System

Abstract

A system in accordance with an embodiment is directed to checking international trademark databases in real time to quickly ascertain the availability of names.SOLUTION: A system according to an embodiment includes a trademark database check part, an inspection part, and a notification part. The trademark database checking unit checks the trademark database in real time. The scrutinizing unit automatically scrutinizes the registration / trademark database of each country. The notification unit checks the availability of the name and notifies the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of requiring a time-consuming and laborious process of checking international trademark databases in real time to confirm the availability of a name.

[0005] The system of the embodiment aims to check international trademark databases in real time to quickly confirm the availability of a name. [Means for solving the problem]

[0006] The system according to the embodiment comprises a trademark database check unit, a review unit, and a notification unit. The trademark database check unit checks the trademark database in real time. The review unit automatically reviews the registration and trademark databases of each country. The notification unit checks the availability of the name and notifies the user. [Effects of the Invention]

[0007] The system according to the embodiment can check international trademark databases in real time to quickly confirm the availability of a name. [Brief explanation 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. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 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 device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The Global Name Safe System, an embodiment of the present invention, is a system that streamlines the process of deciding on service and company names. The system uses advanced AI and APIs to check international trademark databases in real time, expediting the naming process and reducing legal risks and hassle. As a result, the Global Name Safe System centralizes management from name selection to legal confirmation, allowing businesses to launch and develop products more smoothly.

[0029] The global name safe system according to an embodiment includes a trademark database check unit, a scrutiny unit, and a notification unit. The trademark database check unit checks the trademark database in real time. For example, the generation AI checks the international trademark database in real time based on a name candidate entered by a user. The generation AI uses a text generation AI (e.g., LLM) to check whether the name is already registered. For example, when a user enters a "new company name," the generation AI checks whether the name is already registered. The scrutiny unit automatically scrutinizes each country's registration and trademark database. For example, it accesses each country's trademark database through an API, and the RPA automatically collects and analyzes the data. For example, the scrutiny unit accesses each country's trademark database through an API, and the RPA automatically collects and analyzes the data. The notification unit confirms the usability of the name and notifies the user. For example, the generation AI checks the trademark database, and the API and RPA scrutinize each country's database. If the usability of the name is confirmed, the notification unit notifies the user. As a result, the global name safe system according to the embodiment swiftly and accurately verifies the availability of a name and notifies the user, thereby streamlining the naming process and reducing legal risks and hassle.

[0030] The trademark database check unit can conduct searches taking into account the phonetic or semantic similarity of names. For example, when the generation AI checks the trademark database, the trademark database check unit introduces an algorithm that takes into account the phonetic or semantic similarity of names. For example, names with the same pronunciation or similar meanings will also be included in the search. This allows for a broader search by taking into account the phonetic and semantic similarity of names, improving the accuracy of the naming process.

[0031] The Trademark Database Checking Department can automatically generate alternative names based on the results of the trademark database check and suggest them to the user. For example, the Trademark Database Checking Department will develop an algorithm that uses a generation AI to automatically generate usable alternative names based on the results of the trademark database check. For example, it will suggest names with similar meanings or pronunciations. This will enable the automatic generation of alternative names based on the results of the trademark database check, making it possible to quickly suggest appropriate names to users.

[0032] The Scrutiny Department can simultaneously scrutinize not only the trademark database but also the availability of domain names or social media handles. For example, using APIs and RPA, the Scrutiny Department can build a system that simultaneously scrutinizes not only the trademark database but also the availability of domain names and social media handles. For example, it can automatically collect data from each platform. This will ensure the consistency and availability of names by simultaneously scrutinizing not only the trademark database but also the availability of domain names and social media handles.

[0033] The Scrutiny Department will automatically scrutinize regional commercial registry databases in addition to trademark databases, making it possible to avoid region-specific risks. For example, the Scrutiny Department will build a system that automatically scrutinizes regional commercial registry databases in addition to trademark databases for each country. For example, in order to avoid region-specific risks, data for each region will be collected. This will allow scrutinizing regional commercial registry databases as well, making it possible to avoid region-specific risks and select safer names.

[0034] The Reconciliation Department can update the results of trademark database reconciliation in real time and provide users with the latest information. The Reconciliation Department can build a system that uses, for example, API and RPA, to update the results of trademark database reconciliation in real time and provide users with the latest information. For example, changes to the database can be reflected immediately. In this way, by updating the results of trademark database reconciliation in real time, users can be provided with the latest information and support quick decision-making.

