system

Abstract

An object of the system according to the embodiment is to create a virtual influencer suitable for a brand image of a company and efficiently utilize the virtual influencer.SOLUTION: A system includes a customization unit, a generation unit, a quality check unit, an event holding unit, a communication unit, and a detection unit. The customization unit creates a virtual influencer tailored to the brand image of the company. The generation unit generates a PR moving image using the virtual influencer created by the customization unit. The quality check unit checks quality of the PR moving image generated by the generation unit. The event holding unit holds a virtual event using the PR moving picture checked by the quality check unit. The communication unit communicates with the participants in real time in the virtual event held by the event holding unit. The detection unit analyzes the content generated by the generation unit and detects specific malicious content.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies lack efficient means for creating and utilizing virtual influencers that match a company's brand image, and there is room for improvement.

[0005] The system according to the embodiment aims to create virtual influencers that match a company's brand image and utilize them efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a customization unit, a generation unit, a quality check unit, an event hosting unit, a communication unit, and a detection unit. The customization unit creates a virtual influencer that matches a company's brand image. The generation unit generates a promotional video using the virtual influencer created by the customization unit. The quality check unit checks the quality of the promotional video generated by the generation unit. The event hosting unit hosts a virtual event using the promotional video checked by the quality check unit. The communication unit communicates in real time with participants at the virtual event hosted by the event hosting unit. The detection unit analyzes the content generated by the generation unit and detects specific malicious content. [Effects of the Invention]

[0007] The system according to the embodiment can create virtual influencers that match a company's brand image and utilize them efficiently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A platform according to an embodiment of the present invention is a system for creating customizable, AI-generated virtual influencers that companies can use as brand ambassadors. The system creates virtual influencers tailored to a company's brand image, generates promotional videos, hosts virtual events, and checks the quality and safety of the generated content. For example, a company can create a customizable, AI-generated virtual influencer. The virtual influencer's appearance and personality can be tailored to match the company's brand image, and the generation AI generates them. The virtual influencer can then be used to generate promotional videos and host virtual events. The generation AI generates promotional videos based on a scenario provided by the company. The virtual influencer can also communicate with participants in real time. Furthermore, a security service is provided to verify that the generated content is not malicious. The generation AI analyzes the generated content and verifies that it does not contain malicious content. This allows the platform to easily create virtual influencers tailored to a company's brand image and conduct effective promotional activities. The security service also prevents the abuse of virtual influencers and provides safe content. This allows the platform to easily create virtual influencers that match the brand image and carry out effective PR activities, while its security services prevent the misuse of virtual influencers and provide safe content.

[0029] A virtual influencer creation system according to an embodiment includes a customization unit, a generation unit, a quality check unit, an event hosting unit, a communication unit, and a detection unit. The customization unit creates a virtual influencer that matches a company's brand image. For example, the customization unit sets the virtual influencer's appearance and personality based on the company's brand image. The generation unit uses a generation AI to generate a promotional video using the virtual influencer created by the customization unit. For example, the generation unit generates a promotional video based on a scenario provided by the company. The quality check unit checks the quality of the promotional video generated by the generation unit. For example, the quality check unit evaluates the image quality, sound quality, and accuracy of the content. The event hosting unit hosts a virtual event using the promotional video checked by the quality check unit. For example, the event hosting unit conducts live streaming and interactive sessions. The communication unit communicates with participants in real time at the virtual event hosted by the event hosting unit. For example, the communication unit interacts with participants using chat or video calls. The detection unit analyzes the content generated by the generation unit and detects malicious content. For example, the detection unit checks whether the generated promotional video contains inappropriate content. This allows the virtual influencer creation system according to the embodiment to easily create virtual influencers that match a company's brand image and conduct effective promotional activities. Furthermore, the security service prevents the misuse of virtual influencers and provides safe content.

[0030] The customization department can analyze a company's past brand campaign data and suggest characteristics for a virtual influencer. The customization department uses generative AI to analyze a company's past brand campaign data. For example, the customization department analyzes the successful elements of past campaigns and suggests the appearance and personality of a virtual influencer based on that. The customization department can also extract characteristics that are well-received by a specific target demographic from past campaign data and reflect them in a virtual influencer. The customization department can also suggest a virtual influencer that incorporates improvements based on feedback from past campaigns. This enables effective PR activities by providing virtual influencers based on a company's past success stories.

[0031] When customizing a virtual influencer, the customization department can take into account specific trends in a company's target market. The customization department uses the generation AI to analyze trends in the company's target market. For example, the customization department can use the generation AI to create a virtual influencer that reflects current fashion industry trends. The customization department can also adjust the virtual influencer's appearance and personality based on the preferences of consumers in the target market. The customization department can also use the generation AI to create a virtual influencer that incorporates the latest social media trends. This enables more effective marketing by providing virtual influencers that reflect trends in the target market.

[0032] When customizing a virtual influencer, the customization department can analyze the influencer strategies of a company's competitors and propose points of differentiation. The customization department uses generative AI to analyze the influencer strategies of a company's competitors. For example, the customization department analyzes the characteristics of a competitor's influencers and proposes a virtual influencer with unique characteristics to compete with them. The customization department can also create a virtual influencer that emphasizes the company's unique brand image while referring to competitors' success stories. The customization department can also find weaknesses in a competitor's influencer strategy and customize the virtual influencer to complement them. This makes it possible to provide a virtual influencer that emphasizes the company's unique brand image by differentiating it from competitors.

[0033] The customization department can take into account the geographical market characteristics of the company when customizing the virtual influencer. The customization department uses the generation AI to analyze the geographical market characteristics of the company. For example, the customization department causes the generation AI to create a virtual influencer that suits the culture and customs of each region. The customization department can also adjust the appearance and personality of the virtual influencer based on the geographical market characteristics. The customization department can also cause the generation AI to create a virtual influencer that suits the preferences of consumers in each region. This makes it possible to provide virtual influencers that reflect the geographical market characteristics, thereby increasing the marketing effectiveness in each region.

[0034] When customizing the virtual influencer, the customization department can analyze the company's social media activities and reflect relevant characteristics. The customization department uses the generation AI to analyze the company's social media activities. For example, the customization department can analyze the content of the company's social media posts and adjust the virtual influencer's appearance and personality based on that. The customization department can also determine the virtual influencer's characteristics based on the reactions of the company's followers on social media. The customization department can also use the generation AI to create a virtual influencer that reflects the content of the company's social media campaign. This allows the company to deliver a more consistent brand message by providing a virtual influencer that reflects its social media activities.

[0035] When customizing a virtual influencer, the customization department can adjust the customization method by reflecting a company's past feedback. The customization department uses generative AI to analyze a company's past feedback. For example, the customization department can improve the virtual influencer's appearance or personality based on the company's past feedback. The customization department can also provide customization options based on a company's past feedback to meet specific requests. The customization department can also optimize the virtual influencer customization process by referring to the company's past feedback. This makes it possible to provide a virtual influencer that reflects past feedback, thereby enabling customization that meets a company's requests.

[0036] When generating a promotional video, the generation unit can adjust the level of detail in the video based on the company's brand message. The generation unit uses generation AI to adjust the level of detail in the promotional video based on the company's brand message. For example, if the company's brand message is simple, the generation unit can use generation AI to generate a promotional video with simple expression. Conversely, if the company's brand message is detailed, the generation AI can generate a promotional video that includes detailed information. The generation unit can also adjust the visual effects and the amount of text based on the company's brand message. This allows for the generation of promotional videos based on the company's brand message, thereby communicating a more consistent brand message.

