System

Abstract

To provide a system.SOLUTION: A system includes: means for performing generation of a name; means for checking the possibility of registering the generated name in different countries; means for checking whether or not the generated name infringes a trademark in different countries; means for creating a list of the results of the checking; and means for assembling application information such as registration.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] When considering a company or service name for the development of a new service or business, it is necessary to check not only the suitability of the name (meaning, image, etc.), but also the rights and procedures to ensure that it can be registered in each country and that it does not infringe on trademarks. However, these confirmation procedures are time-consuming and difficult to carry out efficiently. [Means for solving the problem]

[0005] The present invention provides a system including a means for generating names, a means for confirming whether the generated names can be registered in each country, a means for confirming whether the generated names infringe trademarks in each country, a means for listing the above confirmation results, and a means for organizing application information for registration, etc. This makes it possible to efficiently confirm rights and procedures when considering and implementing names. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13]FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 16] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system according to the embodiment 2 of the second embodiment in the case where an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0011] 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.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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 wide area network (WAN) and / or a local area network (LAN).

[0018] 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.

[0019] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. 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.

[0020] 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 (e.g., voice and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0024] 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.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The system of the present invention uses generative AI as a means of generating names. The generative AI generates appropriate names based on specific parameters (e.g., industry, target demographic, service content, etc.). The generated names are evaluated by a means for checking the possibility of registration in each country and a means for checking trademark infringement. These means are performed by linking related APIs and accessing trademark regulations and registration information of each country. The confirmation results are compiled into a list and organized together with application information such as registration. This improves the efficiency of planning and procedures.

[0029] "Example 2"

[0030] As a specific example, consider the case of thinking of a name for a newly developed game app. Generative AI generates an appropriate name based on parameters such as the game genre (e.g. RPG, puzzle, action, etc.), target demographic (e.g. children, adults, men, women, etc.), and game characteristics (e.g. fantasy, real-time, multiplayer, etc.). The generated name is evaluated by a method to check for registration feasibility and trademark infringement in each country, and the confirmation results are compiled into a list. This aims to streamline planning and procedures.

[0031] The process flow of each embodiment will be described below.

[0032] "Example 1"

[0033] Step 1: Generative AI generates an appropriate name based on certain parameters (e.g. industry, target demographic, service content, etc.).

[0034] Step 2: The generated name is evaluated using a methodology to check for its registrability in each country. This is done through the integration of relevant APIs, which have access to each country's trademark regulations and registration information.

[0035] Step 3: Similarly, the generated names are evaluated by a means to check for trademark infringement. Step 4: The results of the check are compiled into a list and organized together with the application information for registration, etc. This will streamline the planning and procedures.

[0036] "Example 2"

[0037] Step 1: When thinking of a name for a new game app to be developed, the generative AI generates an appropriate name based on parameters such as the game genre (e.g., RPG, puzzle, action, etc.), target demographic (e.g., children, adults, men, women, etc.), and game features (e.g., fantasy, real-time, multiplayer, etc.).

[0038] Step 2: The generated name is evaluated using a methodology to check for its registrability in each country. This is done through the integration of relevant APIs, which have access to each country's trademark regulations and registration information.

[0039] Step 3: Similarly, the generated names are evaluated by a means to check for trademark infringement. Step 4: The results of the check are compiled into a list and organized together with the application information for registration, etc. This will streamline the planning and procedures.

[0040] Example 1

[0041] Next, a description will be given of Example 1 of the embodiment 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".

[0042] In the modern commercial environment, the selection of an appropriate name is crucial, as the name of a product or service directly affects the brand value. However, when considering the international market, it is difficult to efficiently evaluate the possibility of trademark registration in different countries and the risk of trademark infringement. With traditional methods, such evaluations require a huge amount of time and resources, which is a heavy burden especially for small and medium-sized enterprises and start-ups. There is a need for a method to solve this problem and perform everything from name generation to legal risk assessment quickly and efficiently.

[0043] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0044] In this invention, the server includes a means for inputting a prompt sentence that takes into account a specific industry, target demographic, and service content into a generative AI to generate an appropriate name, a means for evaluating the registrability of the generated name in each country and the risk of trademark infringement through the linkage of a trademark registration database and an API that searches trademark law regulation information, and a means for listing the above evaluation results and organizing application information for registration, etc. This enables users to quickly and efficiently select a name that takes legal risks into account.

[0045] "Specific parameters" are conditions such as industry, target demographic, and service content that are taken into account when generating a name.

[0046] A "name" is a word or combination used to identify a product, service, company, etc.

[0047] "Registrability in each country" refers to whether a name can be registered as a trademark in a particular country.

[0048] "Trademark infringement" is a legal issue that arises when someone unfairly violates the trademark rights of others, especially when they use it without permission.

[0049] "Listing methods" are ways of organizing and visualizing information in a table format.

[0050] "Means for organizing application information for registration, etc." refers to a method for systematically organizing and managing information related to trademark registration of names and other applications.

[0051] "Generative AI" is an artificial intelligence model that automatically generates new content and ideas based on input data and conditions.

[0052] A "prompt" is an instruction or question used to instruct a generative AI model on a specific task.

[0053] The "API for referencing the trademark registration database" is an interface that accesses external systems to inquire about whether a trademark has been registered and for detailed information.

[0054] The "API for searching trademark law and regulation information" is a programming interface for searching for trademark laws and regulation information for specific countries or regions.

[0055] In the embodiment of the present invention, the server, the terminal, and the user work together to carry out the work. The server is equipped with a generative AI model (e.g., a machine learning model using TensorFlow or PyTorch) and has a function of generating a prompt sentence and proposing an appropriate name based on specific parameters (e.g., industry, target demographic, service content) provided by the user via the terminal.

[0056] The terminal acts as an interface to receive user input and transmits the input parameters to the server. For example, if a user inputs a prompt such as "Generate the name of a cloud service targeted at business people in the IT industry," this information is sent from the terminal to the server.

[0057] The server receives the prompt and uses a generative AI model to generate multiple name suggestions. The generated name suggestions are then queried by APIs that reference trademark registration databases and APIs that search trademark law and regulatory information to evaluate the possibility of trademark registration and the risk of trademark infringement. These APIs are used, for example, to access international trademark information databases and trademark law and regulatory information for specific countries.

[0058] The obtained information is organized by the server and displayed to the user via the terminal. The display clearly indicates the likelihood of registration for each proposed name and the risk of trademark infringement. The user can use this information to select the most suitable name.

[0059] This allows users to quickly and efficiently determine names for new products and services while taking into account legal risks. Especially when considering international markets, the system offers great convenience and significantly reduces the time and cost of the name selection process.

[0060] The flow of the identification process will be described with reference to FIG.

[0061] Step 1: Accepting input from the user

[0062] The user inputs parameters such as the specific industry, target demographic, and service content through the terminal. This input information is used to create a prompt for name generation. The terminal receives the input from the user and sends it to the server. At this time, the input parameters act as conditions for generating an appropriate name.

[0063] Step 2: Generate prompts and names

[0064] The server generates a prompt based on parameters received from the user. This prompt is input into a generative AI model and used to generate a name that matches certain conditions. The generative AI model uses this prompt as the basis for data processing and calculations to output a series of name suggestions. For example, if the prompt is "Generate a name for a cloud service targeted at business people in the IT industry," the AI ​​model will generate multiple names that fit the conditions.

[0065] Step 3: Check for trademark registrability and trademark infringement

[0066] For each generated name, the server queries the relevant APIs to assess the possibility of trademark registration and the risk of trademark infringement. This process uses APIs that refer to the trademark registration database and APIs that search trademark law and regulatory information. For each name, the information obtained from these APIs is processed and calculated to assess the name's possibility of registration and the risk of trademark infringement, and the results are returned to the server.

[0067] Step 4: List and present the results

[0068] Based on the information obtained in step 3, the server lists and organizes the registration possibility and trademark infringement risk of each name. This organized information is presented to the user via the terminal. The presented information indicates the registration possibility and trademark infringement risk for each generated name. The user uses this information as a reference to select the final name. This step enables the user to quickly select an appropriate name while taking legal risks into account.

[0069] (Application example 1)

[0070] Next, a description will be given of Application Example 1 of Form 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."

[0071] When deciding on names for commercial facilities or products to be used in virtual space, it is difficult to generate creative names while efficiently checking whether the names can be registered as trademarks in each country and whether there is a risk of trademark infringement. To solve this problem, a system is needed that automates name generation and quickly evaluates the legal issues of generated names.

[0072] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0073] In this invention, the server includes a means for generating names based on parameters such as industry, target demographic, and service content, a means for evaluating the registrability of the generated names in each country and the risk of trademark infringement, and a means for providing the user with a list of the evaluation results. This enables the user to efficiently generate creative names, quickly understand the legal issues involved, and select an appropriate name and apply for registration.

[0074] A "means for generating names" is a function that uses artificial intelligence technology to automatically generate appropriate names based on input parameters (industry, target demographic, service content, etc.).

[0075] "Means for verifying the registrability of the generated name in each country" refers to a function that runs a process to determine whether a particular name is registrable in accordance with the laws and trademark registration regulations of each country.

[0076] The "means of verifying whether the generated name will not infringe on trademarks in each country" refers to a function that analyzes and determines whether the created name is likely to conflict with existing trademarks by referring to country-specific trademark databases.

[0077] The "means of listing the above confirmation results" refers to a function that organizes and displays in list form the confirmation results regarding the registrability of a name and the risk of trademark infringement.

[0078] A "means of organizing application information for registration, etc." is a function that assists the user in the process of managing and organizing documents and information related to registration applications that they need to submit.

[0079] "Means for suggesting names to be applied to commercial facilities and products within a virtual space" refers to a function that generates and suggests names for stores and products to be used within a virtual environment.

[0080] "Using generative AI based on parameters such as industry, target demographic, and service content" refers to the process in which artificial intelligence automatically generates appropriate naming suggestions based on information such as the specified industry, target customer demographic, and characteristics of the services provided.

[0081] "Through integration of relevant APIs" means that the assessment of the possibility of trademark registration and the risk of trademark infringement is carried out automatically online, utilizing external application programming interfaces (APIs).

[0082] The embodiment of the present invention is a system that combines name generation and legal evaluation. The system is composed of a server-based software application that is accessed by a user via a terminal. The server is equipped with a generative AI model and an external API access function. Specifically, a program written in Python uses a generative AI model (e.g., GPT-3 or its successor) to generate a name based on parameters provided by the user (industry, target demographic, service content, etc.). The generated name is used to evaluate the registrability and risk of trademark infringement in each country through an external API.

[0083] Users access the service through devices such as smartphones or head-mounted displays and input the necessary parameters. The server receives this input, generates and evaluates names, and feeds the results back to the user. Through this process, users can efficiently and safely decide on names for commercial facilities and products to be used in virtual space.

[0084] For example, if you input the parameters "cafe," "youth," and "environmentally friendly," the generative AI model will suggest names such as "GreenSip Cafe" and "EcoBrew Corner" based on these parameters. The server then uses an external API to evaluate the registrability and trademark infringement risk of these names in each country and provide the results to the user. This process allows users to understand the risks involved in name selection in advance and make appropriate decisions.

[0085] The flow of the identification process will be described with reference to FIG.

[0086] Step 1: The user inputs parameters such as industry, target demographic, and service content through the terminal. This input is sent to the server as basic data for name generation. The data sent from the terminal is received by the server and temporarily stored for further processing.

[0087] Step 2: The server generates a name using a generative AI model based on the parameters received in step 1. In this process, the input parameters are analyzed by the AI ​​model to generate a relevant name. The generated name is temporarily stored by the server for the next step.

[0088] Step 3: The server evaluates the generated name for its registrability in each country and the risk of trademark infringement through the relevant API. In this evaluation process, the server accesses external trademark databases to investigate whether the generated name may conflict with existing trademarks. The evaluation results are collected and organized by the server.

[0089] Step 4: The server lists the evaluation results obtained in step 3 and displays them to the user. In this step, information on the possibility of registration and the risk of trademark infringement is provided to the user in an easy-to-understand format. Based on this information, the user can make a final selection of the name to be used.

[0090] Step 5: Based on the name finally selected by the user, the server organizes the application information for registration, etc., and provides it to the user. In this process, the documents and information required for the registration application using the selected name are organized, helping the user to complete the application process smoothly.

[0091] Through these steps, users can efficiently and safely select the names of their stores and products to be used in the virtual space and proceed with the necessary registration procedures. Each processing step involves data exchange between the server and the device, data processing using a generative AI model, information collection and evaluation through linkage with external APIs, and information provision to the user.

[0092] Example 2

[0093] Next, a second embodiment of the second embodiment will be described. 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."

[0094] When coming up with a name for new content, it is a time-consuming and labor-intensive task to come up with creative proposals while simultaneously evaluating the possibility of international trademark registration and the risk of trademark infringement. In particular, when considering use in multiple markets, it can be difficult to quickly identify an attractive name that meets legal constraints.

[0095] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0096] In this invention, the server includes a means for generating a name based on the attributes of the content, a means for checking the international registrability of the generated name and the presence or absence of trademark infringement, a means for listing the check results by category and presenting them on a user interface, and a means for organizing application information for registration, etc. based on the name selected by the user and automating the procedures. This makes it possible to quickly propose creative names and simultaneously evaluate their legal applicability.

[0097] "Content attributes" are basic elements for defining content, such as the genre, target demographic, and characteristics of the content.

[0098] "Name generation" is the process of creating a suitable name for new content based on the attributes of the input content.

[0099] "International regisability and trademark infringement check" refers to the act of investigating whether a particular name is available in the trademark registers of various countries around the world and whether there is a risk of infringement due to its similarity to other trademarks.

[0100] "Listing the confirmation results by category and presenting them in a user interface" means classifying the investigated information on the possibility of registration and the presence or absence of trademark infringement, and displaying this information in a form that is visually easy for users to understand.

[0101] "Organizing application information for registrations and other purposes based on the name selected by the user and automating procedures" means consolidating information related to the necessary registrations and applications using the name selected by the user, and streamlining and automating these processes.

[0102] A "generative AI model" is a system that uses artificial intelligence technology to automatically create new data or text from specific input information.

[0103] "API integration" refers to integration through a programming interface that enables different software or services to share functions and exchange data with each other.

[0104] An "API for accessing external databases" is an interface that enables a program to directly query databases managed by other organizations on the Internet.

[0105] In an embodiment of the present invention, the system includes a process for generating a name, checking its international registrability and trademark infringement, and finally presenting the information to a user. This process is mainly composed of software that runs on a server and a terminal that accepts user operations.

[0106] The server uses a generative AI model to generate a name based on the attributes of the input content. For example, a text generation model such as GPT (Generative Pre-trained Transformer) developed by OpenAI is used as the generative AI model. The generated name is checked for international registration possibility and trademark infringement through API connection to an external database. Through this API connection, the server accesses trademark databases from around the world to check whether the proposed name conflicts with existing trademarks.

[0107] The terminal is responsible for accepting input from the user and sending it to the server. Through the terminal, the user specifies attributes such as the genre of the content, the target demographic, and the characteristics of the content. For example, if the user inputs the attributes "Genre: RPG, Target demographic: Adults, Characteristics: Fantasy, Real-time, Multiplayer", this information is formatted as a prompt sentence and sent to the generative AI model on the server.

[0108] The server responds by presenting the generated name and the results of its legal evaluation to the user via the terminal. The user can then use this information to select the most appropriate name and proceed to the next steps, such as registering that name or applying for a trademark. Through this process, the user can decide on a name for new content efficiently and with an understanding of the legal risks.

[0109] The flow of the identification process will be described with reference to FIG.

[0110] Step 1: The user uses the device to input the attributes of the content (genre, target demographic, features, etc.). This input becomes the basic data for the generative AI model to process. The device receives this input data and creates a prompt sentence based on it.

[0111] Step 2: The terminal sends the created prompt to the server. The server receives the prompt and generates a name using a generative AI model. From the input prompt, the server outputs a name that is appropriate for the content.

[0112] Step 3: The server checks the generated name against external databases to verify the name's international registrability and trademark infringement. At this stage, the server takes the generated name as input and the search results from external databases as output.

[0113] Step 4: The server receives the search results from the external database, organizes the information into a list, and sends it to the terminal. This list includes the registrability of the name and the risk of trademark infringement. The server processes the search results and sends the organized information to the terminal.

[0114] Step 5: The terminal presents the list received from the server to the user. The user selects the name that he / she thinks is most appropriate from the presented list. The terminal receives the user's selection and transmits the information to the server.

[0115] Step 6: The server organizes the registration and other application information based on the name selected by the user, and automates the registration process for the user. At this stage, the server processes the selected name and the necessary application information, and guides the user on the next steps.

[0116] Through these steps, the system enables users to efficiently and effectively determine names for new content and automate its registration and trademark verification.

[0117] (Application example 2)

[0118] Next, a description will be given of Application Example 2 of Form 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."

[0119] When launching a new product or brand in the online market, coming up with an appropriate product or brand name is a critical factor that directly impacts its success. However, coming up with a creative and attractive product or brand name is not easy, and it is necessary to comprehensively evaluate whether the name has sufficient online searchability, whether it can secure buzz on social media, and whether it can be trademarked. Efficiently generating and evaluating names that meet these requirements is a major challenge, especially for small and medium-sized businesses.

[0120] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0121] In this invention, the server includes a means for generating product names and brand names, a means for checking the online searchability of the generated product names and brand names, a means for evaluating whether the generated product names and brand names have topicality on SNS, and a means for checking the possibility of trademark registration of the generated product names and brand names. This allows businesses to efficiently generate creative and attractive product names and brand names, and to comprehensively evaluate the names' online searchability, topicality on SNS, and possibility of trademark registration. This system allows businesses to reduce risks when introducing new products and brands to the market and increase the chances of success.

[0122] "Means for generating product and brand names" refers to a function that automatically creates appropriate product and brand names based on the product category, target demographic, and product features designated by the user.

[0123] "Means to verify online searchability" is a function that evaluates whether the generated product or brand name can be easily searched for on the Internet.

[0124] The "means for evaluating whether a product name or brand name has potential to be discussed on social media" is a function for judging whether a generated product name or brand name is likely to be discussed by users on a social networking service.

[0125] "Means to confirm the possibility of trademark registration" is a function that investigates whether the generated product name or brand name can be registered as a trademark, in other words, whether there is any overlap or similarity with other trademarks.

[0126] "Through integration with related APIs" refers to programming interfaces used to automatically collect and process information by integrating with external databases and services.

[0127] An embodiment of the present invention relates to an automated system for generating and evaluating product and brand names in an online market. A server executes a program that generates product and brand names using a generative AI model. The program generates a prompt sentence based on input information from a user, such as the product category, target demographic, and product features, and transmits the prompt sentence to the generative AI model. The generative AI model proposes creative and attractive product and brand names based on the transmitted prompt sentence.

[0128] For each generated product and brand name, the server also runs a program that evaluates its online searchability, social media buzzworthiness, and trademarkability. This evaluation is automated through the associated API, accessing online databases and trademark registry databases to verify the uniqueness and availability of the generated names.

[0129] The system is developed using programming languages ​​such as Python and JavaScript, and utilizes libraries for natural language processing such as TensorFlow, PyTorch, and NLTK. Using these technologies, the server performs complex text analysis and data processing to provide useful information to users.

[0130] As a specific example, the prompt "Brand name of fashion T-shirts for young people made from eco-friendly materials" is generated to reflect the product category "fashion," the target demographic "young people," and the product feature "made from eco-friendly materials." The generative AI model proposes multiple brand names based on this prompt, and these names are automatically evaluated for online searchability, buzzworthiness on social media, and possibility of trademark registration. Through this process, businesses can efficiently select attractive product and brand names and prepare for market launch.

[0131] The flow of the identification process will be described with reference to FIG.

[0132] Step 1: The user inputs the product category, target demographic, and product features. This input is sent to the server as the basic data for generating product names and brand names.

[0133] Step 2: The server generates a prompt sentence based on the input product category, target demographic, and product features. This prompt sentence serves as an instruction for the generative AI model to generate the product name and brand name.

[0134] Step 3: The generative AI model generates product and brand names based on the received prompt text. During this process, the AI ​​model uses text generation techniques to suggest multiple candidates.

[0135] Step 4: The server checks the online searchability of the generated product and brand names. This process involves analyzing existing search engine results on the Internet to assess the uniqueness of the generated names.

[0136] Step 5: At the same time, the server evaluates the popularity of the generated product and brand names on social media based on the analysis of social media trends and hashtags.

[0137] Step 6: The server then checks for trademark registration feasibility by querying national and international trademark databases to see if the generated name conflicts with existing trademarks.

[0138] Step 7: The server reports the results of the evaluation of online searchability, social media popularity, and trademark registration potential to the user. The report includes the recommendation level and potential problems of the generated product and brand names.

[0139] Through this process, users can efficiently obtain creative and practical product and brand names, thus increasing the chances of success in the online market.

