system

Abstract

The system according to the embodiment aims to visualize search results and provide them to the user. [Solution] A system according to an embodiment includes an analysis unit, a generation unit, and a display unit. The analysis unit analyzes search results. The generation unit generates a summary video based on the information analyzed by the analysis unit. The display unit displays the video generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies often display search results using text or photos, which is not suited to the way users use the site.

[0005] The system according to the embodiment aims to visualize search results and provide them to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and a display unit. The analysis unit analyzes search results. The generation unit generates a summary video based on the information analyzed by the analysis unit. The display unit displays the video generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can visualize the search results and provide them to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The video summary browser system according to an embodiment of the present invention visualizes and conveys the content of a user's search. When a user clicks a "Convert to video" button (an example of a video conversion button) on the search screen, a generation AI analyzes the currently displayed screen and search content, generates a video summary, and displays it on the user's smartphone. For example, if a user searches for "interesting cafes" and wants to view the results in video format, they simply click the "Convert to video" button. The generation AI analyzes the text and images in the search results to extract important information, such as the cafe's name, location, rating, and photos. The generation AI then generates a video summary based on the extracted information. For example, the cafe's name, location, and rating may be displayed as text, and photos may be displayed in a slideshow format. The generation AI can also customize the style and content of the video according to the user's preferences. The generated video summary is displayed on the user's smartphone. By watching the video, users can intuitively understand the search results. For example, they can check the cafe's atmosphere and location through the video. This mechanism allows users to obtain information in video format while maintaining the ease of use of traditional search. This is particularly convenient when using a smartphone, as users can search using short keywords without having to read large amounts of text. For example, when searching for travel information, users can simply press the "Convert to video" button to view information about their travel destination in video format. This allows the video summary browser system to provide users with the content they search for in a video format that is easy to understand intuitively.

[0029] A video summary browser system according to an embodiment includes an analysis unit, a generation unit, and a display unit. The analysis unit analyzes search results. For example, the analysis unit analyzes text and images in the search results and extracts important information. For example, the analysis unit uses text mining technology to extract important keywords from the text in the search results. The analysis unit can also use image analysis technology to extract important features from images in the search results. For example, the analysis unit analyzes image features and extracts important information. The generation unit generates a summary video based on the information analyzed by the analysis unit. The generation unit generates a summary video based on the extracted information, for example, using a generation AI. The generation unit summarizes the extracted text information, for example, using a text generation AI (e.g., LLM). The generation unit can also summarize the extracted image information using a multimodal generation AI. For example, the generation unit generates a slideshow of images and overlays text information. The display unit displays the video generated by the generation unit. For example, the display unit displays the generated summary video on a user's smartphone. The display unit displays the video full screen on, for example, a smartphone screen. The display unit can also select the optimal display method depending on the user's device. For example, the display unit provides display methods compatible with different devices, such as smartphones, tablets, and PCs. This allows the video summary browser system according to the embodiment to provide the content searched by the user in a video format that is easy to understand intuitively.

[0030] The video summary browser system includes a customization unit that customizes the style or content of a video according to a user's preferences. The customization unit customizes the style or content of a video according to the user's preferences. The customization unit generates a video style according to the user's preferences, for example, using a generation AI. For example, if the user is relaxed, the customization unit generates a video with calming music and a slow pace. If the user is excited, the customization unit can also generate a video with up-tempo music and dynamic effects. Furthermore, if the user is stressed, the customization unit can generate a simple, highly visible video. For example, the customization unit estimates the user's emotions and adjusts the video style based on the estimated emotions. This improves user satisfaction by providing videos that meet the user's preferences. Some or all of the above-described processing in the customization unit may be performed using or without the generation AI. For example, the customization unit may input user emotion data into the generation AI and cause the generation AI to generate a video style based on the emotion.

[0031] The video digest browser system includes a reception unit that receives a "Convert to Video" button displayed on a search screen. The reception unit initiates video digest generation when the user presses the "Convert to Video" button on the search screen. For example, the reception unit displays the "Convert to Video" button on a screen displaying search results. When the user presses this button, the reception unit transmits the currently displayed screen and the search results to the analysis unit. This allows the user to easily generate a video digest without performing any special operations. For example, if a user searches for "interesting cafes" and wants to view the search results as a video, they simply press the "Convert to Video" button. The reception unit not only receives user operations but also estimates the user's emotions and adjusts the display method of the buttons based on the estimated emotions. For example, if the user is nervous, the reception unit displays a simple, highly visible button. Alternatively, if the user is relaxed, the reception unit can display a button containing detailed information. This allows the user to easily generate a video digest.

[0032] The analysis unit can analyze the text or images of the search results and extract important information. For example, the analysis unit can analyze the text of the search results using text mining technology. For example, the analysis unit can extract important keywords from the text and summarize the information based on the keywords. The analysis unit can also analyze the images of the search results using image analysis technology. For example, the analysis unit can analyze image features and extract important information. The analysis unit can also analyze videos of the search results. For example, the analysis unit can analyze video frames and extract important scenes. This allows the analysis unit to extract important information from the text, images, and videos of the search results and improve the accuracy of the summarized videos. Some or all of the above-mentioned processing in the analysis unit can be performed using or without the generation AI. For example, the analysis unit can input the text and images of the search results into the generation AI and have the generation AI extract important information.

[0033] The generation unit can generate a summary video based on the extracted information. The generation unit generates a summary video based on the extracted information using, for example, a generation AI. The generation unit summarizes the extracted text information using, for example, a text generation AI (e.g., LLM). The generation unit can also summarize the extracted image information using a multimodal generation AI. For example, the generation unit generates an image slideshow and overlays text information. The generation unit can also summarize the extracted video information. For example, the generation unit extracts important scenes and generates a summary video based on the extracted scenes. This allows the generation unit to generate a summary video based on the extracted information and provide useful information to the user. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the extracted information to the generation AI and cause the generation AI to generate a summary video.

[0034] The display unit can display the generated summary video on the user's smartphone. For example, the display unit displays the generated summary video on the user's smartphone. For example, the display unit displays the video full screen on the smartphone screen. The display unit can also select an optimal display method depending on the user's device. For example, the display unit provides display methods compatible with different devices, such as smartphones, tablets, and PCs. The display unit can also estimate the user's emotions and adjust the video display method based on the estimated emotions. For example, if the user is nervous, the display unit can provide a simple, highly visible display method. On the other hand, if the user is relaxed, the display unit can provide a display method including detailed information. This allows the user to intuitively obtain information by displaying the generated summary video on the smartphone. Some or all of the above-described processing in the display unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the generated summary video to the generation AI and have the generation AI select the optimal display method.

