system

Abstract

The system according to the embodiment aims to create idols that match the user's preferences, support multiple languages, and filter performance content. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a performance unit, a language support unit, and a filtering unit. The reception unit receives user input. The generation unit generates idols based on information received by the reception unit. The performance unit performs a performance using the idols generated by the generation unit. The language support unit supports the performance performed by the performance unit in multiple languages. The filtering unit filters the content of the performance performed by the performance unit.

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 technologies do not adequately support idol performances in multiple languages ​​or filter performance content, so there is room for improvement.

[0005] The system according to the embodiment aims to create idols that match the user's preferences, support multiple languages, and filter performance content. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a performance unit, a language support unit, and a filtering unit. The reception unit receives user input. The generation unit generates idols based on information received by the reception unit. The performance unit allows the idols generated by the generation unit to perform a performance. The language support unit supports the performance performed by the performance unit in multiple languages. The filtering unit filters the content of the performance performed by the performance unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate idols that match the user's preferences, and can support multiple languages ​​and filter performance content. [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) An idol generation system according to an embodiment of the present invention uses generative AI to create idols that are active 24 hours a day, 365 days a year, have characteristics tailored to the user's preferences, and are multilingual. In this system, users input the idol's characteristics, and the generative AI generates the idol based on those characteristics. The generated idol performs according to the user's preferences and is active 24 hours a day, 365 days a year. The system is also multilingual, allowing the idol to communicate in the language selected by the user. Furthermore, the generative AI has a filtering function to avoid the risk of controversy, ensuring a consistently positive experience for users without disappointment. For example, when inputting the idol's characteristics, users can specify details such as appearance, personality, and performance style. They can select hair color, eye shape, and tone of voice, and this information is input into the generative AI. The generative AI then analyzes the input information and generates the idol. The generative AI generates an idol with an appearance and personality tailored to the user's preferences and creates a program for the idol's performance. For example, an idol can be generated that performs dances and songs that match the music genre selected by the user. The generated idols can be active 24 hours a day, 365 days a year, allowing users to access and enjoy their idols' performances at any time. The system also supports multiple languages, allowing users to communicate with the idols in the language of their choice. For example, multiple languages ​​are supported, including English, Japanese, and Spanish. Furthermore, the generative AI is equipped with a filtering function to prevent the risk of inappropriate comments or actions from idols toward users, ensuring a consistently positive experience. For example, the generative AI can analyze user comments and filter inappropriate content to avoid the risk of inappropriate comments. This system allows users to enjoy idol performances tailored to their preferences, anytime, anywhere.Furthermore, since there is no risk of online outrage, users can support their idols with peace of mind. For example, users can enjoy their idols' performances in between work, or communicate with their idols while relaxing in the middle of the night. This allows the idol generation system to create idols that suit the user's preferences, operate 24 hours a day, 365 days a year, support multiple languages, and avoid the risk of online outrage.

[0029] An idol generation system according to an embodiment includes a reception unit, a generation unit, a performance unit, a language support unit, and a filtering unit. The reception unit provides an interface for a user to input characteristics of an idol. The user can specify, for example, the idol's appearance, personality, performance style, and other details. The reception unit can receive information in the form of, for example, text input, voice input, or image input. The generation unit uses generative AI to generate an idol based on the information received by the reception unit. The generation unit, for example, generates an idol with an appearance and personality that matches the user's preferences. The generation unit uses generative AI to generate the idol's appearance and personality based on the characteristics specified by the user. For example, the generative AI generates an idol based on information selected by the user, such as hair color, eye shape, and tone of voice. The performance unit creates a program for the idol generated by the generation unit to perform a performance. For example, the performance unit generates an idol that performs dances and songs that match a music genre selected by the user. The performance unit uses generative AI to generate an idol's performance based on the music genre specified by the user. For example, the generative AI generates idols who perform dances and songs that match the music genre selected by the user. The language support unit supports the performances performed by the performance unit in multiple languages. The language support unit can communicate with the idols in, for example, a language selected by the user. The language support unit uses the generative AI to generate performances for the idols based on a language specified by the user. For example, the generative AI creates a program for communicating with the idols in a language selected by the user. The filtering unit filters the content of the performances performed by the performance unit. For example, the filtering unit analyzes user comments and filters out inappropriate content. The filtering unit uses the generative AI to analyze user comments and filter out inappropriate content.For example, generative AI analyzes user comments and filters out inappropriate content to avoid the risk of controversy. As a result, the idol creation system according to the embodiment can create idols that match the user's preferences, operate 24 hours a day, 365 days a year, support multiple languages, and avoid the risk of controversy.

[0030] The reception unit can accept information in which the user specifies in detail the idol's appearance, personality, and performance style. For example, the reception unit accepts information in which the user specifies in detail the idol's appearance, personality, and performance style. The user can select, for example, hair color, eye shape, and tone of voice. The reception unit can accept information in the form of, for example, text input, voice input, or image input. This allows the user to specify the idol's characteristics in detail. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the information entered by the user to a generation AI, which can then analyze the information to generate the idol's characteristics.

[0031] The generation unit can generate idols with appearances and personalities that match the user's specifications. For example, the generation unit generates idols with appearances and personalities that match the user's specifications. The generation unit uses generative AI to generate the idol's appearance and personality based on the characteristics specified by the user. For example, the generative AI generates idols based on information selected by the user, such as hair color, eye shape, and tone of voice. This makes it possible to generate idols that match the user's preferences. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input information entered by the user into the generation AI, and the generation AI can analyze the information to generate the idol's characteristics.

[0032] The performance unit can perform dances and songs that match the music genre selected by the user. For example, the performance unit performs dances and songs that match the music genre selected by the user. The performance unit uses generative AI to generate an idol's performance based on the music genre specified by the user. For example, the generative AI generates an idol that performs dances and songs that match the music genre selected by the user. This makes it possible to provide a performance that matches the music genre selected by the user. Some or all of the above-mentioned processing in the performance unit may be performed using AI, for example, or may be performed without using AI. For example, the performance unit can input information entered by the user into the generation AI, and the generation AI can analyze the information to generate the idol's performance.

[0033] The language support unit can communicate with the idol in a language selected by the user. The language support unit communicates with the idol in, for example, a language selected by the user. The language support unit uses a generative AI to generate an idol's performance based on a language specified by the user. For example, the generative AI creates a program for communicating with the idol in a language selected by the user. This makes it possible to communicate with the idol in the language selected by the user. Some or all of the above-mentioned processing in the language support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the language support unit can input information entered by the user into a generation AI, which then analyzes the information to generate an idol's performance.

