System

Abstract

An object of a system according to an embodiment is to generate video content in accordance with a specific category by effectively utilizing in-house information.SOLUTION: A system includes an information collection unit, an analysis unit, and a generation unit. The information collection unit collects necessary data from in-house information sources. The analysis unit analyzes the data collected by the information collection unit. The generation unit generates moving image content according to the specific category on the basis of the data analyzed by the analysis unit.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 have not been able to effectively utilize in-house information to generate video content tailored to specific categories, and there is room for improvement.

[0005] The system according to the embodiment aims to generate video content in accordance with a specific category by effectively utilizing in-house information. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, and a generation unit. The information collection unit collects necessary data from internal information sources. The analysis unit analyzes the data collected by the information collection unit. The generation unit generates video content according to a specific category based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize in-house information to generate video content in accordance with a specific category. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The automatic video creation system according to the embodiment of the present invention collects necessary data from internal information sources, analyzes it using a generation AI, and generates video content according to a specific category. This not only improves the quality of the material, but also increases the impact and impression it leaves on viewers.

[0029] An automatic video creation system according to an embodiment includes an information collection unit, an analysis unit, and a generation unit. The information collection unit collects necessary data from internal information sources. For example, it collects detailed information about products and services from a company's website. The information collection unit can also collect frequently asked questions and their answers from answers. The information collection unit can also collect business-related videos from BizTV. For example, it collects product specifications from a company's website and user feedback from answers. The analysis unit analyzes the data collected by the information collection unit. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM). The generation AI can also analyze data such as text, images, and audio using a multimodal generation AI. The generation AI can also extract and analyze important parts of the data. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information from the data and perform analysis based on that information. The generation unit generates video content tailored to a specific category based on the data analyzed by the analysis unit. For example, the generation unit generates a video introducing a service. The generation unit can also generate a video of operating procedures. The generation unit can also generate a video of implementation examples. For example, the generation unit generates a service introduction video that highlights product features. The generation unit generates an operating procedure video that provides step-by-step instructions on specific operating methods. The generation unit generates an implementation example video that introduces actual implementation examples and demonstrates their effects and benefits. In this way, the automatic video creation system according to the embodiment can collect and analyze necessary data from internal information sources and generate video content tailored to a specific category. For example, the generation AI creates visually appealing videos based on the collected data. The generation AI increases image resolution and removes audio noise. The generation AI adds visual effects and animations to enhance the impact on the viewer. The generation AI adds text and graphics to emphasize important points.Generative AI employs storytelling techniques to capture audience attention.

[0030] The information collection unit can automatically detect the update frequency of each information source and prioritize collecting the latest information. For example, when the generation AI collects information, the information collection unit automatically detects the update frequency of each information source and prioritizes collecting the latest information. For example, it analyzes the update frequency of a website and prioritizes obtaining the latest product information. The information collection unit can also analyze the update frequency of answers and prioritize obtaining the latest user feedback. The information collection unit can also analyze the update frequency of BizTV and prioritize obtaining the latest business-related videos. In this way, by prioritizing the collection of the latest information, the analysis by the generation AI becomes more accurate.

[0031] The information collection unit can evaluate the reliability of information and prioritize analysis of highly reliable information. For example, when the generation AI collects information, the information collection unit evaluates the reliability of information and prioritizes analysis of highly reliable information. For example, it prioritizes analysis of official information on websites. The information collection unit can also evaluate the reliability of answers and prioritize analysis of highly reliable user feedback. The information collection unit can also evaluate the reliability of BizTV and prioritize analysis of highly reliable business-related videos. In this way, by prioritizing analysis of highly reliable information, the analysis by the generation AI becomes more accurate.

[0032] The information collection unit can also collect information from external public databases or social media and integrate it with internal information. For example, when the generation AI collects information, the information collection unit collects information from external public databases and social media and integrates it with internal information. For example, the information collection unit can collect the latest product reviews from Twitter and integrate it with information from the website. The information collection unit can also collect user feedback from Facebook and integrate it with answer information. The information collection unit can also collect open government data and integrate it with information from BizTV. In this way, by collecting information from external public databases and social media and integrating it with internal information, a wider variety of information can be used for analysis.

[0033] The information collection unit can also analyze audio data or image data and use it in combination with text information. For example, when the generation AI is collecting information, the information collection unit can also analyze audio data or image data and use it in combination with text information. For example, a product image on a website can be used in combination with a description. The information collection unit can also analyze interview audio and use it in combination with text information. The information collection unit can also analyze meeting recordings and use it in combination with text information. In this way, by analyzing audio data and image data and using it in combination with text information, richer content can be generated.

