system

Abstract

The system according to this embodiment aims to efficiently determine the names of products and services. [Solution] The system according to the embodiment comprises a proposal unit, an evaluation unit, a display unit, an analysis unit, a risk assessment unit, and a monitoring unit. The proposal unit proposes a name. The evaluation unit evaluates whether the name proposed by the proposal unit is suitable for the culture and sensibilities of the target market. The display unit crawls registration and trademark databases of various countries and displays legal risks using color coding. The analysis unit analyzes competitive trends and points of differentiation and automatically proposes candidate names that maintain a competitive advantage. The risk assessment unit analyzes pronunciation and linguistic risks and evaluates potential risks in each culture. The monitoring unit autonomously monitors trademark objections and legal risks and provides risk notifications before problems occur.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, when determining the name of a product or service, there are many confirmation items such as multilingual support, competition analysis, and confirmation of legal risks, which poses a problem of taking time and effort.

[0005] The system according to the embodiment aims to efficiently determine the name of a product or service.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a proposal unit, an evaluation unit, a display unit, an analysis unit, a risk assessment unit, and a monitoring unit. The proposal unit proposes a name. The evaluation unit evaluates whether the name proposed by the proposal unit is suitable for the culture and sensibilities of the target market. The display unit crawls registration and trademark databases in various countries and displays legal risks using color coding. The analysis unit analyzes competitive trends and points of differentiation and automatically proposes candidate names that maintain a competitive advantage. The risk assessment unit analyzes pronunciation and linguistic risks and evaluates potential risks in each culture. The monitoring unit autonomously monitors trademark objections and legal risks and provides risk notifications before problems occur. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently determine the names of products and services. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent "Global Name Safe" according to an embodiment of the present invention is a system that automates and streamlines many of the verification tasks that occur when determining the name of a product or service. This system includes a suggestion unit that proposes names, an evaluation unit that assesses whether the proposed name is suitable for the culture and sensibilities of the target market, a display unit that crawls national registration and trademark databases and displays legal risks in color, an analysis unit that analyzes competitive trends and points of differentiation and automatically proposes candidate names that maintain a competitive advantage, a risk assessment unit that analyzes pronunciation and linguistic risks and evaluates potential risks in each culture, and a monitoring unit that autonomously monitors trademark objections and legal risks and provides risk notifications before problems occur. For example, when a user is thinking of a name for a new product or service, the generation AI proposes names. This generation AI assesses in real time whether it is suitable for the culture and sensibilities of the target market. For example, it makes suggestions considering names that are preferred in a particular country or region, or names that should be avoided. Next, it links APIs and RPA to crawl national registration and trademark databases and displays legal risks in color. This makes it possible to check whether the proposed name is already registered or whether it may infringe trademark rights. Furthermore, the AI ​​agent analyzes competitive trends and points of differentiation, automatically suggesting candidate names that maintain a competitive advantage. For example, it ensures uniqueness by avoiding names used by competitors or similar names. It also analyzes pronunciation and linguistic risks, evaluating potential risks in each culture. For example, it prevents international trouble by avoiding names that have inappropriate meanings in certain languages. Finally, it autonomously monitors trademark objections and legal risks, providing risk notifications before problems occur. This prevents problems after release. This system allows individual verification and consideration to be automated on a single AI agent, automatically generating suitable name suggestions and significantly streamlining operations. As a result, Global Name Safe can streamline operations by integrating and automating everything from name suggestion and evaluation to legal risk display, competitive analysis, risk assessment, and monitoring.

[0029] The Global Name Safe according to this embodiment comprises a proposal unit, an evaluation unit, a display unit, an analysis unit, a risk assessment unit, and a monitoring unit. The proposal unit proposes a name. The proposal unit proposes a name using, for example, a generative AI. The generative AI can generate a name that is suitable for the culture and sensibilities of the target market using a natural language generation model or a machine learning algorithm. The evaluation unit evaluates whether the name proposed by the proposal unit is suitable for the culture and sensibilities of the target market. The evaluation unit performs an evaluation using, for example, a generative AI, taking into account the cultural background and characteristics of the sensibilities of the target market. The generative AI can learn data on the culture and sensibilities of the target market and evaluate the suitability of the name. The display unit crawls registration and trademark databases in each country and displays legal risks in color. The display unit crawls the database using, for example, an API and RPA in conjunction and displays legal risks visually. The display unit can display legal risks such as patent infringement risk and trademark infringement risk in color. The analysis department analyzes competitive trends and points of differentiation, and automatically proposes candidate names that maintain a competitive advantage. For example, the analysis department uses AI to analyze competitor names and market share to identify points of differentiation. The analysis department can propose unique names that are not similar to those of competitors. The risk assessment department analyzes pronunciation and linguistic risks and evaluates potential risks in each culture. For example, the risk assessment department uses AI to analyze the difficulty of pronunciation and the possibility of linguistic misunderstanding. By avoiding names that have inappropriate meanings in specific languages, the risk assessment department can prevent international troubles. The monitoring department autonomously monitors trademark objections and legal risks, and provides risk notifications before problems occur. For example, the monitoring department uses AI to monitor trademark objections and legal risks in real time. By providing risk notifications before problems occur, the monitoring department can prevent problems after release. As a result, Global Name Safe according to this embodiment can streamline operations by integrating and automating everything from name proposal and evaluation to legal risk display, competitive analysis, risk assessment, and monitoring.

[0030] The proposal department proposes names. For example, the proposal department uses generative AI to propose names. Generative AI can generate names that are suitable for the culture and sensibilities of the target market by using natural language generation models and machine learning algorithms. Specifically, generative AI learns from a large amount of text data and generates names based on the cultural background and sensibilities of the target market. For example, generative AI considers the language, history, social trends, and consumer preferences of the target market to propose an appropriate name. Generative AI utilizes natural language processing technology to understand the linguistic characteristics and cultural nuances of the target market and generates name candidates. Furthermore, generative AI can learn from past successes and failures to improve the accuracy of name proposals. For example, it can analyze the characteristics of past successful brand names and product names and generate names that incorporate those characteristics. In addition, generative AI can reflect the latest trends and fashions of the target market in real time and always propose names based on the latest information. This allows the proposal department to efficiently propose attractive names that are suitable for the target market.

[0031] The evaluation department assesses whether the name proposed by the proposal department is compatible with the culture and sensibilities of the target market. The evaluation department uses, for example, generative AI to consider the cultural background and characteristics of the target market's sensibilities. Generative AI can learn data on the culture and sensibilities of the target market and evaluate the suitability of the name. Specifically, the generative AI considers the cultural background, history, social values, and consumer preferences of the target market to evaluate whether the proposed name is appropriate. The generative AI uses natural language processing technology to analyze the meaning and nuances of the name and evaluate its acceptance in the target market. In addition, the generative AI can collect consumer feedback and opinions in the target market and evaluate the name based on them. For example, it can analyze online surveys and social media comments and reflect consumer reactions in the evaluation. Furthermore, the generative AI analyzes the names and brand strategies of competitors in the target market to evaluate whether the proposed name is competitive. This allows the evaluation department to accurately evaluate names that are compatible with the target market and select the optimal name.

[0032] The display unit crawls national registration and trademark databases and displays legal risks using color coding. For example, the display unit uses APIs and RPA to crawl databases and visually display legal risks. Specifically, the display unit accesses national trademark databases to check whether a proposed name is already registered. It accesses databases using APIs and automatically collects data using RPA. The collected data is used to assess legal risks, and risks such as patent infringement and trademark infringement are displayed using color coding. For example, already registered names are displayed in red, and names with a high probability of registration are displayed in green. This allows users to grasp legal risks at a glance. Furthermore, the display unit provides detailed information on legal risks, enabling users to understand specific risks. For example, it displays which countries a particular name is registered in and for what goods or services it is registered. This helps users accurately understand legal risks and take appropriate measures.

[0033] The analysis department analyzes competitive trends and points of differentiation, and automatically proposes candidate names that maintain a competitive advantage. For example, the analysis department uses AI to analyze the names and market share of competitors and identify points of differentiation. Specifically, the analysis department analyzes the names and brand strategies of competitors and evaluates whether the proposed names are competitive. The AI ​​analyzes the characteristics of competitors' names, market share, and consumer evaluations, and evaluates the degree to which the proposed names are differentiated. Furthermore, the analysis department analyzes trends in the target market and consumer preferences to evaluate whether the proposed names will be accepted in the market. For example, by proposing names that reflect the latest trends and consumer preferences, a competitive advantage can be maintained. In this way, the analysis department can differentiate itself from competitors and propose competitive names.

[0034] The Risk Assessment Department analyzes pronunciation and linguistic risks to evaluate the potential risks in each culture. For example, the Risk Assessment Department uses AI to analyze the difficulty of pronunciation and the possibility of linguistic misunderstanding. Specifically, the Risk Assessment Department analyzes how the proposed name is pronounced in each country's language and evaluates the difficulty of pronunciation and the possibility of misunderstanding. The AI ​​learns the linguistic characteristics and pronunciation rules of each country and evaluates whether the proposed name is easy to pronounce. The AI ​​also analyzes whether the proposed name has an inappropriate meaning in a particular language and evaluates the linguistic risk. For example, avoiding names with inappropriate meanings in a particular language can prevent international trouble. Furthermore, the Risk Assessment Department considers the cultural background and social values ​​of each country and evaluates whether the proposed name is culturally appropriate. This allows the Risk Assessment Department to accurately evaluate pronunciation and linguistic risks and select an internationally appropriate name.

[0035] The monitoring unit autonomously monitors trademark objections and legal risks, and provides risk notifications before problems occur. For example, the monitoring unit uses AI to monitor trademark objections and legal risks in real time. Specifically, the monitoring unit monitors trademark registration databases and legal information in various countries in real time to detect objections and legal risks to proposed names. The AI ​​analyzes trademark registration databases and legal information to detect signs of objections and legal risks. For example, if an objection occurs to a proposed name, the monitoring unit immediately provides a risk notification and urges the user to take countermeasures. Furthermore, the monitoring unit provides detailed information on legal risks so that users can understand the specific risks. This allows the monitoring unit to provide risk notifications before problems occur and prevent troubles after release.

