system

Abstract

The system according to this embodiment aims to enable professional artists and designers to overcome creative blocks and generate fresh ideas. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's past works and related text data. The analysis unit analyzes the data collected by the collection unit to understand the user's style and themes. The generation unit generates a new visual concept based on the analysis results obtained by the analysis unit. The provision unit provides the visual concept generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to generate fresh ideas when professional artists or designers are trapped in creative blocks.

[0005] The system according to the embodiment aims to eliminate creative blocks of professional artists or designers and generate fresh ideas.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's past works and related text data. The analysis unit analyzes the data collected by the collection unit to understand the user's style and themes. The generation unit generates a new visual concept based on the analysis results obtained by the analysis unit. The provision unit provides the visual concept generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows professional artists and designers to overcome creative blocks and generate fresh ideas. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Creative Agent System according to an embodiment of the present invention is a platform for professional artists and designers to overcome creative blocks and generate fresh ideas. This system collects the user's past works and related text data, extracts meaning from the data using natural language processing AI, and understands the user's style and themes. Furthermore, it uses image generation AI to analyze past works and art styles and generates new visual concepts based on that analysis. This system provides customized ideas to overcome the user's creative blocks. For example, when a user is working on a new project, it suggests new ideas and concepts based on past works. This allows the user to gain fresh ideas and maintain their creativity. It also provides an intuitive and user-friendly interface, making it easy for users to operate. Furthermore, it is offered on a subscription model, allowing users to access it whenever needed. This mechanism significantly reduces the time spent generating ideas, allowing users to dedicate more time to other creative activities. Moreover, the quality and originality of ideas will dramatically improve, and the market size across the entire creative industry could reach hundreds of billions of yen annually. Thus, the Creative Agent System can overcome users' creative blocks and generate fresh ideas.

[0029] The creative agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's past works and related text data. The user's past works include, but are not limited to, paintings, photographs, and designs. The collection unit collects the user's past works in digital format, for example. The collection unit can also collect related text data. For example, the collection unit collects articles, blogs, and comments written by the user. The analysis unit uses natural language processing AI to extract meaning from the collected data and understand the user's style and themes. The natural language processing AI uses techniques such as morphological analysis, grammatical analysis, and semantic analysis. The analysis unit uses morphological analysis to segment the text data and grammatical analysis to analyze the structure of sentences, for example. The analysis unit can also extract meaning from the text data using semantic analysis. The generation unit uses image generation AI to analyze past works and art styles and generate new visual concepts based on them. Image generation AI may employ technologies such as GAN (Generative Adversarial Network) or VAE (Variational Autoencoder). The generation unit may generate new visual concepts using GAN, for example. The generation unit may also generate visual concepts using VAE. The provision unit provides the generated visual concepts to the user. The provision unit may, for example, display the generated visual concepts on the user's device. The provision unit may also print and provide the generated visual concepts. This allows the creative agent system according to the embodiment to overcome the user's creative block and generate fresh ideas. Some or all of the above-described processes in the collection unit, analysis unit, generation unit, and provision unit may be performed using AI, for example, or without AI. For example, the collection unit may collect the user's past works using AI, the analysis unit may analyze the collected data using AI, the generation unit may generate new visual concepts using AI based on the analysis results, and the provision unit may provide the generated visual concepts using AI.

[0030] The data collection unit collects users' past works and related text data. These past works include, but are not limited to, paintings, photographs, and designs. The unit collects users' past works in digital format, for example. Specifically, it scans paintings and photographs created by users in the past and saves them as digital data. Design data can also be collected in the file format of the design software used by the user. This allows the data collection unit to comprehensively understand the history of the user's creative activities. Furthermore, the data collection unit can also collect related text data. For example, it collects articles, blogs, and comments written by users. This includes texts posted on online platforms, personal notes, and diaries. To collect this text data, the data collection unit can efficiently collect publicly available data from the internet using web scraping techniques and APIs. It also has a function to directly upload text data provided by users. This allows the data collection unit to build a rich dataset for the analysis unit, comprehensively collecting the user's sources of creative inspiration.

[0031] The analysis unit uses natural language processing AI to extract meaning from collected data and understand the user's style and themes. The natural language processing AI employs techniques such as morphological analysis, grammatical analysis, and semantic analysis. Specifically, morphological analysis is used to divide text data into words and identify the part of speech of each word. Grammatical analysis is used to analyze sentence structure and extract grammatical elements such as subject, predicate, and object. Furthermore, semantic analysis is used to extract meaning from text data and understand the user's themes and emotions in their writing. For example, it can identify themes the user is interested in and frequently used expressions from the content of articles and blogs written by the user. The analysis unit integrates these analysis results to reveal the characteristics of the user's creative style and themes. The analysis unit also uses image analysis technology to analyze collected visual data. For example, image recognition technology is used to extract features such as color, composition, and motifs from the user's past works. This allows the analysis unit to understand the user's visual style in detail and build foundational data to provide to the generation unit. Furthermore, the analysis unit organizes the collected data using clustering and classification algorithms to clarify the user's creative tendencies. This allows the analysis unit to comprehensively understand the user's style and themes, providing crucial input for the generation unit to generate new visual concepts.