[0035] During analysis, the analysis unit can adjust the analysis algorithm by referring to the user's past search history. For example, the analysis unit optimizes the analysis algorithm by referring to the user's past search history. The analysis unit analyzes the user's past search history, for example, using a generation AI. For example, the analysis unit may prioritize analyzing similar cafe information based on information about cafes previously searched by the user. The analysis unit may also prioritize analyzing related travel information based on information about travel destinations previously searched by the user. Furthermore, the analysis unit may prioritize analyzing information related to keywords previously searched by the user. By referring to the past search history, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit may input the user's past search history into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0036] During analysis, the analysis unit can evaluate the reliability of search results and prioritize analysis of highly reliable information. The analysis unit, for example, evaluates the reliability of search results and prioritizes analysis of highly reliable information. The analysis unit, for example, uses a generation AI to evaluate the reliability of search results. For example, the analysis unit prioritizes analysis of information from highly reliable websites. The analysis unit can also prioritize analysis of posts that have received high user ratings. Furthermore, the analysis unit can prioritize analysis of information from official accounts. This makes it possible to provide highly reliable information to users by prioritizing analysis of highly reliable information. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the reliability of search results into the generation AI and have the generation AI perform the reliability evaluation.

[0037] During analysis, the analysis unit can prioritize analysis of highly relevant information based on the user's geographical location information. For example, the analysis unit prioritizes analysis of highly relevant information taking into account the user's geographical location information. The analysis unit analyzes the user's geographical location information, for example, using a generation AI. For example, the analysis unit prioritizes analysis of information about cafes close to the user's current location. Furthermore, if the user is searching for information about a travel destination, the analysis unit can prioritize analysis of information about tourist spots in that area. Furthermore, if the user is searching for information about a specific city, the analysis unit can prioritize analysis of the latest information about that city. This makes it possible to provide the user with highly relevant information by taking geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit may input the user's geographical location information into the generation AI and cause the generation AI to analyze highly relevant information.

[0038] During the analysis, the analysis unit can analyze the user's social media activity and analyze related information. The analysis unit, for example, analyzes the user's social media activity and analyzes the related information. The analysis unit, for example, uses a generation AI to analyze the user's social media activity. For example, the analysis unit prioritizes analyzing information about cafes that the user has "liked" on social media. The analysis unit can also prioritize analyzing information about travel destinations introduced by influencers the user follows. Furthermore, the analysis unit can prioritize analyzing information related to posts shared by the user. This makes it possible to provide the user with highly relevant information by analyzing social media activity. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's social media activity into the generation AI and have the generation AI analyze the related information.

[0039] When generating a summarized video, the generation unit can adjust the level of detail of the video based on the priority of the search results. The generation unit adjusts the level of detail of the video based on, for example, the importance of the search results. The generation unit evaluates the importance of the search results using, for example, a generation AI. For example, the generation unit generates a video including detailed explanations when there is a lot of important information. The generation unit can also generate a concise video when there is little important information. Furthermore, if specific information is particularly important, the generation unit can generate a video that emphasizes that information. In this way, by adjusting the level of detail of the video based on the importance of the search results, it is possible to provide useful information to the user. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the importance of the search results to the generation AI and cause the generation AI to adjust the level of detail of the video.

[0040] When generating a summary video, the generation unit can apply different generation algorithms depending on the category of the search result. For example, the generation unit applies different generation algorithms depending on the category of the search result. The generation unit analyzes the category of the search result using, for example, a generation AI. For example, in the case of cafe information, the generation unit generates a video that emphasizes the cafe's atmosphere and menu. In addition, in the case of travel information, the generation unit can generate a video that emphasizes tourist spots and accommodations. Furthermore, in the case of product reviews, the generation unit can generate a video that emphasizes product features and user ratings. In this way, by applying a generation algorithm depending on the category of the search result, it is possible to provide useful information to users. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the category of the search result into the generation AI and cause the generation AI to apply different generation algorithms.

[0041] When generating a summarized video, the generation unit can determine the priority of videos based on the time when search results were submitted. The generation unit determines the priority of videos based on, for example, the time when search results were submitted. The generation unit analyzes the time when search results were submitted, for example, using a generation AI. For example, the generation unit prioritizes reflecting the latest information in the video. The generation unit can also briefly summarize old information and explain new information in detail. Furthermore, if the time when the information was submitted is particularly important, the generation unit can generate a video that emphasizes that information. In this way, by determining the priority of videos based on the time when the information was submitted, the latest information can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the time when search results were submitted to the generation AI and have the generation AI determine the priority of videos.

[0042] When generating a summarized video, the generation unit can adjust the order of the videos based on the relevance of the search results. The generation unit adjusts the order of the videos based on, for example, the relevance of the search results. The generation unit evaluates the relevance of the search results using, for example, a generation AI. For example, the generation unit displays the most relevant information first. The generation unit can also postpone less relevant information. Furthermore, if specific information is particularly relevant, the generation unit can generate a video that emphasizes that information. In this way, by adjusting the order of the videos based on relevance, it is possible to provide useful information to the user. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the relevance of the search results into the generation AI and have the generation AI adjust the order of the videos.

[0043] When displaying a video, the display unit can select an optimal display method by referring to the user's past viewing history. The display unit selects the optimal display method by referring to the user's past viewing history, for example. The display unit analyzes the user's viewing history, for example, using a generation AI. For example, the display unit provides a similar display method based on the style of videos the user has previously viewed. The display unit can also display related information based on the content of videos the user has previously viewed. Furthermore, the display unit can preferentially display highly rated videos based on the ratings of videos the user has previously viewed. This makes it possible to provide an optimal display method for the user by referring to the past viewing history. Some or all of the above-described processing in the display unit may be performed using or without the generation AI. For example, the display unit can input the user's viewing history into the generation AI and have the generation AI select the optimal display method.

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

[0045] When displaying videos, the display unit can prioritize displaying highly relevant videos by taking into account the user's geographical location information. For example, the display unit prioritizes displaying highly relevant videos by taking into account the user's geographical location information. The display unit analyzes the user's geographical location information using, for example, a generation AI. For example, the display unit can prioritize displaying videos of cafes close to the user's current location. Furthermore, if the user is searching for information on a travel destination, the display unit can prioritize displaying videos of tourist spots in the area. Furthermore, if the user is searching for information on a specific city, the display unit can prioritize displaying videos of the latest information on that city. In this way, by taking the geographical location information into account, highly relevant videos can be provided to the user. Some or all of the above-described processing in the display unit may be performed using or without the generation AI. For example, the display unit can input the user's geographical location information to the generation AI and cause the generation AI to display highly relevant videos.

