System

Abstract

An object of the system according to the embodiment is to effectively convey the concept of a work and propose an optimal strategy on social media.SOLUTION: A system according to an embodiment includes a sentence generation unit, a strategy suggestion unit, and a video production support unit. A sentence generation part understands the concept and feature of the work by using the generation AI and generates a sentence effectively expressing it. The strategy proposal unit customizes an approach on social media on the basis of the sentence generated by the sentence generation unit, and proposes an optimal strategy according to a purpose. A video production support part supports the production of the work introduction video on the basis of the strategy proposed by the strategy proposal part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to effectively communicate the concept of art or music works, and people often struggle with how to present them on social media.

[0005] The system according to the embodiment aims to effectively communicate the concept of a work and propose an optimal strategy on social media. [Means for solving the problem]

[0006] The system according to the embodiment includes a text generation unit, a strategy proposal unit, and a video production support unit. The text generation unit uses generative AI to understand the concept and characteristics of a work and generate text that effectively expresses them. The strategy proposal unit customizes the approach on social media based on the text generated by the text generation unit and proposes the optimal strategy according to the purpose. The video production support unit supports the production of a work introduction video based on the strategy proposed by the strategy proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively communicate the concept of a work and propose an optimal strategy on social media. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention utilizes generative AI to effectively communicate the appeal of art and music works. This system understands the concept and characteristics of a work, generates text that effectively expresses it, customizes social media approaches, proposes optimal strategies according to the purpose, and supports the production of videos to introduce the work. This enables the system to appeal to a diverse audience and spread better art to the world.

[0029] A system according to an embodiment includes a text generation unit, a strategy proposal unit, and a video production support unit. The text generation unit uses a generative AI to understand the concept and characteristics of a work and generate text that effectively expresses them. For example, the generative AI uses a text generation AI (e.g., GPT-3) to generate text that details the background, creative intent, and techniques used in an artwork. The generative AI can also use a multimodal generative AI to understand the concept and characteristics of a work and generate text that effectively expresses them. The strategy proposal unit customizes a social media approach based on the text generated by the text generation unit and proposes an optimal strategy according to the purpose. For example, the generative AI proposes post content, posting timing, hashtag selection, and other information aimed at a specific target audience based on prompts including the target audience and purpose. The video production support unit supports the production of a work introduction video based on the strategy proposed by the strategy proposal unit. For example, the generative AI generates text for the video's scenario and narration based on prompts including the video's purpose and content, and makes suggestions to enhance the visual appeal. This allows the system to effectively communicate the appeal of a work using the generative AI.

[0030] The sentence generation unit can collect user feedback on generated sentences in real time and automatically improve the sentences based on that feedback. For example, the sentence generation unit collects user feedback on sentences generated by the generation AI in real time and automatically improves the sentences based on that feedback. For example, it analyzes user comments and ratings and adjusts the content of the sentences. It also adjusts the tone and style of the sentences generated by the generation AI based on user feedback. For example, it prioritizes the use of expressions that receive a lot of positive feedback. It also builds a system in which the generation AI improves sentences based on feedback collected in real time. For example, it updates the content of the sentences to reflect user opinions. In this way, it is possible to generate more effective sentences by automatically improving the sentences based on user feedback.

[0031] The text generation unit can add depth to a work by including the historical background and cultural significance of the work in the generated text. For example, the text generation unit can include the historical background of the work in the text generated by the generative AI. For example, it can provide a detailed explanation of the era and cultural background in which the artwork was created. It can also include the cultural significance of the work in the text generated by the generative AI. For example, it can describe the influence a musical work has had on a particular culture or society. It can also add depth to a work by combining the historical background and cultural significance of the work in the text generated by the generative AI. For example, it can explain how the work came to be in its current form. This can add depth to a work by including the historical background and cultural significance of the work.

