system

Abstract

The system according to the embodiment aims to effectively communicate the appeal of a work and propose an optimal presentation strategy. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit receives work information from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates text that conveys the appeal of the work based on the information analyzed by the analysis unit. The proposal unit proposes a presentation strategy based on the text generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult for artists to effectively communicate the appeal of their work, and there is a risk that their work will be buried.

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

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit receives work information from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates text that conveys the appeal of the work based on the information analyzed by the analysis unit. The proposal unit proposes a presentation strategy based on the text generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively convey the appeal of a work and propose an optimal presentation strategy. [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 text generation system according to an embodiment of the present invention allows artists to effectively communicate the compelling story and concept of their artwork. This system allows users to input information about the artwork, and a generation AI analyzes the information to generate text that conveys the artwork's appeal and further suggests optimal presentation strategies on social media. For example, a user inputs the title, concept, and background information of the artwork. This information is then input into the generation AI, which then uses that information to generate text that compellingly expresses the artwork's story and concept. Based on the generated text, the system suggests optimal posting content, timing, and hashtag usage on social media. For example, for Instagram posts, the system suggests the appropriate caption to accompany the image and the best time to post. This system allows artists to effectively express the essence of their artwork and customize their approach on social media. This allows great artwork to be shared with a wide audience without being overlooked. The text generation system thus allows artists to effectively communicate the appeal of their artwork and optimize their presentation on social media.

[0029] A text generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a suggestion unit. The reception unit receives work information from a user. The work information from the user includes, but is not limited to, text, images, audio, and video. The reception unit receives, for example, the title, concept, and background information of the work input by the user. The analysis unit analyzes the information received by the reception unit. The analysis is performed using, for example, text analysis, image analysis, audio analysis, and other methods, but is not limited to these methods. The analysis unit extracts the story and concept of the work using text analysis. The analysis unit can also analyze visual elements of the work using image analysis. The analysis unit can also analyze audio elements of the work using audio analysis. The generation unit generates text that conveys the appeal of the work based on the information analyzed by the analysis unit. The generation unit generates text that appealingly expresses the story and concept of the work using, for example, a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The generation unit generates text that brings out the appeal of the work, for example, using sentiment analysis and keyword extraction. The suggestion unit proposes an optimal presentation strategy on social media based on the text generated by the generation unit. The suggestion unit proposes, for example, optimal posting content, timing, and hashtag usage, depending on the characteristics of the social media and the user's purpose. For example, in the case of a post on Instagram, the suggestion unit suggests what caption should be added to the image, what time of day the post should be made, etc. In this way, the text generation system according to the embodiment can efficiently analyze information about the user's work, generate attractive text, and propose an optimal presentation strategy.

[0030] The suggestion unit can suggest post content according to the characteristics of the social media. For example, the suggestion unit suggests optimal post content according to the characteristics of the social media. Social media characteristics include, but are not limited to, for example, frequency of posts, user responses, and algorithm characteristics. For example, in the case of Instagram, the suggestion unit can suggest captions along with visually appealing images. In addition, in the case of X (formerly Twitter (registered trademark)), the suggestion unit can also suggest short and to-the-point post content. Furthermore, in the case of Facebook (registered trademark), the suggestion unit can also suggest post content that includes detailed information. This enables effective presentations by suggesting optimal post content according to the characteristics of the social media.

[0031] The suggestion unit can suggest a posting timing according to the user's purpose. The suggestion unit, for example, suggests the optimal posting timing according to the user's purpose. The user's purpose includes, but is not limited to, for example, promoting a product, sharing information, and increasing followers. For example, if the purpose is to promote a product, the suggestion unit can suggest posting during a time period when the user's target demographic is most active. Furthermore, if the purpose is to share information, the suggestion unit can also suggest posting during a time period when the user's followers are most online. Furthermore, if the purpose is to increase followers, the suggestion unit can also suggest posting during a time period when engagement is high. In this way, by suggesting the optimal posting timing according to the user's purpose, effective presentations can be made.

[0032] The suggestion unit can suggest how to use hashtags. The suggestion unit, for example, suggests the optimal way to use hashtags. Examples of how to use hashtags include, but are not limited to, popular hashtags and highly relevant hashtags. For example, the suggestion unit can suggest using popular hashtags in the case of a post on Instagram. The suggestion unit can also suggest using highly relevant hashtags in the case of a post on X (formerly Twitter). Furthermore, the suggestion unit can suggest using hashtags related to a specific community in the case of a post on Facebook. This allows for effective presentations on social media by suggesting the optimal way to use hashtags.

[0033] The generation unit can generate text that expresses the story and concept of the work. The generation unit uses, for example, a generation AI to generate text that appealingly expresses the story and concept of the work. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates text that brings out the appeal of the work, for example, using sentiment analysis or keyword extraction. The generation unit generates text that explains in detail, for example, the background of the painting's creation and the techniques used, and brings out its appeal. In addition, in the case of a musical work, the generation unit can also generate text that explains the composer's intentions and the characteristics of the music. Furthermore, in the case of a video work, the generation unit can also generate text that explains the video's story and filming techniques. In this way, the appeal of the work can be effectively conveyed by generating text that appealingly expresses the story and concept of the work.