[0140] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0141] "Example 1"

[0142] The present invention combines a system that generates names, checks whether they can be registered in each country, checks for trademark infringement, lists the check results, and organizes application information such as registration, with an emotion engine that recognizes the emotions of the user. Specifically, the emotion engine recognizes the emotions of the user and generates a name based on the emotions. For example, if the user is expressing an emotion of joy, a name that gives a positive impression that matches the emotion is generated. The emotion engine also checks whether the name generated based on the user's emotions can be registered in each country and whether there is trademark infringement. This makes it possible to generate names that reflect the emotions of the user and evaluate them.

[0143] "Example 2"

[0144] As a concrete example, consider the case of thinking of a name for a new game app to be developed. When a user is excited about the development of the game, the emotion engine recognizes that emotion and generates a name that expresses that excitement. The generated name is evaluated by a means for checking the possibility of registration in each country, and is also evaluated by a means for checking trademark infringement. This makes it possible to generate and evaluate names that reflect the user's emotions.

[0145] The process flow of each embodiment will be described below.

[0146] "Example 1"

[0147] Step 1: The emotion engine recognizes the user's emotion.

[0148] Step 2: Name generation is performed based on the emotions recognized by the emotion engine.

[0149] Step 3: Check the registerability of the generated name in each country.

[0150] Step 4: Check the generated name for trademark infringement.

[0151] Step 5: List the confirmation results and organize the application information for registration, etc.

[0152] "Example 2"

[0153] Step 1: When a user expresses excitement about game development, the emotion engine recognizes that emotion.

[0154] Step 2: The emotion engine generates a name that expresses the feeling of excitement based on the recognized feeling of excitement.

[0155] Step 3: Check the registerability of the generated name in each country.

[0156] Step 4: Check the generated name for trademark infringement.

[0157] Step 5: List the confirmation results and organize the application information for registration, etc.

[0158] Example 1

[0159] Next, a description will be given of Example 1 of the embodiment 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".

[0160] In the modern business environment, it is extremely important to give a suitable name to a new product or service. The name forms the first impression of the product or service and is an element that is memorable to consumers. However, it is extremely difficult to find a name that can be registered in each country and does not pose a risk of trademark infringement while taking into account cultural background and language differences. In addition, conventional technologies do not provide a method to generate a name that reflects the emotions and values ​​of the companies and individuals who bring the product or service to the market.

[0161] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0162] In this invention, the server includes a means for collecting user emotion data, a means for analyzing the collected emotion data, and a means for generating a name by combining the analysis result with specific parameters provided by the user. This makes it possible to generate a name that reflects the user's emotions and values, can be registered in each country, and has no risk of trademark infringement. In addition, by linking related APIs, it is possible to quickly check the registrability of the name in each country and whether or not there is a trademark infringement, which is expected to improve the efficiency and accuracy of the naming process for new products and services in a business environment.

[0163] "User emotional data" refers to information about a user's emotional state collected from facial expressions, tone of voice, body language, etc.

[0164] "Means for analyzing the collected emotional data" refers to processes or algorithms that process the collected emotional data and identify the user's current emotional state.

[0165] "Specific parameters" are specific information such as industry, target demographic, and service content provided by the user when generating a name.

[0166] "Name Generator" refers to a process or algorithm that generates an appropriate name based on collected sentiment data and certain parameters provided by the user.

[0167] "Country availability verification procedures" refers to the process or service that checks whether a generated name can be legally registered in a particular country.

[0168] "Trademark verification measures" refers to processes or services that verify whether the generated name infringes existing trademark rights in a particular country.

[0169] "Means for listing the verification results" refers to a process or algorithm that organizes and displays in a list the verification results regarding the registrability of the name in each country and whether there is any trademark infringement.

[0170] "Means of organizing application information for registration, etc." refers to a process or system that systematically organizes the information required for name registration or trademark application, facilitating the procedure.

[0171] In an embodiment of the present invention, the terminal and the server work together to collect emotion data from the user, analyze the data, generate a name, and legally verify the generated name. The terminal is a device that collects the user's emotion data, records the user's facial expressions and voice through a camera and microphone, and transmits them to the server in an analyzable format. This process uses facial expression recognition software (e.g., OpenCV) and voice analysis libraries (e.g., WebRTC).

[0172] On the server side, a neural network model (e.g., a model using TensorFlow) is used to analyze the received emotion data. The model identifies the user's emotional state and feeds the results back into the name generation process. A generative AI model (e.g., a GPT-based model) is used to generate the name based on the user's emotion and a prompt that reflects certain parameters (e.g., "Generate a service name for a young target audience in the health food industry that expresses the emotion of joy").

[0173] The legal check of the generated name is carried out using a related API that provides access to a trademark registration database, allowing the server to check whether the generated name can be registered in each country and whether there is a risk of trademark infringement, and to report the results to the user in a list.

[0174] This method allows the generation of names that reflect the user's emotions and their legal verification to be carried out automatically through a series of processes, significantly improving the efficiency and satisfaction of naming products and services.

[0175] The flow of the identification process will be described with reference to FIG.

[0176] Step 1: Collect user emotion data

[0177] The device collects the user's facial expressions and voice using a camera and microphone. This collection process uses facial recognition software and voice analysis libraries to collect data that suggests the user's emotions. The collected data (input) is sent to a server for sentiment analysis (output).

[0178] Step 2: Analyze the sentiment data

[0179] The server analyzes the received emotion data using a neural network model to identify the user's emotional state. From the input emotion data, the model extracts features and identifies the emotion based on them (output). This output is used in the name generation process.

[0180] Step 3: Generate names

[0181] The generative AI model on the server generates an appropriate name using a prompt sentence that combines the identified emotional state with specific parameters provided by the user (industry, target demographic, service content). In this step, the emotional state and parameters are given as input, and the generative AI model processes them to generate a new name (output).

[0182] Step 4: Check for registrability and trademark infringement

[0183] For the generated name, the server checks the name's registrability and trademark infringement status in each country through the relevant API. In this process, the generated name is used as input, and the API returns information on registrability and trademark infringement status (output).

[0184] Step 5: List and report findings

[0185] Finally, the server lists the results of the verification of registrability and trademark infringement, organizes this information, and reports it to the user. In this step, the verification result data is used as input, and an organized information list is output. This list is provided to the user via the terminal as a reference for making the final name selection.

[0186] (Application example 1)

[0187] Next, a description will be given of Application Example 1 of Form 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."

[0188] Conventional name generation systems have the problem that it is difficult to generate names that fully reflect the user's feelings and sensibilities. Furthermore, the process of verifying whether the generated name is registrable in each country and whether it infringes on trademarks is complicated and requires a great deal of time and effort. These issues are factors that reduce the speed and efficiency of business, especially for businesses that operate in international virtual spaces.

[0189] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0190] In this invention, the server includes a means for reading the user's emotions, a means for using a generative AI to generate a name that gives an impression that matches the emotion based on the emotion read, and a means for verifying the registrability of the generated name in each country and the presence or absence of trademark infringement through the linkage of related APIs. This enables the rapid generation of a name that reflects the user's emotions and sensibilities, and reduces the risk of using the name internationally.

[0191] A "means for generating names" is a function that automatically devise and output appropriate names or designations based on specific parameters and the user's emotional data.

[0192] "National name registrability checks for generated names" refers to the process of determining whether a proposed name can be used under the laws and registry systems of different countries.

[0193] "Means to verify that the generated name does not violate trademark laws in each country" refers to a function that verifies whether the proposed name violates the trademark laws of each country.

[0194] The "means of listing the above confirmation results" refers to the process of organizing the results of name generation and subsequent registration feasibility and trademark infringement confirmation into a visually easy-to-understand list.

[0195] "Means of organizing application information for registrations, etc." refers to the process of systematically preparing and managing the information required when making official registrations or applications for the use of a name.

[0196] "Means of reading user emotions" means technology that identifies and analyzes the user's current emotional state from the user's facial expressions, tone of voice, or other emotional expressions.

[0197] "Means for generating names based on emotions read" refers to a function that uses emotional data obtained from a user as input and creates a name that matches or responds to that emotion.

[0198] In the embodiment of the present invention, a system built on a server is the center. This system is configured by combining a generative AI model, an emotion reading function, and an API linking function. The server executes a program developed in the Python language and implements a generative AI model using a machine learning library such as TensorFlow or PyTorch. In addition, the emotion reading function uses face recognition technology and voice analysis technology to grasp the user's emotional state through the terminal.

[0199] Users access the system via a device such as a smartphone or head-mounted display. The device sends the user's input information (industry, target demographic, service content, etc.) to the server, while at the same time capturing the user's emotions through a camera and microphone and sending that data to the server. Based on this data, the server uses a generative AI model to generate a name that matches the emotions. The server then checks the registration feasibility of the generated name in each country and whether it infringes on trademarks in cooperation with the relevant APIs.

[0200] As a specific example of usage, a prompt sentence such as "Industry: games, Target demographic: young people, Service content: VR experience game, Emotion: joy" is entered by the user and sent to the server via the terminal. The server uses this prompt sentence and the user's emotion data to generate a name such as "JoyVR Arcade" and checks whether this name can be trademarked and whether there is any trademark infringement. This allows the user to quickly obtain a name that reflects their emotions and has low legal risk.

[0201] The flow of the identification process will be described with reference to FIG.

[0202] Step 1: The user sends input information such as industry, target demographic, and service content, as well as the user's emotional data captured using the device's camera and microphone, to the server via the device. In this step, the input information and emotional data are sent to the server, which becomes the basis for processing.

[0203] Step 2: The server uses a generative AI model to generate a suitable name based on the received industry, target demographic, and service content information and sentiment data. In this process, the server analyzes the input information and outputs a corresponding name. The generative AI model runs an algorithm to create a highly relevant name based on the input parameters and sentiment data.

[0204] Step 3: For the generated name, the server checks the registrability and trademark infringement in each country through the relevant API. In this step, the server sends the generated name to the API and receives the confirmation results of registrability and trademark infringement from the API. The server analyzes these results and lists the final confirmation results.

[0205] Step 4: The server compiles a list of the verification results and sends it to the user's terminal. The user can view this list through the terminal and check the availability and legal risk of the generated name. In this final step, the user obtains the generated name and its related information on its registration possibility in each country and whether there is any trademark infringement.

[0206] Example 2

[0207] Next, a second embodiment of the second embodiment will be described. 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."

[0208] In the past, it was difficult to balance creativity with legality when coming up with names for game apps and other software products. In particular, there was no system that could generate names that reflected users' emotions and intentions, and quickly evaluate whether the names were internationally registrable and avoided trademark infringement. This resulted in a significant amount of time and resources being wasted in the product naming process.

[0209] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0210] In this invention, the server includes a means for generating a name based on the genre, target demographic, and characteristics of the game app provided by the user, and the user's emotional state, a means for checking the international registrability of the generated name and evaluating the risk of trademark infringement, and a means for providing the user with a list of the above-mentioned confirmation results. This enables rapid generation and evaluation of names that take into account legal constraints while still allowing for creativity.

[0211] The "means for generating a name" is a function that automatically creates an appropriate and original name based on parameters provided by the user (e.g., the genre of the game app, the target demographic, characteristics, and the user's emotional state).

[0212] The "means of verifying the registrability of the generated name in each country" is a function that automatically verifies whether a name is registrable in each country's trademark registration system.

[0213] The "means of verifying whether the generated name will not infringe on trademarks in each country" refers to a function that automatically evaluates whether the proposed name is likely to conflict with existing trademarks by referencing international trademark databases.

[0214] The "means of listing the above confirmation results" is a function that presents to the user the evaluation results of the registrability of a name and the risk of trademark infringement in a clear and organized format.

[0215] "Means of organizing application information for registration, etc." is a function that organizes the information required when making a trademark registration or other application based on the name proposed by the user, making the procedure easier.

[0216] In an embodiment of the present invention, a system is configured to generate a name for a game application and evaluate its registrability and risk of trademark infringement. This system is mainly composed of a server, a terminal, and software that links these.

[0217] The server implements name generation using a generative AI model, as well as registrability and trademark infringement assessment via a trademark search API. The generative AI model used utilizes state-of-the-art natural language processing techniques, such as OpenAI's GPT series, to generate creative names based on input parameters. Trademark searches are performed through APIs that access public databases such as the USPTO's TESS and the European Union Intellectual Property Office's (EUIPO) eSearch.

[0218] The terminal is responsible for the user inputting information about the game app (genre, target demographic, features, and the user's emotional state) and sending this information to the server as a prompt sentence. The terminal also displays the generated name and its evaluation results sent from the server, allowing the user to make a final selection.

[0219] As a concrete example, the prompt text "Genre: RPG, Target demographic: Adult, Features: Fantasy, Multiplayer, User emotion: Excitement" is entered into the terminal. This information is sent to the server, and the generative AI model proposes the name "FantasyQuest Online". The name's registrability and risk of trademark infringement are then evaluated through a trademark search API, and the results are returned to the terminal as a list.

[0220] This allows users to choose a suitable name for their game app efficiently and without legal risk.

[0221] The flow of the identification process will be described with reference to FIG.

[0222] Step 1: Accepting user input

[0223] The user inputs information about the game app under development (genre, target demographic, characteristics, and the user's emotional state) into the device. This information is formatted as a prompt text and prepared to be sent to the server. The input data is processed into specific instructions for naming generation. For example, a prompt text like "Genre: RPG, target demographic: adults, characteristics: fantasy, multiplayer, user's emotion: excitement" might be generated.

[0224] Step 2: Generate names

[0225] The server starts name generation using a generative AI model based on the received prompt. This model analyzes the input game app information and generates a creative name that matches it. The generative AI model outputs a name such as "FantasyQuest Online" from the prompt.

[0226] Step 3: Trademark Search and Evaluation

[0227] For each generated name, the server uses a trademark search API to assess its registrability in each country and its risk of trademark infringement. During this process, the name is checked against public trademark databases to check for conflicts with existing trademarks. The result of this operation is a legal risk assessment for the name.

[0228] Step 4: List and submit results

[0229] The server creates a list of the results of the trademark search and evaluation and sends the list to the terminal, which contains information about the registrability of the generated names and the risk of trademark infringement. The terminal receives this information and displays it to the user.

[0230] Step 5: User makes final choice

[0231] Users can refer to the information displayed on their device to select the most suitable name for their game app. The selection process involves a balance between creativity and legal safety when coming up with the final name.

[0232] (Application example 2)

[0233] Next, a description will be given of Application Example 2 of Form 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."

[0234] Conventional name generation systems can simply generate names randomly or based on existing databases, but they have difficulty efficiently proposing names that consider legal risks while also taking into account user emotions and specific requirements for game development (e.g., genre, target demographic, features). In addition, the task of checking whether the generated names are internationally registrable and do not cause trademark infringement is time-consuming, and this process needs to be made more efficient.

[0235] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0236] In this invention, the server includes a means for recognizing the genre, target demographic, game features, and emotions of the game based on information input by the user, a means for generating a name using a generative AI based on this information, and a means for automatically checking the registrability of the generated name in each country and the presence or absence of trademark infringement. This makes it possible to propose names that reflect the user's emotions and requests and to automatically evaluate their legal risks.

[0237] The "means for generating a name" is a function that suggests new game names based on input parameters such as the game genre, target demographic, characteristics, and user emotions.

[0238] "Means to confirm the registrability of the generated name in each country" is a function that automatically investigates and evaluates whether the proposed name is eligible for trademark registration in accordance with the laws of each country.

[0239] The "means of verifying that the generated name does not infringe on trademarks in each country" refers to a function that automatically verifies whether the proposed name conflicts with existing trademarks using each country's trademark database.

[0240] The "means of listing the confirmation results" is a function for organizing the confirmation results of the above-mentioned registration possibility and trademark infringement in a list and providing it to the user.

[0241] The "means of organizing application information for registration, etc." is a function that organizes the information required for trademark registration and other applications related to the proposed name, enabling the application process to be carried out efficiently.

[0242] "Means for recognizing the game genre, target demographic, game features and emotions based on user input" refers to a function that receives specific information about the game under development and the emotions of the game developer and users as input, and understands and analyzes this information.

[0243] The "means for generating a name based on this information" is a function that executes a process of combining the game genre, target demographic, characteristics, and emotional data to create a new game name that matches these.

[0244] The embodiment of the present invention includes a terminal (e.g., a smartphone or tablet), a server, a generative AI model (e.g., OpenAI's GPT), and a trademark database API. The terminal receives inputs from the user, such as the game genre, target demographic, features, and emotions, and transmits this information to the server. The server generates an appropriate game name using the generative AI model based on the received information. In this process, the input information is provided to the AI ​​as a prompt, and the AI ​​creates a name based on this. The generated name is then checked by the server through the trademark database API for registration possibility in each country and for the presence or absence of trademark infringement. The confirmation results are listed by the server and presented to the user through the terminal. The user selects the most suitable name based on the provided information, and this selection is again transmitted to the server. Finally, the server organizes the application information for the selected name, and helps the user to smoothly proceed with the trademark registration procedure, etc.

[0245] As a specific example, if a user inputs the genre of the game they are developing as "RPG," the target demographic as "adults," the characteristics as "fantasy, multiplayer," and the emotion as "adventurous," this information is sent from the terminal to the server. The server provides this information to a generative AI model, which generates a name based on the prompt "genre: RPG, target demographic: adults, characteristics: fantasy, multiplayer, emotion: adventurous." This invention enables users to efficiently create game names that reflect their own intentions and simultaneously evaluate legal risks.

[0246] The flow of the identification process will be described with reference to FIG.

[0247] Step 1: The user uses the terminal to input information such as the game genre, target demographic, features, and emotions. This input information is sent to the server. In this step, the user's intentions and the specific requirements for the game they want to develop are digitized and serve as input to the server.

[0248] Step 2: The server generates a name using a generative AI model based on the received information. This process takes into account the input game genre, target demographic, characteristics, and emotions, and calculates the best name for these parameters. The generated name is used as input for the subsequent process.

[0249] Step 3: The server checks whether the generated name can be registered in each country and whether there is any trademark infringement. This check is done through the relevant API, accessing the trademark database of each country to verify whether the name is registerable and does not constitute trademark infringement. The output of this step is the result of the check on registration and trademark infringement.

[0250] Step 4: The results of the verification are compiled into a list and sent from the server to the terminal. The user can use this list to evaluate the legal risk of the generated name. The results of the verification are listed and include information on the possibility of registration for each name and whether there is any trademark infringement.

[0251] Step 5: After the user has finally selected the name, the server organizes the application information for registration, etc. In this step, the information required for trademark registration and other application processes related to the selected name is organized, helping the user to efficiently proceed with these applications.

[0252] Through these processing steps, users can efficiently generate names that reflect their own feelings and game development requirements, evaluate the legal risks, and then select and apply for an appropriate name.

[0253] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0254] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0255] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0256] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0257] [Second embodiment]

[0258] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0259] 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.

[0260] 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 wide area network (WAN) and / or a local area network (LAN).

[0261] 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.

[0262] 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 the voice according to instructions from the processor 46.

[0263] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0264] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0265] Fig. 4 shows an example of 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.

[0266] 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.

[0267] 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.

[0268] 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.

[0269] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0270] "Example 1"

[0271] The system of the present invention uses generative AI as a means of generating names. The generative AI generates appropriate names based on specific parameters (e.g., industry, target demographic, service content, etc.). The generated names are evaluated by a means for checking the possibility of registration in each country and a means for checking trademark infringement. These means are performed by linking related APIs and accessing trademark regulations and registration information of each country. The confirmation results are compiled into a list and organized together with application information such as registration. This improves the efficiency of planning and procedures.

[0272] "Example 2"

[0273] As a specific example, consider the case of thinking of a name for a newly developed game app. Generative AI generates an appropriate name based on parameters such as the game genre (e.g. RPG, puzzle, action, etc.), target demographic (e.g. children, adults, men, women, etc.), and game characteristics (e.g. fantasy, real-time, multiplayer, etc.). The generated name is evaluated by a method to check for registration feasibility and trademark infringement in each country, and the confirmation results are compiled into a list. This aims to streamline planning and procedures.

[0274] The process flow of each embodiment will be described below.

[0275] "Example 1"

[0276] Step 1: Generative AI generates an appropriate name based on certain parameters (e.g. industry, target demographic, service content, etc.).

[0277] Step 2: The generated name is evaluated using a methodology to check for its registrability in each country. This is done through the integration of relevant APIs, which have access to each country's trademark regulations and registration information.

[0278] Step 3: Similarly, the generated names are evaluated by a means to check for trademark infringement. Step 4: The results of the check are compiled into a list and organized together with the application information for registration, etc. This will streamline the planning and procedures.

[0279] "Example 2"

[0280] Step 1: When thinking of a name for a new game app to be developed, the generative AI generates an appropriate name based on parameters such as the game genre (e.g., RPG, puzzle, action, etc.), target demographic (e.g., children, adults, men, women, etc.), and game features (e.g., fantasy, real-time, multiplayer, etc.).