[0036] The proposal department proposes names using generative AI. For example, the proposal department uses generative AI to propose names. Generative AI can generate names that are appropriate to the culture and sensibilities of the target market using natural language generation models and machine learning algorithms. For example, the generative AI learns data on the culture and sensibilities of the target market and evaluates the suitability of the names. The generative AI can generate names considering the cultural background and sensibilities characteristics of the target market. As a result, using generative AI improves the accuracy of name proposals.

[0037] The evaluation unit uses generative AI to assess whether a name is suitable for the target market's culture and sensibilities. For example, the evaluation unit uses generative AI to evaluate while considering the cultural background and characteristics of the target market's sensibilities. The generative AI can learn data on the target market's culture and sensibilities and evaluate the suitability of a name. The generative AI evaluates whether a name is suitable for the target market's culture and sensibilities in real time. For example, the generative AI considers names that are preferred or should be avoided in specific countries or regions. As a result, using generative AI improves the accuracy of the evaluation of whether a name is suitable for the target market's culture and sensibilities.

[0038] The display unit crawls national registration and trademark databases and displays legal risks using color coding. The display unit can, for example, crawl databases by linking APIs and RPA and visually display legal risks. The display unit can display legal risks such as patent infringement risk and trademark infringement risk using color coding. For example, the display unit can display patent infringement risk in red and trademark infringement risk in yellow. The display unit needs to clearly define the types of legal risks and the criteria for color coding. For example, it can display legal risks such as patent infringement risk and trademark infringement risk using color coding. This makes it easier to visually grasp the risks by displaying legal risks using color coding. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data obtained by crawling national registration and trademark databases into a generating AI and have the generating AI perform the color coding display of legal risks.

[0039] The analysis unit analyzes competitive trends and differentiation points and automatically proposes candidate names that maintain a competitive advantage. For example, the analysis unit uses AI to analyze the names and market share of competitors and identify differentiation points. The analysis unit can propose unique names that are not similar to those of competitors. For example, the analysis unit retrieves the names of competitors from a database and calculates the similarity using AI. The analysis unit proposes names with low similarity to those of competitors as candidate names. The analysis unit needs to clarify the criteria for differentiation points. For example, it analyzes differentiation points such as market share and product characteristics. By analyzing competitive trends and differentiation points, it can propose candidate names that maintain a competitive advantage. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input competitor name data into a generating AI and have the generating AI propose candidate names that maintain a competitive advantage.

[0040] The risk assessment unit analyzes pronunciation and linguistic risks to evaluate the potential risks of each culture. For example, the risk assessment unit uses AI to analyze the difficulty of pronunciation and the likelihood of linguistic misunderstanding. The risk assessment unit can prevent international trouble by avoiding names that have inappropriate meanings in a particular language. For example, the risk assessment unit uses AI to analyze audio data to evaluate the difficulty of pronunciation. The risk assessment unit uses AI to analyze text data to evaluate the likelihood of linguistic misunderstanding. The risk assessment unit needs to clarify the criteria for pronunciation and linguistic risks. For example, it should clarify the criteria for the difficulty of pronunciation and the likelihood of linguistic misunderstanding. This will allow the risk assessment unit to evaluate the potential risks of each culture by analyzing pronunciation and linguistic risks. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input audio data for evaluating the difficulty of pronunciation into a generating AI and have the generating AI perform the evaluation of the difficulty of pronunciation.

[0041] The proposal department analyzes past name proposal history and selects the optimal proposal method. The proposal department analyzes past name proposal history using, for example, a generative AI. The generative AI can learn from past name proposal history data and select the optimal proposal method. The proposal department analyzes past name proposal history and selects the optimal proposal method. The proposal department analyzes trends in names preferred by users in the past and proposes similar names. The proposal department analyzes the reasons why users rejected names in the past and makes suggestions to avoid similar problems. The proposal department proposes names with similar success patterns based on successful examples of names chosen by users in the past. In this way, the optimal proposal method can be selected by analyzing past name proposal history. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input past name proposal history data into a generative AI and have the generative AI select the optimal proposal method.

[0042] The suggestion unit filters suggestions based on the user's current projects and areas of interest. For example, the suggestion unit uses generative AI to identify the user's current projects and areas of interest. The generative AI learns data about the user's projects and areas of interest and can perform filtering. The suggestion unit filters suggestions based on the user's current projects and areas of interest. For example, the suggestion unit prioritizes suggesting names related to the user's current projects. The suggestion unit suggests names containing keywords related to the user's areas of interest. The suggestion unit suggests names related to areas the user has shown interest in in the past. This allows the suggestion unit to suggest highly relevant names by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input data on the user's projects and areas of interest into the generative AI and have the generative AI perform the filtering.

[0043] The suggestion unit, when making suggestions, prioritizes suggesting highly relevant names by considering the user's geographical location. The suggestion unit identifies the user's geographical location using, for example, a generative AI. The generative AI can identify the user's geographical location using data such as GPS data or IP addresses. The suggestion unit prioritizes suggesting highly relevant names by considering the user's geographical location. For example, if the user is in a specific region, the suggestion unit suggests a name preferred in that region. If the user is traveling, the suggestion unit suggests a name appropriate to the culture of the destination. If the user is in a specific city, the suggestion unit suggests a name that aligns with the trends of that city. In this way, by considering the user's geographical location, highly relevant names can be suggested. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's geographical location into the generative AI and have the generative AI suggest highly relevant names.

[0044] The suggestion unit analyzes the user's social media activity and proposes relevant names when making a suggestion. The suggestion unit analyzes the user's social media activity using, for example, generative AI. The generative AI can learn data about the user's social media activity and propose relevant names. The suggestion unit analyzes the user's social media activity and proposes relevant names. The suggestion unit proposes names that include keywords the user frequently uses on social media. The suggestion unit proposes names based on the trends of influencers the user follows. The suggestion unit analyzes the user's social media activity history and proposes relevant names. In this way, relevant names can be proposed by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input data on the user's social media activity into the generative AI and have the generative AI perform the suggestion of relevant names.

[0045] The evaluation unit adjusts the level of detail in its evaluation based on the importance of the name. The evaluation unit identifies the importance of a name, for example, using a generative AI. The generative AI can identify the importance of a name using data such as the market value of the name or the influence of the brand. The evaluation unit adjusts the level of detail in its evaluation based on the importance of the name. For example, the evaluation unit provides a detailed evaluation for important names. For common names, the evaluation unit provides a concise evaluation. For names important in a specific market, the evaluation unit provides a market-specific evaluation. By adjusting the level of detail in the evaluation based on the importance of the name, a more appropriate evaluation can be provided. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data that identifies the importance of a name into a generative AI and have the generative AI perform the adjustment of the level of detail in the evaluation.

[0046] The evaluation unit applies different evaluation algorithms depending on the category of the name during the evaluation process. The evaluation unit identifies the category of the name, for example, using a generative AI. The generative AI can identify the category of a name using data such as the product category or service category of the name. The evaluation unit applies different evaluation algorithms depending on the category of the name. For example, in the case of a product name, the evaluation unit performs an evaluation that emphasizes consumer response. In the case of a service name, the evaluation unit performs an evaluation that emphasizes user convenience. In the case of a brand name, the evaluation unit performs an evaluation that emphasizes brand image. By applying different evaluation algorithms depending on the category of the name, a more appropriate evaluation can be provided. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data to identify the category of the name into the generative AI and have the generative AI execute the application of the evaluation algorithm.

[0047] The evaluation unit determines the priority of evaluations based on the submission timing of the names during the evaluation process. The evaluation unit identifies the submission timing of names, for example, using a generative AI. The generative AI can identify the submission timing of names using data such as the submission date and time and submission order. The evaluation unit determines the priority of evaluations based on the submission timing of the names. The evaluation unit, for example, prioritizes evaluating names submitted earlier. The evaluation unit postpones evaluating names submitted later. If the submission timing is related to a specific event, the evaluation unit performs an evaluation tailored to that event. This allows for a more appropriate evaluation by determining the priority of evaluations based on the submission timing of the names. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data identifying the submission timing of names into a generative AI and have the generative AI determine the priority of evaluations.

[0048] The evaluation unit adjusts the order of evaluation based on the relevance of the names during the evaluation process. The evaluation unit identifies the relevance of names, for example, using a generative AI. The generative AI can identify the relevance of names using data such as the relevance of names to the target market and the relevance of names to competing products. The evaluation unit adjusts the order of evaluation based on the relevance of the names. The evaluation unit, for example, prioritizes the evaluation of names with high relevance. The evaluation unit postpones the evaluation of names with low relevance. The evaluation unit prioritizes the evaluation of names with high relevance in a particular market. This allows for the provision of more appropriate evaluations by adjusting the order of evaluation based on the relevance of the names. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data to identify the relevance of names into a generative AI and have the generative AI perform the adjustment of the evaluation order.

[0049] The display unit adjusts the level of detail displayed based on the importance of the legal risk when displaying information. The display unit identifies the importance of the legal risk, for example, using a generating AI. The generating AI can identify the importance of the legal risk using data such as the impact and probability of occurrence of the legal risk. The display unit adjusts the level of detail displayed based on the importance of the legal risk. For example, the display unit displays detailed information for important legal risks. For general legal risks, the display unit displays concise information. For legal risks that are important in a particular market, the display unit displays market-specific information. This allows for the provision of more appropriate information by adjusting the level of detail displayed based on the importance of the legal risk. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data that identifies the importance of the legal risk into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0050] The display unit applies different display algorithms depending on the legal risk category during display. The display unit identifies the legal risk category, for example, using a generating AI. The generating AI can identify the legal risk category using data such as patent risk and trademark risk. The display unit applies different display algorithms depending on the legal risk category. For example, in the case of trademark risk, the display unit emphasizes trademark registration information. In the case of copyright risk, the display unit emphasizes copyright information. In the case of patent risk, the display unit emphasizes patent information. By applying different display algorithms depending on the legal risk category, more appropriate information can be provided. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data to identify the legal risk category into the generating AI and have the generating AI perform the application of the display algorithm.