[0037] When generating a promotional video, the generation unit can apply different generation algorithms depending on the company's target market. The generation unit uses generation AI to apply different generation algorithms depending on the company's target market. For example, the generation unit can apply a generation algorithm that uses pop and colorful expressions to a promotional video aimed at younger generations. The generation unit can also apply a generation algorithm that uses subdued colors and simple expressions to a promotional video aimed at older generations. The generation unit can also apply a generation algorithm that uses technical terms and visuals appropriate for a specific industry to a promotional video aimed at that industry. This allows for more effective marketing by generating promotional videos tailored to the target market.

[0038] When generating a promotional video, the generation unit can improve the accuracy of generation by referring to the results of a company's past promotional videos. The generation unit uses the generation AI to analyze the results of a company's past promotional videos. For example, the generation unit analyzes the number of views and engagement rates of a company's past promotional videos and adjusts the generation algorithm based on that. The generation unit can also refer to feedback on a company's past promotional videos and have the generation AI generate a promotional video that incorporates improvements. The generation unit can also extract the success factors of a company's past promotional videos and have the generation AI generate a promotional video that reflects those factors. This allows for more effective promotional activities by generating promotional videos that reflect the results of past promotional videos.

[0039] When generating promotional videos, the generation unit can determine the priority of videos based on the timing of a company's campaign. The generation unit uses generation AI to determine the priority of promotional videos based on the timing of a company's campaign. For example, the generation unit prioritizes generating the most important promotional videos immediately before the start of a company's campaign. The generation unit can also generate promotional videos tailored to specific events during the company's campaign period. The generation unit can also generate a comprehensive promotional video after the company's campaign has ended. This allows for more effective marketing by generating promotional videos according to the campaign timing.

[0040] When generating promotional videos, the generation unit can adjust the order of the videos based on the relevance of the company. The generation unit uses generation AI to adjust the order of the promotional videos based on the relevance of the company. For example, the generation unit may first generate promotional videos related to the company's main products. The generation unit may also prioritize generating promotional videos related to the company's new products. The generation unit may also postpone promotional videos related to the company's past products. This allows for more effective promotional activities by generating promotional videos based on the relevance of the company.

[0041] When generating a promotional video, the generation unit can adjust the use of technical terms in the video according to the company's level of expertise. The generation unit uses generation AI to adjust the use of technical terms in the promotional video according to the company's level of expertise. For example, the generation unit avoids technical terms and uses easy-to-understand expressions for promotional videos aimed at general consumers. The generation unit can also use a lot of technical terms to provide detailed information for promotional videos aimed at industry experts. The generation unit can also use expressions that include explanations of technical terms for promotional videos aimed at beginners. This allows for more effective promotional activities by generating promotional videos that suit the company's level of expertise.

[0042] During quality checks, the quality check department can adjust the level of detail of the check based on the company's brand guidelines. The quality check department uses AI to perform quality checks based on the company's brand guidelines. For example, the quality check department performs quality checks that strictly follow the company's brand guidelines. The quality check department can also perform quality checks that focus on specific elements based on the company's brand guidelines. The quality check department can also check visual elements and text content based on the company's brand guidelines. In this way, performing quality checks based on the company's brand guidelines can maintain the brand image.

[0043] The quality check department can conduct quality checks taking into account the specific reactions of a company's target market. The quality check department uses AI to analyze the reactions of a company's target market. For example, the quality check department sets standards for quality checks based on feedback from consumers in the target market. The quality check department can also adjust the content of quality checks based on the culture and customs of the target market. The quality check department can also conduct quality checks that are tailored to the preferences of consumers in the target market. This allows for more effective PR activities by conducting quality checks that take into account the reactions of the target market.

[0044] When conducting quality checks, the quality check department can improve the accuracy of the checks by referring to the company's past quality check results. The quality check department uses AI to analyze the company's past quality check results. For example, the quality check department can conduct quality checks that incorporate improvements based on the company's past quality check results. The quality check department can also extract specific problems from the company's past quality check results and conduct quality checks based on those. The quality check department can also optimize the quality check standards based on the company's past quality check results. This enables more accurate quality checks by conducting quality checks that reflect the past quality check results.

[0045] The quality check department can conduct quality checks taking into account the geographical market characteristics of the company. The quality check department uses AI to analyze the geographical market characteristics of the company. For example, the quality check department conducts quality checks that are tailored to the culture and customs of each region. The quality check department can also adjust the content of the quality checks based on the geographical market characteristics. The quality check department can also conduct quality checks that are tailored to the preferences of consumers in each region. In this way, by conducting quality checks that take into account geographical market characteristics, it is possible to increase the marketing effectiveness in each region.

[0046] During quality checks, the quality check department can analyze a company's social media activities and reflect related check items. The quality check department uses AI to analyze a company's social media activities. For example, the quality check department analyzes the content of a company's social media posts and sets quality check items based on that. The quality check department can also determine the quality check standards by referring to the reactions of the company's social media followers. The quality check department can also perform quality checks that reflect the content of the company's social media campaigns. In this way, by performing quality checks that reflect the company's social media activities, a more consistent brand message can be communicated.

[0047] The quality check department can adjust the check method by reflecting the company's past feedback during quality checks. The quality check department uses AI to analyze the company's past feedback. For example, the quality check department can improve the quality check standards based on the company's past feedback. The quality check department can also set quality check items according to specific requests from the company's past feedback. The quality check department can also optimize the quality check process by referring to the company's past feedback. This makes it possible to perform quality checks that reflect past feedback and meet the company's requests.

[0048] When hosting an event, the event hosting department can adjust the level of detail of the event based on the company's brand message. The event hosting department uses AI to adjust the level of detail of the event based on the company's brand message. For example, if the company's brand message is simple, the event hosting department can host a virtual event with simple expression. Conversely, if the company's brand message is detailed, the event hosting department can host a virtual event with detailed information. The event hosting department can also adjust the visual effects and the amount of text based on the company's brand message. This allows for a more consistent brand message to be communicated by hosting an event based on the company's brand message.

[0049] When holding an event, the event organization department can apply different event formats depending on the company's target market. The event organization department uses AI to apply different event formats depending on the company's target market. For example, the event organization department can use pop and colorful expressions for a virtual event aimed at younger people. The event organization department can also use subdued colors and simple expressions for a virtual event aimed at older people. The event organization department can also use technical terms and visuals appropriate for a specific industry for a virtual event aimed at that industry. This allows for more effective marketing by applying an event format according to the target market.

[0050] When holding an event, the event planning department can refer to the results of a company's past events to improve the accuracy of the event. The event planning department uses AI to analyze the results of a company's past events. For example, the event planning department can analyze the number of participants and engagement rates of a company's past events and adjust the content of the event based on that. The event planning department can also refer to feedback from a company's past events to hold an event that incorporates areas for improvement. The event planning department can also extract the success factors of a company's past events and hold an event that reflects those factors. This makes it possible to hold a more effective event by holding an event that reflects the results of past events.

[0051] When holding an event, the event organizing department can adjust the event taking into account the geographical market characteristics of the company. The event organizing department uses AI to analyze the geographical market characteristics of the company. For example, the event organizing department holds an event that suits the culture and customs of each region. The event organizing department can also adjust the content of the event based on the geographical market characteristics. The event organizing department can also hold an event that suits the preferences of consumers in each region. In this way, by holding an event that takes into account the geographical market characteristics, it is possible to increase the marketing effectiveness in each region.

[0052] When holding an event, the event organization department can analyze a company's social media activities and reflect related event content. The event organization department uses AI to analyze a company's social media activities. For example, the event organization department analyzes the content of a company's social media posts and sets the event content based on that. The event organization department can also decide the event content by taking into consideration the reactions of the company's followers on social media. The event organization department can also hold an event that reflects the content of the company's social media campaign. In this way, by holding an event that reflects the company's social media activities, a more consistent brand message can be communicated.