[0281] Step 2: The generated name is evaluated using a methodology to check for its registrability in each country. This is done through the integration of relevant APIs, which have access to each country's trademark regulations and registration information.

[0282] Step 3: Similarly, the generated names are evaluated by a means to check for trademark infringement. Step 4: The results of the check are compiled into a list and organized together with the application information for registration, etc. This will streamline the planning and procedures.

[0283] Example 1

[0284] Next, a description will be given of Example 1 of Form 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".

[0285] In the modern commercial environment, the selection of an appropriate name is crucial, as the name of a product or service directly affects the brand value. However, when considering the international market, it is difficult to efficiently evaluate the possibility of trademark registration in different countries and the risk of trademark infringement. With traditional methods, such evaluations require a huge amount of time and resources, which is a heavy burden especially for small and medium-sized enterprises and start-ups. There is a need for a method to solve this problem and perform everything from name generation to legal risk assessment quickly and efficiently.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0287] In this invention, the server includes a means for inputting a prompt sentence that takes into account a specific industry, target demographic, and service content into a generative AI to generate an appropriate name, a means for evaluating the registrability of the generated name in each country and the risk of trademark infringement through the linkage of a trademark registration database and an API that searches trademark law regulation information, and a means for listing the above evaluation results and organizing application information for registration, etc. This enables users to quickly and efficiently select a name that takes legal risks into account.

[0288] "Specific parameters" are conditions such as industry, target demographic, and service content that are taken into account when generating a name.

[0289] A "name" is a word or combination used to identify a product, service, company, etc.

[0290] "Registrability in each country" refers to whether a name can be registered as a trademark in a particular country.

[0291] "Trademark infringement" is a legal issue that arises when someone unfairly violates the trademark rights of others, especially when they use it without permission.

[0292] "Listing methods" are ways of organizing and visualizing information in a table format.

[0293] "Means for organizing application information for registration, etc." refers to a method for systematically organizing and managing information related to trademark registration of names and other applications.

[0294] "Generative AI" is an artificial intelligence model that automatically generates new content and ideas based on input data and conditions.

[0295] A "prompt" is an instruction or question used to instruct a generative AI model on a specific task.

[0296] The "API for referencing the trademark registration database" is an interface that accesses external systems to inquire about whether a trademark has been registered and for detailed information.

[0297] The "API for searching trademark law and regulation information" is a programming interface for searching for trademark laws and regulation information for specific countries or regions.

[0298] In the embodiment of the present invention, the server, the terminal, and the user work together to carry out the work. The server is equipped with a generative AI model (e.g., a machine learning model using TensorFlow or PyTorch) and has a function of generating a prompt sentence and proposing an appropriate name based on specific parameters (e.g., industry, target demographic, service content) provided by the user via the terminal.

[0299] The terminal acts as an interface to receive user input and transmits the input parameters to the server. For example, if a user inputs a prompt such as "Generate the name of a cloud service targeted at business people in the IT industry," this information is sent from the terminal to the server.

[0300] The server receives the prompt and uses a generative AI model to generate multiple name suggestions. The generated name suggestions are then queried by APIs that reference trademark registration databases and APIs that search trademark law and regulatory information to evaluate the possibility of trademark registration and the risk of trademark infringement. These APIs are used, for example, to access international trademark information databases and trademark law and regulatory information for specific countries.

[0301] The obtained information is organized by the server and displayed to the user via the terminal. The display clearly indicates the likelihood of registration for each proposed name and the risk of trademark infringement. The user can use this information to select the most suitable name.

[0302] This allows users to quickly and efficiently determine names for new products and services while taking into account legal risks. Especially when considering international markets, the system offers great convenience and significantly reduces the time and cost of the name selection process.

[0303] The flow of the identification process will be described with reference to FIG.

[0304] Step 1: Accepting input from the user

[0305] The user inputs parameters such as the specific industry, target demographic, and service content through the terminal. This input information is used to create a prompt for name generation. The terminal receives the input from the user and sends it to the server. At this time, the input parameters act as conditions for generating an appropriate name.

[0306] Step 2: Generate prompts and names

[0307] The server generates a prompt based on parameters received from the user. This prompt is input into a generative AI model and used to generate a name that matches certain conditions. The generative AI model uses this prompt as the basis for data processing and calculations to output a series of name suggestions. For example, if the prompt is "Generate a name for a cloud service targeted at business people in the IT industry," the AI ​​model will generate multiple names that fit the conditions.

[0308] Step 3: Check for trademark registrability and trademark infringement

[0309] For each generated name, the server queries the relevant APIs to assess the possibility of trademark registration and the risk of trademark infringement. This process uses APIs that refer to the trademark registration database and APIs that search trademark law and regulatory information. For each name, the information obtained from these APIs is processed and calculated to assess the name's possibility of registration and the risk of trademark infringement, and the results are returned to the server.

[0310] Step 4: List and present the results

[0311] Based on the information obtained in step 3, the server lists and organizes the registration possibility and trademark infringement risk of each name. This organized information is presented to the user via the terminal. The presented information indicates the registration possibility and trademark infringement risk for each generated name. The user uses this information as a reference to select the final name. This step enables the user to quickly select an appropriate name while taking legal risks into account.

[0312] (Application example 1)

[0313] Next, a description will be given of Application Example 1 of Form 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".

[0314] When deciding on names for commercial facilities or products to be used in virtual space, it is difficult to generate creative names while efficiently checking whether the names can be registered as trademarks in each country and whether there is a risk of trademark infringement. To solve this problem, a system is needed that automates name generation and quickly evaluates the legal issues of generated names.

[0315] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0316] In this invention, the server includes a means for generating names based on parameters such as industry, target demographic, and service content, a means for evaluating the registrability of the generated names in each country and the risk of trademark infringement, and a means for providing the user with a list of the evaluation results. This enables the user to efficiently generate creative names, quickly understand the legal issues involved, and select an appropriate name and apply for registration.

[0317] A "means for generating names" is a function that uses artificial intelligence technology to automatically generate appropriate names based on input parameters (industry, target demographic, service content, etc.).

[0318] "Means for verifying the registrability of the generated name in each country" refers to a function that runs a process to determine whether a particular name is registrable in accordance with the laws and trademark registration regulations of each country.

[0319] The "means of verifying whether the generated name will not infringe on trademarks in each country" refers to a function that analyzes and determines whether the created name is likely to conflict with existing trademarks by referring to country-specific trademark databases.

[0320] The "means of listing the above confirmation results" refers to a function that organizes and displays in list form the confirmation results regarding the registrability of a name and the risk of trademark infringement.

[0321] A "means of organizing application information for registration, etc." is a function that assists the user in the process of managing and organizing documents and information related to registration applications that they need to submit.

[0322] "Means for suggesting names to be applied to commercial facilities and products within a virtual space" refers to a function that generates and suggests names for stores and products to be used within a virtual environment.

[0323] "Using generative AI based on parameters such as industry, target demographic, and service content" refers to the process in which artificial intelligence automatically generates appropriate naming suggestions based on information such as the specified industry, target customer demographic, and characteristics of the services provided.

[0324] "Through integration of relevant APIs" means that the assessment of the possibility of trademark registration and the risk of trademark infringement is carried out automatically online, utilizing external application programming interfaces (APIs).

[0325] The embodiment of the present invention is a system that combines name generation and legal evaluation. The system is composed of a server-based software application that is accessed by a user via a terminal. The server is equipped with a generative AI model and an external API access function. Specifically, a program written in Python uses a generative AI model (e.g., GPT-3 or its successor) to generate a name based on parameters provided by the user (industry, target demographic, service content, etc.). The generated name is used to evaluate the registrability and risk of trademark infringement in each country through an external API.

[0326] Users access the service through devices such as smartphones or head-mounted displays and input the necessary parameters. The server receives this input, generates and evaluates names, and feeds the results back to the user. Through this process, users can efficiently and safely decide on names for commercial facilities and products to be used in virtual space.

[0327] For example, if you input the parameters "cafe," "youth," and "environmentally friendly," the generative AI model will suggest names such as "GreenSip Cafe" and "EcoBrew Corner" based on these parameters. The server then uses an external API to evaluate the registrability and trademark infringement risk of these names in each country and provide the results to the user. This process allows users to understand the risks involved in name selection in advance and make appropriate decisions.

[0328] The flow of the identification process will be described with reference to FIG.

[0329] Step 1: The user inputs parameters such as industry, target demographic, and service content through the terminal. This input is sent to the server as basic data for name generation. The data sent from the terminal is received by the server and temporarily stored for further processing.

[0330] Step 2: The server generates a name using a generative AI model based on the parameters received in step 1. In this process, the input parameters are analyzed by the AI ​​model to generate a relevant name. The generated name is temporarily stored by the server for the next step.

[0331] Step 3: The server evaluates the generated name for its registrability in each country and the risk of trademark infringement through the relevant API. In this evaluation process, the server accesses external trademark databases to investigate whether the generated name may conflict with existing trademarks. The evaluation results are collected and organized by the server.

[0332] Step 4: The server lists the evaluation results obtained in step 3 and displays them to the user. In this step, information on the possibility of registration and the risk of trademark infringement is provided to the user in an easy-to-understand format. Based on this information, the user can make a final selection of the name to be used.

[0333] Step 5: Based on the name finally selected by the user, the server organizes the application information for registration, etc., and provides it to the user. In this process, the documents and information required for the registration application using the selected name are organized, helping the user to complete the application process smoothly.

[0334] Through these steps, users can efficiently and safely select the names of their stores and products to be used in the virtual space and proceed with the necessary registration procedures. Each processing step involves data exchange between the server and the device, data processing using a generative AI model, information collection and evaluation through linkage with external APIs, and information provision to the user.

[0335] Example 2

[0336] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0337] When coming up with a name for new content, it is a time-consuming and labor-intensive task to come up with creative proposals while simultaneously evaluating the possibility of international trademark registration and the risk of trademark infringement. In particular, when considering use in multiple markets, it can be difficult to quickly identify an attractive name that meets legal constraints.

[0338] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0339] In this invention, the server includes a means for generating a name based on the attributes of the content, a means for checking the international registrability of the generated name and the presence or absence of trademark infringement, a means for listing the check results by category and presenting them on a user interface, and a means for organizing application information for registration, etc. based on the name selected by the user and automating the procedures. This makes it possible to quickly propose creative names and simultaneously evaluate their legal applicability.

[0340] "Content attributes" are basic elements for defining content, such as the genre, target demographic, and characteristics of the content.

[0341] "Name generation" is the process of creating a suitable name for new content based on the attributes of the input content.

[0342] "International regisability and trademark infringement check" refers to the act of investigating whether a particular name is available in the trademark registers of various countries around the world and whether there is a risk of infringement due to its similarity to other trademarks.

[0343] "Listing the confirmation results by category and presenting them in a user interface" means classifying the investigated information on the possibility of registration and the presence or absence of trademark infringement, and displaying this information in a form that is visually easy for users to understand.

[0344] "Organizing application information for registrations and other purposes based on the name selected by the user and automating procedures" means consolidating information related to the necessary registrations and applications using the name selected by the user, and streamlining and automating these processes.

[0345] A "generative AI model" is a system that uses artificial intelligence technology to automatically create new data or text from specific input information.

[0346] "API integration" refers to integration through a programming interface that enables different software or services to share functions and exchange data with each other.

[0347] An "API for accessing external databases" is an interface that enables a program to directly query databases managed by other organizations on the Internet.

[0348] In an embodiment of the present invention, the system includes a process for generating a name, checking its international registrability and trademark infringement, and finally presenting the information to a user. This process is mainly composed of software that runs on a server and a terminal that accepts user operations.

[0349] The server uses a generative AI model to generate a name based on the attributes of the input content. For example, a text generation model such as GPT (Generative Pre-trained Transformer) developed by OpenAI is used as the generative AI model. The generated name is checked for international registration possibility and trademark infringement through API connection to an external database. Through this API connection, the server accesses trademark databases from around the world to check whether the proposed name conflicts with existing trademarks.

[0350] The terminal is responsible for accepting input from the user and sending it to the server. Through the terminal, the user specifies attributes such as the genre of the content, the target demographic, and the characteristics of the content. For example, if the user inputs the attributes "Genre: RPG, Target demographic: Adults, Characteristics: Fantasy, Real-time, Multiplayer", this information is formatted as a prompt sentence and sent to the generative AI model on the server.

[0351] The server responds by presenting the generated name and the results of its legal evaluation to the user via the terminal. The user can then use this information to select the most appropriate name and proceed to the next steps, such as registering that name or applying for a trademark. Through this process, the user can decide on a name for new content efficiently and with an understanding of the legal risks.

[0352] The flow of the identification process will be described with reference to FIG.

[0353] Step 1: The user uses the device to input the attributes of the content (genre, target demographic, features, etc.). This input becomes the basic data for the generative AI model to process. The device receives this input data and creates a prompt sentence based on it.

[0354] Step 2: The terminal sends the created prompt to the server. The server receives the prompt and generates a name using a generative AI model. From the input prompt, the server outputs a name that is appropriate for the content.

[0355] Step 3: The server checks the generated name against external databases to verify the name's international registrability and trademark infringement. At this stage, the server takes the generated name as input and the search results from external databases as output.

[0356] Step 4: The server receives the search results from the external database, organizes the information into a list, and sends it to the terminal. This list includes the registrability of the name and the risk of trademark infringement. The server processes the search results and sends the organized information to the terminal.

[0357] Step 5: The terminal presents the list received from the server to the user. The user selects the name that he / she thinks is most appropriate from the presented list. The terminal receives the user's selection and transmits the information to the server.

[0358] Step 6: The server organizes the registration and other application information based on the name selected by the user, and automates the registration process for the user. At this stage, the server processes the selected name and the necessary application information, and guides the user on the next steps.

[0359] Through these steps, the system enables users to efficiently and effectively determine names for new content and automate its registration and trademark verification.

[0360] (Application example 2)

[0361] Next, a description will be given of Application Example 2 of Form 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".

[0362] When launching a new product or brand in the online market, coming up with an appropriate product or brand name is a critical factor that directly impacts its success. However, coming up with a creative and attractive product or brand name is not easy, and it is necessary to comprehensively evaluate whether the name has sufficient online searchability, whether it can secure buzz on social media, and whether it can be trademarked. Efficiently generating and evaluating names that meet these requirements is a major challenge, especially for small and medium-sized businesses.

[0363] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0364] In this invention, the server includes a means for generating product names and brand names, a means for checking the online searchability of the generated product names and brand names, a means for evaluating whether the generated product names and brand names have topicality on SNS, and a means for checking the possibility of trademark registration of the generated product names and brand names. This allows businesses to efficiently generate creative and attractive product names and brand names, and to comprehensively evaluate the names' online searchability, topicality on SNS, and possibility of trademark registration. This system allows businesses to reduce risks when introducing new products and brands to the market and increase the chances of success.

[0365] "Means for generating product and brand names" refers to a function that automatically creates appropriate product and brand names based on the product category, target demographic, and product features designated by the user.

[0366] "Means to verify online searchability" is a function that evaluates whether the generated product or brand name can be easily searched for on the Internet.

[0367] The "means for evaluating whether a product name or brand name has potential to be discussed on social media" is a function for judging whether a generated product name or brand name is likely to be discussed by users on a social networking service.

[0368] "Means to confirm the possibility of trademark registration" is a function that investigates whether the generated product name or brand name can be registered as a trademark, in other words, whether there is any overlap or similarity with other trademarks.

[0369] "Through integration with related APIs" refers to programming interfaces used to automatically collect and process information by integrating with external databases and services.

[0370] An embodiment of the present invention relates to an automated system for generating and evaluating product and brand names in an online market. A server executes a program that generates product and brand names using a generative AI model. The program generates a prompt sentence based on input information from a user, such as the product category, target demographic, and product features, and transmits the prompt sentence to the generative AI model. The generative AI model proposes creative and attractive product and brand names based on the transmitted prompt sentence.

[0371] For each generated product and brand name, the server also runs a program that evaluates its online searchability, social media buzzworthiness, and trademarkability. This evaluation is automated through the associated API, accessing online databases and trademark registry databases to verify the uniqueness and availability of the generated names.

[0372] The system is developed using programming languages ​​such as Python and JavaScript, and utilizes libraries for natural language processing such as TensorFlow, PyTorch, and NLTK. Using these technologies, the server performs complex text analysis and data processing to provide useful information to users.

[0373] As a specific example, the prompt "Brand name of fashion T-shirts for young people made from eco-friendly materials" is generated to reflect the product category "fashion," the target demographic "young people," and the product feature "made from eco-friendly materials." The generative AI model proposes multiple brand names based on this prompt, and these names are automatically evaluated for online searchability, buzzworthiness on social media, and possibility of trademark registration. Through this process, businesses can efficiently select attractive product and brand names and prepare for market launch.

[0374] The flow of the identification process will be described with reference to FIG.

[0375] Step 1: The user inputs the product category, target demographic, and product features. This input is sent to the server as the basic data for generating product names and brand names.

[0376] Step 2: The server generates a prompt sentence based on the input product category, target demographic, and product features. This prompt sentence serves as an instruction for the generative AI model to generate the product name and brand name.

[0377] Step 3: The generative AI model generates product and brand names based on the received prompt text. During this process, the AI ​​model uses text generation techniques to suggest multiple candidates.

[0378] Step 4: The server checks the online searchability of the generated product and brand names. This process involves analyzing existing search engine results on the Internet to assess the uniqueness of the generated names.

[0379] Step 5: At the same time, the server evaluates the popularity of the generated product and brand names on social media based on the analysis of social media trends and hashtags.

[0380] Step 6: The server then checks for trademark registration feasibility by querying national and international trademark databases to see if the generated name conflicts with existing trademarks.

[0381] Step 7: The server reports the results of the evaluation of online searchability, social media popularity, and trademark registration potential to the user. The report includes the recommendation level and potential problems of the generated product and brand names.

[0382] Through this process, users can efficiently obtain creative and practical product and brand names, thus increasing the chances of success in the online market.

[0383] In addition, an emotion engine that estimates the emotion of the user may be further combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0384] "Example 1"

[0385] The present invention combines a system that generates names, checks whether they can be registered in each country, checks for trademark infringement, lists the check results, and organizes application information such as registration, with an emotion engine that recognizes the emotions of the user. Specifically, the emotion engine recognizes the emotions of the user and generates a name based on the emotions. For example, if the user is expressing an emotion of joy, a name that gives a positive impression that matches the emotion is generated. The emotion engine also checks whether the name generated based on the user's emotions can be registered in each country and whether there is trademark infringement. This makes it possible to generate names that reflect the emotions of the user and evaluate them.

[0386] "Example 2"

[0387] As a concrete example, consider the case of thinking of a name for a new game app to be developed. When a user is excited about the development of the game, the emotion engine recognizes that emotion and generates a name that expresses that excitement. The generated name is evaluated by a means for checking the possibility of registration in each country, and is also evaluated by a means for checking trademark infringement. This makes it possible to generate and evaluate names that reflect the user's emotions.

[0388] The process flow of each embodiment will be described below.

[0389] "Example 1"

[0390] Step 1: The emotion engine recognizes the user's emotion.

[0391] Step 2: Name generation is performed based on the emotions recognized by the emotion engine.

[0392] Step 3: Check the registerability of the generated name in each country.

[0393] Step 4: Check the generated name for trademark infringement.

[0394] Step 5: List the confirmation results and organize the application information for registration, etc.

[0395] "Example 2"

[0396] Step 1: When a user expresses excitement about game development, the emotion engine recognizes that emotion.

[0397] Step 2: The emotion engine generates a name that expresses the feeling of excitement based on the recognized feeling of excitement.

[0398] Step 3: Check the registerability of the generated name in each country.

[0399] Step 4: Check the generated name for trademark infringement.

[0400] Step 5: List the confirmation results and organize the application information for registration, etc.

[0401] Example 1

[0402] Next, a description will be given of Example 1 of Form 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".

[0403] In the modern business environment, it is extremely important to give a suitable name to a new product or service. The name forms the first impression of the product or service and is an element that is memorable to consumers. However, it is extremely difficult to find a name that can be registered in each country and does not pose a risk of trademark infringement while taking into account cultural background and language differences. In addition, conventional technologies do not provide a method to generate a name that reflects the emotions and values ​​of the companies and individuals who bring the product or service to the market.

[0404] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0405] In this invention, the server includes a means for collecting user emotion data, a means for analyzing the collected emotion data, and a means for generating a name by combining the analysis result with specific parameters provided by the user. This makes it possible to generate a name that reflects the user's emotions and values, can be registered in each country, and has no risk of trademark infringement. In addition, by linking related APIs, it is possible to quickly check the registrability of the name in each country and whether or not there is a trademark infringement, which is expected to improve the efficiency and accuracy of the naming process for new products and services in a business environment.

[0406] "User emotional data" refers to information about a user's emotional state collected from facial expressions, tone of voice, body language, etc.