[0051] The display unit adjusts the display order based on the filing date of the legal risks when displaying them. The display unit identifies the filing date of the legal risks, for example, using a generating AI. The generating AI can identify the filing date of the legal risks using data such as the filing date and order of the legal risks. The display unit adjusts the display order based on the filing date of the legal risks. The display unit, for example, prioritizes displaying legal risks that were filed earlier. The display unit postpones displaying legal risks that were filed later. If the filing date is related to a specific event, the display unit displays the information in a way that is appropriate for that event. This allows for the provision of more relevant information by adjusting the display order based on the filing date of the legal risks. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data that identifies the filing date of the legal risks into the generating AI and have the generating AI perform the adjustment of the display order.

[0052] The display unit adjusts the display order based on the relevance of legal risks during display. The display unit identifies the relevance of legal risks, for example, using generative AI. The generative AI can identify the relevance of legal risks using data such as the relevance of legal risks to target markets and relevance to competing products. The display unit adjusts the display order based on the relevance of legal risks. The display unit, for example, prioritizes displaying highly relevant legal risks. The display unit postpones displaying less relevant legal risks. The display unit prioritizes displaying legal risks that are highly relevant in a particular market. This allows for the provision of more appropriate information by adjusting the display order based on the relevance of legal risks. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data that identifies the relevance of legal risks into the generative AI and have the generative AI perform the adjustment of the display order.

[0053] The analysis unit improves the accuracy of its analysis by considering the interrelationships of competitors. The analysis unit identifies the interrelationships of competitors, for example, using generative AI. Generative AI can identify the interrelationships of competitors using data such as market share and product characteristics of competitors. The analysis unit improves the accuracy of its analysis by considering the interrelationships of competitors. The analysis unit performs its analysis by considering the alliance relationships between competing companies. The analysis unit performs its analysis by considering the competitive relationships between competing companies. The analysis unit performs its analysis by considering the market share between competing companies. In this way, the accuracy of the analysis can be improved by considering the interrelationships of competitors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data that identifies the interrelationships of competitors into the generative AI and have the generative AI perform the improvement of the accuracy of the analysis.

[0054] The analysis unit performs analysis while considering the attribute information of competitors. The analysis unit identifies the attribute information of competitors, for example, using a generative AI. The generative AI can identify the attribute information of competitors using data such as the size of the competitors' companies and their market segments. The analysis unit performs analysis while considering the attribute information of competitors. The analysis unit performs analysis while considering the industry of the competitors' companies. The analysis unit performs analysis while considering the size of the competitors' companies. The analysis unit performs analysis while considering the regional characteristics of the competitors' companies. By considering the attribute information of competitors, a more appropriate analysis can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input data to identify the attribute information of competitors into a generative AI and have the generative AI perform the analysis.

[0055] The analysis unit performs its analysis while considering the geographical distribution of competitors. The analysis unit identifies the geographical distribution of competitors, for example, using generative AI. Generative AI can identify the geographical distribution of competitors using data such as the market share of competitors and the strength of competitors in each region. The analysis unit performs its analysis while considering the geographical distribution of competitors. The analysis unit performs its analysis while considering the location of competitor companies. The analysis unit performs its analysis while considering the geographical distribution of market share of competitor companies. The analysis unit performs its analysis while considering the strength of competitor companies in each region. This allows for the provision of a more appropriate analysis by considering the geographical distribution of competitors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data to identify the geographical distribution of competitors into generative AI and have the generative AI perform the analysis.

[0056] The analysis unit improves the accuracy of its analysis by referring to relevant literature from competitors during the analysis. The analysis unit identifies relevant literature from competitors, for example, using a generative AI. The generative AI can identify relevant literature from competitors using data such as patent documents and technical papers from competitors. The analysis unit improves the accuracy of its analysis by referring to relevant literature from competitors. The analysis unit performs analysis by referring to patent documents from competitor companies. The analysis unit performs analysis by referring to research papers from competitor companies. The analysis unit performs analysis by referring to market reports from competitor companies. In this way, the accuracy of the analysis can be improved by referring to relevant literature from competitors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input data for identifying relevant literature from competitors into the generative AI and have the generative AI perform the improvement of the accuracy of the analysis.

[0057] The risk assessment unit predicts current risk by referring to past risk data during risk assessment. The risk assessment unit analyzes past risk data using, for example, a generative AI. The generative AI can learn from past risk data and predict current risk. The risk assessment unit predicts current risk by referring to past risk data. The risk assessment unit predicts current risk based on, for example, past risk data. The risk assessment unit analyzes past risk data and grasps risk trends. The risk assessment unit predicts the frequency of risk occurrence by referring to past risk data. In this way, current risk can be predicted by referring to past risk data. Some or all of the above processes in the risk assessment unit may be performed using, for example, AI, or without using AI. For example, the risk assessment unit can input past risk data into a generative AI and have the generative AI perform a prediction of current risk.

[0058] The risk assessment unit applies different risk assessment methods to each category of name during the risk assessment process. For example, the risk assessment unit uses a generative AI to identify the category of a name. The generative AI can identify the category of a name using data such as the product category or service category of the name. The risk assessment unit applies different risk assessment methods to each category of name. For example, in the case of a product name, the risk assessment unit performs a risk assessment that emphasizes consumer response. In the case of a service name, the risk assessment unit performs a risk assessment that emphasizes user convenience. In the case of a brand name, the risk assessment unit performs a risk assessment that emphasizes brand image. By applying different risk assessment methods to each category of name, a more appropriate risk assessment can be provided. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input data to identify the category of a name into the generative AI and have the generative AI perform the application of risk assessment methods.

[0059] The risk assessment unit analyzes changes in risk based on the submission timing of names during the risk assessment. The risk assessment unit identifies the submission timing of names, for example, using a generative AI. The generative AI can identify the submission timing of names using data such as the submission date and time and submission order. The risk assessment unit analyzes changes in risk based on the submission timing of names. The risk assessment unit prioritizes the assessment of risks for names submitted earlier. The risk assessment unit postpones the assessment of risks for names submitted later. If the submission timing is related to a specific event, the risk assessment unit performs a risk assessment tailored to that event. This allows for a more appropriate risk assessment by analyzing changes in risk based on the submission timing of names. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input data identifying the submission timing of names into a generative AI and have the generative AI perform the analysis of changes in risk.

[0060] The risk assessment unit analyzes risk by referring to relevant market data for the name during the risk assessment. The risk assessment unit identifies relevant market data for the name using, for example, a generative AI. The generative AI can analyze risk using the relevant market data for the name. The risk assessment unit analyzes risk by referring to relevant market data for the name. The risk assessment unit evaluates the risk for the name based on the relevant market data. The risk assessment unit analyzes the relevant market data and grasps the risk trends. The risk assessment unit predicts the frequency of risk occurrence by referring to the relevant market data. This allows for a more appropriate risk assessment by referring to relevant market data for the name. Some or all of the above processes in the risk assessment unit may be performed using, for example, AI, or not using AI. For example, the risk assessment unit can input relevant market data for the name into a generative AI and have the generative AI perform the risk analysis.

[0061] The monitoring unit optimizes the monitoring algorithm by referring to past monitoring data during monitoring. The monitoring unit analyzes past monitoring data using, for example, a generative AI. The generative AI can learn from past monitoring data and optimize the monitoring algorithm. The monitoring unit optimizes the monitoring algorithm by referring to past monitoring data. The monitoring unit optimizes the current monitoring algorithm based on, for example, past monitoring data. The monitoring unit analyzes past monitoring data and improves the accuracy of monitoring. The monitoring unit improves the efficiency of monitoring by referring to past monitoring data. In this way, the monitoring algorithm can be optimized by referring to past monitoring data. Some or all of the above processes in the monitoring unit may be performed using, for example, AI, or without using AI. For example, the monitoring unit can input past monitoring data into a generative AI and have the generative AI perform the optimization of the monitoring algorithm.

[0062] The monitoring unit weights the monitoring data based on the submission date of the names during monitoring. The monitoring unit identifies the submission date of names, for example, using a generation AI. The generation AI can identify the submission date of names using data such as the submission date and order. The monitoring unit weights the monitoring data based on the submission date of the names. For example, the monitoring unit prioritizes the weighting of monitoring data for names submitted earlier. The monitoring unit postpones the weighting of monitoring data for names submitted later. If the submission date is related to a specific event, the monitoring unit weights the monitoring data in accordance with that event. This allows for more appropriate monitoring by weighting the monitoring data based on the submission date of the names. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data identifying the submission date of names into a generation AI and have the generation AI perform the weighting of the monitoring data.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The proposal department can analyze past name proposal history and select the optimal proposal method. For example, it can use generative AI to analyze past name proposal history, analyze the trends of names that users have preferred in the past, and propose similar names. It can also analyze the reasons why users have rejected names in the past and make suggestions that avoid similar problems. Furthermore, it can propose names with similar success patterns based on successful examples of names chosen by users in the past. In this way, by analyzing past name proposal history, the optimal proposal method can be selected.

[0065] The evaluation unit can adjust the level of detail in its evaluations based on the importance of the name. For example, it can use generation AI to identify the importance of a name using data such as its market value and brand influence, and provide a detailed evaluation for important names. For general names, it can provide a concise evaluation. Furthermore, for names that are important in a specific market, it can provide a market-specific evaluation. In this way, by adjusting the level of detail in the evaluation based on the importance of the name, it is possible to provide a more appropriate evaluation.

[0066] The evaluation unit can apply different evaluation algorithms depending on the category of the name during the evaluation process. For example, by using a generation AI to identify the category of the name using data such as the product category or service category, it can perform evaluations that prioritize consumer response in the case of product names, evaluations that prioritize user convenience in the case of service names, and evaluations that prioritize brand image in the case of brand names. By applying different evaluation algorithms depending on the category of the name, it is possible to provide more appropriate evaluations.

