System

Abstract

To provide a system that consistently checks the suitability of a name, possibilities of registration in respective countries, and a risk of trademark infringement to make procedures efficient.SOLUTION: A system comprises an analysis part, a check part, and a rearrangement part. The analysis part analyzes the suitability of a name. The check part checks possibilities of registrations and risks of trademark infringement in respective countries based upon candidates for the name generated by the analysis part. The rearrangement part rearranges application information on registration or trademark registration based upon the result obtained by the check part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to consistently check the suitability of a name, its registrability in each country, and the risk of trademark infringement, so there is a need for efficient procedures.

[0005] The system of the embodiment aims to streamline procedures by consistently checking the suitability of a name, its registrability in each country, and the risk of trademark infringement. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a check unit, and a sorting unit. The analysis unit analyzes the suitability of a name. The check unit checks the registrability or risk of trademark infringement in each country based on the name candidates generated by the analysis unit. The sorting unit sorts the registration or trademark registration application information based on the results obtained by the check unit. Effect of the Invention

[0007] The system according to the embodiment can consistently check the suitability of a name, its registrability in each country, and the risk of trademark infringement, streamlining procedures. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

[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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that improves the efficiency of the consideration and implementation of company and service names when starting a business, developing a new service, or considering a new business. This system is composed of the following steps by linking the generation AI and related APIs (market research and databases for each country). First, the user inputs the company name or service name under consideration. Next, the generation AI analyzes the suitability of the name (meaning, image, etc.) and lists appropriate candidates. At this time, the generation AI refers to market research data and databases for each country to evaluate the meaning and image of the name. Next, the generation AI checks the possibility of registration in each country and the risk of trademark infringement. Specifically, it accesses the trademark database and registration database of each country through the related API to check whether the input name has already been registered and whether there is a risk of trademark infringement. This result is also listed and provided to the user. In addition, the generation AI organizes application information for registration and trademark registration and provides it to the user. This allows the user to proceed with the procedure efficiently. For example, the necessary documents and procedural steps can be automatically listed and provided to the user to make the procedure more efficient. This system will contribute to the efficiency of planning and procedures by making the process of considering and implementing company and service names more efficient when starting a business, developing a new service, or considering a new business more efficient.

[0029] The system according to the embodiment includes an analysis unit, a check unit, and a sorting unit. The analysis unit analyzes the suitability of a name. The suitability of a name includes, for example, cultural suitability, ease of pronunciation, appropriateness of meaning, and the like, but is not limited to such examples. The analysis unit, for example, uses a generation AI to analyze the meaning and image of the name. The generation AI evaluates the meaning and image of the name using, for example, a text generation AI (for example, LLM). The analysis unit can also use the generation AI to refer to market research data and databases of each country to evaluate the suitability of the name. For example, the generation AI collects related market research data and evaluates the meaning and image of the name. The check unit checks the possibility of registration in each country and the risk of trademark infringement based on the name candidates generated by the analysis unit. For example, the check unit accesses the trademark databases and registration databases of each country through related APIs to check whether the input name has already been registered or there is a risk of trademark infringement. For example, the check unit accesses the trademark databases of each country to check whether the name has already been registered. The checking unit can also access the registration databases of each country to check whether the name can be registered. The sorting unit sorts the application information for registration or trademark registration based on the results obtained by the checking unit. The sorting unit, for example, lists the necessary documents and procedural steps and provides them to the user. For example, the sorting unit lists the documents necessary for registration or trademark registration applications and provides them to the user. The sorting unit can also list the procedural steps and provide them to the user. As a result, the system according to the embodiment can check the suitability of the name, the possibility of registration, and the risk of trademark infringement all at once and sort the application information, thereby enabling efficient name consideration and implementation.

[0030] The analysis unit analyzes the suitability of the name. The suitability of the name includes, for example, cultural suitability, ease of pronunciation, appropriateness of meaning, etc., but is not limited to such examples. The analysis unit, for example, uses a generation AI to analyze the meaning and image of the name. The generation AI evaluates the meaning and image of the name using, for example, a text generation AI (for example, LLM). Specifically, the generation AI learns a large amount of text data and analyzes the cultural background and historical meaning of the name. For example, it can evaluate how a particular name is received in a particular culture or region, and the positive or negative image that the name has. In addition, the generation AI can also use voice recognition technology to evaluate the ease of pronunciation of the name. This analyzes how the name is pronounced in different languages ​​and accents, and evaluates the ease of pronunciation. In addition, the generation AI analyzes the meaning of the name and related words to evaluate the appropriateness of the name's meaning, and determines whether the name is appropriate in a particular context. For example, it can evaluate how the name is received in a particular industry or market. In addition, the analysis unit can use the generation AI to refer to market research data and databases for each country to evaluate the suitability of the name. For example, the generation AI can collect related market research data and evaluate the meaning and image of the name. This allows the analysis unit to evaluate the suitability of the name from multiple perspectives and provide the user with the most suitable name candidates.

[0031] The checking unit checks the possibility of registration in each country and the risk of trademark infringement based on the name candidates generated by the analyzing unit. For example, the checking unit accesses the trademark database and the registration database of each country through the related API to check whether the input name has already been registered and whether there is a risk of trademark infringement. Specifically, the checking unit accesses the trademark database of each country to check whether the name is already registered. The checking unit can also access the registration database of each country to check whether the name is registrable. For example, if a specific name is already registered as a trademark, the checking unit checks whether it is legally problematic to use the name. The checking unit also checks whether the name is registrable in a specific country or region and provides the result to the user. Furthermore, the checking unit analyzes how similar the name is to an existing trademark in order to evaluate the risk of trademark infringement. This allows the checking unit to evaluate the risk of the name being confused with an existing trademark and provide appropriate advice to the user. For example, if the name is similar in sound or spelling to an existing trademark, the checking unit can evaluate the risk and suggest alternatives to the user. This enables the checking department to quickly and accurately assess the registrability of a name and the risk of trademark infringement, and provide users with information to minimize legal risks.

[0032] The sorting unit sorts the application information for registration or trademark registration based on the results obtained by the checking unit. The sorting unit, for example, lists the necessary documents and procedural steps and provides them to the user. Specifically, the sorting unit lists the documents necessary for the application for registration or trademark registration and provides them to the user. The sorting unit can also list the procedural steps and provide them to the user. For example, the sorting unit lists the documents and procedures necessary for registration or trademark registration in a specific country or region in detail and provides them to the user in an easy-to-understand manner. This allows the user to prepare all the necessary documents without missing anything and to proceed smoothly with the procedure. Furthermore, the sorting unit can provide the format and entry examples of documents submitted by the user to assist the user in accurately creating the documents. For example, the sorting unit provides templates for application documents for registration or trademark registration so that the user can accurately enter the necessary information. The sorting unit can also track the progress of the procedure and notify the user in a timely manner. This allows the user to grasp the progress of the procedure and take the necessary measures quickly. Furthermore, the sorting unit can check and correct the documents submitted by the user to support the smooth progress of the application. This allows the sorting unit to assist users in efficiently completing registration and trademark procedures, thereby increasing the success rate of the procedures.

[0033] The system includes a collection unit that collects market research data. The collection unit collects the market research data. The market research data includes, but is not limited to, consumer surveys, sales data, and competitive analysis. For example, the collection unit conducts a consumer survey to collect consumer opinions. The collection unit can also collect sales data and analyze product sales. Furthermore, the collection unit can perform competitive analysis to grasp the trends of competitors. For example, the collection unit conducts a consumer survey to collect consumer opinions. The consumer survey is conducted, for example, by an online survey or a telephone interview. The sales data is collected, for example, from POS data or online sales data. The competitive analysis is conducted, for example, by collecting and analyzing information from competitors' websites or market reports. In this way, the collection unit can more accurately evaluate the suitability of the name by collecting the market research data.

[0034] The system includes an access unit that accesses a trademark database and a registration database of each country. The access unit accesses the trademark database and the registration database of each country. The trademark database includes, for example, but is not limited to, a database of a trademark registration agency of each country. The registration database includes, for example, but is not limited to, a database of a registration agency of each country. The access unit, for example, accesses the database of the trademark registration agency of each country to check whether the name is already registered. The access unit can also access the database of the registration agency of each country to check whether the name is registrable. For example, the access unit accesses the database of the trademark registration agency of each country to check whether the name is already registered. The trademark database includes, for example, information such as a trademark registration number, a registration date, and a registrant's name. The registration database includes, for example, information such as a registration number, a registration date, and a registrant's name. This allows the access unit to accurately evaluate the registrability and the trademark infringement risk by accessing the database of each country.

[0035] The system includes a listing unit that lists necessary documents and procedural steps. The listing unit lists necessary documents and procedural steps. The necessary documents include, but are not limited to, for example, an application form, a certificate, and a contract. The procedural steps include, but are not limited to, for example, an application procedure, an examination procedure, and a registration procedure. The listing unit lists, for example, documents required for an application for registration or trademark registration, and provides them to the user. The listing unit can also list procedural steps and provide them to the user. For example, the listing unit lists, for example, documents required for an application for registration or trademark registration, and provides them to the user. The necessary documents include, for example, an application form, a certificate, and a contract. The procedural steps include, for example, an application procedure, an examination procedure, and a registration procedure. As a result, the listing unit can improve the efficiency of the procedure by listing the necessary documents and procedural steps.

[0036] The analysis unit can optimize the evaluation criteria by referring to past success cases and failure cases when analyzing a name. For example, the analysis unit optimizes the evaluation criteria by referring to past success cases and failure cases when analyzing a name. Past success cases and failure cases include, but are not limited to, a case database and a case study. The analysis unit optimizes the evaluation criteria by analyzing, for example, past success cases and failure cases using a generation AI. For example, the generation AI analyzes the characteristics of names that have been successful in the past and highly evaluates names with similar characteristics. The generation AI can also analyze the characteristics of names that have failed in the past and lowly evaluate names with similar characteristics. Furthermore, the generation AI can dynamically adjust the evaluation criteria by referring to a database of success cases and failure cases. For example, the generation AI optimizes the evaluation criteria by referring to a database of past success cases and failure cases. This allows the analysis unit to optimize the name evaluation criteria by referring to past cases.

[0037] The analysis unit can apply an evaluation algorithm specialized for a specific industry or market when analyzing a name. The analysis unit, for example, applies an evaluation algorithm specialized for a specific industry or market when analyzing a name. Specific industries and markets include, but are not limited to, the IT industry, fashion market, and food and beverage industry. The analysis unit, for example, uses a generation AI to apply an evaluation algorithm specialized for a specific industry or market. For example, the generation AI applies an algorithm that emphasizes a technical image when analyzing a name for the IT industry. The generation AI can also apply an algorithm that emphasizes trends and styles when analyzing a name for the fashion industry. Furthermore, the generation AI can also apply an algorithm that emphasizes deliciousness and health when analyzing a name for the food and beverage industry. For example, the generation AI applies an algorithm that emphasizes a technical image when analyzing a name for the IT industry. The technical image includes, for example, innovation, advanced technology, and reliability. When analyzing a name for the fashion industry, the generation AI applies an algorithm that emphasizes trends and styles. Trends and styles include, for example, the latest fashion trends, the beauty of the design, and brand image. When analyzing names for the food and beverage industry, the generative AI applies algorithms that emphasize deliciousness and healthiness, which include, for example, good taste, high nutritional value, and health consciousness. This allows the analysis unit to provide more appropriate name candidates by applying evaluation algorithms specialized for specific industries and markets.

[0038] The analysis unit can evaluate the region-specific meaning and image based on the geographical location information of the user when analyzing the name. For example, the analysis unit evaluates the region-specific meaning and image based on the geographical location information of the user when analyzing the name. The geographical location information includes, for example, GPS data, culture and customs specific to the region, and the like, but is not limited to such examples. The analysis unit evaluates the region-specific meaning and image by taking into consideration the geographical location information of the user, for example, using the generation AI. For example, when the user is in Japan, the generation AI evaluates the name by placing emphasis on the Japanese meaning and image. In addition, when the user is in the United States, the generation AI can also evaluate the name by placing emphasis on the English meaning and image. Furthermore, when the user is in France, the generation AI can also evaluate the name by placing emphasis on the French meaning and image. For example, when the user is in Japan, the generation AI evaluates the name by placing emphasis on the Japanese meaning and image. The Japanese meaning and image include, for example, cultural suitability, ease of pronunciation, appropriateness of meaning, and cultural suitability. The English meaning and image include, for example, ease of pronunciation, appropriateness of meaning, and cultural suitability. The meaning and image of French language include, for example, ease of pronunciation, appropriateness of meaning, cultural suitability, etc. This allows the analysis unit to evaluate the region-specific meaning and image by taking into account the user's geographical location information.