[0035] The review unit can classify the results of the trademark database review by different industries or uses and suggest the most suitable names to the user. For example, the review unit can build a system that classifies the results of the trademark database review by different industries or uses and suggest the most suitable names to the user. For example, it can display the usage status of names by industry. In this way, by classifying the results of the trademark database review by different industries or uses, it becomes possible to suggest more suitable names to the user.

[0036] The notification department can display the trademark database check results as infographics that are visually easy to understand. For example, the notification department will develop a system that automatically generates infographics to visually display the trademark database check results. For example, the system will show the name usage and similarity in graphs and charts. In this way, displaying the trademark database check results as infographics will make it easier for users to intuitively understand.

[0037] The notification department can automatically translate the results of trademark database checks into different languages ​​and obtain feedback from an international perspective. The notification department, for example, can build a system that automatically translates the results of trademark database checks into different languages ​​and collects feedback from an international perspective. For example, translation into English, French, Chinese, etc. This allows the results of trademark database checks to be automatically translated into different languages ​​and feedback from an international perspective to be obtained.

[0038] The review unit can propose specific risk avoidance measures to users based on the results of legal risk assessment. For example, the review unit builds a system in which a generation AI proposes specific risk avoidance measures to users based on the results of legal risk assessment. For example, it presents candidates for names with low risk. This makes it easier for users to avoid legal risks by proposing specific risk avoidance measures based on the results of legal risk assessment.

[0039] The review unit can classify the results of legal risk assessment by different jurisdictions and suggest the most appropriate name to the user. For example, the review unit builds a system that classifies the results of legal risk assessment by different jurisdictions and suggests the most appropriate name to the user. For example, it suggests names that take into account the legal requirements of each jurisdiction. In this way, by classifying the results of legal risk assessment by different jurisdictions, it becomes possible to suggest more appropriate names to the user.

[0040] The review unit can convert the results of the legal risk assessment into a visual note or a mind map to make them visually easier to understand. The review unit, for example, builds a system that converts the results of the legal risk assessment into a visual note or a mind map to make them visually easier to understand. For example, names of high risks can be displayed in different colors. In this way, converting the results of the legal risk assessment into a visual note or a mind map makes it easier for users to intuitively understand.

[0041] The notification unit enables the generation AI to automatically monitor the progress of a business launch or product development and notify the user of the progress. The notification unit, for example, builds a system in which the generation AI automatically monitors the progress of a business launch or product development and notifies the user of the progress. For example, it sends a notification upon completion of each step. This allows the generation AI to automatically monitor the progress of a business launch or product development and notify the user of the progress, making it easier to manage the progress of the project.

[0042] The notification unit allows the generation AI to consider the user's business model or market strategy and provide optimal advice. The notification unit, for example, builds a system in which the generation AI considers the user's business model and market strategy and provides optimal advice. For example, it analyzes the business's target market and competitive situation. This allows the generation AI to consider the user's business model and market strategy and provide optimal advice, thereby improving the probability of business success.

[0043] The notification unit can seamlessly link the business launch or product development process between different devices, allowing the user to access it anywhere. The notification unit, for example, builds a system that seamlessly links the business launch or product development process between different devices, allowing the user to access it anywhere. For example, it can be made accessible from smartphones and tablets. This allows the business launch or product development process to be seamlessly linked between different devices, allowing the user to access it anywhere, improving convenience.

[0044] The notification unit allows the generation AI to refer to the user's project history and suggest similar processes. The notification unit, for example, builds a system in which the generation AI refers to the user's past project history and suggests similar processes. For example, it analyzes patterns of past successful projects. This allows the generation AI to refer to the user's past project history and suggest similar processes, improving the probability of project success.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The Global Name Safe System can further include a cultural compatibility evaluator that evaluates the cultural compatibility of names entered by users. For example, it evaluates how a name is perceived in a particular culture or region and provides feedback to help users avoid culturally inappropriate names. This helps avoid cultural misunderstandings in international business operations. For example, it can issue an alert to help users avoid names that have negative connotations in a particular region. It can also recommend names that have positive connotations in a particular culture. Furthermore, if a name is related to a particular cultural event or holiday, the cultural compatibility evaluator can evaluate its relevance and recommend its use at an appropriate time.