[0046] The display unit can analyze the user's social media activity and display related videos when displaying videos. The display unit, for example, analyzes the user's social media activity and displays related videos. The display unit, for example, uses a generation AI to analyze the user's social media activity. For example, the display unit can prioritize displaying videos of cafes that the user has "liked" on social media. The display unit can also prioritize displaying videos of travel destinations introduced by influencers the user follows. Furthermore, the display unit can prioritize displaying videos related to posts shared by the user. In this way, by analyzing social media activity, it is possible to provide videos that are highly relevant to the user. Some or all of the above-described processing in the display unit may be performed using or without the generation AI. For example, the display unit can input the user's social media activity into the generation AI and cause the generation AI to display related videos.

[0047] During customization, the customization unit can select the optimal customization method by referring to the user's past customization history. The customization unit, for example, selects the optimal customization method by referring to the user's past customization history. The customization unit, for example, uses a generation AI to analyze the user's customization history. For example, the customization unit can suggest a similar style based on a video style previously selected by the user. The customization unit can also generate a video including similar elements based on music and effects previously customized by the user. Furthermore, the customization unit can suggest the optimal customization based on customization methods that the user has previously given high ratings. In this way, by referring to the past customization history, the optimal customization method for the user can be provided. Some or all of the above-described processing in the customization unit may be performed using or without the generation AI. For example, the customization unit can input the user's customization history into the generation AI and cause the generation AI to select the optimal customization method.

[0048] During customization, the customization unit can adjust the customization content based on the user's current areas of interest. The customization unit adjusts the customization content based on the user's current areas of interest, for example. The customization unit analyzes the user's areas of interest using, for example, a generation AI. For example, if the user is currently interested in cafes, the customization unit can suggest a video style related to cafes. Furthermore, if the user is currently interested in travel, the customization unit can suggest a video style related to travel. Furthermore, if the user is currently interested in a specific event, the customization unit can suggest a video style related to the event. This improves user satisfaction by providing customization based on the user's current areas of interest. Some or all of the above-described processing in the customization unit may be performed using or without the generation AI. For example, the customization unit can input the user's areas of interest to the generation AI and cause the generation AI to adjust the customization content.

[0049] During customization, the customization unit can select the optimal customization method by taking into account the user's geographical location information. For example, the customization unit selects the optimal customization method by taking into account the user's geographical location information. The customization unit analyzes the user's geographical location information, for example, using a generation AI. For example, the customization unit may suggest customization related to the area based on information about cafes near the user's current location. Furthermore, if the user is searching for information about a travel destination, the customization unit may suggest customization related to tourist spots in the area. Furthermore, if the user is searching for information about a specific city, the customization unit may suggest customization related to the city. In this way, by taking into account the geographical location information, the optimal customization method for the user can be provided. Some or all of the above-described processing in the customization unit may be performed using or without the generation AI. For example, the customization unit may input the user's geographical location information into the generation AI and cause the generation AI to select the optimal customization method.

[0050] During customization, the customization unit can analyze the user's social media activity and suggest related customization content. For example, the customization unit analyzes the user's social media activity and suggests related customization content. The customization unit analyzes the user's social media activity using, for example, a generation AI. For example, the customization unit can suggest customization related to a cafe based on information about a cafe that the user has "liked" on social media. The customization unit can also suggest customization related to a travel destination based on information about the travel destination introduced by an influencer the user follows. Furthermore, the customization unit can also suggest customization related to information related to posts shared by the user. In this way, by analyzing social media activity, optimal customization content can be provided for the user. Some or all of the above-described processing in the customization unit may be performed using or without the generation AI. For example, the customization unit can input the user's social media activity into the generation AI and cause the generation AI to suggest related customization content.

[0051] When a button is pressed, the reception unit can select an optimal operation method by referring to the user's past operation history. The reception unit, for example, selects an optimal operation method by referring to the user's past operation history. The reception unit, for example, uses a generation AI to analyze the user's operation history. For example, the reception unit suggests a similar operation method based on an operation method (voice, text, etc.) that the user has used in the past. The reception unit can also suggest an optimal operation method based on an operation method that the user has given a high rating in the past. Furthermore, the reception unit can predict and suggest an operation method to be used in a specific time period based on the user's past operation history. In this way, by referring to the past operation history, it is possible to provide an optimal operation method for the user. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's operation history into the generation AI and have the generation AI select an optimal operation method.

[0052] When a button is pressed, the reception unit can select the optimal operation method by taking into account the user's device information. The reception unit, for example, selects the optimal operation method by taking into account the user's device information. The reception unit, for example, uses a generation AI to analyze the user's device information. For example, if the user is using a smartphone, the reception unit can provide an operation method tailored to the screen size. Furthermore, if the user is using a tablet, the reception unit can also provide an operation method optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can also provide a simple and highly visible operation method. In this way, by taking into account the device information, the optimal operation method for the user can be provided. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's device information into the generation AI and cause the generation AI to select the optimal operation method.

[0053] When a button is pressed, the reception unit can select the optimal operation method by taking into account the user's geographical location information. The reception unit selects the optimal operation method by taking into account, for example, the user's geographical location information. The reception unit analyzes the user's geographical location information, for example, using a generation AI. For example, the reception unit can suggest an operation method related to the area based on information about cafes near the user's current location. Furthermore, if the user is searching for information about a travel destination, the reception unit can suggest an operation method related to tourist spots in the area. Furthermore, if the user is searching for information about a specific city, the reception unit can suggest an operation method related to that city. In this way, by taking into account the geographical location information, the optimal operation method for the user can be provided. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal operation method.

[0054] When the button is pressed, the reception unit can analyze the user's social media activity and suggest related operation methods. The reception unit, for example, analyzes the user's social media activity and suggests related operation methods. The reception unit, for example, uses a generation AI to analyze the user's social media activity. For example, the reception unit can suggest operation methods related to a cafe based on information about a cafe that the user has "liked" on social media. The reception unit can also suggest operation methods related to a travel destination based on information about the travel destination introduced by an influencer the user follows. Furthermore, the reception unit can also suggest operation methods related to information related to posts shared by the user. In this way, by analyzing social media activity, it is possible to provide the optimal operation method for the user. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's social media activity into the generation AI and have the generation AI execute the suggestion of related operation methods.