[0032] The text generation unit generates text in different languages, allowing the work to appeal to an international audience. The text generation unit generates text in different languages, for example, using a generative AI. For example, it generates text that conveys the appeal of the work in multiple languages, such as English, French, and Chinese. In addition, to make the text generated by the generative AI multilingual, it takes into account the cultural nuances of each language. For example, it uses different expressions and styles for each language. In addition, to appeal to an international audience, a system is built to provide the text generated by the generative AI in different languages. For example, it displays the text in a language selected by the user. In this way, by generating text in different languages, the work can appeal to an international audience.

[0033] The text generation unit uses speech synthesis technology to provide the generated text as narration, introducing the work both visually and audibly. The text generation unit, for example, uses speech synthesis technology to provide text generated by the generation AI as narration. For example, an introduction to the work is played aloud to introduce the work both visually and audibly. A system is also constructed that uses speech synthesis technology to provide the generated text as narration. For example, a text selected by the user is played aloud. Furthermore, to introduce the work both visually and audibly, the text generated by the generation AI is combined with speech synthesis technology. For example, it is used as a narration for a video. This allows the appeal of the work to be conveyed more effectively by introducing the work both visually and audibly.

[0034] The strategy proposal unit can analyze a user's past posts and reactions, and based on that, suggest optimal post content. The strategy proposal unit, for example, uses a generation AI to analyze a user's past posts and reactions, and based on that, suggest optimal post content. For example, it analyzes engagement data on past posts and generates effective post content. In addition, a system is built in which the generation AI suggests optimal post content based on the user's past reaction data. For example, it prioritizes suggestions of themes and topics that users have responded to the most. In addition, the generation AI analyzes a user's past posts and reactions, and customizes the post content. For example, it generates post content that matches the user's preferences. In this way, it is possible to suggest optimal post content by analyzing a user's past posts and reactions.

[0035] The strategy proposal department can analyze competitors' social media strategies and propose the optimal strategy based on the results. For example, the strategy proposal department uses a generation AI to analyze competitors' social media strategies and propose the optimal strategy based on the results. For example, it generates post content that references competitors' success stories. It also builds a system that analyzes competitors' social media strategies and has the generation AI propose a strategy to differentiate them. For example, it proposes hashtags and themes that competitors are not using. It also analyzes competitors' strategies and generates post content to differentiate them. For example, it proposes post content that exploits competitors' weaknesses. In this way, it is possible to propose the optimal strategy by analyzing competitors' social media strategies.

[0036] The strategy proposal unit can incorporate timely elements according to seasons and events. For example, the strategy proposal unit incorporates timely elements according to seasons and events into the strategies proposed by the generation AI. For example, it generates post content that matches seasonal events such as Christmas and Halloween. Furthermore, in order to reflect timely elements according to seasons and events, a system is constructed in which the generation AI proposes the optimal strategy. For example, it suggests hashtags and themes related to specific events. Furthermore, the generation AI generates post content that matches seasons and events and incorporates timely elements. For example, it suggests post content that matches the change of seasons or specific holidays. In this way, more effective post content can be generated by incorporating timely elements according to seasons and events.

[0037] The video production support unit can collect viewer feedback on the generated scenario in real time and automatically improve the scenario based on that feedback. For example, the video production support unit collects viewer feedback on a scenario generated by the generation AI in real time and automatically improves the scenario based on that feedback. For example, it analyzes viewer comments and ratings and adjusts the content of the scenario. It also adjusts the tone and style of the scenario generated by the generation AI based on viewer feedback. For example, it prioritizes the use of expressions that receive a lot of positive feedback. It also builds a system that allows the generation AI to improve the scenario based on feedback collected in real time. For example, it updates the content of the scenario to reflect viewer opinions. In this way, it is possible to generate more effective video scenarios by automatically improving the scenario based on viewer feedback.

[0038] The video production support unit can include the technical details and production process of the work in the generated scenario to enhance its visual appeal. For example, the video production support unit includes the technical details of the work in the scenario generated by the generative AI. For example, it generates a scenario that provides detailed explanations of the production techniques and materials used in an art work. It also includes the production process of the work in the scenario generated by the generative AI. For example, it generates a scenario that describes the recording of a musical work or scenes of instruments being played. It also combines technical details and the production process in the scenario generated by the generative AI to enhance its visual appeal. For example, it generates a scenario that explains how a work was completed. In this way, the visual appeal can be enhanced by including the technical details and production process of the work.