[0053] When holding an event, the event organization department can adjust the event method by reflecting the company's past feedback. The event organization department uses AI to analyze the company's past feedback. For example, the event organization department can improve the content of the event based on the company's past feedback. The event organization department can also set event content that meets specific requests from the company's past feedback. The event organization department can also optimize the event process by referring to the company's past feedback. This makes it possible to hold an event that reflects past feedback and meets the company's requests.

[0054] When communicating, the communications department can adjust the level of detail in the communication based on the company's brand message. The communications department uses AI to adjust the level of detail in the communication based on the company's brand message. For example, if the company's brand message is simple, the communications department can use simple expressions. Conversely, if the company's brand message is detailed, the communications department can use more detailed information. The communications department can also adjust the visual effects and amount of text based on the company's brand message. This allows for a more consistent brand message to be communicated by communicating based on the company's brand message.

[0055] When communicating, the communications department can apply different communication methods depending on the company's target market. The communications department uses AI to apply different communication methods depending on the company's target market. For example, the communications department can use pop and colorful expressions when communicating with younger generations. The communications department can also use subdued colors and simple expressions when communicating with older generations. The communications department can also use technical terms and visuals appropriate for a specific industry when communicating with that industry. This allows for more effective marketing by applying communication methods according to the target market.

[0056] When communicating, the communications department can improve the accuracy of their communications by referring to the results of the company's past communications. The communications department uses AI to analyze the results of the company's past communications. For example, the communications department can incorporate points for improvement into communications based on the results of the company's past communications. The communications department can also incorporate points for improvement into communications by referring to feedback on the company's past communications. The communications department can also extract factors that contributed to the success of the company's past communications and reflect these in communications. In this way, more effective communications are possible by communicating in a way that reflects the results of past communications.

[0057] The communications department can adjust communications taking into account the geographical market characteristics of the company. The communications department uses AI to analyze the geographical market characteristics of the company. For example, the communications department can tailor communications to suit the culture and customs of each region. The communications department can also adjust the content of communications based on the geographical market characteristics. The communications department can also tailor communications to suit the preferences of consumers in each region. In this way, communications that take into account geographical market characteristics can increase the marketing effectiveness in each region.

[0058] When communicating, the communications department can analyze the company's social media activities and reflect relevant communication content. The communications department uses AI to analyze the company's social media activities. For example, the communications department analyzes the content of the company's social media posts and sets the content of the communication based on that. The communications department can also determine the content of the communication by taking into account the reactions of the company's followers on social media. The communications department can also create communications that reflect the content of the company's social media campaigns. In this way, communications that reflect the company's social media activities can send a more consistent brand message.

[0059] The communications department can adjust the communication method by reflecting the company's past feedback when communicating. The communications department uses AI to analyze the company's past feedback. For example, the communications department can improve the content of communications based on the company's past feedback. The communications department can also set communication content that meets specific requests from the company's past feedback. The communications department can also optimize the communication process by referring to the company's past feedback. This makes it possible to communicate in a way that reflects past feedback, thereby meeting the company's requests.

[0060] During detection, the detection unit can adjust the detection detail level based on the company's brand guidelines. The detection unit uses AI to perform detection based on the company's brand guidelines. For example, the detection unit performs detection that strictly follows the company's brand guidelines. The detection unit can also perform detection that focuses on specific elements based on the company's brand guidelines. The detection unit can also detect visual elements and text content based on the company's brand guidelines. This allows the company to maintain its brand image by performing detection based on the company's brand guidelines.

[0061] The detection unit can perform detection taking into account the specific reactions of a company's target market. The detection unit uses AI to analyze the reactions of a company's target market. For example, the detection unit sets detection criteria based on feedback from consumers in the target market. The detection unit can also adjust the content of detection based on the culture and habits of the target market. The detection unit can also perform detection that is tailored to the preferences of consumers in the target market. This allows for more effective PR activities by performing detection that takes into account the reactions of the target market.

[0062] During detection, the detection unit can improve the accuracy of detection by referring to the company's past detection results. The detection unit uses AI to analyze the company's past detection results. For example, the detection unit performs detection incorporating improvements based on the company's past detection results. The detection unit can also extract specific issues from the company's past detection results and perform detection based on those. The detection unit can also optimize the detection criteria based on the company's past detection results. This enables more accurate detection by performing detection that reflects past detection results.

[0063] The detection unit can perform detection taking into account the geographical market characteristics of the company. The detection unit uses AI to analyze the geographical market characteristics of the company. For example, the detection unit performs detection tailored to the culture and customs of each region. The detection unit can also adjust the content of the detection based on the geographical market characteristics. The detection unit can also perform detection tailored to the preferences of consumers in each region. In this way, by performing detection taking into account the geographical market characteristics, it is possible to increase the marketing effectiveness in each region.

[0064] At the time of detection, the detection unit can analyze the company's social media activities and reflect related detection items. The detection unit uses AI to analyze the company's social media activities. For example, the detection unit can analyze the content of the company's social media posts and set detection items based on that. The detection unit can also determine detection criteria by referring to the reactions of the company's followers on social media. The detection unit can also perform detection that reflects the content of the company's social media campaigns. This allows for detection that reflects the company's social media activities, enabling a more consistent brand message to be sent.

[0065] The detection unit can adjust the detection method by reflecting the company's past feedback during detection. The detection unit uses AI to analyze the company's past feedback. For example, the detection unit can improve the detection criteria based on the company's past feedback. The detection unit can also set detection items based on specific requests from the company's past feedback. The detection unit can also optimize the detection process by referring to the company's past feedback. This makes it possible to perform detection that reflects past feedback and meets the company's requests.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The customization department can reflect a company's CSR (corporate social responsibility) activities when customizing a virtual influencer. For example, the customization department can use a generation AI to create a virtual influencer that reflects a company's environmental protection activities. The customization department can also set the virtual influencer's personality and behavior based on the company's social contribution activities. Furthermore, the customization department can suggest virtual influencers that reflect the company's ethical values. This enables PR activities that emphasize corporate social responsibility by providing virtual influencers that reflect a company's CSR activities.

[0068] Event organizers can reflect a company's sustainability goals when hosting a virtual event. For example, the event organizers can host a virtual event with an environmentally conscious theme. The event organizers can also host an event that includes a session introducing the company's sustainability activities. Furthermore, the event organizers can provide participants with educational content related to sustainability. In this way, by hosting a virtual event that reflects the company's sustainability goals, the company's environmental awareness can be emphasized.

[0069] The detection unit can make the language of the generated content multilingual. For example, the detection unit can detect content in multiple languages, such as English, Japanese, and Spanish. The detection unit can also perform detection taking into account the linguistic nuances and slang of a specific region. Furthermore, the detection unit can adjust the detection criteria to take into account differences in meaning between different languages. This multilingual detection can ensure the quality of content from a global perspective.

[0070] The customization department can reflect a company's history and traditions when customizing a virtual influencer. For example, the customization department can use a generation AI to create a virtual influencer that reflects an episode from the company's founding. The customization department can also set the virtual influencer's personality to reflect the company's traditional values. Furthermore, the customization department can suggest virtual influencers that reflect the company's historical events and important milestones. This allows the company's brand story to be emphasized by providing virtual influencers that reflect the company's history and traditions.