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

[0056] The analysis unit can also estimate the user's search intent and determine the priority of analysis based on the estimated search intent. For example, if the user places importance on the "atmosphere of the cafe," the analysis unit can prioritize analyzing information about the cafe's interior and atmosphere. Also, if the user places importance on the "menu of the cafe," the analysis unit can prioritize analyzing information about the menu. Furthermore, if the user places importance on the "ratings of the cafe," the analysis unit can prioritize analyzing information about the ratings. This makes it possible to provide more appropriate information by performing analysis according to the user's search intent.

[0057] The customization unit can also refer to the user's past viewing history and perform customization based on the viewing history. For example, it can suggest a similar style based on the style of a video the user has previously viewed. It can also display related information based on the content of a video the user has previously viewed. It can also suggest optimal customization based on the style of a video the user has previously given a high rating. In this way, by referring to the past viewing history, it is possible to provide the optimal customization method for the user.

[0058] The reception unit can also refer to the user's operation history and suggest the optimal operation method based on the operation history. For example, it can suggest a similar operation method based on the operation method (voice, text, etc.) that the user has used in the past. It can also suggest the optimal operation method based on the operation method that the user has given a high rating in the past. Furthermore, it can predict and suggest the operation method that will be used in a specific time period based on the user's past operation history. In this way, it is possible to provide the optimal operation method for the user by referring to the past operation history.

[0059] The analysis unit can also prioritize analysis of highly relevant information by taking into account the user's geographical location information. For example, the analysis unit can prioritize analysis of information about cafes close to the user's current location. Also, if the user is searching for information about a travel destination, the analysis unit can prioritize analysis of information about tourist spots in that area. Furthermore, if the user is searching for information about a specific city, the analysis unit can prioritize analysis of the latest information about that city. In this way, by taking into account the geographical location information, it is possible to provide the user with highly relevant information.

[0060] The generation unit can evaluate the reliability of search results and prioritize reflecting highly reliable information in the summarized video. For example, information from highly reliable websites can be prioritized in the video. Also, posts with high user ratings can be prioritized in the video. Furthermore, information from official accounts can be prioritized in the video. This allows highly reliable information to be provided to users by prioritized in the summarized video.

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

[0062] Step 1: The analysis unit analyzes the search results. For example, the analysis unit analyzes the text and images of the search results and extracts important information. Specifically, the analysis unit uses text mining technology to extract important keywords from the text of the search results, and uses image analysis technology to extract important features from the images of the search results. Step 2: The generation unit generates a summary video based on the information analyzed by the analysis unit. For example, the generation unit generates the summary video based on information extracted using a generation AI. Specifically, the generation unit summarizes the extracted text information using a text generation AI (e.g., LLM) and summarizes the extracted image information using a multimodal generation AI. For example, the generation unit generates a slideshow of images and overlays the text information. Step 3: The display unit displays the video generated by the generation unit. For example, the display unit displays the generated summary video on the user's smartphone. Specifically, the display unit displays the video full screen on the smartphone screen and selects the optimal display method depending on the user's device. For example, it provides display methods that are compatible with different devices, such as smartphones, tablets, and PCs.

[0063] (Example 2) The video summary browser system according to an embodiment of the present invention visualizes and conveys the content of a user's search. When a user clicks a "Convert to video" button (an example of a video conversion button) on the search screen, a generation AI analyzes the currently displayed screen and search content, generates a video summary, and displays it on the user's smartphone. For example, if a user searches for "interesting cafes" and wants to view the results in video format, they simply click the "Convert to video" button. The generation AI analyzes the text and images in the search results to extract important information, such as the cafe's name, location, rating, and photos. The generation AI then generates a video summary based on the extracted information. For example, the cafe's name, location, and rating may be displayed as text, and photos may be displayed in a slideshow format. The generation AI can also customize the style and content of the video according to the user's preferences. The generated video summary is displayed on the user's smartphone. By watching the video, users can intuitively understand the search results. For example, they can check the cafe's atmosphere and location through the video. This mechanism allows users to obtain information in video format while maintaining the ease of use of traditional search. This is particularly convenient when using a smartphone, as users can search using short keywords without having to read large amounts of text. For example, when searching for travel information, users can simply press the "Convert to video" button to view information about their travel destination in video format. This allows the video summary browser system to provide users with the content they search for in a video format that is easy to understand intuitively.

[0064] A video summary browser system according to an embodiment includes an analysis unit, a generation unit, and a display unit. The analysis unit analyzes search results. For example, the analysis unit analyzes text and images in the search results and extracts important information. For example, the analysis unit uses text mining technology to extract important keywords from the text in the search results. The analysis unit can also use image analysis technology to extract important features from images in the search results. For example, the analysis unit analyzes image features and extracts important information. The generation unit generates a summary video based on the information analyzed by the analysis unit. The generation unit generates a summary video based on the extracted information, for example, using a generation AI. The generation unit summarizes the extracted text information, for example, using a text generation AI (e.g., LLM). The generation unit can also summarize the extracted image information using a multimodal generation AI. For example, the generation unit generates a slideshow of images and overlays text information. The display unit displays the video generated by the generation unit. For example, the display unit displays the generated summary video on a user's smartphone. The display unit displays the video full screen on, for example, a smartphone screen. The display unit can also select the optimal display method depending on the user's device. For example, the display unit provides display methods compatible with different devices, such as smartphones, tablets, and PCs. This allows the video summary browser system according to the embodiment to provide the content searched by the user in a video format that is easy to understand intuitively.

[0065] The video summary browser system includes a customization unit that customizes the style or content of a video according to a user's preferences. The customization unit customizes the style or content of a video according to the user's preferences. The customization unit generates a video style according to the user's preferences, for example, using a generation AI. For example, if the user is relaxed, the customization unit generates a video with calming music and a slow pace. If the user is excited, the customization unit can also generate a video with up-tempo music and dynamic effects. Furthermore, if the user is stressed, the customization unit can generate a simple, highly visible video. For example, the customization unit estimates the user's emotions and adjusts the video style based on the estimated emotions. This improves user satisfaction by providing videos that meet the user's preferences. Some or all of the above-described processing in the customization unit may be performed using or without the generation AI. For example, the customization unit may input user emotion data into the generation AI and cause the generation AI to generate a video style based on the emotion.