[0067] The display unit can adjust the level of detail displayed based on the importance of the legal risk. For example, it can use a generation AI to identify the importance of a legal risk using data such as its impact and probability of occurrence, and display detailed information for important legal risks. Conversely, it can display concise information for general legal risks. Furthermore, for legal risks that are important in a specific market, it can display market-specific information. By adjusting the level of detail displayed based on the importance of the legal risk, it is possible to provide more appropriate information.

[0068] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of competitors. For example, it can use generative AI to identify interrelationships of competitors using data such as market share and product characteristics, and perform analysis while considering alliances between competing companies. It can also perform analysis while considering the competitive relationships between competing companies. Furthermore, it can perform analysis while considering the market share of competing companies. In this way, the accuracy of the analysis can be improved by considering the interrelationships of competitors.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The proposal team proposes a name. The proposal team uses generative AI to propose a name and generates a name that is suitable for the culture and sensibilities of the target market using natural language generation models and machine learning algorithms. Step 2: The evaluation department assesses whether the name proposed by the proposal department is suitable for the culture and sensibilities of the target market. The evaluation department uses generative AI to consider the cultural background and sensibility characteristics of the target market when making its evaluation. Step 3: The display unit crawls the registration and trademark databases of each country and displays legal risks using color coding. The display unit uses APIs and RPA to crawl the database and visually displays legal risks such as patent infringement risk and trademark infringement risk. Step 4: The analysis unit analyzes competitive trends and points of differentiation, and automatically proposes candidate names that maintain a competitive advantage. The analysis unit uses AI to analyze the names and market share of competitors, identifies points of differentiation, and proposes unique names that are not similar to those of competitors. Step 5: The Risk Assessment Department analyzes pronunciation and linguistic risks to assess potential risks in each culture. The Risk Assessment Department uses AI to analyze the difficulty of pronunciation and the possibility of linguistic misunderstandings, preventing international problems by avoiding names that have inappropriate meanings in specific languages. Step 6: The monitoring unit autonomously monitors trademark objections and legal risks and provides risk notifications before problems occur. The monitoring unit uses AI to monitor trademark objections and legal risks in real time and provides risk notifications before problems occur, thereby preventing post-release troubles.

[0071] (Example of form 2) The AI ​​agent "Global Name Safe" according to an embodiment of the present invention is a system that automates and streamlines many of the verification tasks that occur when determining the name of a product or service. This system includes a suggestion unit that proposes names, an evaluation unit that assesses whether the proposed name is suitable for the culture and sensibilities of the target market, a display unit that crawls national registration and trademark databases and displays legal risks in color, an analysis unit that analyzes competitive trends and points of differentiation and automatically proposes candidate names that maintain a competitive advantage, a risk assessment unit that analyzes pronunciation and linguistic risks and evaluates potential risks in each culture, and a monitoring unit that autonomously monitors trademark objections and legal risks and provides risk notifications before problems occur. For example, when a user is thinking of a name for a new product or service, the generation AI proposes names. This generation AI assesses in real time whether it is suitable for the culture and sensibilities of the target market. For example, it makes suggestions considering names that are preferred in a particular country or region, or names that should be avoided. Next, it links APIs and RPA to crawl national registration and trademark databases and displays legal risks in color. This makes it possible to check whether the proposed name is already registered or whether it may infringe trademark rights. Furthermore, the AI ​​agent analyzes competitive trends and points of differentiation, automatically suggesting candidate names that maintain a competitive advantage. For example, it ensures uniqueness by avoiding names used by competitors or similar names. It also analyzes pronunciation and linguistic risks, evaluating potential risks in each culture. For example, it prevents international trouble by avoiding names that have inappropriate meanings in certain languages. Finally, it autonomously monitors trademark objections and legal risks, providing risk notifications before problems occur. This prevents problems after release. This system allows individual verification and consideration to be automated on a single AI agent, automatically generating suitable name suggestions and significantly streamlining operations. As a result, Global Name Safe can streamline operations by integrating and automating everything from name suggestion and evaluation to legal risk display, competitive analysis, risk assessment, and monitoring.

[0072] The Global Name Safe according to this embodiment comprises a proposal unit, an evaluation unit, a display unit, an analysis unit, a risk assessment unit, and a monitoring unit. The proposal unit proposes a name. The proposal unit proposes a name using, for example, a generative AI. The generative AI can generate a name that is suitable for the culture and sensibilities of the target market using a natural language generation model or a machine learning algorithm. The evaluation unit evaluates whether the name proposed by the proposal unit is suitable for the culture and sensibilities of the target market. The evaluation unit performs an evaluation using, for example, a generative AI, taking into account the cultural background and characteristics of the sensibilities of the target market. The generative AI can learn data on the culture and sensibilities of the target market and evaluate the suitability of the name. The display unit crawls registration and trademark databases in each country and displays legal risks in color. The display unit crawls the database using, for example, an API and RPA in conjunction and displays legal risks visually. The display unit can display legal risks such as patent infringement risk and trademark infringement risk in color. The analysis department analyzes competitive trends and points of differentiation, and automatically proposes candidate names that maintain a competitive advantage. For example, the analysis department uses AI to analyze competitor names and market share to identify points of differentiation. The analysis department can propose unique names that are not similar to those of competitors. The risk assessment department analyzes pronunciation and linguistic risks and evaluates potential risks in each culture. For example, the risk assessment department uses AI to analyze the difficulty of pronunciation and the possibility of linguistic misunderstanding. By avoiding names that have inappropriate meanings in specific languages, the risk assessment department can prevent international troubles. The monitoring department autonomously monitors trademark objections and legal risks, and provides risk notifications before problems occur. For example, the monitoring department uses AI to monitor trademark objections and legal risks in real time. By providing risk notifications before problems occur, the monitoring department can prevent problems after release. As a result, Global Name Safe according to this embodiment can streamline operations by integrating and automating everything from name proposal and evaluation to legal risk display, competitive analysis, risk assessment, and monitoring.

[0073] The proposal department proposes names. For example, the proposal department uses generative AI to propose names. Generative AI can generate names that are suitable for the culture and sensibilities of the target market by using natural language generation models and machine learning algorithms. Specifically, generative AI learns from a large amount of text data and generates names based on the cultural background and sensibilities of the target market. For example, generative AI considers the language, history, social trends, and consumer preferences of the target market to propose an appropriate name. Generative AI utilizes natural language processing technology to understand the linguistic characteristics and cultural nuances of the target market and generates name candidates. Furthermore, generative AI can learn from past successes and failures to improve the accuracy of name proposals. For example, it can analyze the characteristics of past successful brand names and product names and generate names that incorporate those characteristics. In addition, generative AI can reflect the latest trends and fashions of the target market in real time and always propose names based on the latest information. This allows the proposal department to efficiently propose attractive names that are suitable for the target market.

[0074] The evaluation department assesses whether the name proposed by the proposal department is compatible with the culture and sensibilities of the target market. The evaluation department uses, for example, generative AI to consider the cultural background and characteristics of the target market's sensibilities. Generative AI can learn data on the culture and sensibilities of the target market and evaluate the suitability of the name. Specifically, the generative AI considers the cultural background, history, social values, and consumer preferences of the target market to evaluate whether the proposed name is appropriate. The generative AI uses natural language processing technology to analyze the meaning and nuances of the name and evaluate its acceptance in the target market. In addition, the generative AI can collect consumer feedback and opinions in the target market and evaluate the name based on them. For example, it can analyze online surveys and social media comments and reflect consumer reactions in the evaluation. Furthermore, the generative AI analyzes the names and brand strategies of competitors in the target market to evaluate whether the proposed name is competitive. This allows the evaluation department to accurately evaluate names that are compatible with the target market and select the optimal name.

[0075] The display unit crawls national registration and trademark databases and displays legal risks using color coding. For example, the display unit uses APIs and RPA to crawl databases and visually display legal risks. Specifically, the display unit accesses national trademark databases to check whether a proposed name is already registered. It accesses databases using APIs and automatically collects data using RPA. The collected data is used to assess legal risks, and risks such as patent infringement and trademark infringement are displayed using color coding. For example, already registered names are displayed in red, and names with a high probability of registration are displayed in green. This allows users to grasp legal risks at a glance. Furthermore, the display unit provides detailed information on legal risks, enabling users to understand specific risks. For example, it displays which countries a particular name is registered in and for what goods or services it is registered. This helps users accurately understand legal risks and take appropriate measures.

[0076] The analysis department analyzes competitive trends and points of differentiation, and automatically proposes candidate names that maintain a competitive advantage. For example, the analysis department uses AI to analyze the names and market share of competitors and identify points of differentiation. Specifically, the analysis department analyzes the names and brand strategies of competitors and evaluates whether the proposed names are competitive. The AI ​​analyzes the characteristics of competitors' names, market share, and consumer evaluations, and evaluates the degree to which the proposed names are differentiated. Furthermore, the analysis department analyzes trends in the target market and consumer preferences to evaluate whether the proposed names will be accepted in the market. For example, by proposing names that reflect the latest trends and consumer preferences, a competitive advantage can be maintained. In this way, the analysis department can differentiate itself from competitors and propose competitive names.

[0077] The Risk Assessment Department analyzes pronunciation and linguistic risks to evaluate the potential risks in each culture. For example, the Risk Assessment Department uses AI to analyze the difficulty of pronunciation and the possibility of linguistic misunderstanding. Specifically, the Risk Assessment Department analyzes how the proposed name is pronounced in each country's language and evaluates the difficulty of pronunciation and the possibility of misunderstanding. The AI ​​learns the linguistic characteristics and pronunciation rules of each country and evaluates whether the proposed name is easy to pronounce. The AI ​​also analyzes whether the proposed name has an inappropriate meaning in a particular language and evaluates the linguistic risk. For example, avoiding names with inappropriate meanings in a particular language can prevent international trouble. Furthermore, the Risk Assessment Department considers the cultural background and social values ​​of each country and evaluates whether the proposed name is culturally appropriate. This allows the Risk Assessment Department to accurately evaluate pronunciation and linguistic risks and select an internationally appropriate name.