[0034] When generating content according to a category, the generation unit can analyze the viewer's past viewing data and generate content that matches the viewer's preferences. For example, when the generation AI generates content according to a category, the generation unit analyzes the past viewing data and generates content that matches the viewer's preferences. For example, the generation unit generates a new service introduction video based on data of a service introduction video that was viewed in the past. The generation unit can also generate a new operation procedure video based on data of an operation procedure video that was viewed in the past. The generation unit can also generate a new implementation case video based on data of an implementation case video that was viewed in the past. This makes it easier to attract the viewer's attention by generating content that matches the viewer's preferences.

[0035] When generating content according to a category, the generation unit can generate cross-category content by taking into account the relevance between different categories. For example, when the generation AI generates content according to a category, the generation unit generates cross-category content by taking into account the relevance between different categories. For example, the generation unit generates a video that combines a service introduction and operation procedures. The generation unit can also generate a video that combines a service introduction and implementation examples. The generation unit can also generate a video that combines operation procedures and implementation examples. In this way, by generating cross-category content by taking into account the relevance between different categories, it is possible to enhance the impact and impression on viewers.

[0036] When generating content according to a category, the generation unit can simultaneously generate content in different languages ​​to accommodate international audiences. For example, when the generation AI generates content according to a category, the generation unit can simultaneously generate content in different languages ​​to accommodate international audiences. For example, the generation unit can simultaneously generate service introduction videos in English and Japanese. The generation unit can also simultaneously generate operation procedure videos in English and Spanish. The generation unit can also simultaneously generate case study videos in English and Chinese. This allows content to be generated simultaneously in different languages ​​to accommodate international audiences, thereby reaching a wider audience.

[0037] The generation unit can enhance not only visual elements but also auditory elements when generating content that fits a category. For example, when the generation AI generates content that fits a category, the generation unit enhances not only visual elements but also auditory elements. For example, the generation unit can add high-quality narration to a service introduction video. The generation unit can also add sound effects to an operation procedure video. The generation unit can also add background music to an implementation case video. In this way, by enhancing not only visual elements but also auditory elements, it is possible to increase the impact and impression on the viewer.

[0038] When improving the quality of materials, the generation unit can automatically evaluate the quality of images and audio and perform optimal editing. For example, when the generation AI improves the quality of materials, the generation unit can automatically evaluate the quality of images and audio and perform optimal editing. For example, the generation unit can automatically improve the image resolution. The generation unit can also automatically remove audio noise. The generation unit can also automatically add visual effects and animations. In this way, by automatically evaluating the quality of images and audio and performing optimal editing, it is possible to increase the impact and impression on viewers.

[0039] When improving the quality of material, the generation unit can try out different editing styles and select the most effective style. For example, when the generation AI improves the quality of material, the generation unit can try out different editing styles and select the most effective style. For example, the generation unit can apply multiple filters and select the most visually appealing one. The generation unit can also try out multiple narration styles and select the most effective one. The generation unit can also try out multiple music styles and select the one that evokes the most emotion. In this way, by trying out different editing styles and selecting the most effective style, it is possible to increase the impact and impression on the viewer.

[0040] The generation unit can incorporate 3D graphics or VR technology to enhance the visual impact when improving the quality of the material. For example, when the generation AI improves the quality of the material, the generation unit can incorporate 3D graphics or VR technology to enhance the visual impact. For example, the generation unit can add a 3D model to a product introduction video. The generation unit can also incorporate VR technology into operation procedure videos. The generation unit can also add 3D animation to case study videos. In this way, by incorporating 3D graphics or VR technology to enhance the visual impact, it is possible to increase the impact and impression on the viewer.

[0041] When improving the quality of materials, the generation unit can try out different music or narration and select the most effective one. For example, when the generation AI improves the quality of materials, the generation unit can try out different music or narration and select the most effective one. For example, the generation unit can try out multiple background music and select the one that evokes the most emotion. The generation unit can also try out multiple narration styles and select the most effective one. The generation unit can also try out multiple sound effects and select the one that is most visually appealing. In this way, by trying out different music and narration and selecting the most effective one, it is possible to increase the impact and impression on the viewer.

[0042] The generation unit can analyze the viewer's gaze tracking data and emphasize important points. For example, the generation AI analyzes the viewer's gaze tracking data and emphasizes important points. For example, it adds text and graphics to parts that the viewer focuses on. The generation unit can also add visual effects to scenes where the viewer's gaze is focused. The generation unit can also change the camera angle to match the timing of the viewer's gaze movement. In this way, by analyzing the viewer's gaze tracking data and emphasizing important points, it is possible to increase the impact and impression on the viewer.