[0039] The analysis unit can analyze the user's social media activity when analyzing the name and suggest related name candidates. The analysis unit, for example, analyzes the user's social media activity when analyzing the name and suggests related name candidates. Social media activity includes, for example, the content of posts, the number of followers, and the engagement rate, but is not limited to such examples. The analysis unit, for example, uses a generation AI to analyze the user's social media activity and suggests related name candidates. For example, the generation AI analyzes hashtags frequently used by the user and suggests related name candidates. The generation AI can also analyze the content of accounts followed by the user and suggest related name candidates. Furthermore, the generation AI can analyze the content of posts by the user and suggest related name candidates. For example, the generation AI analyzes hashtags frequently used by the user and suggests related name candidates. Hashtags include, for example, keywords related to a specific theme or topic. The content of the accounts followed includes, for example, fields and topics in which the user is interested. The post content includes, for example, information and opinions that the user regularly transmits. This allows the analysis unit to suggest related name candidates by analyzing the user's social media activity.

[0040] The check unit can optimize the evaluation criteria by referring to past precedents and legal data when checking the possibility of registration or the risk of trademark infringement. For example, the check unit optimizes the evaluation criteria by referring to past precedents and legal data when checking the possibility of registration or the risk of trademark infringement. Past precedents and legal data include, but are not limited to, a precedent database and legal documents. The check unit, for example, uses a generation AI to analyze past precedents and legal data and optimize the evaluation criteria. For example, the generation AI refers to a past precedent database and adjusts the evaluation criteria based on similar cases. The generation AI can also optimize the evaluation criteria based on the latest legal information by referring to a legal database. Furthermore, the generation AI can analyze past trademark infringement cases and improve the accuracy of risk assessment. For example, the generation AI refers to a past precedent database and adjusts the evaluation criteria based on similar cases. The precedent database includes, for example, detailed information on precedents and the contents of judgments. The legal database includes, for example, legal provisions and interpretations, legal guidelines, and the like. Past trademark infringement cases include, for example, information on the similarity and scope of use of trademarks. This allows the checking department to optimize evaluation criteria by referring to past precedents and legal data.

[0041] The check unit can apply an evaluation algorithm specialized for a specific industry or market when checking the possibility of registration or the risk of trademark infringement. For example, the check unit applies an evaluation algorithm specialized for a specific industry or market when checking the possibility of registration or the risk of trademark infringement. The specific industry or market includes, but is not limited to, the IT industry, the fashion market, the food and beverage industry, and the like. The check unit applies an evaluation algorithm specialized for a specific industry or market using, for example, a generation AI. For example, the generation AI applies an algorithm that emphasizes technical elements when checking names for the IT industry. In addition, the generation AI can also apply an algorithm that emphasizes trends and styles when checking names for the fashion industry. Furthermore, the generation AI can also apply an algorithm that emphasizes deliciousness and health when checking names for the food and beverage industry. For example, the generation AI applies an algorithm that emphasizes technical elements when checking names for the IT industry. The technical elements include, for example, innovation, advancedness, and reliability. When checking names for the fashion industry, the generation AI applies an algorithm that emphasizes trends and styles. Trends and styles include, for example, the latest fashion trends, the beauty of design, and brand image. When checking names for the food and beverage industry, the Generative AI applies algorithms that emphasize deliciousness and healthiness. Deliciousness and healthiness include, for example, good taste, high nutritional value, and health consciousness. This allows the Check Department to make more appropriate risk assessments by applying evaluation algorithms specialized for specific industries and markets.

[0042] The check unit can evaluate region-specific risks by considering the geographic location information of the user when checking the possibility of registration or the risk of trademark infringement. For example, the check unit evaluates region-specific risks by considering the geographic location information of the user when checking the possibility of registration or the risk of trademark infringement. The geographic location information includes, for example, GPS data, culture and customs specific to the region, but is not limited to such examples. The check unit evaluates region-specific risks by considering the geographic location information of the user using, for example, a generation AI. For example, when the user is in Japan, the generation AI evaluates risks by preferentially referring to a Japanese trademark database. In addition, when the user is in the United States, the generation AI can also evaluate risks by preferentially referring to a US trademark database. Furthermore, when the user is in France, the generation AI can also evaluate risks by preferentially referring to a French trademark database. For example, when the user is in Japan, the generation AI evaluates risks by preferentially referring to a Japanese trademark database. The Japanese trademark database includes, for example, information such as a trademark registration number, a registration date, and a registrant's name. The American trademark database includes, for example, information such as a trademark registration number, a registration date, and a registrant's name. The French trademark database contains, for example, information such as the trademark registration number, registration date and the name of the registrant, which allows the checking department to assess local-specific risks by taking into account the user's geographic location.

[0043] The check unit can analyze the social media activity of the user and suggest related risks when checking the possibility of registration or the risk of trademark infringement. For example, when checking the possibility of registration or the risk of trademark infringement, the check unit analyzes the social media activity of the user and suggests related risks. Social media activities include, for example, but are not limited to, the content of posts, the number of followers, and the engagement rate. The check unit, for example, uses a generation AI to analyze the social media activity of the user and suggests related risks. For example, the generation AI analyzes hashtags frequently used by the user and suggests related risks. The generation AI can also analyze the content of accounts followed by the user and suggest related risks. Furthermore, the generation AI can analyze the content of posts by the user and suggest related risks. For example, the generation AI analyzes hashtags frequently used by the user and suggests related risks. Hashtags include, for example, keywords related to a specific theme or topic. The content of the accounts followed includes, for example, fields and topics in which the user is interested. The post content includes, for example, information and opinions that the user regularly transmits. This allows the check unit to suggest related risks by analyzing the social media activity of the user.

[0044] The sorting unit can select the optimal sorting method by referring to past application data when sorting application information. For example, the sorting unit selects the optimal sorting method by referring to past application data when sorting application information. Past application data includes, for example, an application history database, a case study, and the like, but is not limited to such examples. The sorting unit, for example, uses a generation AI to analyze past application data and select the optimal sorting method. For example, the generation AI refers to past successful application data and proposes a similar sorting method. In addition, the generation AI can also refer to past unsuccessful application data and propose a sorting method that avoids similar mistakes. Furthermore, the generation AI can also dynamically select the optimal sorting method by referring to a past application database. For example, the generation AI refers to past successful application data and proposes a similar sorting method. The application history database includes, for example, detailed information and results of applications. The case study includes, for example, analysis and evaluation based on specific cases. This allows the sorting unit to select the optimal sorting method by referring to past application data.

[0045] The sorting unit can apply a sorting algorithm specialized for a specific industry or market when sorting the application information. For example, the sorting unit applies a sorting algorithm specialized for a specific industry or market when sorting the application information. The specific industry or market includes, but is not limited to, the IT industry, fashion market, and food and beverage industry. The sorting unit applies a sorting algorithm specialized for a specific industry or market using, for example, a generation AI. For example, the generation AI applies an algorithm that emphasizes technical elements when sorting application information for the IT industry. In addition, the generation AI can also apply an algorithm that emphasizes trends and styles when sorting application information for the fashion industry. In addition, the generation AI can also apply an algorithm that emphasizes deliciousness and health when sorting application information for the food and beverage industry. For example, the generation AI applies an algorithm that emphasizes technical elements when sorting application information for the IT industry. The technical elements include, for example, innovation, advancedness, and reliability. When sorting application information for the fashion industry, the generation AI applies an algorithm that emphasizes trends and styles. Trends and styles include, for example, the latest fashion trends, beauty of design, and brand image. When sorting application information for the food and beverage industry, the generation AI applies algorithms that emphasize deliciousness and healthiness. Deliciousness and healthiness include, for example, good taste, high nutritional value, and health consciousness. This enables the sorting department to sort application information more appropriately by applying sorting algorithms specialized for specific industries and markets.

[0046] The sorting unit can sort the information specific to the region in consideration of the geographical location information of the user when sorting the application information. For example, the sorting unit sorts the information specific to the region in consideration of the geographical location information of the user when sorting the application information. The geographical location information includes, for example, GPS data, culture and customs specific to the region, and the like, but is not limited to such examples. The sorting unit sorts the information specific to the region in consideration of the geographical location information of the user, for example, using a generation AI. For example, the generation AI sorts the application information based on the legal requirements of Japan when the user is in Japan. Furthermore, the generation AI can also sort the application information based on the legal requirements of the United States when the user is in the United States. Furthermore, the generation AI can also sort the application information based on the legal requirements of France when the user is in France. For example, the generation AI sorts the application information based on the legal requirements of Japan when the user is in Japan. The legal requirements of Japan include, for example, trademark registration procedures and necessary documents. The legal requirements of the United States include, for example, trademark registration procedures and necessary documents. The legal requirements of France include, for example, trademark registration procedures and necessary documents. This allows the sorting unit to sort the information specific to the region by considering the geographical location information of the user.

[0047] The organizing unit can analyze the social media activity of the user and organize the related information when organizing the application information. For example, the organizing unit analyzes the social media activity of the user and organizes the related information when organizing the application information. The social media activity includes, for example, the content of the post, the number of followers, and the engagement rate, but is not limited to such examples. The organizing unit, for example, uses a generation AI to analyze the social media activity of the user and organizes the related information. For example, the generation AI analyzes hashtags frequently used by the user and organizes the related application information. The generation AI can also analyze the content of the accounts followed by the user and organize the related application information. Furthermore, the generation AI can analyze the content of the user's post and organize the related application information. For example, the generation AI analyzes hashtags frequently used by the user and organizes the related application information. The hashtag includes, for example, keywords related to a specific theme or topic. The content of the followed account includes, for example, fields and topics in which the user is interested. The post content includes, for example, information and opinions that the user transmits on a daily basis. This allows the organizing unit to organize the related information by analyzing the social media activity of the user.

[0048] The collection unit can select the optimal collection method by referring to past data when collecting market research data. For example, the collection unit selects the optimal collection method by referring to past data when collecting market research data. Past data includes, but is not limited to, databases, case studies, and the like. The collection unit, for example, uses the generation AI to analyze past data and select the optimal collection method. For example, the generation AI can refer to successful market research data in the past and propose a similar collection method. The generation AI can also refer to unsuccessful market research data in the past and propose a collection method that avoids similar mistakes. Furthermore, the generation AI can dynamically select the optimal collection method by referring to a past market research database. For example, the generation AI can refer to successful market research data in the past and propose a similar collection method. The database includes, for example, past market research data and analysis results. The case study includes, for example, analysis and evaluation based on a specific case. This allows the collection unit to select the optimal collection method by referring to past data.

[0049] The collection unit can apply a collection algorithm specialized for a specific industry or market when collecting market research data. The collection unit, for example, applies a collection algorithm specialized for a specific industry or market when collecting market research data. The specific industry or market includes, for example, the IT industry, the fashion market, the food and beverage industry, and the like, but is not limited to such examples. The collection unit, for example, uses a generation AI to apply a collection algorithm specialized for a specific industry or market. For example, the generation AI applies an algorithm that emphasizes technical elements when collecting market research data for the IT industry. In addition, the generation AI can also apply an algorithm that emphasizes trends and styles when collecting market research data for the fashion industry. In addition, the generation AI can also apply an algorithm that emphasizes deliciousness and health when collecting market research data for the food and beverage industry. For example, the generation AI applies an algorithm that emphasizes technical elements when collecting market research data for the IT industry. The technical elements include, for example, innovation, advancedness, and reliability. In collecting market research data for the fashion industry, the generation AI applies an algorithm that emphasizes trends and styles. Trends and styles include, for example, the latest fashion trends, the beauty of design, and brand image. When collecting market research data for the food and beverage industry, the generative AI applies algorithms that emphasize deliciousness and healthiness. Deliciousness and healthiness include, for example, good taste, high nutritional value, and health consciousness. This enables the collection unit to collect more appropriate data by applying collection algorithms specialized for specific industries and markets.