[0071] The event organizing department can reflect the voices of company employees when hosting a virtual event. For example, the event organizing department can host an event that includes a panel discussion in which company employees participate. The event organizing department can also hold sessions that include employee interviews. Furthermore, the event organizing department can set the event content to reflect the opinions and ideas of employees. In this way, by hosting a virtual event that reflects the voices of company employees, it is possible to strengthen internal communication within a company.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The customization department creates a virtual influencer that matches the company's brand image. For example, the customization department sets the virtual influencer's appearance and personality based on the company's brand image. Step 2: The generation unit uses the generation AI to generate a promotional video using the virtual influencer created by the customization unit. For example, the generation unit generates a promotional video based on a scenario provided by the company. Step 3: The quality check unit checks the quality of the promotional video generated by the generation unit. For example, the quality check unit evaluates the image quality, sound quality, and accuracy of the content. Step 4: The Event Organizing Department will host a virtual event using the promotional video reviewed by the Quality Checking Department. For example, the Event Organizing Department will conduct live streaming and interactive sessions. Step 5: The communication department communicates with participants in real time at the virtual event hosted by the event hosting department. For example, the communication department interacts with participants using chat or video calls. Step 6: The detection unit analyzes the content generated by the generation unit and detects malicious content. For example, the detection unit checks whether the generated promotional video contains inappropriate content.

[0074] (Example 2) A platform according to an embodiment of the present invention is a system for creating customizable, AI-generated virtual influencers that companies can use as brand ambassadors. The system creates virtual influencers tailored to a company's brand image, generates promotional videos, hosts virtual events, and checks the quality and safety of the generated content. For example, a company can create a customizable, AI-generated virtual influencer. The virtual influencer's appearance and personality can be tailored to match the company's brand image, and the generation AI generates them. The virtual influencer can then be used to generate promotional videos and host virtual events. The generation AI generates promotional videos based on a scenario provided by the company. The virtual influencer can also communicate with participants in real time. Furthermore, a security service is provided to verify that the generated content is not malicious. The generation AI analyzes the generated content and verifies that it does not contain malicious content. This allows the platform to easily create virtual influencers tailored to a company's brand image and conduct effective promotional activities. The security service also prevents the abuse of virtual influencers and provides safe content. This allows the platform to easily create virtual influencers that match the brand image and carry out effective PR activities, while its security services prevent the misuse of virtual influencers and provide safe content.

[0075] A virtual influencer creation system according to an embodiment includes a customization unit, a generation unit, a quality check unit, an event hosting unit, a communication unit, and a detection unit. The customization unit creates a virtual influencer that matches a company's brand image. For example, the customization unit sets the virtual influencer's appearance and personality based on the company's brand image. The generation unit uses a generation AI to generate a promotional video using the virtual influencer created by the customization unit. For example, the generation unit generates a promotional video based on a scenario provided by the company. The quality check unit checks the quality of the promotional video generated by the generation unit. For example, the quality check unit evaluates the image quality, sound quality, and accuracy of the content. The event hosting unit hosts a virtual event using the promotional video checked by the quality check unit. For example, the event hosting unit conducts live streaming and interactive sessions. The communication unit communicates with participants in real time at the virtual event hosted by the event hosting unit. For example, the communication unit interacts with participants using chat or video calls. The detection unit analyzes the content generated by the generation unit and detects malicious content. For example, the detection unit checks whether the generated promotional video contains inappropriate content. This allows the virtual influencer creation system according to the embodiment to easily create virtual influencers that match a company's brand image and conduct effective promotional activities. Furthermore, the security service prevents the misuse of virtual influencers and provides safe content.

[0076] The customization unit can estimate the user's emotions and adjust the virtual influencer's appearance and personality based on the estimated user emotions. The customization unit estimates the user's emotions using a generation AI. For example, the customization unit can estimate the user's emotions using facial expression recognition technology. The customization unit can also estimate the user's emotions using voice analysis technology. The customization unit can also estimate the user's emotions using text analysis technology. The customization unit then adjusts the virtual influencer's appearance and personality based on the estimated user emotions. For example, if the user has positive emotions, the generation AI can create a virtual influencer with a bright and energetic appearance and personality. If the user has calm emotions, the generation AI can create a virtual influencer with a calm and relaxed appearance and personality. If the user is excited, the generation AI can create a virtual influencer with an energetic and lively appearance and personality. This makes it possible to provide a more personalized influencer by generating a virtual influencer according to the user's emotions.

[0077] The customization department can analyze a company's past brand campaign data and suggest characteristics for a virtual influencer. The customization department uses generative AI to analyze a company's past brand campaign data. For example, the customization department analyzes the successful elements of past campaigns and suggests the appearance and personality of a virtual influencer based on that. The customization department can also extract characteristics that are well-received by a specific target demographic from past campaign data and reflect them in a virtual influencer. The customization department can also suggest a virtual influencer that incorporates improvements based on feedback from past campaigns. This enables effective PR activities by providing virtual influencers based on a company's past success stories.

[0078] When customizing a virtual influencer, the customization department can take into account specific trends in a company's target market. The customization department uses the generation AI to analyze trends in the company's target market. For example, the customization department can use the generation AI to create a virtual influencer that reflects current fashion industry trends. The customization department can also adjust the virtual influencer's appearance and personality based on the preferences of consumers in the target market. The customization department can also use the generation AI to create a virtual influencer that incorporates the latest social media trends. This enables more effective marketing by providing virtual influencers that reflect trends in the target market.

[0079] When customizing a virtual influencer, the customization department can analyze the influencer strategies of a company's competitors and propose points of differentiation. The customization department uses generative AI to analyze the influencer strategies of a company's competitors. For example, the customization department analyzes the characteristics of a competitor's influencers and proposes a virtual influencer with unique characteristics to compete with them. The customization department can also create a virtual influencer that emphasizes the company's unique brand image while referring to competitors' success stories. The customization department can also find weaknesses in a competitor's influencer strategy and customize the virtual influencer to complement them. This makes it possible to provide a virtual influencer that emphasizes the company's unique brand image by differentiating it from competitors.

[0080] The customization unit can estimate a user's emotions and determine the priorities for customization of the virtual influencer based on the estimated user's emotions. The customization unit estimates the user's emotions using generative AI. For example, the customization unit can estimate the user's emotions using facial expression recognition technology. The customization unit can also estimate the user's emotions using voice analysis technology. The customization unit can also estimate the user's emotions using text analysis technology. Next, the customization unit determines the priorities for customization of the virtual influencer based on the estimated user's emotions. For example, if the user is feeling stressed, simple customization options can be provided preferentially. On the other hand, if the user is relaxed, detailed customization options can be provided and adjustments can be made to suit the user's preferences. On the other hand, if the user is in a hurry, the most important customization items can be set preferentially. This enables more efficient customization by determining the priorities for customization according to the user's emotions.

[0081] The customization department can take into account the geographical market characteristics of the company when customizing the virtual influencer. The customization department uses the generation AI to analyze the geographical market characteristics of the company. For example, the customization department causes the generation AI to create a virtual influencer that suits the culture and customs of each region. The customization department can also adjust the appearance and personality of the virtual influencer based on the geographical market characteristics. The customization department can also cause the generation AI to create a virtual influencer that suits the preferences of consumers in each region. This makes it possible to provide virtual influencers that reflect the geographical market characteristics, thereby increasing the marketing effectiveness in each region.

[0082] When customizing the virtual influencer, the customization department can analyze the company's social media activities and reflect relevant characteristics. The customization department uses the generation AI to analyze the company's social media activities. For example, the customization department can analyze the content of the company's social media posts and adjust the virtual influencer's appearance and personality based on that. The customization department can also determine the virtual influencer's characteristics based on the reactions of the company's followers on social media. The customization department can also use the generation AI to create a virtual influencer that reflects the content of the company's social media campaign. This allows the company to deliver a more consistent brand message by providing a virtual influencer that reflects its social media activities.