[0034] The filtering unit can analyze user comments and filter out inappropriate content. For example, the filtering unit analyzes user comments and filters out inappropriate content. The filtering unit uses generative AI to analyze user comments and filter out inappropriate content. For example, the generative AI analyzes user comments and filters out inappropriate content, thereby avoiding the risk of a social outrage. This filters out inappropriate content and provides a positive experience for users. Some or all of the above-described processing in the filtering unit may be performed using AI, for example, or may be performed without using AI. For example, the filtering unit can input information entered by the user into a generation AI, which then analyzes the information to filter out inappropriate content.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, the reception unit automatically displays as candidates the characteristics of idols that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest characteristics to be used in a specific time period from the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to the generation AI, and the generation AI can analyze the information to suggest the optimal input method.

[0036] The reception unit can dynamically change the input items based on the user's current interests and trends. The reception unit dynamically changes the input items based on, for example, the user's current interests and trends. The reception unit can suggest related idol characteristics based on trends and topics recently searched by the user. The reception unit can also customize the input items based on genres and themes in which the user is interested. Furthermore, the reception unit can analyze social media trends followed by the user and automatically input related characteristics. This allows the input items to be dynamically changed based on the user's interests and trends. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's interests and trend data into the generation AI, and the generation AI can analyze the information to dynamically change the input items.

[0037] The reception unit can suggest region-specific idol characteristics based on the user's geographical location information. The reception unit can suggest region-specific idol characteristics based on the user's geographical location information, for example. For example, if the user is in a specific region, the reception unit can suggest characteristics of idols popular in that region. Also, if the user is traveling, the reception unit can suggest idol characteristics specific to the region the user is visiting. Furthermore, the reception unit can suggest relevant idol characteristics based on the culture and trends of the region in which the user lives. This makes it possible to suggest region-specific idol characteristics based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, or without, AI, for example. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then analyze the information to suggest region-specific idol characteristics.

[0038] The reception unit can analyze the user's social media activity and automatically input related idol features. The reception unit, for example, analyzes the user's social media activity and automatically inputs related idol features. The reception unit can suggest related idol features based on, for example, accounts and hashtags followed by the user. The reception unit can also automatically input related idol features based on posts that the user has recently "liked." Furthermore, the reception unit can analyze trends in online communities in which the user participates and suggest related idol features. This allows related idol features to be automatically input based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI, and the generation AI can analyze the information to automatically input related idol features.

[0039] The generation unit can generate idols with higher accuracy by referring to the user's past preferences and selection history during generation. For example, the generation unit can generate idols with higher accuracy by referring to the user's past preferences and selection history during generation. For example, the generation unit can generate idols with similar characteristics based on the characteristics of idols previously selected by the user. The generation unit can also analyze preference trends from the user's past selection history and generate idols based on the analysis. Furthermore, the generation unit can generate optimal idols by referring to the user's past favorite music genres and performance styles. This allows for the generation of highly accurate idols based on the user's past preferences and selection history. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's past preferences and selection history data into the generation AI, which can then analyze the information needed for the generation AI to generate highly accurate idols.

[0040] The generation unit can customize the idol's characteristics based on the user's current living situation and areas of interest at the time of generation. The generation unit customizes the idol's characteristics based on the user's current living situation and areas of interest at the time of generation, for example. The generation unit generates relevant idol characteristics based on, for example, topics or hobbies in which the user is currently interested. The generation unit can also generate appropriate idol characteristics according to the user's living situation (e.g., student, working person, housewife, etc.). Furthermore, the generation unit can generate relevant idol characteristics based on events or activities in which the user is currently participating. This allows the idol's characteristics to be customized based on the user's current living situation and areas of interest. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input the user's current living situation and area of ​​interest data into the generation AI, and the generation AI can analyze the information to customize the idol's characteristics.

[0041] The generation unit can generate region-specific idols by taking into account the user's geographical location information during generation. For example, the generation unit generates region-specific idols by taking into account the user's geographical location information during generation. For example, if the user is in a specific region, the generation unit generates idols based on the culture and trends of that region. Also, if the user is traveling, the generation unit can generate idols specific to the region the user is visiting. Furthermore, the generation unit can generate related idols based on the culture and trends of the region in which the user lives. This allows region-specific idols to be generated based on the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information data into the generation AI, which can then analyze the information to generate region-specific idols.

[0042] The generation unit can analyze the user's social media activity and generate related idols at the time of generation. For example, the generation unit can analyze the user's social media activity and generate related idols at the time of generation. The generation unit can generate related idols based on, for example, accounts and hashtags followed by the user. The generation unit can also generate related idols based on posts that the user has recently "liked." Furthermore, the generation unit can analyze trends in online communities in which the user participates and generate related idols. This allows related idols to be generated based on the user's social media activity. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input the user's social media activity data into the generation AI, which can then analyze the information to generate related idols.

[0043] The performance unit can provide an optimal performance by referring to the user's past viewing history during a performance. For example, the performance unit can provide an optimal performance by referring to the user's past viewing history during a performance. For example, the performance unit can analyze trends in performances the user has viewed in the past and provide a similar performance. The performance unit can also identify a user's preferred genre or style from the user's past viewing history and provide a performance based on that. Furthermore, the performance unit can provide an optimal performance by referring to performances that the user has previously rated highly. This allows an optimal performance to be provided based on the user's past viewing history. Some or all of the above-described processing in the performance unit can be performed using, for example, AI, or without AI. For example, the performance unit can input the user's viewing history data into a generation AI, which can analyze the information needed for the generation AI to provide an optimal performance.

[0044] The performance unit can customize the performance content based on the user's current living situation and areas of interest during the performance. For example, the performance unit customizes the performance content based on the user's current living situation and areas of interest during the performance. For example, the performance unit provides a relevant performance based on the user's current topics of interest or hobbies. The performance unit can also provide an appropriate performance tailored to the user's living situation (e.g., student, working professional, housewife, etc.). Furthermore, the performance unit can also provide a relevant performance based on an event or activity in which the user is currently participating. This allows the performance content to be customized based on the user's current living situation and areas of interest. Some or all of the above-described processing in the performance unit may be performed using, or without, AI. For example, the performance unit can input the user's living situation and area of ​​interest data into the generation AI, which can then analyze the information to customize the performance content.

[0045] The performance unit can provide a region-specific performance by taking into account the user's geographical location information during a performance. For example, the performance unit can provide a region-specific performance by taking into account the user's geographical location information during a performance. For example, if the user is in a specific region, the performance unit can provide a performance based on the culture and trends of that region. Also, if the user is traveling, the performance unit can provide a performance specific to the region the user is visiting. Furthermore, the performance unit can provide a relevant performance based on the culture and trends of the region in which the user lives. This allows a region-specific performance to be provided based on the user's geographical location information. Some or all of the above-described processing in the performance unit may be performed using, for example, AI, or may be performed without using AI. For example, the performance unit can input the user's geographical location information data into a generation AI, which can analyze the information to provide a region-specific performance.