[0050] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting market research data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting market research data. The geographical location information includes, for example, GPS data, culture and customs specific to a region, and the like, but is not limited to such examples. The collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user, for example, using the generation AI. For example, when the user is in Japan, the generation AI preferentially collects Japanese market data. In addition, when the user is in the United States, the generation AI can also preferentially collect American market data. Furthermore, when the user is in France, the generation AI can also preferentially collect French market data. For example, when the user is in Japan, the generation AI preferentially collects Japanese market data. The Japanese market data includes, for example, information on consumer preferences and purchasing behavior. The American market data includes, for example, information on consumer preferences and purchasing behavior. The French market data includes, for example, information on consumer preferences and purchasing behavior. This allows the collection unit to prioritize collection of highly relevant data by taking into account the geographical location information of the user.

[0051] The collection unit can analyze the social media activity of the user and collect related data preferentially when collecting market research data. For example, the collection unit analyzes the social media activity of the user and collects related data preferentially when collecting market research data. Social media activities include, for example, post content, number of followers, engagement rate, etc., but are not limited to such examples. The collection unit, for example, uses a generation AI to analyze the social media activity of the user and collects related data preferentially. For example, the generation AI analyzes hashtags frequently used by the user and collects related market data preferentially. The generation AI can also analyze the content of accounts followed by the user and collect related market data preferentially. Furthermore, the generation AI can analyze the content of posts by the user and collect related market data preferentially. For example, the generation AI analyzes hashtags frequently used by the user and collects related market data preferentially. Hashtags include, for example, keywords related to specific themes and topics. The content of the accounts followed includes, for example, fields and topics in which the user is interested. The post content includes, for example, information and opinions that the user transmits on a daily basis. This allows the collection unit to prioritize collection of relevant data by analyzing the user's social media activity.

[0052] The access unit can select the optimal access method by referring to past access history when accessing the database. For example, the access unit selects the optimal access method by referring to past access history when accessing the database. The past access history includes, for example, access logs, case studies, and the like, but is not limited to such examples. The access unit analyzes the past access history using, for example, a generation AI to select the optimal access method. For example, the generation AI can refer to past successful access history and propose a similar access method. The generation AI can also refer to past unsuccessful access history and propose an access method that avoids similar mistakes. Furthermore, the generation AI can also dynamically select the optimal access method by referring to a past access history database. For example, the generation AI can refer to past successful access history and propose a similar access method. The access log includes, for example, detailed information and results of past accesses. The case study includes, for example, analysis and evaluation based on a specific case. This allows the access unit to select the optimal access method by referring to the past access history.

[0053] The access unit can apply an access algorithm specialized for a specific industry or market when accessing the database. The access unit, for example, applies an access algorithm specialized for a specific industry or market when accessing the database. Specific industries and markets include, but are not limited to, the IT industry, fashion market, and food and beverage industry. The access unit, for example, uses a generation AI to apply an access algorithm specialized for a specific industry or market. For example, the generation AI applies an algorithm that emphasizes technical elements when accessing a database for the IT industry. In addition, the generation AI can also apply an algorithm that emphasizes trends and styles when accessing a database for the fashion industry. Furthermore, the generation AI can also apply an algorithm that emphasizes deliciousness and health when accessing a database for the food and beverage industry. For example, the generation AI applies an algorithm that emphasizes technical elements when accessing a database for the IT industry. Technical elements include, for example, innovation, advanced technology, and reliability. When accessing a database for the fashion industry, the generation AI applies an algorithm that emphasizes trends and styles. Trends and styles include, for example, the latest fashion trends, design beauty, and brand image. When accessing a database for the food and beverage industry, the Generative AI applies an algorithm that emphasizes deliciousness and healthiness. Deliciousness and healthiness include, for example, good taste, high nutritional value, and health consciousness. This allows the Access Department to apply access algorithms specialized for specific industries and markets, enabling more appropriate database access.

[0054] The access unit can preferentially access a highly relevant database in consideration of the geographical location information of the user when accessing the database. For example, the access unit preferentially accesses a highly relevant database in consideration of the geographical location information of the user when accessing the database. The geographical location information includes, for example, GPS data, culture and customs specific to a region, and the like, but is not limited to such examples. The access unit preferentially accesses a highly relevant database in consideration of the geographical location information of the user, for example, using a generation AI. For example, the generation AI preferentially accesses a Japanese database when the user is in Japan. Furthermore, the generation AI can also preferentially access an American database when the user is in the United States. Furthermore, the generation AI can also preferentially access a French database when the user is in France. For example, the generation AI preferentially accesses a Japanese database when the user is in Japan. The Japanese database includes, for example, trademark registration information and registration information. The American database includes, for example, trademark registration information and registration information. The French database includes, for example, trademark registration information and registration information. As a result, the access unit can preferentially access a highly relevant database by considering the geographical location information of the user.

[0055] The access unit can analyze the social media activity of the user and preferentially access the related database when accessing the database. For example, the access unit can analyze the social media activity of the user and preferentially access the related database when accessing the database. The social media activity includes, for example, the content of the post, the number of followers, the engagement rate, etc., but is not limited to such examples. The access unit can analyze the social media activity of the user using, for example, a generation AI and preferentially access the related database. For example, the generation AI can analyze hashtags frequently used by the user and preferentially access the related database. The generation AI can also analyze the content of the accounts the user follows and preferentially access the related database. Furthermore, the generation AI can analyze the content of the post of the user and preferentially access the related database. For example, the generation AI can analyze hashtags frequently used by the user and preferentially access the related database. The hashtag includes, for example, keywords related to a specific theme or topic. The content of the followed account includes, for example, fields and topics in which the user is interested. The post content includes, for example, information and opinions that the user transmits on a daily basis. This allows the access unit to preferentially access the related database by analyzing the social media activity of the user.

[0056] The listing unit can select the optimal listing method by referring to past listing data when listing. For example, the listing unit selects the optimal listing method by referring to past listing data when listing. Past listing data includes, for example, databases, case studies, and the like, but is not limited to such examples. The listing unit, for example, uses a generation AI to analyze past listing data and select the optimal listing method. For example, the generation AI can refer to past successful listing data and propose a similar listing method. In addition, the generation AI can also refer to past unsuccessful listing data and propose a listing method that avoids similar mistakes. Furthermore, the generation AI can refer to a past listing database and dynamically select the optimal listing method. For example, the generation AI can refer to past successful listing data and propose a similar listing method. The database includes, for example, past listing data and analysis results. The case study includes, for example, analysis and evaluation based on a specific case. This allows the listing unit to select the optimal listing method by referring to past listing data.

[0057] The listing unit can apply a listing algorithm specialized for a specific industry or market when listing. For example, the listing unit applies a listing algorithm specialized for a specific industry or market when listing. The specific industry or market includes, but is not limited to, the IT industry, fashion market, and food and beverage industry. For example, the listing unit applies a listing algorithm specialized for a specific industry or market using a generation AI. For example, the generation AI applies an algorithm that emphasizes technical elements when listing for the IT industry. In addition, the generation AI can also apply an algorithm that emphasizes trends and styles when listing for the fashion industry. Furthermore, the generation AI can also apply an algorithm that emphasizes deliciousness and health when listing for the food and beverage industry. For example, the generation AI applies an algorithm that emphasizes technical elements when listing for the IT industry. The technical elements include, for example, innovation, advancedness, and reliability. When listing for the fashion industry, the generation AI applies an algorithm that emphasizes trends and styles. Trends and styles include, for example, the latest fashion trends, beautiful designs, and brand images. When creating a list for the food and beverage industry, the generation AI applies an algorithm that emphasizes deliciousness and healthiness. Deliciousness and healthiness include, for example, good taste, high nutritional value, and health consciousness. This allows the list creation unit to create a more appropriate list by applying a list creation algorithm specialized for a specific industry or market.

[0058] The listing unit can prioritize listing highly relevant items in consideration of the geographical location information of the user when listing. For example, the listing unit prioritizes listing highly relevant items in consideration of the geographical location information of the user when listing. The geographical location information includes, for example, GPS data, culture and customs specific to a region, and the like, but is not limited to such examples. The listing unit prioritizes listing highly relevant items in consideration of the geographical location information of the user, for example, using a generation AI. For example, when the user is in Japan, the generation AI lists based on Japanese legal requirements. In addition, when the user is in the United States, the generation AI can also list based on American legal requirements. In addition, when the user is in France, the generation AI can also list based on French legal requirements. For example, when the user is in Japan, the generation AI lists based on Japanese legal requirements. The legal requirements of the United States include, for example, trademark registration procedures and necessary documents. The legal requirements of the United States include, for example, trademark registration procedures and necessary documents. The legal requirements of France include, for example, trademark registration procedures and necessary documents. This allows the listing unit to prioritize listing of highly relevant items by taking into account the geographical location information of the user.

[0059] The listing unit can analyze the social media activity of the user at the time of listing and prioritize listing related items. For example, the listing unit can analyze the social media activity of the user at the time of listing and prioritize listing related items. Social media activities include, for example, post content, number of followers, engagement rate, etc., but are not limited to such examples. The listing unit can analyze the social media activity of the user using, for example, a generation AI and prioritize listing related items. For example, the generation AI can analyze hashtags frequently used by the user and prioritize listing related items. In addition, the generation AI can also analyze the content of accounts followed by the user and prioritize listing related items. In addition, the generation AI can analyze the content of posts by the user and prioritize listing related items. For example, the generation AI can analyze hashtags frequently used by the user and prioritize listing related items. Hashtags include, for example, keywords related to specific themes or topics. The content of the accounts followed includes, for example, fields and topics in which the user is interested. The post content includes, for example, information and opinions that the user transmits on a daily basis. This allows the listing unit to prioritize listing of related items by analyzing the user's social media activity.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The analysis unit may also evaluate the phonological characteristics of the name when analyzing the suitability of the name. For example, the analysis unit may evaluate how the sound and rhythm of the name are perceived in a particular culture or language. This may determine whether the name is easy to pronounce and remember. The analysis unit may also use speech recognition technology to evaluate the phonological characteristics of the name. For example, the pronunciation of the name may be analyzed using speech recognition technology to evaluate the difficulty of pronunciation. Furthermore, the analysis unit may refer to a phonological database to evaluate the phonological characteristics of the name. For example, the phonological database includes phonological patterns and pronunciation rules for each language. This allows the analysis unit to provide more appropriate name candidates by evaluating the phonological characteristics of the name.

[0062] The collection unit may also collect data in real time when collecting market research data. For example, social media trends and news articles may be collected in real time and used as data for evaluating the suitability of names. The collection unit may also provide the data collected in real time to the analysis unit and dynamically adjust the evaluation criteria for names. Furthermore, the collection unit may provide the data collected in real time to a user and suggest name candidates that reflect the latest market trends. For example, the collection unit may collect social media trends in real time and suggest name candidates. In this way, the collection unit may provide more appropriate name candidates by collecting data in real time.

[0063] The access department can also take into account the update frequency of the database when accessing the trademark database or the registry database of each country. For example, if the database is frequently updated, the access department increases the access frequency to obtain the latest information. The access department can also dynamically adjust the access schedule based on the update frequency of the database. Furthermore, the access department can provide the latest information to the user based on the update frequency of the database. For example, if the database is updated daily, the access department accesses the database daily to obtain the latest information. In this way, the access department can provide more accurate information by taking into account the update frequency of the database.

[0064] The listing unit can also refer to the user's past procedure history when listing the necessary documents and procedure steps. For example, the listing unit can refer to the history of procedures that the user has performed in the past and automatically list them when a similar procedure is required. The listing unit can also optimize the procedure steps based on the user's past procedure history. Furthermore, the listing unit can also automatically generate the necessary documents by referring to the user's past procedure history. For example, the listing unit can refer to the history of procedures that the user has performed in the past and automatically generate the necessary documents. In this way, the listing unit can improve the efficiency of procedures by referring to the user's past procedure history.

[0065] When analyzing a name, the analysis unit can optimize the evaluation criteria by referring to past success and failure cases. For example, the generation AI can analyze the characteristics of names that have been successful in the past and highly evaluate names with similar characteristics. The generation AI can also analyze the characteristics of names that have failed in the past and highly evaluate names with similar characteristics. Furthermore, the generation AI can dynamically adjust the evaluation criteria by referring to a database of success and failure cases. This allows the analysis unit to optimize the name evaluation criteria by referring to past cases.