[0039] The video production support unit can generate video scenarios in different formats. The video production support unit generates video scenarios in different formats, for example, using a generation AI. For example, it generates scenarios suitable for short videos or live streaming. In addition, a system is constructed in which the generation AI proposes the optimal scenario, taking into account the characteristics of each format. For example, it generates a concise scenario for short videos and an interactive scenario for live streaming. In addition, the generation AI generates the optimal video scenario for each different format. For example, it proposes a scenario to increase viewer engagement. In this way, by generating video scenarios in different formats, viewer engagement can be increased.

[0040] The video production support unit can incorporate visual effects and animations into the generated scenario to enhance its visual impact. The video production support unit, for example, incorporates visual effects and animations into the scenario generated by the generation AI. For example, visual effects are added to emphasize the features of the work. In addition, a system is built that combines visual effects and animations to enhance the visual impact of the generated scenario. For example, animations that reproduce the movements of an artwork are added. In addition, the generation AI generates a scenario that incorporates visual effects and animations to enhance its visual impact. For example, the production process of the work is expressed in animation. In this way, the visual impact can be enhanced by incorporating visual effects and animations.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The system can also be equipped with a recommendation unit that analyzes a user's past browsing history and recommends the most suitable art or music works for each individual user. For example, similar works can be recommended based on the genre and style of works the user has previously viewed. The system can also build a recommendation algorithm based on the user's browsing history to enable the generation AI to discover new works. For example, it can recommend works that the user has not seen yet but may be interested in. The generation AI can also generate a customized recommendation list for each individual user based on the user's browsing history. This allows the system to utilize the user's past browsing history to recommend the most suitable works for each individual user.

[0043] The system can also be equipped with a feedback collection unit that collects user feedback in real time and improves the work recommendation algorithm based on that feedback. For example, the system can analyze the ratings and comments users have made on recommended works and adjust the recommendation algorithm. The system can also adjust the genre and style of works recommended by the generation AI based on user feedback. For example, works with a lot of positive feedback can be given priority in recommendation. It is also possible to build a system in which the generation AI improves the recommendation algorithm based on feedback collected in real time. This allows the recommendation algorithm to be automatically improved based on user feedback, enabling more effective work recommendations.

[0044] The system can also be equipped with a purchase history analysis unit that analyzes a user's past purchase history and recommends the most suitable art and music works for each individual user. For example, similar works can be recommended based on the genre and style of works the user has previously purchased. The system can also build a recommendation algorithm based on the user's purchase history to enable the generation AI to discover new works. For example, it can recommend works that the user has not yet purchased but may be interested in. The generation AI can also generate a customized recommendation list for each individual user based on the user's purchase history. This makes it possible to utilize the user's past purchase history to recommend the most suitable works for each individual user.

[0045] The system can also be equipped with a viewing history analysis unit that analyzes a user's past viewing history and recommends the most suitable art or music works for each individual user. For example, similar works can be recommended based on the genre and style of works the user has previously viewed. The system can also build a recommendation algorithm based on the user's viewing history to enable the generation AI to discover new works. For example, it can recommend works that the user has not yet viewed but may be interested in. The generation AI can also generate a customized recommendation list for each individual user based on the user's viewing history. This allows the system to utilize the user's past viewing history to recommend the most suitable works for each individual user.