[0066] The video digest browser system includes a reception unit that receives a "Convert to Video" button displayed on a search screen. The reception unit initiates video digest generation when the user presses the "Convert to Video" button on the search screen. For example, the reception unit displays the "Convert to Video" button on a screen displaying search results. When the user presses this button, the reception unit transmits the currently displayed screen and the search results to the analysis unit. This allows the user to easily generate a video digest without performing any special operations. For example, if a user searches for "interesting cafes" and wants to view the search results as a video, they simply press the "Convert to Video" button. The reception unit not only receives user operations but also estimates the user's emotions and adjusts the display method of the buttons based on the estimated emotions. For example, if the user is nervous, the reception unit displays a simple, highly visible button. Alternatively, if the user is relaxed, the reception unit can display a button containing detailed information. This allows the user to easily generate a video digest.

[0067] The analysis unit can analyze the text or images of the search results and extract important information. For example, the analysis unit can analyze the text of the search results using text mining technology. For example, the analysis unit can extract important keywords from the text and summarize the information based on the keywords. The analysis unit can also analyze the images of the search results using image analysis technology. For example, the analysis unit can analyze image features and extract important information. The analysis unit can also analyze videos of the search results. For example, the analysis unit can analyze video frames and extract important scenes. This allows the analysis unit to extract important information from the text, images, and videos of the search results and improve the accuracy of the summarized videos. Some or all of the above-mentioned processing in the analysis unit can be performed using or without the generation AI. For example, the analysis unit can input the text and images of the search results into the generation AI and have the generation AI extract important information.

[0068] The generation unit can generate a summary video based on the extracted information. The generation unit generates a summary video based on the extracted information using, for example, a generation AI. The generation unit summarizes the extracted text information using, for example, a text generation AI (e.g., LLM). The generation unit can also summarize the extracted image information using a multimodal generation AI. For example, the generation unit generates an image slideshow and overlays text information. The generation unit can also summarize the extracted video information. For example, the generation unit extracts important scenes and generates a summary video based on the extracted scenes. This allows the generation unit to generate a summary video based on the extracted information and provide useful information to the user. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input the extracted information to the generation AI and cause the generation AI to generate a summary video.

[0069] The display unit can display the generated summary video on the user's smartphone. For example, the display unit displays the generated summary video on the user's smartphone. For example, the display unit displays the video full screen on the smartphone screen. The display unit can also select an optimal display method depending on the user's device. For example, the display unit provides display methods compatible with different devices, such as smartphones, tablets, and PCs. The display unit can also estimate the user's emotions and adjust the video display method based on the estimated emotions. For example, if the user is nervous, the display unit can provide a simple, highly visible display method. On the other hand, if the user is relaxed, the display unit can provide a display method including detailed information. This allows the user to intuitively obtain information by displaying the generated summary video on the smartphone. Some or all of the above-described processing in the display unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the generated summary video to the generation AI and have the generation AI select the optimal display method.

[0070] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and determines the analysis priority based on the estimated emotions. The analysis unit, for example, estimates the user's emotions using a generative AI. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit estimates the emotions based on heart rate fluctuations. This allows for analysis according to the user's emotions and provides more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to determine the priority of analysis based on emotion.

[0071] During analysis, the analysis unit can adjust the analysis algorithm by referring to the user's past search history. For example, the analysis unit optimizes the analysis algorithm by referring to the user's past search history. The analysis unit analyzes the user's past search history, for example, using a generation AI. For example, the analysis unit may prioritize analyzing similar cafe information based on information about cafes previously searched by the user. The analysis unit may also prioritize analyzing related travel information based on information about travel destinations previously searched by the user. Furthermore, the analysis unit may prioritize analyzing information related to keywords previously searched by the user. By referring to the past search history, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit may input the user's past search history into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0072] During analysis, the analysis unit can evaluate the reliability of search results and prioritize analysis of highly reliable information. The analysis unit, for example, evaluates the reliability of search results and prioritizes analysis of highly reliable information. The analysis unit, for example, uses a generation AI to evaluate the reliability of search results. For example, the analysis unit prioritizes analysis of information from highly reliable websites. The analysis unit can also prioritize analysis of posts that have received high user ratings. Furthermore, the analysis unit can prioritize analysis of information from official accounts. This makes it possible to provide highly reliable information to users by prioritizing analysis of highly reliable information. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the reliability of search results into the generation AI and have the generation AI perform the reliability evaluation.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. The analysis unit, for example, estimates the user's emotions using a generative AI. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit estimates the emotions based on heart rate fluctuations. This improves user satisfaction by providing a display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0074] During analysis, the analysis unit can prioritize analysis of highly relevant information based on the user's geographical location information. For example, the analysis unit prioritizes analysis of highly relevant information taking into account the user's geographical location information. The analysis unit analyzes the user's geographical location information, for example, using a generation AI. For example, the analysis unit prioritizes analysis of information about cafes close to the user's current location. Furthermore, if the user is searching for information about a travel destination, the analysis unit can prioritize analysis of information about tourist spots in that area. Furthermore, if the user is searching for information about a specific city, the analysis unit can prioritize analysis of the latest information about that city. This makes it possible to provide the user with highly relevant information by taking geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit may input the user's geographical location information into the generation AI and cause the generation AI to analyze highly relevant information.

[0075] During the analysis, the analysis unit can analyze the user's social media activity and analyze related information. The analysis unit, for example, analyzes the user's social media activity and analyzes the related information. The analysis unit, for example, uses a generation AI to analyze the user's social media activity. For example, the analysis unit prioritizes analyzing information about cafes that the user has "liked" on social media. The analysis unit can also prioritize analyzing information about travel destinations introduced by influencers the user follows. Furthermore, the analysis unit can prioritize analyzing information related to posts shared by the user. This makes it possible to provide the user with highly relevant information by analyzing social media activity. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's social media activity into the generation AI and have the generation AI analyze the related information.

[0076] The generation unit can estimate the user's emotions and adjust the style of the summarized video based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the style of the summarized video based on the estimated emotions. The generation unit, for example, estimates the user's emotions using a generation AI. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit estimates the emotions based on heart rate fluctuations. This improves user satisfaction by providing a video style that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the video style based on the emotion.

[0077] When generating a summarized video, the generation unit can adjust the level of detail of the video based on the priority of the search results. The generation unit adjusts the level of detail of the video based on, for example, the importance of the search results. The generation unit evaluates the importance of the search results using, for example, a generation AI. For example, the generation unit generates a video including detailed explanations when there is a lot of important information. The generation unit can also generate a concise video when there is little important information. Furthermore, if specific information is particularly important, the generation unit can generate a video that emphasizes that information. In this way, by adjusting the level of detail of the video based on the importance of the search results, it is possible to provide useful information to the user. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the importance of the search results to the generation AI and cause the generation AI to adjust the level of detail of the video.