[0043] The generation unit can collect viewer feedback in real time and dynamically adjust the content of the video. For example, the generation unit uses a generation AI to collect viewer feedback in real time and dynamically adjust the content of the video. For example, the generation unit can change video scenes based on viewer comments. The generation unit can also adjust the content of the video based on viewer evaluation scores. The generation unit can also change the story of the video based on viewer survey results. In this way, by collecting viewer feedback in real time and dynamically adjusting the content of the video, it is possible to increase the impact and impression it leaves on viewers.

[0044] The generation unit can generate content that corresponds to viewers in different cultural spheres. For example, the generation AI generates content that corresponds to viewers in different cultural spheres. For example, the generation unit adds narration in Japanese for Japanese viewers. The generation unit can also add narration in English for American viewers. The generation unit can also add subtitles in Chinese for Chinese viewers. In this way, by generating content that corresponds to viewers in different cultural spheres, it is possible to reach a wider audience.

[0045] The generation unit can incorporate not only visual elements but also tactile elements. For example, the generation AI can incorporate not only visual elements but also tactile elements. For example, it can use a device that provides tactile feedback. The generation unit can also add tactile effects. The generation unit can also adjust the timing and intensity of the tactile feedback. In this way, by incorporating not only visual elements but also tactile elements, it is possible to increase the impact and impression on the viewer.

[0046] When automatically distributing videos, the generation unit can analyze the viewer's viewing history and determine the optimal distribution timing. For example, when the generation AI automatically distributes videos, the generation unit analyzes the viewer's viewing history and determines the optimal distribution timing. For example, the generation unit distributes videos during times when the viewer is most active. The generation unit can also analyze the viewer's past viewing patterns and determine the optimal distribution timing. The generation unit can also adjust the distribution timing taking into account the viewer's region and time zone. In this way, by analyzing the viewer's viewing history and determining the optimal distribution timing, it is possible to increase the impact and impression on the viewer.

[0047] When automatically distributing videos, the generation unit can monitor the network conditions of the distribution destination in real time and select the optimal distribution method. For example, when the generation AI automatically distributes videos, the generation unit can monitor the network conditions of the distribution destination in real time and select the optimal distribution method. For example, if the network is congested, it can distribute low-resolution videos. The generation unit can also adjust the bitrate according to the network bandwidth. The generation unit can also monitor network delays and packet loss and select the optimal streaming protocol. In this way, by monitoring the network conditions of the distribution destination in real time and selecting the optimal distribution method, it is possible to increase the impact and impression on viewers.

[0048] The generation unit can automatically optimize distribution across different platforms when automatically distributing videos. For example, when the generation AI automatically distributes videos, the generation unit automatically optimizes distribution across different platforms. For example, it can simultaneously optimize distribution on YouTube and Facebook. The generation unit can also simultaneously optimize distribution on Vimeo and Instagram. The generation unit can also set the optimal bitrate and resolution for each different platform. This automatically optimizing distribution across different platforms can increase the impact and impression on viewers.

[0049] When automatically distributing videos, the generation unit can distribute them in the optimal format according to the viewer's device. For example, when the generation AI automatically distributes videos, the generation unit distributes them in the optimal format according to the viewer's device. For example, vertical videos are distributed for smartphones. The generation unit can also distribute high-resolution videos for tablets. The generation unit can also distribute widescreen videos for PCs. This allows for distribution in the optimal format according to the viewer's device, thereby increasing the impact and impression on the viewer.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The generation unit can analyze the viewer's past viewing data and generate content that matches the viewer's preferences. For example, a new service introduction video is generated based on data from a service introduction video that was previously viewed. The generation unit can also generate a new operation procedure video based on data from an operation procedure video that was previously viewed. The generation unit can also generate a new implementation case video based on data from an implementation case video that was previously viewed. This makes it easier to attract the viewer's attention by generating content that matches the viewer's preferences.

[0052] The generation unit can generate cross-category content by taking into consideration the relevance between different categories. For example, a video is generated that combines a service introduction with operation procedures. The generation unit can also generate a video that combines a service introduction with an implementation example. The generation unit can also generate a video that combines operation procedures with an implementation example. In this way, by generating cross-category content by taking into consideration the relevance between different categories, it is possible to increase the impact and impression it leaves on viewers.

[0053] The generation unit can simultaneously generate content in different languages ​​to cater to an international audience. For example, service introduction videos can be generated simultaneously in English and Japanese. The generation unit can also simultaneously generate operation procedure videos in English and Spanish. The generation unit can also simultaneously generate case study videos in English and Chinese. This allows content to be generated simultaneously in different languages ​​to cater to an international audience, thereby reaching a wider audience.