[0046] The performance unit can analyze the user's social media activity during a performance and provide a relevant performance. For example, the performance unit can analyze the user's social media activity during a performance and provide a relevant performance. For example, the performance unit can provide a relevant performance based on accounts or hashtags the user follows. The performance unit can also provide a relevant performance based on posts the user has recently "liked." Furthermore, the performance unit can analyze trends in online communities in which the user participates and provide a relevant performance. This makes it possible to provide a relevant performance based on the user's social media activity. Some or all of the above-described processing in the performance unit can be performed using, for example, AI, or can be performed without using AI. For example, the performance unit can input the user's social media activity data into a generation AI, which can analyze the information to provide a relevant performance.

[0047] The language support unit can suggest the optimal language by referring to the user's past language selection history during language support. For example, the language support unit can suggest the optimal language by referring to the user's past language selection history during language support. For example, the language support unit can automatically display languages ​​that the user has frequently used in the past as candidates. The language support unit can also analyze the language selection patterns that the user has used in the past and suggest the optimal language. Furthermore, the language support unit can predict and suggest the language to be used during a specific time period based on the user's past language selection history. This makes it possible to suggest the optimal language based on the user's past language selection history. Some or all of the above-mentioned processing in the language support unit may be performed using, for example, AI, or may be performed without using AI. For example, the language support unit can input the user's language selection history data into a generation AI and analyze the information for the generation AI to suggest the optimal language.

[0048] The language support unit can customize language options based on the user's current living situation and areas of interest during language support. For example, the language support unit customizes language options based on the user's current living situation and areas of interest during language support. The language support unit can suggest related languages ​​based on, for example, topics or hobbies that the user is currently interested in. The language support unit can also suggest appropriate languages ​​based on the user's living situation (e.g., student, working professional, housewife, etc.). Furthermore, the language support unit can suggest related languages ​​based on events or activities in which the user is currently participating. This allows language options to be customized based on the user's current living situation and areas of interest. Some or all of the above-described processing in the language support unit may be performed using, or without, AI, for example. For example, the language support unit can input data on the user's living situation and areas of interest into the generation AI, and the generation AI can analyze the information to customize the language options.

[0049] The language support unit can suggest a region-specific language by taking into account the user's geographical location information during language support. For example, the language support unit can suggest a region-specific language by taking into account the user's geographical location information during language support. For example, if the user is in a specific region, the language support unit can suggest a language commonly used in that region. Also, if the user is traveling, the language support unit can suggest a language specific to the region the user is visiting. Furthermore, the language support unit can suggest a related language based on the culture and trends of the region in which the user lives. This makes it possible to suggest a region-specific language based on the user's geographical location information. Some or all of the above-described processing in the language support unit may be performed using, or without, AI, for example. For example, the language support unit can input the user's geographical location information data into a generation AI, which can then analyze the information to suggest a region-specific language.

[0050] The language support unit can analyze the user's social media activities and suggest related languages ​​during language support. For example, the language support unit can analyze the user's social media activities and suggest related languages ​​during language support. The language support unit can suggest related languages ​​based on, for example, accounts and hashtags followed by the user. The language support unit can also suggest related languages ​​based on posts recently liked by the user. Furthermore, the language support unit can analyze trends in online communities in which the user participates and suggest related languages. This allows related languages ​​to be suggested based on the user's social media activities. Some or all of the above-described processing in the language support unit can be performed using, or without, AI, for example. For example, the language support unit can input the user's social media activity data into the generation AI and analyze the information to enable the generation AI to suggest related languages.

[0051] The filtering unit can perform optimal filtering by referring to the user's past comment history when filtering. The filtering unit can perform optimal filtering by referring to the user's past comment history when filtering, for example. The filtering unit can, for example, filter similar content based on comments that the user found unpleasant in the past. The filtering unit can also preferentially display favorable content from the user's past comment history. Furthermore, the filtering unit can also perform optimal filtering by referring to comments that the user has previously given high ratings. This allows optimal filtering to be performed based on the user's past comment history. Some or all of the above-mentioned processing in the filtering unit can be performed using, for example, AI, or can be performed without using AI. For example, the filtering unit can input the user's comment history data into the generation AI, and analyze the information for the generation AI to perform optimal filtering.

[0052] The filtering unit can customize the filtering content based on the user's current living situation and areas of interest during filtering. For example, the filtering unit customizes the filtering content based on the user's current living situation and areas of interest during filtering. The filtering unit, for example, preferentially displays content related to topics or hobbies in which the user is currently interested. The filtering unit can also display appropriate content according to the user's living situation (e.g., student, working professional, housewife, etc.). Furthermore, the filtering unit can also preferentially display content related to events or activities in which the user is currently participating. This allows the filtering content to be customized based on the user's current living situation and areas of interest. Some or all of the above-described processing in the filtering unit may be performed using, or without, AI. For example, the filtering unit can input data on the user's living situation and areas of interest into the generation AI, and the generation AI can analyze the information to customize the filtering content.

[0053] The filtering unit can perform region-specific filtering by taking into account the user's geographical location information when filtering. For example, the filtering unit can perform region-specific filtering by taking into account the user's geographical location information when filtering. For example, if the user is in a specific region, the filtering unit can perform filtering based on the language and culture commonly used in that region. Also, if the user is traveling, the filtering unit can perform region-specific filtering for the user's destination. Furthermore, the filtering unit can filter related content based on the culture and trends of the region in which the user lives. This allows region-specific filtering to be performed based on the user's geographical location information. Some or all of the above-described processing in the filtering unit can be performed using, for example, AI, or can be performed without using AI. For example, the filtering unit can input the user's geographical location information data to the generation AI, which can then analyze the information to perform region-specific filtering.

[0054] The filtering unit can analyze the user's social media activity and perform relevant filtering during filtering. For example, the filtering unit can analyze the user's social media activity and perform relevant filtering during filtering. The filtering unit can filter relevant content based on accounts and hashtags followed by the user, for example. The filtering unit can also filter relevant content based on posts that the user has recently "liked." Furthermore, the filtering unit can analyze trends in online communities in which the user participates and filter relevant content. This allows relevant filtering to be performed based on the user's social media activity. Some or all of the above-described processing in the filtering unit can be performed using, or without, AI, for example. For example, the filtering unit can input the user's social media activity data into a generation AI, which can then analyze the information for relevant filtering.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as candidates the characteristics of idols that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest characteristics that will be used in a specific time period from the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0057] The generation unit can customize the idol's characteristics based on the user's current life situation and areas of interest. For example, the generation unit generates relevant idol characteristics based on the user's current topics of interest and hobbies. The generation unit can also generate appropriate idol characteristics according to the user's life situation (e.g., student, working person, housewife, etc.). Furthermore, the generation unit can also generate relevant idol characteristics based on events and activities in which the user is currently participating. This allows the idol's characteristics to be customized based on the user's current life situation and areas of interest.

[0058] The performance unit can provide an optimal performance by referring to the user's past viewing history. For example, it can analyze the trends of performances the user has viewed in the past and provide a similar performance. The performance unit can also identify the user's preferred genre or style from the user's past viewing history and provide a performance based on that. Furthermore, the performance unit can provide an optimal performance by referring to performances that the user has given high ratings to in the past. This makes it possible to provide an optimal performance based on the user's past viewing history.

