engine to analyze the user's emotions.

[1268] Input: Data packets arriving at the server and real-time data such as the user's facial expressions, voice, typing speed, and gestures.

[1269] Specific operation: The server's emotion engine analyzes this data and recognizes the user's emotional state (happiness, surprise, stress, etc.).

[1270] Output: Analysis results about the user's emotional state.

[1271] Step 4:

[1272] The server provides data to the AI ​​to generate naming suggestions.

[1273] Input: Input data and emotion data.

[1274] Specific operation: The server provides these data to the generation AI and instructs it to generate naming suggestions. The generation AI generates naming suggestions based on the provided information.

[1275] Output: Generated naming ideas (e.g. "PureSkin Botanicals" or "EcoGlam Naturals").

[1276] Step 5:

[1277] The server sends the generated naming proposal to the device.

[1278] Input: Generated naming ideas.

[1279] Specific operation: The server assembles the naming proposals into a data packet, encrypts it, and sends it to the terminal.

[1280] Output: Data packets arrive at the terminal.

[1281] Step 6:

[1282] The device displays naming suggestions to the user

[1283] Input: The received data packet.

[1284] Specific operation: The terminal decodes the data packet and displays naming suggestions on the user interface.

[1285] Output: The naming proposal is displayed on the terminal screen.

[1286] Step 7:

[1287] User enters feedback

[1288] Input: The displayed naming suggestion.

[1289] Specific operation: The user inputs their evaluation and requests for revisions to the naming proposals, and facial expressions and voice data are also analyzed by the emotion engine.

[1290] Output: Feedback and emotion data.

[1291] Step 8:

[1292] The device sends feedback to the server

[1293] Input: Feedback and emotion data.

[1294] Specific operation: The device assembles this data into a data packet, encrypts it, and sends it to the server.

[1295] Output: The data packet arrives at the server.

[1296] Step 9:

[1297] The server re-feeds the feedback and sentiment data and generates revised naming proposals.

[1298] Input: Feedback and emotion data.

[1299] Specific actions: The server analyzes this data and feeds it back to the generation AI, instructing it to generate revised naming proposals.

[1300] Output: A newly generated revised naming proposal (e.g., "PureGlow Organics").

[1301] Step 10:

[1302] The server sends the revised naming proposal to the device and presents it to the user.

[1303] Input: The newly generated revised naming proposal.

[1304] Specific operation: The server assembles the revised naming proposal into a data packet, encrypts it, and sends it to the device, which decrypts the data packet and displays it on the user interface.

[1305] Output: The revised naming proposal is displayed on the terminal screen.

[1306] 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.

[1307] 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.

[1308] 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.

[1309] [Fourth embodiment]

[1310] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1311] 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.

[1312] 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).

[1313] 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.

[1314] 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.

[1315] 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).

[1316] 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.

[1317] 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.

[1318] 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.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] 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."

[1323] This invention relates to a system that allows users to input requirements and generates original and attractive naming suggestions using a generation AI. The basic configuration of this system consists of a terminal where the user inputs the requirements, a terminal that sends the data to a server, generates naming suggestions using a generation AI, and returns the generated naming suggestions as feedback to the user, and a server that revises and generates new naming suggestions based on the feedback.

[1324] The program processing of this system will be specifically explained below.

[1325] 1. The user enters the requirements

[1326] Users simply launch the Creative Name Genie application on their device, create a new project, and enter the concept information, keywords, and related images needed to come up with a new product name, for example.

[1327] Example: If a user wants to come up with a name for a new organic cosmetics brand, they can enter keywords like "natural," "gentle on the skin," and "beauty" and upload an image.

[1328] 2. The device sends the data to the server

[1329] The device generates a data packet containing the input requirements, keywords, and images, encrypts it, and sends it to the server, ensuring data security.

[1330] The server converts the received data into the required format and prepares it as data that can be processed by the generation AI.

[1331] 3. The server provides the data to the AI ​​to generate naming suggestions.

[1332] The server provides the supplied data as input to the AI ​​generator, which then generates naming suggestions based on that data. The AI ​​generator uses a large amount of data set it has learned to suggest original names that match the user's requirements.

[1333] Example: Generative AI uses the keywords "natural," "skin-friendly," and "beauty" to generate naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals."

[1334] 4. The server sends the generated naming proposal to the device.

[1335] The server collects the naming suggestions received from the AI ​​generator into a data packet, encrypts it, and sends it to the device, ensuring that the generated naming suggestions are delivered securely to the user's device.

[1336] The device decodes the received naming suggestions and displays them in a user interface, where the user can review them and provide feedback.

[1337] 5. User provides feedback

[1338] Users can view the displayed naming suggestions and then enter their evaluation and requests for revisions, such as "Make it a little simpler" or "Add specific keywords."

[1339] The terminal transmits this feedback back to the server.

[1340] 6. The server re-supplies the data to the AI ​​based on the feedback and generates revised naming proposals.

[1341] The server analyzes the feedback from the user and provides the feedback to the generation AI again, instructing it to generate revised naming proposals.

[1342] The AI ​​then takes new feedback into account and generates more optimized naming suggestions, such as "PureGlow Organics."

[1343] 7. The server sends the revised naming proposal to the device and displays it to the user.

[1344] The server again receives a revised naming proposal from the generation AI and sends it to the device, which again displays it on the user interface for final confirmation by the user.

[1345] This process is repeated until the user selects and finalizes the best naming suggestion. This system efficiently generates high-quality naming suggestions and allows for a process that can be tailored to the user's needs.

[1346] The processing flow will be explained below.

[1347] Step 1: User enters requirements

[1348] The user opens the device and launches the "Creative Name Genie" application.

[1349] A user creates a new project and selects a naming category (e.g., brand name, product name, pet name).

[1350] The user enters naming requirements and concept information (e.g., keywords, target market, characteristics, etc.) into text fields.

[1351] If the user has a related image, upload the image.

[1352] Step 2: The device sends the data to the server

[1353] The terminal assembles the input requirements, concept information, and images into a single data packet.

[1354] The device encrypts the collected data packets and sends them to the server using a secure protocol (e.g., HTTPS).

[1355] Step 3: The server receives the data and provides it to the generation AI.

[1356] The server decrypts the data packets received from the terminal and stores them in a database.

[1357] The server converts the stored data into the required format to match the input format of the generating AI.

[1358] The server supplies the converted data to the generation AI and sends a request to generate naming suggestions.

[1359] Step 4: Generative AI generates naming ideas

[1360] The generative AI analyzes the data provided and extracts relevant keywords and concepts.

[1361] The generative AI generates original and attractive naming ideas based on the large amount of naming data it has learned.

[1362] Evaluate the generated naming ideas and select the most appropriate candidate.