[0046] The system can also be equipped with a rating history analysis unit that analyzes users' past rating history and recommends the most suitable art and music works for each individual user. For example, similar works can be recommended based on the genres and styles of works that the user has previously given high ratings. The system can also build a recommendation algorithm based on the user's rating history to enable the generation AI to discover new works. For example, it can recommend works that the user has not yet rated but may be interested in. The generation AI can also generate a customized recommendation list for each individual user based on the user's rating history. This makes it possible to utilize the user's past rating history to recommend the most suitable works for each individual user.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The text generation unit uses generative AI to understand the concept and characteristics of the work and generate text that effectively expresses them. For example, the generative AI uses text generation AI (e.g., GPT-3) to generate text that provides a detailed explanation of the background, creative intent, and techniques used in the artwork. The generative AI can also use multimodal generative AI to understand the concept and characteristics of the work and generate text that effectively expresses them. Step 2: The strategy proposal unit customizes social media approaches based on the text generated by the text generation unit and proposes optimal strategies according to the objectives. For example, based on prompts including the target audience and objectives, the generation AI suggests post content, posting timing, hashtag selection, etc. aimed at a specific target audience. Step 3: The Video Production Support Department supports the production of the product introduction video based on the strategy proposed by the Strategy Proposal Department. For example, the Generative AI generates the video's scenario and narration text based on prompts including the video's purpose and content, and makes suggestions to enhance its visual appeal.

[0049] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to effectively communicate the appeal of art and music works. This system understands the concept and characteristics of a work, generates text that effectively expresses it, customizes social media approaches, proposes optimal strategies according to the purpose, and supports the production of videos to introduce the work. This enables the system to appeal to a diverse audience and spread better art to the world.

[0050] A system according to an embodiment includes a text generation unit, a strategy proposal unit, and a video production support unit. The text generation unit uses a generative AI to understand the concept and characteristics of a work and generate text that effectively expresses them. For example, the generative AI uses a text generation AI (e.g., GPT-3) to generate text that details the background, creative intent, and techniques used in an artwork. The generative AI can also use a multimodal generative AI to understand the concept and characteristics of a work and generate text that effectively expresses them. The strategy proposal unit customizes a social media approach based on the text generated by the text generation unit and proposes an optimal strategy according to the purpose. For example, the generative AI proposes post content, posting timing, hashtag selection, and other information aimed at a specific target audience based on prompts including the target audience and purpose. The video production support unit supports the production of a work introduction video based on the strategy proposed by the strategy proposal unit. For example, the generative AI generates text for the video's scenario and narration based on prompts including the video's purpose and content, and makes suggestions to enhance the visual appeal. This allows the system to effectively communicate the appeal of a work using the generative AI.

[0051] The sentence generation unit generates sentences that emphasize the emotional aspects of the work and can appeal to the user's emotions using an emotion engine. The sentence generation unit, for example, uses a generation AI to generate sentences that emphasize the emotional aspects of the work. For example, it describes in detail the emotions evoked by the colors and shapes of an artwork to appeal to the user's emotions. It also uses an emotion engine to add emotional elements to the generated sentences. For example, it generates expressions that emphasize the emotions evoked by the melody and rhythm of a musical piece. It also combines the emotion engine with sentences generated by the generation AI to generate sentences that appeal to the user's emotions. For example, it emotionally expresses the background and creative intentions of the work. In this way, it is possible to appeal to the user's emotions by emphasizing the emotional aspects of the work.

[0052] The sentence generation unit can collect user feedback on generated sentences in real time and automatically improve the sentences based on that feedback. For example, the sentence generation unit collects user feedback on sentences generated by the generation AI in real time and automatically improves the sentences based on that feedback. For example, it analyzes user comments and ratings and adjusts the content of the sentences. It also adjusts the tone and style of the sentences generated by the generation AI based on user feedback. For example, it prioritizes the use of expressions that receive a lot of positive feedback. It also builds a system in which the generation AI improves sentences based on feedback collected in real time. For example, it updates the content of the sentences to reflect user opinions. In this way, it is possible to generate more effective sentences by automatically improving the sentences based on user feedback.

[0053] The text generation unit can add depth to a work by including the historical background and cultural significance of the work in the generated text. For example, the text generation unit can include the historical background of the work in the text generated by the generative AI. For example, it can provide a detailed explanation of the era and cultural background in which the artwork was created. It can also include the cultural significance of the work in the text generated by the generative AI. For example, it can describe the influence a musical work has had on a particular culture or society. It can also add depth to a work by combining the historical background and cultural significance of the work in the text generated by the generative AI. For example, it can explain how the work came to be in its current form. This can add depth to a work by including the historical background and cultural significance of the work.

