system

Abstract

The system according to the embodiment aims to automatically generate related words based on a theme as character names, and to help users remember the words. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a character generation unit, and a display unit. The reception unit receives a theme input from a user. The generation unit generates related words based on the theme received by the reception unit. The character generation unit automatically generates a character name based on the word generated by the generation unit and its meaning. The display unit displays the character name generated by the character generation unit in the game.

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 techniques have had the problem that it is difficult to generate a large number of character names in a game in a short amount of time, and there is a lack of means to help players memorize and solidify words.

[0005] The system according to the embodiment aims to automatically generate related words based on a theme as character names, and to help users remember the words. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a character generation unit, and a display unit. The reception unit receives a theme input from a user. The generation unit generates related words based on the theme received by the reception unit. The character generation unit automatically generates a character name based on the word generated by the generation unit and its meaning. The display unit displays the character name generated by the character generation unit in the game. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate related words based on a theme as character names, thereby helping to solidify the words in memory. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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. 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) A character generation system according to an embodiment of the present invention is a system for games in which a large number of characters appear in a short period of time. When a user specifies a theme, a generation AI automatically generates character names based on words and their meanings that fit the theme. This character generation system allows users to memorize words while playing the game. For example, when a user specifies a theme for a game, the generation AI generates related words based on the specified theme and automatically generates character names that include the meanings of the words. The generated character names are assigned to characters appearing in the game. Users can naturally memorize the generated character names and their meanings by playing the game. This system allows users to learn while having fun, making it an effective learning tool, especially for children and students. Furthermore, the generation AI can automatically generate a variety of words based on the theme, enabling learning tailored to the user's interests. This allows the character generation system to memorize words while playing the game.

[0029] A character generation system according to an embodiment includes a reception unit, a generation unit, a character generation unit, and a display unit. The reception unit receives a theme input from a user. The theme specified by the user may include, but is not limited to, a game genre, a character personality, and a story setting. The reception unit may receive the theme input by the user in text format. The reception unit may also receive the theme via voice input. For example, the user may dictate the theme using a microphone, and the reception unit may convert the voice into text. The generation unit uses a generation AI to generate related words based on the theme received by the reception unit. The generated words may include, but are not limited to, synonyms, related words, and frequently used words. For example, the generation AI may automatically generate words related to the theme. The generation unit may also generate a variety of words tailored to the user's interests. For example, the generation AI may analyze the user's past theme input history and generate related words. The character generation unit automatically generates a character name using the words generated by the generation unit and their meanings. The character name is determined taking into consideration, for example, the length of the name, the sound of the name, cultural background, etc., but is not limited to these examples. The character generation unit, for example, automatically generates a character name based on the generated word. The character generation unit can also generate a character name that includes the meaning of the generated word. The display unit displays the character name generated by the character generation unit in the game. The display unit, for example, displays the character name on the game screen. The display unit can also add effects to visually emphasize the character name. For example, the character name is highlighted using a color or font size. This allows the character generation system according to the embodiment to help the user solidify the word in their memory through the game. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs a prompt to generate a word related to a theme to the generation AI, and the generation AI generates the word.Some or all of the above-described processing in the character generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character generation unit inputs a prompt to the generation AI to generate a character name based on the generated words, and the generation AI generates the character name. Some or all of the above-described processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit inputs a prompt to the generation AI to display the generated character name, and the generation AI generates a display method.

[0030] The reception unit can analyze the user's past theme input history and select the optimal theme input method. For example, the reception unit prioritizes and suggests theme input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit stores the user's past theme input history in a database and selects the optimal theme input method using an analysis algorithm. The reception unit can also predict and suggest a theme input method to be used during a specific time period based on the user's past theme input history. For example, the reception unit can analyze the user's past input time and suggest the optimal input timing. The reception unit can also analyze trends in themes entered by the user in the past and automatically suggest related themes. For example, the reception unit can extract and suggest related keywords based on the user's past theme input history. In this way, the optimal theme input method can be selected by analyzing the user's past theme input history. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past theme input history into the generation AI and have the generation AI select the optimal theme input method.

[0031] When a theme is input, the reception unit can filter the theme based on the user's current interests. The reception unit can suggest related themes based on keywords recently searched by the user, for example. For example, the reception unit can store the user's search history in a database and extract related themes. The reception unit can also suggest interesting themes based on content recently viewed by the user. For example, the reception unit can analyze the user's browsing history and suggest related themes. The reception unit can also suggest related themes based on topics in social media groups in which the user participates. For example, the reception unit can analyze the user's social media activity and extract related themes. This allows the themes to be filtered based on the user's current interests, thereby suggesting more relevant themes. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's interest data into the generation AI and have the generation AI filter related themes.

[0032] When inputting a theme, the reception unit can prioritize accepting highly relevant themes by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes suggesting themes related to that area. For example, the reception unit acquires the user's geographical location information from GPS data and extracts related themes. Furthermore, if the user is traveling, the reception unit can prioritize suggesting themes related to the user's travel destination. For example, the reception unit acquires information about the user's travel destination and suggests related themes. Furthermore, if the user is at home, the reception unit can prioritize suggesting themes related to events or news around the user's home. For example, the reception unit extracts related themes based on the location information of the user's home. In this way, by taking the user's geographical location information into account, highly relevant themes can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's geographical location information to the generation AI and cause the generation AI to extract related themes.

[0033] When a theme is input, the reception unit can analyze the user's social media activity and suggest related themes. The reception unit can suggest related themes based on, for example, the content of posts that the user has recently "liked." For example, the reception unit can store the user's social media activity in a database and extract related themes using an analysis algorithm. The reception unit can also suggest interesting themes based on the content of posts from accounts the user follows. For example, the reception unit can analyze the content of posts from accounts the user follows and suggest related themes. The reception unit can also suggest related themes based on the topics of groups the user participates in. For example, the reception unit can analyze the topics of groups the user participates in and extract related themes. In this way, related themes can be suggested by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to extract related themes.