[0032] The generation unit uses image generation AI to analyze past works and art styles and generate new visual concepts based on that analysis. The image generation AI employs technologies such as GAN (Generative Adversarial Network) and VAE (Variational Autoencoder). Specifically, when generating new visual concepts using GAN, the generation unit uses the user's past works as training data and generates high-quality visual concepts by having the generative network and discriminative network compete. The generative network generates new images, and the discriminative network determines whether the generated images are genuine or fake. By repeating this process, the generative network improves its ability to generate more realistic and creative visual concepts. When generating visual concepts using VAE, the generation unit maps the user's past works into a latent space and samples new visual concepts from that latent space. This allows the generation unit to generate diverse visual concepts based on the user's style and themes. Furthermore, the generation unit also has the function to adjust the generated visual concepts based on user feedback. For example, by providing evaluations and comments on the generated visual concepts, the generation unit can reflect that feedback and improve the next generation process. This allows the generation unit to continuously generate and provide visual concepts that meet the user's needs and preferences.

[0033] The service provider delivers the generated visual concepts to the user. Specifically, it displays the generated visual concepts on the user's device. The service provider can also print and deliver the generated visual concepts. For example, it can display the generated visual concepts on the user's smartphone or tablet, allowing the user to review them immediately. The service provider can also offer a service to print the generated visual concepts in high resolution and mail them to the user. Furthermore, the service provider has a function that allows users to upload the generated visual concepts to an online platform and share them with other users. This allows users to widely publish their creative results and receive feedback from other users. The service provider can also provide an API for directly integrating the generated visual concepts into the user's project. This allows users to easily incorporate the generated visual concepts into their design projects and presentations. In addition, the service provider has a function to customize the generated visual concepts according to the user's preferences. For example, by specifying specific colors and styles, the service provider can generate and deliver a visual concept that meets those requirements. This allows the service provider to offer flexible services that meet users' creative needs and improve user satisfaction.

[0034] The interface unit can provide an intuitive and user-friendly interface. For example, the interface unit can provide an interface that takes ease of operation into consideration. For example, the interface unit can adopt a visually easy-to-understand design so that users can operate it intuitively. The interface unit can also simplify the operation procedure so that users can operate it easily. For example, the interface unit can enable users to complete operations in fewer steps. This makes the system easier to use by providing an interface that users can easily operate. Some or all of the above processing in the interface unit may be performed using AI, for example, or not using AI. For example, the interface unit can analyze the user's operation history using AI and provide an optimal interface.

[0035] The administration department can manage the subscription model. For example, the administration department manages user subscription information. For example, the administration department manages user contract status and pricing structures. The administration department can also manage user subscription renewals and cancellations. For example, the administration department guides users through the necessary procedures when they renew their subscriptions. By managing the subscription model, users can access it whenever they need it. Some or all of the above processes in the administration department may be performed using AI, for example, or not using AI. For example, the administration department can use AI to manage user subscription information and suggest the optimal subscription plan.

[0036] The data collection unit can collect the user's past works and related text data. For example, the data collection unit can collect the user's past works in digital format. For example, the data collection unit can collect paintings, photographs, designs, etc., created by the user. The data collection unit can also collect related text data. For example, the data collection unit can collect articles, blogs, comments, etc., written by the user. By collecting the user's past works and related text data, it is possible to provide data to understand the user's style and themes. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can collect the user's past works using AI and collect related text data using AI.

[0037] The analysis unit can use natural language processing AI to extract meaning from collected data and understand the user's style and themes. For example, the analysis unit can use morphological analysis to divide text data and grammatical analysis to analyze sentence structure. For example, the analysis unit can use morphological analysis to divide text data into words and grammatical analysis to analyze sentence structure. The analysis unit can also use semantic analysis to extract meaning from text data. For example, the analysis unit can use semantic analysis to extract meaning from text data and understand the user's style and themes. In this way, by using natural language processing AI, meaning can be extracted from collected data and the user's style and themes can be understood. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze collected data using AI to understand the user's style and themes.

[0038] The generation unit can use image generation AI to analyze past works and art styles and generate new visual concepts based on that analysis. For example, the generation unit can use a GAN (Generative Adversarial Network) to generate new visual concepts. Alternatively, the generation unit can use a VAE (Variational Autoencoder) to generate visual concepts. This means that by using image generation AI, it is possible to analyze past works and art styles and generate new visual concepts based on that analysis. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can analyze past works and art styles using AI and generate new visual concepts using AI.