[0047] The Global Name Safe System may further include a visual evaluator that evaluates the visual impression of a name. For example, it may evaluate how a name looks in a logo or brand design and recommend visually appealing names. This may enhance brand consistency and visual appeal. For example, it may evaluate how a name harmonizes with a particular font or color. It may also simulate how a name would be incorporated into a logo design and suggest the optimal design. Furthermore, the visual evaluator may evaluate how a name looks in advertising and marketing materials and provide feedback to maximize visual impact.

[0048] The Global Name Safe System may further include a phonetic evaluator that evaluates the phonetic impression of a name. For example, it may evaluate how a name sounds spoken and recommend phonetically appealing names. This may optimize how a name is perceived in advertising or presentations. For example, it may evaluate whether a name is easy to pronounce. It may also evaluate how a name blends with a particular phonetic tone or rhythm. The phonetic evaluator may also evaluate how a name is recognized by a speech recognition system and provide feedback to improve speech recognition accuracy.

[0049] The Global Name Safe System may further include an SEO evaluator that evaluates the SEO (search engine optimization) effectiveness of a name. For example, it evaluates how a name appears in search engines and recommends names with high SEO effectiveness, thereby increasing a brand's online visibility. For example, it may evaluate how a name relates to specific keywords. It may also evaluate how a name influences search engine algorithms. Furthermore, the SEO evaluator may evaluate how a name is shared on social media and provide feedback to increase engagement on social media.

[0050] The Global Name Safe System can further include a legal risk assessment module that evaluates the legal risk of names. For example, it can assess whether a name is legally problematic in a specific jurisdiction and recommend names with lower legal risks. This can prevent legal trouble before it occurs. For example, it can assess whether a name infringes on a specific trademark or copyright. It can also assess whether a name has restrictions on use in a specific jurisdiction. Furthermore, the legal risk assessment module can evaluate whether a name complies with specific industry regulations and provide feedback to minimize legal risks.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The trademark database checker checks the trademark database in real time. For example, the generation AI checks the international trademark database in real time based on the name candidate entered by the user. The generation AI uses a text generation AI (e.g., LLM) to check whether the name is already registered. Step 2: The Scrutiny Department automatically scrutinizes the trademark registration and databases of each country. For example, the Scrutiny Department accesses the trademark databases of each country through APIs, and the RPA automatically collects and analyzes the data. Step 3: The notification department checks the availability of the name and notifies the user. For example, if the generation AI checks the trademark database, and the API and RPA scrutinize the databases of each country, and the availability of the name is confirmed, the notification department notifies the user.

[0053] (Example 2) The Global Name Safe System, an embodiment of the present invention, is a system that streamlines the process of deciding on service and company names. The system uses advanced AI and APIs to check international trademark databases in real time, expediting the naming process and reducing legal risks and hassle. As a result, the Global Name Safe System centralizes management from name selection to legal confirmation, allowing businesses to launch and develop products more smoothly.

[0054] The global name safe system according to an embodiment includes a trademark database check unit, a scrutiny unit, and a notification unit. The trademark database check unit checks the trademark database in real time. For example, the generation AI checks the international trademark database in real time based on a name candidate entered by a user. The generation AI uses a text generation AI (e.g., LLM) to check whether the name is already registered. For example, when a user enters a "new company name," the generation AI checks whether the name is already registered. The scrutiny unit automatically scrutinizes each country's registration and trademark database. For example, it accesses each country's trademark database through an API, and the RPA automatically collects and analyzes the data. For example, the scrutiny unit accesses each country's trademark database through an API, and the RPA automatically collects and analyzes the data. The notification unit confirms the usability of the name and notifies the user. For example, the generation AI checks the trademark database, and the API and RPA scrutinize each country's database. If the usability of the name is confirmed, the notification unit notifies the user. As a result, the global name safe system according to the embodiment swiftly and accurately verifies the availability of a name and notifies the user, thereby streamlining the naming process and reducing legal risks and hassle.

[0055] The trademark database check unit can conduct searches taking into account the phonetic or semantic similarity of names. For example, when the generation AI checks the trademark database, the trademark database check unit introduces an algorithm that takes into account the phonetic or semantic similarity of names. For example, names with the same pronunciation or similar meanings will also be included in the search. This allows for a broader search by taking into account the phonetic and semantic similarity of names, improving the accuracy of the naming process.