[0034] When generating words, the generation unit can adjust the level of detail of the generated words based on the importance of the theme. For example, in the case of an important theme, the generation unit generates words that include detailed descriptions. For example, the generation unit adjusts the level of detail of the generated words using an algorithm that evaluates the importance of the theme. The generation unit can also generate concise words in the case of a general theme. For example, the generation unit adjusts the length and level of detail of the generated words based on the importance of the theme. The generation unit can also generate words that include technical terms in the case of a theme related to a specific field of expertise. For example, the generation unit generates words that include technical terms based on the importance of the theme. In this way, by adjusting the level of detail of the generated words based on the importance of the theme, more appropriate words can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input theme importance data into the generation AI and cause the generation AI to adjust the level of detail of the words to be generated.

[0035] When generating words, the generation unit can apply different generation algorithms depending on the theme category. For example, in the case of a science-related theme, the generation unit applies an algorithm that generates scientific terms. For example, the generation unit selects an appropriate generation algorithm using an algorithm that classifies the theme category. In addition, in the case of an art-related theme, the generation unit can apply an algorithm that generates art terms. For example, the generation unit selects a generation algorithm based on the theme category. In addition, in the case of a sports-related theme, the generation unit can apply an algorithm that generates sports terms. For example, the generation unit selects an appropriate generation algorithm based on the theme category. In this way, by applying different generation algorithms depending on the theme category, more appropriate words can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input theme category data into the generation AI and cause the generation AI to select an appropriate generation algorithm.

[0036] When generating words, the generation unit can determine the generation priority based on the time of submission of the theme. For example, in the case of an urgent theme, the generation unit generates words with priority. For example, the generation unit determines the generation priority using an algorithm that evaluates the time of submission of the theme. The generation unit can also generate words with normal priority in the case of a regular theme. For example, the generation unit adjusts the generation priority based on the time of submission of the theme. The generation unit can also postpone word generation in the case of a long-term theme. For example, the generation unit determines the generation priority based on the time of submission of the theme. In this way, by determining the generation priority based on the time of submission of the theme, words can be generated in a more appropriate order. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input theme submission time data into the generation AI and have the generation AI determine the generation priority.

[0037] When generating words, the generation unit can adjust the order of generation based on thematic relevance. For example, for a theme with high relevance, the generation unit generates words preferentially. For example, the generation unit adjusts the order of generation using an algorithm that evaluates thematic relevance. The generation unit can also generate words in a normal order for a theme with medium relevance. For example, the generation unit adjusts the order of generation based on thematic relevance. The generation unit can also generate words at a later date for a theme with low relevance. For example, the generation unit adjusts the order of generation based on thematic relevance. In this way, adjusting the order of generation based on thematic relevance allows words to be generated in a more appropriate order. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input thematic relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0038] When generating a character name, the character generation unit can adjust the level of detail of the character name based on the importance of the generated word. For example, in the case of an important word, the character generation unit generates a character name that includes a detailed description. For example, the character generation unit adjusts the level of detail of the character name using an algorithm that evaluates the importance of the word. The character generation unit can also generate concise character names for common words. For example, the character generation unit adjusts the length and level of detail of the character name based on the importance of the word. The character generation unit can also generate character names that include technical terms for words related to a specific field of expertise. For example, the character generation unit generates character names that include technical terms based on the importance of the word. In this way, by adjusting the level of detail of the character name based on the importance of the generated word, more appropriate character names can be generated. Some or all of the above-described processing in the character generation unit may be performed using, or without, a generation AI. For example, the character generation unit can input word importance data into the generation AI and cause the generation AI to adjust the level of detail of the character name.

[0039] When generating a character name, the character generation unit can apply different generation algorithms depending on the word category. For example, for a science-related word, the character generation unit applies an algorithm that generates scientific terms. For example, the character generation unit selects an appropriate generation algorithm using an algorithm that classifies word categories. Furthermore, for a word related to art, the character generation unit can also apply an algorithm that generates art terms. For example, the character generation unit selects a generation algorithm based on the word category. Furthermore, for a word related to sports, the character generation unit can also apply an algorithm that generates sports terms. For example, the character generation unit selects an appropriate generation algorithm based on the word category. In this way, by applying different generation algorithms depending on the word category, more appropriate character names can be generated. Some or all of the above-described processing in the character generation unit may be performed using, or without, a generation AI. For example, the character generation unit may input word category data into the generation AI and cause the generation AI to select an appropriate generation algorithm.

[0040] When generating character names, the character generation unit can determine the priority of character names based on the time of word submission. For example, in the case of urgent words, the character generation unit prioritizes generating character names. For example, the character generation unit determines the priority of character names using an algorithm that evaluates the time of word submission. The character generation unit can also generate character names with normal priority for regular words. For example, the character generation unit adjusts the priority of character names based on the time of word submission. The character generation unit can also generate character names for long-term words at a later date. For example, the character generation unit determines the priority of character names based on the time of word submission. In this way, by determining the priority of character names based on the time of word submission, character names can be generated in a more appropriate order. Some or all of the above-described processing in the character generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character generation unit can input word submission time data into the generation AI and cause the generation AI to determine the priority of character names.

[0041] When generating character names, the character generation unit can adjust the order of character names based on word relevance. For example, the character generation unit prioritizes generating character names for words with high relevance. For example, the character generation unit adjusts the order of character names using an algorithm that evaluates word relevance. The character generation unit can also generate character names in the normal order for words with medium relevance. For example, the character generation unit adjusts the order of character names based on word relevance. The character generation unit can also postpone generating character names for words with low relevance. For example, the character generation unit adjusts the order of character names based on word relevance. In this way, adjusting the order of character names based on word relevance allows character names to be generated in a more appropriate order. Some or all of the above-described processing in the character generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character generation unit can input word relevance data to the generation AI and cause the generation AI to adjust the order of character names.