[0054] The generation unit can enhance not only visual elements but also auditory elements. For example, it can add high-quality narration to a service introduction video. The generation unit can also add sound effects to an operation procedure video. The generation unit can also add background music to an implementation case study video. In this way, by enhancing not only visual elements but also auditory elements, it is possible to increase the impact and impression on the viewer.

[0055] The generation unit can analyze viewer gaze tracking data and emphasize important points. For example, it can add text or graphics to areas where viewers are paying attention. The generation unit can also add visual effects to scenes where viewers' gazes are focused. The generation unit can also change the camera angle to match the timing of the viewer's gaze movement. In this way, by analyzing viewer gaze tracking data and emphasizing important points, it is possible to increase the impact and impression on the viewer.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The information gathering department collects the necessary data from internal sources, such as product and service details from the website, frequently asked questions and their answers from answers, and business-related videos from BizTV. Step 2: The analysis unit analyzes the data collected by the information collection unit. For example, the generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze data such as text, images, and audio, and extract and analyze important parts. Step 3: The generator generates video content according to a specific category based on the data analyzed by the analyzer, such as a video introducing the service, a video explaining operation procedures, or a video showing implementation examples.

[0058] (Example 2) The automatic video creation system according to the embodiment of the present invention collects necessary data from internal information sources, analyzes it using a generation AI, and generates video content according to a specific category. This not only improves the quality of the material, but also increases the impact and impression it leaves on viewers.

[0059] An automatic video creation system according to an embodiment includes an information collection unit, an analysis unit, and a generation unit. The information collection unit collects necessary data from internal information sources. For example, it collects detailed information about products and services from a company's website. The information collection unit can also collect frequently asked questions and their answers from answers. The information collection unit can also collect business-related videos from BizTV. For example, it collects product specifications from a company's website and user feedback from answers. The analysis unit analyzes the data collected by the information collection unit. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM). The generation AI can also analyze data such as text, images, and audio using a multimodal generation AI. The generation AI can also extract and analyze important parts of the data. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information from the data and perform analysis based on that information. The generation unit generates video content tailored to a specific category based on the data analyzed by the analysis unit. For example, the generation unit generates a video introducing a service. The generation unit can also generate a video of operating procedures. The generation unit can also generate a video of implementation examples. For example, the generation unit generates a service introduction video that highlights product features. The generation unit generates an operating procedure video that provides step-by-step instructions on specific operating methods. The generation unit generates an implementation example video that introduces actual implementation examples and demonstrates their effects and benefits. In this way, the automatic video creation system according to the embodiment can collect and analyze necessary data from internal information sources and generate video content tailored to a specific category. For example, the generation AI creates visually appealing videos based on the collected data. The generation AI increases image resolution and removes audio noise. The generation AI adds visual effects and animations to enhance the impact on the viewer. The generation AI adds text and graphics to emphasize important points.Generative AI employs storytelling techniques to capture audience attention.

[0060] The information collection unit can automatically detect the update frequency of each information source and prioritize collecting the latest information. For example, when the generation AI collects information, the information collection unit automatically detects the update frequency of each information source and prioritizes collecting the latest information. For example, it analyzes the update frequency of a website and prioritizes obtaining the latest product information. The information collection unit can also analyze the update frequency of answers and prioritize obtaining the latest user feedback. The information collection unit can also analyze the update frequency of BizTV and prioritize obtaining the latest business-related videos. In this way, by prioritizing the collection of the latest information, the analysis by the generation AI becomes more accurate.

[0061] The information collection unit can evaluate the reliability of information and prioritize analysis of highly reliable information. For example, when the generation AI collects information, the information collection unit evaluates the reliability of information and prioritizes analysis of highly reliable information. For example, it prioritizes analysis of official information on websites. The information collection unit can also evaluate the reliability of answers and prioritize analysis of highly reliable user feedback. The information collection unit can also evaluate the reliability of BizTV and prioritize analysis of highly reliable business-related videos. In this way, by prioritizing analysis of highly reliable information, the analysis by the generation AI becomes more accurate.

[0062] The information collection unit can use the emotion estimation function to analyze the emotional nuances of the collected information and prioritize the use of positive information. The information collection unit can, for example, use the emotion estimation function to analyze the emotional nuances of the collected information and prioritize the use of positive information. For example, the information collection unit can prioritize the use of positive product reviews on websites. The information collection unit can also prioritize the use of positive user feedback on answers. The information collection unit can also prioritize the use of positive business-related videos on BizTV. In this way, by prioritizing the use of positive information, the impact and impression on viewers can be increased.