[0078] The monitoring unit autonomously monitors trademark objections and legal risks, and provides risk notifications before problems occur. For example, the monitoring unit uses AI to monitor trademark objections and legal risks in real time. Specifically, the monitoring unit monitors trademark registration databases and legal information in various countries in real time to detect objections and legal risks to proposed names. The AI ​​analyzes trademark registration databases and legal information to detect signs of objections and legal risks. For example, if an objection occurs to a proposed name, the monitoring unit immediately provides a risk notification and urges the user to take countermeasures. Furthermore, the monitoring unit provides detailed information on legal risks so that users can understand the specific risks. This allows the monitoring unit to provide risk notifications before problems occur and prevent troubles after release.

[0079] The proposal department proposes names using generative AI. For example, the proposal department uses generative AI to propose names. Generative AI can generate names that are appropriate to the culture and sensibilities of the target market using natural language generation models and machine learning algorithms. For example, the generative AI learns data on the culture and sensibilities of the target market and evaluates the suitability of the names. The generative AI can generate names considering the cultural background and sensibilities characteristics of the target market. As a result, using generative AI improves the accuracy of name proposals.

[0080] The evaluation unit uses generative AI to assess whether a name is suitable for the target market's culture and sensibilities. For example, the evaluation unit uses generative AI to evaluate while considering the cultural background and characteristics of the target market's sensibilities. The generative AI can learn data on the target market's culture and sensibilities and evaluate the suitability of a name. The generative AI evaluates whether a name is suitable for the target market's culture and sensibilities in real time. For example, the generative AI considers names that are preferred or should be avoided in specific countries or regions. As a result, using generative AI improves the accuracy of the evaluation of whether a name is suitable for the target market's culture and sensibilities.

[0081] The display unit crawls national registration and trademark databases and displays legal risks using color coding. The display unit can, for example, crawl databases by linking APIs and RPA and visually display legal risks. The display unit can display legal risks such as patent infringement risk and trademark infringement risk using color coding. For example, the display unit can display patent infringement risk in red and trademark infringement risk in yellow. The display unit needs to clearly define the types of legal risks and the criteria for color coding. For example, it can display legal risks such as patent infringement risk and trademark infringement risk using color coding. This makes it easier to visually grasp the risks by displaying legal risks using color coding. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data obtained by crawling national registration and trademark databases into a generating AI and have the generating AI perform the color coding display of legal risks.

[0082] The analysis unit analyzes competitive trends and differentiation points and automatically proposes candidate names that maintain a competitive advantage. For example, the analysis unit uses AI to analyze the names and market share of competitors and identify differentiation points. The analysis unit can propose unique names that are not similar to those of competitors. For example, the analysis unit retrieves the names of competitors from a database and calculates the similarity using AI. The analysis unit proposes names with low similarity to those of competitors as candidate names. The analysis unit needs to clarify the criteria for differentiation points. For example, it analyzes differentiation points such as market share and product characteristics. By analyzing competitive trends and differentiation points, it can propose candidate names that maintain a competitive advantage. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input competitor name data into a generating AI and have the generating AI propose candidate names that maintain a competitive advantage.

[0083] The risk assessment unit analyzes pronunciation and linguistic risks to evaluate the potential risks of each culture. For example, the risk assessment unit uses AI to analyze the difficulty of pronunciation and the likelihood of linguistic misunderstanding. The risk assessment unit can prevent international trouble by avoiding names that have inappropriate meanings in a particular language. For example, the risk assessment unit uses AI to analyze audio data to evaluate the difficulty of pronunciation. The risk assessment unit uses AI to analyze text data to evaluate the likelihood of linguistic misunderstanding. The risk assessment unit needs to clarify the criteria for pronunciation and linguistic risks. For example, it should clarify the criteria for the difficulty of pronunciation and the likelihood of linguistic misunderstanding. This will allow the risk assessment unit to evaluate the potential risks of each culture by analyzing pronunciation and linguistic risks. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input audio data for evaluating the difficulty of pronunciation into a generating AI and have the generating AI perform the evaluation of the difficulty of pronunciation.

[0084] The monitoring unit autonomously monitors trademark objections and legal risks and provides risk notifications before problems occur. The monitoring unit monitors trademark objections and legal risks in real time, for example, using AI. By providing risk notifications before problems occur, the monitoring unit can prevent post-release troubles. The monitoring unit provides risk notifications based on, for example, the frequency of trademark objections and the type of legal risk. The monitoring unit needs to clarify the criteria for trademark objections and legal risks. For example, it should clarify the criteria for the frequency of objections and the type of legal risk. This allows the monitoring unit to autonomously monitor trademark objections and legal risks and provide risk notifications before problems occur. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input data on trademark objections and legal risks into a generating AI and have the generating AI execute risk notifications.

[0085] The suggestion unit estimates the user's emotions and adjusts the timing of name suggestions based on the estimated emotions. The suggestion unit estimates the user's emotions, for example, using generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition and voice analysis. The suggestion unit adjusts the timing of name suggestions based on the user's emotions. For example, if the user is feeling stressed, the suggestion unit suggests a name at a time when the user can relax. If the user is concentrating, the suggestion unit suggests a name when the user's concentration is high. If the user is tired, the suggestion unit suggests a name after a break when the user has refreshed. By adjusting the timing of name suggestions based on the user's emotions, names can be suggested at a more appropriate time. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the timing of name suggestions.

[0086] The proposal department analyzes past name proposal history and selects the optimal proposal method. The proposal department analyzes past name proposal history using, for example, a generative AI. The generative AI can learn from past name proposal history data and select the optimal proposal method. The proposal department analyzes past name proposal history and selects the optimal proposal method. The proposal department analyzes trends in names preferred by users in the past and proposes similar names. The proposal department analyzes the reasons why users rejected names in the past and makes suggestions to avoid similar problems. The proposal department proposes names with similar success patterns based on successful examples of names chosen by users in the past. In this way, the optimal proposal method can be selected by analyzing past name proposal history. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input past name proposal history data into a generative AI and have the generative AI select the optimal proposal method.

[0087] The suggestion unit filters suggestions based on the user's current projects and areas of interest. For example, the suggestion unit uses generative AI to identify the user's current projects and areas of interest. The generative AI learns data about the user's projects and areas of interest and can perform filtering. The suggestion unit filters suggestions based on the user's current projects and areas of interest. For example, the suggestion unit prioritizes suggesting names related to the user's current projects. The suggestion unit suggests names containing keywords related to the user's areas of interest. The suggestion unit suggests names related to areas the user has shown interest in in the past. This allows the suggestion unit to suggest highly relevant names by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input data on the user's projects and areas of interest into the generative AI and have the generative AI perform the filtering.

[0088] The suggestion unit estimates the user's emotions and determines the priority of suggested names based on the estimated emotions. The suggestion unit estimates the user's emotions, for example, using generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition and voice analysis. The suggestion unit determines the priority of suggested names based on the user's emotions. For example, if the user is relaxed, the suggestion unit suggests multiple names to broaden the options. If the user is in a hurry, the suggestion unit prioritizes suggesting the most appropriate name. If the user is excited, the suggestion unit prioritizes suggesting impactful names. In this way, by determining the priority of suggested names based on the user's emotions, more appropriate names can be suggested. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user emotion data into generative AI and have the generative AI perform the determination of name priority.

[0089] The suggestion unit, when making suggestions, prioritizes suggesting highly relevant names by considering the user's geographical location. The suggestion unit identifies the user's geographical location using, for example, a generative AI. The generative AI can identify the user's geographical location using data such as GPS data or IP addresses. The suggestion unit prioritizes suggesting highly relevant names by considering the user's geographical location. For example, if the user is in a specific region, the suggestion unit suggests a name preferred in that region. If the user is traveling, the suggestion unit suggests a name appropriate to the culture of the destination. If the user is in a specific city, the suggestion unit suggests a name that aligns with the trends of that city. In this way, by considering the user's geographical location, highly relevant names can be suggested. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input the user's geographical location into the generative AI and have the generative AI suggest highly relevant names.

[0090] The suggestion unit analyzes the user's social media activity and proposes relevant names when making a suggestion. The suggestion unit analyzes the user's social media activity using, for example, generative AI. The generative AI can learn data about the user's social media activity and propose relevant names. The suggestion unit analyzes the user's social media activity and proposes relevant names. The suggestion unit proposes names that include keywords the user frequently uses on social media. The suggestion unit proposes names based on the trends of influencers the user follows. The suggestion unit analyzes the user's social media activity history and proposes relevant names. In this way, relevant names can be proposed by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input data on the user's social media activity into the generative AI and have the generative AI perform the suggestion of relevant names.

[0091] The evaluation unit estimates the user's emotions and adjusts the way the evaluation is presented based on the estimated emotions. The evaluation unit estimates the user's emotions, for example, using generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition and voice analysis. The evaluation unit adjusts the way the evaluation is presented based on the user's emotions. For example, if the user is relaxed, the evaluation unit provides a detailed evaluation. If the user is in a hurry, the evaluation unit provides a concise evaluation. If the user is excited, the evaluation unit provides a visually appealing evaluation. In this way, by adjusting the way the evaluation is presented based on the user's emotions, a more appropriate evaluation can be provided. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into generative AI and have the generative AI perform the adjustment of the evaluation presentation.

[0092] The evaluation unit adjusts the level of detail in its evaluation based on the importance of the name. The evaluation unit identifies the importance of a name, for example, using a generative AI. The generative AI can identify the importance of a name using data such as the market value of the name or the influence of the brand. The evaluation unit adjusts the level of detail in its evaluation based on the importance of the name. For example, the evaluation unit provides a detailed evaluation for important names. For common names, the evaluation unit provides a concise evaluation. For names important in a specific market, the evaluation unit provides a market-specific evaluation. By adjusting the level of detail in the evaluation based on the importance of the name, a more appropriate evaluation can be provided. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data that identifies the importance of a name into a generative AI and have the generative AI perform the adjustment of the level of detail in the evaluation.