[0042] When displaying a character name, the display unit can select the optimal display method by referring to the user's past operation history. For example, the display unit prioritizes display methods that the user has previously preferred. For example, the display unit stores the user's past operation history in a database and selects the optimal display method using an analysis algorithm. The display unit can also predict and suggest a display method to be used during a specific time period based on the user's past operation history. For example, the display unit can analyze the user's past operation time and suggest the optimal display timing. The display unit can also analyze trends in display methods used by the user in the past and automatically suggest related display methods. For example, the display unit can extract and suggest related display methods based on the user's past operation history. This allows the optimal display method to be selected by referring to the user's past operation history. Some or all of the above-described processing in the display unit can be performed using, or without, a generation AI. For example, the display unit can input the user's past operation history data into the generation AI and have the generation AI select the optimal display method.

[0043] When displaying character names, the display unit can customize the display content based on the user's current interests and concerns. For example, the display unit highlights related character names based on keywords recently searched by the user. For example, the display unit stores the user's search history in a database and extracts related character names. The display unit can also customize and display interesting character names based on content recently viewed by the user. For example, the display unit analyzes the user's browsing history and suggests related character names. The display unit can also customize and display related character names based on topics in social media groups in which the user participates. For example, the display unit analyzes the user's social media activity and extracts related character names. This enables more relevant display by customizing the display content based on the user's current interests and concerns. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's interest data into the generation AI and cause the generation AI to customize and display related character names.

[0044] When displaying a character name, the display unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method tailored to the screen size. For example, the display unit acquires the user's device information and selects the optimal display method. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. For example, the display unit selects the optimal display method based on the user's device information. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. For example, the display unit selects the optimal display method based on the user's device information. In this way, the optimal display method can be selected by taking the user's device information into consideration. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0045] When displaying a character name, the display unit can analyze the user's social media activity to suggest display content. The display unit can suggest related character names based on, for example, the content of posts that the user has recently "liked." For example, the display unit stores the user's social media activity in a database and extracts related character names using an analysis algorithm. The display unit can also suggest interesting character names based on the content of posts from accounts the user follows. For example, the display unit can analyze the content of posts from accounts the user follows and suggest related character names. The display unit can also suggest related character names based on the topics of groups the user participates in. For example, the display unit can analyze the topics of groups the user participates in and extract related character names. In this way, related character names can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the display unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the display unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related character names.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can analyze the user's past theme input history and select the optimal theme input method. For example, it can prioritize and suggest theme input methods (such as voice and text) that the user has frequently used in the past. The reception unit stores the user's past theme input history in a database and selects the optimal theme input method using an analysis algorithm. The reception unit can also predict and suggest a theme input method to be used during a specific time period based on the user's past theme input history. For example, the reception unit can analyze the user's past input times and suggest the optimal input timing. The reception unit can also analyze trends in the themes the user has previously input and automatically suggest related themes. For example, the reception unit can extract and suggest related keywords based on the user's past theme input history. In this way, the optimal theme input method can be selected by analyzing the user's past theme input history. Some or all of the above-described processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can input the user's past theme input history into the generation AI and have the generation AI select the optimal theme input method.

[0048] When a theme is input, the reception unit can filter the theme based on the user's current interests. For example, the reception unit can suggest related themes based on keywords recently searched by the user. The reception unit stores the user's search history in a database and extracts related themes. The reception unit can also suggest interesting themes based on content recently viewed by the user. The reception unit analyzes the user's browsing history and suggests related themes. The reception unit can also suggest related themes based on topics in social media groups in which the user participates. The reception unit analyzes the user's social media activity and extracts related themes. This allows the reception unit to filter the themes based on the user's current interests and suggests more relevant themes. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's interest data into the generation AI and have the generation AI filter related themes.

[0049] When inputting a theme, the reception unit can prioritize accepting highly relevant themes by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes suggesting themes related to that area. The reception unit acquires the user's geographical location information from GPS data and extracts related themes. Furthermore, if the user is traveling, the reception unit can prioritize suggesting themes related to the user's travel destination. The reception unit acquires information about the user's travel destination and suggests related themes. Furthermore, if the user is at home, the reception unit can prioritize suggesting themes related to events and news around the user's home. The reception unit extracts related themes based on the user's home location information. In this way, by taking the user's geographical location information into account, highly relevant themes can be prioritized. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to extract related themes.

[0050] When generating words, the generation unit can adjust the level of detail of the generated words based on the importance of the theme. For example, for an important theme, the generation unit generates words that include detailed descriptions. The generation unit adjusts the level of detail of the generated words using an algorithm that evaluates the importance of the theme. The generation unit can also generate concise words for a general theme. The generation unit adjusts the length and level of detail of the generated words based on the importance of the theme. The generation unit can also generate words that include technical terms for a theme related to a specific field of expertise. The generation unit generates words that include technical terms based on the importance of the theme. In this way, by adjusting the level of detail of the generated words based on the importance of the theme, more appropriate words can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input theme importance data into the generation AI and cause the generation AI to adjust the level of detail of the words to be generated.

[0051] When generating words, the generation unit can apply different generation algorithms depending on the theme category. For example, for a science-related theme, an algorithm that generates scientific terms is applied. The generation unit selects an appropriate generation algorithm using an algorithm that classifies the theme category. The generation unit can also apply an algorithm that generates art terms for a theme related to art. The generation unit selects a generation algorithm based on the theme category. The generation unit can also apply an algorithm that generates sports terms for a theme related to sports. The generation unit selects an appropriate generation algorithm based on the theme category. In this way, by applying different generation algorithms depending on the theme category, more appropriate words can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input theme category data into the generation AI and cause the generation AI to select an appropriate generation algorithm.