[0083] When customizing a virtual influencer, the customization department can adjust the customization method by reflecting a company's past feedback. The customization department uses generative AI to analyze a company's past feedback. For example, the customization department can improve the virtual influencer's appearance or personality based on the company's past feedback. The customization department can also provide customization options based on a company's past feedback to meet specific requests. The customization department can also optimize the virtual influencer customization process by referring to the company's past feedback. This makes it possible to provide a virtual influencer that reflects past feedback, thereby enabling customization that meets a company's requests.

[0084] The generation unit can estimate the user's emotions and adjust the presentation of the promotional video based on the estimated user emotions. The generation unit estimates the user's emotions using a generation AI. For example, the generation unit can estimate the user's emotions using facial expression recognition technology. The generation unit can also estimate the user's emotions using voice analysis technology. The generation unit can also estimate the user's emotions using text analysis technology. The generation unit then adjusts the presentation of the promotional video based on the estimated user emotions. For example, if the user is relaxed, the generation AI can generate a promotional video that progresses at a leisurely pace. If the user is in a hurry, the generation AI can generate a promotional video that emphasizes the shortest route. If the user is excited, the generation AI can generate a promotional video with visually stimulating effects. This enables more effective promotional activities by generating promotional videos that correspond to the user's emotions.

[0085] When generating a promotional video, the generation unit can adjust the level of detail in the video based on the company's brand message. The generation unit uses generation AI to adjust the level of detail in the promotional video based on the company's brand message. For example, if the company's brand message is simple, the generation unit can use generation AI to generate a promotional video with simple expression. Conversely, if the company's brand message is detailed, the generation AI can generate a promotional video that includes detailed information. The generation unit can also adjust the visual effects and the amount of text based on the company's brand message. This allows for the generation of promotional videos based on the company's brand message, thereby communicating a more consistent brand message.

[0086] When generating a promotional video, the generation unit can apply different generation algorithms depending on the company's target market. The generation unit uses generation AI to apply different generation algorithms depending on the company's target market. For example, the generation unit can apply a generation algorithm that uses pop and colorful expressions to a promotional video aimed at younger generations. The generation unit can also apply a generation algorithm that uses subdued colors and simple expressions to a promotional video aimed at older generations. The generation unit can also apply a generation algorithm that uses technical terms and visuals appropriate for a specific industry to a promotional video aimed at that industry. This allows for more effective marketing by generating promotional videos tailored to the target market.

[0087] When generating a promotional video, the generation unit can improve the accuracy of generation by referring to the results of a company's past promotional videos. The generation unit uses the generation AI to analyze the results of a company's past promotional videos. For example, the generation unit analyzes the number of views and engagement rates of a company's past promotional videos and adjusts the generation algorithm based on that. The generation unit can also refer to feedback on a company's past promotional videos and have the generation AI generate a promotional video that incorporates improvements. The generation unit can also extract the success factors of a company's past promotional videos and have the generation AI generate a promotional video that reflects those factors. This allows for more effective promotional activities by generating promotional videos that reflect the results of past promotional videos.

[0088] The generation unit can estimate the user's emotions and adjust the length of the promotional video based on the estimated user emotions. The generation unit estimates the user's emotions using a generation AI. For example, the generation unit can estimate the user's emotions using facial expression recognition technology. The generation unit can also estimate the user's emotions using voice analysis technology. The generation unit can also estimate the user's emotions using text analysis technology. The generation unit then adjusts the length of the promotional video based on the estimated user emotions. For example, if the user is in a hurry, the generation AI can generate a short promotional video that gets to the point. If the user is relaxed, the generation AI can generate a longer promotional video that includes detailed explanations. If the user is excited, the generation AI can generate a promotional video with visually stimulating effects. This allows for more effective promotional activities by adjusting the length of the promotional video according to the user's emotions.

[0089] When generating promotional videos, the generation unit can determine the priority of videos based on the timing of a company's campaign. The generation unit uses generation AI to determine the priority of promotional videos based on the timing of a company's campaign. For example, the generation unit prioritizes generating the most important promotional videos immediately before the start of a company's campaign. The generation unit can also generate promotional videos tailored to specific events during the company's campaign period. The generation unit can also generate a comprehensive promotional video after the company's campaign has ended. This allows for more effective marketing by generating promotional videos according to the campaign timing.

[0090] When generating promotional videos, the generation unit can adjust the order of the videos based on the relevance of the company. The generation unit uses generation AI to adjust the order of the promotional videos based on the relevance of the company. For example, the generation unit may first generate promotional videos related to the company's main products. The generation unit may also prioritize generating promotional videos related to the company's new products. The generation unit may also postpone promotional videos related to the company's past products. This allows for more effective promotional activities by generating promotional videos based on the relevance of the company.

[0091] When generating a promotional video, the generation unit can adjust the use of technical terms in the video according to the company's level of expertise. The generation unit uses generation AI to adjust the use of technical terms in the promotional video according to the company's level of expertise. For example, the generation unit avoids technical terms and uses easy-to-understand expressions for promotional videos aimed at general consumers. The generation unit can also use a lot of technical terms to provide detailed information for promotional videos aimed at industry experts. The generation unit can also use expressions that include explanations of technical terms for promotional videos aimed at beginners. This allows for more effective promotional activities by generating promotional videos that suit the company's level of expertise.

[0092] The quality check unit can estimate a user's emotions and adjust the quality check criteria for the promotional video based on the estimated user emotions. The quality check unit estimates the user's emotions using AI. For example, the quality check unit can estimate the user's emotions using facial expression recognition technology. The quality check unit can also estimate the user's emotions using voice analysis technology. The quality check unit can also estimate the user's emotions using text analysis technology. Next, the quality check unit adjusts the quality check criteria for the promotional video based on the estimated user emotions. For example, if a user tends to give strict ratings, the quality check criteria can be set strict. On the other hand, if a user tends to give lenient ratings, the quality check criteria can be set lenient. Furthermore, if a user is sensitive to a particular element, the quality check can be performed with an emphasis on that element. This allows for more appropriate quality checks by setting quality check criteria according to the user's emotions.

[0093] During quality checks, the quality check department can adjust the level of detail of the check based on the company's brand guidelines. The quality check department uses AI to perform quality checks based on the company's brand guidelines. For example, the quality check department performs quality checks that strictly follow the company's brand guidelines. The quality check department can also perform quality checks that focus on specific elements based on the company's brand guidelines. The quality check department can also check visual elements and text content based on the company's brand guidelines. In this way, performing quality checks based on the company's brand guidelines can maintain the brand image.

[0094] The quality check department can conduct quality checks taking into account the specific reactions of a company's target market. The quality check department uses AI to analyze the reactions of a company's target market. For example, the quality check department sets standards for quality checks based on feedback from consumers in the target market. The quality check department can also adjust the content of quality checks based on the culture and customs of the target market. The quality check department can also conduct quality checks that are tailored to the preferences of consumers in the target market. This allows for more effective PR activities by conducting quality checks that take into account the reactions of the target market.

[0095] When conducting quality checks, the quality check department can improve the accuracy of the checks by referring to the company's past quality check results. The quality check department uses AI to analyze the company's past quality check results. For example, the quality check department can conduct quality checks that incorporate improvements based on the company's past quality check results. The quality check department can also extract specific problems from the company's past quality check results and conduct quality checks based on those. The quality check department can also optimize the quality check standards based on the company's past quality check results. This enables more accurate quality checks by conducting quality checks that reflect the past quality check results.