[0066] When analyzing names, the analysis unit can apply evaluation algorithms specialized for specific industries or markets. For example, when analyzing names for the IT industry, the generation AI can apply an algorithm that emphasizes a technical image. When analyzing names for the fashion industry, it can also apply an algorithm that emphasizes trends and style. Furthermore, when analyzing names for the food and beverage industry, it can apply an algorithm that emphasizes deliciousness and health. In this way, the analysis unit can provide more appropriate name candidates by applying evaluation algorithms specialized for specific industries and markets.

[0067] When analyzing a name, the analysis unit can evaluate region-specific meanings and images based on the user's geographic location information. For example, if the user is in Japan, the generation AI can evaluate the name by placing emphasis on Japanese meanings and images. Also, if the user is in the United States, the generation AI can evaluate the name by placing emphasis on English meanings and images. Furthermore, if the user is in France, the generation AI can evaluate the name by placing emphasis on French meanings and images. This allows the analysis unit to evaluate region-specific meanings and images by taking into account the user's geographic location information.

[0068] When analyzing a name, the analysis unit can analyze the user's social media activity and suggest related name candidates. For example, the generation AI can analyze hashtags frequently used by the user and suggest related name candidates. It can also analyze the content of accounts the user follows and suggest related name candidates. It can also analyze the content of the user's posts and suggest related name candidates. This allows the analysis unit to suggest related name candidates by analyzing the user's social media activity.

[0069] The process flow of the first embodiment will be briefly described below.

[0070] Step 1: The analysis unit analyzes the suitability of the name. The suitability of a name includes cultural suitability, ease of pronunciation, appropriateness of meaning, etc. The analysis unit uses a generative AI to analyze the meaning and image of the name, and further evaluates it by referring to market research data and databases for each country. Step 2: The Checking Department checks the registrability and risk of trademark infringement in each country based on the name candidates generated by the Analysis Department. The Checking Department accesses the trademark database and registration database of each country through the relevant API to check whether the input name has already been registered or there is a risk of trademark infringement. Step 3: The sorting unit sorts the application information for registration and trademark registration based on the results obtained by the checking unit. The sorting unit creates a list of necessary documents and procedural steps and provides it to the user.

[0071] (Example 2) The system according to the embodiment of the present invention is a system that improves the efficiency of the consideration and implementation of company and service names when starting a business, developing a new service, or considering a new business. This system is composed of the following steps by linking the generation AI and related APIs (market research and databases for each country). First, the user inputs the company name or service name under consideration. Next, the generation AI analyzes the suitability of the name (meaning, image, etc.) and lists appropriate candidates. At this time, the generation AI refers to market research data and databases for each country to evaluate the meaning and image of the name. Next, the generation AI checks the possibility of registration in each country and the risk of trademark infringement. Specifically, it accesses the trademark database and registration database of each country through the related API to check whether the input name has already been registered and whether there is a risk of trademark infringement. This result is also listed and provided to the user. In addition, the generation AI organizes application information for registration and trademark registration and provides it to the user. This allows the user to proceed with the procedure efficiently. For example, the necessary documents and procedural steps can be automatically listed and provided to the user to make the procedure more efficient. This system will contribute to the efficiency of planning and procedures by making the process of considering and implementing company and service names more efficient when starting a business, developing a new service, or considering a new business more efficient.

[0072] The system according to the embodiment includes an analysis unit, a check unit, and a sorting unit. The analysis unit analyzes the suitability of a name. The suitability of a name includes, for example, cultural suitability, ease of pronunciation, appropriateness of meaning, and the like, but is not limited to such examples. The analysis unit, for example, uses a generation AI to analyze the meaning and image of the name. The generation AI evaluates the meaning and image of the name using, for example, a text generation AI (for example, LLM). The analysis unit can also use the generation AI to refer to market research data and databases of each country to evaluate the suitability of the name. For example, the generation AI collects related market research data and evaluates the meaning and image of the name. The check unit checks the possibility of registration in each country and the risk of trademark infringement based on the name candidates generated by the analysis unit. For example, the check unit accesses the trademark databases and registration databases of each country through related APIs to check whether the input name has already been registered or there is a risk of trademark infringement. For example, the check unit accesses the trademark databases of each country to check whether the name has already been registered. The checking unit can also access the registration databases of each country to check whether the name can be registered. The sorting unit sorts the application information for registration or trademark registration based on the results obtained by the checking unit. The sorting unit, for example, lists the necessary documents and procedural steps and provides them to the user. For example, the sorting unit lists the documents necessary for registration or trademark registration applications and provides them to the user. The sorting unit can also list the procedural steps and provide them to the user. As a result, the system according to the embodiment can check the suitability of the name, the possibility of registration, and the risk of trademark infringement all at once and sort the application information, thereby enabling efficient name consideration and implementation.

[0073] The analysis unit analyzes the suitability of the name. The suitability of the name includes, for example, cultural suitability, ease of pronunciation, appropriateness of meaning, etc., but is not limited to such examples. The analysis unit, for example, uses a generation AI to analyze the meaning and image of the name. The generation AI evaluates the meaning and image of the name using, for example, a text generation AI (for example, LLM). Specifically, the generation AI learns a large amount of text data and analyzes the cultural background and historical meaning of the name. For example, it can evaluate how a particular name is received in a particular culture or region, and the positive or negative image that the name has. In addition, the generation AI can also use voice recognition technology to evaluate the ease of pronunciation of the name. This analyzes how the name is pronounced in different languages ​​and accents, and evaluates the ease of pronunciation. In addition, the generation AI analyzes the meaning of the name and related words to evaluate the appropriateness of the name's meaning, and determines whether the name is appropriate in a particular context. For example, it can evaluate how the name is received in a particular industry or market. In addition, the analysis unit can use the generation AI to refer to market research data and databases for each country to evaluate the suitability of the name. For example, the generation AI can collect related market research data and evaluate the meaning and image of the name. This allows the analysis unit to evaluate the suitability of the name from multiple perspectives and provide the user with the most suitable name candidates.

[0074] The checking unit checks the possibility of registration in each country and the risk of trademark infringement based on the name candidates generated by the analyzing unit. For example, the checking unit accesses the trademark database and the registration database of each country through the related API to check whether the input name has already been registered and whether there is a risk of trademark infringement. Specifically, the checking unit accesses the trademark database of each country to check whether the name is already registered. The checking unit can also access the registration database of each country to check whether the name is registrable. For example, if a specific name is already registered as a trademark, the checking unit checks whether it is legally problematic to use the name. The checking unit also checks whether the name is registrable in a specific country or region and provides the result to the user. Furthermore, the checking unit analyzes how similar the name is to an existing trademark in order to evaluate the risk of trademark infringement. This allows the checking unit to evaluate the risk of the name being confused with an existing trademark and provide appropriate advice to the user. For example, if the name is similar in sound or spelling to an existing trademark, the checking unit can evaluate the risk and suggest alternatives to the user. This enables the checking department to quickly and accurately assess the registrability of a name and the risk of trademark infringement, and provide users with information to minimize legal risks.

[0075] The sorting unit sorts the application information for registration or trademark registration based on the results obtained by the checking unit. The sorting unit, for example, lists the necessary documents and procedural steps and provides them to the user. Specifically, the sorting unit lists the documents necessary for the application for registration or trademark registration and provides them to the user. The sorting unit can also list the procedural steps and provide them to the user. For example, the sorting unit lists the documents and procedures necessary for registration or trademark registration in a specific country or region in detail and provides them to the user in an easy-to-understand manner. This allows the user to prepare all the necessary documents without missing anything and to proceed smoothly with the procedure. Furthermore, the sorting unit can provide the format and entry examples of documents submitted by the user to assist the user in accurately creating the documents. For example, the sorting unit provides templates for application documents for registration or trademark registration so that the user can accurately enter the necessary information. The sorting unit can also track the progress of the procedure and notify the user in a timely manner. This allows the user to grasp the progress of the procedure and take the necessary measures quickly. Furthermore, the sorting unit can check and correct the documents submitted by the user to support the smooth progress of the application. This allows the sorting unit to assist users in efficiently completing registration and trademark procedures, thereby increasing the success rate of the procedures.

[0076] The system includes a collection unit that collects market research data. The collection unit collects the market research data. The market research data includes, but is not limited to, consumer surveys, sales data, and competitive analysis. For example, the collection unit conducts a consumer survey to collect consumer opinions. The collection unit can also collect sales data and analyze product sales. Furthermore, the collection unit can perform competitive analysis to grasp the trends of competitors. For example, the collection unit conducts a consumer survey to collect consumer opinions. The consumer survey is conducted, for example, by an online survey or a telephone interview. The sales data is collected, for example, from POS data or online sales data. The competitive analysis is conducted, for example, by collecting and analyzing information from competitors' websites or market reports. In this way, the collection unit can more accurately evaluate the suitability of the name by collecting the market research data.

[0077] The system includes an access unit that accesses a trademark database and a registration database of each country. The access unit accesses the trademark database and the registration database of each country. The trademark database includes, for example, but is not limited to, a database of a trademark registration agency of each country. The registration database includes, for example, but is not limited to, a database of a registration agency of each country. The access unit, for example, accesses the database of the trademark registration agency of each country to check whether the name is already registered. The access unit can also access the database of the registration agency of each country to check whether the name is registrable. For example, the access unit accesses the database of the trademark registration agency of each country to check whether the name is already registered. The trademark database includes, for example, information such as a trademark registration number, a registration date, and a registrant's name. The registration database includes, for example, information such as a registration number, a registration date, and a registrant's name. This allows the access unit to accurately evaluate the registrability and the trademark infringement risk by accessing the database of each country.

[0078] The system includes a listing unit that lists necessary documents and procedural steps. The listing unit lists necessary documents and procedural steps. The necessary documents include, but are not limited to, for example, an application form, a certificate, and a contract. The procedural steps include, but are not limited to, for example, an application procedure, an examination procedure, and a registration procedure. The listing unit lists, for example, documents required for an application for registration or trademark registration, and provides them to the user. The listing unit can also list procedural steps and provide them to the user. For example, the listing unit lists, for example, documents required for an application for registration or trademark registration, and provides them to the user. The necessary documents include, for example, an application form, a certificate, and a contract. The procedural steps include, for example, an application procedure, an examination procedure, and a registration procedure. As a result, the listing unit can improve the efficiency of the procedure by listing the necessary documents and procedural steps.

[0079] The analysis unit can estimate the user's emotions and adjust the evaluation criteria for the name candidates based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the evaluation criteria for the name candidates based on the estimated user's emotions. The user's emotions include, for example, excitement, anxiety, relaxation, etc., but are not limited to such examples. The analysis unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the analysis unit can also estimate the user's emotions using a questionnaire result. For example, the analysis unit conducts a questionnaire for the user and evaluates the emotion. As a result, the analysis unit can provide more appropriate name candidates by adjusting the evaluation criteria for the name based on the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is excited, the analysis unit causes the generation AI to preferentially evaluate names that have positive connotations or images. In addition, when the user is feeling anxious, the analysis unit can cause the generation AI to preferentially evaluate names that give a sense of security. Furthermore, when the user is relaxed, the analysis unit can cause the generation AI to preferentially evaluate names that have a soft sound. This allows the analysis unit to provide more appropriate name candidates by adjusting the name evaluation criteria based on the user's emotions.

[0080] The analysis unit can optimize the evaluation criteria by referring to past success cases and failure cases when analyzing a name. For example, the analysis unit optimizes the evaluation criteria by referring to past success cases and failure cases when analyzing a name. Past success cases and failure cases include, but are not limited to, a case database and a case study. The analysis unit optimizes the evaluation criteria by analyzing, for example, past success cases and failure cases using a generation AI. For example, the generation AI analyzes the characteristics of names that have been successful in the past and highly evaluates names with similar characteristics. The generation AI can also analyze the characteristics of names that have failed in the past and lowly evaluate names with similar characteristics. Furthermore, the generation AI can dynamically adjust the evaluation criteria by referring to a database of success cases and failure cases. For example, the generation AI optimizes the evaluation criteria by referring to a database of past success cases and failure cases. This allows the analysis unit to optimize the name evaluation criteria by referring to past cases.