[0056] The Trademark Database Checking Department can automatically generate alternative names based on the results of the trademark database check and suggest them to the user. For example, the Trademark Database Checking Department will develop an algorithm that uses a generation AI to automatically generate usable alternative names based on the results of the trademark database check. For example, it will suggest names with similar meanings or pronunciations. This will enable the automatic generation of alternative names based on the results of the trademark database check, making it possible to quickly suggest appropriate names to users.

[0057] The trademark database check unit can use the emotion estimation function to analyze the emotional response to the name entered by the user and preferentially suggest names that evoke positive emotions. For example, the trademark database check unit uses the emotion estimation function to analyze the emotional response to the name entered by the user in real time and preferentially suggest names that evoke positive emotions. For example, the trademark database check unit analyzes the user's facial expressions and voice. This allows the unit to analyze the user's emotional response and suggest names that evoke positive emotions, thereby improving user satisfaction.

[0058] The Scrutiny Department can simultaneously scrutinize not only the trademark database but also the availability of domain names or social media handles. For example, using APIs and RPA, the Scrutiny Department can build a system that simultaneously scrutinizes not only the trademark database but also the availability of domain names and social media handles. For example, it can automatically collect data from each platform. This will ensure the consistency and availability of names by simultaneously scrutinizing not only the trademark database but also the availability of domain names and social media handles.

[0059] The Scrutiny Department will automatically scrutinize regional commercial registry databases in addition to trademark databases, making it possible to avoid region-specific risks. For example, the Scrutiny Department will build a system that automatically scrutinizes regional commercial registry databases in addition to trademark databases for each country. For example, in order to avoid region-specific risks, data for each region will be collected. This will allow scrutinizing regional commercial registry databases as well, making it possible to avoid region-specific risks and select safer names.

[0060] The Reconciliation Department can update the results of trademark database reconciliation in real time and provide users with the latest information. The Reconciliation Department can build a system that uses, for example, API and RPA, to update the results of trademark database reconciliation in real time and provide users with the latest information. For example, changes to the database can be reflected immediately. In this way, by updating the results of trademark database reconciliation in real time, users can be provided with the latest information and support quick decision-making.

[0061] The review unit can classify the results of the trademark database review by different industries or uses and suggest the most suitable names to the user. For example, the review unit can build a system that classifies the results of the trademark database review by different industries or uses and suggest the most suitable names to the user. For example, it can display the usage status of names by industry. In this way, by classifying the results of the trademark database review by different industries or uses, it becomes possible to suggest more suitable names to the user.

[0062] The review unit can use the emotion estimation function to collect users' emotional reactions to the trademark database review results and identify names that are likely to resonate emotionally. For example, the review unit uses the emotion estimation function to collect users' emotional reactions to the trademark database review results in real time and build a system that identifies names that are likely to resonate emotionally. For example, the review unit selects names based on emotion scores. In this way, by collecting users' emotional reactions using the emotion estimation function, names that are likely to resonate emotionally can be identified and user satisfaction improved.

[0063] The notification department can display the trademark database check results as infographics that are visually easy to understand. For example, the notification department will develop a system that automatically generates infographics to visually display the trademark database check results. For example, the system will show the name usage and similarity in graphs and charts. In this way, displaying the trademark database check results as infographics will make it easier for users to intuitively understand.

[0064] The notification department can automatically translate the results of trademark database checks into different languages ​​and obtain feedback from an international perspective. The notification department, for example, can build a system that automatically translates the results of trademark database checks into different languages ​​and collects feedback from an international perspective. For example, translation into English, French, Chinese, etc. This allows the results of trademark database checks to be automatically translated into different languages ​​and feedback from an international perspective to be obtained.

[0065] The notification unit can use the emotion estimation function to collect other users' emotional reactions to the name entered by the user and display popular names in a ranking format. The notification unit, for example, uses the emotion estimation function to build a system that collects other users' emotional reactions to the name entered by the user and displays popular names in a ranking format. For example, a ranking is created based on the emotion score. In this way, by collecting other users' emotional reactions using the emotion estimation function, popular names are displayed in a ranking format to assist the user in making a selection.