[0052] When generating words, the generation unit can determine the generation priority based on the time when the theme was submitted. For example, in the case of an urgent theme, word generation is prioritized. The generation unit determines the generation priority using an algorithm that evaluates the time when the theme was submitted. The generation unit can also generate words with normal priority in the case of a regular theme. The generation unit adjusts the generation priority based on the time when the theme was submitted. The generation unit can also postpone word generation in the case of a long-term theme. The generation unit determines the generation priority based on the time when the theme was submitted. In this way, by determining the generation priority based on the time when the theme was submitted, words can be generated in a more appropriate order. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input theme submission time data into the generation AI and have the generation AI determine the generation priority.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit receives a theme input from the user. Themes specified by the user include, but are not limited to, the game genre, character personality, and story setting. The reception unit, for example, receives the theme input by the user in text format. The reception unit can also receive the theme using voice input. For example, the user dictates the theme using a microphone, and the reception unit converts the voice into text. Step 2: The generation unit uses the generation AI to generate related words based on the theme received by the reception unit. The generated words include, but are not limited to, synonyms, related words, and frequently used words. For example, the generation AI automatically generates words related to the theme. The generation unit can also generate a variety of words tailored to the user's interests. For example, the generation AI analyzes the user's past theme input history and generates related words. Step 3: The character generation unit automatically generates a character name based on the word generated by the generation unit and its meaning. The character name is determined taking into consideration, for example, but not limited to, the length of the name, the sound of the name, and the cultural background. The character generation unit automatically generates a character name based on, for example, the generated word. The character generation unit can also generate a character name that includes the meaning of the generated word. Step 4: The display unit displays the character name generated by the character generation unit in the game. For example, the display unit displays the character name on the game screen. The display unit can also add effects to visually emphasize the character name. For example, the character name can be highlighted by changing the color or font size.

[0055] (Example 2) A character generation system according to an embodiment of the present invention is a system for games in which a large number of characters appear in a short period of time. When a user specifies a theme, a generation AI automatically generates character names based on words and their meanings that fit the theme. This character generation system allows users to memorize words while playing the game. For example, when a user specifies a theme for a game, the generation AI generates related words based on the specified theme and automatically generates character names that include the meanings of the words. The generated character names are assigned to characters appearing in the game. Users can naturally memorize the generated character names and their meanings by playing the game. This system allows users to learn while having fun, making it an effective learning tool, especially for children and students. Furthermore, the generation AI can automatically generate a variety of words based on the theme, enabling learning tailored to the user's interests. This allows the character generation system to memorize words while playing the game.

[0056] A character generation system according to an embodiment includes a reception unit, a generation unit, a character generation unit, and a display unit. The reception unit receives a theme input from a user. The theme specified by the user may include, but is not limited to, a game genre, a character personality, and a story setting. The reception unit may receive the theme input by the user in text format. The reception unit may also receive the theme via voice input. For example, the user may dictate the theme using a microphone, and the reception unit may convert the voice into text. The generation unit uses a generation AI to generate related words based on the theme received by the reception unit. The generated words may include, but are not limited to, synonyms, related words, and frequently used words. For example, the generation AI may automatically generate words related to the theme. The generation unit may also generate a variety of words tailored to the user's interests. For example, the generation AI may analyze the user's past theme input history and generate related words. The character generation unit automatically generates a character name using the words generated by the generation unit and their meanings. The character name is determined taking into consideration, for example, the length of the name, the sound of the name, cultural background, etc., but is not limited to these examples. The character generation unit, for example, automatically generates a character name based on the generated word. The character generation unit can also generate a character name that includes the meaning of the generated word. The display unit displays the character name generated by the character generation unit in the game. The display unit, for example, displays the character name on the game screen. The display unit can also add effects to visually emphasize the character name. For example, the character name is highlighted using a color or font size. This allows the character generation system according to the embodiment to help the user solidify the word in their memory through the game. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs a prompt to generate a word related to a theme to the generation AI, and the generation AI generates the word.Some or all of the above-described processing in the character generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character generation unit inputs a prompt to the generation AI to generate a character name based on the generated words, and the generation AI generates the character name. Some or all of the above-described processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit inputs a prompt to the generation AI to display the generated character name, and the generation AI generates a display method.

[0057] The reception unit can estimate the user's emotions and adjust the timing of theme input based on the estimated user emotions. For example, if the user is excited, the reception unit displays an interface prompting the user to immediately input a theme. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit calculates an emotion score based on changes in the user's facial expression and adjusts the timing of theme input. Furthermore, if the user is relaxed, the reception unit can delay the timing of theme input and encourage the user to input at a slower pace. For example, the reception unit records the user's voice and estimates the emotion using voice analysis technology. The reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of theme input. Furthermore, if the user is feeling stressed, the reception unit can temporarily suspend the theme input and display content that will help the user relax. For example, the reception unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotion using an emotion estimation algorithm. The reception unit calculates an emotion score based on fluctuations in the heart rate and adjusts the timing of theme input. This allows the theme to be input at a more appropriate timing by adjusting the timing of theme input according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0058] The reception unit can analyze the user's past theme input history and select the optimal theme input method. For example, the reception unit prioritizes and suggests theme input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit stores the user's past theme input history in a database and selects the optimal theme input method using an analysis algorithm. The reception unit can also predict and suggest a theme input method to be used during a specific time period based on the user's past theme input history. For example, the reception unit can analyze the user's past input time and suggest the optimal input timing. The reception unit can also analyze trends in themes entered by the user in the past and automatically suggest related themes. For example, the reception unit can extract and suggest related keywords based on the user's past theme input history. In this way, the optimal theme input method can be selected by analyzing the user's past theme input history. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past theme input history into the generation AI and have the generation AI select the optimal theme input method.

[0059] When a theme is input, the reception unit can filter the theme based on the user's current interests. The reception unit can suggest related themes based on keywords recently searched by the user, for example. For example, the reception unit can store the user's search history in a database and extract related themes. The reception unit can also suggest interesting themes based on content recently viewed by the user. For example, the reception unit can analyze the user's browsing history and suggest related themes. The reception unit can also suggest related themes based on topics in social media groups in which the user participates. For example, the reception unit can analyze the user's social media activity and extract related themes. This allows the themes to be filtered based on the user's current interests, thereby suggesting more relevant themes. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's interest data into the generation AI and have the generation AI filter related themes.