[0081] The analysis unit can apply an evaluation algorithm specialized for a specific industry or market when analyzing a name. The analysis unit, for example, applies an evaluation algorithm specialized for a specific industry or market when analyzing a name. Specific industries and markets include, but are not limited to, the IT industry, fashion market, and food and beverage industry. The analysis unit, for example, uses a generation AI to apply an evaluation algorithm specialized for a specific industry or market. For example, the generation AI applies an algorithm that emphasizes a technical image when analyzing a name for the IT industry. The generation AI can also apply an algorithm that emphasizes trends and styles when analyzing a name for the fashion industry. Furthermore, the generation AI can also apply an algorithm that emphasizes deliciousness and health when analyzing a name for the food and beverage industry. For example, the generation AI applies an algorithm that emphasizes a technical image when analyzing a name for the IT industry. The technical image includes, for example, innovation, advanced technology, and reliability. When analyzing a name for the fashion industry, the generation AI applies an algorithm that emphasizes trends and styles. Trends and styles include, for example, the latest fashion trends, the beauty of the design, and brand image. When analyzing names for the food and beverage industry, the generative AI applies algorithms that emphasize deliciousness and healthiness, which include, for example, good taste, high nutritional value, and health consciousness. This allows the analysis unit to provide more appropriate name candidates by applying evaluation algorithms specialized for specific industries and markets.

[0082] The analysis unit can estimate the user's emotion and determine the priority order of the name candidates based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and determines the priority order of the name candidates based on the estimated user's emotion. The user's emotion includes, for example, excitement, anxiety, relaxation, etc., but is not limited to such examples. The analysis unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the analysis unit can also estimate the user's emotion using a questionnaire result. For example, the analysis unit conducts a questionnaire for the user and evaluates the emotion. As a result, the analysis unit can provide more appropriate name candidates by determining the priority order of the name based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is excited, the analysis unit may cause the generation AI to preferentially place names with positive connotations or images at the top of the list. In addition, when the user is feeling anxious, the analysis unit may cause the generation AI to preferentially place names that give a sense of security at the top of the list. Furthermore, when the user is relaxed, the analysis unit may cause the generation AI to preferentially place names with soft sounds at the top of the list. In this way, the analysis unit can provide more appropriate name candidates by determining the priority order of names based on the user's emotions.

[0083] The analysis unit can evaluate the region-specific meaning and image based on the geographical location information of the user when analyzing the name. For example, the analysis unit evaluates the region-specific meaning and image based on the geographical location information of the user when analyzing the name. The geographical location information includes, for example, GPS data, culture and customs specific to the region, and the like, but is not limited to such examples. The analysis unit evaluates the region-specific meaning and image by taking into consideration the geographical location information of the user, for example, using the generation AI. For example, when the user is in Japan, the generation AI evaluates the name by placing emphasis on the Japanese meaning and image. In addition, when the user is in the United States, the generation AI can also evaluate the name by placing emphasis on the English meaning and image. Furthermore, when the user is in France, the generation AI can also evaluate the name by placing emphasis on the French meaning and image. For example, when the user is in Japan, the generation AI evaluates the name by placing emphasis on the Japanese meaning and image. The Japanese meaning and image include, for example, cultural suitability, ease of pronunciation, appropriateness of meaning, and cultural suitability. The English meaning and image include, for example, ease of pronunciation, appropriateness of meaning, and cultural suitability. The meaning and image of French language include, for example, ease of pronunciation, appropriateness of meaning, cultural suitability, etc. This allows the analysis unit to evaluate the region-specific meaning and image by taking into account the user's geographical location information.

[0084] The analysis unit can analyze the user's social media activity when analyzing the name and suggest related name candidates. The analysis unit, for example, analyzes the user's social media activity when analyzing the name and suggests related name candidates. Social media activity includes, for example, the content of posts, the number of followers, and the engagement rate, but is not limited to such examples. The analysis unit, for example, uses a generation AI to analyze the user's social media activity and suggests related name candidates. For example, the generation AI analyzes hashtags frequently used by the user and suggests related name candidates. The generation AI can also analyze the content of accounts followed by the user and suggest related name candidates. Furthermore, the generation AI can analyze the content of posts by the user and suggest related name candidates. For example, the generation AI analyzes hashtags frequently used by the user and suggests related name candidates. Hashtags include, for example, keywords related to a specific theme or topic. The content of the accounts followed includes, for example, fields and topics in which the user is interested. The post content includes, for example, information and opinions that the user regularly transmits. This allows the analysis unit to suggest related name candidates by analyzing the user's social media activity.

[0085] The check unit can estimate the user's emotions and adjust the evaluation criteria for the possibility of registration and the risk of trademark infringement based on the estimated user's emotions. The check unit, for example, estimates the user's emotions and adjusts the evaluation criteria for the possibility of registration and the risk of trademark infringement based on the estimated user's emotions. The user's emotions include, for example, excitement, anxiety, relaxation, etc., but are not limited to such examples. The check unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the check unit takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The check unit can also estimate the user's emotions using voice analysis technology. For example, the check unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the check unit can estimate the user's emotions using a questionnaire result. For example, the check unit conducts a questionnaire for the user and evaluates the emotion. As a result, the check unit adjusts the evaluation criteria for the possibility of registration and the risk of trademark infringement based on the user's emotions, thereby enabling a more appropriate risk assessment. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the user is excited, the check unit adjusts the generation AI's tendency to underestimate the risk and performs a careful evaluation. Also, if the user is feeling anxious, the check unit can adjust the generation AI's tendency to overestimate the risk and perform an evaluation that gives a sense of security. Furthermore, if the user is relaxed, the check unit can make a balanced evaluation. This allows the check unit to adjust the evaluation criteria based on the user's emotions, enabling more appropriate risk evaluation.

[0086] The check unit can optimize the evaluation criteria by referring to past precedents and legal data when checking the possibility of registration or the risk of trademark infringement. For example, the check unit optimizes the evaluation criteria by referring to past precedents and legal data when checking the possibility of registration or the risk of trademark infringement. Past precedents and legal data include, but are not limited to, a precedent database and legal documents. The check unit, for example, uses a generation AI to analyze past precedents and legal data and optimize the evaluation criteria. For example, the generation AI refers to a past precedent database and adjusts the evaluation criteria based on similar cases. The generation AI can also optimize the evaluation criteria based on the latest legal information by referring to a legal database. Furthermore, the generation AI can analyze past trademark infringement cases and improve the accuracy of risk assessment. For example, the generation AI refers to a past precedent database and adjusts the evaluation criteria based on similar cases. The precedent database includes, for example, detailed information on precedents and the contents of judgments. The legal database includes, for example, legal provisions and interpretations, legal guidelines, and the like. Past trademark infringement cases include, for example, information on the similarity and scope of use of trademarks. This allows the checking department to optimize evaluation criteria by referring to past precedents and legal data.

[0087] The check unit can apply an evaluation algorithm specialized for a specific industry or market when checking the possibility of registration or the risk of trademark infringement. For example, the check unit applies an evaluation algorithm specialized for a specific industry or market when checking the possibility of registration or the risk of trademark infringement. The specific industry or market includes, but is not limited to, the IT industry, the fashion market, the food and beverage industry, and the like. The check unit applies an evaluation algorithm specialized for a specific industry or market using, for example, a generation AI. For example, the generation AI applies an algorithm that emphasizes technical elements when checking names for the IT industry. In addition, the generation AI can also apply an algorithm that emphasizes trends and styles when checking names for the fashion industry. Furthermore, the generation AI can also apply an algorithm that emphasizes deliciousness and health when checking names for the food and beverage industry. For example, the generation AI applies an algorithm that emphasizes technical elements when checking names for the IT industry. The technical elements include, for example, innovation, advancedness, and reliability. When checking names for the fashion industry, the generation AI applies an algorithm that emphasizes trends and styles. Trends and styles include, for example, the latest fashion trends, the beauty of design, and brand image. When checking names for the food and beverage industry, the Generative AI applies algorithms that emphasize deliciousness and healthiness. Deliciousness and healthiness include, for example, good taste, high nutritional value, and health consciousness. This allows the Check Department to make more appropriate risk assessments by applying evaluation algorithms specialized for specific industries and markets.

[0088] The check unit can estimate the user's emotion and determine the priority of the check results based on the estimated user's emotion. The check unit, for example, estimates the user's emotion and determines the priority of the check results based on the estimated user's emotion. The user's emotion includes, for example, excitement, anxiety, relaxation, etc., but is not limited to such examples. The check unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the check unit takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The check unit can also estimate the user's emotion using voice analysis technology. For example, the check unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the check unit can also estimate the user's emotion using a questionnaire result. For example, the check unit conducts a questionnaire on the user and evaluates the emotion. As a result, the check unit determines the priority of the check results based on the user's emotion, thereby enabling more appropriate risk assessment. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, the check unit may cause the generation AI to preferentially display low-risk results when the user is excited. Also, the check unit may cause the generation AI to preferentially display high-risk results when the user is feeling anxious. Furthermore, the check unit may cause the generation AI to preferentially display balanced results when the user is relaxed. This allows the check unit to determine the priority order of check results based on the user's emotions, thereby enabling more appropriate risk assessment.

[0089] The check unit can evaluate region-specific risks by considering the geographic location information of the user when checking the possibility of registration or the risk of trademark infringement. For example, the check unit evaluates region-specific risks by considering the geographic location information of the user when checking the possibility of registration or the risk of trademark infringement. The geographic location information includes, for example, GPS data, culture and customs specific to the region, but is not limited to such examples. The check unit evaluates region-specific risks by considering the geographic location information of the user using, for example, a generation AI. For example, when the user is in Japan, the generation AI evaluates risks by preferentially referring to a Japanese trademark database. In addition, when the user is in the United States, the generation AI can also evaluate risks by preferentially referring to a US trademark database. Furthermore, when the user is in France, the generation AI can also evaluate risks by preferentially referring to a French trademark database. For example, when the user is in Japan, the generation AI evaluates risks by preferentially referring to a Japanese trademark database. The Japanese trademark database includes, for example, information such as a trademark registration number, a registration date, and a registrant's name. The American trademark database includes, for example, information such as a trademark registration number, a registration date, and a registrant's name. The French trademark database contains, for example, information such as the trademark registration number, registration date and the name of the registrant, which allows the checking department to assess local-specific risks by taking into account the user's geographic location.

[0090] The check unit can analyze the social media activity of the user and suggest related risks when checking the possibility of registration or the risk of trademark infringement. For example, when checking the possibility of registration or the risk of trademark infringement, the check unit analyzes the social media activity of the user and suggests related risks. Social media activities include, for example, but are not limited to, the content of posts, the number of followers, and the engagement rate. The check unit, for example, uses a generation AI to analyze the social media activity of the user and suggests related risks. For example, the generation AI analyzes hashtags frequently used by the user and suggests related risks. The generation AI can also analyze the content of accounts followed by the user and suggest related risks. Furthermore, the generation AI can analyze the content of posts by the user and suggest related risks. For example, the generation AI analyzes hashtags frequently used by the user and suggests related risks. Hashtags include, for example, keywords related to a specific theme or topic. The content of the accounts followed includes, for example, fields and topics in which the user is interested. The post content includes, for example, information and opinions that the user regularly transmits. This allows the check unit to suggest related risks by analyzing the social media activity of the user.

[0091] The sorting unit can estimate the user's emotion and adjust the sorting method of the application information based on the estimated user's emotion. The sorting unit, for example, estimates the user's emotion and adjusts the sorting method of the application information based on the estimated user's emotion. The user's emotion includes, for example, excitement, anxiety, relaxation, etc., but is not limited to such examples. The sorting unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the sorting unit takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The sorting unit can also estimate the user's emotion using voice analysis technology. For example, the sorting unit analyzes the tone and speed of the user's voice and estimates the emotion. Furthermore, the sorting unit can estimate the user's emotion using a questionnaire result. For example, the sorting unit conducts a questionnaire on the user and evaluates the emotion. As a result, the sorting unit adjusts the sorting method of the application information based on the user's emotion, thereby making it possible to sort the application information more appropriately. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is excited, the organizing unit may provide the generation AI with a simple and intuitive organizing method. When the user is feeling anxious, the organizing unit may provide the generation AI with a detailed and reassuring organizing method. Furthermore, when the user is relaxed, the organizing unit may provide the generation AI with a balanced organizing method. This allows the organizing unit to organize the application information more appropriately by adjusting the organizing method based on the user's emotions.