[0078] When generating a summary video, the generation unit can apply different generation algorithms depending on the category of the search result. For example, the generation unit applies different generation algorithms depending on the category of the search result. The generation unit analyzes the category of the search result using, for example, a generation AI. For example, in the case of cafe information, the generation unit generates a video that emphasizes the cafe's atmosphere and menu. In addition, in the case of travel information, the generation unit can generate a video that emphasizes tourist spots and accommodations. Furthermore, in the case of product reviews, the generation unit can generate a video that emphasizes product features and user ratings. In this way, by applying a generation algorithm depending on the category of the search result, it is possible to provide useful information to users. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the category of the search result into the generation AI and cause the generation AI to apply different generation algorithms.

[0079] The generation unit can estimate the user's emotions and adjust the length of the summary video based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the summary video based on the estimated emotions. The generation unit, for example, estimates the user's emotions using a generation AI. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit estimates the emotions based on heart rate fluctuations. This improves user satisfaction by providing a video length that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the length of the video based on the emotion.

[0080] When generating a summarized video, the generation unit can determine the priority of videos based on the time when search results were submitted. The generation unit determines the priority of videos based on, for example, the time when search results were submitted. The generation unit analyzes the time when search results were submitted, for example, using a generation AI. For example, the generation unit prioritizes reflecting the latest information in the video. The generation unit can also briefly summarize old information and explain new information in detail. Furthermore, if the time when the information was submitted is particularly important, the generation unit can generate a video that emphasizes that information. In this way, by determining the priority of videos based on the time when the information was submitted, the latest information can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the time when search results were submitted to the generation AI and have the generation AI determine the priority of videos.

[0081] When generating a summarized video, the generation unit can adjust the order of the videos based on the relevance of the search results. The generation unit adjusts the order of the videos based on, for example, the relevance of the search results. The generation unit evaluates the relevance of the search results using, for example, a generation AI. For example, the generation unit displays the most relevant information first. The generation unit can also postpone less relevant information. Furthermore, if specific information is particularly relevant, the generation unit can generate a video that emphasizes that information. In this way, by adjusting the order of the videos based on relevance, it is possible to provide useful information to the user. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the relevance of the search results into the generation AI and have the generation AI adjust the order of the videos.

[0082] The display unit can estimate the user's emotion and adjust the video display method based on the estimated user's emotion. The display unit, for example, estimates the user's emotion and adjusts the video display method based on the estimated emotion. The display unit, for example, estimates the user's emotion using a generative AI. For example, the display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The display unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the display unit analyzes the tone and speed of the voice to estimate the emotion. Furthermore, the display unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the display unit estimates the emotion based on heart rate fluctuations. This improves user satisfaction by providing a display method that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the display unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0083] When displaying a video, the display unit can select an optimal display method by referring to the user's past viewing history. The display unit selects the optimal display method by referring to the user's past viewing history, for example. The display unit analyzes the user's viewing history, for example, using a generation AI. For example, the display unit provides a similar display method based on the style of videos the user has previously viewed. The display unit can also display related information based on the content of videos the user has previously viewed. Furthermore, the display unit can preferentially display highly rated videos based on the ratings of videos the user has previously viewed. This makes it possible to provide an optimal display method for the user by referring to the past viewing history. Some or all of the above-described processing in the display unit may be performed using or without the generation AI. For example, the display unit can input the user's viewing history into the generation AI and have the generation AI select the optimal display method.

[0084] When displaying a video, the display unit can select the optimal display method by taking into account the user's device information. For example, the display unit selects the optimal display method by taking into account the user's device information. The display unit analyzes the user's device information using, for example, a generation AI. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. In this way, by taking into account the device information, the optimal display method for the user can be provided. Some or all of the above-described processing in the display unit may be performed using or without the generation AI. For example, the display unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0085] The display unit can estimate a user's emotion and adjust the display order of videos based on the estimated user's emotion. The display unit, for example, estimates a user's emotion and adjusts the display order of videos based on the estimated emotion. The display unit estimates a user's emotion, for example, using a generative AI. For example, the display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The display unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the display unit analyzes the tone and speed of the voice to estimate the emotion. Furthermore, the display unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the display unit estimates the emotion based on heart rate fluctuations. This improves user satisfaction by providing a display order that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the display unit may input user emotion data into the generation AI and cause the generation AI to adjust the display order based on the emotion.

[0086] When displaying videos, the display unit can prioritize displaying highly relevant videos by taking into account the user's geographical location information. For example, the display unit prioritizes displaying highly relevant videos by taking into account the user's geographical location information. The display unit analyzes the user's geographical location information using, for example, a generation AI. For example, the display unit can prioritize displaying videos of cafes close to the user's current location. Furthermore, if the user is searching for information on a travel destination, the display unit can prioritize displaying videos of tourist spots in the area. Furthermore, if the user is searching for information on a specific city, the display unit can prioritize displaying videos of the latest information on that city. In this way, by taking the geographical location information into account, highly relevant videos can be provided to the user. Some or all of the above-described processing in the display unit may be performed using or without the generation AI. For example, the display unit can input the user's geographical location information to the generation AI and cause the generation AI to display highly relevant videos.

[0087] The display unit can analyze the user's social media activity and display related videos when displaying videos. The display unit, for example, analyzes the user's social media activity and displays related videos. The display unit, for example, uses a generation AI to analyze the user's social media activity. For example, the display unit can prioritize displaying videos of cafes that the user has "liked" on social media. The display unit can also prioritize displaying videos of travel destinations introduced by influencers the user follows. Furthermore, the display unit can prioritize displaying videos related to posts shared by the user. In this way, by analyzing social media activity, it is possible to provide videos that are highly relevant to the user. Some or all of the above-described processing in the display unit may be performed using or without the generation AI. For example, the display unit can input the user's social media activity into the generation AI and cause the generation AI to display related videos.

[0088] The customization unit can estimate the user's emotions and adjust the video customization method based on the estimated user emotions. The customization unit, for example, estimates the user's emotions and adjusts the video customization method based on the estimated emotions. The customization unit, for example, estimates the user's emotions using a generative AI. For example, the customization unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The customization unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the customization unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the customization unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the customization unit estimates the emotions based on heart rate fluctuations. This improves user satisfaction by providing a customization method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the customization unit may input user emotion data into the generation AI and cause the generation AI to adjust the customization method based on the emotion.