[0093] The evaluation unit applies different evaluation algorithms depending on the category of the name during the evaluation process. The evaluation unit identifies the category of the name, for example, using a generative AI. The generative AI can identify the category of a name using data such as the product category or service category of the name. The evaluation unit applies different evaluation algorithms depending on the category of the name. For example, in the case of a product name, the evaluation unit performs an evaluation that emphasizes consumer response. In the case of a service name, the evaluation unit performs an evaluation that emphasizes user convenience. In the case of a brand name, the evaluation unit performs an evaluation that emphasizes brand image. By applying different evaluation algorithms depending on the category of the name, a more appropriate evaluation can be provided. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data to identify the category of the name into the generative AI and have the generative AI execute the application of the evaluation algorithm.

[0094] The evaluation unit estimates the user's emotions and adjusts the length of the evaluation based on the estimated emotions. The evaluation unit estimates the user's emotions using, for example, generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition and voice analysis. The evaluation unit adjusts the length of the evaluation based on the user's emotions. For example, if the user is relaxed, the evaluation unit provides a detailed evaluation. If the user is in a hurry, the evaluation unit provides a concise evaluation. If the user is excited, the evaluation unit provides a visually appealing evaluation. By adjusting the length of the evaluation based on the user's emotions, a more appropriate evaluation can be provided. Some or all of the above processing in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit can input user emotion data into generative AI and have the generative AI perform the adjustment of the evaluation length.

[0095] The evaluation unit determines the priority of evaluations based on the submission timing of the names during the evaluation process. The evaluation unit identifies the submission timing of names, for example, using a generative AI. The generative AI can identify the submission timing of names using data such as the submission date and time and submission order. The evaluation unit determines the priority of evaluations based on the submission timing of the names. The evaluation unit, for example, prioritizes evaluating names submitted earlier. The evaluation unit postpones evaluating names submitted later. If the submission timing is related to a specific event, the evaluation unit performs an evaluation tailored to that event. This allows for a more appropriate evaluation by determining the priority of evaluations based on the submission timing of the names. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data identifying the submission timing of names into a generative AI and have the generative AI determine the priority of evaluations.

[0096] The evaluation unit adjusts the order of evaluation based on the relevance of the names during the evaluation process. The evaluation unit identifies the relevance of names, for example, using a generative AI. The generative AI can identify the relevance of names using data such as the relevance of names to the target market and the relevance of names to competing products. The evaluation unit adjusts the order of evaluation based on the relevance of the names. The evaluation unit, for example, prioritizes the evaluation of names with high relevance. The evaluation unit postpones the evaluation of names with low relevance. The evaluation unit prioritizes the evaluation of names with high relevance in a particular market. This allows for the provision of more appropriate evaluations by adjusting the order of evaluation based on the relevance of the names. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input data to identify the relevance of names into a generative AI and have the generative AI perform the adjustment of the evaluation order.

[0097] The display unit estimates the user's emotions and adjusts the display method based on the estimated emotions. The display unit estimates the user's emotions, for example, using generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition and voice analysis. The display unit adjusts the display method based on the user's emotions. For example, if the user is relaxed, the display unit displays detailed information. If the user is in a hurry, the display unit displays concise information. If the user is excited, the display unit provides a visually appealing display. In this way, by adjusting the display method based on the user's emotions, more appropriate information can be provided. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into generative AI and have the generative AI perform the adjustment of the display method.

[0098] The display unit adjusts the level of detail displayed based on the importance of the legal risk when displaying information. The display unit identifies the importance of the legal risk, for example, using a generating AI. The generating AI can identify the importance of the legal risk using data such as the impact and probability of occurrence of the legal risk. The display unit adjusts the level of detail displayed based on the importance of the legal risk. For example, the display unit displays detailed information for important legal risks. For general legal risks, the display unit displays concise information. For legal risks that are important in a particular market, the display unit displays market-specific information. This allows for the provision of more appropriate information by adjusting the level of detail displayed based on the importance of the legal risk. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data that identifies the importance of the legal risk into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0099] The display unit applies different display algorithms depending on the legal risk category during display. The display unit identifies the legal risk category, for example, using a generating AI. The generating AI can identify the legal risk category using data such as patent risk and trademark risk. The display unit applies different display algorithms depending on the legal risk category. For example, in the case of trademark risk, the display unit emphasizes trademark registration information. In the case of copyright risk, the display unit emphasizes copyright information. In the case of patent risk, the display unit emphasizes patent information. By applying different display algorithms depending on the legal risk category, more appropriate information can be provided. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data to identify the legal risk category into the generating AI and have the generating AI perform the application of the display algorithm.

[0100] The display unit estimates the user's emotions and determines the display priority based on the estimated user emotions. The display unit estimates the user's emotions, for example, using generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition and voice analysis. The display unit determines the display priority based on the user's emotions. For example, if the user is relaxed, the display unit prioritizes displaying detailed information. If the user is in a hurry, the display unit prioritizes displaying concise information. If the user is excited, the display unit prioritizes displaying visually appealing information. In this way, more appropriate information can be provided by determining the display priority based on the user's emotions. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into generative AI and have the generative AI perform the determination of the display priority.

[0101] The display unit adjusts the display order based on the filing date of the legal risks when displaying them. The display unit identifies the filing date of the legal risks, for example, using a generating AI. The generating AI can identify the filing date of the legal risks using data such as the filing date and order of the legal risks. The display unit adjusts the display order based on the filing date of the legal risks. The display unit, for example, prioritizes displaying legal risks that were filed earlier. The display unit postpones displaying legal risks that were filed later. If the filing date is related to a specific event, the display unit displays the information in a way that is appropriate for that event. This allows for the provision of more relevant information by adjusting the display order based on the filing date of the legal risks. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data that identifies the filing date of the legal risks into the generating AI and have the generating AI perform the adjustment of the display order.

[0102] The display unit adjusts the display order based on the relevance of legal risks during display. The display unit identifies the relevance of legal risks, for example, using generative AI. The generative AI can identify the relevance of legal risks using data such as the relevance of legal risks to target markets and relevance to competing products. The display unit adjusts the display order based on the relevance of legal risks. The display unit, for example, prioritizes displaying highly relevant legal risks. The display unit postpones displaying less relevant legal risks. The display unit prioritizes displaying legal risks that are highly relevant in a particular market. This allows for the provision of more appropriate information by adjusting the display order based on the relevance of legal risks. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data that identifies the relevance of legal risks into the generative AI and have the generative AI perform the adjustment of the display order.

[0103] The analysis unit estimates the user's emotions and adjusts the analysis criteria based on the estimated emotions. The analysis unit estimates the user's emotions using, for example, generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition and voice analysis. The analysis unit adjusts the analysis criteria based on the user's emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis. If the user is in a hurry, the analysis unit performs a concise analysis. If the user is excited, the analysis unit performs a visually appealing analysis. By adjusting the analysis criteria based on the user's emotions, a more appropriate analysis can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into generative AI and have the generative AI perform the adjustment of the analysis criteria.

[0104] The analysis unit improves the accuracy of its analysis by considering the interrelationships of competitors. The analysis unit identifies the interrelationships of competitors, for example, using generative AI. Generative AI can identify the interrelationships of competitors using data such as market share and product characteristics of competitors. The analysis unit improves the accuracy of its analysis by considering the interrelationships of competitors. The analysis unit performs its analysis by considering the alliance relationships between competing companies. The analysis unit performs its analysis by considering the competitive relationships between competing companies. The analysis unit performs its analysis by considering the market share between competing companies. In this way, the accuracy of the analysis can be improved by considering the interrelationships of competitors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data that identifies the interrelationships of competitors into the generative AI and have the generative AI perform the improvement of the accuracy of the analysis.

[0105] The analysis unit performs analysis while considering the attribute information of competitors. The analysis unit identifies the attribute information of competitors, for example, using a generative AI. The generative AI can identify the attribute information of competitors using data such as the size of the competitors' companies and their market segments. The analysis unit performs analysis while considering the attribute information of competitors. The analysis unit performs analysis while considering the industry of the competitors' companies. The analysis unit performs analysis while considering the size of the competitors' companies. The analysis unit performs analysis while considering the regional characteristics of the competitors' companies. By considering the attribute information of competitors, a more appropriate analysis can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input data to identify the attribute information of competitors into a generative AI and have the generative AI perform the analysis.

[0106] The analysis unit estimates the user's emotions and adjusts the order in which the analysis results are displayed based on the estimated emotions. The analysis unit estimates the user's emotions using, for example, generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition and voice analysis. The analysis unit adjusts the order in which the analysis results are displayed based on the user's emotions. For example, if the user is relaxed, the analysis unit prioritizes displaying detailed analysis results. If the user is in a hurry, the analysis unit prioritizes displaying concise analysis results. If the user is excited, the analysis unit prioritizes displaying visually appealing analysis results. This allows for the provision of more appropriate information by adjusting the order in which the analysis results are displayed based on the user's emotions. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the analysis results.

[0107] The analysis unit performs its analysis while considering the geographical distribution of competitors. The analysis unit identifies the geographical distribution of competitors, for example, using generative AI. Generative AI can identify the geographical distribution of competitors using data such as the market share of competitors and the strength of competitors in each region. The analysis unit performs its analysis while considering the geographical distribution of competitors. The analysis unit performs its analysis while considering the location of competitor companies. The analysis unit performs its analysis while considering the geographical distribution of market share of competitor companies. The analysis unit performs its analysis while considering the strength of competitor companies in each region. This allows for the provision of a more appropriate analysis by considering the geographical distribution of competitors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data to identify the geographical distribution of competitors into generative AI and have the generative AI perform the analysis.