[0096] The quality check unit can estimate the user's emotions and determine the priority of quality checks based on the estimated user's emotions. The quality check unit estimates the user's emotions using AI. For example, the quality check unit can estimate the user's emotions using facial expression recognition technology. The quality check unit can also estimate the user's emotions using voice analysis technology. The quality check unit can also estimate the user's emotions using text analysis technology. Next, the quality check unit determines the priority of quality checks based on the estimated user's emotions. For example, if the user is feeling stressed, important quality check items can be prioritized. Alternatively, if the user is relaxed, detailed quality checks can be performed. Alternatively, if the user is in a hurry, the most important quality check items can be prioritized. This enables more efficient quality checks by prioritizing quality checks according to the user's emotions.

[0097] The quality check department can conduct quality checks taking into account the geographical market characteristics of the company. The quality check department uses AI to analyze the geographical market characteristics of the company. For example, the quality check department conducts quality checks that are tailored to the culture and customs of each region. The quality check department can also adjust the content of the quality checks based on the geographical market characteristics. The quality check department can also conduct quality checks that are tailored to the preferences of consumers in each region. In this way, by conducting quality checks that take into account geographical market characteristics, it is possible to increase the marketing effectiveness in each region.

[0098] During quality checks, the quality check department can analyze a company's social media activities and reflect related check items. The quality check department uses AI to analyze a company's social media activities. For example, the quality check department analyzes the content of a company's social media posts and sets quality check items based on that. The quality check department can also determine the quality check standards by referring to the reactions of the company's social media followers. The quality check department can also perform quality checks that reflect the content of the company's social media campaigns. In this way, by performing quality checks that reflect the company's social media activities, a more consistent brand message can be communicated.

[0099] The quality check department can adjust the check method by reflecting the company's past feedback during quality checks. The quality check department uses AI to analyze the company's past feedback. For example, the quality check department can improve the quality check standards based on the company's past feedback. The quality check department can also set quality check items according to specific requests from the company's past feedback. The quality check department can also optimize the quality check process by referring to the company's past feedback. This makes it possible to perform quality checks that reflect past feedback and meet the company's requests.

[0100] The event hosting unit can estimate the user's emotions and adjust the content of the virtual event based on the estimated user's emotions. The event hosting unit estimates the user's emotions using AI. For example, the event hosting unit estimates the user's emotions using facial expression recognition technology. The event hosting unit can also estimate the user's emotions using voice analysis technology. The event hosting unit can also estimate the user's emotions using text analysis technology. Next, the event hosting unit adjusts the content of the virtual event based on the estimated user's emotions. For example, if the user is relaxed, a virtual event that proceeds at a leisurely pace can be held. If the user is excited, a virtual event with visually stimulating effects can be held. If the user is stressed, a simple, highly visible virtual event can be held. This enables more effective events by holding virtual events that correspond to the user's emotions.

[0101] When hosting an event, the event hosting department can adjust the level of detail of the event based on the company's brand message. The event hosting department uses AI to adjust the level of detail of the event based on the company's brand message. For example, if the company's brand message is simple, the event hosting department can host a virtual event with simple expression. Conversely, if the company's brand message is detailed, the event hosting department can host a virtual event with detailed information. The event hosting department can also adjust the visual effects and the amount of text based on the company's brand message. This allows for a more consistent brand message to be communicated by hosting an event based on the company's brand message.

[0102] When holding an event, the event organization department can apply different event formats depending on the company's target market. The event organization department uses AI to apply different event formats depending on the company's target market. For example, the event organization department can use pop and colorful expressions for a virtual event aimed at younger people. The event organization department can also use subdued colors and simple expressions for a virtual event aimed at older people. The event organization department can also use technical terms and visuals appropriate for a specific industry for a virtual event aimed at that industry. This allows for more effective marketing by applying an event format according to the target market.

[0103] When holding an event, the event planning department can refer to the results of a company's past events to improve the accuracy of the event. The event planning department uses AI to analyze the results of a company's past events. For example, the event planning department can analyze the number of participants and engagement rates of a company's past events and adjust the content of the event based on that. The event planning department can also refer to feedback from a company's past events to hold an event that incorporates areas for improvement. The event planning department can also extract the success factors of a company's past events and hold an event that reflects those factors. This makes it possible to hold a more effective event by holding an event that reflects the results of past events.

[0104] The event hosting unit can estimate the user's emotions and determine the priority of events based on the estimated user's emotions. The event hosting unit estimates the user's emotions using AI. For example, the event hosting unit estimates the user's emotions using facial expression recognition technology. The event hosting unit can also estimate the user's emotions using voice analysis technology. The event hosting unit can also estimate the user's emotions using text analysis technology. Next, the event hosting unit determines the priority of events based on the estimated user's emotions. For example, if the user is feeling stressed, important events can be held with priority. If the user is relaxed, detailed events can be held with priority. If the user is in a hurry, the most important events can be set with priority. This enables more efficient event management by prioritizing events according to the user's emotions.

[0105] When holding an event, the event organizing department can adjust the event taking into account the geographical market characteristics of the company. The event organizing department uses AI to analyze the geographical market characteristics of the company. For example, the event organizing department holds an event that suits the culture and customs of each region. The event organizing department can also adjust the content of the event based on the geographical market characteristics. The event organizing department can also hold an event that suits the preferences of consumers in each region. In this way, by holding an event that takes into account the geographical market characteristics, it is possible to increase the marketing effectiveness in each region.

[0106] When holding an event, the event organization department can analyze a company's social media activities and reflect related event content. The event organization department uses AI to analyze a company's social media activities. For example, the event organization department analyzes the content of a company's social media posts and sets the event content based on that. The event organization department can also decide the event content by taking into consideration the reactions of the company's followers on social media. The event organization department can also hold an event that reflects the content of the company's social media campaign. In this way, by holding an event that reflects the company's social media activities, a more consistent brand message can be communicated.

[0107] When holding an event, the event organization department can adjust the event method by reflecting the company's past feedback. The event organization department uses AI to analyze the company's past feedback. For example, the event organization department can improve the content of the event based on the company's past feedback. The event organization department can also set event content that meets specific requests from the company's past feedback. The event organization department can also optimize the event process by referring to the company's past feedback. This makes it possible to hold an event that reflects past feedback and meets the company's requests.

[0108] The communication unit can estimate the user's emotions and adjust the communication method based on the estimated user's emotions. The communication unit estimates the user's emotions using AI. For example, the communication unit estimates the user's emotions using facial expression recognition technology. The communication unit can also estimate the user's emotions using voice analysis technology. Furthermore, the communication unit can estimate the user's emotions using text analysis technology. Next, the communication unit adjusts the communication method based on the estimated user's emotions. For example, if the user is relaxed, communication can be performed at a leisurely pace. If the user is excited, communication can be performed with visually stimulating effects. If the user is stressed, communication can be performed in a simple, highly visible manner. This enables more effective communication by performing communication according to the user's emotions.

[0109] When communicating, the communications department can adjust the level of detail in the communication based on the company's brand message. The communications department uses AI to adjust the level of detail in the communication based on the company's brand message. For example, if the company's brand message is simple, the communications department can use simple expressions. Conversely, if the company's brand message is detailed, the communications department can use more detailed information. The communications department can also adjust the visual effects and amount of text based on the company's brand message. This allows for a more consistent brand message to be communicated by communicating based on the company's brand message.