[1363] Step 5: The server sends the naming proposal to the device

[1364] The server compiles the naming suggestions received from the generation AI into a data packet.

[1365] The server encrypts the collected data packets and sends them to the terminal.

[1366] Step 6: The device displays naming suggestions to the user

[1367] The terminal decrypts the data packet received from the server.

[1368] The decrypted naming proposal is read and displayed in the user interface.

[1369] The user reviews the naming proposal and provides feedback.

[1370] Step 7: User Provides Feedback

[1371] The user enters requests and feedback (e.g., corrections, additional information) for the provided naming proposal.

[1372] The user finalizes the feedback and presses the submit button.

[1373] Step 8: The device sends feedback to the server

[1374] The terminal packages the input feedback into data packets.

[1375] The terminal encrypts the feedback data packet and sends it to the server.

[1376] Step 9: The server receives the feedback and re-feeds it to the generating AI.

[1377] The server decodes the feedback packet received from the terminal and adds it to the database.

[1378] The server converts the added feedback into a format suitable for the generated AI.

[1379] The server re-feeds the feedback to the generation AI and sends a request to generate revised naming suggestions.

[1380] Step 10: Generative AI generates revised naming ideas

[1381] The generative AI analyzes the provided feedback and implements any necessary modifications.

[1382] The generative AI generates revised naming suggestions.

[1383] Evaluate the revised naming proposals and select the most appropriate candidate.

[1384] Step 11: The server sends the revised naming proposal to the device.

[1385] The server compiles the revised naming proposals received from the generation AI into a data packet.

[1386] The server encrypts the collected data packets and sends them to the terminal.

[1387] Step 12: The device displays the revised naming proposal to the user.

[1388] The terminal again decrypts the data packets received from the server.

[1389] The decoded revised naming proposal is displayed again in the user interface.

[1390] The user confirms the revised naming proposal.

[1391] Step 13: Finalize the naming idea

[1392] The user finally selects the naming proposal they like and presses the confirm button.

[1393] The terminal assembles the selected final naming proposal into a data packet and transmits it to the server.

[1394] The server saves the final naming proposal in the database as final information, completing the project.

[1395] Example 1

[1396] 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."

[1397] Conventional naming proposal generation systems have limitations in generating original names that perfectly match user requirements. Furthermore, the process of regenerating names based on user feedback is ineffective, resulting in a large amount of manual revisions. Such systems make it difficult to increase user satisfaction, and efficient generation of naming proposals is required.

[1398] 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.

[1399] In this invention, the server includes means for converting received data into JSON format, means for supplying the data to a generative AI model to generate naming suggestions, and means for analyzing feedback and resupplying the data to the generative AI model to generate revised naming suggestions, thereby enabling user feedback to be effectively reflected and the generated naming suggestions to be revised quickly and reliably.

[1400] A "user" is a person or organization that uses the system to generate and provide feedback on naming suggestions.

[1401] "Requirements" are data such as concept information, keywords, and related images that are necessary for the user to generate naming suggestions.

[1402] "Device" refers to an electronic device used by a user to input requirements and review and provide feedback on naming proposals, including smartphones, tablets, and PCs.

[1403] A "data packet" is a format in which data to be transmitted is collected and sent securely by encryption.

[1404] "Server" is a computer system for receiving data, feeding the data to the generative AI model, generating naming suggestions, analyzing feedback, and generating revised naming suggestions.

[1405] A "generative AI model" is an artificial intelligence model that utilizes machine learning and natural language processing techniques and is used to generate original naming ideas based on user requirements.

[1406] "Feedback" refers to evaluations and correction requests that users input for generated naming proposals.

[1407] "JSON format" is a standard data format used by servers to exchange data, and is an abbreviation for JavaScript Object Notation (JSON).

[1408] "AES-256 encryption" means an algorithm used to encrypt data at a high level and is a 256-bit key length version of the Advanced Encryption Standard (AES).

[1409] This invention relates to a system that generates original and attractive naming suggestions using a generative AI model based on user input requirements. This system consists of a terminal where the user inputs the requirements, a terminal that sends the data to a server, generates naming suggestions using a generative AI model, and returns the generated naming suggestions as feedback to the user, and a server that revises and generates new naming suggestions based on the feedback.

[1410] To implement this system, the following hardware and software are required:

[1411] Hardware used

[1412] 1. User device: A smartphone, tablet, or PC that users use to enter requirements and review and provide feedback on naming ideas.

[1413] 2. Server: A cloud-based server with a powerful CPU, GPU, sufficient memory, and storage capacity, used to receive data, feed it to the generative AI model, generate naming suggestions, and generate revised naming suggestions.

[1414] Software used

[1415] 1. Terminal software: "Creative Name Genie" application, which provides an interface for users to input requirements and review and provide feedback on naming suggestions.

[1416] 2. Server software: Data management software, generative AI models (e.g., GPT-4), data conversion, encryption, data transmission, naming proposal generation, and feedback analysis.

[1417] System Overview

[1418] User operations

[1419] Users launch the Creative Name Genie application on their device and create a new project. They then enter the concept information, keywords, and related images needed to come up with a new product name. For example, if a user wants to come up with a name for a new organic cosmetics brand, they can enter keywords such as "natural," "gentle on the skin," and "beauty" and upload an image.

[1420] Server Processing

[1421] The device generates a data packet containing the user's input requirements, keywords, and images, encrypts it using AES-256, and sends it to the server. The server then converts the received data into JSON format and supplies it to a generative AI model (such as GPT-4) to generate creative naming suggestions. The generative AI model uses a large dataset to suggest the best naming suggestions for the given requirements. For example, based on the keywords "natural," "gentle on the skin," and "beauty," naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals" are generated.

[1422] Naming proposal feedback and regeneration

[1423] The generated naming suggestions are then encrypted again using AES-256 and sent to the device. The user then uses the device to review the displayed naming suggestions and enter their evaluation and correction requests. For example, they can enter requests such as "Make it a little simpler" or "Add specific keywords." The device then encrypts this feedback again using AES-256 and sends it to the server. The server analyzes the user feedback and supplies it to the generative AI model as a new dataset to generate further optimized naming suggestions. For example, a new suggestion such as "PureGlow Organics" is generated.

[1424] Prompt Sentence Examples

[1425] Examples of prompts for generative AI models include:

[1426] "Think of a name for a new organic cosmetics brand. The keywords are 'natural,' 'gentle on the skin,' and 'beauty.'"

[1427] This system can generate efficient and high-quality naming suggestions based on user requirements and adjust the optimal naming suggestions based on user feedback.

[1428] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1429] Step 1:

[1430] A user launches the "Creative Name Genie" application on their device. They then create a new project and input concept information and keywords for the name. They can also upload related images. Keywords and images such as "natural," "gentle on the skin," and "beauty" are used as input. The output is a data packet containing these requirements, keywords, and images.