[0034] The reception unit can analyze the user's past work information submission history and select the reception method. The reception unit, for example, analyzes the user's past work information submission history and selects the optimal reception method. The past work information submission history includes, for example, the number of submissions, the submission content, the submission date and time, etc., but is not limited to these examples. The reception unit, for example, preferentially suggests reception methods that the user has frequently used in the past. The reception unit can also analyze trends in works submitted by the user in the past and select the optimal reception method. Furthermore, the reception unit can also suggest the optimal reception method for a specific time period based on the user's past submission history. In this way, the optimal reception method can be selected by analyzing the user's past submission history.

[0035] The reception unit may perform filtering based on the user's current project or area of ​​interest when receiving the work information. For example, the reception unit may perform filtering based on the user's current project or area of ​​interest when receiving the work information. Examples of current projects and areas of interest include, but are not limited to, project themes and topics of interest. For example, the reception unit may preferentially receive work information related to a project the user is currently working on. The reception unit may also filter related work information based on the user's area of ​​interest. Furthermore, the reception unit may also receive optimal work information according to the progress of the user's current project. Thus, by filtering based on the user's current project or area of ​​interest, highly relevant work information can be received.

[0036] The reception unit may prioritize receiving highly relevant information when receiving artwork information, taking into consideration the user's geographical location information. For example, the reception unit may prioritize receiving highly relevant information when receiving artwork information, taking into consideration the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if the user is in a specific area, the reception unit may prioritize receiving artwork information related to that area. Furthermore, the reception unit may prioritize receiving artwork information related to nearby events or exhibitions based on the user's current location. Furthermore, the reception unit may prioritize receiving artwork information related to trends specific to the area, based on the user's geographical location information. This allows for prioritized reception of highly relevant artwork information by taking into consideration the user's geographical location information.

[0037] The reception unit may analyze the user's social media activity and receive related information when receiving the work information. For example, the reception unit may analyze the user's social media activity and receive related information when receiving the work information. Social media activity may include, but is not limited to, the content of posts, the number of followers, and the engagement rate. For example, the reception unit may preferentially receive work information related to themes that the user frequently posts about on social media. The reception unit may also receive related work information based on the interests of the user's social media followers. Furthermore, the reception unit may analyze the user's social media activity history and receive optimal work information. This allows the user's social media activity to be analyzed and related work information to be preferentially received.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the work during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the work during analysis. The importance of a work includes, but is not limited to, user ratings, number of views, expert ratings, etc. For example, the analysis unit performs a detailed analysis of works with high importance. The analysis unit can also perform a simplified analysis of works with low importance. Furthermore, the analysis unit can adjust the depth of the analysis depending on the importance of the work. In this way, by adjusting the level of detail of the analysis depending on the importance of the work, more appropriate analysis results can be provided.

[0039] The analysis unit can apply different analysis algorithms depending on the category of the work during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the work during analysis. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms. For example, the analysis unit applies a color analysis algorithm to a painting. The analysis unit can also apply a sound wave analysis algorithm to a musical work. Furthermore, the analysis unit can apply a frame analysis algorithm to a video work. In this way, by applying an analysis algorithm depending on the category of the work, more appropriate analysis results can be provided.

[0040] The analysis unit can determine the priority of analysis based on the submission date of the work during analysis. For example, the analysis unit determines the priority of analysis based on the submission date of the work during analysis. The submission date includes, but is not limited to, for example, the submission date and time, the submission period, etc. For example, the analysis unit prioritizes the analysis of recently submitted works. The analysis unit can also postpone works that were submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the submission date. In this way, by determining the priority of analysis based on the submission date of the work, the analysis can be performed in a more appropriate order.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the works during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the works during analysis. The relevance of the works includes, but is not limited to, for example, a match in theme or similarity in content. For example, the analysis unit prioritizes the analysis of highly relevant works. The analysis unit can also postpone works with low relevance. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the works. In this way, by adjusting the order of analysis based on the relevance of the works, the analysis can be performed in a more appropriate order.

[0042] The generation unit can adjust the level of detail of the sentence based on the importance of the work during generation. For example, the generation unit adjusts the level of detail of the sentence based on the importance of the work during generation. The importance of the work includes, for example, user ratings, number of views, expert ratings, etc., but is not limited to these examples. For example, the generation unit generates detailed sentences for works with high importance. The generation unit can also generate simplified sentences for works with low importance. Furthermore, the generation unit can adjust the level of detail of the sentences according to the importance of the work. In this way, more appropriate sentences can be generated by adjusting the level of detail of the sentences according to the importance of the work.