[0110] When communicating, the communications department can apply different communication methods depending on the company's target market. The communications department uses AI to apply different communication methods depending on the company's target market. For example, the communications department can use pop and colorful expressions when communicating with younger generations. The communications department can also use subdued colors and simple expressions when communicating with older generations. The communications department can also use technical terms and visuals appropriate for a specific industry when communicating with that industry. This allows for more effective marketing by applying communication methods according to the target market.

[0111] When communicating, the communications department can improve the accuracy of their communications by referring to the results of the company's past communications. The communications department uses AI to analyze the results of the company's past communications. For example, the communications department can incorporate points for improvement into communications based on the results of the company's past communications. The communications department can also incorporate points for improvement into communications by referring to feedback on the company's past communications. The communications department can also extract factors that contributed to the success of the company's past communications and reflect these in communications. In this way, more effective communications are possible by communicating in a way that reflects the results of past communications.

[0112] The communication unit can estimate the user's emotions and determine communication priorities based on the estimated user emotions. The communication unit estimates the user's emotions using AI. For example, the communication unit can estimate the user's emotions using facial expression recognition technology. The communication unit can also estimate the user's emotions using voice analysis technology. The communication unit can also estimate the user's emotions using text analysis technology. Next, the communication unit determines communication priorities based on the estimated user emotions. For example, if the user is feeling stressed, important communication items can be prioritized. Also, if the user is relaxed, detailed communication can be performed. Also, if the user is in a hurry, the most important communication items can be prioritized. This enables more efficient communication by setting communication priorities according to the user's emotions.

[0113] The communications department can adjust communications taking into account the geographical market characteristics of the company. The communications department uses AI to analyze the geographical market characteristics of the company. For example, the communications department can tailor communications to suit the culture and customs of each region. The communications department can also adjust the content of communications based on the geographical market characteristics. The communications department can also tailor communications to suit the preferences of consumers in each region. In this way, communications that take into account geographical market characteristics can increase the marketing effectiveness in each region.

[0114] When communicating, the communications department can analyze the company's social media activities and reflect relevant communication content. The communications department uses AI to analyze the company's social media activities. For example, the communications department analyzes the content of the company's social media posts and sets the content of the communication based on that. The communications department can also determine the content of the communication by taking into account the reactions of the company's followers on social media. The communications department can also create communications that reflect the content of the company's social media campaigns. In this way, communications that reflect the company's social media activities can send a more consistent brand message.

[0115] The communications department can adjust the communication method by reflecting the company's past feedback when communicating. The communications department uses AI to analyze the company's past feedback. For example, the communications department can improve the content of communications based on the company's past feedback. The communications department can also set communication content that meets specific requests from the company's past feedback. The communications department can also optimize the communication process by referring to the company's past feedback. This makes it possible to communicate in a way that reflects past feedback, thereby meeting the company's requests.

[0116] The detection unit can estimate the user's emotions and adjust the detection criteria for malicious content based on the estimated user's emotions. The detection unit estimates the user's emotions using AI. For example, the detection unit can estimate the user's emotions using facial expression recognition technology. The detection unit can also estimate the user's emotions using voice analysis technology. The detection unit can also estimate the user's emotions using text analysis technology. Next, the detection unit adjusts the detection criteria for malicious content based on the estimated user's emotions. For example, if the user tends to give strict ratings, the detection criteria can be set strict. On the other hand, if the user tends to give lenient ratings, the detection criteria can be set lenient. Furthermore, if the user is sensitive to a particular element, detection can be performed with an emphasis on that element. This allows detection criteria to be set according to the user's emotions, enabling more appropriate detection of malicious content.

[0117] During detection, the detection unit can adjust the detection detail level based on the company's brand guidelines. The detection unit uses AI to perform detection based on the company's brand guidelines. For example, the detection unit performs detection that strictly follows the company's brand guidelines. The detection unit can also perform detection that focuses on specific elements based on the company's brand guidelines. The detection unit can also detect visual elements and text content based on the company's brand guidelines. This allows the company to maintain its brand image by performing detection based on the company's brand guidelines.

[0118] The detection unit can perform detection taking into account the specific reactions of a company's target market. The detection unit uses AI to analyze the reactions of a company's target market. For example, the detection unit sets detection criteria based on feedback from consumers in the target market. The detection unit can also adjust the content of detection based on the culture and habits of the target market. The detection unit can also perform detection that is tailored to the preferences of consumers in the target market. This allows for more effective PR activities by performing detection that takes into account the reactions of the target market.

[0119] During detection, the detection unit can improve the accuracy of detection by referring to the company's past detection results. The detection unit uses AI to analyze the company's past detection results. For example, the detection unit performs detection incorporating improvements based on the company's past detection results. The detection unit can also extract specific issues from the company's past detection results and perform detection based on those. The detection unit can also optimize the detection criteria based on the company's past detection results. This enables more accurate detection by performing detection that reflects past detection results.

[0120] The detection unit can estimate the user's emotions and determine the detection priority based on the estimated user's emotions. The detection unit uses AI to estimate the user's emotions. For example, the detection unit can estimate the user's emotions using facial expression recognition technology. The detection unit can also estimate the user's emotions using voice analysis technology. The detection unit can also estimate the user's emotions using text analysis technology. The detection unit then determines the detection priority based on the estimated user's emotions. For example, if the user is feeling stressed, important detection items can be prioritized. If the user is relaxed, detailed detection can be performed. If the user is in a hurry, the most important detection items can be prioritized. This allows for more efficient detection by setting detection priorities according to the user's emotions.

[0121] The detection unit can perform detection taking into account the geographical market characteristics of the company. The detection unit uses AI to analyze the geographical market characteristics of the company. For example, the detection unit performs detection tailored to the culture and customs of each region. The detection unit can also adjust the content of the detection based on the geographical market characteristics. The detection unit can also perform detection tailored to the preferences of consumers in each region. In this way, by performing detection taking into account the geographical market characteristics, it is possible to increase the marketing effectiveness in each region.

[0122] At the time of detection, the detection unit can analyze the company's social media activities and reflect related detection items. The detection unit uses AI to analyze the company's social media activities. For example, the detection unit can analyze the content of the company's social media posts and set detection items based on that. The detection unit can also determine detection criteria by referring to the reactions of the company's followers on social media. The detection unit can also perform detection that reflects the content of the company's social media campaigns. This allows for detection that reflects the company's social media activities, enabling a more consistent brand message to be sent.