[1431] Step 2:

[1432] The terminal generates a data packet based on the requirements, keywords, and images entered by the user, encrypts it using AES-256, and sends it to the server. The data entered by the user is used as input, and the output is an encrypted data packet. Specifically, the terminal starts sending data by clicking the "Send" button.

[1433] Step 3:

[1434] The server receives the incoming data packets and decrypts them using AES-256 encryption to extract the data. The encrypted data packets are used as input, and the output is data containing decrypted requirements, keywords, and images. The server converts this data into JSON format, making it ready for the generative AI model to process.

[1435] Step 4:

[1436] The server supplies the converted data to the generative AI model to generate naming suggestions. The inputs are requirements, keywords, and images converted into JSON format. The output is the naming suggestions generated by the generative AI model. Specifically, the generative AI model generates original naming suggestions based on a large dataset.

[1437] Step 5:

[1438] The server re-encrypts the naming proposal received from the generative AI model using AES-256 and sends it to the terminal. The naming proposal from the generative AI model is used as input, and the output is a data packet of the encrypted naming proposal. The server sends this data packet to the terminal.

[1439] Step 6:

[1440] The terminal decrypts the received data packet and displays the naming proposal on the user interface. The encrypted naming proposal data packet is used as input, and the output is the decrypted naming proposal. The user checks the displayed naming proposal and is ready to enter feedback.

[1441] Step 7:

[1442] The user inputs their evaluation and correction requests for the displayed naming proposals. The input is the user's feedback, and the output is a data packet containing the evaluation and correction requests. Specifically, the user clicks the "Send Feedback" button.

[1443] Step 8:

[1444] The device encrypts the feedback with AES-256 and sends it to the server. The input is the user's feedback, and the output is the encrypted feedback data packet.

[1445] Step 9:

[1446] The server receives the feedback and decrypts it using AES-256 encryption to extract the data. The encrypted feedback is used as input, and the output is the decrypted feedback data. The server analyzes this feedback and feeds it into a generative AI model as a new dataset.

[1447] Step 10:

[1448] The server provides the analyzed feedback to the generative AI model to generate revised naming suggestions. The analyzed feedback is used as input, and the revised naming suggestions are the output. Specifically, the generative AI model reflects the feedback and generates new naming suggestions.

[1449] Step 11:

[1450] The server re-encrypts the revised naming proposal received from the generative AI model using AES-256 and sends it to the terminal. The revised naming proposal is used as input, and the output is a data packet of the encrypted revised naming proposal.

[1451] Step 12:

[1452] The terminal decrypts the received data packet and displays the revised naming proposal on the user interface. The encrypted revised naming proposal data packet is used as input, and the output is the decrypted revised naming proposal. The user reviews the revised naming proposal, and this process is repeated until the best naming proposal is determined.

[1453] (Application example 1)

[1454] 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."

[1455] A system that can effectively generate new naming suggestions is needed for autonomous vehicles. In particular, it is necessary for the user to intuitively input information via the vehicle's dashboard or voice recognition system, review the generated naming suggestions, and generate new naming suggestions based on the feedback efficiently. However, conventional methods have the problem that it is difficult for users to easily generate naming suggestions.

[1456] 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.

[1457] In this invention, the server includes: means for a user to input requirements via a voice recognition system or a touch screen; means for a vehicle terminal to encrypt and transmit the input data packet to the server; means for the server to supply data to a generative AI model and generate naming suggestions; means for the server to encrypt and transmit the generated naming suggestions to the vehicle terminal; means for the vehicle terminal to display the naming suggestions to the user; means for the user to input feedback via a voice recognition system or a touch screen; means for the vehicle terminal to encrypt and transmit the feedback to the server; means for the server to supply data together with the feedback to the generative AI model again and generate revised naming suggestions; and means for the server to encrypt and transmit the revised naming suggestions to the vehicle terminal. This makes it possible to effectively and efficiently generate naming suggestions in an environment where the user can input names intuitively, and to obtain optimal naming suggestions through a revision process.

[1458] "Means for users to input requirements via a voice recognition system or touch screen" refers to a device or function that allows a vehicle user to input requirements related to naming of the vehicle through voice commands or by operating a touch screen on the dashboard.

[1459] "Means for encrypting and transmitting data packets input by a vehicle terminal to a server" refers to a computer system within a vehicle that forms information input by a user into a data packet, encrypts it, and securely transmits it to a remote server.

[1460] "Means by which the server supplies data to the generative AI model and generates naming suggestions" refers to the function or process by which the server provides data to the generative AI model and the AI ​​automatically generates naming suggestions based on that data.

[1461] "Means for the server to encrypt and transmit the generated naming proposals to the vehicle's terminal" refers to the process of assembling the generated naming proposals into data packets, encrypting them, and securely transmitting them to the terminal in the vehicle.

[1462] The "means for the vehicle terminal to display the naming suggestions to the user" refers to a device or function that displays the generated naming suggestions on the dashboard or touch screen of the vehicle.

[1463] "Means for users to input feedback via a voice recognition system or touch screen" refers to a device or function that allows users to input their opinions or requests for corrections to the generated naming proposals via voice commands or a touch screen.

[1464] The "means for the vehicle terminal to encrypt the feedback and transmit it to the server" refers to an in-vehicle computer system that assembles the user's feedback into data packets, encrypts them, and transmits them securely to the server.

[1465] "Means for the server to again provide data to the generative AI model together with feedback to generate revised naming proposals" refers to the process in which the server provides data to the generative AI model based on user feedback and generates revised naming proposals.

[1466] The "means for the server to encrypt and transmit the revised naming proposal to the vehicle's terminal" refers to the process of assembling the generated revised naming proposal into a data packet, encrypting it, and securely transmitting it to the terminal in the vehicle.

[1467] The present invention relates to a system that allows users to input requirements via a voice recognition system or touch screen, and generates original and attractive naming ideas using a generative AI model. The basic components of this system include a vehicle terminal, a cloud server, a generative AI model, and an interface for incorporating user feedback.

[1468] First, the user uses the touchscreen or voice recognition system on the vehicle's dashboard to input the concept information and keywords needed for the naming proposal. For example, they can enter keywords such as "innovation," "future," or "eco," and upload related images. This data is then formed into a data packet by the vehicle's terminal and sent to the cloud server using TLS / SSL encryption.

[1469] The cloud server then analyzes the received data and provides it to a generative AI model (such as OpenAI GPT-4). The generative AI model uses a large dataset to generate appropriate naming suggestions based on the input requirements. For example, using the keywords "innovation," "future," and "eco," it can generate naming suggestions such as "EcoFuture Drive" and "Green Innovate Rover."