[0108] The analysis unit improves the accuracy of its analysis by referring to relevant literature from competitors during the analysis. The analysis unit identifies relevant literature from competitors, for example, using a generative AI. The generative AI can identify relevant literature from competitors using data such as patent documents and technical papers from competitors. The analysis unit improves the accuracy of its analysis by referring to relevant literature from competitors. The analysis unit performs analysis by referring to patent documents from competitor companies. The analysis unit performs analysis by referring to research papers from competitor companies. The analysis unit performs analysis by referring to market reports from competitor companies. In this way, the accuracy of the analysis can be improved by referring to relevant literature from competitors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input data for identifying relevant literature from competitors into the generative AI and have the generative AI perform the improvement of the accuracy of the analysis.

[0109] The risk assessment unit estimates the user's emotions and adjusts the risk assessment method based on the estimated user emotions. The risk assessment unit estimates the user's emotions, for example, using generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition and voice analysis. The risk assessment unit adjusts the risk assessment method based on the user's emotions. For example, if the user is relaxed, the risk assessment unit performs a detailed risk assessment. If the user is in a hurry, the risk assessment unit performs a concise risk assessment. If the user is excited, the risk assessment unit performs a visually appealing risk assessment. In this way, by adjusting the risk assessment method based on the user's emotions, a more appropriate risk assessment can be provided. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input user emotion data into generative AI and have the generative AI perform the adjustment of the risk assessment method.

[0110] The risk assessment unit predicts current risk by referring to past risk data during risk assessment. The risk assessment unit analyzes past risk data using, for example, a generative AI. The generative AI can learn from past risk data and predict current risk. The risk assessment unit predicts current risk by referring to past risk data. The risk assessment unit predicts current risk based on, for example, past risk data. The risk assessment unit analyzes past risk data and grasps risk trends. The risk assessment unit predicts the frequency of risk occurrence by referring to past risk data. In this way, current risk can be predicted by referring to past risk data. Some or all of the above processes in the risk assessment unit may be performed using, for example, AI, or without using AI. For example, the risk assessment unit can input past risk data into a generative AI and have the generative AI perform a prediction of current risk.

[0111] The risk assessment unit applies different risk assessment methods to each category of name during the risk assessment process. For example, the risk assessment unit uses a generative AI to identify the category of a name. The generative AI can identify the category of a name using data such as the product category or service category of the name. The risk assessment unit applies different risk assessment methods to each category of name. For example, in the case of a product name, the risk assessment unit performs a risk assessment that emphasizes consumer response. In the case of a service name, the risk assessment unit performs a risk assessment that emphasizes user convenience. In the case of a brand name, the risk assessment unit performs a risk assessment that emphasizes brand image. By applying different risk assessment methods to each category of name, a more appropriate risk assessment can be provided. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input data to identify the category of a name into the generative AI and have the generative AI perform the application of risk assessment methods.

[0112] The risk assessment unit estimates the user's emotions and adjusts the importance of the risk assessment based on the estimated user emotions. The risk assessment unit estimates the user's emotions, for example, using generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition and voice analysis. The risk assessment unit adjusts the importance of the risk assessment based on the user's emotions. For example, if the user is relaxed, the risk assessment unit performs a detailed risk assessment. If the user is in a hurry, the risk assessment unit performs a concise risk assessment. If the user is excited, the risk assessment unit performs a visually appealing risk assessment. This allows for a more appropriate risk assessment by adjusting the importance of the risk assessment based on the user's emotions. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input user emotion data into generative AI and have the generative AI perform the adjustment of the importance of the risk assessment.

[0113] The risk assessment unit analyzes changes in risk based on the submission timing of names during the risk assessment. The risk assessment unit identifies the submission timing of names, for example, using a generative AI. The generative AI can identify the submission timing of names using data such as the submission date and time and submission order. The risk assessment unit analyzes changes in risk based on the submission timing of names. The risk assessment unit prioritizes the assessment of risks for names submitted earlier. The risk assessment unit postpones the assessment of risks for names submitted later. If the submission timing is related to a specific event, the risk assessment unit performs a risk assessment tailored to that event. This allows for a more appropriate risk assessment by analyzing changes in risk based on the submission timing of names. Some or all of the above processing in the risk assessment unit may be performed using AI, for example, or without AI. For example, the risk assessment unit can input data identifying the submission timing of names into a generative AI and have the generative AI perform the analysis of changes in risk.

[0114] The risk assessment unit analyzes risk by referring to relevant market data for the name during the risk assessment. The risk assessment unit identifies relevant market data for the name using, for example, a generative AI. The generative AI can analyze risk using the relevant market data for the name. The risk assessment unit analyzes risk by referring to relevant market data for the name. The risk assessment unit evaluates the risk for the name based on the relevant market data. The risk assessment unit analyzes the relevant market data and grasps the risk trends. The risk assessment unit predicts the frequency of risk occurrence by referring to the relevant market data. This allows for a more appropriate risk assessment by referring to relevant market data for the name. Some or all of the above processes in the risk assessment unit may be performed using, for example, AI, or not using AI. For example, the risk assessment unit can input relevant market data for the name into a generative AI and have the generative AI perform the risk analysis.

[0115] The monitoring unit estimates the user's emotions and adjusts the monitoring method based on the estimated emotions. The monitoring unit estimates the user's emotions, for example, using generative AI. Generative AI can estimate the user's emotions using technologies such as facial recognition and voice analysis. The monitoring unit adjusts the monitoring method based on the user's emotions. For example, if the user is relaxed, the monitoring unit performs detailed monitoring. If the user is in a hurry, the monitoring unit performs concise monitoring. If the user is excited, the monitoring unit performs visually appealing monitoring. By adjusting the monitoring method based on the user's emotions, more appropriate monitoring can be provided. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into generative AI and have the generative AI adjust the monitoring method.

[0116] The monitoring unit optimizes the monitoring algorithm by referring to past monitoring data during monitoring. The monitoring unit analyzes past monitoring data using, for example, a generative AI. The generative AI can learn from past monitoring data and optimize the monitoring algorithm. The monitoring unit optimizes the monitoring algorithm by referring to past monitoring data. The monitoring unit optimizes the current monitoring algorithm based on, for example, past monitoring data. The monitoring unit analyzes past monitoring data and improves the accuracy of monitoring. The monitoring unit improves the efficiency of monitoring by referring to past monitoring data. In this way, the monitoring algorithm can be optimized by referring to past monitoring data. Some or all of the above processes in the monitoring unit may be performed using, for example, AI, or without using AI. For example, the monitoring unit can input past monitoring data into a generative AI and have the generative AI perform the optimization of the monitoring algorithm.

[0117] The monitoring unit weights the monitoring data based on the submission date of the names during monitoring. The monitoring unit identifies the submission date of names, for example, using a generation AI. The generation AI can identify the submission date of names using data such as the submission date and order. The monitoring unit weights the monitoring data based on the submission date of the names. For example, the monitoring unit prioritizes the weighting of monitoring data for names submitted earlier. The monitoring unit postpones the weighting of monitoring data for names submitted later. If the submission date is related to a specific event, the monitoring unit weights the monitoring data in accordance with that event. This allows for more appropriate monitoring by weighting the monitoring data based on the submission date of the names. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data identifying the submission date of names into a generation AI and have the generation AI perform the weighting of the monitoring data.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The suggestion function can estimate the user's emotions and adjust the timing of name suggestions based on those emotions. For example, by using generative AI to estimate the user's emotions, if the user is feeling stressed, it can suggest a name at a time when the user can relax. If the user is concentrating, it can suggest a name when their concentration is high. Furthermore, if the user is tired, it can suggest a name after a break when they have refreshed. In this way, by adjusting the timing of name suggestions based on the user's emotions, it is possible to suggest a name at a more appropriate time.

[0120] The proposal department can analyze past name proposal history and select the optimal proposal method. For example, it can use generative AI to analyze past name proposal history, analyze the trends of names that users have preferred in the past, and propose similar names. It can also analyze the reasons why users have rejected names in the past and make suggestions that avoid similar problems. Furthermore, it can propose names with similar success patterns based on successful examples of names chosen by users in the past. In this way, by analyzing past name proposal history, the optimal proposal method can be selected.

[0121] The evaluation unit can estimate the user's emotions and adjust the way the evaluation is presented based on those emotions. For example, by using generative AI to estimate the user's emotions, it can provide a detailed evaluation if the user is relaxed, a concise evaluation if the user is in a hurry, and a visually appealing evaluation if the user is excited. By adjusting the way the evaluation is presented based on the user's emotions, it is possible to provide a more appropriate evaluation.

[0122] The evaluation unit can adjust the level of detail in its evaluations based on the importance of the name. For example, it can use generation AI to identify the importance of a name using data such as its market value and brand influence, and provide a detailed evaluation for important names. For general names, it can provide a concise evaluation. Furthermore, for names that are important in a specific market, it can provide a market-specific evaluation. In this way, by adjusting the level of detail in the evaluation based on the importance of the name, it is possible to provide a more appropriate evaluation.

[0123] The evaluation unit can apply different evaluation algorithms depending on the category of the name during the evaluation process. For example, by using a generation AI to identify the category of the name using data such as the product category or service category, it can perform evaluations that prioritize consumer response in the case of product names, evaluations that prioritize user convenience in the case of service names, and evaluations that prioritize brand image in the case of brand names. By applying different evaluation algorithms depending on the category of the name, it is possible to provide more appropriate evaluations.

[0124] The display unit can estimate the user's emotions and adjust the display method based on those emotions. For example, by using generative AI to estimate the user's emotions, it can display detailed information if the user is relaxed, concise information if the user is in a hurry, and visually appealing information if the user is excited. In addition, it can provide more appropriate information by adjusting the display method based on the user's emotions.

[0125] The display unit can adjust the level of detail displayed based on the importance of the legal risk. For example, it can use a generation AI to identify the importance of a legal risk using data such as its impact and probability of occurrence, and display detailed information for important legal risks. Conversely, it can display concise information for general legal risks. Furthermore, for legal risks that are important in a specific market, it can display market-specific information. By adjusting the level of detail displayed based on the importance of the legal risk, it is possible to provide more appropriate information.