[0043] The generation unit can apply different generation algorithms depending on the category of the work during generation. For example, the generation unit applies different generation algorithms depending on the category of the work during generation. Examples of generation algorithms include, but are not limited to, natural language generation algorithms and template-based generation. For example, the generation unit applies a generation algorithm specialized in color expression to a painting. The generation unit can also apply a generation algorithm based on music theory to a musical work. Furthermore, the generation unit can apply a generation algorithm based on video analysis to a video work. In this way, by applying a generation algorithm depending on the category of the work, more appropriate sentences can be generated.

[0044] The generation unit can determine the priority of sentences based on the submission time of the work at the time of generation. For example, the generation unit determines the priority of sentences based on the submission time of the work at the time of generation. The submission time includes, for example, but is not limited to, the submission date and time, the submission period, etc. For example, the generation unit generates sentences with priority for works that have been submitted recently. The generation unit can also postpone works that have been submitted earlier. Furthermore, the generation unit can adjust the generation order of sentences based on the submission time. In this way, by determining the priority of sentences based on the submission time of the work, sentences can be generated in a more appropriate order.

[0045] The generation unit can adjust the order of sentences based on the relevance of the works during generation. For example, the generation unit adjusts the order of sentences based on the relevance of the works during generation. The relevance of the works includes, but is not limited to, for example, a match in theme or similarity in content. For example, the generation unit can prioritize generating sentences for highly relevant works. The generation unit can also postpone generating sentences for less relevant works. Furthermore, the generation unit can adjust the generation order of sentences based on the relevance of the works. In this way, by adjusting the order of sentences based on the relevance of the works, sentences can be generated in a more appropriate order.

[0046] The suggestion unit may adjust the level of detail of the suggestion based on the characteristics of the social media when making a suggestion. For example, the suggestion unit may adjust the level of detail of the suggestion based on the characteristics of the social media when making a suggestion. Examples of social media characteristics include, but are not limited to, posting frequency, user responses, and algorithm characteristics. For example, the suggestion unit may make visually appealing suggestions in the case of Instagram. Furthermore, the suggestion unit may make short and to-the-point suggestions in the case of X (formerly Twitter). Furthermore, the suggestion unit may make suggestions including detailed information in the case of Facebook. As a result, by adjusting the level of detail of the suggestion based on the characteristics of the social media, more appropriate suggestions can be made.

[0047] The suggestion unit can apply different suggestion algorithms depending on the user's purpose when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the user's purpose when making a suggestion. Examples of suggestion algorithms include, but are not limited to, recommendation algorithms and optimization algorithms. For example, the suggestion unit makes suggestions that increase engagement when the purpose is to increase followers. Furthermore, the suggestion unit can make visually attractive suggestions when the purpose is to increase brand awareness. Furthermore, the suggestion unit can make suggestions that increase purchasing motivation when the purpose is sales promotion. In this way, by applying a suggestion algorithm depending on the user's purpose, more appropriate suggestions can be made.

[0048] The suggestion unit can determine the priority of suggestions based on the time of posting on social media when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the time of posting on social media when making suggestions. The time of posting includes, but is not limited to, for example, the date and time of posting and the posting period. For example, the suggestion unit makes prominent suggestions during times of day when there are many posts. The suggestion unit can also make detailed suggestions during times of day when there are few posts. Furthermore, the suggestion unit can adjust the priority of suggestions based on the time of posting. Thus, by determining the priority of suggestions based on the time of posting on social media, suggestions can be made in a more appropriate order.

[0049] The suggestion unit can adjust the order of suggestions based on social media relevance when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on social media relevance when making suggestions. Social media relevance includes, but is not limited to, for example, matching of themes and similarity of content. For example, the suggestion unit prioritizes suggestions for highly relevant posts. The suggestion unit can also postpone posts with low relevance. Furthermore, the suggestion unit can adjust the order of suggestions based on social media relevance. As a result, by adjusting the order of suggestions based on social media relevance, suggestions can be made in a more appropriate order.

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

[0051] The analysis unit can analyze a user's past posting history and understand trends in posting content. For example, it can analyze the themes and styles of posts the user has made in the past and reflect these in current posts. The analysis unit can also analyze the reactions of the user's followers and understand what posts are gaining the most engagement. Furthermore, the analysis unit can analyze the user's posting frequency and time period and suggest an optimal posting schedule. This makes it possible to utilize the user's past posting history to suggest more effective posting content and schedules.

[0052] The generation unit can generate sentences related to local cultures and events by taking into account the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate sentences including information related to the culture and events of that region. The generation unit can also generate sentences related to local trends and topics based on the user's current location. Furthermore, the generation unit can generate sentences that take into account the local language and dialect based on the user's geographical location information. This makes it possible to generate sentences that are more closely related to the region by utilizing the user's geographical location information.

[0053] The suggestion unit can analyze a user's influence on social media and adjust the content of suggestions based on that influence. For example, if the user is an influencer with many followers, suggestions that appeal to a wider audience can be made. Also, if the user has a strong influence in a specific niche community, suggestions specialized for that community can be made. Furthermore, fluctuations in the user's influence can be analyzed in real time and suggestions can be made at the optimal time. This allows for more effective suggestions to be made based on the user's influence.