[0089] During customization, the customization unit can select the optimal customization method by referring to the user's past customization history. The customization unit, for example, selects the optimal customization method by referring to the user's past customization history. The customization unit, for example, uses a generation AI to analyze the user's customization history. For example, the customization unit can suggest a similar style based on a video style previously selected by the user. The customization unit can also generate a video including similar elements based on music and effects previously customized by the user. Furthermore, the customization unit can suggest the optimal customization based on customization methods that the user has previously given high ratings. In this way, by referring to the past customization history, the optimal customization method for the user can be provided. Some or all of the above-described processing in the customization unit may be performed using or without the generation AI. For example, the customization unit can input the user's customization history into the generation AI and cause the generation AI to select the optimal customization method.

[0090] During customization, the customization unit can adjust the customization content based on the user's current areas of interest. The customization unit adjusts the customization content based on the user's current areas of interest, for example. The customization unit analyzes the user's areas of interest using, for example, a generation AI. For example, if the user is currently interested in cafes, the customization unit can suggest a video style related to cafes. Furthermore, if the user is currently interested in travel, the customization unit can suggest a video style related to travel. Furthermore, if the user is currently interested in a specific event, the customization unit can suggest a video style related to the event. This improves user satisfaction by providing customization based on the user's current areas of interest. Some or all of the above-described processing in the customization unit may be performed using or without the generation AI. For example, the customization unit can input the user's areas of interest to the generation AI and cause the generation AI to adjust the customization content.

[0091] The customization unit can estimate the user's emotions and determine the priorities of customization based on the estimated user emotions. The customization unit, for example, estimates the user's emotions and determines the priorities of customization based on the estimated emotions. The customization unit, for example, estimates the user's emotions using a generative AI. For example, the customization unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The customization unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the customization unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the customization unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the customization unit estimates the emotions based on heart rate fluctuations. This improves user satisfaction by providing customization priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the customization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the customization unit may input user emotion data into the generation AI and cause the generation AI to determine customization priorities based on the emotion.

[0092] During customization, the customization unit can select the optimal customization method by taking into account the user's geographical location information. For example, the customization unit selects the optimal customization method by taking into account the user's geographical location information. The customization unit analyzes the user's geographical location information, for example, using a generation AI. For example, the customization unit may suggest customization related to the area based on information about cafes near the user's current location. Furthermore, if the user is searching for information about a travel destination, the customization unit may suggest customization related to tourist spots in the area. Furthermore, if the user is searching for information about a specific city, the customization unit may suggest customization related to the city. In this way, by taking into account the geographical location information, the optimal customization method for the user can be provided. Some or all of the above-described processing in the customization unit may be performed using or without the generation AI. For example, the customization unit may input the user's geographical location information into the generation AI and cause the generation AI to select the optimal customization method.

[0093] During customization, the customization unit can analyze the user's social media activity and suggest related customization content. For example, the customization unit analyzes the user's social media activity and suggests related customization content. The customization unit analyzes the user's social media activity using, for example, a generation AI. For example, the customization unit can suggest customization related to a cafe based on information about a cafe that the user has "liked" on social media. The customization unit can also suggest customization related to a travel destination based on information about the travel destination introduced by an influencer the user follows. Furthermore, the customization unit can also suggest customization related to information related to posts shared by the user. In this way, by analyzing social media activity, optimal customization content can be provided for the user. Some or all of the above-described processing in the customization unit may be performed using or without the generation AI. For example, the customization unit can input the user's social media activity into the generation AI and cause the generation AI to suggest related customization content.

[0094] The reception unit can estimate the user's emotion and adjust the button display method based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and adjusts the button display method based on the estimated emotion. The reception unit, for example, estimates the user's emotion using a generative AI. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice to estimate the emotion. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit estimates the emotion based on heart rate fluctuations. This improves user satisfaction by providing a button display method that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data into the generation AI and cause the generation AI to adjust the button display method based on the emotion.

[0095] When a button is pressed, the reception unit can select an optimal operation method by referring to the user's past operation history. The reception unit, for example, selects an optimal operation method by referring to the user's past operation history. The reception unit, for example, uses a generation AI to analyze the user's operation history. For example, the reception unit suggests a similar operation method based on an operation method (voice, text, etc.) that the user has used in the past. The reception unit can also suggest an optimal operation method based on an operation method that the user has given a high rating in the past. Furthermore, the reception unit can predict and suggest an operation method to be used in a specific time period based on the user's past operation history. In this way, by referring to the past operation history, it is possible to provide an optimal operation method for the user. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's operation history into the generation AI and have the generation AI select an optimal operation method.

[0096] When a button is pressed, the reception unit can select the optimal operation method by taking into account the user's device information. The reception unit, for example, selects the optimal operation method by taking into account the user's device information. The reception unit, for example, uses a generation AI to analyze the user's device information. For example, if the user is using a smartphone, the reception unit can provide an operation method tailored to the screen size. Furthermore, if the user is using a tablet, the reception unit can also provide an operation method optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can also provide a simple and highly visible operation method. In this way, by taking into account the device information, the optimal operation method for the user can be provided. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's device information into the generation AI and cause the generation AI to select the optimal operation method.

[0097] The reception unit can estimate the user's emotion and adjust the button display order based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and adjusts the button display order based on the estimated emotion. The reception unit, for example, estimates the user's emotion using a generation AI. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice to estimate the emotion. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit estimates the emotion based on heart rate fluctuations. This improves user satisfaction by providing a button display order that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data into the generation AI and cause the generation AI to adjust the display order of buttons based on the emotion.

[0098] When a button is pressed, the reception unit can select the optimal operation method by taking into account the user's geographical location information. The reception unit selects the optimal operation method by taking into account, for example, the user's geographical location information. The reception unit analyzes the user's geographical location information, for example, using a generation AI. For example, the reception unit can suggest an operation method related to the area based on information about cafes near the user's current location. Furthermore, if the user is searching for information about a travel destination, the reception unit can suggest an operation method related to tourist spots in the area. Furthermore, if the user is searching for information about a specific city, the reception unit can suggest an operation method related to that city. In this way, by taking into account the geographical location information, the optimal operation method for the user can be provided. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal operation method.