[0123] The detection unit can adjust the detection method by reflecting the company's past feedback during detection. The detection unit uses AI to analyze the company's past feedback. For example, the detection unit can improve the detection criteria based on the company's past feedback. The detection unit can also set detection items based on specific requests from the company's past feedback. The detection unit can also optimize the detection process by referring to the company's past feedback. This makes it possible to perform detection that reflects past feedback and meets the company's requests. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned customization unit, generation unit, quality check unit, event hosting unit, communication unit, and detection unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the customization unit is realized by the control unit 46A of the smart device 14 and estimates the user's emotions and adjusts the appearance and personality of the virtual influencer. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a promotional video. The quality check unit is realized by the specific processing unit 290 of the data processing device 12 and checks the quality of the generated promotional video. The event hosting unit is realized by the control unit 46A of the smart device 14 and hosts a virtual event. The communication unit is realized by the control unit 46A of the smart device 14 and communicates with participants in real time. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects malicious content in the generated content. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned customization unit, generation unit, quality check unit, event hosting unit, communication unit, and detection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the customization unit is realized by the control unit 46A of the smart glasses 214 and estimates the user's emotions and adjusts the appearance and personality of the virtual influencer. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a promotional video. The quality check unit is realized by the specific processing unit 290 of the data processing device 12 and checks the quality of the generated promotional video. The event hosting unit is realized by the control unit 46A of the smart glasses 214 and hosts a virtual event. The communication unit is realized by the control unit 46A of the smart glasses 214 and communicates with participants in real time. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects malicious content in the generated content. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned customization unit, generation unit, quality check unit, event hosting unit, communication unit, and detection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the customization unit is realized by the control unit 46A of the headset type terminal 314 and estimates the user's emotions and adjusts the appearance and personality of the virtual influencer. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a promotional video. The quality check unit is realized by the specific processing unit 290 of the data processing device 12 and checks the quality of the generated promotional video. The event hosting unit is realized by the control unit 46A of the headset type terminal 314 and hosts a virtual event. The communication unit is realized by the control unit 46A of the headset type terminal 314 and communicates with participants in real time. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects malicious content in the generated content. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned customization unit, generation unit, quality check unit, event hosting unit, communication unit, and detection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the customization unit is realized by the control unit 46A of the robot 414 and estimates the user's emotions and adjusts the appearance and personality of the virtual influencer. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a promotional video. The quality check unit is realized by the specific processing unit 290 of the data processing device 12 and checks the quality of the generated promotional video. The event hosting unit is realized by the control unit 46A of the robot 414 and hosts a virtual event. The communication unit is realized by the control unit 46A of the robot 414 and communicates with participants in real time. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects malicious content in the generated content.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The customization department can reflect a company's CSR (corporate social responsibility) activities when customizing a virtual influencer. For example, the customization department can use a generation AI to create a virtual influencer that reflects a company's environmental protection activities. The customization department can also set the virtual influencer's personality and behavior based on the company's social contribution activities. Furthermore, the customization department can suggest virtual influencers that reflect the company's ethical values. This enables PR activities that emphasize corporate social responsibility by providing virtual influencers that reflect a company's CSR activities.

[0126] The generation unit can estimate the user's emotions and adjust the music of the promotional video based on the estimated user emotions. For example, if the user is relaxed, the generation AI can generate a promotional video using calm music. If the user is excited, the generation AI can also generate a promotional video using energetic music. Furthermore, if the user is sad, the generation AI can generate a promotional video using moving music. This makes it possible to provide a more effective promotional video by using music that corresponds to the user's emotions.

[0127] The quality check unit can analyze the visual elements of the generated content and adjust them based on the user's emotions. For example, if the user is feeling stressed, the quality check unit can prioritize a visually simple design. If the user is feeling relaxed, the quality check unit can also evaluate a detailed design. Furthermore, if the user is excited, the quality check unit can evaluate a design that includes visually stimulating elements. This allows for more appropriate quality checks by adjusting the visual elements according to the user's emotions.

[0128] Event organizers can reflect a company's sustainability goals when hosting a virtual event. For example, the event organizers can host a virtual event with an environmentally conscious theme. The event organizers can also host an event that includes a session introducing the company's sustainability activities. Furthermore, the event organizers can provide participants with educational content related to sustainability. In this way, by hosting a virtual event that reflects the company's sustainability goals, the company's environmental awareness can be emphasized.

[0129] The communication unit can estimate the user's emotions and adjust the tone of communication based on the estimated user's emotions. For example, if the user is relaxed, the communication unit can use a calm tone. If the user is excited, the communication unit can use an energetic tone. Furthermore, if the user is sad, the communication unit can use a comforting tone. This allows for more effective dialogue by communicating in a tone that corresponds to the user's emotions.

[0130] The detection unit can make the language of the generated content multilingual. For example, the detection unit can detect content in multiple languages, such as English, Japanese, and Spanish. The detection unit can also perform detection taking into account the linguistic nuances and slang of a specific region. Furthermore, the detection unit can adjust the detection criteria to take into account differences in meaning between different languages. This multilingual detection can ensure the quality of content from a global perspective.

[0131] The customization department can reflect a company's history and traditions when customizing a virtual influencer. For example, the customization department can use a generation AI to create a virtual influencer that reflects an episode from the company's founding. The customization department can also set the virtual influencer's personality to reflect the company's traditional values. Furthermore, the customization department can suggest virtual influencers that reflect the company's historical events and important milestones. This allows the company's brand story to be emphasized by providing virtual influencers that reflect the company's history and traditions.

[0132] The generation unit can estimate the user's emotions and adjust the narration of the promotional video based on the estimated user emotions. For example, if the user is relaxed, the generation unit can narrate in a calm voice. If the user is excited, the generation unit can narrate in an energetic voice. Furthermore, if the user is sad, the generation unit can narrate in a comforting voice. This allows the generation unit to provide a more effective promotional video by providing narration that matches the user's emotions.

[0133] The event organizing department can reflect the voices of company employees when hosting a virtual event. For example, the event organizing department can host an event that includes a panel discussion in which company employees participate. The event organizing department can also hold sessions that include employee interviews. Furthermore, the event organizing department can set the event content to reflect the opinions and ideas of employees. In this way, by hosting a virtual event that reflects the voices of company employees, it is possible to strengthen internal communication within a company.

[0134] The quality check unit can estimate the user's emotions and adjust the quality check feedback based on the estimated user's emotions. For example, the quality check unit can provide concise and clear feedback when the user is stressed. Also, the quality check unit can provide detailed feedback when the user is relaxed. Furthermore, the quality check unit can emphasize positive feedback when the user is excited. This allows for more effective quality checks by providing feedback according to the user's emotions.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The customization department creates a virtual influencer that matches the company's brand image. For example, the customization department sets the virtual influencer's appearance and personality based on the company's brand image. Step 2: The generation unit uses the generation AI to generate a promotional video using the virtual influencer created by the customization unit. For example, the generation unit generates a promotional video based on a scenario provided by the company. Step 3: The quality check unit checks the quality of the promotional video generated by the generation unit. For example, the quality check unit evaluates the image quality, sound quality, and accuracy of the content. Step 4: The Event Organizing Department will host a virtual event using the promotional video reviewed by the Quality Checking Department. For example, the Event Organizing Department will conduct live streaming and interactive sessions. Step 5: The communication department communicates with participants in real time at the virtual event hosted by the event hosting department. For example, the communication department interacts with participants using chat or video calls. Step 6: The detection unit analyzes the content generated by the generation unit and detects malicious content. For example, the detection unit checks whether the generated promotional video contains inappropriate content.

[0137] 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.

[0138] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] 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.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Explanation of symbols]

[0209] 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 customization department creates virtual influencers that match a company's brand image, and a generation unit that generates a promotional video using the virtual influencer created by the customization unit; a quality check unit that checks the quality of the promotional video generated by the generation unit; an event hosting unit that hosts a virtual event using the promotional video checked by the quality check unit; a communication unit that communicates in real time with participants in the virtual event held by the event holding unit; a detection unit that analyzes the content generated by the generation unit and detects specific malicious content. A system characterized by:

2. The customization unit Estimate the user's emotions and adjust the appearance and personality of the virtual influencer based on the estimated user emotions.

2. The system of claim 1.

3. The customization unit Analyze a company's past brand campaign data and propose the characteristics of virtual influencers 2. The system of claim 1.

4. The customization unit Consider specific trends in your target market when customizing virtual influencers 2. The system of claim 1.

5. The customization unit Analyze the influencer strategies of your competitors and offer key points when customizing virtual influencers 2. The system of claim 1.

6. The customization unit Estimating user emotions and determining priorities for customization of virtual influencers based on the estimated user emotions 2. The system of claim 1.

7. The customization unit Consider your company's geographic market characteristics when customizing virtual influencers 2. The system of claim 1.

8. The customization unit Analyze a company's social media activity to reflect relevant characteristics when customizing virtual influencers 2. The system of claim 1.

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