[0054] The generation unit can analyze the user's past writing history and adjust the style of the text to be generated. For example, it can analyze the style and tone of texts the user has previously written and generate new texts based on that. The generation unit can also understand the user's preferences and tendencies and generate texts in a style that matches those preferences. Furthermore, the generation unit can take into account the user's past feedback and generate texts that reflect areas for improvement. In this way, it is possible to utilize the user's past writing history to generate texts that are more suited to the user.

[0055] The reception unit can analyze the user's social media activity history and propose the optimal reception method. For example, it can identify the social media platform the user frequently uses and propose the optimal reception method for that platform. It can also analyze the user's posting content and engagement rate to select the most effective reception method. Furthermore, it can take into account the reactions of the user's followers and propose a reception method that is attractive to them. In this way, it is possible to propose a more effective reception method by utilizing the user's social media activity history.

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

[0057] Step 1: The reception unit receives work information from the user. The work information from the user includes text, images, audio, video, etc. For example, the reception unit receives the title, concept, background information, etc. of the work input by the user. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as text analysis, image analysis, and audio analysis. For example, text analysis is used to extract the story and concept of the work, image analysis is used to analyze the visual elements of the work, and audio analysis is used to analyze the audio elements of the work. Step 3: The generation unit generates text that conveys the appeal of the work based on the information analyzed by the analysis unit. The generation unit uses generative AI to generate text that appealingly expresses the story and concept of the work. For example, it uses sentiment analysis and keyword extraction to generate text that brings out the appeal of the work. Step 4: The suggestion unit proposes an optimal presentation strategy for social media based on the text generated by the generation unit. The suggestion unit suggests the optimal posting content, timing, and hashtag usage based on the characteristics of the social media and the user's purpose. For example, in the case of an Instagram post, it suggests what kind of caption should accompany the image and what time of day the post should be made.

[0058] (Example 2) A text generation system according to an embodiment of the present invention allows artists to effectively communicate the compelling story and concept of their artwork. This system allows users to input information about the artwork, and a generation AI analyzes the information to generate text that conveys the artwork's appeal and further suggests optimal presentation strategies on social media. For example, a user inputs the title, concept, and background information of the artwork. This information is then input into the generation AI, which then uses that information to generate text that compellingly expresses the artwork's story and concept. Based on the generated text, the system suggests optimal posting content, timing, and hashtag usage on social media. For example, for Instagram posts, the system suggests the appropriate caption to accompany the image and the best time to post. This system allows artists to effectively express the essence of their artwork and customize their approach on social media. This allows great artwork to be shared with a wide audience without being overlooked. The text generation system thus allows artists to effectively communicate the appeal of their artwork and optimize their presentation on social media.

[0059] A text generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a suggestion unit. The reception unit receives work information from a user. The work information from the user includes, but is not limited to, text, images, audio, and video. The reception unit receives, for example, the title, concept, and background information of the work input by the user. The analysis unit analyzes the information received by the reception unit. The analysis is performed using, for example, text analysis, image analysis, audio analysis, and other methods, but is not limited to these methods. The analysis unit extracts the story and concept of the work using text analysis. The analysis unit can also analyze visual elements of the work using image analysis. The analysis unit can also analyze audio elements of the work using audio analysis. The generation unit generates text that conveys the appeal of the work based on the information analyzed by the analysis unit. The generation unit generates text that appealingly expresses the story and concept of the work using, for example, a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The generation unit generates text that brings out the appeal of the work, for example, using sentiment analysis and keyword extraction. The suggestion unit proposes an optimal presentation strategy on social media based on the text generated by the generation unit. The suggestion unit proposes, for example, optimal posting content, timing, and hashtag usage, depending on the characteristics of the social media and the user's purpose. For example, in the case of a post on Instagram, the suggestion unit suggests what caption should be added to the image, what time of day the post should be made, etc. In this way, the text generation system according to the embodiment can efficiently analyze information about the user's work, generate attractive text, and propose an optimal presentation strategy.

[0060] The suggestion unit can suggest post content according to the characteristics of the social media. For example, the suggestion unit suggests optimal post content according to the characteristics of the social media. Social media characteristics include, but are not limited to, for example, frequency of posts, user responses, and algorithm characteristics. For example, in the case of Instagram, the suggestion unit can suggest captions along with visually appealing images. In addition, in the case of X (formerly Twitter), the suggestion unit can also suggest short and to-the-point post content. Furthermore, in the case of Facebook, the suggestion unit can also suggest post content that includes detailed information. This enables effective presentations by suggesting optimal post content according to the characteristics of the social media.

[0061] The suggestion unit can suggest a posting timing according to the user's purpose. The suggestion unit, for example, suggests the optimal posting timing according to the user's purpose. The user's purpose includes, but is not limited to, for example, promoting a product, sharing information, and increasing followers. For example, if the purpose is to promote a product, the suggestion unit can suggest posting during a time period when the user's target demographic is most active. Furthermore, if the purpose is to share information, the suggestion unit can also suggest posting during a time period when the user's followers are most online. Furthermore, if the purpose is to increase followers, the suggestion unit can also suggest posting during a time period when engagement is high. In this way, by suggesting the optimal posting timing according to the user's purpose, effective presentations can be made.