[1470] The generated naming suggestions are then packaged again into a data packet, encrypted, and sent to the vehicle's terminal, which decodes the suggestions and displays them on the dashboard or touchscreen. The user can review the displayed naming suggestions and enter feedback, such as requests to make them simpler or to add specific keywords.

[1471] The user's feedback is again generated as a data packet from the vehicle's terminal, encrypted, and sent to the server. The cloud server then inputs the feedback into the generative AI model again, generating revised naming suggestions. This process is repeated until a naming suggestion that satisfies the user is generated.

[1472] For example, if the user enters the following prompt:

[1473] Generate naming ideas for your vehicle.

[1474] Keywords: Innovation, Future, Eco

[1475] Concept: Eco-friendly autonomous driving technology

[1476] Related images: (Ecosystem image URL)

[1477] Based on this prompt, the system generates naming suggestions such as "EcoFuture Drive" or "Green Innovate Rover." Further revisions are suggested based on the user's feedback, allowing the user to find the optimal name.

[1478] The hardware requires an on-board computer system, a voice recognition microphone, and a touchscreen, while the software requires a generative AI model (OpenAI GPT-4) hosted on a cloud server, a data encryption and data packet generation module, and an in-vehicle user interface application.

[1479] This allows users to input information intuitively, efficiently generate naming suggestions based on the generative AI model, and obtain the optimal naming suggestions through a revision process.

[1480] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1481] Step 1:

[1482] The device accepts user input. Specifically, the user uses a voice recognition system or touchscreen to input keywords, concepts, and related images related to the proposed name. The input data is then formed into a data packet within the device. Keywords such as "innovation," "future," and "eco" and related images are inserted as input. This data packet is then ready to be fed to the generative AI model.

[1483] Step 2:

[1484] The device encrypts the generated data packet. TLS / SSL encryption is used to enhance data security. The device then sends this encrypted data packet to the cloud server. If the data transmission is successful, the device waits for a response.

[1485] Step 3:

[1486] The server receives the data sent from the device and decrypts the data packets. During this process, the server analyzes the received data and converts it into a format that can be fed to the generative AI model. The converted data becomes, for example, a prompt sentence: "Generate a name proposal for a vehicle. Keywords: innovation, future, eco-concept: environmentally friendly autonomous driving technology." This prompt sentence is passed to the generative AI model.

[1487] Step 4:

[1488] The server inputs a prompt to the generative AI model, which then generates naming suggestions. The generative AI model then creates naming suggestions based on the prompt and returns the results to the server. Specifically, naming suggestions such as "EcoFuture Drive" and "Green Innovate Rover" are generated.

[1489] Step 5:

[1490] The server assembles the generated naming proposals into a data packet and encrypts it again. The encrypted data packet is sent to the terminal. When the server completes the transmission, it waits for a response.

[1491] Step 6:

[1492] The device receives and decrypts the data packets sent by the server, converts them into a format that is displayed in the user interface, and displays them as naming suggestions on the dashboard or touchscreen, where the user can review them and provide feedback.

[1493] Step 7:

[1494] The user provides feedback on the proposed name, such as "Make it simpler" or "Add specific keywords" through a voice recognition system or touch screen. This feedback is then sent to the device as a data packet.

[1495] Step 8:

[1496] The device encrypts the feedback data packet. TLS / SSL encryption is also used to ensure security. The device then sends the encrypted feedback data packet to the cloud server. Once the transmission is complete, the device waits for a response.

[1497] Step 9:

[1498] The server receives the feedback data and decrypts it. It then analyzes the received feedback and converts it into a format that can be fed to the generative AI model. Based on the feedback, it inputs a prompt sentence into the generative AI model again to generate new naming suggestions.

[1499] Step 10:

[1500] The server generates revised naming proposals based on the generative AI model and assembles them into data packets, which are then re-encrypted and sent to the vehicle's terminal. This process is repeated until a revised naming proposal is sent to the terminal.

[1501] This allows the user to efficiently generate naming suggestions in an intuitive input environment, and to obtain the optimal naming suggestion through a revision process.

[1502] 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.

[1503] This invention relates to a system that generates original and attractive naming suggestions using a generative AI and an emotion engine after a user inputs requirements. The basic configuration of this system consists of a terminal where the user inputs requirements, a means for sending the data to a server, generating naming suggestions using generative AI, presenting the generated naming suggestions to the user and collecting feedback, and a server that modifies and generates naming suggestions based on the feedback and emotion data.

[1504] Furthermore, in the present invention, by incorporating an emotion engine that recognizes the user's emotions, it is possible to provide naming suggestions that further increase user satisfaction.

[1505] The program processing using this system and emotion engine will be specifically explained below.

[1506] 1. The user enters the requirements

[1507] The user launches the Creative Name Genie application on their device, creates a new project, selects a naming category (e.g., brand name, product name, pet name), and enters the necessary concept information, keywords, and related images.

[1508] Example: If a user wants to come up with a name for a new organic cosmetics brand, they can enter keywords like "natural," "gentle on the skin," and "beauty" and upload an image.

[1509] 2. The device sends the data to the server

[1510] The device generates a data packet containing the input requirements, keywords, and images, encrypts it, and sends it to the server, ensuring data security.

[1511] The server converts the received data into the required format and prepares it as data that can be processed by the generative AI and emotion engine.

[1512] 3. The emotion engine recognizes the user's emotions

[1513] The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received from the device in real time to recognize the user's emotional state (e.g., joy, surprise, stress, etc.).

[1514] Example: If the user smiles while typing, the emotion engine detects the user's positive emotion.

[1515] 4. The server supplies the data to the AI ​​to generate naming suggestions.

[1516] The server provides the input data and emotional data to the AI ​​generator, instructing it to generate naming suggestions. The AI ​​then generates original naming suggestions based on the information provided.

[1517] Example: Generative AI generates naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals" by taking into account keywords such as "natural," "skin-friendly," and "beauty" as well as positive emotional data from users.

[1518] 5. The server sends the generated naming proposal to the device.

[1519] The server collects the naming suggestions received from the AI ​​generator into a data packet, encrypts it, and sends it to the device, ensuring that the generated naming suggestions are delivered securely to the user's device.

[1520] The device decodes the received naming suggestions and displays them in a user interface, where the user can review them and provide feedback.

[1521] 6. User provides feedback

[1522] The user can then rate or request revisions to the displayed naming ideas. Furthermore, the emotion engine continuously analyzes the user's facial expressions and voice during this process.

[1523] For example, you can input requests such as "Make it a little simpler" or "Add specific keywords," and the emotion engine will collect emotional data at that time.

[1524] 7. The device sends feedback to the server

[1525] The terminal assembles the input feedback and emotion data into a data packet, encrypts it, and transmits it to the server.