[0060] The reception unit can estimate the user's emotion and determine the priority of the input themes based on the estimated user emotion. For example, if the user is excited, the reception unit processes the input themes with a higher priority. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit calculates an emotion score based on changes in facial expression and determines the priority of the themes. The reception unit can also set a lower priority for the input themes if the user is relaxed. For example, the reception unit records the user's voice and estimates the emotion using voice analysis technology. The reception unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of the themes. The reception unit can also temporarily suspend the priority of the input themes if the user is feeling stressed. For example, the reception unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. The reception unit calculates an emotion score based on heart rate fluctuations and determines the priority of the themes. This allows the themes to be processed in a more appropriate order by determining the priority of themes according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0061] When inputting a theme, the reception unit can prioritize accepting highly relevant themes by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes suggesting themes related to that area. For example, the reception unit acquires the user's geographical location information from GPS data and extracts related themes. Furthermore, if the user is traveling, the reception unit can prioritize suggesting themes related to the user's travel destination. For example, the reception unit acquires information about the user's travel destination and suggests related themes. Furthermore, if the user is at home, the reception unit can prioritize suggesting themes related to events or news around the user's home. For example, the reception unit extracts related themes based on the location information of the user's home. In this way, by taking the user's geographical location information into account, highly relevant themes can be prioritized. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's geographical location information to the generation AI and cause the generation AI to extract related themes.

[0062] When a theme is input, the reception unit can analyze the user's social media activity and suggest related themes. The reception unit can suggest related themes based on, for example, the content of posts that the user has recently "liked." For example, the reception unit can store the user's social media activity in a database and extract related themes using an analysis algorithm. The reception unit can also suggest interesting themes based on the content of posts from accounts the user follows. For example, the reception unit can analyze the content of posts from accounts the user follows and suggest related themes. The reception unit can also suggest related themes based on the topics of groups the user participates in. For example, the reception unit can analyze the topics of groups the user participates in and extract related themes. In this way, related themes can be suggested by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to extract related themes.

[0063] The generation unit can estimate the user's emotions and adjust the expression of words to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates words with softer expressions. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit calculates an emotion score based on changes in the facial expression and adjusts the expression of words. The generation unit can also generate words with stronger expressions if the user is excited. For example, the generation unit records the user's voice and estimates the emotion using voice analysis technology. The generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the expression of words. The generation unit can also generate simple, easy-to-understand words if the user is stressed. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The generation unit calculates an emotion score based on fluctuations in heart rate and adjusts the expression of words. This allows for the generation of more appropriate words by adjusting the way words are expressed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0064] When generating words, the generation unit can adjust the level of detail of the generated words based on the importance of the theme. For example, in the case of an important theme, the generation unit generates words that include detailed descriptions. For example, the generation unit adjusts the level of detail of the generated words using an algorithm that evaluates the importance of the theme. The generation unit can also generate concise words in the case of a general theme. For example, the generation unit adjusts the length and level of detail of the generated words based on the importance of the theme. The generation unit can also generate words that include technical terms in the case of a theme related to a specific field of expertise. For example, the generation unit generates words that include technical terms based on the importance of the theme. In this way, by adjusting the level of detail of the generated words based on the importance of the theme, more appropriate words can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input theme importance data into the generation AI and cause the generation AI to adjust the level of detail of the words to be generated.

[0065] When generating words, the generation unit can apply different generation algorithms depending on the theme category. For example, in the case of a science-related theme, the generation unit applies an algorithm that generates scientific terms. For example, the generation unit selects an appropriate generation algorithm using an algorithm that classifies the theme category. In addition, in the case of an art-related theme, the generation unit can apply an algorithm that generates art terms. For example, the generation unit selects a generation algorithm based on the theme category. In addition, in the case of a sports-related theme, the generation unit can apply an algorithm that generates sports terms. For example, the generation unit selects an appropriate generation algorithm based on the theme category. In this way, by applying different generation algorithms depending on the theme category, more appropriate words can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input theme category data into the generation AI and cause the generation AI to select an appropriate generation algorithm.

[0066] The generation unit can estimate the user's emotion and adjust the length of words to be generated based on the estimated user emotion. For example, if the user is relaxed, the generation unit generates longer words. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit calculates an emotion score based on changes in the facial expression and adjusts the length of the words. The generation unit can also generate shorter words if the user is in a hurry. For example, the generation unit records the user's voice and estimates the emotion using voice analysis technology. The generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the words. The generation unit can also generate words of appropriate length if the user is excited. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The generation unit calculates an emotion score based on fluctuations in heart rate and adjusts the length of the words. In this way, more appropriate words can be generated by adjusting the length of the words according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0067] When generating words, the generation unit can determine the generation priority based on the time of submission of the theme. For example, in the case of an urgent theme, the generation unit generates words with priority. For example, the generation unit determines the generation priority using an algorithm that evaluates the time of submission of the theme. The generation unit can also generate words with normal priority in the case of a regular theme. For example, the generation unit adjusts the generation priority based on the time of submission of the theme. The generation unit can also postpone word generation in the case of a long-term theme. For example, the generation unit determines the generation priority based on the time of submission of the theme. In this way, by determining the generation priority based on the time of submission of the theme, words can be generated in a more appropriate order. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input theme submission time data into the generation AI and have the generation AI determine the generation priority.