[0062] The suggestion unit can suggest how to use hashtags. The suggestion unit, for example, suggests the optimal way to use hashtags. Examples of how to use hashtags include, but are not limited to, popular hashtags and highly relevant hashtags. For example, the suggestion unit can suggest using popular hashtags in the case of a post on Instagram. The suggestion unit can also suggest using highly relevant hashtags in the case of a post on X (formerly Twitter). Furthermore, the suggestion unit can suggest using hashtags related to a specific community in the case of a post on Facebook. This allows for effective presentations on social media by suggesting the optimal way to use hashtags.

[0063] The generation unit can generate text that expresses the story and concept of the work. The generation unit uses, for example, a generation AI to generate text that appealingly expresses the story and concept of the work. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates text that brings out the appeal of the work, for example, using sentiment analysis or keyword extraction. The generation unit generates text that explains in detail, for example, the background of the painting's creation and the techniques used, and brings out its appeal. In addition, in the case of a musical work, the generation unit can also generate text that explains the composer's intentions and the characteristics of the music. Furthermore, in the case of a video work, the generation unit can also generate text that explains the video's story and filming techniques. In this way, the appeal of the work can be effectively conveyed by generating text that appealingly expresses the story and concept of the work.

[0064] The reception unit can estimate the user's emotions and adjust the timing of receiving the work information based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the timing of receiving the work information based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, facial expression recognition, text analysis, and voice analysis. For example, if the user is feeling stressed, the reception unit can receive the work information during a time when the user is able to relax. Furthermore, if the user is excited, the reception unit can also receive the work information immediately. Furthermore, if the user is tired, the reception unit can also receive the work information after the user has rested. In this way, by adjusting the timing of receiving the work information according to the user's emotions, the work information can be received at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0065] The reception unit can analyze the user's past work information submission history and select the reception method. The reception unit, for example, analyzes the user's past work information submission history and selects the optimal reception method. The past work information submission history includes, for example, the number of submissions, the submission content, the submission date and time, etc., but is not limited to these examples. The reception unit, for example, preferentially suggests reception methods that the user has frequently used in the past. The reception unit can also analyze trends in works submitted by the user in the past and select the optimal reception method. Furthermore, the reception unit can also suggest the optimal reception method for a specific time period based on the user's past submission history. In this way, the optimal reception method can be selected by analyzing the user's past submission history.

[0066] The reception unit may perform filtering based on the user's current project or area of ​​interest when receiving the work information. For example, the reception unit may perform filtering based on the user's current project or area of ​​interest when receiving the work information. Examples of current projects and areas of interest include, but are not limited to, project themes and topics of interest. For example, the reception unit may preferentially receive work information related to a project the user is currently working on. The reception unit may also filter related work information based on the user's area of ​​interest. Furthermore, the reception unit may also receive optimal work information according to the progress of the user's current project. Thus, by filtering based on the user's current project or area of ​​interest, highly relevant work information can be received.

[0067] The reception unit can estimate the user's emotions and determine the priority of the artwork information to be received based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of the artwork information to be received based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, facial expression recognition, text analysis, and voice analysis. For example, when the user is feeling stressed, the reception unit can postpone receiving artwork information of lower importance. Furthermore, when the user is relaxed, the reception unit can also prioritize receiving artwork information of higher importance. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving artwork information of higher urgency. In this way, by determining the priority of the artwork information according to the user's emotions, the artwork information can be received in a more appropriate order. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0068] The reception unit may prioritize receiving highly relevant information when receiving artwork information, taking into consideration the user's geographical location information. For example, the reception unit may prioritize receiving highly relevant information when receiving artwork information, taking into consideration the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if the user is in a specific area, the reception unit may prioritize receiving artwork information related to that area. Furthermore, the reception unit may prioritize receiving artwork information related to nearby events or exhibitions based on the user's current location. Furthermore, the reception unit may prioritize receiving artwork information related to trends specific to the area, based on the user's geographical location information. This allows for prioritized reception of highly relevant artwork information by taking into consideration the user's geographical location information.

[0069] The reception unit may analyze the user's social media activity and receive related information when receiving the work information. For example, the reception unit may analyze the user's social media activity and receive related information when receiving the work information. Social media activity may include, but is not limited to, the content of posts, the number of followers, and the engagement rate. For example, the reception unit may preferentially receive work information related to themes that the user frequently posts about on social media. The reception unit may also receive related work information based on the interests of the user's social media followers. Furthermore, the reception unit may analyze the user's social media activity history and receive optimal work information. This allows the user's social media activity to be analyzed and related work information to be preferentially received.