[1526] The server analyzes the received data and provides feedback and emotion data to the generative AI.

[1527] 8. The server re-supplies the data to the AI ​​based on the emotion data and generates revised naming suggestions.

[1528] The server issues a command to the AI ​​generator based on the feedback and emotional data to generate revised naming suggestions. The AI ​​generator takes the new feedback and emotional data into account and generates optimized naming suggestions.

[1529] For example, a new suggestion could be generated such as "PureGlow Organics."

[1530] 9. The server sends the revised naming proposal to the device and presents it to the user.

[1531] The server receives a revised naming proposal from the AI ​​again and sends it to the device, which displays it again on the user interface for final confirmation by the user.

[1532] This process is repeated until the user selects and finalizes the optimal naming suggestion.This system generates efficient and high-quality naming suggestions that reflect the user's emotional state, thereby improving user satisfaction.

[1533] The processing flow will be explained below.

[1534] Step 1:

[1535] A user launches the "Creative Name Genie" application on their device. They create a new project and select a naming category (e.g., brand name, product name, pet name). Next, the user enters naming requirements and concept information (e.g., keywords, target market, characteristics, etc.) into text fields and uploads related images, if available.

[1536] Step 2:

[1537] The device combines the input requirements, concept information, and images into a single data packet, which the device encrypts and sends to the server using a secure protocol (e.g., HTTPS).

[1538] Step 3:

[1539] The server decrypts the data packets received from the device and stores them in a database. The server then converts the stored data into the required format and prepares it as data that can be processed by the generative AI and emotion engine.

[1540] Step 4:

[1541] The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received from the device in real time to recognize the user's emotional state (e.g., joy, surprise, stress, etc.). For example, if the user smiles while typing, the emotion engine detects the user's positive emotion.

[1542] Step 5:

[1543] The server provides the input data along with emotional data to the AI ​​and instructs it to generate naming suggestions. The AI ​​generates original naming suggestions based on the information provided. For example, the AI ​​might consider keywords such as "natural," "skin-friendly," and "beauty" along with the user's positive emotional data to generate naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals."

[1544] Step 6:

[1545] The server collects the naming suggestions received from the AI ​​generator into a data packet, encrypts it, and sends it to the device. The device decrypts the received naming suggestions and displays them on the user interface. The user can review these suggestions and provide feedback.

[1546] Step 7:

[1547] The user can then rate the displayed naming proposals and input any corrections they wish to make. Furthermore, the emotion engine continuously analyzes the user's facial expressions and voice during this process. For example, if a request is made such as "I'd like it to be a little simpler" or "I'd like a specific keyword added," the emotion engine will also collect emotional data at that time.

[1548] Step 8:

[1549] The device assembles the input feedback and emotion data into a data packet, encrypts it, and sends it to the server, which analyzes the received data and provides the feedback and emotion data to the generative AI.

[1550] Step 9:

[1551] The server then issues a new command to the AI ​​based on the feedback and emotional data, instructing it to generate a revised naming proposal. The AI ​​then takes the new feedback and emotional data into account and generates an optimized naming proposal, such as "PureGlow Organics."

[1552] Step 10:

[1553] The server receives a revised naming proposal from the AI ​​again and sends it to the device. The device displays the revised naming proposal again on the user interface. The user reviews the revised naming proposal and again judges whether it is good or bad.

[1554] Step 11:

[1555] The user finally selects their favorite naming plan and presses the confirm button. The device assembles the final naming plan into a data packet and sends it to the server. The server saves the final naming plan in the database as final information, and the project is completed.

[1556] Example 2

[1557] 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."

[1558] Conventional naming generation systems generate naming suggestions based solely on user requirements and feedback, which means they are unable to fully reflect the user's emotions and intentions. This can lead to a decline in the quality of the generated naming suggestions and user satisfaction. Furthermore, even in the process of incorporating user feedback to generate revised naming suggestions, emotional data is not taken into account, making it difficult to generate suggestions that reflect the user's true intentions.

[1559] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1560] In this invention, the server includes: means for a user to input requirements; means for a terminal to transmit the input data to the server; means for the server to supply data to a generative AI model and generate naming suggestions; means for the server to transmit the generated naming suggestions to the terminal; means for the terminal to display the naming suggestions to the user; means for a user to input feedback; means for the terminal to transmit the feedback to the server; means for the server to resupply data to the generative AI model together with the feedback and generate revised naming suggestions; means for the server to transmit the revised naming suggestions to the terminal; means for the terminal to use an emotion engine that recognizes the user's emotions and transmit user emotion data to the server; and means for the server to supply data to the generative AI model based on the emotion data and adjust the generated naming suggestions. This enables the generation of high-quality naming suggestions that reflect the user's emotional state and true intentions.

[1561] The "means for inputting requirements" is a system component that allows a user to input their own wishes and necessary conditions.

[1562] The "means for transmitting data to a server" is a system component for transmitting information input by a user from a terminal to a server.

[1563] The "means for supplying data to the generative AI model" is a system component that allows the server to pass the user's input data and emotion data to the generative AI model and generate naming suggestions.

[1564] A "means for generating naming suggestions" is a system component for using a generative AI model to create new naming suggestions based on supplied data.

[1565] The "means for transmitting the generated naming proposal" is a system component for transmitting the generated naming proposal from the server to the terminal.

[1566] The "means for displaying naming suggestions to the user" is a system component for displaying the naming suggestions received by the terminal on the user interface.

[1567] The "means for inputting feedback" is a system component that allows the user to input evaluations and requests for corrections to the displayed naming proposals.

[1568] The "means for transmitting feedback to the server" is a system component for transmitting feedback data including the user's evaluation and correction requests from the terminal to the server.

[1569] The "means for using the emotion engine and transmitting user emotion data" is a system component that enables the terminal to recognize the user's emotional state and transmit the data to the server.

[1570] The "means for supplying data to the generative AI model based on emotional data and adjusting the generated naming suggestions" is a system component that enables the server to supply data to the generative AI model taking emotional data into consideration and optimize the generated naming suggestions.

[1571] The "means for generating revised naming suggestions" is a system component for generating revised naming suggestions using a generative AI model based on the input feedback and sentiment data.

[1572] This invention is a system that generates original and attractive naming suggestions using a generative AI model and an emotion engine after a user inputs requirements.The basic configuration of this system consists of a terminal where the user inputs requirements, a means for sending the data to a server, generating naming suggestions using a generative AI model, presenting the generated naming suggestions to the user and collecting feedback, and a server that modifies and generates naming suggestions based on the feedback and emotion data.

[1573] Furthermore, by incorporating an emotion engine that recognizes the user's emotions, the present invention can provide naming suggestions that further increase user satisfaction. Below, we will specifically explain the program processing using this system and emotion engine.