[0059] The language support unit can suggest the most suitable language by referring to the user's past language selection history. For example, languages ​​that the user has frequently used in the past can be automatically displayed as candidates. The language support unit can also analyze the language selection patterns that the user has used in the past and suggest the most suitable language. Furthermore, the language support unit can predict and suggest the language that will be used in a specific time period based on the user's past language selection history. This makes it possible to suggest the most suitable language based on the user's past language selection history.

[0060] The filtering unit can perform optimal filtering by referring to the user's past comment history. For example, similar content is filtered based on comments that the user found unpleasant in the past. The filtering unit can also preferentially display favorable content from the user's past comment history. Furthermore, the filtering unit can also perform optimal filtering by referring to comments that the user has given high ratings in the past. This allows optimal filtering to be performed based on the user's past comment history.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit provides an interface for the user to input the idol's characteristics. The user can specify, for example, the idol's appearance, personality, performance style, etc. in detail. The reception unit can accept information in the form of, for example, text input, voice input, image input, etc. Step 2: The generation unit uses generative AI to generate an idol based on the information received by the reception unit. For example, the generation unit generates an idol with an appearance and personality that matches the user's preferences. The generation unit uses generative AI to generate the idol's appearance and personality based on the characteristics specified by the user. For example, the generative AI generates an idol based on information such as hair color, eye shape, and voice tone selected by the user. Step 3: The performance unit creates a program for the idol generated by the generation unit to perform. For example, the performance unit generates an idol that performs a dance or song that matches the music genre selected by the user. The performance unit uses generative AI to generate a performance for the idol based on the music genre specified by the user. For example, the generative AI generates an idol that performs a dance or song that matches the music genre selected by the user. Step 4: The language support unit supports the performance performed by the performance unit in multiple languages. For example, the language support unit can communicate with the idol in a language selected by the user. The language support unit uses generative AI to generate the idol's performance based on the language specified by the user. For example, the generative AI creates a program for communicating with the idol in the language selected by the user. Step 5: The filtering unit filters the content of the performance performed by the performance unit. For example, the filtering unit analyzes user comments and filters out inappropriate content. The filtering unit uses generative AI to analyze user comments and filter out inappropriate content. For example, generative AI analyzes user comments and filters out inappropriate content, thereby avoiding the risk of a controversy.

[0063] (Example 2) An idol generation system according to an embodiment of the present invention uses generative AI to create idols that are active 24 hours a day, 365 days a year, have characteristics tailored to the user's preferences, and are multilingual. In this system, users input the idol's characteristics, and the generative AI generates the idol based on those characteristics. The generated idol performs according to the user's preferences and is active 24 hours a day, 365 days a year. The system is also multilingual, allowing the idol to communicate in the language selected by the user. Furthermore, the generative AI has a filtering function to avoid the risk of controversy, ensuring a consistently positive experience for users without disappointment. For example, when inputting the idol's characteristics, users can specify details such as appearance, personality, and performance style. They can select hair color, eye shape, and tone of voice, and this information is input into the generative AI. The generative AI then analyzes the input information and generates the idol. The generative AI generates an idol with an appearance and personality tailored to the user's preferences and creates a program for the idol's performance. For example, an idol can be generated that performs dances and songs that match the music genre selected by the user. The generated idols can be active 24 hours a day, 365 days a year, allowing users to access and enjoy their idols' performances at any time. The system also supports multiple languages, allowing users to communicate with the idols in the language of their choice. For example, multiple languages ​​are supported, including English, Japanese, and Spanish. Furthermore, the generative AI is equipped with a filtering function to prevent the risk of inappropriate comments or actions from idols toward users, ensuring a consistently positive experience. For example, the generative AI can analyze user comments and filter inappropriate content to avoid the risk of inappropriate comments. This system allows users to enjoy idol performances tailored to their preferences, anytime, anywhere.Furthermore, since there is no risk of online outrage, users can support their idols with peace of mind. For example, users can enjoy their idols' performances in between work, or communicate with their idols while relaxing in the middle of the night. This allows the idol generation system to create idols that suit the user's preferences, operate 24 hours a day, 365 days a year, support multiple languages, and avoid the risk of online outrage.

[0064] An idol generation system according to an embodiment includes a reception unit, a generation unit, a performance unit, a language support unit, and a filtering unit. The reception unit provides an interface for a user to input characteristics of an idol. The user can specify, for example, the idol's appearance, personality, performance style, and other details. The reception unit can receive information in the form of, for example, text input, voice input, or image input. The generation unit uses generative AI to generate an idol based on the information received by the reception unit. The generation unit, for example, generates an idol with an appearance and personality that matches the user's preferences. The generation unit uses generative AI to generate the idol's appearance and personality based on the characteristics specified by the user. For example, the generative AI generates an idol based on information selected by the user, such as hair color, eye shape, and tone of voice. The performance unit creates a program for the idol generated by the generation unit to perform a performance. For example, the performance unit generates an idol that performs dances and songs that match a music genre selected by the user. The performance unit uses generative AI to generate an idol's performance based on the music genre specified by the user. For example, the generative AI generates idols who perform dances and songs that match the music genre selected by the user. The language support unit supports the performances performed by the performance unit in multiple languages. The language support unit can communicate with the idols in, for example, a language selected by the user. The language support unit uses the generative AI to generate performances for the idols based on a language specified by the user. For example, the generative AI creates a program for communicating with the idols in a language selected by the user. The filtering unit filters the content of the performances performed by the performance unit. For example, the filtering unit analyzes user comments and filters out inappropriate content. The filtering unit uses the generative AI to analyze user comments and filter out inappropriate content.For example, generative AI analyzes user comments and filters out inappropriate content to avoid the risk of controversy. As a result, the idol creation system according to the embodiment can create idols that match the user's preferences, operate 24 hours a day, 365 days a year, support multiple languages, and avoid the risk of controversy.

[0065] The reception unit can accept information in which the user specifies in detail the idol's appearance, personality, and performance style. For example, the reception unit accepts information in which the user specifies in detail the idol's appearance, personality, and performance style. The user can select, for example, hair color, eye shape, and tone of voice. The reception unit can accept information in the form of, for example, text input, voice input, or image input. This allows the user to specify the idol's characteristics in detail. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the information entered by the user to a generation AI, which can then analyze the information to generate the idol's characteristics.

[0066] The generation unit can generate idols with appearances and personalities that match the user's specifications. For example, the generation unit generates idols with appearances and personalities that match the user's specifications. The generation unit uses generative AI to generate the idol's appearance and personality based on the characteristics specified by the user. For example, the generative AI generates idols based on information selected by the user, such as hair color, eye shape, and tone of voice. This makes it possible to generate idols that match the user's preferences. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input information entered by the user into the generation AI, and the generation AI can analyze the information to generate the idol's characteristics.