[0407] "Means for analyzing the collected emotional data" refers to processes or algorithms that process the collected emotional data and identify the user's current emotional state.

[0408] "Specific parameters" are specific information such as industry, target demographic, and service content provided by the user when generating a name.

[0409] "Name Generator" refers to a process or algorithm that generates an appropriate name based on collected sentiment data and certain parameters provided by the user.

[0410] "Country availability verification procedures" refers to the process or service that checks whether a generated name can be legally registered in a particular country.

[0411] "Trademark verification measures" refers to processes or services that verify whether the generated name infringes existing trademark rights in a particular country.

[0412] "Means for listing the verification results" refers to a process or algorithm that organizes and displays in a list the verification results regarding the registrability of the name in each country and whether there is any trademark infringement.

[0413] "Means of organizing application information for registration, etc." refers to a process or system that systematically organizes the information required for name registration or trademark application, facilitating the procedure.

[0414] In an embodiment of the present invention, the terminal and the server work together to collect emotion data from the user, analyze the data, generate a name, and legally verify the generated name. The terminal is a device that collects the user's emotion data, records the user's facial expressions and voice through a camera and microphone, and transmits them to the server in an analyzable format. This process uses facial expression recognition software (e.g., OpenCV) and voice analysis libraries (e.g., WebRTC).

[0415] On the server side, a neural network model (e.g., a model using TensorFlow) is used to analyze the received emotion data. The model identifies the user's emotional state and feeds the results back into the name generation process. A generative AI model (e.g., a GPT-based model) is used to generate the name based on the user's emotion and a prompt that reflects certain parameters (e.g., "Generate a service name for a young target audience in the health food industry that expresses the emotion of joy").

[0416] The legal check of the generated name is carried out using a related API that provides access to a trademark registration database, allowing the server to check whether the generated name can be registered in each country and whether there is a risk of trademark infringement, and to report the results to the user in a list.

[0417] This method allows the generation of names that reflect the user's emotions and their legal verification to be carried out automatically through a series of processes, significantly improving the efficiency and satisfaction of naming products and services.

[0418] The flow of the identification process will be described with reference to FIG.

[0419] Step 1: Collect user emotion data

[0420] The device collects the user's facial expressions and voice using a camera and microphone. This collection process uses facial recognition software and voice analysis libraries to collect data that suggests the user's emotions. The collected data (input) is sent to a server for sentiment analysis (output).

[0421] Step 2: Analyze the sentiment data

[0422] The server analyzes the received emotion data using a neural network model to identify the user's emotional state. From the input emotion data, the model extracts features and identifies the emotion based on them (output). This output is used in the name generation process.

[0423] Step 3: Generate names

[0424] The generative AI model on the server generates an appropriate name using a prompt sentence that combines the identified emotional state with specific parameters provided by the user (industry, target demographic, service content). In this step, the emotional state and parameters are given as input, and the generative AI model processes them to generate a new name (output).

[0425] Step 4: Check for registrability and trademark infringement

[0426] For the generated name, the server checks the name's registrability and trademark infringement status in each country through the relevant API. In this process, the generated name is used as input, and the API returns information on registrability and trademark infringement status (output).

[0427] Step 5: List and report findings

[0428] Finally, the server lists the results of the verification of registrability and trademark infringement, organizes this information, and reports it to the user. In this step, the verification result data is used as input, and an organized information list is output. This list is provided to the user via the terminal as a reference for making the final name selection.

[0429] (Application example 1)

[0430] Next, a description will be given of Application Example 1 of Form 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".

[0431] Conventional name generation systems have the problem that it is difficult to generate names that fully reflect the user's feelings and sensibilities. Furthermore, the process of verifying whether the generated name is registrable in each country and whether it infringes on trademarks is complicated and requires a great deal of time and effort. These issues are factors that reduce the speed and efficiency of business, especially for businesses that operate in international virtual spaces.

[0432] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0433] In this invention, the server includes a means for reading the user's emotions, a means for using a generative AI to generate a name that gives an impression that matches the emotion based on the emotion read, and a means for verifying the registrability of the generated name in each country and the presence or absence of trademark infringement through the linkage of related APIs. This enables the rapid generation of a name that reflects the user's emotions and sensibilities, and reduces the risk of using the name internationally.

[0434] A "means for generating names" is a function that automatically devise and output appropriate names or designations based on specific parameters and the user's emotional data.

[0435] "National name registrability checks for generated names" refers to the process of determining whether a proposed name can be used under the laws and registry systems of different countries.

[0436] "Means to verify that the generated name does not violate trademark laws in each country" refers to a function that verifies whether the proposed name violates the trademark laws of each country.

[0437] The "means of listing the above confirmation results" refers to the process of organizing the results of name generation and subsequent registration feasibility and trademark infringement confirmation into a visually easy-to-understand list.

[0438] "Means of organizing application information for registrations, etc." refers to the process of systematically preparing and managing the information required when making official registrations or applications for the use of a name.

[0439] "Means of reading user emotions" means technology that identifies and analyzes the user's current emotional state from the user's facial expressions, tone of voice, or other emotional expressions.

[0440] "Means for generating names based on emotions read" refers to a function that uses emotional data obtained from a user as input and creates a name that matches or responds to that emotion.

[0441] In the embodiment of the present invention, a system built on a server is the center. This system is configured by combining a generative AI model, an emotion reading function, and an API linking function. The server executes a program developed in the Python language and implements a generative AI model using a machine learning library such as TensorFlow or PyTorch. In addition, the emotion reading function uses face recognition technology and voice analysis technology to grasp the user's emotional state through the terminal.

[0442] Users access the system via a device such as a smartphone or head-mounted display. The device sends the user's input information (industry, target demographic, service content, etc.) to the server, while at the same time capturing the user's emotions through a camera and microphone and sending that data to the server. Based on this data, the server uses a generative AI model to generate a name that matches the emotions. The server then checks the registration feasibility of the generated name in each country and whether it infringes on trademarks in cooperation with the relevant APIs.

[0443] As a specific example of usage, a prompt sentence such as "Industry: games, Target demographic: young people, Service content: VR experience game, Emotion: joy" is entered by the user and sent to the server via the terminal. The server uses this prompt sentence and the user's emotion data to generate a name such as "JoyVR Arcade" and checks whether this name can be trademarked and whether there is any trademark infringement. This allows the user to quickly obtain a name that reflects their emotions and has low legal risk.

[0444] The flow of the identification process will be described with reference to FIG.

[0445] Step 1: The user sends input information such as industry, target demographic, and service content, as well as the user's emotional data captured using the device's camera and microphone, to the server via the device. In this step, the input information and emotional data are sent to the server, which becomes the basis for processing.

[0446] Step 2: The server uses a generative AI model to generate a suitable name based on the received industry, target demographic, and service content information and sentiment data. In this process, the server analyzes the input information and outputs a corresponding name. The generative AI model runs an algorithm to create a highly relevant name based on the input parameters and sentiment data.

[0447] Step 3: For the generated name, the server checks the registrability and trademark infringement in each country through the relevant API. In this step, the server sends the generated name to the API and receives the confirmation results of registrability and trademark infringement from the API. The server analyzes these results and lists the final confirmation results.

[0448] Step 4: The server compiles a list of the verification results and sends it to the user's terminal. The user can view this list through the terminal and check the availability and legal risk of the generated name. In this final step, the user obtains the generated name and its related information on its registration possibility in each country and whether there is any trademark infringement.

[0449] Example 2

[0450] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0451] In the past, it was difficult to balance creativity with legality when coming up with names for game apps and other software products. In particular, there was no system that could generate names that reflected users' emotions and intentions, and quickly evaluate whether the names were internationally registrable and avoided trademark infringement. This resulted in a significant amount of time and resources being wasted in the product naming process.

[0452] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0453] In this invention, the server includes a means for generating a name based on the genre, target demographic, and characteristics of the game app provided by the user, and the user's emotional state, a means for checking the international registrability of the generated name and evaluating the risk of trademark infringement, and a means for providing the user with a list of the above-mentioned confirmation results. This enables rapid generation and evaluation of names that take into account legal constraints while still allowing for creativity.

[0454] The "means for generating a name" is a function that automatically creates an appropriate and original name based on parameters provided by the user (e.g., the genre of the game app, the target demographic, characteristics, and the user's emotional state).

[0455] The "means of verifying the registrability of the generated name in each country" is a function that automatically verifies whether a name is registrable in each country's trademark registration system.

[0456] The "means of verifying whether the generated name will not infringe on trademarks in each country" refers to a function that automatically evaluates whether the proposed name is likely to conflict with existing trademarks by referencing international trademark databases.

[0457] The "means of listing the above confirmation results" is a function that presents to the user the evaluation results of the registrability of a name and the risk of trademark infringement in a clear and organized format.

[0458] "Means of organizing application information for registration, etc." is a function that organizes the information required when making a trademark registration or other application based on the name proposed by the user, making the procedure easier.

[0459] In an embodiment of the present invention, a system is configured to generate a name for a game application and evaluate its registrability and risk of trademark infringement. This system is mainly composed of a server, a terminal, and software that links these.

[0460] The server implements name generation using a generative AI model, as well as registrability and trademark infringement assessment via a trademark search API. The generative AI model used utilizes state-of-the-art natural language processing techniques, such as OpenAI's GPT series, to generate creative names based on input parameters. Trademark searches are performed through APIs that access public databases such as the USPTO's TESS and the European Union Intellectual Property Office's (EUIPO) eSearch.

[0461] The terminal is responsible for the user inputting information about the game app (genre, target demographic, features, and the user's emotional state) and sending this information to the server as a prompt sentence. The terminal also displays the generated name and its evaluation results sent from the server, allowing the user to make a final selection.

[0462] As a concrete example, the prompt text "Genre: RPG, Target demographic: Adult, Features: Fantasy, Multiplayer, User emotion: Excitement" is entered into the terminal. This information is sent to the server, and the generative AI model proposes the name "FantasyQuest Online". The name's registrability and risk of trademark infringement are then evaluated through a trademark search API, and the results are returned to the terminal as a list.

[0463] This allows users to choose a suitable name for their game app efficiently and without legal risk.

[0464] The flow of the identification process will be described with reference to FIG.

[0465] Step 1: Accepting user input

[0466] The user inputs information about the game app under development (genre, target demographic, characteristics, and the user's emotional state) into the device. This information is formatted as a prompt text and prepared to be sent to the server. The input data is processed into specific instructions for naming generation. For example, a prompt text like "Genre: RPG, target demographic: adults, characteristics: fantasy, multiplayer, user's emotion: excitement" might be generated.

[0467] Step 2: Generate names

[0468] The server starts name generation using a generative AI model based on the received prompt. This model analyzes the input game app information and generates a creative name that matches it. The generative AI model outputs a name such as "FantasyQuest Online" from the prompt.

[0469] Step 3: Trademark Search and Evaluation

[0470] For each generated name, the server uses a trademark search API to assess its registrability in each country and its risk of trademark infringement. During this process, the name is checked against public trademark databases to check for conflicts with existing trademarks. The result of this operation is a legal risk assessment for the name.

[0471] Step 4: List and submit results

[0472] The server creates a list of the results of the trademark search and evaluation and sends the list to the terminal, which contains information about the registrability of the generated names and the risk of trademark infringement. The terminal receives this information and displays it to the user.

[0473] Step 5: User makes final choice

[0474] Users can refer to the information displayed on their device to select the most suitable name for their game app. The selection process involves a balance between creativity and legal safety when coming up with the final name.

[0475] (Application example 2)

[0476] Next, a description will be given of Application Example 2 of Form 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".

[0477] Conventional name generation systems can simply generate names randomly or based on existing databases, but they have difficulty efficiently proposing names that consider legal risks while also taking into account user emotions and specific requirements for game development (e.g., genre, target demographic, features). In addition, the task of checking whether the generated names are internationally registrable and do not cause trademark infringement is time-consuming, and this process needs to be made more efficient.

[0478] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0479] In this invention, the server includes a means for recognizing the genre, target demographic, game features, and emotions of the game based on information input by the user, a means for generating a name using a generative AI based on this information, and a means for automatically checking the registrability of the generated name in each country and the presence or absence of trademark infringement. This makes it possible to propose names that reflect the user's emotions and requests and to automatically evaluate their legal risks.

[0480] The "means for generating a name" is a function that suggests new game names based on input parameters such as the game genre, target demographic, characteristics, and user emotions.

[0481] "Means to confirm the registrability of the generated name in each country" is a function that automatically investigates and evaluates whether the proposed name is eligible for trademark registration in accordance with the laws of each country.

[0482] The "means of verifying that the generated name does not infringe on trademarks in each country" refers to a function that automatically verifies whether the proposed name conflicts with existing trademarks using each country's trademark database.

[0483] The "means of listing the confirmation results" is a function for organizing the confirmation results of the above-mentioned registration possibility and trademark infringement in a list and providing it to the user.

[0484] The "means of organizing application information for registration, etc." is a function that organizes the information required for trademark registration and other applications related to the proposed name, enabling the application process to be carried out efficiently.

[0485] "Means for recognizing the game genre, target demographic, game features and emotions based on user input" refers to a function that receives specific information about the game under development and the emotions of the game developer and users as input, and understands and analyzes this information.

[0486] The "means for generating a name based on this information" is a function that executes a process of combining the game genre, target demographic, characteristics, and emotional data to create a new game name that matches these.

[0487] The embodiment of the present invention includes a terminal (e.g., a smartphone or tablet), a server, a generative AI model (e.g., OpenAI's GPT), and a trademark database API. The terminal receives inputs from the user, such as the game genre, target demographic, features, and emotions, and transmits this information to the server. The server generates an appropriate game name using the generative AI model based on the received information. In this process, the input information is provided to the AI ​​as a prompt, and the AI ​​creates a name based on this. The generated name is then checked by the server through the trademark database API for registration possibility in each country and for the presence or absence of trademark infringement. The confirmation results are listed by the server and presented to the user through the terminal. The user selects the most suitable name based on the provided information, and this selection is again transmitted to the server. Finally, the server organizes the application information for the selected name, and helps the user to smoothly proceed with the trademark registration procedure, etc.

[0488] As a specific example, if a user inputs the genre of the game they are developing as "RPG," the target demographic as "adults," the characteristics as "fantasy, multiplayer," and the emotion as "adventurous," this information is sent from the terminal to the server. The server provides this information to a generative AI model, which generates a name based on the prompt "genre: RPG, target demographic: adults, characteristics: fantasy, multiplayer, emotion: adventurous." This invention enables users to efficiently create game names that reflect their own intentions and simultaneously evaluate legal risks.

[0489] The flow of the identification process will be described with reference to FIG.

[0490] Step 1: The user uses the terminal to input information such as the game genre, target demographic, features, and emotions. This input information is sent to the server. In this step, the user's intentions and the specific requirements for the game they want to develop are digitized and serve as input to the server.

[0491] Step 2: The server generates a name using a generative AI model based on the received information. This process takes into account the input game genre, target demographic, characteristics, and emotions, and calculates the best name for these parameters. The generated name is used as input for the subsequent process.

[0492] Step 3: The server checks whether the generated name can be registered in each country and whether there is any trademark infringement. This check is done through the relevant API, accessing the trademark database of each country to verify whether the name is registerable and does not constitute trademark infringement. The output of this step is the result of the check on registration and trademark infringement.

[0493] Step 4: The results of the verification are compiled into a list and sent from the server to the terminal. The user can use this list to evaluate the legal risk of the generated name. The results of the verification are listed and include information on the possibility of registration for each name and whether there is any trademark infringement.

[0494] Step 5: After the user has finally selected the name, the server organizes the application information for registration, etc. In this step, the information required for trademark registration and other application processes related to the selected name is organized, helping the user to efficiently proceed with these applications.

[0495] Through these processing steps, users can efficiently generate names that reflect their own feelings and game development requirements, evaluate the legal risks, and then select and apply for an appropriate name.

[0496] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0497] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0498] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0499] 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 smart glasses 214.

[0500] [Third embodiment]

[0501] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0502] 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.

[0503] 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 wide area network (WAN) and / or a local area network (LAN).

[0504] 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.

[0505] 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 the voice according to instructions from the processor 46.

[0506] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0507] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0508] Fig. 6 shows an example of 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.

[0509] 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.

[0510] 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.

[0511] In the headset type terminal 314, 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.

[0512] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0513] "Example 1"

[0514] The system of the present invention uses generative AI as a means of generating names. The generative AI generates appropriate names based on specific parameters (e.g., industry, target demographic, service content, etc.). The generated names are evaluated by a means for checking the possibility of registration in each country and a means for checking trademark infringement. These means are performed by linking related APIs and accessing trademark regulations and registration information of each country. The confirmation results are compiled into a list and organized together with application information such as registration. This improves the efficiency of planning and procedures.

[0515] "Example 2"

[0516] As a specific example, consider the case of thinking of a name for a newly developed game app. Generative AI generates an appropriate name based on parameters such as the game genre (e.g. RPG, puzzle, action, etc.), target demographic (e.g. children, adults, men, women, etc.), and game characteristics (e.g. fantasy, real-time, multiplayer, etc.). The generated name is evaluated by a method to check for registration feasibility and trademark infringement in each country, and the confirmation results are compiled into a list. This aims to streamline planning and procedures.

[0517] The process flow of each embodiment will be described below.

[0518] "Example 1"

[0519] Step 1: Generative AI generates an appropriate name based on certain parameters (e.g. industry, target demographic, service content, etc.).

[0520] Step 2: The generated name is evaluated using a methodology to check for its registrability in each country. This is done through the integration of relevant APIs, which have access to each country's trademark regulations and registration information.

[0521] Step 3: Similarly, the generated names are evaluated by a means to check for trademark infringement. Step 4: The results of the check are compiled into a list and organized together with the application information for registration, etc. This will streamline the planning and procedures.

[0522] "Example 2"

[0523] Step 1: When thinking of a name for a new game app to be developed, the generative AI generates an appropriate name based on parameters such as the game genre (e.g., RPG, puzzle, action, etc.), target demographic (e.g., children, adults, men, women, etc.), and game features (e.g., fantasy, real-time, multiplayer, etc.).

[0524] Step 2: The generated name is evaluated using a methodology to check for its registrability in each country. This is done through the integration of relevant APIs, which have access to each country's trademark regulations and registration information.

[0525] Step 3: Similarly, the generated names are evaluated by a means to check for trademark infringement. Step 4: The results of the check are compiled into a list and organized together with the application information for registration, etc. This will streamline the planning and procedures.

[0526] Example 1

[0527] Next, a description will be given of Example 1 of the embodiment 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".

[0528] In the modern commercial environment, the selection of an appropriate name is crucial, as the name of a product or service directly affects the brand value. However, when considering the international market, it is difficult to efficiently evaluate the possibility of trademark registration in different countries and the risk of trademark infringement. With traditional methods, such evaluations require a huge amount of time and resources, which is a heavy burden especially for small and medium-sized enterprises and start-ups. There is a need for a method to solve this problem and perform everything from name generation to legal risk assessment quickly and efficiently.

[0529] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0530] In this invention, the server includes a means for inputting a prompt sentence that takes into account a specific industry, target demographic, and service content into a generative AI to generate an appropriate name, a means for evaluating the registrability of the generated name in each country and the risk of trademark infringement through the linkage of a trademark registration database and an API that searches trademark law regulation information, and a means for listing the above evaluation results and organizing application information for registration, etc. This enables users to quickly and efficiently select a name that takes legal risks into account.

[0531] "Specific parameters" are conditions such as industry, target demographic, and service content that are taken into account when generating a name.

[0532] A "name" is a word or combination used to identify a product, service, company, etc.

[0533] "Registrability in each country" refers to whether a name can be registered as a trademark in a particular country.

[0534] "Trademark infringement" is a legal issue that arises when someone unfairly violates the trademark rights of others, especially when they use it without permission.

[0535] "Listing methods" are ways of organizing and visualizing information in a table format.

[0536] "Means for organizing application information for registration, etc." refers to a method for systematically organizing and managing information related to trademark registration of names and other applications.

[0537] "Generative AI" is an artificial intelligence model that automatically generates new content and ideas based on input data and conditions.

[0538] A "prompt" is an instruction or question used to instruct a generative AI model on a specific task.

[0539] The "API for referencing the trademark registration database" is an interface that accesses external systems to inquire about whether a trademark has been registered and for detailed information.

[0540] The "API for searching trademark law and regulation information" is a programming interface for searching for trademark laws and regulation information for specific countries or regions.

[0541] In the embodiment of the present invention, the server, the terminal, and the user work together to carry out the work. The server is equipped with a generative AI model (e.g., a machine learning model using TensorFlow or PyTorch) and has a function of generating a prompt sentence and proposing an appropriate name based on specific parameters (e.g., industry, target demographic, service content) provided by the user via the terminal.

[0542] The terminal acts as an interface to receive user input and transmits the input parameters to the server. For example, if a user inputs a prompt such as "Generate the name of a cloud service targeted at business people in the IT industry," this information is sent from the terminal to the server.