[0063] The information collection unit can also collect information from external public databases or social media and integrate it with internal information. For example, when the generation AI collects information, the information collection unit collects information from external public databases and social media and integrates it with internal information. For example, the information collection unit can collect the latest product reviews from Twitter and integrate it with information from the website. The information collection unit can also collect user feedback from Facebook and integrate it with answer information. The information collection unit can also collect open government data and integrate it with information from BizTV. In this way, by collecting information from external public databases and social media and integrating it with internal information, a wider variety of information can be used for analysis.

[0064] The information collection unit can also analyze audio data or image data and use it in combination with text information. For example, when the generation AI is collecting information, the information collection unit can also analyze audio data or image data and use it in combination with text information. For example, a product image on a website can be used in combination with a description. The information collection unit can also analyze interview audio and use it in combination with text information. The information collection unit can also analyze meeting recordings and use it in combination with text information. In this way, by analyzing audio data and image data and using it in combination with text information, richer content can be generated.

[0065] The information collection unit can use the emotion estimation function to analyze the emotional reactions to the collected information in real time and prioritize collecting information based on the user's emotions. For example, the information collection unit can use the emotion estimation function to analyze the emotional reactions to the collected information in real time and prioritize collecting information based on the user's emotions. For example, the information collection unit can prioritize collecting positive product reviews on websites. The information collection unit can also prioritize collecting positive user feedback on answers. The information collection unit can also prioritize collecting positive business-related videos on BizTV. In this way, by prioritized collection of information based on the user's emotions, the impact and impression on viewers can be increased.

[0066] When generating content according to a category, the generation unit can analyze the viewer's past viewing data and generate content that matches the viewer's preferences. For example, when the generation AI generates content according to a category, the generation unit analyzes the past viewing data and generates content that matches the viewer's preferences. For example, the generation unit generates a new service introduction video based on data of a service introduction video that was viewed in the past. The generation unit can also generate a new operation procedure video based on data of an operation procedure video that was viewed in the past. The generation unit can also generate a new implementation case video based on data of an implementation case video that was viewed in the past. This makes it easier to attract the viewer's attention by generating content that matches the viewer's preferences.

[0067] When generating content according to a category, the generation unit can generate cross-category content by taking into account the relevance between different categories. For example, when the generation AI generates content according to a category, the generation unit generates cross-category content by taking into account the relevance between different categories. For example, the generation unit generates a video that combines a service introduction and operation procedures. The generation unit can also generate a video that combines a service introduction and implementation examples. The generation unit can also generate a video that combines operation procedures and implementation examples. In this way, by generating cross-category content by taking into account the relevance between different categories, it is possible to enhance the impact and impression on viewers.

[0068] The generation unit can use the emotion estimation function to generate content that incorporates storytelling based on the viewer's emotions. The generation unit, for example, uses the emotion estimation function to generate content that incorporates storytelling based on the viewer's emotions. For example, a story that evokes positive emotions can be incorporated into a service introduction video. The generation unit can also incorporate an inspiring story into an operation procedure video. The generation unit can also incorporate an emotional story into an implementation case study video. In this way, by generating content that incorporates storytelling based on the viewer's emotions, it is possible to increase the impact and impression on the viewer.

[0069] When generating content according to a category, the generation unit can simultaneously generate content in different languages ​​to accommodate international audiences. For example, when the generation AI generates content according to a category, the generation unit can simultaneously generate content in different languages ​​to accommodate international audiences. For example, the generation unit can simultaneously generate service introduction videos in English and Japanese. The generation unit can also simultaneously generate operation procedure videos in English and Spanish. The generation unit can also simultaneously generate case study videos in English and Chinese. This allows content to be generated simultaneously in different languages ​​to accommodate international audiences, thereby reaching a wider audience.

[0070] The generation unit can enhance not only visual elements but also auditory elements when generating content that fits a category. For example, when the generation AI generates content that fits a category, the generation unit enhances not only visual elements but also auditory elements. For example, the generation unit can add high-quality narration to a service introduction video. The generation unit can also add sound effects to an operation procedure video. The generation unit can also add background music to an implementation case video. In this way, by enhancing not only visual elements but also auditory elements, it is possible to increase the impact and impression on the viewer.

[0071] The generation unit can use the emotion estimation function to generate interactive content based on the viewer's emotions. The generation unit, for example, uses the emotion estimation function to generate interactive content based on the viewer's emotions. For example, the generation unit generates a service introduction video whose content changes depending on the viewer's emotions. The generation unit can also generate an interactive video whose operation procedures change depending on the viewer's emotions. The generation unit can also generate an interactive video whose implementation examples change depending on the viewer's emotions. In this way, by generating interactive content based on the viewer's emotions, it is possible to increase the impact and impression on the viewer.