[0067] The performance unit can perform dances and songs that match the music genre selected by the user. For example, the performance unit performs dances and songs that match the music genre selected by the user. The performance unit uses generative AI to generate an idol's performance based on the music genre specified by the user. For example, the generative AI generates an idol that performs dances and songs that match the music genre selected by the user. This makes it possible to provide a performance that matches the music genre selected by the user. Some or all of the above-mentioned processing in the performance unit may be performed using AI, for example, or may be performed without using AI. For example, the performance unit can input information entered by the user into the generation AI, and the generation AI can analyze the information to generate the idol's performance.

[0068] The language support unit can communicate with the idol in a language selected by the user. The language support unit communicates with the idol in, for example, a language selected by the user. The language support unit uses a generative AI to generate an idol's performance based on a language specified by the user. For example, the generative AI creates a program for communicating with the idol in a language selected by the user. This makes it possible to communicate with the idol in the language selected by the user. Some or all of the above-mentioned processing in the language support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the language support unit can input information entered by the user into a generation AI, which then analyzes the information to generate an idol's performance.

[0069] The filtering unit can analyze user comments and filter out inappropriate content. For example, the filtering unit analyzes user comments and filters out inappropriate content. The filtering unit uses generative AI to analyze user comments and filter out inappropriate content. For example, the generative AI analyzes user comments and filters out inappropriate content, thereby avoiding the risk of a social outrage. This filters out inappropriate content and provides a positive experience for users. Some or all of the above-described processing in the filtering unit may be performed using AI, for example, or may be performed without using AI. For example, the filtering unit can input information entered by the user into a generation AI, which then analyzes the information to filter out inappropriate content.

[0070] The reception unit can estimate the user's emotions and adjust the idol feature input interface based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the idol feature input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface to minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed customization options to allow the user to enjoy input. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow the user to quickly input the idol's features. This allows the interface to be adjusted according to the user's emotions, providing a more comfortable input experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then analyze the information to adjust the interface based on the emotion.

[0071] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. For example, the reception unit automatically displays as candidates the characteristics of idols that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest characteristics to be used in a specific time period from the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to the generation AI, and the generation AI can analyze the information to suggest the optimal input method.

[0072] The reception unit can dynamically change the input items based on the user's current interests and trends. The reception unit dynamically changes the input items based on, for example, the user's current interests and trends. The reception unit can suggest related idol characteristics based on trends and topics recently searched by the user. The reception unit can also customize the input items based on genres and themes in which the user is interested. Furthermore, the reception unit can analyze social media trends followed by the user and automatically input related characteristics. This allows the input items to be dynamically changed based on the user's interests and trends. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's interests and trend data into the generation AI, and the generation AI can analyze the information to dynamically change the input items.

[0073] The reception unit can estimate the user's emotions and prioritize input items based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and prioritize input items based on the estimated user emotions. For example, if the user is tired, the reception unit can prioritize displaying the most important items to simplify the input procedure. Furthermore, if the user is excited, the reception unit can provide detailed customization options to make the input process more enjoyable. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying items that can be input in the shortest time. This allows the prioritization of input items based on the user's emotions and provides efficient input. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 can be performed using AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then analyze the information to determine the priority of input items.

[0074] The reception unit can suggest region-specific idol characteristics based on the user's geographical location information. The reception unit can suggest region-specific idol characteristics based on the user's geographical location information, for example. For example, if the user is in a specific region, the reception unit can suggest characteristics of idols popular in that region. Also, if the user is traveling, the reception unit can suggest idol characteristics specific to the region the user is visiting. Furthermore, the reception unit can suggest relevant idol characteristics based on the culture and trends of the region in which the user lives. This makes it possible to suggest region-specific idol characteristics based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, or without, AI, for example. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then analyze the information to suggest region-specific idol characteristics.

[0075] The reception unit can analyze the user's social media activity and automatically input related idol features. The reception unit, for example, analyzes the user's social media activity and automatically inputs related idol features. The reception unit can suggest related idol features based on, for example, accounts and hashtags followed by the user. The reception unit can also automatically input related idol features based on posts that the user has recently "liked." Furthermore, the reception unit can analyze trends in online communities in which the user participates and suggest related idol features. This allows related idol features to be automatically input based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI, and the generation AI can analyze the information to automatically input related idol features.

[0076] The generation unit can estimate the user's emotions and adjust the appearance and personality of the generated idol based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the appearance and personality of the generated idol based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an idol with a calm and friendly appearance and personality. If the user is excited, the generation unit can also generate an idol with an energetic and colorful appearance and personality. Furthermore, if the user is sad, the generation unit can generate an idol with a comforting and gentle appearance and personality. This allows the idol's appearance and personality to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can 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-mentioned processing in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotional data into the generation AI, and the generation AI can analyze the information to adjust the idol's appearance and personality.

[0077] The generation unit can generate idols with higher accuracy by referring to the user's past preferences and selection history during generation. For example, the generation unit can generate idols with higher accuracy by referring to the user's past preferences and selection history during generation. For example, the generation unit can generate idols with similar characteristics based on the characteristics of idols previously selected by the user. The generation unit can also analyze preference trends from the user's past selection history and generate idols based on the analysis. Furthermore, the generation unit can generate optimal idols by referring to the user's past favorite music genres and performance styles. This allows for the generation of highly accurate idols based on the user's past preferences and selection history. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's past preferences and selection history data into the generation AI, which can then analyze the information needed for the generation AI to generate highly accurate idols.

[0078] The generation unit can customize the idol's characteristics based on the user's current living situation and areas of interest at the time of generation. The generation unit customizes the idol's characteristics based on the user's current living situation and areas of interest at the time of generation, for example. The generation unit generates relevant idol characteristics based on, for example, topics or hobbies in which the user is currently interested. The generation unit can also generate appropriate idol characteristics according to the user's living situation (e.g., student, working person, housewife, etc.). Furthermore, the generation unit can generate relevant idol characteristics based on events or activities in which the user is currently participating. This allows the idol's characteristics to be customized based on the user's current living situation and areas of interest. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input the user's current living situation and area of ​​interest data into the generation AI, and the generation AI can analyze the information to customize the idol's characteristics.

[0079] The generation unit can estimate the user's emotions and adjust the generated idol's performance style based on the estimated user's emotions. For example, the generation unit can estimate the user's emotions and adjust the generated idol's performance style based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a calm and relaxed performance style. If the user is excited, the generation unit can also generate an energetic and dynamic performance style. Furthermore, if the user is sad, the generation unit can generate a comforting and gentle performance style. This allows the idol's performance style to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can 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-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's emotion data into the generation AI, and the generation AI can analyze the information to adjust the idol's performance style.