[0543] The server receives the prompt and uses a generative AI model to generate multiple name suggestions. The generated name suggestions are then queried by APIs that reference trademark registration databases and APIs that search trademark law and regulatory information to evaluate the possibility of trademark registration and the risk of trademark infringement. These APIs are used, for example, to access international trademark information databases and trademark law and regulatory information for specific countries.

[0544] The obtained information is organized by the server and displayed to the user via the terminal. The display clearly indicates the likelihood of registration for each proposed name and the risk of trademark infringement. The user can use this information to select the most suitable name.

[0545] This allows users to quickly and efficiently determine names for new products and services while taking into account legal risks. Especially when considering international markets, the system offers great convenience and significantly reduces the time and cost of the name selection process.

[0546] The flow of the identification process will be described with reference to FIG.

[0547] Step 1: Accepting input from the user

[0548] The user inputs parameters such as the specific industry, target demographic, and service content through the terminal. This input information is used to create a prompt for name generation. The terminal receives the input from the user and sends it to the server. At this time, the input parameters act as conditions for generating an appropriate name.

[0549] Step 2: Generate prompts and names

[0550] The server generates a prompt based on parameters received from the user. This prompt is input into a generative AI model and used to generate a name that matches certain conditions. The generative AI model uses this prompt as the basis for data processing and calculations to output a series of name suggestions. For example, if the prompt is "Generate a name for a cloud service targeted at business people in the IT industry," the AI ​​model will generate multiple names that fit the conditions.

[0551] Step 3: Check for trademark registrability and trademark infringement

[0552] For each generated name, the server queries the relevant APIs to assess the possibility of trademark registration and the risk of trademark infringement. This process uses APIs that refer to the trademark registration database and APIs that search trademark law and regulatory information. For each name, the information obtained from these APIs is processed and calculated to assess the name's possibility of registration and the risk of trademark infringement, and the results are returned to the server.

[0553] Step 4: List and present the results

[0554] Based on the information obtained in step 3, the server lists and organizes the registration possibility and trademark infringement risk of each name. This organized information is presented to the user via the terminal. The presented information indicates the registration possibility and trademark infringement risk for each generated name. The user uses this information as a reference to select the final name. This step enables the user to quickly select an appropriate name while taking legal risks into account.

[0555] (Application example 1)

[0556] Next, a description will be given of Application Example 1 of Form 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".

[0557] When deciding on names for commercial facilities or products to be used in virtual space, it is difficult to generate creative names while efficiently checking whether the names can be registered as trademarks in each country and whether there is a risk of trademark infringement. To solve this problem, a system is needed that automates name generation and quickly evaluates the legal issues of generated names.

[0558] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0559] In this invention, the server includes a means for generating names based on parameters such as industry, target demographic, and service content, a means for evaluating the registrability of the generated names in each country and the risk of trademark infringement, and a means for providing the user with a list of the evaluation results. This enables the user to efficiently generate creative names, quickly understand the legal issues involved, and select an appropriate name and apply for registration.

[0560] A "means for generating names" is a function that uses artificial intelligence technology to automatically generate appropriate names based on input parameters (industry, target demographic, service content, etc.).

[0561] "Means for verifying the registrability of the generated name in each country" refers to a function that runs a process to determine whether a particular name is registrable in accordance with the laws and trademark registration regulations of each country.

[0562] The "means of verifying whether the generated name will not infringe on trademarks in each country" refers to a function that analyzes and determines whether the created name is likely to conflict with existing trademarks by referring to country-specific trademark databases.

[0563] The "means of listing the above confirmation results" refers to a function that organizes and displays in list form the confirmation results regarding the registrability of a name and the risk of trademark infringement.

[0564] A "means of organizing application information for registration, etc." is a function that assists the user in the process of managing and organizing documents and information related to registration applications that they need to submit.

[0565] "Means for suggesting names to be applied to commercial facilities and products within a virtual space" refers to a function that generates and suggests names for stores and products to be used within a virtual environment.

[0566] "Using generative AI based on parameters such as industry, target demographic, and service content" refers to the process in which artificial intelligence automatically generates appropriate naming suggestions based on information such as the specified industry, target customer demographic, and characteristics of the services provided.

[0567] "Through integration of relevant APIs" means that the assessment of the possibility of trademark registration and the risk of trademark infringement is carried out automatically online, utilizing external application programming interfaces (APIs).

[0568] The embodiment of the present invention is a system that combines name generation and legal evaluation. The system is composed of a server-based software application that is accessed by a user via a terminal. The server is equipped with a generative AI model and an external API access function. Specifically, a program written in Python uses a generative AI model (e.g., GPT-3 or its successor) to generate a name based on parameters provided by the user (industry, target demographic, service content, etc.). The generated name is used to evaluate the registrability and risk of trademark infringement in each country through an external API.

[0569] Users access the service through devices such as smartphones or head-mounted displays and input the necessary parameters. The server receives this input, generates and evaluates names, and feeds the results back to the user. Through this process, users can efficiently and safely decide on names for commercial facilities and products to be used in virtual space.

[0570] For example, if you input the parameters "cafe," "youth," and "environmentally friendly," the generative AI model will suggest names such as "GreenSip Cafe" and "EcoBrew Corner" based on these parameters. The server then uses an external API to evaluate the registrability and trademark infringement risk of these names in each country and provide the results to the user. This process allows users to understand the risks involved in name selection in advance and make appropriate decisions.

[0571] The flow of the identification process will be described with reference to FIG.

[0572] Step 1: The user inputs parameters such as industry, target demographic, and service content through the terminal. This input is sent to the server as basic data for name generation. The data sent from the terminal is received by the server and temporarily stored for further processing.

[0573] Step 2: The server generates a name using a generative AI model based on the parameters received in step 1. In this process, the input parameters are analyzed by the AI ​​model to generate a relevant name. The generated name is temporarily stored by the server for the next step.

[0574] Step 3: The server evaluates the generated name for its registrability in each country and the risk of trademark infringement through the relevant API. In this evaluation process, the server accesses external trademark databases to investigate whether the generated name may conflict with existing trademarks. The evaluation results are collected and organized by the server.

[0575] Step 4: The server lists the evaluation results obtained in step 3 and displays them to the user. In this step, information on the possibility of registration and the risk of trademark infringement is provided to the user in an easy-to-understand format. Based on this information, the user can make a final selection of the name to be used.

[0576] Step 5: Based on the name finally selected by the user, the server organizes the application information for registration, etc., and provides it to the user. In this process, the documents and information required for the registration application using the selected name are organized, helping the user to complete the application process smoothly.

[0577] Through these steps, users can efficiently and safely select the names of their stores and products to be used in the virtual space and proceed with the necessary registration procedures. Each processing step involves data exchange between the server and the device, data processing using a generative AI model, information collection and evaluation through linkage with external APIs, and information provision to the user.

[0578] Example 2

[0579] Next, a second embodiment of the second form will be described. 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".

[0580] When coming up with a name for new content, it is a time-consuming and labor-intensive task to come up with creative proposals while simultaneously evaluating the possibility of international trademark registration and the risk of trademark infringement. In particular, when considering use in multiple markets, it can be difficult to quickly identify an attractive name that meets legal constraints.

[0581] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0582] In this invention, the server includes a means for generating a name based on the attributes of the content, a means for checking the international registrability of the generated name and the presence or absence of trademark infringement, a means for listing the check results by category and presenting them on a user interface, and a means for organizing application information for registration, etc. based on the name selected by the user and automating the procedures. This makes it possible to quickly propose creative names and simultaneously evaluate their legal applicability.

[0583] "Content attributes" are basic elements for defining content, such as the genre, target demographic, and characteristics of the content.

[0584] "Name generation" is the process of creating a suitable name for new content based on the attributes of the input content.

[0585] "International regisability and trademark infringement check" refers to the act of investigating whether a particular name is available in the trademark registers of various countries around the world and whether there is a risk of infringement due to its similarity to other trademarks.

[0586] "Listing the confirmation results by category and presenting them in a user interface" means classifying the investigated information on the possibility of registration and the presence or absence of trademark infringement, and displaying this information in a form that is visually easy for users to understand.

[0587] "Organizing application information for registrations and other purposes based on the name selected by the user and automating procedures" means consolidating information related to the necessary registrations and applications using the name selected by the user, and streamlining and automating these processes.

[0588] A "generative AI model" is a system that uses artificial intelligence technology to automatically create new data or text from specific input information.

[0589] "API integration" refers to integration through a programming interface that enables different software or services to share functions and exchange data with each other.

[0590] An "API for accessing external databases" is an interface that enables a program to directly query databases managed by other organizations on the Internet.

[0591] In an embodiment of the present invention, the system includes a process for generating a name, checking its international registrability and trademark infringement, and finally presenting the information to a user. This process is mainly composed of software that runs on a server and a terminal that accepts user operations.

[0592] The server uses a generative AI model to generate a name based on the attributes of the input content. For example, a text generation model such as GPT (Generative Pre-trained Transformer) developed by OpenAI is used as the generative AI model. The generated name is checked for international registration possibility and trademark infringement through API connection to an external database. Through this API connection, the server accesses trademark databases from around the world to check whether the proposed name conflicts with existing trademarks.

[0593] The terminal is responsible for accepting input from the user and sending it to the server. Through the terminal, the user specifies attributes such as the genre of the content, the target demographic, and the characteristics of the content. For example, if the user inputs the attributes "Genre: RPG, Target demographic: Adults, Characteristics: Fantasy, Real-time, Multiplayer", this information is formatted as a prompt sentence and sent to the generative AI model on the server.

[0594] The server responds by presenting the generated name and the results of its legal evaluation to the user via the terminal. The user can then use this information to select the most appropriate name and proceed to the next steps, such as registering that name or applying for a trademark. Through this process, the user can decide on a name for new content efficiently and with an understanding of the legal risks.

[0595] The flow of the identification process will be described with reference to FIG.

[0596] Step 1: The user uses the device to input the attributes of the content (genre, target demographic, features, etc.). This input becomes the basic data for the generative AI model to process. The device receives this input data and creates a prompt sentence based on it.

[0597] Step 2: The terminal sends the created prompt to the server. The server receives the prompt and generates a name using a generative AI model. From the input prompt, the server outputs a name that is appropriate for the content.

[0598] Step 3: The server checks the generated name against external databases to verify the name's international registrability and trademark infringement. At this stage, the server takes the generated name as input and the search results from external databases as output.

[0599] Step 4: The server receives the search results from the external database, organizes the information into a list, and sends it to the terminal. This list includes the registrability of the name and the risk of trademark infringement. The server processes the search results and sends the organized information to the terminal.

[0600] Step 5: The terminal presents the list received from the server to the user. The user selects the name that he / she thinks is most appropriate from the presented list. The terminal receives the user's selection and transmits the information to the server.

[0601] Step 6: The server organizes the registration and other application information based on the name selected by the user, and automates the registration process for the user. At this stage, the server processes the selected name and the necessary application information, and guides the user on the next steps.

[0602] Through these steps, the system enables users to efficiently and effectively determine names for new content and automate its registration and trademark verification.

[0603] (Application example 2)

[0604] Next, a description will be given of Application Example 2 of Form 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".

[0605] When launching a new product or brand in the online market, coming up with an appropriate product or brand name is a critical factor that directly impacts its success. However, coming up with a creative and attractive product or brand name is not easy, and it is necessary to comprehensively evaluate whether the name has sufficient online searchability, whether it can secure buzz on social media, and whether it can be trademarked. Efficiently generating and evaluating names that meet these requirements is a major challenge, especially for small and medium-sized businesses.

[0606] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0607] In this invention, the server includes a means for generating product names and brand names, a means for checking the online searchability of the generated product names and brand names, a means for evaluating whether the generated product names and brand names have topicality on SNS, and a means for checking the possibility of trademark registration of the generated product names and brand names. This allows businesses to efficiently generate creative and attractive product names and brand names, and to comprehensively evaluate the names' online searchability, topicality on SNS, and possibility of trademark registration. This system allows businesses to reduce risks when introducing new products and brands to the market and increase the chances of success.

[0608] "Means for generating product and brand names" refers to a function that automatically creates appropriate product and brand names based on the product category, target demographic, and product features designated by the user.

[0609] "Means to verify online searchability" is a function that evaluates whether the generated product or brand name can be easily searched for on the Internet.

[0610] The "means for evaluating whether a product name or brand name has potential to be discussed on social media" is a function for judging whether a generated product name or brand name is likely to be discussed by users on a social networking service.

[0611] "Means to confirm the possibility of trademark registration" is a function that investigates whether the generated product name or brand name can be registered as a trademark, in other words, whether there is any overlap or similarity with other trademarks.

[0612] "Through integration with related APIs" refers to programming interfaces used to automatically collect and process information by integrating with external databases and services.

[0613] An embodiment of the present invention relates to an automated system for generating and evaluating product and brand names in an online market. A server executes a program that generates product and brand names using a generative AI model. The program generates a prompt sentence based on input information from a user, such as the product category, target demographic, and product features, and transmits the prompt sentence to the generative AI model. The generative AI model proposes creative and attractive product and brand names based on the transmitted prompt sentence.

[0614] For each generated product and brand name, the server also runs a program that evaluates its online searchability, social media buzzworthiness, and trademarkability. This evaluation is automated through the associated API, accessing online databases and trademark registry databases to verify the uniqueness and availability of the generated names.

[0615] The system is developed using programming languages ​​such as Python and JavaScript, and utilizes libraries for natural language processing such as TensorFlow, PyTorch, and NLTK. Using these technologies, the server performs complex text analysis and data processing to provide useful information to users.

[0616] As a specific example, the prompt "Brand name of fashion T-shirts for young people made from eco-friendly materials" is generated to reflect the product category "fashion," the target demographic "young people," and the product feature "made from eco-friendly materials." The generative AI model proposes multiple brand names based on this prompt, and these names are automatically evaluated for online searchability, buzzworthiness on social media, and possibility of trademark registration. Through this process, businesses can efficiently select attractive product and brand names and prepare for market launch.

[0617] The flow of the identification process will be described with reference to FIG.

[0618] Step 1: The user inputs the product category, target demographic, and product features. This input is sent to the server as the basic data for generating product names and brand names.

[0619] Step 2: The server generates a prompt sentence based on the input product category, target demographic, and product features. This prompt sentence serves as an instruction for the generative AI model to generate the product name and brand name.

[0620] Step 3: The generative AI model generates product and brand names based on the received prompt text. During this process, the AI ​​model uses text generation techniques to suggest multiple candidates.

[0621] Step 4: The server checks the online searchability of the generated product and brand names. This process involves analyzing existing search engine results on the Internet to assess the uniqueness of the generated names.

[0622] Step 5: At the same time, the server evaluates the popularity of the generated product and brand names on social media based on the analysis of social media trends and hashtags.

[0623] Step 6: The server then checks for trademark registration feasibility by querying national and international trademark databases to see if the generated name conflicts with existing trademarks.

[0624] Step 7: The server reports the results of the evaluation of online searchability, social media popularity, and trademark registration potential to the user. The report includes the recommendation level and potential problems of the generated product and brand names.

[0625] Through this process, users can efficiently obtain creative and practical product and brand names, thus increasing the chances of success in the online market.

[0626] In addition, an emotion engine that estimates the emotion of the user may be further combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0627] "Example 1"

[0628] The present invention combines a system that generates names, checks whether they can be registered in each country, checks for trademark infringement, lists the check results, and organizes application information such as registration, with an emotion engine that recognizes the emotions of the user. Specifically, the emotion engine recognizes the emotions of the user and generates a name based on the emotions. For example, if the user is expressing an emotion of joy, a name that gives a positive impression that matches the emotion is generated. The emotion engine also checks whether the name generated based on the user's emotions can be registered in each country and whether there is trademark infringement. This makes it possible to generate names that reflect the emotions of the user and evaluate them.

[0629] "Example 2"

[0630] As a concrete example, consider the case of thinking of a name for a new game app to be developed. When a user is excited about the development of the game, the emotion engine recognizes that emotion and generates a name that expresses that excitement. The generated name is evaluated by a means for checking the possibility of registration in each country, and is also evaluated by a means for checking trademark infringement. This makes it possible to generate and evaluate names that reflect the user's emotions.

[0631] The process flow of each embodiment will be described below.

[0632] "Example 1"

[0633] Step 1: The emotion engine recognizes the user's emotion.

[0634] Step 2: Name generation is performed based on the emotions recognized by the emotion engine.

[0635] Step 3: Check the registerability of the generated name in each country.

[0636] Step 4: Check the generated name for trademark infringement.

[0637] Step 5: List the confirmation results and organize the application information for registration, etc.

[0638] "Example 2"

[0639] Step 1: When a user expresses excitement about game development, the emotion engine recognizes that emotion.

[0640] Step 2: The emotion engine generates a name that expresses the feeling of excitement based on the recognized feeling of excitement.

[0641] Step 3: Check the registerability of the generated name in each country.

[0642] Step 4: Check the generated name for trademark infringement.

[0643] Step 5: List the confirmation results and organize the application information for registration, etc.

[0644] Example 1

[0645] Next, a description will be given of Example 1 of the embodiment 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".

[0646] In the modern business environment, it is extremely important to give a suitable name to a new product or service. The name forms the first impression of the product or service and is an element that is memorable to consumers. However, it is extremely difficult to find a name that can be registered in each country and does not pose a risk of trademark infringement while taking into account cultural background and language differences. In addition, conventional technologies do not provide a method to generate a name that reflects the emotions and values ​​of the companies and individuals who bring the product or service to the market.

[0647] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0648] In this invention, the server includes a means for collecting user emotion data, a means for analyzing the collected emotion data, and a means for generating a name by combining the analysis result with specific parameters provided by the user. This makes it possible to generate a name that reflects the user's emotions and values, can be registered in each country, and has no risk of trademark infringement. In addition, by linking related APIs, it is possible to quickly check the registrability of the name in each country and whether or not there is a trademark infringement, which is expected to improve the efficiency and accuracy of the naming process for new products and services in a business environment.

[0649] "User emotional data" refers to information about a user's emotional state collected from facial expressions, tone of voice, body language, etc.

[0650] "Means for analyzing the collected emotional data" refers to processes or algorithms that process the collected emotional data and identify the user's current emotional state.

[0651] "Specific parameters" are specific information such as industry, target demographic, and service content provided by the user when generating a name.

[0652] "Name Generator" refers to a process or algorithm that generates an appropriate name based on collected sentiment data and certain parameters provided by the user.

[0653] "Country availability verification procedures" refers to the process or service that checks whether a generated name can be legally registered in a particular country.

[0654] "Trademark verification measures" refers to processes or services that verify whether the generated name infringes existing trademark rights in a particular country.

[0655] "Means for listing the verification results" refers to a process or algorithm that organizes and displays in a list the verification results regarding the registrability of the name in each country and whether there is any trademark infringement.

[0656] "Means of organizing application information for registration, etc." refers to a process or system that systematically organizes the information required for name registration or trademark application, facilitating the procedure.

[0657] In an embodiment of the present invention, the terminal and the server work together to collect emotion data from the user, analyze the data, generate a name, and legally verify the generated name. The terminal is a device that collects the user's emotion data, records the user's facial expressions and voice through a camera and microphone, and transmits them to the server in an analyzable format. This process uses facial expression recognition software (e.g., OpenCV) and voice analysis libraries (e.g., WebRTC).

[0658] On the server side, a neural network model (e.g., a model using TensorFlow) is used to analyze the received emotion data. The model identifies the user's emotional state and feeds the results back into the name generation process. A generative AI model (e.g., a GPT-based model) is used to generate the name based on the user's emotion and a prompt that reflects certain parameters (e.g., "Generate a service name for a young target audience in the health food industry that expresses the emotion of joy").

[0659] The legal check of the generated name is carried out using a related API that provides access to a trademark registration database, allowing the server to check whether the generated name can be registered in each country and whether there is a risk of trademark infringement, and to report the results to the user in a list.

[0660] This method allows the generation of names that reflect the user's emotions and their legal verification to be carried out automatically through a series of processes, significantly improving the efficiency and satisfaction of naming products and services.

[0661] The flow of the identification process will be described with reference to FIG.

[0662] Step 1: Collect user emotion data

[0663] The device collects the user's facial expressions and voice using a camera and microphone. This collection process uses facial recognition software and voice analysis libraries to collect data that suggests the user's emotions. The collected data (input) is sent to a server for sentiment analysis (output).

[0664] Step 2: Analyze the sentiment data

[0665] The server analyzes the received emotion data using a neural network model to identify the user's emotional state. From the input emotion data, the model extracts features and identifies the emotion based on them (output). This output is used in the name generation process.

[0666] Step 3: Generate names

[0667] The generative AI model on the server generates an appropriate name using a prompt sentence that combines the identified emotional state with specific parameters provided by the user (industry, target demographic, service content). In this step, the emotional state and parameters are given as input, and the generative AI model processes them to generate a new name (output).