[0092] The sorting unit can select the optimal sorting method by referring to past application data when sorting application information. For example, the sorting unit selects the optimal sorting method by referring to past application data when sorting application information. Past application data includes, for example, an application history database, a case study, and the like, but is not limited to such examples. The sorting unit, for example, uses a generation AI to analyze past application data and select the optimal sorting method. For example, the generation AI refers to past successful application data and proposes a similar sorting method. In addition, the generation AI can also refer to past unsuccessful application data and propose a sorting method that avoids similar mistakes. Furthermore, the generation AI can also dynamically select the optimal sorting method by referring to a past application database. For example, the generation AI refers to past successful application data and proposes a similar sorting method. The application history database includes, for example, detailed information and results of applications. The case study includes, for example, analysis and evaluation based on specific cases. This allows the sorting unit to select the optimal sorting method by referring to past application data.

[0093] The sorting unit can apply a sorting algorithm specialized for a specific industry or market when sorting the application information. For example, the sorting unit applies a sorting algorithm specialized for a specific industry or market when sorting the application information. The specific industry or market includes, but is not limited to, the IT industry, fashion market, and food and beverage industry. The sorting unit applies a sorting algorithm specialized for a specific industry or market using, for example, a generation AI. For example, the generation AI applies an algorithm that emphasizes technical elements when sorting application information for the IT industry. In addition, the generation AI can also apply an algorithm that emphasizes trends and styles when sorting application information for the fashion industry. In addition, the generation AI can also apply an algorithm that emphasizes deliciousness and health when sorting application information for the food and beverage industry. For example, the generation AI applies an algorithm that emphasizes technical elements when sorting application information for the IT industry. The technical elements include, for example, innovation, advancedness, and reliability. When sorting application information for the fashion industry, the generation AI applies an algorithm that emphasizes trends and styles. Trends and styles include, for example, the latest fashion trends, beauty of design, and brand image. When sorting application information for the food and beverage industry, the generation AI applies algorithms that emphasize deliciousness and healthiness. Deliciousness and healthiness include, for example, good taste, high nutritional value, and health consciousness. This enables the sorting department to sort application information more appropriately by applying sorting algorithms specialized for specific industries and markets.

[0094] The sorting unit can estimate the user's emotion and determine the priority of the application information based on the estimated user's emotion. The sorting unit, for example, estimates the user's emotion and determines the priority of the application information based on the estimated user's emotion. The user's emotion includes, for example, excitement, anxiety, relaxation, etc., but is not limited to such examples. The sorting unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the sorting unit takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The sorting unit can also estimate the user's emotion using voice analysis technology. For example, the sorting unit analyzes the tone and speed of the user's voice and estimates the emotion. Furthermore, the sorting unit can estimate the user's emotion using a questionnaire result. For example, the sorting unit conducts a questionnaire on the user and evaluates the emotion. As a result, the sorting unit determines the priority of the application information based on the user's emotion, making it possible to organize the application information more appropriately. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is excited, the sorting unit arranges important information at the top of the list. In addition, when the user is feeling anxious, the sorting unit can arrange information that gives a sense of security at the top of the list. Furthermore, when the user is relaxed, the sorting unit can arrange balanced information at the top of the list. This enables the sorting unit to organize the application information more appropriately by determining the priority order of the application information based on the user's emotions.

[0095] The sorting unit can sort the information specific to the region in consideration of the geographical location information of the user when sorting the application information. For example, the sorting unit sorts the information specific to the region in consideration of the geographical location information of the user when sorting the application information. The geographical location information includes, for example, GPS data, culture and customs specific to the region, and the like, but is not limited to such examples. The sorting unit sorts the information specific to the region in consideration of the geographical location information of the user, for example, using a generation AI. For example, the generation AI sorts the application information based on the legal requirements of Japan when the user is in Japan. Furthermore, the generation AI can also sort the application information based on the legal requirements of the United States when the user is in the United States. Furthermore, the generation AI can also sort the application information based on the legal requirements of France when the user is in France. For example, the generation AI sorts the application information based on the legal requirements of Japan when the user is in Japan. The legal requirements of Japan include, for example, trademark registration procedures and necessary documents. The legal requirements of the United States include, for example, trademark registration procedures and necessary documents. The legal requirements of France include, for example, trademark registration procedures and necessary documents. This allows the sorting unit to sort the information specific to the region by considering the geographical location information of the user.

[0096] The organizing unit can analyze the social media activity of the user and organize the related information when organizing the application information. For example, the organizing unit analyzes the social media activity of the user and organizes the related information when organizing the application information. The social media activity includes, for example, the content of the post, the number of followers, and the engagement rate, but is not limited to such examples. The organizing unit, for example, uses a generation AI to analyze the social media activity of the user and organizes the related information. For example, the generation AI analyzes hashtags frequently used by the user and organizes the related application information. The generation AI can also analyze the content of the accounts followed by the user and organize the related application information. Furthermore, the generation AI can analyze the content of the user's post and organize the related application information. For example, the generation AI analyzes hashtags frequently used by the user and organizes the related application information. The hashtag includes, for example, keywords related to a specific theme or topic. The content of the followed account includes, for example, fields and topics in which the user is interested. The post content includes, for example, information and opinions that the user transmits on a daily basis. This allows the organizing unit to organize the related information by analyzing the social media activity of the user.

[0097] The collection unit can estimate the user's emotions and adjust the method of collecting market research data based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and adjusts the method of collecting market research data based on the estimated user's emotions. The user's emotions include, for example, excitement, anxiety, relaxation, etc., but are not limited to such examples. The collection unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the collection unit can also estimate the user's emotions using a questionnaire result. For example, the collection unit conducts a questionnaire on the user and evaluates the emotions. As a result, the collection unit adjusts the method of collecting market research data based on the user's emotions, thereby enabling more appropriate data collection. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is excited, the collection unit causes the generation AI to preferentially collect positive market data. Also, when the user is feeling anxious, the collection unit can cause the generation AI to preferentially collect market data for reducing risk. Furthermore, when the user is relaxed, the collection unit can cause the generation AI to collect balanced market data. This allows the collection unit to adjust the method of collecting market research data based on the user's emotions, thereby enabling more appropriate data collection.

[0098] The collection unit can select the optimal collection method by referring to past data when collecting market research data. For example, the collection unit selects the optimal collection method by referring to past data when collecting market research data. Past data includes, but is not limited to, databases, case studies, and the like. The collection unit, for example, uses the generation AI to analyze past data and select the optimal collection method. For example, the generation AI can refer to successful market research data in the past and propose a similar collection method. The generation AI can also refer to unsuccessful market research data in the past and propose a collection method that avoids similar mistakes. Furthermore, the generation AI can dynamically select the optimal collection method by referring to a past market research database. For example, the generation AI can refer to successful market research data in the past and propose a similar collection method. The database includes, for example, past market research data and analysis results. The case study includes, for example, analysis and evaluation based on a specific case. This allows the collection unit to select the optimal collection method by referring to past data.

[0099] The collection unit can apply a collection algorithm specialized for a specific industry or market when collecting market research data. The collection unit, for example, applies a collection algorithm specialized for a specific industry or market when collecting market research data. The specific industry or market includes, for example, the IT industry, the fashion market, the food and beverage industry, and the like, but is not limited to such examples. The collection unit, for example, uses a generation AI to apply a collection algorithm specialized for a specific industry or market. For example, the generation AI applies an algorithm that emphasizes technical elements when collecting market research data for the IT industry. In addition, the generation AI can also apply an algorithm that emphasizes trends and styles when collecting market research data for the fashion industry. In addition, the generation AI can also apply an algorithm that emphasizes deliciousness and health when collecting market research data for the food and beverage industry. For example, the generation AI applies an algorithm that emphasizes technical elements when collecting market research data for the IT industry. The technical elements include, for example, innovation, advancedness, and reliability. In collecting market research data for the fashion industry, the generation AI applies an algorithm that emphasizes trends and styles. Trends and styles include, for example, the latest fashion trends, the beauty of design, and brand image. When collecting market research data for the food and beverage industry, the generative AI applies algorithms that emphasize deliciousness and healthiness. Deliciousness and healthiness include, for example, good taste, high nutritional value, and health consciousness. This enables the collection unit to collect more appropriate data by applying collection algorithms specialized for specific industries and markets.

[0100] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated user's emotion. The user's emotion includes, for example, excitement, anxiety, relaxation, etc., but is not limited to such examples. The collection unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The collection unit can also estimate the user's emotion using voice analysis technology. For example, the collection unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the collection unit can also estimate the user's emotion using a questionnaire result. For example, the collection unit conducts a questionnaire on the user and evaluates the emotion. As a result, the collection unit determines the priority of data to be collected based on the user's emotion, thereby enabling more appropriate data collection. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is excited, the collection unit causes the generation AI to preferentially place positive market data at the top of the list. In addition, when the user is feeling anxious, the collection unit may cause the generation AI to preferentially place market data for reducing risk at the top of the list. Furthermore, when the user is relaxed, the collection unit may cause the generation AI to preferentially place balanced market data at the top of the list. This allows the collection unit to determine the priority of data to be collected based on the user's emotions, thereby enabling more appropriate data collection.

[0101] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting market research data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting market research data. The geographical location information includes, for example, GPS data, culture and customs specific to a region, and the like, but is not limited to such examples. The collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user, for example, using the generation AI. For example, when the user is in Japan, the generation AI preferentially collects Japanese market data. In addition, when the user is in the United States, the generation AI can also preferentially collect American market data. Furthermore, when the user is in France, the generation AI can also preferentially collect French market data. For example, when the user is in Japan, the generation AI preferentially collects Japanese market data. The Japanese market data includes, for example, information on consumer preferences and purchasing behavior. The American market data includes, for example, information on consumer preferences and purchasing behavior. The French market data includes, for example, information on consumer preferences and purchasing behavior. This allows the collection unit to prioritize collection of highly relevant data by taking into account the geographical location information of the user.

[0102] The collection unit can analyze the social media activity of the user and collect related data preferentially when collecting market research data. For example, the collection unit analyzes the social media activity of the user and collects related data preferentially when collecting market research data. Social media activities include, for example, post content, number of followers, engagement rate, etc., but are not limited to such examples. The collection unit, for example, uses a generation AI to analyze the social media activity of the user and collects related data preferentially. For example, the generation AI analyzes hashtags frequently used by the user and collects related market data preferentially. The generation AI can also analyze the content of accounts followed by the user and collect related market data preferentially. Furthermore, the generation AI can analyze the content of posts by the user and collect related market data preferentially. For example, the generation AI analyzes hashtags frequently used by the user and collects related market data preferentially. Hashtags include, for example, keywords related to specific themes and topics. The content of the accounts followed includes, for example, fields and topics in which the user is interested. The post content includes, for example, information and opinions that the user transmits on a daily basis. This allows the collection unit to prioritize collection of relevant data by analyzing the user's social media activity.

[0103] The access unit can estimate the user's emotion and adjust the method of accessing the database based on the estimated user's emotion. The access unit, for example, estimates the user's emotion and adjusts the method of accessing the database based on the estimated user's emotion. The user's emotion includes, for example, excitement, anxiety, relaxation, etc., but is not limited to such examples. The access unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the access unit takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The access unit can also estimate the user's emotion using voice analysis technology. For example, the access unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the access unit can also estimate the user's emotion using a questionnaire result. For example, the access unit conducts a questionnaire on the user and evaluates the emotion. As a result, the access unit adjusts the method of accessing the database based on the user's emotion, thereby enabling more appropriate database access. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, if the access unit detects that the user is excited, the generation AI provides a quick access method. If the user is anxious, the access unit may also detect that the generation AI provides a detailed access method. If the user is relaxed, the access unit may also detect that the generation AI provides a balanced access method. This allows the access unit to adjust the access method based on the user's emotions, thereby enabling more appropriate database access.

[0104] The access unit can select the optimal access method by referring to past access history when accessing the database. For example, the access unit selects the optimal access method by referring to past access history when accessing the database. The past access history includes, for example, access logs, case studies, and the like, but is not limited to such examples. The access unit analyzes the past access history using, for example, a generation AI to select the optimal access method. For example, the generation AI can refer to past successful access history and propose a similar access method. The generation AI can also refer to past unsuccessful access history and propose an access method that avoids similar mistakes. Furthermore, the generation AI can also dynamically select the optimal access method by referring to a past access history database. For example, the generation AI can refer to past successful access history and propose a similar access method. The access log includes, for example, detailed information and results of past accesses. The case study includes, for example, analysis and evaluation based on a specific case. This allows the access unit to select the optimal access method by referring to the past access history.