[0072] When improving the quality of materials, the generation unit can automatically evaluate the quality of images and audio and perform optimal editing. For example, when the generation AI improves the quality of materials, the generation unit can automatically evaluate the quality of images and audio and perform optimal editing. For example, the generation unit can automatically improve the image resolution. The generation unit can also automatically remove audio noise. The generation unit can also automatically add visual effects and animations. In this way, by automatically evaluating the quality of images and audio and performing optimal editing, it is possible to increase the impact and impression on viewers.

[0073] When improving the quality of material, the generation unit can try out different editing styles and select the most effective style. For example, when the generation AI improves the quality of material, the generation unit can try out different editing styles and select the most effective style. For example, the generation unit can apply multiple filters and select the most visually appealing one. The generation unit can also try out multiple narration styles and select the most effective one. The generation unit can also try out multiple music styles and select the one that evokes the most emotion. In this way, by trying out different editing styles and selecting the most effective style, it is possible to increase the impact and impression on the viewer.

[0074] The generation unit can use the emotion estimation function to add visual and sound effects based on the viewer's emotions. The generation unit, for example, uses the emotion estimation function to add visual and sound effects based on the viewer's emotions. For example, a bright color visual effect can be added to elicit positive emotions. The generation unit can also add music to match moving scenes. The generation unit can also add sound effects to heighten tension. In this way, adding visual and sound effects based on the viewer's emotions can increase the impact and impression on the viewer.

[0075] The generation unit can incorporate 3D graphics or VR technology to enhance the visual impact when improving the quality of the material. For example, when the generation AI improves the quality of the material, the generation unit can incorporate 3D graphics or VR technology to enhance the visual impact. For example, the generation unit can add a 3D model to a product introduction video. The generation unit can also incorporate VR technology into operation procedure videos. The generation unit can also add 3D animation to case study videos. In this way, by incorporating 3D graphics or VR technology to enhance the visual impact, it is possible to increase the impact and impression on the viewer.

[0076] When improving the quality of materials, the generation unit can try out different music or narration and select the most effective one. For example, when the generation AI improves the quality of materials, the generation unit can try out different music or narration and select the most effective one. For example, the generation unit can try out multiple background music and select the one that evokes the most emotion. The generation unit can also try out multiple narration styles and select the most effective one. The generation unit can also try out multiple sound effects and select the one that is most visually appealing. In this way, by trying out different music and narration and selecting the most effective one, it is possible to increase the impact and impression on the viewer.

[0077] The generation unit can use the emotion estimation function to perform customized editing based on the viewer's emotions. The generation unit, for example, uses the emotion estimation function to perform customized editing based on the viewer's emotions. For example, the generation unit performs editing with bright colors to elicit positive emotions. The generation unit can also add music to match moving scenes. The generation unit can also add sound effects to increase tension. In this way, customized editing based on the viewer's emotions can increase the impact and impression on the viewer.

[0078] The generation unit can analyze the viewer's gaze tracking data and emphasize important points. For example, the generation AI analyzes the viewer's gaze tracking data and emphasizes important points. For example, it adds text and graphics to parts that the viewer focuses on. The generation unit can also add visual effects to scenes where the viewer's gaze is focused. The generation unit can also change the camera angle to match the timing of the viewer's gaze movement. In this way, by analyzing the viewer's gaze tracking data and emphasizing important points, it is possible to increase the impact and impression on the viewer.

[0079] The generation unit can collect viewer feedback in real time and dynamically adjust the content of the video. For example, the generation unit uses a generation AI to collect viewer feedback in real time and dynamically adjust the content of the video. For example, the generation unit can change video scenes based on viewer comments. The generation unit can also adjust the content of the video based on viewer evaluation scores. The generation unit can also change the story of the video based on viewer survey results. In this way, by collecting viewer feedback in real time and dynamically adjusting the content of the video, it is possible to increase the impact and impression it leaves on viewers.

[0080] The generation unit can use the emotion estimation function to enhance storytelling based on the viewer's emotions. The generation unit, for example, uses the emotion estimation function to enhance storytelling based on the viewer's emotions. For example, the generation unit can add a moving story to elicit positive emotions. The generation unit can also add a suspenseful story to increase tension. The generation unit can also change the development of the story depending on the viewer's emotions. In this way, by enhancing storytelling based on the viewer's emotions, it is possible to increase the impact and impression on the viewer.

[0081] The generation unit can generate content that corresponds to viewers in different cultural spheres. For example, the generation AI generates content that corresponds to viewers in different cultural spheres. For example, the generation unit adds narration in Japanese for Japanese viewers. The generation unit can also add narration in English for American viewers. The generation unit can also add subtitles in Chinese for Chinese viewers. In this way, by generating content that corresponds to viewers in different cultural spheres, it is possible to reach a wider audience.