[0054] The text generation unit generates text in different languages, allowing the work to appeal to an international audience. The text generation unit generates text in different languages, for example, using a generative AI. For example, it generates text that conveys the appeal of the work in multiple languages, such as English, French, and Chinese. In addition, to make the text generated by the generative AI multilingual, it takes into account the cultural nuances of each language. For example, it uses different expressions and styles for each language. In addition, to appeal to an international audience, a system is built to provide the text generated by the generative AI in different languages. For example, it displays the text in a language selected by the user. In this way, by generating text in different languages, the work can appeal to an international audience.

[0055] The text generation unit uses speech synthesis technology to provide the generated text as narration, introducing the work both visually and audibly. The text generation unit, for example, uses speech synthesis technology to provide text generated by the generation AI as narration. For example, an introduction to the work is played aloud to introduce the work both visually and audibly. A system is also constructed that uses speech synthesis technology to provide the generated text as narration. For example, a text selected by the user is played aloud. Furthermore, to introduce the work both visually and audibly, the text generated by the generation AI is combined with speech synthesis technology. For example, it is used as a narration for a video. This allows the appeal of the work to be conveyed more effectively by introducing the work both visually and audibly.

[0056] The sentence generation unit can use the emotion estimation function to identify the sentence pattern that most moves the user and generate sentences based on that pattern. The sentence generation unit, for example, uses the emotion estimation function to identify the sentence pattern that most moves the user. For example, expressions and phrases that evoke emotion are extracted based on past data. Then, the generation AI generates sentences based on the identified emotion pattern. For example, sentences that describe moving episodes or scenes are generated. Furthermore, by combining the emotion estimation function, a system is built that generates sentences that most move the user. For example, patterns with high emotion scores are used preferentially. This makes it possible to identify the sentence pattern that most moves the user and generate sentences based on that pattern, thereby strongly appealing to the user's emotions.

[0057] The strategy proposal unit can analyze a user's past posts and reactions, and based on that, suggest optimal post content. The strategy proposal unit, for example, uses a generation AI to analyze a user's past posts and reactions, and based on that, suggest optimal post content. For example, it analyzes engagement data on past posts and generates effective post content. In addition, a system is built in which the generation AI suggests optimal post content based on the user's past reaction data. For example, it prioritizes suggestions of themes and topics that users have responded to the most. In addition, the generation AI analyzes a user's past posts and reactions, and customizes the post content. For example, it generates post content that matches the user's preferences. In this way, it is possible to suggest optimal post content by analyzing a user's past posts and reactions.

[0058] The strategy proposal unit can reflect the user's emotion estimation results and create posts that are likely to resonate emotionally. The strategy proposal unit, for example, reflects the user's emotion estimation results in the strategy proposed by the generation AI and creates posts that are likely to resonate emotionally. For example, it prioritizes posting themes that evoke strong positive emotions. In addition, a system is constructed in which the generation AI suggests post content that is likely to resonate emotionally based on the emotion estimation results. For example, it uses expressions and phrases with high emotion scores. In addition, the generation AI customizes the post content by reflecting the user's emotion estimation results. For example, it generates post content that matches the user's emotions. In this way, posts that are likely to resonate emotionally can be created by reflecting the user's emotion estimation results.

[0059] The strategy proposal department can analyze competitors' social media strategies and propose the optimal strategy based on the results. For example, the strategy proposal department uses a generation AI to analyze competitors' social media strategies and propose the optimal strategy based on the results. For example, it generates post content that references competitors' success stories. It also builds a system that analyzes competitors' social media strategies and has the generation AI propose a strategy to differentiate them. For example, it proposes hashtags and themes that competitors are not using. It also analyzes competitors' strategies and generates post content to differentiate them. For example, it proposes post content that exploits competitors' weaknesses. In this way, it is possible to propose the optimal strategy by analyzing competitors' social media strategies.