[0066] The reconciliation unit can use the emotion estimation function to analyze the user's emotional response to the database reconciliation results and prioritize notifying positive results. For example, the reconciliation unit uses the emotion estimation function to analyze the user's emotional response to the database reconciliation results for each country in real time and build a system that prioritizes notifying positive results. For example, the notification content is adjusted based on the emotion score. In this way, by analyzing the user's emotional response using the emotion estimation function, positive results are prioritized and user satisfaction is improved.

[0067] The review unit can propose specific risk avoidance measures to users based on the results of legal risk assessment. For example, the review unit builds a system in which a generation AI proposes specific risk avoidance measures to users based on the results of legal risk assessment. For example, it presents candidates for names with low risk. This makes it easier for users to avoid legal risks by proposing specific risk avoidance measures based on the results of legal risk assessment.

[0068] The review unit can classify the results of legal risk assessment by different jurisdictions and suggest the most appropriate name to the user. For example, the review unit builds a system that classifies the results of legal risk assessment by different jurisdictions and suggests the most appropriate name to the user. For example, it suggests names that take into account the legal requirements of each jurisdiction. In this way, by classifying the results of legal risk assessment by different jurisdictions, it becomes possible to suggest more appropriate names to the user.

[0069] The review unit can convert the results of the legal risk assessment into a visual note or a mind map to make them visually easier to understand. The review unit, for example, builds a system that converts the results of the legal risk assessment into a visual note or a mind map to make them visually easier to understand. For example, names of high risks can be displayed in different colors. In this way, converting the results of the legal risk assessment into a visual note or a mind map makes it easier for users to intuitively understand.

[0070] The scrutiny unit can use the emotion estimation function to collect other users' emotional reactions to the legal risk assessment results and identify risk avoidance measures that are likely to be emotionally relatable. The scrutiny unit, for example, uses the emotion estimation function to collect other users' emotional reactions to the legal risk assessment results and builds a system that identifies risk avoidance measures that are likely to be emotionally relatable. For example, the scrutiny unit selects risk avoidance measures based on emotion scores. In this way, by collecting other users' emotional reactions using the emotion estimation function, risk avoidance measures that are likely to be emotionally relatable can be identified and user satisfaction can be improved.

[0071] The notification unit enables the generation AI to automatically monitor the progress of a business launch or product development and notify the user of the progress. The notification unit, for example, builds a system in which the generation AI automatically monitors the progress of a business launch or product development and notifies the user of the progress. For example, it sends a notification upon completion of each step. This allows the generation AI to automatically monitor the progress of a business launch or product development and notify the user of the progress, making it easier to manage the progress of the project.

[0072] The notification unit allows the generation AI to consider the user's business model or market strategy and provide optimal advice. The notification unit, for example, builds a system in which the generation AI considers the user's business model and market strategy and provides optimal advice. For example, it analyzes the business's target market and competitive situation. This allows the generation AI to consider the user's business model and market strategy and provide optimal advice, thereby improving the probability of business success.

[0073] The notification unit can use the emotion estimation function to analyze the user's emotional reactions in the business launch or product development process and prioritize the execution of processes that elicit positive emotions. For example, the notification unit uses the emotion estimation function to analyze the user's emotional reactions in the business launch or product development process in real time and build a system that prioritizes the execution of processes that elicit positive emotions. For example, the notification unit adjusts the process based on the emotion score. In this way, by analyzing the user's emotional reactions using the emotion estimation function, processes that elicit positive emotions are prioritized and user satisfaction is improved.

[0074] The notification unit can seamlessly link the business launch or product development process between different devices, allowing the user to access it anywhere. The notification unit, for example, builds a system that seamlessly links the business launch or product development process between different devices, allowing the user to access it anywhere. For example, it can be made accessible from smartphones and tablets. This allows the business launch or product development process to be seamlessly linked between different devices, allowing the user to access it anywhere, improving convenience.

[0075] The notification unit allows the generation AI to refer to the user's project history and suggest similar processes. The notification unit, for example, builds a system in which the generation AI refers to the user's past project history and suggests similar processes. For example, it analyzes patterns of past successful projects. This allows the generation AI to refer to the user's past project history and suggest similar processes, improving the probability of project success.