[1574] A user launches an application such as "Creative Name Genie" on their device and creates a new project. Here, the user selects a naming category (e.g., brand name, product name, pet name) and enters the necessary concept information, keywords, and related images. For example, when thinking of a name for a new organic cosmetics brand, a user enters keywords such as "natural," "gentle on the skin," and "beauty," and uploads an image of the skin care products.

[1575] The terminal assembles these input data into data packets, encrypts them, and sends them to the server. The security of this data is maintained using TLS (Transport Layer Security).

[1576] The server converts the received data into the required format and prepares it as data that can be processed by the generative AI model and emotion engine. The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received in real time from the device to recognize the user's emotional state (e.g., joy, surprise, stress, etc.). For example, if the user smiles while typing, the emotion engine detects that positive emotion.

[1577] Next, the server supplies the input data and emotional data to a generative AI model and instructs it to generate naming suggestions. As an example of a generative AI model, GPT-3 can be used. The generative AI model generates original naming suggestions based on the information provided. As a specific example, by combining the keywords "natural," "skin-friendly," and "beauty" with the user's positive emotional data, naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals" are generated.

[1578] The generated naming suggestions are packaged in encrypted data packets from the server and sent to the device. The device decrypts the packets and displays them on the user interface. The user can then review the displayed naming suggestions and enter their evaluations or requests for revisions. The emotion engine continuously analyzes the user's facial expressions and voice while they are entering feedback, and generates emotion data.

[1579] The feedback and emotion data entered by the user is sent from the device to a server, which analyzes the data and feeds it to a generative AI model. The generative AI model generates revised naming suggestions based on the new feedback and emotion data. For example, a new suggestion such as "PureGlow Organics" is generated.

[1580] The regenerated naming suggestions are sent from the server to the device and redisplayed on the user interface, allowing the user to repeat this process until the best naming suggestion is selected and finalized.

[1581] This system generates efficient and high-quality naming suggestions that reflect the user's emotional state, thereby improving user satisfaction.

[1582] "Think of a name for a new organic cosmetics brand. Keywords: natural, gentle, beauty."

[1583] Examples include:

[1584] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1585] Program processing steps

[1586] Step 1:

[1587] User enters requirements

[1588] What it does: A user launches the Creative Name Genie application on their device, creates a new project, selects a naming category (e.g., brand name, product name, pet name), and enters the required concept information, keywords, and related images.

[1589] Input: Category, concept information, keywords, related images

[1590] Output: Generate input data (categories, concept information, keywords, images)

[1591] Step 2:

[1592] The device sends the entered data to the server.

[1593] Specific operation: The device assembles the requirements, keywords, and images entered by the user into a data packet, encrypts it, and sends it to the server.

[1594] Input: User input data (categories, concept information, keywords, images)

[1595] Data processing: Packetization and encryption of input data (using TLS)

[1596] Output: Encrypted data packet

[1597] Step 3:

[1598] The server prepares the input data

[1599] What it does: The server decrypts the encrypted data it receives and converts it into a format that can be processed by the generative AI model and emotion engine.

[1600] Input: Encrypted data packet

[1601] Data processing: Data interpretation and format conversion

[1602] Output: Data converted into a processable format

[1603] Step 4:

[1604] Emotion engine recognizes user emotions

[1605] Specific operation: The emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. received in real time from the device to recognize the user's emotional state.

[1606] Input: Real-time user data (facial expressions, voice, typing speed, gestures)

[1607] Data Computation: Emotional state analysis and emotional data generation

[1608] Output: Emotion data

[1609] Step 5:

[1610] The server supplies data to the generative AI model to generate naming suggestions.

[1611] Specific operation: The server provides input data and emotion data to a generative AI model (e.g., GPT-3) and instructs it to generate naming suggestions. The generative AI model generates naming suggestions based on the provided information.

[1612] Input: Input data, emotion data converted into a processable format

[1613] Data Computation: Data computation using generative AI models

[1614] Output: Generated naming ideas

[1615] Step 6:

[1616] The server sends the generated naming proposal to the device.

[1617] How it works: The server compiles the naming ideas obtained from the generative AI model into a data packet, encrypts it, and sends it to the device, which then decrypts it and displays it on the user interface.

[1618] Input: Generated naming ideas

[1619] Data processing: Packetization and encryption of naming proposals, decryption after receiving

[1620] Output: Naming proposal displayed in the user interface

[1621] Step 7:

[1622] User enters feedback

[1623] Specific operation: The user inputs their evaluation and correction requests for the displayed naming proposals. The emotion engine analyzes their facial expressions and voices and generates emotion data.

[1624] Input: Evaluation of naming proposals, requests for revisions, user facial expressions and voice

[1625] Data computation: Emotional state analysis, emotional data generation

[1626] Output: Feedback data, emotion data

[1627] Step 8:

[1628] The device sends feedback to the server

[1629] Specific operation: The device assembles the input feedback and emotion data into a data packet, encrypts it, and sends it to the server.

[1630] Input: Feedback data, emotion data

[1631] Data processing: Packetization and encryption of feedback and emotion data

[1632] Output: Encrypted data packet

[1633] Step 9:

[1634] The server re-feeds the data to the generative AI model to generate revised naming proposals.

[1635] Specific operation: The server provides feedback and emotion data to the generative AI model and instructs it to generate revised naming suggestions. The generative AI model generates optimized naming suggestions based on the new feedback and emotion data.

[1636] Input: Feedback and emotion data

[1637] Data computation: Recomputing data using generative AI models

[1638] Output: Revised naming proposal

[1639] Step 10:

[1640] The server sends the revised naming proposal to the device and presents it to the user.

[1641] How it works: The server assembles revised naming suggestions from the generative AI model into a data packet, encrypts it, and sends it to the device. The device decrypts it and displays it again on the user interface. This process can be repeated until the user selects and finalizes the best naming suggestion.

[1642] Input: Revised naming proposal

[1643] Data processing: Packetization and encryption of the revised naming proposal, and decryption after receiving it

[1644] Output: Revised naming proposal displayed in the user interface

[1645] (Application example 2)

[1646] 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."

[1647] Conventional naming suggestion generation systems have difficulty generating naming suggestions that take user emotions into account, and have not been able to sufficiently increase user satisfaction. In addition, the process of revising generated naming suggestions based on feedback is cumbersome, making them difficult to use for users.

[1648] 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.

[1649] In this invention, the server includes a means for analyzing the user's emotions using an emotion engine and supplying the analysis results to the generation AI to generate revised naming suggestions, and a revision process for regenerating based on the user's feedback and emotion data. This makes it possible to generate original and attractive naming suggestions that take the user's emotions into consideration, thereby improving user satisfaction.