[0080] The generation unit can generate region-specific idols by taking into account the user's geographical location information during generation. For example, the generation unit generates region-specific idols by taking into account the user's geographical location information during generation. For example, if the user is in a specific region, the generation unit generates idols based on the culture and trends of that region. Also, if the user is traveling, the generation unit can generate idols specific to the region the user is visiting. Furthermore, the generation unit can generate related idols based on the culture and trends of the region in which the user lives. This allows region-specific idols to be generated based on the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's geographical location information data into the generation AI, which can then analyze the information to generate region-specific idols.

[0081] The generation unit can analyze the user's social media activity and generate related idols at the time of generation. For example, the generation unit can analyze the user's social media activity and generate related idols at the time of generation. The generation unit can generate related idols based on, for example, accounts and hashtags followed by the user. The generation unit can also generate related idols based on posts that the user has recently "liked." Furthermore, the generation unit can analyze trends in online communities in which the user participates and generate related idols. This allows related idols to be generated based on the user's social media activity. Some or all of the above-described processing in the generation unit can be performed using, or without, AI. For example, the generation unit can input the user's social media activity data into the generation AI, which can then analyze the information to generate related idols.

[0082] The performance unit can estimate the user's emotions and adjust the content of the performance based on the estimated user emotions. For example, the performance unit can estimate the user's emotions and adjust the content of the performance based on the estimated user emotions. For example, if the user is relaxed, the performance unit can provide a calm and relaxing performance. Furthermore, if the user is excited, the performance unit can provide an energetic and dynamic performance. Furthermore, if the user is sad, the performance unit can provide a comforting and gentle performance. This allows the content of the performance to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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-mentioned processing in the performance unit can be performed using, for example, AI, or without AI. For example, the performance unit can input the user's emotion data into the generation AI, and the generation AI can analyze the information to adjust the content of the performance.

[0083] The performance unit can provide an optimal performance by referring to the user's past viewing history during a performance. For example, the performance unit can provide an optimal performance by referring to the user's past viewing history during a performance. For example, the performance unit can analyze trends in performances the user has viewed in the past and provide a similar performance. The performance unit can also identify a user's preferred genre or style from the user's past viewing history and provide a performance based on that. Furthermore, the performance unit can provide an optimal performance by referring to performances that the user has previously rated highly. This allows an optimal performance to be provided based on the user's past viewing history. Some or all of the above-described processing in the performance unit can be performed using, for example, AI, or without AI. For example, the performance unit can input the user's viewing history data into a generation AI, which can analyze the information needed for the generation AI to provide an optimal performance.

[0084] The performance unit can customize the performance content based on the user's current living situation and areas of interest during the performance. For example, the performance unit customizes the performance content based on the user's current living situation and areas of interest during the performance. For example, the performance unit provides a relevant performance based on the user's current topics of interest or hobbies. The performance unit can also provide an appropriate performance tailored to the user's living situation (e.g., student, working professional, housewife, etc.). Furthermore, the performance unit can also provide a relevant performance based on an event or activity in which the user is currently participating. This allows the performance content to be customized based on the user's current living situation and areas of interest. Some or all of the above-described processing in the performance unit may be performed using, or without, AI. For example, the performance unit can input the user's living situation and area of ​​interest data into the generation AI, which can then analyze the information to customize the performance content.

[0085] The performance unit can estimate the user's emotions and adjust the order of performances based on the estimated user emotions. For example, the performance unit can estimate the user's emotions and adjust the order of performances based on the estimated user emotions. For example, if the user is relaxed, the performance unit can start with a calm performance. If the user is excited, the performance unit can start with an energetic performance. Furthermore, if the user is sad, the performance unit can start with a comforting performance. This allows the order of performances to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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-mentioned processing in the performance unit can be performed using, for example, AI, or without AI. For example, the performance unit can input the user's emotion data into the generation AI, and the generation AI can analyze the information to adjust the order of performances.

[0086] The performance unit can provide a region-specific performance by taking into account the user's geographical location information during a performance. For example, the performance unit can provide a region-specific performance by taking into account the user's geographical location information during a performance. For example, if the user is in a specific region, the performance unit can provide a performance based on the culture and trends of that region. Also, if the user is traveling, the performance unit can provide a performance specific to the region the user is visiting. Furthermore, the performance unit can provide a relevant performance based on the culture and trends of the region in which the user lives. This allows a region-specific performance to be provided based on the user's geographical location information. Some or all of the above-described processing in the performance unit may be performed using, for example, AI, or may be performed without using AI. For example, the performance unit can input the user's geographical location information data into a generation AI, which can analyze the information to provide a region-specific performance.

[0087] The performance unit can analyze the user's social media activity during a performance and provide a relevant performance. For example, the performance unit can analyze the user's social media activity during a performance and provide a relevant performance. For example, the performance unit can provide a relevant performance based on accounts or hashtags the user follows. The performance unit can also provide a relevant performance based on posts the user has recently "liked." Furthermore, the performance unit can analyze trends in online communities in which the user participates and provide a relevant performance. This makes it possible to provide a relevant performance based on the user's social media activity. Some or all of the above-described processing in the performance unit can be performed using, for example, AI, or can be performed without using AI. For example, the performance unit can input the user's social media activity data into a generation AI, which can analyze the information to provide a relevant performance.

[0088] The language support unit can estimate a user's emotions and adjust language options based on the estimated user emotions. For example, the language support unit can estimate a user's emotions and adjust language options based on the estimated user emotions. For example, if the user is nervous, the language support unit can prioritize displaying the user's native language to provide a sense of security. Furthermore, if the user is relaxed, the language support unit can provide multiple language options to increase freedom of choice. Furthermore, if the user is in a hurry, the language support unit can prioritize displaying the most frequently used language. This allows language options to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 language support unit can be performed using, for example, AI, or without AI. For example, the language support unit can input user emotion data into the generation AI and analyze the information for the generation AI to adjust the language options.

[0089] The language support unit can suggest the optimal language by referring to the user's past language selection history during language support. For example, the language support unit can suggest the optimal language by referring to the user's past language selection history during language support. For example, the language support unit can automatically display languages ​​that the user has frequently used in the past as candidates. The language support unit can also analyze the language selection patterns that the user has used in the past and suggest the optimal language. Furthermore, the language support unit can predict and suggest the language to be used during a specific time period based on the user's past language selection history. This makes it possible to suggest the optimal language based on the user's past language selection history. Some or all of the above-mentioned processing in the language support unit may be performed using, for example, AI, or may be performed without using AI. For example, the language support unit can input the user's language selection history data into a generation AI and analyze the information for the generation AI to suggest the optimal language.

[0090] The language support unit can customize language options based on the user's current living situation and areas of interest during language support. For example, the language support unit customizes language options based on the user's current living situation and areas of interest during language support. The language support unit can suggest related languages ​​based on, for example, topics or hobbies that the user is currently interested in. The language support unit can also suggest appropriate languages ​​based on the user's living situation (e.g., student, working professional, housewife, etc.). Furthermore, the language support unit can suggest related languages ​​based on events or activities in which the user is currently participating. This allows language options to be customized based on the user's current living situation and areas of interest. Some or all of the above-described processing in the language support unit may be performed using, or without, AI, for example. For example, the language support unit can input data on the user's living situation and areas of interest into the generation AI, and the generation AI can analyze the information to customize the language options.