[0105] The access unit can apply an access algorithm specialized for a specific industry or market when accessing the database. The access unit, for example, applies an access algorithm specialized for a specific industry or market when accessing the database. Specific industries and markets include, but are not limited to, the IT industry, fashion market, and food and beverage industry. The access unit, for example, uses a generation AI to apply an access algorithm specialized for a specific industry or market. For example, the generation AI applies an algorithm that emphasizes technical elements when accessing a database for the IT industry. In addition, the generation AI can also apply an algorithm that emphasizes trends and styles when accessing a database for the fashion industry. Furthermore, the generation AI can also apply an algorithm that emphasizes deliciousness and health when accessing a database for the food and beverage industry. For example, the generation AI applies an algorithm that emphasizes technical elements when accessing a database for the IT industry. Technical elements include, for example, innovation, advanced technology, and reliability. When accessing a database for the fashion industry, the generation AI applies an algorithm that emphasizes trends and styles. Trends and styles include, for example, the latest fashion trends, design beauty, and brand image. When accessing a database for the food and beverage industry, the Generative AI applies an algorithm that emphasizes deliciousness and healthiness. Deliciousness and healthiness include, for example, good taste, high nutritional value, and health consciousness. This allows the Access Department to apply access algorithms specialized for specific industries and markets, enabling more appropriate database access.

[0106] The access unit can estimate the user's emotion and determine the priority of the database to be accessed based on the estimated user's emotion. The access unit, for example, estimates the user's emotion and determines the priority of the database to be accessed based on the estimated user's emotion. The user's emotion includes, for example, excitement, anxiety, relaxation, etc., but is not limited to such examples. The access unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the access unit takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The access unit can also estimate the user's emotion using voice analysis technology. For example, the access unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the access unit can also estimate the user's emotion using a questionnaire result. For example, the access unit conducts a questionnaire on the user and evaluates the emotion. As a result, the access unit determines the priority of the database to be accessed based on the user's emotion, thereby enabling more appropriate database access. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is excited, the access unit allows the generation AI to preferentially access important databases. Also, when the user is feeling anxious, the access unit allows the generation AI to preferentially access databases that provide a sense of security. Furthermore, when the user is relaxed, the access unit allows the generation AI to preferentially access balanced databases. This allows the access unit to determine the priority order of databases based on the user's emotions, enabling more appropriate database access.

[0107] The access unit can preferentially access a highly relevant database in consideration of the geographical location information of the user when accessing the database. For example, the access unit preferentially accesses a highly relevant database in consideration of the geographical location information of the user when accessing the database. The geographical location information includes, for example, GPS data, culture and customs specific to a region, and the like, but is not limited to such examples. The access unit preferentially accesses a highly relevant database in consideration of the geographical location information of the user, for example, using a generation AI. For example, the generation AI preferentially accesses a Japanese database when the user is in Japan. Furthermore, the generation AI can also preferentially access an American database when the user is in the United States. Furthermore, the generation AI can also preferentially access a French database when the user is in France. For example, the generation AI preferentially accesses a Japanese database when the user is in Japan. The Japanese database includes, for example, trademark registration information and registration information. The American database includes, for example, trademark registration information and registration information. The French database includes, for example, trademark registration information and registration information. As a result, the access unit can preferentially access a highly relevant database by considering the geographical location information of the user.

[0108] The access unit can analyze the social media activity of the user and preferentially access the relevant database when accessing the database. For example, the access unit can analyze the social media activity of the user and preferentially access the relevant database when accessing the database. The social media activity includes, for example, the content of the post, the number of followers, the engagement rate, etc., but is not limited to such examples. The access unit can analyze the social media activity of the user using, for example, a generation AI and preferentially access the relevant database. For example, the generation AI can analyze hashtags frequently used by the user and preferentially access the relevant database. The generation AI can also analyze the content of the accounts the user follows and preferentially access the relevant database. Furthermore, the generation AI can analyze the content of the post of the user and preferentially access the relevant database. For example, the generation AI can analyze hashtags frequently used by the user and preferentially access the relevant database. The hashtag includes, for example, keywords related to a specific theme or topic. The content of the followed account includes, for example, fields and topics in which the user is interested. The post content includes, for example, information and opinions that the user transmits on a daily basis. This allows the access unit to preferentially access the relevant database by analyzing the social media activity of the user.

[0109] The listing unit can estimate the user's emotion and adjust the listing method based on the estimated user's emotion. The listing unit, for example, estimates the user's emotion and adjusts the listing method based on the estimated user's emotion. The user's emotion includes, for example, excitement, anxiety, relaxation, etc., but is not limited to such examples. The listing unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the listing unit takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The listing unit can also estimate the user's emotion using voice analysis technology. For example, the listing unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the listing unit can also estimate the user's emotion using a questionnaire result. For example, the listing unit conducts a questionnaire on the user and evaluates the emotion. As a result, the listing unit adjusts the listing method based on the user's emotion, thereby enabling more appropriate listing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is excited, the list generation unit provides a simple and intuitive listing method. When the user is anxious, the list generation unit may provide a detailed and reassuring listing method. When the user is relaxed, the list generation unit may provide a balanced listing method. This allows the list generation unit to adjust the listing method based on the user's emotions, thereby enabling more appropriate listing.

[0110] The listing unit can select the optimal listing method by referring to past listing data when listing. For example, the listing unit selects the optimal listing method by referring to past listing data when listing. Past listing data includes, for example, databases, case studies, and the like, but is not limited to such examples. The listing unit, for example, uses a generation AI to analyze past listing data and select the optimal listing method. For example, the generation AI can refer to past successful listing data and propose a similar listing method. In addition, the generation AI can also refer to past unsuccessful listing data and propose a listing method that avoids similar mistakes. Furthermore, the generation AI can refer to a past listing database and dynamically select the optimal listing method. For example, the generation AI can refer to past successful listing data and propose a similar listing method. The database includes, for example, past listing data and analysis results. The case study includes, for example, analysis and evaluation based on a specific case. This allows the listing unit to select the optimal listing method by referring to past listing data.

[0111] The listing unit can apply a listing algorithm specialized for a specific industry or market when listing. For example, the listing unit applies a listing algorithm specialized for a specific industry or market when listing. The specific industry or market includes, but is not limited to, the IT industry, fashion market, and food and beverage industry. For example, the listing unit applies a listing algorithm specialized for a specific industry or market using a generation AI. For example, the generation AI applies an algorithm that emphasizes technical elements when listing for the IT industry. In addition, the generation AI can also apply an algorithm that emphasizes trends and styles when listing for the fashion industry. Furthermore, the generation AI can also apply an algorithm that emphasizes deliciousness and health when listing for the food and beverage industry. For example, the generation AI applies an algorithm that emphasizes technical elements when listing for the IT industry. The technical elements include, for example, innovation, advancedness, and reliability. When listing for the fashion industry, the generation AI applies an algorithm that emphasizes trends and styles. Trends and styles include, for example, the latest fashion trends, beautiful designs, and brand images. When creating a list for the food and beverage industry, the generation AI applies an algorithm that emphasizes deliciousness and healthiness. Deliciousness and healthiness include, for example, good taste, high nutritional value, and health consciousness. This allows the list creation unit to create a more appropriate list by applying a list creation algorithm specialized for a specific industry or market.

[0112] The listing unit can estimate the user's emotion and determine the priority of items to be listed based on the estimated user's emotion. The listing unit, for example, estimates the user's emotion and determines the priority of items to be listed based on the estimated user's emotion. The user's emotion includes, for example, excitement, anxiety, relaxation, etc., but is not limited to such examples. The listing unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the listing unit takes a picture of the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The listing unit can also estimate the user's emotion using voice analysis technology. For example, the listing unit analyzes the tone and speed of the user's voice to estimate the emotion. Furthermore, the listing unit can also estimate the user's emotion using a questionnaire result. For example, the listing unit conducts a questionnaire on the user and evaluates the emotion. As a result, the listing unit determines the priority of items to be listed based on the user's emotion, thereby enabling more appropriate listing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is excited, the list creation unit places important items at the top of the list. Also, when the user is feeling anxious, the list creation unit can place items that give a sense of security at the top of the list. Furthermore, when the user is relaxed, the list creation unit can place balanced items at the top of the list. This allows the list creation unit to determine the priority order of items to be listed based on the user's emotions, thereby enabling more appropriate listing.

[0113] The listing unit can prioritize listing highly relevant items in consideration of the geographical location information of the user when listing. For example, the listing unit prioritizes listing highly relevant items in consideration of the geographical location information of the user when listing. The geographical location information includes, for example, GPS data, culture and customs specific to a region, and the like, but is not limited to such examples. The listing unit prioritizes listing highly relevant items in consideration of the geographical location information of the user, for example, using a generation AI. For example, when the user is in Japan, the generation AI lists based on Japanese legal requirements. In addition, when the user is in the United States, the generation AI can also list based on American legal requirements. In addition, when the user is in France, the generation AI can also list based on French legal requirements. For example, when the user is in Japan, the generation AI lists based on Japanese legal requirements. The legal requirements of the United States include, for example, trademark registration procedures and necessary documents. The legal requirements of the United States include, for example, trademark registration procedures and necessary documents. The legal requirements of France include, for example, trademark registration procedures and necessary documents. This allows the listing unit to prioritize listing of highly relevant items by taking into account the geographical location information of the user.

[0114] The listing unit can analyze the social media activity of the user at the time of listing and prioritize listing related items. For example, the listing unit can analyze the social media activity of the user at the time of listing and prioritize listing related items. Social media activities include, for example, post content, number of followers, engagement rate, etc., but are not limited to such examples. The listing unit can analyze the social media activity of the user using, for example, a generation AI and prioritize listing related items. For example, the generation AI can analyze hashtags frequently used by the user and prioritize listing related items. In addition, the generation AI can also analyze the content of accounts followed by the user and prioritize listing related items. In addition, the generation AI can analyze the content of posts by the user and prioritize listing related items. For example, the generation AI can analyze hashtags frequently used by the user and prioritize listing related items. Hashtags include, for example, keywords related to specific themes or topics. The content of the accounts followed includes, for example, fields and topics in which the user is interested. The post content includes, for example, information and opinions that the user transmits on a daily basis. This allows the listing unit to prioritize listing of related items by analyzing the user's social media activity.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The analysis unit may also evaluate the phonological characteristics of the name when analyzing the suitability of the name. For example, the analysis unit may evaluate how the sound and rhythm of the name are perceived in a particular culture or language. This may determine whether the name is easy to pronounce and remember. The analysis unit may also use speech recognition technology to evaluate the phonological characteristics of the name. For example, the pronunciation of the name may be analyzed using speech recognition technology to evaluate the difficulty of pronunciation. Furthermore, the analysis unit may refer to a phonological database to evaluate the phonological characteristics of the name. For example, the phonological database includes phonological patterns and pronunciation rules for each language. This allows the analysis unit to provide more appropriate name candidates by evaluating the phonological characteristics of the name.

[0117] The collection unit may also collect data in real time when collecting market research data. For example, social media trends and news articles may be collected in real time and used as data for evaluating the suitability of names. The collection unit may also provide the data collected in real time to the analysis unit and dynamically adjust the evaluation criteria for names. Furthermore, the collection unit may provide the data collected in real time to a user and suggest name candidates that reflect the latest market trends. For example, the collection unit may collect social media trends in real time and suggest name candidates. In this way, the collection unit may provide more appropriate name candidates by collecting data in real time.

[0118] The access department can also take into account the update frequency of the database when accessing the trademark database or the registry database of each country. For example, if the database is frequently updated, the access department increases the access frequency to obtain the latest information. The access department can also dynamically adjust the access schedule based on the update frequency of the database. Furthermore, the access department can provide the latest information to the user based on the update frequency of the database. For example, if the database is updated daily, the access department accesses the database daily to obtain the latest information. In this way, the access department can provide more accurate information by taking into account the update frequency of the database.

[0119] The listing unit can also refer to the user's past procedure history when listing the necessary documents and procedure steps. For example, the listing unit can refer to the history of procedures that the user has performed in the past and automatically list them when a similar procedure is required. The listing unit can also optimize the procedure steps based on the user's past procedure history. Furthermore, the listing unit can also automatically generate the necessary documents by referring to the user's past procedure history. For example, the listing unit can refer to the history of procedures that the user has performed in the past and automatically generate the necessary documents. In this way, the listing unit can improve the efficiency of procedures by referring to the user's past procedure history.