[0668] Step 4: Check for registrability and trademark infringement

[0669] For the generated name, the server checks the name's registrability and trademark infringement status in each country through the relevant API. In this process, the generated name is used as input, and the API returns information on registrability and trademark infringement status (output).

[0670] Step 5: List and report findings

[0671] Finally, the server lists the results of the verification of registrability and trademark infringement, organizes this information, and reports it to the user. In this step, the verification result data is used as input, and an organized information list is output. This list is provided to the user via the terminal as a reference for making the final name selection.

[0672] (Application example 1)

[0673] Next, a description will be given of Application Example 1 of Form 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".

[0674] Conventional name generation systems have the problem that it is difficult to generate names that fully reflect the user's feelings and sensibilities. Furthermore, the process of verifying whether the generated name is registrable in each country and whether it infringes on trademarks is complicated and requires a great deal of time and effort. These issues are factors that reduce the speed and efficiency of business, especially for businesses that operate in international virtual spaces.

[0675] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0676] In this invention, the server includes a means for reading the user's emotions, a means for using a generative AI to generate a name that gives an impression that matches the emotion based on the emotion read, and a means for verifying the registrability of the generated name in each country and the presence or absence of trademark infringement through the linkage of related APIs. This enables the rapid generation of a name that reflects the user's emotions and sensibilities, and reduces the risk of using the name internationally.

[0677] A "means for generating names" is a function that automatically devise and output appropriate names or designations based on specific parameters and the user's emotional data.

[0678] "National name registrability checks for generated names" refers to the process of determining whether a proposed name can be used under the laws and registry systems of different countries.

[0679] "Means to verify that the generated name does not violate trademark laws in each country" refers to a function that verifies whether the proposed name violates the trademark laws of each country.

[0680] The "means of listing the above confirmation results" refers to the process of organizing the results of name generation and subsequent registration feasibility and trademark infringement confirmation into a visually easy-to-understand list.

[0681] "Means of organizing application information for registrations, etc." refers to the process of systematically preparing and managing the information required when making official registrations or applications for the use of a name.

[0682] "Means of reading user emotions" means technology that identifies and analyzes the user's current emotional state from the user's facial expressions, tone of voice, or other emotional expressions.

[0683] "Means for generating names based on emotions read" refers to a function that uses emotional data obtained from a user as input and creates a name that matches or responds to that emotion.

[0684] In the embodiment of the present invention, a system built on a server is the center. This system is configured by combining a generative AI model, an emotion reading function, and an API linking function. The server executes a program developed in the Python language and implements a generative AI model using a machine learning library such as TensorFlow or PyTorch. In addition, the emotion reading function uses face recognition technology and voice analysis technology to grasp the user's emotional state through the terminal.

[0685] Users access the system via a device such as a smartphone or head-mounted display. The device sends the user's input information (industry, target demographic, service content, etc.) to the server, while at the same time capturing the user's emotions through a camera and microphone and sending that data to the server. Based on this data, the server uses a generative AI model to generate a name that matches the emotions. The server then checks the registration feasibility of the generated name in each country and whether it infringes on trademarks in cooperation with the relevant APIs.

[0686] As a specific example of usage, a prompt sentence such as "Industry: games, Target demographic: young people, Service content: VR experience game, Emotion: joy" is entered by the user and sent to the server via the terminal. The server uses this prompt sentence and the user's emotion data to generate a name such as "JoyVR Arcade" and checks whether this name can be trademarked and whether there is any trademark infringement. This allows the user to quickly obtain a name that reflects their emotions and has low legal risk.

[0687] The flow of the identification process will be described with reference to FIG.

[0688] Step 1: The user sends input information such as industry, target demographic, and service content, as well as the user's emotional data captured using the device's camera and microphone, to the server via the device. In this step, the input information and emotional data are sent to the server, which becomes the basis for processing.

[0689] Step 2: The server uses a generative AI model to generate a suitable name based on the received industry, target demographic, and service content information and sentiment data. In this process, the server analyzes the input information and outputs a corresponding name. The generative AI model runs an algorithm to create a highly relevant name based on the input parameters and sentiment data.

[0690] Step 3: For the generated name, the server checks the registrability and trademark infringement in each country through the relevant API. In this step, the server sends the generated name to the API and receives the confirmation results of registrability and trademark infringement from the API. The server analyzes these results and lists the final confirmation results.

[0691] Step 4: The server compiles a list of the verification results and sends it to the user's terminal. The user can view this list through the terminal and check the availability and legal risk of the generated name. In this final step, the user obtains the generated name and its related information on its registration possibility in each country and whether there is any trademark infringement.

[0692] Example 2

[0693] Next, a second embodiment of the second form will be described. 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".

[0694] In the past, it was difficult to balance creativity with legality when coming up with names for game apps and other software products. In particular, there was no system that could generate names that reflected users' emotions and intentions, and quickly evaluate whether the names were internationally registrable and avoided trademark infringement. This resulted in a significant amount of time and resources being wasted in the product naming process.

[0695] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0696] In this invention, the server includes a means for generating a name based on the genre, target demographic, and characteristics of the game app provided by the user, and the user's emotional state, a means for checking the international registrability of the generated name and evaluating the risk of trademark infringement, and a means for providing the user with a list of the above-mentioned confirmation results. This enables rapid generation and evaluation of names that take into account legal constraints while still allowing for creativity.

[0697] The "means for generating a name" is a function that automatically creates an appropriate and original name based on parameters provided by the user (e.g., the genre of the game app, the target demographic, characteristics, and the user's emotional state).

[0698] The "means of verifying the registrability of the generated name in each country" is a function that automatically verifies whether a name is registrable in each country's trademark registration system.

[0699] The "means of verifying whether the generated name will not infringe on trademarks in each country" refers to a function that automatically evaluates whether the proposed name is likely to conflict with existing trademarks by referencing international trademark databases.

[0700] The "means of listing the above confirmation results" is a function that presents to the user the evaluation results of the registrability of a name and the risk of trademark infringement in a clear and organized format.

[0701] "Means of organizing application information for registration, etc." is a function that organizes the information required when making a trademark registration or other application based on the name proposed by the user, making the procedure easier.

[0702] In an embodiment of the present invention, a system is configured to generate a name for a game application and evaluate its registrability and risk of trademark infringement. This system is mainly composed of a server, a terminal, and software that links these.

[0703] The server implements name generation using a generative AI model, as well as registrability and trademark infringement assessment via a trademark search API. The generative AI model used utilizes state-of-the-art natural language processing techniques, such as OpenAI's GPT series, to generate creative names based on input parameters. Trademark searches are performed through APIs that access public databases such as the USPTO's TESS and the European Union Intellectual Property Office's (EUIPO) eSearch.

[0704] The terminal is responsible for the user inputting information about the game app (genre, target demographic, features, and the user's emotional state) and sending this information to the server as a prompt sentence. The terminal also displays the generated name and its evaluation results sent from the server, allowing the user to make a final selection.

[0705] As a concrete example, the prompt text "Genre: RPG, Target demographic: Adult, Features: Fantasy, Multiplayer, User emotion: Excitement" is entered into the terminal. This information is sent to the server, and the generative AI model proposes the name "FantasyQuest Online". The name's registrability and risk of trademark infringement are then evaluated through a trademark search API, and the results are returned to the terminal as a list.

[0706] This allows users to choose a suitable name for their game app efficiently and without legal risk.

[0707] The flow of the identification process will be described with reference to FIG.

[0708] Step 1: Accepting user input

[0709] The user inputs information about the game app under development (genre, target demographic, characteristics, and the user's emotional state) into the device. This information is formatted as a prompt text and prepared to be sent to the server. The input data is processed into specific instructions for naming generation. For example, a prompt text like "Genre: RPG, target demographic: adults, characteristics: fantasy, multiplayer, user's emotion: excitement" might be generated.

[0710] Step 2: Generate names

[0711] The server starts name generation using a generative AI model based on the received prompt. This model analyzes the input game app information and generates a creative name that matches it. The generative AI model outputs a name such as "FantasyQuest Online" from the prompt.

[0712] Step 3: Trademark Search and Evaluation

[0713] For each generated name, the server uses a trademark search API to assess its registrability in each country and its risk of trademark infringement. During this process, the name is checked against public trademark databases to check for conflicts with existing trademarks. The result of this operation is a legal risk assessment for the name.

[0714] Step 4: List and submit results

[0715] The server creates a list of the results of the trademark search and evaluation and sends the list to the terminal, which contains information about the registrability of the generated names and the risk of trademark infringement. The terminal receives this information and displays it to the user.

[0716] Step 5: User makes final choice

[0717] Users can refer to the information displayed on their device to select the most suitable name for their game app. The selection process involves a balance between creativity and legal safety when coming up with the final name.

[0718] (Application example 2)

[0719] Next, a description will be given of Application Example 2 of Form 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".

[0720] Conventional name generation systems can simply generate names randomly or based on existing databases, but they have difficulty efficiently proposing names that consider legal risks while also taking into account user emotions and specific requirements for game development (e.g., genre, target demographic, features). In addition, the task of checking whether the generated names are internationally registrable and do not cause trademark infringement is time-consuming, and this process needs to be made more efficient.

[0721] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0722] In this invention, the server includes a means for recognizing the genre, target demographic, game features, and emotions of the game based on information input by the user, a means for generating a name using a generative AI based on this information, and a means for automatically checking the registrability of the generated name in each country and the presence or absence of trademark infringement. This makes it possible to propose names that reflect the user's emotions and requests and to automatically evaluate their legal risks.

[0723] The "means for generating a name" is a function that suggests new game names based on input parameters such as the game genre, target demographic, characteristics, and user emotions.

[0724] "Means to confirm the registrability of the generated name in each country" is a function that automatically investigates and evaluates whether the proposed name is eligible for trademark registration in accordance with the laws of each country.

[0725] The "means of verifying that the generated name does not infringe on trademarks in each country" refers to a function that automatically verifies whether the proposed name conflicts with existing trademarks using each country's trademark database.

[0726] The "means of listing the confirmation results" is a function for organizing the confirmation results of the above-mentioned registration possibility and trademark infringement in a list and providing it to the user.

[0727] The "means of organizing application information for registration, etc." is a function that organizes the information required for trademark registration and other applications related to the proposed name, enabling the application process to be carried out efficiently.

[0728] "Means for recognizing the game genre, target demographic, game features and emotions based on user input" refers to a function that receives specific information about the game under development and the emotions of the game developer and users as input, and understands and analyzes this information.

[0729] The "means for generating a name based on this information" is a function that executes a process of combining the game genre, target demographic, characteristics, and emotional data to create a new game name that matches these.

[0730] The embodiment of the present invention includes a terminal (e.g., a smartphone or tablet), a server, a generative AI model (e.g., OpenAI's GPT), and a trademark database API. The terminal receives inputs from the user, such as the game genre, target demographic, features, and emotions, and transmits this information to the server. The server generates an appropriate game name using the generative AI model based on the received information. In this process, the input information is provided to the AI ​​as a prompt, and the AI ​​creates a name based on this. The generated name is then checked by the server through the trademark database API for registration possibility in each country and for the presence or absence of trademark infringement. The confirmation results are listed by the server and presented to the user through the terminal. The user selects the most suitable name based on the provided information, and this selection is again transmitted to the server. Finally, the server organizes the application information for the selected name, and helps the user to smoothly proceed with the trademark registration procedure, etc.

[0731] As a specific example, if a user inputs the genre of the game they are developing as "RPG," the target demographic as "adults," the characteristics as "fantasy, multiplayer," and the emotion as "adventurous," this information is sent from the terminal to the server. The server provides this information to a generative AI model, which generates a name based on the prompt "genre: RPG, target demographic: adults, characteristics: fantasy, multiplayer, emotion: adventurous." This invention enables users to efficiently create game names that reflect their own intentions and simultaneously evaluate legal risks.

[0732] The flow of the identification process will be described with reference to FIG.

[0733] Step 1: The user uses the terminal to input information such as the game genre, target demographic, features, and emotions. This input information is sent to the server. In this step, the user's intentions and the specific requirements for the game they want to develop are digitized and serve as input to the server.

[0734] Step 2: The server generates a name using a generative AI model based on the received information. This process takes into account the input game genre, target demographic, characteristics, and emotions, and calculates the best name for these parameters. The generated name is used as input for the subsequent process.

[0735] Step 3: The server checks whether the generated name can be registered in each country and whether there is any trademark infringement. This check is done through the relevant API, accessing the trademark database of each country to verify whether the name is registerable and does not constitute trademark infringement. The output of this step is the result of the check on registration and trademark infringement.

[0736] Step 4: The results of the verification are compiled into a list and sent from the server to the terminal. The user can use this list to evaluate the legal risk of the generated name. The results of the verification are listed and include information on the possibility of registration for each name and whether there is any trademark infringement.

[0737] Step 5: After the user has finally selected the name, the server organizes the application information for registration, etc. In this step, the information required for trademark registration and other application processes related to the selected name is organized, helping the user to efficiently proceed with these applications.

[0738] Through these processing steps, users can efficiently generate names that reflect their own feelings and game development requirements, evaluate the legal risks, and then select and apply for an appropriate name.

[0739] 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 voice indicating a user input for 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.

[0740] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0741] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0742] 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.

[0743] [Fourth embodiment]

[0744] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0745] 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.

[0746] 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 wide area network (WAN) and / or a local area network (LAN).

[0747] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0748] 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 the voice according to instructions from the processor 46.

[0749] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0750] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0751] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. 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.

[0752] Fig. 8 shows an example of 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.

[0753] 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.

[0754] 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.

[0755] In the robot 414, 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.

[0756] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0757] "Example 1"

[0758] The system of the present invention uses generative AI as a means of generating names. The generative AI generates appropriate names based on specific parameters (e.g., industry, target demographic, service content, etc.). The generated names are evaluated by a means for checking the possibility of registration in each country and a means for checking trademark infringement. These means are performed by linking related APIs and accessing trademark regulations and registration information of each country. The confirmation results are compiled into a list and organized together with application information such as registration. This improves the efficiency of planning and procedures.

[0759] "Example 2"

[0760] As a specific example, consider the case of thinking of a name for a newly developed game app. Generative AI generates an appropriate name based on parameters such as the game genre (e.g. RPG, puzzle, action, etc.), target demographic (e.g. children, adults, men, women, etc.), and game characteristics (e.g. fantasy, real-time, multiplayer, etc.). The generated name is evaluated by a method to check for registration feasibility and trademark infringement in each country, and the confirmation results are compiled into a list. This aims to streamline planning and procedures.

[0761] The process flow of each embodiment will be described below.

[0762] "Example 1"

[0763] Step 1: Generative AI generates an appropriate name based on certain parameters (e.g. industry, target demographic, service content, etc.).

[0764] Step 2: The generated name is evaluated using a methodology to check for its registrability in each country. This is done through the integration of relevant APIs, which have access to each country's trademark regulations and registration information.

[0765] Step 3: Similarly, the generated names are evaluated by a means to check for trademark infringement. Step 4: The results of the check are compiled into a list and organized together with the application information for registration, etc. This will streamline the planning and procedures.

[0766] "Example 2"

[0767] Step 1: When thinking of a name for a new game app to be developed, the generative AI generates an appropriate name based on parameters such as the game genre (e.g., RPG, puzzle, action, etc.), target demographic (e.g., children, adults, men, women, etc.), and game features (e.g., fantasy, real-time, multiplayer, etc.).

[0768] Step 2: The generated name is evaluated using a methodology to check for its registrability in each country. This is done through the integration of relevant APIs, which have access to each country's trademark regulations and registration information.

[0769] Step 3: Similarly, the generated names are evaluated by a means to check for trademark infringement. Step 4: The results of the check are compiled into a list and organized together with the application information for registration, etc. This will streamline the planning and procedures.

[0770] Example 1

[0771] Next, a description will be given of Example 1 of the embodiment 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".

[0772] In the modern commercial environment, the selection of an appropriate name is crucial, as the name of a product or service directly affects the brand value. However, when considering the international market, it is difficult to efficiently evaluate the possibility of trademark registration in different countries and the risk of trademark infringement. With traditional methods, such evaluations require a huge amount of time and resources, which is a heavy burden especially for small and medium-sized enterprises and start-ups. There is a need for a method to solve this problem and perform everything from name generation to legal risk assessment quickly and efficiently.

[0773] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0774] In this invention, the server includes a means for inputting a prompt sentence that takes into account a specific industry, target demographic, and service content into a generative AI to generate an appropriate name, a means for evaluating the registrability of the generated name in each country and the risk of trademark infringement through the linkage of a trademark registration database and an API that searches trademark law regulation information, and a means for listing the above evaluation results and organizing application information for registration, etc. This enables users to quickly and efficiently select a name that takes legal risks into account.

[0775] "Specific parameters" are conditions such as industry, target demographic, and service content that are taken into account when generating a name.

[0776] A "name" is a word or combination used to identify a product, service, company, etc.

[0777] "Registrability in each country" refers to whether a name can be registered as a trademark in a particular country.

[0778] "Trademark infringement" is a legal issue that arises when someone unfairly violates the trademark rights of others, especially when they use it without permission.

[0779] "Listing methods" are ways of organizing and visualizing information in a table format.

[0780] "Means for organizing application information for registration, etc." refers to a method for systematically organizing and managing information related to trademark registration of names and other applications.

[0781] "Generative AI" is an artificial intelligence model that automatically generates new content and ideas based on input data and conditions.

[0782] A "prompt" is an instruction or question used to instruct a generative AI model on a specific task.

[0783] The "API for referencing the trademark registration database" is an interface that accesses external systems to inquire about whether a trademark has been registered and for detailed information.

[0784] The "API for searching trademark law and regulation information" is a programming interface for searching for trademark laws and regulation information for specific countries or regions.

[0785] In the embodiment of the present invention, the server, the terminal, and the user work together to carry out the work. The server is equipped with a generative AI model (e.g., a machine learning model using TensorFlow or PyTorch) and has a function of generating a prompt sentence and proposing an appropriate name based on specific parameters (e.g., industry, target demographic, service content) provided by the user via the terminal.

[0786] The terminal acts as an interface to receive user input and transmits the input parameters to the server. For example, if a user inputs a prompt such as "Generate the name of a cloud service targeted at business people in the IT industry," this information is sent from the terminal to the server.

[0787] The server receives the prompt and uses a generative AI model to generate multiple name suggestions. The generated name suggestions are then queried by APIs that reference trademark registration databases and APIs that search trademark law and regulatory information to evaluate the possibility of trademark registration and the risk of trademark infringement. These APIs are used, for example, to access international trademark information databases and trademark law and regulatory information for specific countries.

[0788] The obtained information is organized by the server and displayed to the user via the terminal. The display clearly indicates the likelihood of registration for each proposed name and the risk of trademark infringement. The user can use this information to select the most suitable name.

[0789] This allows users to quickly and efficiently determine names for new products and services while taking into account legal risks. Especially when considering international markets, the system offers great convenience and significantly reduces the time and cost of the name selection process.

[0790] The flow of the identification process will be described with reference to FIG.

[0791] Step 1: Accepting input from the user

[0792] The user inputs parameters such as the specific industry, target demographic, and service content through the terminal. This input information is used to create a prompt for name generation. The terminal receives the input from the user and sends it to the server. At this time, the input parameters act as conditions for generating an appropriate name.

[0793] Step 2: Generate prompts and names

[0794] The server generates a prompt based on parameters received from the user. This prompt is input into a generative AI model and used to generate a name that matches certain conditions. The generative AI model uses this prompt as the basis for data processing and calculations to output a series of name suggestions. For example, if the prompt is "Generate a name for a cloud service targeted at business people in the IT industry," the AI ​​model will generate multiple names that fit the conditions.

[0795] Step 3: Check for trademark registrability and trademark infringement

[0796] For each generated name, the server queries the relevant APIs to assess the possibility of trademark registration and the risk of trademark infringement. This process uses APIs that refer to the trademark registration database and APIs that search trademark law and regulatory information. For each name, the information obtained from these APIs is processed and calculated to assess the name's possibility of registration and the risk of trademark infringement, and the results are returned to the server.

[0797] Step 4: List and present the results

[0798] Based on the information obtained in step 3, the server lists and organizes the registration possibility and trademark infringement risk of each name. This organized information is presented to the user via the terminal. The presented information indicates the registration possibility and trademark infringement risk for each generated name. The user uses this information as a reference to select the final name. This step enables the user to quickly select an appropriate name while taking legal risks into account.

[0799] (Application example 1)

[0800] Next, a description will be given of Application Example 1 of Form 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".

[0801] When deciding on names for commercial facilities or products to be used in virtual space, it is difficult to generate creative names while efficiently checking whether the names can be registered as trademarks in each country and whether there is a risk of trademark infringement. To solve this problem, a system is needed that automates name generation and quickly evaluates the legal issues of generated names.