[0126] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on those emotions. For example, it can use generative AI to estimate the user's emotions and perform a detailed analysis if the user is relaxed. If the user is in a hurry, it can perform a concise analysis. Furthermore, if the user is excited, it can perform a visually appealing analysis. By adjusting the analysis criteria based on the user's emotions, it can provide a more appropriate analysis.

[0127] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of competitors. For example, it can use generative AI to identify interrelationships of competitors using data such as market share and product characteristics, and perform analysis while considering alliances between competing companies. It can also perform analysis while considering the competitive relationships between competing companies. Furthermore, it can perform analysis while considering the market share of competing companies. In this way, the accuracy of the analysis can be improved by considering the interrelationships of competitors.

[0128] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated emotions. For example, by using generative AI to estimate the user's emotions, detailed monitoring can be performed if the user is relaxed. Conversely, concise monitoring can be performed if the user is in a hurry. Furthermore, visually appealing monitoring can be performed if the user is excited. In this way, by adjusting the monitoring method based on the user's emotions, more appropriate monitoring can be provided.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The proposal team proposes a name. The proposal team uses generative AI to propose a name and generates a name that is suitable for the culture and sensibilities of the target market using natural language generation models and machine learning algorithms. Step 2: The evaluation department assesses whether the name proposed by the proposal department is suitable for the culture and sensibilities of the target market. The evaluation department uses generative AI to consider the cultural background and sensibility characteristics of the target market when making its evaluation. Step 3: The display unit crawls the registration and trademark databases of each country and displays legal risks using color coding. The display unit uses APIs and RPA to crawl the database and visually displays legal risks such as patent infringement risk and trademark infringement risk. Step 4: The analysis unit analyzes competitive trends and points of differentiation, and automatically proposes candidate names that maintain a competitive advantage. The analysis unit uses AI to analyze the names and market share of competitors, identifies points of differentiation, and proposes unique names that are not similar to those of competitors. Step 5: The Risk Assessment Department analyzes pronunciation and linguistic risks to assess potential risks in each culture. The Risk Assessment Department uses AI to analyze the difficulty of pronunciation and the possibility of linguistic misunderstandings, preventing international problems by avoiding names that have inappropriate meanings in specific languages. Step 6: The monitoring unit autonomously monitors trademark objections and legal risks and provides risk notifications before problems occur. The monitoring unit uses AI to monitor trademark objections and legal risks in real time and provides risk notifications before problems occur, thereby preventing post-release troubles.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0133] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the proposal unit, evaluation unit, display unit, analysis unit, risk assessment unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the smart device 14 and proposes a name using a generation AI. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates whether it is suitable for the culture and sensibilities of the target market using a generation AI. The display unit is implemented by the control unit 46A of the smart device 14 and displays legal risks in color by linking APIs and RPA. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes competitive trends and points of differentiation, and automatically proposes candidate names that maintain a competitive advantage. The risk assessment unit is implemented by the control unit 46A of the smart device 14 and analyzes pronunciation and linguistic risks to evaluate the potential risks of each culture. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and autonomously monitors trademark objections and legal risks, providing risk notification before problems occur. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the proposal unit, evaluation unit, display unit, analysis unit, risk assessment unit, and monitoring unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes a name using a generation AI. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates whether it is suitable for the culture and sensibilities of the target market using a generation AI. The display unit is implemented by the control unit 46A of the smart glasses 214 and displays legal risks in color by linking APIs and RPA. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes competitive trends and points of differentiation, and automatically proposes candidate names that maintain a competitive advantage. The risk assessment unit is implemented by the control unit 46A of the smart glasses 214 and analyzes pronunciation and linguistic risks to evaluate the potential risks of each culture. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and autonomously monitors trademark objections and legal risks, providing risk notification before problems occur. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the proposal unit, evaluation unit, display unit, analysis unit, risk assessment unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes a name using a generation AI. The evaluation unit is implemented by the identification processing unit 290 of the data processing unit 12 and evaluates whether it is suitable for the culture and sensibilities of the target market using a generation AI. The display unit is implemented by the control unit 46A of the headset terminal 314 and displays legal risks in color by linking APIs and RPA. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes competitive trends and points of differentiation, and automatically proposes candidate names that maintain a competitive advantage. The risk assessment unit is implemented by the control unit 46A of the headset terminal 314 and analyzes pronunciation and linguistic risks to evaluate the potential risks of each culture. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and autonomously monitors trademark objections and legal risks, providing risk notification before problems occur. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0171] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0173] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0174] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0175] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0176] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0177] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0178] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0179] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0181] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0183] Each of the multiple elements described above, including the proposal unit, evaluation unit, display unit, analysis unit, risk assessment unit, and monitoring unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the robot 414 and proposes a name using a generation AI. The evaluation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and evaluates whether it is suitable for the culture and sensibilities of the target market using a generation AI. The display unit is implemented by, for example, the control unit 46A of the robot 414 and displays legal risks in color by linking API and RPA. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes competitive trends and points of differentiation, and automatically proposes candidate names that maintain a competitive advantage. The risk assessment unit is implemented by, for example, the control unit 46A of the robot 414 and analyzes pronunciation and linguistic risks to evaluate the potential risks of each culture. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and autonomously monitors trademark objections and legal risks, providing risk notification before problems occur. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0184] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0185] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0186] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0187] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0188] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0189] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0192] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0193] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0194] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0195] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0196] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0197] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0198] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0199] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0200] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0201] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0202] (Note 1) The proposal department that proposes the name, An evaluation unit evaluates whether the name proposed by the aforementioned proposal unit is suitable for the culture and sensibilities of the target market, It crawls the registration and trademark databases of various countries and displays legal risks using color coding. The analysis unit analyzes competitive trends and points of differentiation, and automatically proposes candidate names that maintain a competitive advantage. The Risk Assessment Department analyzes pronunciation and linguistic risks and evaluates the potential risks of each culture, It includes a monitoring unit that autonomously monitors trademark objections and legal risks, and provides risk notifications before problems occur. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Name suggested by a generative AI The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, The generation AI evaluates whether the content is suitable for the culture and sensibilities of the target market. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is We crawl registration and trademark databases in various countries and color-code legal risks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, It analyzes competitive trends and points of differentiation, and automatically suggests candidate names that maintain a competitive advantage. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned risk assessment unit, Analyze pronunciation and linguistic risks to assess the potential risks in each culture. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitor unit is Autonomously monitors trademark opposition and legal risks, and provides risk notification before problems arise. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, It estimates the user's emotions and adjusts the timing of name suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, We will analyze past name proposal history and select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, When making suggestions, filter them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggested names based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When making a proposal, we prioritize suggesting names that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and suggest a relevant name. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, It estimates the user's emotions and adjusts the way evaluations are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, During the evaluation, adjust the level of detail based on the importance of the names. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, During evaluation, different evaluation algorithms are applied depending on the category of the name. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, It estimates the user's emotions and adjusts the length of the rating based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, During the evaluation process, the priority of evaluation will be determined based on when the names were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, During evaluation, the order of evaluation will be adjusted based on the relevance of the names. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying information, adjust the level of detail based on the importance of the legal risk. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is When displaying information, different display algorithms are applied depending on the category of legal risk. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displaying information, adjust the order of display based on the timing of the submission of legal risks. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is When displaying information, adjust the order of display based on the relevance of the legal risks. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, During analysis, consider the interrelationships of competitors to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, During the analysis, the attribute information of competitors will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit, It estimates the user's emotions and adjusts the order in which the analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit, During the analysis, the geographical distribution of competitors will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit, During analysis, we refer to relevant literature from competitors to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned risk assessment unit, We estimate user sentiment and adjust the risk assessment method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned risk assessment unit, When assessing risk, historical risk data is used to predict current risk. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned risk assessment unit, When conducting a risk assessment, different risk assessment methods are applied to each category of name. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned risk assessment unit, The system estimates user sentiment and adjusts the importance of risk assessment based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned risk assessment unit, During risk assessment, analyze changes in risk based on the timing of name submission. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned risk assessment unit, When assessing risk, we analyze risk by referring to relevant market data for the name. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned monitor unit is We estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned monitor unit is During monitoring, the monitoring algorithm is optimized by referring to past monitoring data. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned monitor unit is During monitoring, monitoring data is weighted based on when the name was submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The proposal department that proposes the name, An evaluation unit evaluates whether the name proposed by the aforementioned proposal unit is suitable for the culture and sensibilities of the target market, It crawls the registration and trademark databases of various countries and displays legal risks using color coding. The analysis unit analyzes competitive trends and points of differentiation, and automatically proposes candidate names that maintain a competitive advantage. The Risk Assessment Department analyzes pronunciation and linguistic risks and evaluates the potential risks of each culture, It includes a monitoring unit that autonomously monitors trademark objections and legal risks, and provides risk notifications before problems occur. A system characterized by the following features.

2. The aforementioned proposal section is, Names are proposed by a generative AI. The system according to feature 1.

3. The evaluation unit described above, The generation AI evaluates whether the content is suitable for the culture and sensibilities of the target market. The system according to feature 1.

4. The aforementioned display unit is We crawl registration and trademark databases in various countries and color-code legal risks. The system according to feature 1.

5. The aforementioned analysis unit, It analyzes competitive trends and points of differentiation, and automatically suggests candidate names that maintain a competitive advantage. The system according to feature 1.

6. The aforementioned risk assessment unit, Analyze pronunciation and linguistic risks to assess the potential risks in each culture. The system according to feature 1.

7. The aforementioned monitor unit is Autonomously monitors trademark opposition and legal risks, and provides risk notification before problems arise. The system according to feature 1.

8. The aforementioned proposal section is, It estimates the user's emotions and adjusts the timing of name suggestions based on those estimated emotions. The system according to feature 1.

9. The aforementioned proposal section is, We will analyze past name proposal history and select the most suitable proposal method. The system according to feature 1.

10. The aforementioned proposal section is, When making suggestions, filter them based on the user's current projects and areas of interest. The system according to feature 1.

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