[1650] A "user" is someone who uses the system to generate naming suggestions and provide feedback.

[1651] "Requirements" are conditions or information that a user inputs into the system, such as keywords or categories.

[1652] A "terminal" is a device operated by a user, which inputs requirements, transmits data, displays naming suggestions, etc.

[1653] A "server" is a device that receives data sent from a terminal, processes and generates the data using generative AI or an emotion engine, and returns the results to the terminal.

[1654] "Generative AI" refers to artificial intelligence that generates naming ideas based on given data.

[1655] "Naming suggestions" are names or candidate names generated by the generation AI.

[1656] An "emotion engine" is a technology that analyzes user emotions and reflects the results in generating naming suggestions.

[1657] "Feedback" refers to the user's evaluation of the generated naming proposals and requests for corrections.

[1658] A "data packet" is a unit of digital information that contains user-entered requirements and feedback.

[1659] "Analysis results" refers to the data obtained after the emotion engine analyzes the user's emotions.

[1660] "Revised naming proposals" are naming proposals regenerated by the generation AI, taking into account user feedback and emotional data.

[1661] A "process" is a series of tasks that indicate the overall procedure or processing flow.

[1662] The present invention relates to a system that allows a user to input requirements and generates original and attractive naming ideas using a generative AI and an emotion engine. Specific embodiments are described below.

[1663] 1. User operations

[1664] Users launch a dedicated application on their device and create a new project. At that time, they select a naming category (e.g., brand name, product name, etc.) and enter the necessary concept information, keywords, and related images. For example, if they want to think of a name for a new organic cosmetics brand, they can enter keywords such as "natural," "gentle on the skin," and "beauty" and upload an image.

[1665] 2. Data transmission

[1666] The device generates data packets from the input requirements, keywords, images, etc., and encrypts the data before sending it to the server, ensuring data security.

[1667] 3. Emotion Data Analysis

[1668] When data arrives at the server, the emotion engine analyzes the user's facial expressions, voice, input speed, gestures, etc. in real time to recognize the user's emotional state (happiness, surprise, stress, etc.). For example, if the user smiles while typing, the emotion engine will detect that positive emotion.

[1669] 4. Naming Idea Generation

[1670] The server provides the received input data and emotional data to the generation AI, instructing it to generate naming suggestions. The generation AI generates original naming suggestions based on the information provided. For example, by taking into account the keywords "natural," "skin-friendly," and "beauty" and the user's positive emotional data, it generates naming suggestions such as "PureSkin Botanicals" and "EcoGlam Naturals."

[1671] 5. Submit a naming proposal

[1672] The server packages the generated naming suggestions into a data packet, encrypts it, and sends it to the device. The device then decrypts the received naming suggestions and displays them on a user interface. The user can review these suggestions and provide feedback.

[1673] 6. Processing Feedback

[1674] Users can then evaluate the generated naming proposals and input their feedback and requests for revisions, while their facial expressions and voices are continuously analyzed by the emotion engine. For example, users can input requests such as "Make it a little simpler" or "Add specific keywords." This feedback and emotion data is collected and sent back to the server from the device.

[1675] 7. Generate revised naming ideas

[1676] The server analyzes the received feedback and sentiment data and feeds it into a generative AI to generate revised naming suggestions, such as "PureGlow Organics."

[1677] 8. Final Check

[1678] The terminal receives the revised naming proposals sent again from the server, and the user finally checks them and selects and confirms the most suitable naming proposal.

[1679] Hardware / Software used

[1680] Servers and Terminals

[1681] Emotion Engine and Generative AI Models

[1682] Dedicated application

[1683] Prompt Sentence Examples

[1684] "Generate a name for a new organic cosmetics brand. Keywords are 'natural,' 'gentle on the skin,' and 'beauty.'"

[1685] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1686] Step 1:

[1687] User enters requirements

[1688] Input: The user launches a dedicated application on the device and enters the naming category, necessary concept information, keywords, and related images.

[1689] Specific operation: The user selects the category "Brand Name" and enters keywords such as "natural," "gentle on the skin," and "beauty" along with an image.

[1690] Output: Data entered into the terminal is generated.

[1691] Step 2:

[1692] The device sends the entered data to the server.

[1693] Input: The data generated in step 1.

[1694] Specific operation: The device creates a data packet, encrypts it, and sends it to the server.

[1695] Output: The data packet arrives at the server.

[1696] Step 3:

[1697] The server uses an emotion engine to analyze the user's emotions.

[1698] Input: Data packets arriving at the server and real-time data such as the user's facial expressions, voice, typing speed, and gestures.

[1699] Specific operation: The server's emotion engine analyzes this data and recognizes the user's emotional state (happiness, surprise, stress, etc.).

[1700] Output: Analysis results about the user's emotional state.

[1701] Step 4:

[1702] The server provides data to the AI ​​to generate naming suggestions.

[1703] Input: Input data and emotion data.

[1704] Specific operation: The server provides these data to the generation AI and instructs it to generate naming suggestions. The generation AI generates naming suggestions based on the provided information.

[1705] Output: Generated naming ideas (e.g. "PureSkin Botanicals" or "EcoGlam Naturals").

[1706] Step 5:

[1707] The server sends the generated naming proposal to the device.

[1708] Input: Generated naming ideas.

[1709] Specific operation: The server assembles the naming proposals into a data packet, encrypts it, and sends it to the terminal.

[1710] Output: Data packets arrive at the terminal.

[1711] Step 6:

[1712] The device displays naming suggestions to the user

[1713] Input: The received data packet.

[1714] Specific operation: The terminal decodes the data packet and displays naming suggestions on the user interface.

[1715] Output: The naming proposal is displayed on the terminal screen.

[1716] Step 7:

[1717] User enters feedback

[1718] Input: The displayed naming suggestion.

[1719] Specific operation: The user inputs their evaluation and requests for revisions to the naming proposals, and facial expressions and voice data are also analyzed by the emotion engine.

[1720] Output: Feedback and emotion data.

[1721] Step 8:

[1722] The device sends feedback to the server

[1723] Input: Feedback and emotion data.

[1724] Specific operation: The device assembles this data into a data packet, encrypts it, and sends it to the server.

[1725] Output: The data packet arrives at the server.

[1726] Step 9:

[1727] The server re-feeds the feedback and sentiment data and generates revised naming proposals.

[1728] Input: Feedback and emotion data.

[1729] Specific actions: The server analyzes this data and feeds it back to the generation AI, instructing it to generate revised naming proposals.

[1730] Output: A newly generated revised naming proposal (e.g., "PureGlow Organics").

[1731] Step 10:

[1732] The server sends the revised naming proposal to the device and presents it to the user.

[1733] Input: The newly generated revised naming proposal.