[0802] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0803] In this invention, the server includes a means for generating names based on parameters such as industry, target demographic, and service content, a means for evaluating the registrability of the generated names in each country and the risk of trademark infringement, and a means for providing the user with a list of the evaluation results. This enables the user to efficiently generate creative names, quickly understand the legal issues involved, and select an appropriate name and apply for registration.

[0804] A "means for generating names" is a function that uses artificial intelligence technology to automatically generate appropriate names based on input parameters (industry, target demographic, service content, etc.).

[0805] "Means for verifying the registrability of the generated name in each country" refers to a function that runs a process to determine whether a particular name is registrable in accordance with the laws and trademark registration regulations of each country.

[0806] The "means of verifying whether the generated name will not infringe on trademarks in each country" refers to a function that analyzes and determines whether the created name is likely to conflict with existing trademarks by referring to country-specific trademark databases.

[0807] The "means of listing the above confirmation results" refers to a function that organizes and displays in list form the confirmation results regarding the registrability of a name and the risk of trademark infringement.

[0808] A "means of organizing application information for registration, etc." is a function that assists the user in the process of managing and organizing documents and information related to registration applications that they need to submit.

[0809] "Means for suggesting names to be applied to commercial facilities and products within a virtual space" refers to a function that generates and suggests names for stores and products to be used within a virtual environment.

[0810] "Using generative AI based on parameters such as industry, target demographic, and service content" refers to the process in which artificial intelligence automatically generates appropriate naming suggestions based on information such as the specified industry, target customer demographic, and characteristics of the services provided.

[0811] "Through integration of relevant APIs" means that the assessment of the possibility of trademark registration and the risk of trademark infringement is carried out automatically online, utilizing external application programming interfaces (APIs).

[0812] The embodiment of the present invention is a system that combines name generation and legal evaluation. The system is composed of a server-based software application that is accessed by a user via a terminal. The server is equipped with a generative AI model and an external API access function. Specifically, a program written in Python uses a generative AI model (e.g., GPT-3 or its successor) to generate a name based on parameters provided by the user (industry, target demographic, service content, etc.). The generated name is used to evaluate the registrability and risk of trademark infringement in each country through an external API.

[0813] Users access the service through devices such as smartphones or head-mounted displays and input the necessary parameters. The server receives this input, generates and evaluates names, and feeds the results back to the user. Through this process, users can efficiently and safely decide on names for commercial facilities and products to be used in virtual space.

[0814] For example, if you input the parameters "cafe," "youth," and "environmentally friendly," the generative AI model will suggest names such as "GreenSip Cafe" and "EcoBrew Corner" based on these parameters. The server then uses an external API to evaluate the registrability and trademark infringement risk of these names in each country and provide the results to the user. This process allows users to understand the risks involved in name selection in advance and make appropriate decisions.

[0815] The flow of the identification process will be described with reference to FIG.

[0816] Step 1: The user inputs parameters such as industry, target demographic, and service content through the terminal. This input is sent to the server as basic data for name generation. The data sent from the terminal is received by the server and temporarily stored for further processing.

[0817] Step 2: The server generates a name using a generative AI model based on the parameters received in step 1. In this process, the input parameters are analyzed by the AI ​​model to generate a relevant name. The generated name is temporarily stored by the server for the next step.

[0818] Step 3: The server evaluates the generated name for its registrability in each country and the risk of trademark infringement through the relevant API. In this evaluation process, the server accesses external trademark databases to investigate whether the generated name may conflict with existing trademarks. The evaluation results are collected and organized by the server.

[0819] Step 4: The server lists the evaluation results obtained in step 3 and displays them to the user. In this step, information on the possibility of registration and the risk of trademark infringement is provided to the user in an easy-to-understand format. Based on this information, the user can make a final selection of the name to be used.

[0820] Step 5: Based on the name finally selected by the user, the server organizes the application information for registration, etc., and provides it to the user. In this process, the documents and information required for the registration application using the selected name are organized, helping the user to complete the application process smoothly.

[0821] Through these steps, users can efficiently and safely select the names of their stores and products to be used in the virtual space and proceed with the necessary registration procedures. Each processing step involves data exchange between the server and the device, data processing using a generative AI model, information collection and evaluation through linkage with external APIs, and information provision to the user.

[0822] Example 2

[0823] Next, a second embodiment of the second embodiment will be described. 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".

[0824] When coming up with a name for new content, it is a time-consuming and labor-intensive task to come up with creative proposals while simultaneously evaluating the possibility of international trademark registration and the risk of trademark infringement. In particular, when considering use in multiple markets, it can be difficult to quickly identify an attractive name that meets legal constraints.

[0825] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0826] In this invention, the server includes a means for generating a name based on the attributes of the content, a means for checking the international registrability of the generated name and the presence or absence of trademark infringement, a means for listing the check results by category and presenting them on a user interface, and a means for organizing application information for registration, etc. based on the name selected by the user and automating the procedures. This makes it possible to quickly propose creative names and simultaneously evaluate their legal applicability.

[0827] "Content attributes" are basic elements for defining content, such as the genre, target demographic, and characteristics of the content.

[0828] "Name generation" is the process of creating a suitable name for new content based on the attributes of the input content.

[0829] "International regisability and trademark infringement check" refers to the act of investigating whether a particular name is available in the trademark registers of various countries around the world and whether there is a risk of infringement due to its similarity to other trademarks.

[0830] "Listing the confirmation results by category and presenting them in a user interface" means classifying the investigated information on the possibility of registration and the presence or absence of trademark infringement, and displaying this information in a form that is visually easy for users to understand.

[0831] "Organizing application information for registrations and other purposes based on the name selected by the user and automating procedures" means consolidating information related to the necessary registrations and applications using the name selected by the user, and streamlining and automating these processes.

[0832] A "generative AI model" is a system that uses artificial intelligence technology to automatically create new data or text from specific input information.

[0833] "API integration" refers to integration through a programming interface that enables different software or services to share functions and exchange data with each other.

[0834] An "API for accessing external databases" is an interface that enables a program to directly query databases managed by other organizations on the Internet.

[0835] In an embodiment of the present invention, the system includes a process for generating a name, checking its international registrability and trademark infringement, and finally presenting the information to a user. This process is mainly composed of software that runs on a server and a terminal that accepts user operations.

[0836] The server uses a generative AI model to generate a name based on the attributes of the input content. For example, a text generation model such as GPT (Generative Pre-trained Transformer) developed by OpenAI is used as the generative AI model. The generated name is checked for international registration possibility and trademark infringement through API connection to an external database. Through this API connection, the server accesses trademark databases from around the world to check whether the proposed name conflicts with existing trademarks.

[0837] The terminal is responsible for accepting input from the user and sending it to the server. Through the terminal, the user specifies attributes such as the genre of the content, the target demographic, and the characteristics of the content. For example, if the user inputs the attributes "Genre: RPG, Target demographic: Adults, Characteristics: Fantasy, Real-time, Multiplayer", this information is formatted as a prompt sentence and sent to the generative AI model on the server.

[0838] The server responds by presenting the generated name and the results of its legal evaluation to the user via the terminal. The user can then use this information to select the most appropriate name and proceed to the next steps, such as registering that name or applying for a trademark. Through this process, the user can decide on a name for new content efficiently and with an understanding of the legal risks.

[0839] The flow of the identification process will be described with reference to FIG.

[0840] Step 1: The user uses the device to input the attributes of the content (genre, target demographic, features, etc.). This input becomes the basic data for the generative AI model to process. The device receives this input data and creates a prompt sentence based on it.

[0841] Step 2: The terminal sends the created prompt to the server. The server receives the prompt and generates a name using a generative AI model. From the input prompt, the server outputs a name that is appropriate for the content.

[0842] Step 3: The server checks the generated name against external databases to verify the name's international registrability and trademark infringement. At this stage, the server takes the generated name as input and the search results from external databases as output.

[0843] Step 4: The server receives the search results from the external database, organizes the information into a list, and sends it to the terminal. This list includes the registrability of the name and the risk of trademark infringement. The server processes the search results and sends the organized information to the terminal.

[0844] Step 5: The terminal presents the list received from the server to the user. The user selects the name that he / she thinks is most appropriate from the presented list. The terminal receives the user's selection and transmits the information to the server.

[0845] Step 6: The server organizes the registration and other application information based on the name selected by the user, and automates the registration process for the user. At this stage, the server processes the selected name and the necessary application information, and guides the user on the next steps.

[0846] Through these steps, the system enables users to efficiently and effectively determine names for new content and automate its registration and trademark verification.

[0847] (Application example 2)

[0848] Next, a description will be given of Application Example 2 of Form 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".

[0849] When launching a new product or brand in the online market, coming up with an appropriate product or brand name is a critical factor that directly impacts its success. However, coming up with a creative and attractive product or brand name is not easy, and it is necessary to comprehensively evaluate whether the name has sufficient online searchability, whether it can secure buzz on social media, and whether it can be trademarked. Efficiently generating and evaluating names that meet these requirements is a major challenge, especially for small and medium-sized businesses.

[0850] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0851] In this invention, the server includes a means for generating product names and brand names, a means for checking the online searchability of the generated product names and brand names, a means for evaluating whether the generated product names and brand names have topicality on SNS, and a means for checking the possibility of trademark registration of the generated product names and brand names. This allows businesses to efficiently generate creative and attractive product names and brand names, and to comprehensively evaluate the names' online searchability, topicality on SNS, and possibility of trademark registration. This system allows businesses to reduce risks when introducing new products and brands to the market and increase the chances of success.

[0852] "Means for generating product and brand names" refers to a function that automatically creates appropriate product and brand names based on the product category, target demographic, and product features designated by the user.

[0853] "Means to verify online searchability" is a function that evaluates whether the generated product or brand name can be easily searched for on the Internet.

[0854] The "means for evaluating whether a product name or brand name has potential to be discussed on social media" is a function for judging whether a generated product name or brand name is likely to be discussed by users on a social networking service.

[0855] "Means to confirm the possibility of trademark registration" is a function that investigates whether the generated product name or brand name can be registered as a trademark, in other words, whether there is any overlap or similarity with other trademarks.

[0856] "Through integration with related APIs" refers to programming interfaces used to automatically collect and process information by integrating with external databases and services.

[0857] An embodiment of the present invention relates to an automated system for generating and evaluating product and brand names in an online market. A server executes a program that generates product and brand names using a generative AI model. The program generates a prompt sentence based on input information from a user, such as the product category, target demographic, and product features, and transmits the prompt sentence to the generative AI model. The generative AI model proposes creative and attractive product and brand names based on the transmitted prompt sentence.

[0858] For each generated product and brand name, the server also runs a program that evaluates its online searchability, social media buzzworthiness, and trademarkability. This evaluation is automated through the associated API, accessing online databases and trademark registry databases to verify the uniqueness and availability of the generated names.

[0859] The system is developed using programming languages ​​such as Python and JavaScript, and utilizes libraries for natural language processing such as TensorFlow, PyTorch, and NLTK. Using these technologies, the server performs complex text analysis and data processing to provide useful information to users.

[0860] As a specific example, the prompt "Brand name of fashion T-shirts for young people made from eco-friendly materials" is generated to reflect the product category "fashion," the target demographic "young people," and the product feature "made from eco-friendly materials." The generative AI model proposes multiple brand names based on this prompt, and these names are automatically evaluated for online searchability, buzzworthiness on social media, and possibility of trademark registration. Through this process, businesses can efficiently select attractive product and brand names and prepare for market launch.

[0861] The flow of the identification process will be described with reference to FIG.

[0862] Step 1: The user inputs the product category, target demographic, and product features. This input is sent to the server as the basic data for generating product names and brand names.

[0863] Step 2: The server generates a prompt sentence based on the input product category, target demographic, and product features. This prompt sentence serves as an instruction for the generative AI model to generate the product name and brand name.

[0864] Step 3: The generative AI model generates product and brand names based on the received prompt text. During this process, the AI ​​model uses text generation techniques to suggest multiple candidates.

[0865] Step 4: The server checks the online searchability of the generated product and brand names. This process involves analyzing existing search engine results on the Internet to assess the uniqueness of the generated names.

[0866] Step 5: At the same time, the server evaluates the popularity of the generated product and brand names on social media based on the analysis of social media trends and hashtags.

[0867] Step 6: The server then checks for trademark registration feasibility by querying national and international trademark databases to see if the generated name conflicts with existing trademarks.

[0868] Step 7: The server reports the results of the evaluation of online searchability, social media popularity, and trademark registration potential to the user. The report includes the recommendation level and potential problems of the generated product and brand names.

[0869] Through this process, users can efficiently obtain creative and practical product and brand names, thus increasing the chances of success in the online market.

[0870] In addition, an emotion engine that estimates the emotion of the user may be further combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0871] "Example 1"

[0872] The present invention combines a system that generates names, checks whether they can be registered in each country, checks for trademark infringement, lists the check results, and organizes application information such as registration, with an emotion engine that recognizes the emotions of the user. Specifically, the emotion engine recognizes the emotions of the user and generates a name based on the emotions. For example, if the user is expressing an emotion of joy, a name that gives a positive impression that matches the emotion is generated. The emotion engine also checks whether the name generated based on the user's emotions can be registered in each country and whether there is trademark infringement. This makes it possible to generate names that reflect the emotions of the user and evaluate them.

[0873] "Example 2"

[0874] As a concrete example, consider the case of thinking of a name for a new game app to be developed. When a user is excited about the development of the game, the emotion engine recognizes that emotion and generates a name that expresses that excitement. The generated name is evaluated by a means for checking the possibility of registration in each country, and is also evaluated by a means for checking trademark infringement. This makes it possible to generate and evaluate names that reflect the user's emotions.

[0875] The process flow of each embodiment will be described below.

[0876] "Example 1"

[0877] Step 1: The emotion engine recognizes the user's emotion.

[0878] Step 2: Name generation is performed based on the emotions recognized by the emotion engine.

[0879] Step 3: Check the registerability of the generated name in each country.

[0880] Step 4: Check the generated name for trademark infringement.

[0881] Step 5: List the confirmation results and organize the application information for registration, etc.

[0882] "Example 2"

[0883] Step 1: When a user expresses excitement about game development, the emotion engine recognizes that emotion.

[0884] Step 2: The emotion engine generates a name that expresses the feeling of excitement based on the recognized feeling of excitement.

[0885] Step 3: Check the registerability of the generated name in each country.

[0886] Step 4: Check the generated name for trademark infringement.

[0887] Step 5: List the confirmation results and organize the application information for registration, etc.

[0888] Example 1

[0889] Next, a description will be given of Example 1 of the embodiment 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".

[0890] In the modern business environment, it is extremely important to give a suitable name to a new product or service. The name forms the first impression of the product or service and is an element that is memorable to consumers. However, it is extremely difficult to find a name that can be registered in each country and does not pose a risk of trademark infringement while taking into account cultural background and language differences. In addition, conventional technologies do not provide a method to generate a name that reflects the emotions and values ​​of the companies and individuals who bring the product or service to the market.

[0891] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0892] In this invention, the server includes a means for collecting user emotion data, a means for analyzing the collected emotion data, and a means for generating a name by combining the analysis result with specific parameters provided by the user. This makes it possible to generate a name that reflects the user's emotions and values, can be registered in each country, and has no risk of trademark infringement. In addition, by linking related APIs, it is possible to quickly check the registrability of the name in each country and whether or not there is a trademark infringement, which is expected to improve the efficiency and accuracy of the naming process for new products and services in a business environment.

[0893] "User emotional data" refers to information about a user's emotional state collected from facial expressions, tone of voice, body language, etc.

[0894] "Means for analyzing the collected emotional data" refers to processes or algorithms that process the collected emotional data and identify the user's current emotional state.

[0895] "Specific parameters" are specific information such as industry, target demographic, and service content provided by the user when generating a name.

[0896] "Name Generator" refers to a process or algorithm that generates an appropriate name based on collected sentiment data and certain parameters provided by the user.

[0897] "Country availability verification procedures" refers to the process or service that checks whether a generated name can be legally registered in a particular country.

[0898] "Trademark verification measures" refers to processes or services that verify whether the generated name infringes existing trademark rights in a particular country.

[0899] "Means for listing the verification results" refers to a process or algorithm that organizes and displays in a list the verification results regarding the registrability of the name in each country and whether there is any trademark infringement.

[0900] "Means of organizing application information for registration, etc." refers to a process or system that systematically organizes the information required for name registration or trademark application, facilitating the procedure.

[0901] In an embodiment of the present invention, the terminal and the server work together to collect emotion data from the user, analyze the data, generate a name, and legally verify the generated name. The terminal is a device that collects the user's emotion data, records the user's facial expressions and voice through a camera and microphone, and transmits them to the server in an analyzable format. This process uses facial expression recognition software (e.g., OpenCV) and voice analysis libraries (e.g., WebRTC).

[0902] On the server side, a neural network model (e.g., a model using TensorFlow) is used to analyze the received emotion data. The model identifies the user's emotional state and feeds the results back into the name generation process. A generative AI model (e.g., a GPT-based model) is used to generate the name based on the user's emotion and a prompt that reflects certain parameters (e.g., "Generate a service name for a young target audience in the health food industry that expresses the emotion of joy").

[0903] The legal check of the generated name is carried out using a related API that provides access to a trademark registration database, allowing the server to check whether the generated name can be registered in each country and whether there is a risk of trademark infringement, and to report the results to the user in a list.

[0904] This method allows the generation of names that reflect the user's emotions and their legal verification to be carried out automatically through a series of processes, significantly improving the efficiency and satisfaction of naming products and services.

[0905] The flow of the identification process will be described with reference to FIG.

[0906] Step 1: Collect user emotion data

[0907] The device collects the user's facial expressions and voice using a camera and microphone. This collection process uses facial recognition software and voice analysis libraries to collect data that suggests the user's emotions. The collected data (input) is sent to a server for sentiment analysis (output).

[0908] Step 2: Analyze the sentiment data

[0909] The server analyzes the received emotion data using a neural network model to identify the user's emotional state. From the input emotion data, the model extracts features and identifies the emotion based on them (output). This output is used in the name generation process.

[0910] Step 3: Generate names

[0911] The generative AI model on the server generates an appropriate name using a prompt sentence that combines the identified emotional state with specific parameters provided by the user (industry, target demographic, service content). In this step, the emotional state and parameters are given as input, and the generative AI model processes them to generate a new name (output).

[0912] Step 4: Check for registrability and trademark infringement

[0913] For the generated name, the server checks the name's registrability and trademark infringement status in each country through the relevant API. In this process, the generated name is used as input, and the API returns information on registrability and trademark infringement status (output).

[0914] Step 5: List and report findings

[0915] Finally, the server lists the results of the verification of registrability and trademark infringement, organizes this information, and reports it to the user. In this step, the verification result data is used as input, and an organized information list is output. This list is provided to the user via the terminal as a reference for making the final name selection.

[0916] (Application example 1)

[0917] Next, a description will be given of Application Example 1 of Form 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".

[0918] Conventional name generation systems have the problem that it is difficult to generate names that fully reflect the user's feelings and sensibilities. Furthermore, the process of verifying whether the generated name is registrable in each country and whether it infringes on trademarks is complicated and requires a great deal of time and effort. These issues are factors that reduce the speed and efficiency of business, especially for businesses that operate in international virtual spaces.

[0919] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0920] In this invention, the server includes a means for reading the user's emotions, a means for using a generative AI to generate a name that gives an impression that matches the emotion based on the emotion read, and a means for verifying the registrability of the generated name in each country and the presence or absence of trademark infringement through the linkage of related APIs. This enables the rapid generation of a name that reflects the user's emotions and sensibilities, and reduces the risk of using the name internationally.

[0921] A "means for generating names" is a function that automatically devise and output appropriate names or designations based on specific parameters and the user's emotional data.

[0922] "National name registrability checks for generated names" refers to the process of determining whether a proposed name can be used under the laws and registry systems of different countries.

[0923] "Means to verify that the generated name does not violate trademark laws in each country" refers to a function that verifies whether the proposed name violates the trademark laws of each country.

[0924] The "means of listing the above confirmation results" refers to the process of organizing the results of name generation and subsequent registration feasibility and trademark infringement confirmation into a visually easy-to-understand list.

[0925] "Means of organizing application information for registrations, etc." refers to the process of systematically preparing and managing the information required when making official registrations or applications for the use of a name.

[0926] "Means of reading user emotions" means technology that identifies and analyzes the user's current emotional state from the user's facial expressions, tone of voice, or other emotional expressions.

[0927] "Means for generating names based on emotions read" refers to a function that uses emotional data obtained from a user as input and creates a name that matches or responds to that emotion.

[0928] In the embodiment of the present invention, a system built on a server is the center. This system is configured by combining a generative AI model, an emotion reading function, and an API linking function. The server executes a program developed in the Python language and implements a generative AI model using a machine learning library such as TensorFlow or PyTorch. In addition, the emotion reading function uses face recognition technology and voice analysis technology to grasp the user's emotional state through the terminal.