[0060] The strategy proposal unit can incorporate timely elements according to seasons and events. For example, the strategy proposal unit incorporates timely elements according to seasons and events into the strategies proposed by the generation AI. For example, it generates post content that matches seasonal events such as Christmas and Halloween. Furthermore, in order to reflect timely elements according to seasons and events, a system is constructed in which the generation AI proposes the optimal strategy. For example, it suggests hashtags and themes related to specific events. Furthermore, the generation AI generates post content that matches seasons and events and incorporates timely elements. For example, it suggests post content that matches the change of seasons or specific holidays. In this way, more effective post content can be generated by incorporating timely elements according to seasons and events.

[0061] The strategy proposal unit uses the emotion estimation function to select hashtags based on the user's emotions, thereby increasing post engagement. The strategy proposal unit, for example, uses the emotion estimation function to select hashtags based on the user's emotions. For example, hashtags with strong positive emotions are used preferentially. Furthermore, a system is constructed in which a generation AI suggests optimal hashtags based on the user's emotion estimation results. For example, hashtags with a high emotion score are automatically selected. Furthermore, by combining the emotion estimation function, hashtags based on the user's emotions are selected to increase post engagement. For example, hashtags that are likely to resonate emotionally are used. In this way, by selecting hashtags based on the user's emotions, post engagement can be increased.

[0062] The video production support unit can incorporate emotional elements into the scenario to appeal to the viewer's emotions. The video production support unit, for example, uses generative AI to incorporate emotional elements into the video scenario. For example, it generates a scenario that emotionally depicts the production process of a work or the artist's thoughts. It also combines an emotion engine to add emotional elements to the generated scenario. For example, it depicts episodes or scenes that appeal to the viewer's emotions. It also incorporates emotional elements into the scenario generated by the generative AI to appeal to the viewer's emotions. For example, it emotionally expresses the background of the work or the intention behind its creation. In this way, by incorporating emotional elements into the video scenario, it is possible to appeal to the viewer's emotions.

[0063] The video production support unit can collect viewer feedback on the generated scenario in real time and automatically improve the scenario based on that feedback. For example, the video production support unit collects viewer feedback on a scenario generated by the generation AI in real time and automatically improves the scenario based on that feedback. For example, it analyzes viewer comments and ratings and adjusts the content of the scenario. It also adjusts the tone and style of the scenario generated by the generation AI based on viewer feedback. For example, it prioritizes the use of expressions that receive a lot of positive feedback. It also builds a system that allows the generation AI to improve the scenario based on feedback collected in real time. For example, it updates the content of the scenario to reflect viewer opinions. In this way, it is possible to generate more effective video scenarios by automatically improving the scenario based on viewer feedback.

[0064] The video production support unit can include the technical details and production process of the work in the generated scenario to enhance its visual appeal. For example, the video production support unit includes the technical details of the work in the scenario generated by the generative AI. For example, it generates a scenario that provides detailed explanations of the production techniques and materials used in an art work. It also includes the production process of the work in the scenario generated by the generative AI. For example, it generates a scenario that describes the recording of a musical work or scenes of instruments being played. It also combines technical details and the production process in the scenario generated by the generative AI to enhance its visual appeal. For example, it generates a scenario that explains how a work was completed. In this way, the visual appeal can be enhanced by including the technical details and production process of the work.

[0065] The video production support unit can generate video scenarios in different formats. The video production support unit generates video scenarios in different formats, for example, using a generation AI. For example, it generates scenarios suitable for short videos or live streaming. In addition, a system is constructed in which the generation AI proposes the optimal scenario, taking into account the characteristics of each format. For example, it generates a concise scenario for short videos and an interactive scenario for live streaming. In addition, the generation AI generates the optimal video scenario for each different format. For example, it proposes a scenario to increase viewer engagement. In this way, by generating video scenarios in different formats, viewer engagement can be increased.