[0120] The analysis unit can estimate the user's emotions and adjust the evaluation criteria of name candidates based on the estimated user's emotions. For example, if the user is excited, the generation AI can prioritize names with positive connotations and images. In addition, if the user is feeling anxious, the generation AI can also prioritize names that give a sense of security. Furthermore, if the user is relaxed, the generation AI can also prioritize names that have a soft sound. This allows the analysis unit to provide more appropriate name candidates by adjusting the name evaluation criteria based on the user's emotions.

[0121] When analyzing a name, the analysis unit can optimize the evaluation criteria by referring to past success and failure cases. For example, the generation AI can analyze the characteristics of names that have been successful in the past and highly evaluate names with similar characteristics. The generation AI can also analyze the characteristics of names that have failed in the past and highly evaluate names with similar characteristics. Furthermore, the generation AI can dynamically adjust the evaluation criteria by referring to a database of success and failure cases. This allows the analysis unit to optimize the name evaluation criteria by referring to past cases.

[0122] When analyzing names, the analysis unit can apply evaluation algorithms specialized for specific industries or markets. For example, when analyzing names for the IT industry, the generation AI can apply an algorithm that emphasizes a technical image. When analyzing names for the fashion industry, it can also apply an algorithm that emphasizes trends and style. Furthermore, when analyzing names for the food and beverage industry, it can apply an algorithm that emphasizes deliciousness and health. In this way, the analysis unit can provide more appropriate name candidates by applying evaluation algorithms specialized for specific industries and markets.

[0123] The analysis unit can estimate the user's emotions and prioritize name candidates based on the estimated user's emotions. For example, if the user is excited, the generation AI can prioritize names with positive connotations and images at the top of the list. In addition, if the user is feeling anxious, the generation AI can also prioritize names that give a sense of security at the top of the list. Furthermore, if the user is relaxed, the generation AI can also prioritize names that have a soft sound at the top of the list. This allows the analysis unit to provide more appropriate name candidates by prioritizing names based on the user's emotions.

[0124] When analyzing a name, the analysis unit can evaluate region-specific meanings and images based on the user's geographic location information. For example, if the user is in Japan, the generation AI can evaluate the name by placing emphasis on Japanese meanings and images. Also, if the user is in the United States, the generation AI can evaluate the name by placing emphasis on English meanings and images. Furthermore, if the user is in France, the generation AI can evaluate the name by placing emphasis on French meanings and images. This allows the analysis unit to evaluate region-specific meanings and images by taking into account the user's geographic location information.

[0125] When analyzing a name, the analysis unit can analyze the user's social media activity and suggest related name candidates. For example, the generation AI can analyze hashtags frequently used by the user and suggest related name candidates. It can also analyze the content of accounts the user follows and suggest related name candidates. It can also analyze the content of the user's posts and suggest related name candidates. This allows the analysis unit to suggest related name candidates by analyzing the user's social media activity.

[0126] The process flow of the second embodiment will be briefly described below.

[0127] Step 1: The analysis unit analyzes the suitability of the name. The suitability of a name includes cultural suitability, ease of pronunciation, appropriateness of meaning, etc. The analysis unit uses a generative AI to analyze the meaning and image of the name, and further evaluates it by referring to market research data and databases for each country. Step 2: The Checking Department checks the registrability and risk of trademark infringement in each country based on the name candidates generated by the Analysis Department. The Checking Department accesses the trademark database and registration database of each country through the relevant API to check whether the input name has already been registered or there is a risk of trademark infringement. Step 3: The sorting unit sorts the application information for registration and trademark registration based on the results obtained by the checking unit. The sorting unit creates a list of necessary documents and procedural steps and provides it to the user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0130] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements including the above-mentioned analysis unit, check unit, arrangement unit, collection unit, access unit, and listing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 and analyzes the suitability of the name. The check unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and checks the possibility of registration in each country and the risk of trademark infringement. The arrangement unit is realized, for example, by the control unit 46A of the smart device 14 and organizes application information for registration and trademark registration. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects market research data. The access unit is realized, for example, by the control unit 46A of the smart device 14 and accesses trademark databases and registration databases of each country. The listing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and lists necessary documents and procedural steps. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements including the above-mentioned analysis unit, check unit, sorting unit, collection unit, access unit, and listing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 and analyzes the suitability of the name. The check unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and checks the possibility of registration in each country and the risk of trademark infringement. The sorting unit is realized, for example, by the control unit 46A of the smart glasses 214 and sorts application information for registration and trademark registration. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects market research data. The access unit is realized, for example, by the control unit 46A of the smart glasses 214 and accesses trademark databases and registration databases of each country. The listing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and lists necessary documents and procedural steps. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0150] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0155] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements including the above-mentioned analysis unit, check unit, sorting unit, collection unit, access unit, and listing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314, and analyzes the suitability of the name. The check unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and checks the possibility of registration in each country and the risk of trademark infringement. The sorting unit is realized, for example, by the control unit 46A of the headset type terminal 314, and sorts application information for registration and trademark registration. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects market research data. The access unit is realized, for example, by the control unit 46A of the headset type terminal 314, and accesses trademark databases and registration databases of each country. The listing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and lists necessary documents and procedural steps. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0168] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0170] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0171] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0172] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0174] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0175] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0176] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0178] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0179] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0180] Each of the multiple elements including the above-mentioned analysis unit, check unit, arrangement unit, collection unit, access unit, and listing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414, and analyzes the suitability of the name. The check unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and checks the possibility of registration in each country and the risk of trademark infringement. The arrangement unit is realized, for example, by the control unit 46A of the robot 414, and organizes application information for registration and trademark registration. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects market research data. The access unit is realized, for example, by the control unit 46A of the robot 414, and accesses trademark databases and registration databases of each country. The listing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and lists necessary documents and procedural steps. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0181] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0182] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0183] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0184] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0185] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0186] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0189] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0190] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0191] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0192] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0193] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0194] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0195] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0196] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0197] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0198] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0199] (Appendix 1) an analysis unit for analyzing the suitability of a name; A checking unit that checks the registrability or the risk of trademark infringement in each country based on the name candidates generated by the analyzing unit; and a sorting unit that sorts application information for registration or trademark registration based on the results obtained by the checking unit. A system characterized by: (Appendix 2) Equipping a collection department to collect market research data 2. The system of claim 1, (Appendix 3) Equipped with an access section to trademark databases and registration databases in various countries 2. The system of claim 1, (Appendix 4) Equipped with a listing section that lists the necessary documents and procedural steps 2. The system of claim 1, (Appendix 5) The analysis unit is Inferring user sentiment and adjusting evaluation criteria for name candidates based on the estimated user sentiment 2. The system of claim 1, (Appendix 6) The analysis unit is When analyzing names, optimize the evaluation criteria by referring to past successes and failures. 2. The system of claim 1, (Appendix 7) The analysis unit is Apply industry- or market-specific rating algorithms when parsing names 2. The system of claim 1, (Appendix 8) The analysis unit is Estimate user sentiment and prioritize name candidates based on the estimated user sentiment 2. The system of claim 1, (Appendix 9) The analysis unit is When parsing names, evaluate local connotations and imagery based on the user's geographic location 2. The system of claim 1, (Appendix 10) The analysis unit is During name parsing, it analyzes the user's social media activity to suggest name suggestions 2. The system of claim 1, (Appendix 11) The checking unit is Inferring user sentiment and adjusting the assessment criteria for the possibility of registration and the risk of trademark infringement based on the inferred user sentiment 2. The system of claim 1, (Appendix 12) The checking unit is When checking the possibility of registration or the risk of trademark infringement, the evaluation criteria are optimized by referring to past cases and legal data. 2. The system of claim 1, (Appendix 13) The checking unit is Applying industry- and market-specific evaluation algorithms when checking for registrability and trademark infringement risk 2. The system of claim 1, (Appendix 14) The checking unit is Infer user emotions and prioritize check results based on the estimated user emotions 2. The system of claim 1, (Appendix 15) The checking unit is When checking for registrability and trademark infringement risk, users' geographic location information is taken into account to assess region-specific risks 2. The system of claim 1, (Appendix 16) The checking unit is Analyze users' social media activity and suggest related risks when checking for registrability or trademark infringement risk 2. The system of claim 1, (Appendix 17) The sorting unit includes: Infer user sentiment and adjust how application information is organized based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The sorting unit includes: When organizing application information, select the optimal organization method by referring to past application data. 2. The system of claim 1, (Appendix 19) The sorting unit includes: Apply industry- or market-specific sorting algorithms when sorting applications 2. The system of claim 1, (Appendix 20) The sorting unit includes: Inferring user sentiment and prioritizing application information based on the estimated user sentiment 2. The system of claim 1, (Appendix 21) The sorting unit includes: When sorting application information, sort region-specific information by considering the user's geographic location information. 2. The system of claim 1, (Appendix 22) The sorting unit includes: When sorting application information, analyze users' social media activity and sort relevant information. 2. The system of claim 1, (Appendix 23) The collecting unit includes: To estimate user sentiment and adjust a method for collecting market research data based on the estimated user sentiment 3. The system of claim 2, (Appendix 24) The collecting unit includes: When collecting market research data, choose the best collection method by referring to past data. 3. The system of claim 2, (Appendix 25) The collecting unit includes: Applying industry- or market-specific collection algorithms when collecting market research data 3. The system of claim 2, (Appendix 26) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 3. The system of claim 2, (Appendix 27) The collecting unit includes: When collecting market research data, prioritize collecting data that is more relevant by taking into account the user's geographic location information. 3. The system of claim 2, (Appendix 28) The collecting unit includes: When collecting market research data, analyze users' social media activity and prioritize collecting relevant data. 3. The system of claim 2, (Appendix 29) The access unit is Inferring user emotions and adjusting the method of accessing a database based on the estimated user emotions 4. The system of claim 3. (Appendix 30) The access unit is When accessing a database, the most appropriate access method is selected by referring to past access history. 4. The system of claim 3. (Appendix 31) The access unit is Apply industry- or market-specific access algorithms when accessing databases 4. The system of claim 3. (Appendix 32) The access unit is A user's emotion is estimated, and a priority order of databases to be accessed is determined based on the estimated user's emotion. 4. The system of claim 3. (Appendix 33) The access unit is When accessing a database, the system takes into account the user's geographic location information and prioritizes access to the most relevant databases. 4. The system of claim 3. (Appendix 34) The access unit is When accessing a database, the user's social media activity is analyzed and relevant databases are accessed preferentially. 4. The system of claim 3. (Appendix 35) The list creation unit includes: Infer user sentiment and adjust the listing method based on the estimated user sentiment 5. The system of claim 4. (Appendix 36) The list creation unit includes: When listing, refer to past listing data to select the optimal listing method. 5. The system of claim 4. (Appendix 37) The list creation unit includes: When listing, we apply a listing algorithm that is specialized for a specific industry or market. 5. The system of claim 4. (Appendix 38) The list creation unit includes: Infer user sentiment and prioritize items to be listed based on the inferred user sentiment 5. The system of claim 4. (Appendix 39) The list creation unit includes: When creating a list, it takes into account the user's geographic location to prioritize the most relevant items. 5. The system of claim 4. (Appendix 40) The list creation unit includes: When creating a list, it analyzes the user's social media activity and prioritizes the list of relevant items. 5. The system of claim 4. [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the suitability of a name; A checking unit that checks the registrability or the risk of trademark infringement in each country based on the name candidates generated by the analyzing unit; and a sorting unit that sorts application information for registration or trademark registration based on the results obtained by the checking unit. A system characterized by:

2. Equipping a collection department to collect market research data 2. The system of claim 1 .

3. Equipped with an access section to trademark databases and registration databases in various countries 2. The system of claim 1 .

4. Equipped with a listing section that lists the necessary documents and procedural steps 2. The system of claim 1 .

5. The analysis unit is Inferring user sentiment and adjusting evaluation criteria for name candidates based on the estimated user sentiment 2. The system of claim 1 .

6. The analysis unit is When analyzing names, optimize the evaluation criteria by referring to past successes and failures.

2. The system of claim 1 .

7. The analysis unit is Apply industry- or market-specific rating algorithms when parsing names 2. The system of claim 1 .

8. The analysis unit is Estimate user sentiment and prioritize name candidates based on the estimated user sentiment 2. The system of claim 1 .

9. The analysis unit is When parsing names, evaluate local connotations and imagery based on the user's geographic location 2. The system of claim 1 .

Images