[0091] The language support unit can estimate a user's emotions and determine language priorities based on the estimated user emotions. For example, the language support unit can estimate a user's emotions and determine language priorities based on the estimated user emotions. For example, if the user is nervous, the language support unit can prioritize displaying the user's native language to provide a sense of security. Furthermore, if the user is relaxed, the language support unit can provide multiple language options to increase freedom of choice. Furthermore, if the user is in a hurry, the language support unit can prioritize displaying the most frequently used language. This allows language priorities to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 language support unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the language support unit can input user emotion data into the generation AI, which can then analyze the information to determine language priorities.

[0092] The language support unit can suggest a region-specific language by taking into account the user's geographical location information during language support. For example, the language support unit can suggest a region-specific language by taking into account the user's geographical location information during language support. For example, if the user is in a specific region, the language support unit can suggest a language commonly used in that region. Also, if the user is traveling, the language support unit can suggest a language specific to the region the user is visiting. Furthermore, the language support unit can suggest a related language based on the culture and trends of the region in which the user lives. This makes it possible to suggest a region-specific language based on the user's geographical location information. Some or all of the above-described processing in the language support unit may be performed using, or without, AI, for example. For example, the language support unit can input the user's geographical location information data into a generation AI, which can then analyze the information to suggest a region-specific language.

[0093] The language support unit can analyze the user's social media activities and suggest related languages ​​during language support. For example, the language support unit can analyze the user's social media activities and suggest related languages ​​during language support. The language support unit can suggest related languages ​​based on, for example, accounts and hashtags followed by the user. The language support unit can also suggest related languages ​​based on posts recently liked by the user. Furthermore, the language support unit can analyze trends in online communities in which the user participates and suggest related languages. This allows related languages ​​to be suggested based on the user's social media activities. Some or all of the above-described processing in the language support unit can be performed using, or without, AI, for example. For example, the language support unit can input the user's social media activity data into the generation AI and analyze the information to enable the generation AI to suggest related languages.

[0094] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated user emotions. For example, the filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated user emotions. For example, if the user is feeling stressed, the filtering unit can prioritize displaying positive content and filtering out negative content. Furthermore, if the user is relaxed, the filtering unit can display a wide range of content to attract the user's interest. Furthermore, if the user is in a hurry, the filtering unit can prioritize displaying important information and filtering out unnecessary information. This allows the filtering criteria to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 filtering unit can be performed using, for example, an AI, or without an AI. For example, the filtering unit can input the user's emotion data into the generation AI, which can then analyze the information to adjust the filtering criteria.

[0095] The filtering unit can perform optimal filtering by referring to the user's past comment history when filtering. The filtering unit can perform optimal filtering by referring to the user's past comment history when filtering, for example. The filtering unit can, for example, filter similar content based on comments that the user found unpleasant in the past. The filtering unit can also preferentially display favorable content from the user's past comment history. Furthermore, the filtering unit can also perform optimal filtering by referring to comments that the user has previously given high ratings. This allows optimal filtering to be performed based on the user's past comment history. Some or all of the above-mentioned processing in the filtering unit can be performed using, for example, AI, or can be performed without using AI. For example, the filtering unit can input the user's comment history data into the generation AI, and analyze the information for the generation AI to perform optimal filtering.

[0096] The filtering unit can customize the filtering content based on the user's current living situation and areas of interest during filtering. For example, the filtering unit customizes the filtering content based on the user's current living situation and areas of interest during filtering. The filtering unit, for example, preferentially displays content related to topics or hobbies in which the user is currently interested. The filtering unit can also display appropriate content according to the user's living situation (e.g., student, working professional, housewife, etc.). Furthermore, the filtering unit can also preferentially display content related to events or activities in which the user is currently participating. This allows the filtering content to be customized based on the user's current living situation and areas of interest. Some or all of the above-described processing in the filtering unit may be performed using, or without, AI. For example, the filtering unit can input data on the user's living situation and areas of interest into the generation AI, and the generation AI can analyze the information to customize the filtering content.

[0097] The filtering unit can estimate the user's emotions and determine filtering priorities based on the estimated user emotions. For example, the filtering unit can estimate the user's emotions and determine filtering priorities based on the estimated user emotions. For example, if the user is feeling stressed, the filtering unit can prioritize displaying positive content and filtering out negative content. Furthermore, if the user is relaxed, the filtering unit can display a wide range of content to attract the user's interest. Furthermore, if the user is in a hurry, the filtering unit can prioritize displaying important information and filtering out unnecessary information. This allows filtering priorities to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 filtering unit can be performed using, for example, an AI, or without an AI. For example, the filtering unit can input user emotion data into the generation AI, which can then analyze the information to determine filtering priorities.

[0098] The filtering unit can perform region-specific filtering by taking into account the user's geographical location information when filtering. For example, the filtering unit can perform region-specific filtering by taking into account the user's geographical location information when filtering. For example, if the user is in a specific region, the filtering unit can perform filtering based on the language and culture commonly used in that region. Also, if the user is traveling, the filtering unit can perform region-specific filtering for the user's destination. Furthermore, the filtering unit can filter related content based on the culture and trends of the region in which the user lives. This allows region-specific filtering to be performed based on the user's geographical location information. Some or all of the above-described processing in the filtering unit can be performed using, for example, AI, or can be performed without using AI. For example, the filtering unit can input the user's geographical location information data to the generation AI, which can then analyze the information to perform region-specific filtering.