[0929] Users access the system via a device such as a smartphone or head-mounted display. The device sends the user's input information (industry, target demographic, service content, etc.) to the server, while at the same time capturing the user's emotions through a camera and microphone and sending that data to the server. Based on this data, the server uses a generative AI model to generate a name that matches the emotions. The server then checks the registration feasibility of the generated name in each country and whether it infringes on trademarks in cooperation with the relevant APIs.

[0930] As a specific example of usage, a prompt sentence such as "Industry: games, Target demographic: young people, Service content: VR experience game, Emotion: joy" is entered by the user and sent to the server via the terminal. The server uses this prompt sentence and the user's emotion data to generate a name such as "JoyVR Arcade" and checks whether this name can be trademarked and whether there is any trademark infringement. This allows the user to quickly obtain a name that reflects their emotions and has low legal risk.

[0931] The flow of the identification process will be described with reference to FIG.

[0932] Step 1: The user sends input information such as industry, target demographic, and service content, as well as the user's emotional data captured using the device's camera and microphone, to the server via the device. In this step, the input information and emotional data are sent to the server, which becomes the basis for processing.

[0933] Step 2: The server uses a generative AI model to generate a suitable name based on the received industry, target demographic, and service content information and sentiment data. In this process, the server analyzes the input information and outputs a corresponding name. The generative AI model runs an algorithm to create a highly relevant name based on the input parameters and sentiment data.

[0934] Step 3: For the generated name, the server checks the registrability and trademark infringement in each country through the relevant API. In this step, the server sends the generated name to the API and receives the confirmation results of registrability and trademark infringement from the API. The server analyzes these results and lists the final confirmation results.

[0935] Step 4: The server compiles a list of the verification results and sends it to the user's terminal. The user can view this list through the terminal and check the availability and legal risk of the generated name. In this final step, the user obtains the generated name and its related information on its registration possibility in each country and whether there is any trademark infringement.

[0936] Example 2

[0937] Next, a second embodiment of the second embodiment will be described. 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".

[0938] In the past, it was difficult to balance creativity with legality when coming up with names for game apps and other software products. In particular, there was no system that could generate names that reflected users' emotions and intentions, and quickly evaluate whether the names were internationally registrable and avoided trademark infringement. This resulted in a significant amount of time and resources being wasted in the product naming process.

[0939] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0940] In this invention, the server includes a means for generating a name based on the genre, target demographic, and characteristics of the game app provided by the user, and the user's emotional state, a means for checking the international registrability of the generated name and evaluating the risk of trademark infringement, and a means for providing the user with a list of the above-mentioned confirmation results. This enables rapid generation and evaluation of names that take into account legal constraints while still allowing for creativity.

[0941] The "means for generating a name" is a function that automatically creates an appropriate and original name based on parameters provided by the user (e.g., the genre of the game app, the target demographic, characteristics, and the user's emotional state).

[0942] The "means of verifying the registrability of the generated name in each country" is a function that automatically verifies whether a name is registrable in each country's trademark registration system.

[0943] The "means of verifying whether the generated name will not infringe on trademarks in each country" refers to a function that automatically evaluates whether the proposed name is likely to conflict with existing trademarks by referencing international trademark databases.

[0944] The "means of listing the above confirmation results" is a function that presents to the user the evaluation results of the registrability of a name and the risk of trademark infringement in a clear and organized format.

[0945] "Means of organizing application information for registration, etc." is a function that organizes the information required when making a trademark registration or other application based on the name proposed by the user, making the procedure easier.

[0946] In an embodiment of the present invention, a system is configured to generate a name for a game application and evaluate its registrability and risk of trademark infringement. This system is mainly composed of a server, a terminal, and software that links these.

[0947] The server implements name generation using a generative AI model, as well as registrability and trademark infringement assessment via a trademark search API. The generative AI model used utilizes state-of-the-art natural language processing techniques, such as OpenAI's GPT series, to generate creative names based on input parameters. Trademark searches are performed through APIs that access public databases such as the USPTO's TESS and the European Union Intellectual Property Office's (EUIPO) eSearch.

[0948] The terminal is responsible for the user inputting information about the game app (genre, target demographic, features, and the user's emotional state) and sending this information to the server as a prompt sentence. The terminal also displays the generated name and its evaluation results sent from the server, allowing the user to make a final selection.

[0949] As a concrete example, the prompt text "Genre: RPG, Target demographic: Adult, Features: Fantasy, Multiplayer, User emotion: Excitement" is entered into the terminal. This information is sent to the server, and the generative AI model proposes the name "FantasyQuest Online". The name's registrability and risk of trademark infringement are then evaluated through a trademark search API, and the results are returned to the terminal as a list.

[0950] This allows users to choose a suitable name for their game app efficiently and without legal risk.

[0951] The flow of the identification process will be described with reference to FIG.

[0952] Step 1: Accepting user input

[0953] The user inputs information about the game app under development (genre, target demographic, characteristics, and the user's emotional state) into the device. This information is formatted as a prompt text and prepared to be sent to the server. The input data is processed into specific instructions for naming generation. For example, a prompt text like "Genre: RPG, target demographic: adults, characteristics: fantasy, multiplayer, user's emotion: excitement" might be generated.

[0954] Step 2: Generate names

[0955] The server starts name generation using a generative AI model based on the received prompt. This model analyzes the input game app information and generates a creative name that matches it. The generative AI model outputs a name such as "FantasyQuest Online" from the prompt.

[0956] Step 3: Trademark Search and Evaluation

[0957] For each generated name, the server uses a trademark search API to assess its registrability in each country and its risk of trademark infringement. During this process, the name is checked against public trademark databases to check for conflicts with existing trademarks. The result of this operation is a legal risk assessment for the name.

[0958] Step 4: List and submit results

[0959] The server creates a list of the results of the trademark search and evaluation and sends the list to the terminal, which contains information about the registrability of the generated names and the risk of trademark infringement. The terminal receives this information and displays it to the user.

[0960] Step 5: User makes final choice

[0961] Users can refer to the information displayed on their device to select the most suitable name for their game app. The selection process involves a balance between creativity and legal safety when coming up with the final name.

[0962] (Application example 2)

[0963] Next, a description will be given of Application Example 2 of Form 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".

[0964] Conventional name generation systems can simply generate names randomly or based on existing databases, but they have difficulty efficiently proposing names that consider legal risks while also taking into account user emotions and specific requirements for game development (e.g., genre, target demographic, features). In addition, the task of checking whether the generated names are internationally registrable and do not cause trademark infringement is time-consuming, and this process needs to be made more efficient.

[0965] The specific processing by the specific processing unit 290 of the data processing device 12 is realized by the following means.

[0966] In this invention, the server includes a means for recognizing the genre, target demographic, game features, and emotions of the game based on information input by the user, a means for generating a name using a generative AI based on this information, and a means for automatically checking the registrability of the generated name in each country and the presence or absence of trademark infringement. This makes it possible to propose names that reflect the user's emotions and requests and to automatically evaluate their legal risks.

[0967] The "means for generating a name" is a function that suggests new game names based on input parameters such as the game genre, target demographic, characteristics, and user emotions.

[0968] "Means to confirm the registrability of the generated name in each country" is a function that automatically investigates and evaluates whether the proposed name is eligible for trademark registration in accordance with the laws of each country.

[0969] The "means of verifying that the generated name does not infringe on trademarks in each country" refers to a function that automatically verifies whether the proposed name conflicts with existing trademarks using each country's trademark database.

[0970] The "means of listing the confirmation results" is a function for organizing the confirmation results of the above-mentioned registration possibility and trademark infringement in a list and providing it to the user.

[0971] The "means of organizing application information for registration, etc." is a function that organizes the information required for trademark registration and other applications related to the proposed name, enabling the application process to be carried out efficiently.

[0972] "Means for recognizing the game genre, target demographic, game features and emotions based on user input" refers to a function that receives specific information about the game under development and the emotions of the game developer and users as input, and understands and analyzes this information.

[0973] The "means for generating a name based on this information" is a function that executes a process of combining the game genre, target demographic, characteristics, and emotional data to create a new game name that matches these.

[0974] The embodiment of the present invention includes a terminal (e.g., a smartphone or tablet), a server, a generative AI model (e.g., OpenAI's GPT), and a trademark database API. The terminal receives inputs from the user, such as the game genre, target demographic, features, and emotions, and transmits this information to the server. The server generates an appropriate game name using the generative AI model based on the received information. In this process, the input information is provided to the AI ​​as a prompt, and the AI ​​creates a name based on this. The generated name is then checked by the server through the trademark database API for registration possibility in each country and for the presence or absence of trademark infringement. The confirmation results are listed by the server and presented to the user through the terminal. The user selects the most suitable name based on the provided information, and this selection is again transmitted to the server. Finally, the server organizes the application information for the selected name, and helps the user to smoothly proceed with the trademark registration procedure, etc.

[0975] As a specific example, if a user inputs the genre of the game they are developing as "RPG," the target demographic as "adults," the characteristics as "fantasy, multiplayer," and the emotion as "adventurous," this information is sent from the terminal to the server. The server provides this information to a generative AI model, which generates a name based on the prompt "genre: RPG, target demographic: adults, characteristics: fantasy, multiplayer, emotion: adventurous." This invention enables users to efficiently create game names that reflect their own intentions and simultaneously evaluate legal risks.

[0976] The flow of the identification process will be described with reference to FIG.

[0977] Step 1: The user uses the terminal to input information such as the game genre, target demographic, features, and emotions. This input information is sent to the server. In this step, the user's intentions and the specific requirements for the game they want to develop are digitized and serve as input to the server.

[0978] Step 2: The server generates a name using a generative AI model based on the received information. This process takes into account the input game genre, target demographic, characteristics, and emotions, and calculates the best name for these parameters. The generated name is used as input for the subsequent process.

[0979] Step 3: The server checks whether the generated name can be registered in each country and whether there is any trademark infringement. This check is done through the relevant API, accessing the trademark database of each country to verify whether the name is registerable and does not constitute trademark infringement. The output of this step is the result of the check on registration and trademark infringement.

[0980] Step 4: The results of the verification are compiled into a list and sent from the server to the terminal. The user can use this list to evaluate the legal risk of the generated name. The results of the verification are listed and include information on the possibility of registration for each name and whether there is any trademark infringement.

[0981] Step 5: After the user has finally selected the name, the server organizes the application information for registration, etc. In this step, the information required for trademark registration and other application processes related to the selected name is organized, helping the user to efficiently proceed with these applications.

[0982] Through these processing steps, users can efficiently generate names that reflect their own feelings and game development requirements, evaluate the legal risks, and then select and apply for an appropriate name.

[0983] 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 a voice indicating a user input for 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.

[0984] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0985] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0986] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the robot 414.

[0987] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0988] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0989] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0990] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0991] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0992] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0993] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0994] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0995] 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 Universal Serial Bus (USB) 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.

[0996] In addition, 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 upon request from the data processing device 12.

[0997] 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.

[0998] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing the specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0999] The hardware resource that executes the specific process may be one of these various processors, or may be 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 process may be a single processor.

[1000] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[1001] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[1002] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 description and illustrations, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above description and illustrations omit explanations of technical common sense that do not require explanation in order to enable the implementation of the technology of the present disclosure.

[1003] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[1004] The following is further disclosed regarding the above embodiment.

[1005] (Claim 1) A system including a means for generating names, a means for confirming whether the generated names can be registered in each country, a means for confirming whether the generated names infringe trademarks in each country, a means for listing the results of the above confirmation, and a means for organizing application information for registration, etc.

[1006] (Claim 2) 2. The system according to claim 1, wherein the means for generating names uses a generative AI.

[1007] (Claim 3) The system according to claim 1, characterized in that the means for confirming the possibility of registration in each country and the means for confirming trademark infringement are performed through the linkage of related APIs.

[1008] (Claim 4) 10. The system of claim 1, further comprising an emotion engine for recognizing an emotion of a user.

[1009] (Claim 5) 5. The system of claim 4, wherein the emotion engine generates names based on emotions of a user.

[1010] (Claim 6) 5. The system of claim 4, wherein the emotion engine checks the name generated based on the user's emotion for legality and trademark infringement in each country.

[1011] "Example 1"

[1012] (Claim 1) A means for generating a suitable name based on certain parameters; A means to verify the registrability of the generated names in each country; and A means to check whether the generated name will violate trademarks in each country, A means for listing the above confirmation results; A system that includes a means for organizing application information for registrations, etc.

[1013] (Claim 2) The system according to claim 1, characterized in that the means for generating the name inputs a prompt sentence that takes into account a specific industry, target demographic, and service content into a generation AI.

[1014] (Claim 3) The system according to claim 1, characterized in that the means for confirming the possibility of registration in each country and the means for confirming trademark infringement are performed by linking an API for referencing a trademark registration database and an API for searching trademark law regulation information.

[1015] "Application example 1"

[1016] (Claim 1) A system including a means for generating names, a means for confirming whether the generated names can be registered in each country, a means for confirming whether the generated names infringe trademarks in each country, a means for listing the results of the confirmation, a means for organizing application information for registration, etc., and a means for proposing names to be applied to commercial facilities and products in a virtual space.

[1017] (Claim 2) The system according to claim 1, characterized in that the means for generating names uses a generative AI based on parameters such as industry, target demographic, and service content.

[1018] (Claim 3) The system according to claim 1, characterized in that the means for confirming the possibility of registration in each country and the means for confirming trademark infringement are performed through the linkage of related APIs.

[1019] "Example 2"

[1020] (Claim 1) A means for generating a name based on an attribute of the content; A means of verifying the international registrability of the generated names and whether there is any trademark infringement; A means for listing the verification results by category and presenting them in a user interface; A system that includes a means for organizing application information for registration and other purposes based on the name selected by the user and automating procedures.

[1021] (Claim 2) The system described in claim 1, characterized in that the means for generating the name receives multiple parameters (content genre, target demographic, content features, etc.) as input, and generates an appropriate name by supplying a prompt sentence based on these to a generative AI model.

[1022] (Claim 3) The system according to claim 1, characterized in that the means for verifying the international registrability and the presence or absence of trademark infringement performs a search for the generated name through API integration for accessing an external database and analyzes the results.

[1023] "Application example 2"

[1024] (Claim 1) A system including: a means for generating product names and brand names; a means for confirming the online searchability of the generated product names and brand names; a means for evaluating whether the generated product names and brand names have topicality on social media; a means for confirming the possibility of trademark registration of the generated product names and brand names; a means for listing the results of the above confirmation; and a means for organizing application information for registration, etc.

[1025] (Claim 2) The system according to claim 1, characterized in that the means for generating the product name and brand name is performed by using a generative AI with prompt sentences based on the product category, target demographic, and product features.

[1026] (Claim 3) 2. The system according to claim 1, wherein the means for checking online searchability and the means for checking trademark registrability are performed through the cooperation of related APIs.

[1027] "Example 1 of combining emotion engines"

[1028] (Claim 1) A means for collecting user emotion data; A means for analyzing the collected emotion data; and a means for combining the results of the analysis with certain user-supplied parameters to generate a name; A means to verify the registerability of the generated names in each country; and A means to check whether the generated name will violate trademarks in each country, and A means for listing the above confirmation results; A system that includes a means for organizing application information for registrations, etc.

[1029] (Claim 2) The system described in claim 1, characterized in that the means for generating the name uses a generative AI to generate a prompt sentence based on the user's emotional data and specific parameters, and uses this to generate the name.

[1030] (Claim 3) The system according to claim 1, characterized in that the means for confirming the possibility of registration in each country and the means for confirming trademark infringement are performed through the linkage of related APIs.

[1031] "Application example 1 when combining emotion engines"

[1032] (Claim 1) The system includes a means for generating names, a means for verifying whether the generated names can be registered in each country, a means for verifying whether the generated names infringe trademarks in each country, a means for compiling a list of the verification results, a means for organizing application information for registration, etc., a means for reading a user's emotions, and a means for generating names based on the read emotions.

[1033] (Claim 2) The system according to claim 1, characterized in that the means for generating names uses a generative AI, and further generates a name that gives an impression that matches the user's emotions based on emotional data obtained through a means for reading the user's emotions.

[1034] (Claim 3) The system according to claim 1, characterized in that the means for checking the possibility of registration in each country and the means for checking trademark infringement are performed through the cooperation of related APIs, and that similar checks are also performed on names generated based on user sentiment.

[1035] "Example 2 of combining emotion engines"

[1036] (Claim 1) means for computer generated name generation; A means to verify the registerability of the generated names in each country; and A means to check whether the generated name will violate trademarks in each country, and A means for listing the above confirmation results; A means of organizing application information such as registrations, A means for generating a name taking into consideration attribute information based on a user's input and an emotional state of the user; a means for utilizing an emotion engine to analyze an emotional state of a user; The system includes a means to use trademark search tools to assess the registrability and risk of trademark infringement for proposed names in various countries.

[1037] (Claim 2) 2. The system of claim 1, wherein the means for generating the name uses a generative artificial intelligence model and generates the name taking into account the emotional state of the user.

[1038] (Claim 3) The system according to claim 1, characterized in that the means for confirming the possibility of registration in each country and the means for confirming trademark infringement are performed through cooperation with an external database access interface.

[1039] "Application example 2 when combining emotion engines"

[1040] (Claim 1) A system including: a means for generating a name; a means for confirming whether the generated name can be registered in each country; a means for confirming whether the generated name constitutes a trademark infringement in each country; a means for listing the confirmation results; a means for organizing application information for registration, etc.; a means for recognizing the game genre, target demographic, game features and emotions based on user input; and a means for generating a name based on this information.

[1041] (Claim 2) The system according to claim 1, characterized in that the means for generating the name uses a generative AI to generate a name based on user input information and emotion data.

[1042] (Claim 3) The system according to claim 1, characterized in that the means for confirming the possibility of registration in each country and the means for confirming trademark infringement are carried out through the linkage of related APIs, and further, that it is possible to generate names taking into account the user's feelings. [Explanation of symbols]

[1043] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for generating a suitable name based on certain parameters; A means to verify the registrability of the generated names in each country; and A means to check whether the generated name will violate trademarks in each country, A means for listing the above confirmation results; A system that includes a means for organizing application information for registrations, etc.

2. The means for generating names generates a plurality of names by inputting a prompt sentence to a generative AI model, the prompt sentence instructing the generative AI model to generate names according to a specific industry, target demographic, and service content; The means for confirming registrability in each country accesses a trademark registration database, inquires about the presence or absence of trademark registration, and based on the information obtained, confirms the registrability in each country of the generated multiple names; The means for checking trademark infringement checks whether the generated multiple names infringe trademarks in each country based on information obtained by searching legal information on trademarks; The listing means lists the likelihood of registration and the presence or absence of a risk of trademark infringement for each of the generated names, The organizing means organizes application information related to an application for trademark registration of a name. The system of claim 1 .

3. The means for generating names generates a plurality of names by inputting a prompt sentence to a generative AI model, the prompt sentence instructing the generative AI model to generate names according to a specific industry, target demographic, and service content; The means for verifying registrability in each country accesses a trademark database to verify the registrability in each country of the generated names; The means for checking trademark infringement accesses a trademark database to check whether the generated names infringe trademarks in each country; The listing means lists the likelihood of registration and the presence or absence of a risk of trademark infringement for each of the generated names. The organizing means organizes application information related to the registration application of the name selected by the user. The system of claim 1 .

4. The name generating means generates a plurality of names by inputting a prompt sentence to the generative AI model, the prompt sentence instructing the generation of a name according to the attributes of the content. The system of claim 1 .

5. The name generating means generates a plurality of names by inputting a prompt sentence to the generative AI model, the prompt sentence instructing the generation of a name according to the attributes of the content. The system of claim 1 .

6. The system of claim 5 , wherein the content attributes include a genre, a target demographic, or a characteristic of the content.

7. The organizing means organizes application information related to the application for trademark registration based on the name selected by the user, and performs the registration procedure. The system of claim 1 .

8. A means for generating product names using a generative AI system based on prompt sentences based on the product category, target demographic, and product features; A means for verifying the uniqueness of the generated product names using search engine results; A means for evaluating whether the generated product name has a social media buzz based on an analysis of social media trends or hashtags; A means for checking the possibility of trademark registration of the generated product name by checking the trademark database to see if the generated product name conflicts with existing trademarks; A system including:

9. means for determining an emotional state of the user using an emotion engine; The system of claim 1, wherein the generating means generates a name using a generative AI model based on a prompt sentence that combines the user's emotional state and the specific parameters provided by the user, such as industry, target demographic, and service content.

10. The system according to claim 1, wherein the generating means generates a name for the game app using a generative AI model based on a prompt sentence that combines the genre, target demographic, characteristics, and emotional state of the user related to the game app as the specific parameters provided by the user.

11. The device further includes a means for recognizing a genre, a target demographic, a feature of the game, and an emotion of the game based on information about the game and emotion data of the game developer or the user inputted by the user, The system of claim 1 , wherein the generating means generates a name for the game using a generative AI model based on a prompt sentence that combines the game's genre, target demographic, characteristics, and emotions.

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