[0066] The video production support unit can incorporate visual effects and animations into the generated scenario to enhance its visual impact. The video production support unit, for example, incorporates visual effects and animations into the scenario generated by the generation AI. For example, visual effects are added to emphasize the features of the work. In addition, a system is built that combines visual effects and animations to enhance the visual impact of the generated scenario. For example, animations that reproduce the movements of an artwork are added. In addition, the generation AI generates a scenario that incorporates visual effects and animations to enhance its visual impact. For example, the production process of the work is expressed in animation. In this way, the visual impact can be enhanced by incorporating visual effects and animations.

[0067] The video production support unit can use the emotion estimation function to identify the video pattern that most moves viewers and generate a scenario based on that pattern. The video production support unit, for example, uses the emotion estimation function to identify the video pattern that most moves viewers. For example, it extracts elements of a scenario that evokes emotion based on past data. Then, a generation AI generates a scenario based on the identified emotion pattern. For example, it generates a scenario that depicts an emotional episode or scene. Furthermore, by combining the emotion estimation function, a system is built that generates a video scenario that most moves viewers. For example, it prioritizes the use of patterns with high emotion scores. In this way, it is possible to identify the video pattern that most moves viewers and generate a scenario based on that pattern, thereby strongly appealing to the viewer's emotions.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The system can also be equipped with a recommendation unit that analyzes a user's past browsing history and recommends the most suitable art or music works for each individual user. For example, similar works can be recommended based on the genre and style of works the user has previously viewed. The system can also build a recommendation algorithm based on the user's browsing history to enable the generation AI to discover new works. For example, it can recommend works that the user has not seen yet but may be interested in. The generation AI can also generate a customized recommendation list for each individual user based on the user's browsing history. This allows the system to utilize the user's past browsing history to recommend the most suitable works for each individual user.

[0070] The system can further include a recommendation unit that estimates the user's emotions and recommends works that the user is likely to be interested in based on the estimated emotions. For example, the system can analyze the characteristics of works that have previously moved the user and recommend similar works. The system can also build a recommendation algorithm based on the estimated user emotions to enable the generation AI to discover new works. For example, it can recommend works with themes or styles that are likely to move the user. The generation AI can also generate a customized recommendation list for each individual user based on the estimated user emotions. This makes it possible to recommend works that the user is most likely to be interested in based on their emotions.

[0071] The system can also be equipped with a feedback collection unit that collects user feedback in real time and improves the work recommendation algorithm based on that feedback. For example, the system can analyze the ratings and comments users have made on recommended works and adjust the recommendation algorithm. The system can also adjust the genre and style of works recommended by the generation AI based on user feedback. For example, works with a lot of positive feedback can be given priority in recommendation. It is also possible to build a system in which the generation AI improves the recommendation algorithm based on feedback collected in real time. This allows the recommendation algorithm to be automatically improved based on user feedback, enabling more effective work recommendations.

[0072] The system can further include a recommendation unit that estimates the user's emotions and, based on the estimated emotions, recommends works that are most likely to move the user. For example, the system can analyze the characteristics of works that have previously moved the user and recommend similar works. A recommendation algorithm can also be constructed based on the results of the user's emotion estimation, allowing the generation AI to discover new works. For example, works with themes or styles that are likely to move the user can be recommended. The generation AI can also generate a customized recommendation list for each individual user based on the results of the user's emotion estimation. This allows the system to recommend works that are most likely to move the user based on their emotions.

[0073] The system can also be equipped with a purchase history analysis unit that analyzes a user's past purchase history and recommends the most suitable art and music works for each individual user. For example, similar works can be recommended based on the genre and style of works the user has previously purchased. The system can also build a recommendation algorithm based on the user's purchase history to enable the generation AI to discover new works. For example, it can recommend works that the user has not yet purchased but may be interested in. The generation AI can also generate a customized recommendation list for each individual user based on the user's purchase history. This makes it possible to utilize the user's past purchase history to recommend the most suitable works for each individual user.

[0074] The system can further include a recommendation unit that estimates the user's emotions and recommends works that will most relax the user based on the estimated emotions. For example, the system can analyze the characteristics of works that the user has previously found relaxing and recommend similar works. The system can also build a recommendation algorithm based on the estimated user emotions to enable the generation AI to discover new works. For example, it can recommend works with themes or styles that the user finds relaxing. The generation AI can also generate a customized recommendation list for each individual user based on the estimated user emotions. This allows the system to recommend the most relaxing works based on the user's emotions.