[0082] The generation unit can incorporate not only visual elements but also tactile elements. For example, the generation AI can incorporate not only visual elements but also tactile elements. For example, it can use a device that provides tactile feedback. The generation unit can also add tactile effects. The generation unit can also adjust the timing and intensity of the tactile feedback. In this way, by incorporating not only visual elements but also tactile elements, it is possible to increase the impact and impression on the viewer.

[0083] The generation unit can use the emotion estimation function to add interactive elements based on the viewer's emotions. The generation unit, for example, uses the emotion estimation function to add interactive elements based on the viewer's emotions. For example, the generation unit generates an interactive video whose content changes depending on the viewer's emotions. The generation unit can also generate an interactive story whose choices change depending on the viewer's emotions. The generation unit can also generate interactive content whose ending changes depending on the viewer's emotions. In this way, by adding interactive elements based on the viewer's emotions, it is possible to increase the impact and impression on the viewer.

[0084] When automatically distributing videos, the generation unit can analyze the viewer's viewing history and determine the optimal distribution timing. For example, when the generation AI automatically distributes videos, the generation unit analyzes the viewer's viewing history and determines the optimal distribution timing. For example, the generation unit distributes videos during times when the viewer is most active. The generation unit can also analyze the viewer's past viewing patterns and determine the optimal distribution timing. The generation unit can also adjust the distribution timing taking into account the viewer's region and time zone. In this way, by analyzing the viewer's viewing history and determining the optimal distribution timing, it is possible to increase the impact and impression on the viewer.

[0085] When automatically distributing videos, the generation unit can monitor the network conditions of the distribution destination in real time and select the optimal distribution method. For example, when the generation AI automatically distributes videos, the generation unit can monitor the network conditions of the distribution destination in real time and select the optimal distribution method. For example, if the network is congested, it can distribute low-resolution videos. The generation unit can also adjust the bitrate according to the network bandwidth. The generation unit can also monitor network delays and packet loss and select the optimal streaming protocol. In this way, by monitoring the network conditions of the distribution destination in real time and selecting the optimal distribution method, it is possible to increase the impact and impression on viewers.

[0086] The generation unit can use the emotion estimation function to perform customized delivery based on the viewer's emotions. The generation unit, for example, uses the emotion estimation function to perform customized delivery based on the viewer's emotions. For example, if the viewer has positive emotions, the generation unit can deliver related positive videos. Furthermore, if the viewer has negative emotions, the generation unit can also deliver relaxing videos. Furthermore, the generation unit can adjust the content and delivery timing of the video according to the viewer's emotions. In this way, customized delivery based on the viewer's emotions can increase the impact and impression it leaves on the viewer.

[0087] The generation unit can automatically optimize distribution across different platforms when automatically distributing videos. For example, when the generation AI automatically distributes videos, the generation unit automatically optimizes distribution across different platforms. For example, it can simultaneously optimize distribution on YouTube and Facebook. The generation unit can also simultaneously optimize distribution on Vimeo and Instagram. The generation unit can also set the optimal bitrate and resolution for each different platform. This automatically optimizing distribution across different platforms can increase the impact and impression on viewers.

[0088] When automatically distributing videos, the generation unit can distribute them in the optimal format according to the viewer's device. For example, when the generation AI automatically distributes videos, the generation unit distributes them in the optimal format according to the viewer's device. For example, vertical videos are distributed for smartphones. The generation unit can also distribute high-resolution videos for tablets. The generation unit can also distribute widescreen videos for PCs. This allows for distribution in the optimal format according to the viewer's device, thereby increasing the impact and impression on the viewer.

[0089] The generation unit can use the emotion estimation function to perform personalized delivery based on the viewer's emotions. The generation unit, for example, uses the emotion estimation function to perform personalized delivery based on the viewer's emotions. For example, if the viewer has positive emotions, the generation unit can deliver related positive videos. Furthermore, if the viewer has negative emotions, the generation unit can also deliver relaxing videos. Furthermore, the generation unit can adjust the content and delivery timing of the videos according to the viewer's emotions. In this way, personalized delivery based on the viewer's emotions can increase the impact and impression it leaves on the viewer.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The generation unit can analyze the viewer's past viewing data and generate content that matches the viewer's preferences. For example, a new service introduction video is generated based on data from a service introduction video that was previously viewed. The generation unit can also generate a new operation procedure video based on data from an operation procedure video that was previously viewed. The generation unit can also generate a new implementation case video based on data from an implementation case video that was previously viewed. This makes it easier to attract the viewer's attention by generating content that matches the viewer's preferences.