[1734] Specific operation: The server assembles the revised naming proposal into a data packet, encrypts it, and sends it to the device, which decrypts the data packet and displays it on the user interface.

[1735] Output: The revised naming proposal is displayed on the terminal screen.

[1736] 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.

[1737] 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.

[1738] 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.

[1739] 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.

[1740] 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.

[1741] 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.

[1742] 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).

[1743] 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.

[1744] 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."

[1745] 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.

[1746] 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).

[1747] 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.

[1748] 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.

[1749] 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.

[1750] 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.

[1751] 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.

[1752] 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.

[1753] 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.

[1754] 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.

[1755] 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.

[1756] 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.

[1757] The following is further disclosed regarding the above embodiment.

[1758] (Claim 1)

[1759] a means for a user to input requirements;

[1760] A means for transmitting input data from the terminal to a server;

[1761] A means for the server to provide data to the generation AI to generate naming suggestions;

[1762] A means for the server to transmit the generated naming proposal to the terminal;

[1763] a means for the terminal to display naming proposals to a user;

[1764] a means for the user to input feedback;

[1765] means for the device to send feedback to the server;

[1766] The server re-supplies the data to the AI ​​with feedback to generate revised naming proposals;

[1767] and means for the server to transmit the revised naming proposal to the terminal.

[1768] (Claim 2)

[1769] 2. The system according to claim 1, further comprising means for generating naming proposals by the server supplying the data to a generating AI after the user inputs requirements and the terminal transmits the data to the server.

[1770] (Claim 3)

[1771] 2. The system according to claim 1, further comprising a processing step in cooperation with the above-mentioned means, including a revision process in which the generated naming proposals are regenerated based on user feedback.

[1772] "Example 1"

[1773] (Claim 1)

[1774] a means for a user to input requirements;

[1775] A means for transmitting input data from the terminal to a server;

[1776] A means for the server to convert the received data into JSON format,

[1777] A means for the server to supply data to the generative AI model to generate naming suggestions;

[1778] A server encrypts the generated naming proposals and transmits them to the terminal;

[1779] a means for the terminal to decode the naming proposal and display it to the user;

[1780] a means for the user to input feedback;

[1781] a means for the device to encrypt and transmit the feedback to the server;

[1782] A means for the server to analyze the feedback and re-feed the data to the generative AI model to generate revised naming proposals; and

[1783] A means for the server to encrypt the revised naming proposal and send it to the terminal;

[1784] A system including:

[1785] (Claim 2)

[1786] 2. The system of claim 1, further comprising means for the server to convert the data into JSON format and provide it to a generative AI model to generate naming suggestions after the data is encrypted and sent to the server.

[1787] (Claim 3)

[1788] 2. The system of claim 1, further comprising a processing step in cooperation with the means described above, including a revision process for regenerating naming suggestions based on user feedback.

[1789] "Application Example 1"

[1790] (Claim 1)

[1791] means for the user to input requirements via a voice recognition system or touch screen;

[1792] a means for encrypting and transmitting input data packets to a server by a terminal of the vehicle;

[1793] A means for the server to supply data to the generative AI model to generate naming suggestions;

[1794] A means for the server to encrypt and transmit the generated naming proposal to a terminal of the vehicle;

[1795] a means for displaying the naming proposal to a user on a terminal of the vehicle;

[1796] a means for the user to input feedback via a voice recognition system or a touch screen;

[1797] a means for the vehicle terminal to encrypt the feedback and transmit it to the server;

[1798] The server provides the data to the AI ​​model again along with the feedback to generate revised naming proposals;

[1799] A means for the server to encrypt and transmit the revised naming proposal to a terminal of the vehicle;

[1800] A system including:

[1801] (Claim 2)

[1802] 2. The system of claim 1, further comprising means for generating naming suggestions by feeding the data to a generative AI model after a user inputs requirements and the vehicle terminal transmits the data to the server.

[1803] (Claim 3)

[1804] The system according to claim 1, further comprising a processing step that includes a revision process in which the generated naming proposals are regenerated based on user feedback, and that cooperates with the above-mentioned means and an encrypted communication function between the vehicle's terminal and the server.

[1805] "Example 2: Combining Emotion Engines"

[1806] (Claim 1)

[1807] a means for a user to input requirements;

[1808] A means for transmitting input data from the terminal to a server;

[1809] A means for the server to supply data to the generative AI model to generate naming suggestions;

[1810] A means for the server to transmit the generated naming proposal to the terminal;

[1811] a means for the terminal to display naming proposals to a user;

[1812] a means for the user to input feedback;

[1813] means for the device to send feedback to the server;

[1814] A means for the server to re-feed the data to the generative AI model with feedback to generate revised naming proposals; and

[1815] a means for the server to transmit the revised naming proposal to the terminal;

[1816] a means for the terminal to use an emotion engine for recognizing the user's emotion and transmit the user's emotion data to the server;

[1817] The server supplies data to the generative AI model based on the emotion data and adjusts the generated naming proposals;

[1818] A system including:

[1819] (Claim 2)

[1820] 2. The system of claim 1, further comprising means for generating naming suggestions by feeding the data to a generative AI model after a user inputs requirements and the terminal transmits the data to the server.

[1821] (Claim 3)

[1822] 2. The system of claim 1, further comprising a processing step in cooperation with the above means, including a revision process in which the generated naming suggestions are regenerated based on user feedback and sentiment data.

[1823] "Application example 2 when combining emotion engines"

[1824] (Claim 1)

[1825] a means for a user to input requirements;

[1826] A means for transmitting input data from the terminal to a server;

[1827] A means for the server to provide data to the generation AI to generate naming suggestions;

[1828] A means for the server to transmit the generated naming proposal to the terminal;

[1829] a means for the terminal to display naming proposals to a user;

[1830] a means for the user to input feedback;

[1831] means for the device to send feedback to the server;

[1832] The server uses an emotion engine to analyze the user's emotions, and supplies the analysis results to the generation AI to generate revised ...

Claims

1. a means for a user to input requirements; A means for transmitting input data from the terminal to a server; A means for the server to provide data to the generation AI to generate naming suggestions; A means for the server to transmit the generated naming proposal to the terminal; a means for the terminal to display naming proposals to a user; a means for the user to input feedback; means for the device to send feedback to the server; The server re-supplies the data to the AI ​​with feedback to generate revised naming proposals; and means for the server to transmit the revised naming proposal to the terminal.

2. 2. The system according to claim 1, further comprising means for generating naming proposals by the server supplying the data to a generating AI after the user inputs requirements and the terminal transmits the data to the server.

3. 2. The system according to claim 1, further comprising a processing step in cooperation with the above means, including a revision process in which the generated naming proposals are regenerated based on user feedback.

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