[0076] The notification unit can use the emotion estimation function to collect the emotional reactions of other users in the business launch or product development process and display popular processes in a ranking format. For example, the notification unit uses the emotion estimation function to build a system that collects the emotional reactions of other users in the business launch or product development process and displays popular processes in a ranking format. For example, the system creates a ranking based on the emotion score. In this way, by collecting the emotional reactions of other users using the emotion estimation function, popular processes are displayed in a ranking format to assist the user in making selections.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The Global Name Safe System can further include a cultural compatibility evaluator that evaluates the cultural compatibility of names entered by users. For example, it evaluates how a name is perceived in a particular culture or region and provides feedback to help users avoid culturally inappropriate names. This helps avoid cultural misunderstandings in international business operations. For example, it can issue an alert to help users avoid names that have negative connotations in a particular region. It can also recommend names that have positive connotations in a particular culture. Furthermore, if a name is related to a particular cultural event or holiday, the cultural compatibility evaluator can evaluate its relevance and recommend its use at an appropriate time.

[0079] The Global Name Safe System may further include a visual evaluator that evaluates the visual impression of a name. For example, it may evaluate how a name looks in a logo or brand design and recommend visually appealing names. This may enhance brand consistency and visual appeal. For example, it may evaluate how a name harmonizes with a particular font or color. It may also simulate how a name would be incorporated into a logo design and suggest the optimal design. Furthermore, the visual evaluator may evaluate how a name looks in advertising and marketing materials and provide feedback to maximize visual impact.

[0080] The Global Name Safe System may further include a phonetic evaluator that evaluates the phonetic impression of a name. For example, it may evaluate how a name sounds spoken and recommend phonetically appealing names. This may optimize how a name is perceived in advertising or presentations. For example, it may evaluate whether a name is easy to pronounce. It may also evaluate how a name blends with a particular phonetic tone or rhythm. The phonetic evaluator may also evaluate how a name is recognized by a speech recognition system and provide feedback to improve speech recognition accuracy.

[0081] The Global Name Safe System may further include an SEO evaluator that evaluates the SEO (search engine optimization) effectiveness of a name. For example, it evaluates how a name appears in search engines and recommends names with high SEO effectiveness, thereby increasing a brand's online visibility. For example, it may evaluate how a name relates to specific keywords. It may also evaluate how a name influences search engine algorithms. Furthermore, the SEO evaluator may evaluate how a name is shared on social media and provide feedback to increase engagement on social media.

[0082] The Global Name Safe System can further include a legal risk assessment module that evaluates the legal risk of names. For example, it can assess whether a name is legally problematic in a specific jurisdiction and recommend names with lower legal risks. This can prevent legal trouble before it occurs. For example, it can assess whether a name infringes on a specific trademark or copyright. It can also assess whether a name has restrictions on use in a specific jurisdiction. Furthermore, the legal risk assessment module can evaluate whether a name complies with specific industry regulations and provide feedback to minimize legal risks.

[0083] The Global Name Safe System can further include an emotion estimation unit that estimates a user's emotions and suggests names based on the estimated emotions. For example, it can analyze the user's emotional response to the name entered in real time and prioritize suggesting names that evoke positive emotions. This can improve user satisfaction. For example, it can analyze the user's facial expressions and voice and select a name based on an emotion score. It can also learn patterns of names that the user has used in the past to show positive emotions and suggest new names based on those patterns. Furthermore, the emotion estimation unit can monitor changes in the user's emotions as they select a name and provide feedback at the optimal time.

[0084] The Global Name Safe System can also include an emotional phonological evaluation unit that estimates a user's emotions and evaluates the phonetic and semantic similarities of names based on the estimated emotions. For example, the system can analyze the user's emotional response to the name entered and prioritize suggestions of names with phonemes and meanings that evoke positive emotions. This enables name selection based on the user's emotions. For example, if a user expresses positive emotions toward a specific phoneme or meaning, the system can suggest names with those phonemes and meanings. The system can also learn patterns of names that the user has previously expressed positive emotions toward and suggest new names based on those patterns. Furthermore, the emotional phonological evaluation unit can monitor changes in the user's emotions as they select a name and provide feedback at the optimal time.