[0092] The generation unit can generate cross-category content by taking into consideration the relevance between different categories. For example, a video is generated that combines a service introduction with operation procedures. The generation unit can also generate a video that combines a service introduction with an implementation example. The generation unit can also generate a video that combines operation procedures with an implementation example. In this way, by generating cross-category content by taking into consideration the relevance between different categories, it is possible to increase the impact and impression it leaves on viewers.

[0093] The generation unit can simultaneously generate content in different languages ​​to cater to an international audience. For example, service introduction videos can be generated simultaneously in English and Japanese. The generation unit can also simultaneously generate operation procedure videos in English and Spanish. The generation unit can also simultaneously generate case study videos in English and Chinese. This allows content to be generated simultaneously in different languages ​​to cater to an international audience, thereby reaching a wider audience.

[0094] The generation unit can enhance not only visual elements but also auditory elements. For example, it can add high-quality narration to a service introduction video. The generation unit can also add sound effects to an operation procedure video. The generation unit can also add background music to an implementation case study video. In this way, by enhancing not only visual elements but also auditory elements, it is possible to increase the impact and impression on the viewer.

[0095] The generation unit can analyze viewer gaze tracking data and emphasize important points. For example, it can add text or graphics to areas where viewers are paying attention. The generation unit can also add visual effects to scenes where viewers' gazes are focused. The generation unit can also change the camera angle to match the timing of the viewer's gaze movement. In this way, by analyzing viewer gaze tracking data and emphasizing important points, it is possible to increase the impact and impression on the viewer.

[0096] The generation unit can use the emotion estimation function to generate content that incorporates storytelling based on the viewer's emotions. For example, a story that evokes positive emotions can be incorporated into a service introduction video. The generation unit can also incorporate moving stories into operation procedure videos. The generation unit can also incorporate emotional stories into case study videos. In this way, by generating content that incorporates storytelling based on the viewer's emotions, it is possible to increase the impact and impression it leaves on the viewer.

[0097] The generation unit can use the emotion estimation function to generate interactive content based on the viewer's emotions. For example, it can generate a service introduction video whose content changes depending on the viewer's emotions. The generation unit can also generate an interactive video whose operation procedures change depending on the viewer's emotions. The generation unit can also generate an interactive video whose implementation examples change depending on the viewer's emotions. In this way, by generating interactive content based on the viewer's emotions, it is possible to increase the impact and impression it leaves on the viewer.

[0098] The generation unit can use the emotion estimation function to add visual and sound effects based on the viewer's emotions. For example, bright colored visual effects can be added to elicit positive emotions. The generation unit can also add music to match moving scenes. The generation unit can also add sound effects to heighten tension. In this way, adding visual and sound effects based on the viewer's emotions can increase the impact and impression on the viewer.

[0099] The generation unit can use the emotion estimation function to perform customized editing based on the viewer's emotions. For example, editing can be performed using bright colors to elicit positive emotions. The generation unit can also add music to match moving scenes. The generation unit can also add sound effects to increase tension. In this way, customized editing based on the viewer's emotions can increase the impact and impression on the viewer.

[0100] The generation unit can use the emotion estimation function to deliver personalized content based on the viewer's emotions. For example, if the viewer has positive emotions, the generation unit can deliver related positive videos. In addition, if the viewer has negative emotions, the generation unit can deliver relaxing videos. The generation unit can also adjust the content and delivery timing of the video according to the viewer's emotions. This makes it possible to deliver personalized content based on the viewer's emotions, thereby increasing the impact and impression it leaves on the viewer.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The information gathering department collects the necessary data from internal sources, such as product and service details from the website, frequently asked questions and their answers from answers, and business-related videos from BizTV. Step 2: The analysis unit analyzes the data collected by the information collection unit. For example, the generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze data such as text, images, and audio, and extract and analyze important parts. Step 3: The generator generates video content according to a specific category based on the data analyzed by the analyzer, such as a video introducing the service, a video explaining operation procedures, or a video showing implementation examples.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information gathering department that collects necessary data from internal sources; an analysis unit that analyzes the data collected by the information collection unit; a generation unit that generates video content according to a specific category based on the data analyzed by the analysis unit. A system characterized by:

2. The information collecting unit Collect information from external public databases or social media and integrate it with internal information 2. The system of claim 1.

3. The generation unit When generating content according to the category, the past viewing data of the viewer is analyzed, and content that matches the preferences of the viewer is generated.

2. The system of claim 1.

4. The generation unit When improving the quality of material, the system automatically evaluates the image and audio quality and performs optimal editing.

2. The system of claim 1.

5. The generation unit Analyze your audience's eye-tracking data to highlight key points 2. The system of claim 1.

6. The information collecting unit Analyze the emotional nuances of the information you gather and prioritize positive information.

2. The system of claim 1.

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