[0068] When generating words, the generation unit can adjust the order of generation based on thematic relevance. For example, for a theme with high relevance, the generation unit generates words preferentially. For example, the generation unit adjusts the order of generation using an algorithm that evaluates thematic relevance. The generation unit can also generate words in a normal order for a theme with medium relevance. For example, the generation unit adjusts the order of generation based on thematic relevance. The generation unit can also generate words at a later date for a theme with low relevance. For example, the generation unit adjusts the order of generation based on thematic relevance. In this way, adjusting the order of generation based on thematic relevance allows words to be generated in a more appropriate order. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input thematic relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0069] The character generation unit can estimate the user's emotions and adjust the character name generation method based on the estimated user emotions. For example, if the user is relaxed, the character generation unit generates a soft-sounding character name. For example, the character generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The character generation unit calculates an emotion score based on changes in the facial expression and adjusts the character name generation method. The character generation unit can also generate a powerful-sounding character name if the user is excited. For example, the character generation unit records the user's voice and estimates the emotion using voice analysis technology. The character generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the character name generation method. The character generation unit can also generate a simple, easy-to-remember character name if the user is stressed. For example, the character generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The character generation unit calculates an emotion score based on heart rate fluctuations and adjusts the character name generation method. This allows for the generation of more appropriate character names by adjusting the character name generation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the character generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the character generation unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0070] When generating a character name, the character generation unit can adjust the level of detail of the character name based on the importance of the generated word. For example, in the case of an important word, the character generation unit generates a character name that includes a detailed description. For example, the character generation unit adjusts the level of detail of the character name using an algorithm that evaluates the importance of the word. The character generation unit can also generate concise character names for common words. For example, the character generation unit adjusts the length and level of detail of the character name based on the importance of the word. The character generation unit can also generate character names that include technical terms for words related to a specific field of expertise. For example, the character generation unit generates character names that include technical terms based on the importance of the word. In this way, by adjusting the level of detail of the character name based on the importance of the generated word, more appropriate character names can be generated. Some or all of the above-described processing in the character generation unit may be performed using, or without, a generation AI. For example, the character generation unit can input word importance data into the generation AI and cause the generation AI to adjust the level of detail of the character name.

[0071] When generating a character name, the character generation unit can apply different generation algorithms depending on the word category. For example, for a science-related word, the character generation unit applies an algorithm that generates scientific terms. For example, the character generation unit selects an appropriate generation algorithm using an algorithm that classifies word categories. Furthermore, for a word related to art, the character generation unit can also apply an algorithm that generates art terms. For example, the character generation unit selects a generation algorithm based on the word category. Furthermore, for a word related to sports, the character generation unit can also apply an algorithm that generates sports terms. For example, the character generation unit selects an appropriate generation algorithm based on the word category. In this way, by applying different generation algorithms depending on the word category, more appropriate character names can be generated. Some or all of the above-described processing in the character generation unit may be performed using, or without, a generation AI. For example, the character generation unit may input word category data into the generation AI and cause the generation AI to select an appropriate generation algorithm.

[0072] The character generation unit can estimate the user's emotion and adjust the length of the character name based on the estimated user emotion. For example, if the user is relaxed, the character generation unit generates a longer character name. For example, the character generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The character generation unit calculates an emotion score based on changes in the facial expression and adjusts the length of the character name. The character generation unit can also generate a shorter character name if the user is in a hurry. For example, the character generation unit records the user's voice and estimates the emotion using voice analysis technology. The character generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the character name. The character generation unit can also generate a character name of an appropriate length if the user is excited. For example, the character generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The character generation unit calculates an emotion score based on fluctuations in heart rate and adjusts the length of the character name. This allows for the generation of more appropriate character names by adjusting the length of the character name according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the character generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the character generation unit may input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0073] When generating character names, the character generation unit can determine the priority of character names based on the time of word submission. For example, in the case of urgent words, the character generation unit prioritizes generating character names. For example, the character generation unit determines the priority of character names using an algorithm that evaluates the time of word submission. The character generation unit can also generate character names with normal priority for regular words. For example, the character generation unit adjusts the priority of character names based on the time of word submission. The character generation unit can also generate character names for long-term words at a later date. For example, the character generation unit determines the priority of character names based on the time of word submission. In this way, by determining the priority of character names based on the time of word submission, character names can be generated in a more appropriate order. Some or all of the above-described processing in the character generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character generation unit can input word submission time data into the generation AI and cause the generation AI to determine the priority of character names.

[0074] When generating character names, the character generation unit can adjust the order of character names based on word relevance. For example, the character generation unit prioritizes generating character names for words with high relevance. For example, the character generation unit adjusts the order of character names using an algorithm that evaluates word relevance. The character generation unit can also generate character names in the normal order for words with medium relevance. For example, the character generation unit adjusts the order of character names based on word relevance. The character generation unit can also postpone generating character names for words with low relevance. For example, the character generation unit adjusts the order of character names based on word relevance. In this way, adjusting the order of character names based on word relevance allows character names to be generated in a more appropriate order. Some or all of the above-described processing in the character generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character generation unit can input word relevance data to the generation AI and cause the generation AI to adjust the order of character names.

[0075] The display unit can estimate the user's emotions and adjust the display method of the character name based on the estimated user emotions. For example, if the user is relaxed, the display unit displays the character name in soft colors. For example, the display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The display unit calculates an emotion score based on changes in the facial expression and adjusts the display method. Furthermore, if the user is excited, the display unit can display the character name in vivid colors. For example, the display unit records the user's voice and estimates the emotion using voice analysis technology. The display unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method. Furthermore, if the user is feeling stressed, the display unit can provide a simple, highly visible display method. For example, the display unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The display unit calculates an emotion score based on fluctuations in heart rate and adjusts the display method. This allows for more appropriate display by adjusting the display method of the character name according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, or without, the generation AI. For example, the display unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0076] When displaying a character name, the display unit can select the optimal display method by referring to the user's past operation history. For example, the display unit prioritizes display methods that the user has previously preferred. For example, the display unit stores the user's past operation history in a database and selects the optimal display method using an analysis algorithm. The display unit can also predict and suggest a display method to be used during a specific time period based on the user's past operation history. For example, the display unit can analyze the user's past operation time and suggest the optimal display timing. The display unit can also analyze trends in display methods used by the user in the past and automatically suggest related display methods. For example, the display unit can extract and suggest related display methods based on the user's past operation history. This allows the optimal display method to be selected by referring to the user's past operation history. Some or all of the above-described processing in the display unit can be performed using, or without, a generation AI. For example, the display unit can input the user's past operation history data into the generation AI and have the generation AI select the optimal display method.