[0070] The analysis unit can estimate the user's emotion and adjust the analysis presentation method based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis presentation method based on the estimated user's emotion. Methods for estimating the user's emotion include, but are not limited to, facial expression recognition, text analysis, and voice analysis. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. This allows for adjusting the analysis presentation method according to the user's emotion, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the work during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the work during analysis. The importance of a work includes, but is not limited to, user ratings, number of views, expert ratings, etc. For example, the analysis unit performs a detailed analysis of works with high importance. The analysis unit can also perform a simplified analysis of works with low importance. Furthermore, the analysis unit can adjust the depth of the analysis depending on the importance of the work. In this way, by adjusting the level of detail of the analysis depending on the importance of the work, more appropriate analysis results can be provided.

[0072] The analysis unit can apply different analysis algorithms depending on the category of the work during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the work during analysis. Analysis algorithms include, but are not limited to, machine learning algorithms and statistical analysis algorithms. For example, the analysis unit applies a color analysis algorithm to a painting. The analysis unit can also apply a sound wave analysis algorithm to a musical work. Furthermore, the analysis unit can apply a frame analysis algorithm to a video work. In this way, by applying an analysis algorithm depending on the category of the work, more appropriate analysis results can be provided.

[0073] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. Methods for estimating the user's emotion include, but are not limited to, facial expression recognition, text analysis, and voice analysis. For example, if the user is in a hurry, the analysis unit can perform a short and to-the-point analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can perform a visually stimulating analysis. This allows for adjusting the length of the analysis according to the user's emotion, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] The analysis unit can determine the priority of analysis based on the submission date of the work during analysis. For example, the analysis unit determines the priority of analysis based on the submission date of the work during analysis. The submission date includes, but is not limited to, for example, the submission date and time, the submission period, etc. For example, the analysis unit prioritizes the analysis of recently submitted works. The analysis unit can also postpone works that were submitted earlier. Furthermore, the analysis unit can adjust the order of analysis based on the submission date. In this way, by determining the priority of analysis based on the submission date of the work, the analysis can be performed in a more appropriate order.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the works during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the works during analysis. The relevance of the works includes, but is not limited to, for example, a match in theme or similarity in content. For example, the analysis unit prioritizes the analysis of highly relevant works. The analysis unit can also postpone works with low relevance. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the works. In this way, by adjusting the order of analysis based on the relevance of the works, the analysis can be performed in a more appropriate order.

[0076] The generation unit can estimate the user's emotion and adjust the expression style of the generated sentence based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression style of the generated sentence based on the estimated user emotion. Methods for estimating the user's emotion include, but are not limited to, facial expression recognition, text analysis, and voice analysis. For example, the generation unit uses a relaxed expression style when the user is relaxed. Furthermore, the generation unit can use a concise and to-the-point expression style when the user is in a hurry. Furthermore, the generation unit can use a visually stimulating expression style when the user is excited. In this way, by adjusting the expression style of the sentence according to the user's emotion, more appropriate sentences can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The generation unit can adjust the level of detail of the sentence based on the importance of the work during generation. For example, the generation unit adjusts the level of detail of the sentence based on the importance of the work during generation. The importance of the work includes, for example, user ratings, number of views, expert ratings, etc., but is not limited to these examples. For example, the generation unit generates detailed sentences for works with high importance. The generation unit can also generate simplified sentences for works with low importance. Furthermore, the generation unit can adjust the level of detail of the sentences according to the importance of the work. In this way, more appropriate sentences can be generated by adjusting the level of detail of the sentences according to the importance of the work.

[0078] The generation unit can apply different generation algorithms depending on the category of the work during generation. For example, the generation unit applies different generation algorithms depending on the category of the work during generation. Examples of generation algorithms include, but are not limited to, natural language generation algorithms and template-based generation. For example, the generation unit applies a generation algorithm specialized in color expression to a painting. The generation unit can also apply a generation algorithm based on music theory to a musical work. Furthermore, the generation unit can apply a generation algorithm based on video analysis to a video work. In this way, by applying a generation algorithm depending on the category of the work, more appropriate sentences can be generated.

[0079] The generation unit can estimate the user's emotion and adjust the length of the generated sentence based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the generated sentence based on the estimated user emotion. Methods for estimating the user's emotion include, but are not limited to, facial expression recognition, text analysis, and voice analysis. For example, if the user is in a hurry, the generation unit can generate short, concise sentences. If the user is relaxed, the generation unit can also generate longer sentences with detailed explanations. Furthermore, if the user is excited, the generation unit can generate sentences with visually stimulating effects. This allows for the generation of more appropriate sentences by adjusting the length of the sentences according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0080] The generation unit can determine the priority of sentences based on the submission time of the work at the time of generation. For example, the generation unit determines the priority of sentences based on the submission time of the work at the time of generation. The submission time includes, for example, but is not limited to, the submission date and time, the submission period, etc. For example, the generation unit generates sentences with priority for works that have been submitted recently. The generation unit can also postpone works that have been submitted earlier. Furthermore, the generation unit can adjust the generation order of sentences based on the submission time. In this way, by determining the priority of sentences based on the submission time of the work, sentences can be generated in a more appropriate order.