[0099] The filtering unit can analyze the user's social media activity and perform relevant filtering during filtering. For example, the filtering unit can analyze the user's social media activity and perform relevant filtering during filtering. The filtering unit can filter relevant content based on accounts and hashtags followed by the user, for example. The filtering unit can also filter relevant content based on posts that the user has recently "liked." Furthermore, the filtering unit can analyze trends in online communities in which the user participates and filter relevant content. This allows relevant filtering to be performed based on the user's social media activity. Some or all of the above-described processing in the filtering unit can be performed using, or without, AI, for example. For example, the filtering unit can input the user's social media activity data into a generation AI, which can then analyze the information for relevant filtering. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, performance unit, language support unit, and filtering unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for the user to input the idol's characteristics. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the idol using generative AI. The performance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a program for the generated idol to perform a performance. The language support unit is realized, for example, by the control unit 46A of the smart device 14 and enables communication with the idol in a language selected by the user. The filtering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes user comments and filters out inappropriate content. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, performance unit, language support unit, and filtering unit, described above, 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 control unit 46A of the smart glasses 214 and provides an interface for the user to input the idol's characteristics. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the idol using generative AI. The performance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a program for the generated idol to perform. The language support unit is realized, for example, by the control unit 46A of the smart glasses 214 and enables communication with the idol in a language selected by the user. The filtering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes user comments and filters out inappropriate content. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, performance unit, language support unit, and filtering 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 control unit 46A of the headset-type terminal 314 and provides an interface for the user to input the idol's characteristics. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the idol using generative AI. The performance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a program for the generated idol to perform. The language support unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and enables communication with the idol in a language selected by the user. The filtering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes user comments and filters out inappropriate content. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, performance unit, language support unit, and filtering 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 control unit 46A of the robot 414 and provides an interface for the user to input the idol's characteristics. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the idol using generative AI. The performance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and creates a program for the generated idol to perform a performance. The language support unit is realized, for example, by the control unit 46A of the robot 414 and enables communication with the idol in a language selected by the user. The filtering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes user comments and filters out inappropriate content.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as candidates the characteristics of idols that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest characteristics that will be used in a specific time period from the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0102] The generation unit can customize the idol's characteristics based on the user's current life situation and areas of interest. For example, the generation unit generates relevant idol characteristics based on the user's current topics of interest and hobbies. The generation unit can also generate appropriate idol characteristics according to the user's life situation (e.g., student, working person, housewife, etc.). Furthermore, the generation unit can also generate relevant idol characteristics based on events and activities in which the user is currently participating. This allows the idol's characteristics to be customized based on the user's current life situation and areas of interest.

[0103] The performance unit can provide an optimal performance by referring to the user's past viewing history. For example, it can analyze the trends of performances the user has viewed in the past and provide a similar performance. The performance unit can also identify the user's preferred genre or style from the user's past viewing history and provide a performance based on that. Furthermore, the performance unit can provide an optimal performance by referring to performances that the user has given high ratings to in the past. This makes it possible to provide an optimal performance based on the user's past viewing history.

[0104] The language support unit can suggest the most suitable language by referring to the user's past language selection history. For example, languages ​​that the user has frequently used in the past can be automatically displayed as candidates. The language support unit can also analyze the language selection patterns that the user has used in the past and suggest the most suitable language. Furthermore, the language support unit can predict and suggest the language that will be used in a specific time period based on the user's past language selection history. This makes it possible to suggest the most suitable language based on the user's past language selection history.

[0105] The filtering unit can perform optimal filtering by referring to the user's past comment history. For example, similar content is filtered based on comments that the user found unpleasant in the past. The filtering unit can also preferentially display favorable content from the user's past comment history. Furthermore, the filtering unit can also perform optimal filtering by referring to comments that the user has given high ratings in the past. This allows optimal filtering to be performed based on the user's past comment history.

[0106] The reception unit can estimate the user's emotions and adjust the interface for inputting the idol's characteristics based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit provides a simple and intuitive interface to minimize input steps. If the user is relaxed, the reception unit provides detailed customization options to allow the user to input while enjoying the experience. Furthermore, if the user is in a hurry, the reception unit prioritizes voice input to allow the user to quickly input the idol's characteristics. This allows the interface to be adjusted according to the user's emotions, providing a more comfortable input experience.

[0107] The generation unit can estimate the user's emotions and adjust the appearance and personality of the generated idol based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate an idol with a calm and friendly appearance and personality. If the user is excited, the generation unit can also generate an idol with an energetic and colorful appearance and personality. Furthermore, if the user is sad, the generation unit can generate an idol with a comforting and gentle appearance and personality. In this way, the appearance and personality of the idol can be adjusted according to the user's emotions.

[0108] The performance unit can estimate the user's emotions and adjust the content of the performance based on the estimated user's emotions. For example, if the user is relaxed, the performance unit can provide a calm and relaxing performance. If the user is excited, the performance unit can also provide an energetic and dynamic performance. Furthermore, if the user is sad, the performance unit can also provide a comforting and gentle performance. In this way, the content of the performance can be adjusted according to the user's emotions.

[0109] The language support unit can estimate the user's emotions and adjust language options based on the estimated user emotions. For example, if the user is nervous, the native language can be displayed preferentially to provide a sense of security. The language support unit can also provide multiple language options to increase the user's freedom of choice when the user is relaxed. Furthermore, if the user is in a hurry, the language support unit can also display the most frequently used language preferentially. This allows the language options to be adjusted according to the user's emotions.

[0110] The filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated user emotions. For example, if the user is feeling stressed, positive content is displayed preferentially and negative content is filtered out. The filtering unit can also display a wide range of content to attract the user's interest if the user is relaxed. Furthermore, if the user is in a hurry, the filtering unit can also display important information preferentially and filter out unnecessary information. This allows the filtering criteria to be adjusted according to the user's emotions.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit provides an interface for the user to input the idol's characteristics. The user can specify, for example, the idol's appearance, personality, performance style, etc. in detail. The reception unit can accept information in the form of, for example, text input, voice input, image input, etc. Step 2: The generation unit uses generative AI to generate an idol based on the information received by the reception unit. For example, the generation unit generates an idol with an appearance and personality that matches the user's preferences. The generation unit uses generative AI to generate the idol's appearance and personality based on the characteristics specified by the user. For example, the generative AI generates an idol based on information such as hair color, eye shape, and voice tone selected by the user. Step 3: The performance unit creates a program for the idol generated by the generation unit to perform. For example, the performance unit generates an idol that performs a dance or song that matches the music genre selected by the user. The performance unit uses generative AI to generate a performance for the idol based on the music genre specified by the user. For example, the generative AI generates an idol that performs a dance or song that matches the music genre selected by the user. Step 4: The language support unit supports the performance performed by the performance unit in multiple languages. For example, the language support unit can communicate with the idol in a language selected by the user. The language support unit uses generative AI to generate the idol's performance based on the language specified by the user. For example, the generative AI creates a program for communicating with the idol in the language selected by the user. Step 5: The filtering unit filters the content of the performance performed by the performance unit. For example, the filtering unit analyzes user comments and filters out inappropriate content. The filtering unit uses generative AI to analyze user comments and filter out inappropriate content. For example, generative AI analyzes user comments and filters out inappropriate content, thereby avoiding the risk of a controversy.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 input from a user; a generating unit that generates an idol based on the information received by the receiving unit; a performance section in which the idols generated by the generation section perform a performance; a language support unit that supports the performances performed by the performance unit in multiple languages; a filtering unit that filters the content of the performance performed by the performance unit. A system characterized by:

2. The reception unit Accepts information that allows users to specify details about the idol's appearance, personality, and performance style 2. The system of claim 1.

3. The generation unit Generate idols with appearances and personalities that match user specifications 2. The system of claim 1.

4. The performance section Perform dances and songs to match the music genre selected by the user 2. The system of claim 1.

5. The language support unit Communicate with your idols in the language of your choice 2. The system of claim 1.

6. The filtering unit Analyze user comments and filter inappropriate content 2. The system of claim 1.

7. The reception unit Estimate the user's emotion and adjust the idol's feature input interface based on the estimated user's emotion.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

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