[0077] When displaying character names, the display unit can customize the display content based on the user's current interests and concerns. For example, the display unit highlights related character names based on keywords recently searched by the user. For example, the display unit stores the user's search history in a database and extracts related character names. The display unit can also customize and display interesting character names based on content recently viewed by the user. For example, the display unit analyzes the user's browsing history and suggests related character names. The display unit can also customize and display related character names based on topics in social media groups in which the user participates. For example, the display unit analyzes the user's social media activity and extracts related character names. This enables more relevant display by customizing the display content based on the user's current interests and concerns. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's interest data into the generation AI and cause the generation AI to customize and display related character names.

[0078] The display unit can estimate the user's emotions and adjust the display order of character names based on the estimated user emotions. For example, if the user is relaxed, the display unit displays character names in soft colors. For example, the display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The display unit calculates an emotion score based on changes in the facial expression and adjusts the display order. Furthermore, if the user is excited, the display unit can display character names in vivid colors. For example, the display unit records the user's voice and estimates the emotion using voice analysis technology. The display unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display order. Furthermore, the display unit can provide a simple, highly visible display method when the user is stressed. For example, the display unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The display unit calculates an emotion score based on heart rate fluctuations and adjusts the display order. This allows the display order of character names to be adjusted according to the user's emotions, thereby enabling a more appropriate display order. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, or without, the generation AI. For example, the display unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0079] When displaying a character name, the display unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method tailored to the screen size. For example, the display unit acquires the user's device information and selects the optimal display method. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. For example, the display unit selects the optimal display method based on the user's device information. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. For example, the display unit selects the optimal display method based on the user's device information. In this way, the optimal display method can be selected by taking the user's device information into consideration. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0080] When displaying a character name, the display unit can analyze the user's social media activity to suggest display content. The display unit can suggest related character names based on, for example, the content of posts that the user has recently "liked." For example, the display unit stores the user's social media activity in a database and extracts related character names using an analysis algorithm. The display unit can also suggest interesting character names based on the content of posts from accounts the user follows. For example, the display unit can analyze the content of posts from accounts the user follows and suggest related character names. The display unit can also suggest related character names based on the topics of groups the user participates in. For example, the display unit can analyze the topics of groups the user participates in and extract related character names. In this way, related character names can be suggested by analyzing the user's social media activity. Some or all of the above-described processing in the display unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the display unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related character names. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, generation unit, character generation unit, and display unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives a theme input by the user in text format or voice input. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates related words based on the theme using a generation AI. The character generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates a character name based on the generated word and its meaning. The display unit is implemented, for example, by the output device 40 of the smart device 14 and displays the generated character name in the game. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, generation unit, character generation unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a theme input by the user via voice input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates related words based on the theme using a generation AI. The character generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates the generated word and its meaning as a character name. The display unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated character name to the user by voice. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, generation unit, character generation unit, and display unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives a theme input by the user via voice input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates related words based on the theme using a generation AI. The character generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates the generated word and its meaning as a character name. The display unit is realized, for example, by the display 343 of the headset-type terminal 314 and displays the generated character name in the game. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, character generation unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives a theme input by the user via voice input. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates related words based on the theme using a generation AI. The character generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates the generated word and its meaning as a character name. The display unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated character name to the user by voice.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The reception unit can analyze the user's past theme input history and select the optimal theme input method. For example, it can prioritize and suggest theme input methods (such as voice and text) that the user has frequently used in the past. The reception unit stores the user's past theme input history in a database and selects the optimal theme input method using an analysis algorithm. The reception unit can also predict and suggest a theme input method to be used during a specific time period based on the user's past theme input history. For example, the reception unit can analyze the user's past input times and suggest the optimal input timing. The reception unit can also analyze trends in the themes the user has previously input and automatically suggest related themes. For example, the reception unit can extract and suggest related keywords based on the user's past theme input history. In this way, the optimal theme input method can be selected by analyzing the user's past theme input history. Some or all of the above-described processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can input the user's past theme input history into the generation AI and have the generation AI select the optimal theme input method.

[0083] The reception unit can estimate the user's emotions and adjust the timing of theme input based on the estimated user emotions. For example, if the user is excited, an interface prompting the user to immediately input a theme is displayed. The reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit calculates an emotion score based on changes in facial expression and adjusts the timing of theme input. If the user is relaxed, the reception unit can also delay the timing of theme input and encourage the user to input at a slower pace. The reception unit records the user's voice and estimates the emotion using voice analysis technology. The reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of theme input. If the user is feeling stressed, the reception unit can temporarily suspend the theme input and display relaxing content. The reception unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. The reception unit calculates an emotion score based on heart rate fluctuations and adjusts the timing of theme input. This allows the theme to be input at a more appropriate time by adjusting the timing of theme input according to the user's emotions. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0084] When a theme is input, the reception unit can filter the theme based on the user's current interests. For example, the reception unit can suggest related themes based on keywords recently searched by the user. The reception unit stores the user's search history in a database and extracts related themes. The reception unit can also suggest interesting themes based on content recently viewed by the user. The reception unit analyzes the user's browsing history and suggests related themes. The reception unit can also suggest related themes based on topics in social media groups in which the user participates. The reception unit analyzes the user's social media activity and extracts related themes. This allows the reception unit to filter the themes based on the user's current interests and suggests more relevant themes. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's interest data into the generation AI and have the generation AI filter related themes.