[0081] The generation unit can adjust the order of sentences based on the relevance of the works during generation. For example, the generation unit adjusts the order of sentences based on the relevance of the works during generation. The relevance of the works includes, but is not limited to, for example, a match in theme or similarity in content. For example, the generation unit can prioritize generating sentences for highly relevant works. The generation unit can also postpone generating sentences for less relevant works. Furthermore, the generation unit can adjust the generation order of sentences based on the relevance of the works. In this way, by adjusting the order of sentences based on the relevance of the works, sentences can be generated in a more appropriate order.

[0082] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way the suggestion is expressed based on the estimated user's emotion. Methods for estimating the user's emotion include, but are not limited to, facial expression recognition, text analysis, and voice analysis. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This allows for more appropriate suggestions to be made by adjusting the way the suggestion is expressed based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] The suggestion unit may adjust the level of detail of the suggestion based on the characteristics of the social media when making a suggestion. For example, the suggestion unit may adjust the level of detail of the suggestion based on the characteristics of the social media when making a suggestion. Examples of social media characteristics include, but are not limited to, posting frequency, user responses, and algorithm characteristics. For example, the suggestion unit may make visually appealing suggestions in the case of Instagram. Furthermore, the suggestion unit may make short and to-the-point suggestions in the case of X (formerly Twitter). Furthermore, the suggestion unit may make suggestions including detailed information in the case of Facebook. As a result, by adjusting the level of detail of the suggestion based on the characteristics of the social media, more appropriate suggestions can be made.

[0084] The suggestion unit can apply different suggestion algorithms depending on the user's purpose when making a suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the user's purpose when making a suggestion. Examples of suggestion algorithms include, but are not limited to, recommendation algorithms and optimization algorithms. For example, the suggestion unit makes suggestions that increase engagement when the purpose is to increase followers. Furthermore, the suggestion unit can make visually attractive suggestions when the purpose is to increase brand awareness. Furthermore, the suggestion unit can make suggestions that increase purchasing motivation when the purpose is sales promotion. In this way, by applying a suggestion algorithm depending on the user's purpose, more appropriate suggestions can be made.

[0085] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. Estimation of the user's emotion includes, but is not limited to, methods such as facial expression recognition, text analysis, and voice analysis. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. This allows for more appropriate suggestions to be made by adjusting the length of the suggestion based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The suggestion unit can determine the priority of suggestions based on the time of posting on social media when making suggestions. For example, the suggestion unit determines the priority of suggestions based on the time of posting on social media when making suggestions. The time of posting includes, but is not limited to, for example, the date and time of posting and the posting period. For example, the suggestion unit makes prominent suggestions during times of day when there are many posts. The suggestion unit can also make detailed suggestions during times of day when there are few posts. Furthermore, the suggestion unit can adjust the priority of suggestions based on the time of posting. Thus, by determining the priority of suggestions based on the time of posting on social media, suggestions can be made in a more appropriate order.

[0087] The suggestion unit can adjust the order of suggestions based on social media relevance when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on social media relevance when making suggestions. Social media relevance includes, but is not limited to, for example, matching of themes and similarity of content. For example, the suggestion unit prioritizes suggestions for highly relevant posts. The suggestion unit can also postpone posts with low relevance. Furthermore, the suggestion unit can adjust the order of suggestions based on social media relevance. As a result, by adjusting the order of suggestions based on social media relevance, suggestions can be made in a more appropriate order. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and suggestion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and accepts the title, concept, background information, etc. of the work entered by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs text analysis, image analysis, etc. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates text that conveys the appeal of the work using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal presentation strategy on social media. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and suggestion unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and accepts the title, concept, background information, etc. of the work input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs text analysis, image analysis, etc. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates text that conveys the appeal of the work using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal presentation strategy on social media. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and suggestion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and accepts the title, concept, background information, etc. of the work input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs text analysis, image analysis, etc. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates text that conveys the appeal of the work using a generation AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal presentation strategy on social media. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and accepts the title, concept, background information, etc. of the work input by the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs text analysis, image analysis, etc. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates text that conveys the appeal of the work using a generative AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal presentation strategy on social media.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The analysis unit can analyze a user's past posting history and understand trends in posting content. For example, it can analyze the themes and styles of posts the user has made in the past and reflect these in current posts. The analysis unit can also analyze the reactions of the user's followers and understand what posts are gaining the most engagement. Furthermore, the analysis unit can analyze the user's posting frequency and time period and suggest an optimal posting schedule. This makes it possible to utilize the user's past posting history to suggest more effective posting content and schedules.

[0090] The suggestion unit can estimate the user's emotions and adjust the tone of the post content based on the estimated user emotions. For example, if the user is happy, it can suggest a post with a bright and positive tone. If the user is sad, it can suggest a post with a calm tone. Furthermore, if the user is excited, it can suggest a post with an energetic tone. In this way, by suggesting a post with a tone that matches the user's emotions, it is possible to create a post that is more likely to resonate with users.