[0085] The Global Name Safe System can further include an emotional visual evaluation unit that estimates a user's emotions and evaluates the visual impression of a name based on the estimated emotions. For example, the system can analyze the user's emotional response to the name entered by the user and prioritize suggestions of names with visual designs that evoke positive emotions. This enables the system to select visually appealing names based on the user's emotions. For example, if a user expresses positive emotions toward a particular color or font, the system can suggest names using that color or font. The system can also learn patterns of visual designs that the user has previously expressed positive emotions toward and suggest new names based on those patterns. Furthermore, the emotional visual evaluation unit can monitor changes in the user's emotions as they select a name and provide feedback at the optimal time.

[0086] The Global Name Safe System can further include an emotional legal risk assessment unit that estimates a user's emotions and evaluates the legal risk of a name based on the estimated emotions. For example, the system analyzes the user's emotional response to the name entered by the user and prioritizes suggestions of names with low legal risk that elicit positive emotions. This enables the selection of legally safe names based on the user's emotions. For example, if a user expresses positive emotions toward a specific legal risk, the system can suggest names that avoid that risk. The system can also learn patterns of legal risks for which the user has previously expressed positive emotions and suggest new names based on those patterns. Furthermore, the emotional legal risk assessment unit can monitor changes in the user's emotions as they select a name and provide feedback at the optimal time.

[0087] The Global Name Safe System can further include an emotional SEO evaluation unit that estimates user sentiment and evaluates the SEO effectiveness of a name based on the estimated sentiment. For example, it can analyze the emotional response to the name entered by the user and prioritize suggestions of names with high SEO effectiveness that evoke positive sentiment. This can increase online brand visibility based on user sentiment. For example, if a user expresses positive sentiment toward a specific keyword, it can suggest names that include that keyword. It can also learn patterns of SEO effectiveness where a user has previously expressed positive sentiment and suggest new names based on those patterns. Furthermore, the emotional SEO evaluation unit can monitor changes in the user's sentiment as they select a name and provide feedback at the optimal time.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The trademark database checker checks the trademark database in real time. For example, the generation AI checks the international trademark database in real time based on the name candidate entered by the user. The generation AI uses a text generation AI (e.g., LLM) to check whether the name is already registered. Step 2: The Scrutiny Department automatically scrutinizes the trademark registration and databases of each country. For example, the Scrutiny Department accesses the trademark databases of each country through APIs, and the RPA automatically collects and analyzes the data. Step 3: The notification department checks the availability of the name and notifies the user. For example, if the generation AI checks the trademark database, and the API and RPA scrutinize the databases of each country, and the availability of the name is confirmed, the notification department notifies the user.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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, RAM 30, and 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.

[0096] 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.

[0097] 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 audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0100] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0101] 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.

[0102] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0110] 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, RAM 30, and 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.

[0111] 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.

[0112] 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 audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] 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.

[0116] 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.

[0117] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and 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.

[0126] 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.

[0127] 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 audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0131] 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.

[0132] 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.

[0133] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0137] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0139] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0140] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0141] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0142] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0144] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0145] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0146] 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.

[0147] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0148] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0149] 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.

[0150] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0151] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0152] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0153] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0154] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 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. The Trademark Database Check Department checks the trademark database in real time, The examination department automatically examines the registration and trademark databases of each country, It has a notification section that checks the availability of the name and notifies the user. A system characterized by:

2. The trademark database check unit: Searches are performed taking into account the phonetic or semantic similarity of the name.

2. The system of claim 1.

3. The inspection unit In addition to the trademark database, simultaneously review the availability of domain names or social media handles.

2. The system of claim 1.

4. The notification unit The system according to claim 1, wherein the results of the trademark database check are displayed as visually easy-to-understand infographics.

5. The trademark database check unit: The system of claim 1 , further comprising: an emotion estimation function for analyzing an emotional response to the name entered by the user; and a system for preferentially suggesting names that evoke positive emotions.

6. The inspection unit The system of claim 1 further comprising: a sentiment estimation function for collecting the user's emotional response to the trademark database review results, and identifying names that are likely to resonate emotionally.

7. The notification unit Using an emotion estimation function, the user's emotional response during the business launch or product development process is analyzed, and the process that elicits positive emotions is executed preferentially.

2. The system of claim 1.

8. The inspection unit The system according to claim 1, further comprising: a sentiment estimation function for collecting the emotional responses of other users to legal risk assessment results; and identifying risk avoidance measures that are likely to be emotionally relatable.

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