[0085] The reception unit can estimate the user's emotions and prioritize the input themes based on the estimated user emotions. For example, if the user is excited, the input themes are processed with priority. The reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit calculates an emotion score based on changes in facial expression and determines the priority of the themes. The reception unit can also set a lower priority for the input themes if the user is relaxed. The reception unit records the user's voice and estimates the emotion using voice analysis technology. The reception unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of the themes. The reception unit can also temporarily suspend the priority of the input themes if the user is feeling stressed. The reception unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. The reception unit calculates an emotion score based on heart rate fluctuations and determines the priority of the themes. This allows the themes to be processed in a more appropriate order by prioritizing the themes according to the user's emotions. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0086] When inputting a theme, the reception unit can prioritize accepting highly relevant themes by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes suggesting themes related to that area. The reception unit acquires the user's geographical location information from GPS data and extracts related themes. Furthermore, if the user is traveling, the reception unit can prioritize suggesting themes related to the user's travel destination. The reception unit acquires information about the user's travel destination and suggests related themes. Furthermore, if the user is at home, the reception unit can prioritize suggesting themes related to events and news around the user's home. The reception unit extracts related themes based on the user's home location information. In this way, by taking the user's geographical location information into account, highly relevant themes can be prioritized. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to extract related themes.

[0087] The generation unit can estimate the user's emotions and adjust the expression of words to be generated based on the estimated user emotions. For example, if the user is relaxed, it generates words with softer expressions. The generation unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. It calculates an emotion score based on changes in facial expressions and adjusts the expression of words. The generation unit can also generate words with stronger expressions if the user is excited. The generation unit records the user's voice and estimates their emotions using voice analysis technology. It analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the expression of words. The generation unit can also generate simple, easy-to-understand words if the user is stressed. The generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates their emotions using an emotion estimation algorithm. It calculates an emotion score based on heart rate fluctuations and adjusts the expression of words. This allows the generation of more appropriate words by adjusting the expression of words according to the user's emotions. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0088] When generating words, the generation unit can adjust the level of detail of the generated words based on the importance of the theme. For example, for an important theme, the generation unit generates words that include detailed descriptions. The generation unit adjusts the level of detail of the generated words using an algorithm that evaluates the importance of the theme. The generation unit can also generate concise words for a general theme. The generation unit adjusts the length and level of detail of the generated words based on the importance of the theme. The generation unit can also generate words that include technical terms for a theme related to a specific field of expertise. The generation unit generates words that include technical terms based on the importance of the theme. In this way, by adjusting the level of detail of the generated words based on the importance of the theme, more appropriate words can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input theme importance data into the generation AI and cause the generation AI to adjust the level of detail of the words to be generated.

[0089] When generating words, the generation unit can apply different generation algorithms depending on the theme category. For example, for a science-related theme, an algorithm that generates scientific terms is applied. The generation unit selects an appropriate generation algorithm using an algorithm that classifies the theme category. The generation unit can also apply an algorithm that generates art terms for a theme related to art. The generation unit selects a generation algorithm based on the theme category. The generation unit can also apply an algorithm that generates sports terms for a theme related to sports. The generation unit selects an appropriate generation algorithm based on the theme category. In this way, by applying different generation algorithms depending on the theme category, more appropriate words can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input theme category data into the generation AI and cause the generation AI to select an appropriate generation algorithm.

[0090] The generator can estimate the user's emotions and adjust the length of the words to be generated based on the estimated user emotions. For example, if the user is relaxed, it generates longer words. The generator captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. It calculates an emotion score based on changes in facial expressions and adjusts the length of words. The generator can also generate shorter words if the user is in a hurry. The generator records the user's voice and estimates their emotions using voice analysis technology. It analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of words. The generator can also generate words of appropriate length if the user is excited. The generator collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates their emotions using an emotion estimation algorithm. It calculates an emotion score based on heart rate fluctuations and adjusts the length of words. This allows the generator to generate more appropriate words by adjusting the length of words according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0091] When generating words, the generation unit can determine the generation priority based on the time when the theme was submitted. For example, in the case of an urgent theme, word generation is prioritized. The generation unit determines the generation priority using an algorithm that evaluates the time when the theme was submitted. The generation unit can also generate words with normal priority in the case of a regular theme. The generation unit adjusts the generation priority based on the time when the theme was submitted. The generation unit can also postpone word generation in the case of a long-term theme. The generation unit determines the generation priority based on the time when the theme was submitted. In this way, by determining the generation priority based on the time when the theme was submitted, words can be generated in a more appropriate order. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input theme submission time data into the generation AI and have the generation AI determine the generation priority.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The reception unit receives a theme input from the user. Themes specified by the user include, but are not limited to, the game genre, character personality, and story setting. The reception unit, for example, receives the theme input by the user in text format. The reception unit can also receive the theme using voice input. For example, the user dictates the theme using a microphone, and the reception unit converts the voice into text. Step 2: The generation unit uses the generation AI to generate related words based on the theme received by the reception unit. The generated words include, but are not limited to, synonyms, related words, and frequently used words. For example, the generation AI automatically generates words related to the theme. The generation unit can also generate a variety of words tailored to the user's interests. For example, the generation AI analyzes the user's past theme input history and generates related words. Step 3: The character generation unit automatically generates a character name based on the word generated by the generation unit and its meaning. The character name is determined taking into consideration, for example, but not limited to, the length of the name, the sound of the name, and the cultural background. The character generation unit automatically generates a character name based on, for example, the generated word. The character generation unit can also generate a character name that includes the meaning of the generated word. Step 4: The display unit displays the character name generated by the character generation unit in the game. For example, the display unit displays the character name on the game screen. The display unit can also add effects to visually emphasize the character name. For example, the character name can be highlighted by changing the color or font size.

[0094] 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.

[0095] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] 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 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.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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. a reception unit that receives a theme input from a user; a generation unit that generates related words based on the theme received by the reception unit; a character generation unit that automatically generates a character name using the word generated by the generation unit and its meaning; a display unit that displays the character name generated by the character generation unit in the game. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of theme input based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past theme input history and select the optimal theme input method 2. The system of claim 1.

4. The reception unit Filtering based on the user's current interests as they type 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize the input topics based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit When entering a theme, the most relevant theme is given priority based on the user's geographic location.

2. The system of claim 1.

7. The reception unit When you enter a topic, it analyzes your social media activity and suggests related topics.

2. The system of claim 1.

8. The generation unit Estimate the user's emotions and adjust the expression of generated words based on the estimated user emotions.

2. The system of claim 1.

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