[0091] The generation unit can generate sentences related to local cultures and events by taking into account the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate sentences including information related to the culture and events of that region. The generation unit can also generate sentences related to local trends and topics based on the user's current location. Furthermore, the generation unit can generate sentences that take into account the local language and dialect based on the user's geographical location information. This makes it possible to generate sentences that are more closely related to the region by utilizing the user's geographical location information.

[0092] The reception unit can estimate the user's emotions and adjust the category of work information to be received based on the estimated user's emotions. For example, if the user is relaxed, work information in the creative category can be preferentially received. Also, if the user is stressed, work information in the relaxing category can be preferentially received. Furthermore, if the user is excited, work information in the energetic category can be preferentially received. In this way, by adjusting the category of work information according to the user's emotions, more appropriate work information can be received.

[0093] The suggestion unit can analyze a user's influence on social media and adjust the content of suggestions based on that influence. For example, if the user is an influencer with many followers, suggestions that appeal to a wider audience can be made. Also, if the user has a strong influence in a specific niche community, suggestions specialized for that community can be made. Furthermore, fluctuations in the user's influence can be analyzed in real time and suggestions can be made at the optimal time. This allows for more effective suggestions to be made based on the user's influence.

[0094] The analysis unit can estimate the user's emotions and adjust the visualization method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis results can be provided using detailed graphs and charts. If the user is in a hurry, the analysis results can be provided in a concise and to-the-point visualization. Furthermore, if the user is excited, the analysis results can be provided with visually stimulating effects. In this way, by adjusting the visualization method of the analysis results according to the user's emotions, more appropriate analysis results can be provided.

[0095] The generation unit can analyze the user's past writing history and adjust the style of the text to be generated. For example, it can analyze the style and tone of texts the user has previously written and generate new texts based on that. The generation unit can also understand the user's preferences and tendencies and generate texts in a style that matches those preferences. Furthermore, the generation unit can take into account the user's past feedback and generate texts that reflect areas for improvement. In this way, it is possible to utilize the user's past writing history to generate texts that are more suited to the user.

[0096] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can select the timing to make detailed suggestions. If the user is in a hurry, the suggestion unit can quickly make concise suggestions. Furthermore, if the user is excited, the suggestion unit can select the timing to make energetic suggestions. In this way, more effective suggestions can be made by adjusting the timing of suggestions according to the user's emotions.

[0097] The reception unit can analyze the user's social media activity history and propose the optimal reception method. For example, it can identify the social media platform the user frequently uses and propose the optimal reception method for that platform. It can also analyze the user's posting content and engagement rate to select the most effective reception method. Furthermore, it can take into account the reactions of the user's followers and propose a reception method that is attractive to them. In this way, it is possible to propose a more effective reception method by utilizing the user's social media activity history.

[0098] The generation unit can estimate the user's emotions and adjust the visual elements of the generated text based on the estimated user emotions. For example, if the user is relaxed, text can be generated using calm colors and fonts. If the user is excited, text can be generated using vivid colors and dynamic fonts. Furthermore, if the user is sad, text can be generated using subdued colors and simple fonts. In this way, more appropriate text can be generated by adjusting the visual elements of the text according to the user's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The reception unit receives work information from the user. The work information from the user includes text, images, audio, video, etc. For example, the reception unit receives the title, concept, background information, etc. of the work input by the user. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as text analysis, image analysis, and audio analysis. For example, text analysis is used to extract the story and concept of the work, image analysis is used to analyze the visual elements of the work, and audio analysis is used to analyze the audio elements of the work. Step 3: The generation unit generates text that conveys the appeal of the work based on the information analyzed by the analysis unit. The generation unit uses generative AI to generate text that appealingly expresses the story and concept of the work. For example, it uses sentiment analysis and keyword extraction to generate text that brings out the appeal of the work. Step 4: The suggestion unit proposes an optimal presentation strategy for social media based on the text generated by the generation unit. The suggestion unit suggests the optimal posting content, timing, and hashtag usage based on the characteristics of the social media and the user's purpose. For example, in the case of an Instagram post, it suggests what kind of caption should accompany the image and what time of day the post should be made.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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, in order to avoid confusion and to 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives work information from a user; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates a sentence that conveys the appeal of the work based on the information analyzed by the analysis unit; a suggestion unit that proposes a presentation strategy based on the sentences generated by the generation unit; Equipped with A system characterized by:

2. The proposal unit Suggesting post content based on the characteristics of social media 2. The system of claim 1.

3. The proposal unit Suggesting posting timing based on user purpose 2. The system of claim 1.

4. The proposal unit Suggest ways to use hashtags 2. The system of claim 1.

5. The generation unit Generate sentences that express the story and concept of a work 2. The system of claim 1.

6. The reception unit Estimates user emotions and adjusts the timing of accepting work information based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past submission history and select the reception method.

2. The system of claim 1.

8. The reception unit When accepting work information, filter it based on the user's current projects and areas of interest 2. The system of claim 1.

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