[0075] The system can also be equipped with a viewing history analysis unit that analyzes a user's past viewing history and recommends the most suitable art or music works for each individual user. For example, similar works can be recommended based on the genre and style of works the user has previously viewed. The system can also build a recommendation algorithm based on the user's viewing history to enable the generation AI to discover new works. For example, it can recommend works that the user has not yet viewed but may be interested in. The generation AI can also generate a customized recommendation list for each individual user based on the user's viewing history. This allows the system to utilize the user's past viewing history to recommend the most suitable works for each individual user.

[0076] The system can further include a recommendation unit that estimates the user's emotions and recommends works that excite the user most based on the estimated emotions. For example, the system can analyze the characteristics of works that the user has previously found exciting and recommend similar works. The system can also build a recommendation algorithm based on the user's emotion estimation results to enable the generation AI to discover new works. For example, it can recommend works with themes or styles that excite the user. The generation AI can also generate a customized recommendation list for each individual user based on the user's emotion estimation results. This allows the system to recommend the most exciting works based on the user's emotions.

[0077] The system can also be equipped with a rating history analysis unit that analyzes users' past rating history and recommends the most suitable art and music works for each individual user. For example, similar works can be recommended based on the genres and styles of works that the user has previously given high ratings. The system can also build a recommendation algorithm based on the user's rating history to enable the generation AI to discover new works. For example, it can recommend works that the user has not yet rated but may be interested in. The generation AI can also generate a customized recommendation list for each individual user based on the user's rating history. This makes it possible to utilize the user's past rating history to recommend the most suitable works for each individual user.

[0078] The system can further include a recommendation unit that estimates the user's emotions and recommends works that the user most resonates with based on the estimated emotions. For example, the system can analyze the characteristics of works that the user has previously resonated with and recommend similar works. The system can also build a recommendation algorithm based on the user's emotion estimation results to enable the generation AI to discover new works. For example, it can recommend works with themes or styles that the user easily resonates with. The generation AI can also generate a customized recommendation list tailored to each individual user based on the user's emotion estimation results. This allows the system to recommend works that the user most resonates with based on their emotions.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The text generation unit uses generative AI to understand the concept and characteristics of the work and generate text that effectively expresses them. For example, the generative AI uses text generation AI (e.g., GPT-3) to generate text that provides a detailed explanation of the background, creative intent, and techniques used in the artwork. The generative AI can also use multimodal generative AI to understand the concept and characteristics of the work and generate text that effectively expresses them. Step 2: The strategy proposal unit customizes social media approaches based on the text generated by the text generation unit and proposes optimal strategies according to the objectives. For example, based on prompts including the target audience and objectives, the generation AI suggests post content, posting timing, hashtag selection, etc. aimed at a specific target audience. Step 3: The Video Production Support Department supports the production of the product introduction video based on the strategy proposed by the Strategy Proposal Department. For example, the Generative AI generates the video's scenario and narration text based on prompts including the video's purpose and content, and makes suggestions to enhance its visual appeal.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The 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.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] In the robot 414, 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 robot 414 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.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A text generation section uses generative AI to understand the concept and characteristics of the work and generate text that effectively expresses them. a strategy proposal unit that customizes an approach on social media based on the sentences generated by the sentence generation unit and proposes an optimal strategy according to a purpose; a video production support unit that supports the production of a work introduction video based on the strategy proposed by the strategy proposal unit. A system characterized by:

2. The sentence generation unit Generate text that emphasizes the emotional aspects of the work and appeals to the user's emotions using an emotion engine 2. The system of claim 1.

3. The sentence generation unit The generated sentences are automatically improved based on the feedback from users collected in real time.

2. The system of claim 1.

4. The sentence generation unit The generated text adds depth to the work by including its historical background and cultural significance.

2. The system of claim 1.

5. The sentence generation unit Generate text in different languages ​​to reach an international audience 2. The system of claim 1.

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