[0099] When the button is pressed, the reception unit can analyze the user's social media activity and suggest related operation methods. The reception unit, for example, analyzes the user's social media activity and suggests related operation methods. The reception unit, for example, uses a generation AI to analyze the user's social media activity. For example, the reception unit can suggest operation methods related to a cafe based on information about a cafe that the user has "liked" on social media. The reception unit can also suggest operation methods related to a travel destination based on information about the travel destination introduced by an influencer the user follows. Furthermore, the reception unit can also suggest operation methods related to information related to posts shared by the user. In this way, by analyzing social media activity, it is possible to provide the optimal operation method for the user. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's social media activity into the generation AI and have the generation AI execute the suggestion of related operation methods. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, display unit, customization unit, and reception unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The display unit is realized, for example, by the display 40A of the smart device 14. The customization unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The reception unit is realized, for example, by the touch panel 38A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, display unit, customization unit, and reception unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The display unit is realized, for example, by the display of the smart glasses 214. The customization unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The reception unit is realized, for example, by the touch panel of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, display unit, customization unit, and reception unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The display unit is realized, for example, by the display 343 of the headset type terminal 314. The customization unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The reception unit is realized, for example, by a touch panel of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, display unit, customization unit, and reception unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The display unit is realized, for example, by the display of the robot 414. The customization unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The reception unit is realized, for example, by a touch panel of the robot 414.

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

[0101] The analysis unit can also estimate the user's search intent and determine the priority of analysis based on the estimated search intent. For example, if the user places importance on the "atmosphere of the cafe," the analysis unit can prioritize analyzing information about the cafe's interior and atmosphere. Also, if the user places importance on the "menu of the cafe," the analysis unit can prioritize analyzing information about the menu. Furthermore, if the user places importance on the "ratings of the cafe," the analysis unit can prioritize analyzing information about the ratings. This makes it possible to provide more appropriate information by performing analysis according to the user's search intent.

[0102] The customization unit can also refer to the user's past viewing history and perform customization based on the viewing history. For example, it can suggest a similar style based on the style of a video the user has previously viewed. It can also display related information based on the content of a video the user has previously viewed. It can also suggest optimal customization based on the style of a video the user has previously given a high rating. In this way, by referring to the past viewing history, it is possible to provide the optimal customization method for the user.

[0103] The reception unit can also refer to the user's operation history and suggest the optimal operation method based on the operation history. For example, it can suggest a similar operation method based on the operation method (voice, text, etc.) that the user has used in the past. It can also suggest the optimal operation method based on the operation method that the user has given a high rating in the past. Furthermore, it can predict and suggest the operation method that will be used in a specific time period based on the user's past operation history. In this way, it is possible to provide the optimal operation method for the user by referring to the past operation history.

[0104] The analysis unit can also prioritize analysis of highly relevant information by taking into account the user's geographical location information. For example, the analysis unit can prioritize analysis of information about cafes close to the user's current location. Also, if the user is searching for information about a travel destination, the analysis unit can prioritize analysis of information about tourist spots in that area. Furthermore, if the user is searching for information about a specific city, the analysis unit can prioritize analysis of the latest information about that city. In this way, by taking into account the geographical location information, it is possible to provide the user with highly relevant information.

[0105] The generation unit can evaluate the reliability of search results and prioritize reflecting highly reliable information in the summarized video. For example, information from highly reliable websites can be prioritized in the video. Also, posts with high user ratings can be prioritized in the video. Furthermore, information from official accounts can be prioritized in the video. This allows highly reliable information to be provided to users by prioritized in the summarized video.

[0106] The display unit can also estimate the user's emotions and adjust the video display method based on the estimated emotions. For example, if the user is nervous, the display unit can provide a simple, highly visible display method. If the user is relaxed, the display unit can provide a display method that includes detailed information. Furthermore, if the user is excited, the display unit can provide a display method that adds dynamic effects. In this way, by providing a display method that corresponds to the user's emotions, user satisfaction is improved.

[0107] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can prioritize analyzing detailed information. Also, if the user is nervous, the analysis unit can prioritize analyzing concise information. Furthermore, if the user is excited, the analysis unit can prioritize analyzing dynamic information. This makes it possible to provide more appropriate information by performing analysis according to the user's emotions.

[0108] The generation unit can also estimate the user's emotions and adjust the style of the summarized video based on the estimated emotions. For example, if the user is relaxed, a video with calm music and a slow pace can be generated. If the user is excited, a video with fast-paced music and dynamic effects can be generated. Furthermore, if the user is stressed, a simple video with high visibility can be generated. This improves user satisfaction by providing a video style that matches the user's emotions.

[0109] The customization unit can also estimate the user's emotions and determine the priority of customization based on the estimated emotions. For example, if the user is relaxed, the customization unit can prioritize detailed customization. Also, if the user is nervous, the customization unit can prioritize simple customization. Furthermore, if the user is excited, the customization unit can prioritize dynamic customization. In this way, customization according to the user's emotions can improve user satisfaction.

[0110] The reception unit can also estimate the user's emotions and adjust the button display method based on the estimated emotions. For example, if the user is nervous, the reception unit can display a simple, highly visible button. If the user is relaxed, the reception unit can display a button containing detailed information. Furthermore, if the user is excited, the reception unit can display a button with dynamic effects. This improves user satisfaction by providing a button display method that corresponds to the user's emotions.

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

[0112] Step 1: The analysis unit analyzes the search results. For example, the analysis unit analyzes the text and images of the search results and extracts important information. Specifically, the analysis unit uses text mining technology to extract important keywords from the text of the search results, and uses image analysis technology to extract important features from the images of the search results. Step 2: The generation unit generates a summary video based on the information analyzed by the analysis unit. For example, the generation unit generates the summary video based on information extracted using a generation AI. Specifically, the generation unit summarizes the extracted text information using a text generation AI (e.g., LLM) and summarizes the extracted image information using a multimodal generation AI. For example, the generation unit generates a slideshow of images and overlays the text information. Step 3: The display unit displays the video generated by the generation unit. For example, the display unit displays the generated summary video on the user's smartphone. Specifically, the display unit displays the video full screen on the smartphone screen and selects the optimal display method depending on the user's device. For example, it provides display methods that are compatible with different devices, such as smartphones, tablets, and PCs.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0150] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the search results; a generation unit that generates a summary video based on the information analyzed by the analysis unit; a display unit that displays the moving image generated by the generation unit; A system characterized by:

2. A customization section is provided to customize the style or content of the video according to the user's preferences. The system of claim 1 .

3. It has a reception unit that receives presses of the video conversion button displayed on the search screen. The system of claim 1 .

4. The analysis unit Analyze the text or images in the search results to extract important information The system of claim 1 .

5. The generation unit Generate a summary video based on the extracted information The system of claim 1 .

6. The display unit The generated summary video is displayed on the user's smartphone. The system of claim 1 .

7. The analysis unit Estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. The system of claim 1 .

8. The analysis unit When analyzing, the analysis algorithm is adjusted by referring to the user's past search history. The system of claim 1 .

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