[0039] The service provider can provide the generated visual concept to the user. For example, the service provider can display the generated visual concept on the user's device. For example, the service provider can display the generated visual concept on the user's smartphone or tablet. The service provider can also print and provide the generated visual concept. For example, the service provider can print the generated visual concept on a printer and provide it to the user. By providing the user with the generated visual concept, the user can gain fresh ideas. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the generated visual concept using AI.

[0040] The data collection unit can analyze a user's past submission history and select the optimal data collection method. For example, the data collection unit can analyze the frequency of works previously submitted by the user and collect data during the time periods with the highest submission frequency. The data collection unit can also prioritize the collection of data related to a specific theme or style if the user is focused on that theme or style. For example, the data collection unit can analyze a user's past submission history and prioritize the collection of data related to a specific theme or style. The data collection unit can also collect data related to works with high ratings based on the ratings of works previously submitted by the user. For example, the data collection unit can analyze a user's past submission history and prioritize the collection of data related to works with high ratings. This allows the data collection unit to efficiently collect data by analyzing a user's past submission history and selecting the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can analyze a user's past submission history using AI and select the optimal data collection method using AI.

[0041] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can collect only data related to the user's current projects. For example, the data collection unit can prioritize collecting data related to the user's current projects. The data collection unit can also prioritize collecting data related to areas of interest the user has shown interest in. For example, the data collection unit can prioritize collecting data related to the user's areas of interest. The data collection unit can also filter and collect data related to themes the user has shown interest in in the past. For example, the data collection unit can filter data based on the user's past interests and collect relevant data. This allows for the collection of highly relevant data by filtering data based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can filter data using AI based on the user's current projects and areas of interest.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, the data collection unit can prioritize the collection of data related to the region based on the user's geographical location information. The data collection unit can also prioritize the collection of data related to the travel destination if the user is traveling. For example, the data collection unit can prioritize the collection of data related to the travel destination based on the user's geographical location information. The data collection unit can also prioritize the collection of data related to an event if the user is participating in a specific event. For example, the data collection unit can prioritize the collection of data related to an event based on the user's geographical location information. By collecting highly relevant data while considering the user's geographical location information, the system can provide optimal data tailored to the user's situation. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can consider the user's geographical location information using AI and prioritize the collection of highly relevant data using AI.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to content shared by the user on social media. For example, the data collection unit can analyze a user's social media activity and collect data related to shared content. The data collection unit can also collect data related to accounts followed by the user on social media. For example, the data collection unit can analyze a user's social media activity and collect data related to accounts followed. The data collection unit can also collect data related to posts that a user has "liked" on social media. For example, the data collection unit can analyze a user's social media activity and collect relevant data using AI.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can evaluate the importance of the data and perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can evaluate the importance of the data and perform a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with moderate importance. For example, the analysis unit can evaluate the importance of the data and perform an analysis with an appropriate level of detail on data with moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the data, analysis can be performed efficiently. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can evaluate the importance of the data using AI and adjust the level of detail of the analysis using AI.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing (NLP) algorithm to text data. For example, the analysis unit can apply a natural language processing algorithm to text data and analyze the data. The analysis unit can also apply an image analysis algorithm to image data. For example, the analysis unit can apply an image analysis algorithm to image data and analyze the data. The analysis unit can also apply a speech analysis algorithm to speech data. For example, the analysis unit can apply a speech analysis algorithm to speech data and analyze the data. By applying different analysis algorithms depending on the data category, the analysis unit can provide optimal analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply different analysis algorithms using AI depending on the data category.

[0046] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may evaluate the data submission date and prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. For example, the analysis unit may evaluate the data submission date and postpone the analysis of older data. The analysis unit may also moderately prioritize data with a medium submission date. For example, the analysis unit may evaluate the data submission date and moderately prioritize data with a medium submission date. This allows for efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit may use AI to evaluate the data submission date and use AI to determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can evaluate the relevance of the data and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can evaluate the relevance of the data and postpone the analysis of less relevant data. The analysis unit can also moderately prioritize data with a moderate level of relevance. For example, the analysis unit can evaluate the relevance of the data and moderately prioritize data with a moderate level of relevance. In this way, by adjusting the order of analysis based on the relevance of the data, analysis can be performed efficiently. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can evaluate the relevance of the data using AI and adjust the order of analysis using AI.

[0048] The generation unit can adjust the level of detail of the generated visual concepts based on the importance of past works during the generation process. For example, the generation unit can generate detailed visual concepts based on past works of high importance. For example, the generation unit can evaluate the importance of past works and generate detailed visual concepts based on works of high importance. The generation unit can also generate concise visual concepts based on past works of low importance. For example, the generation unit can evaluate the importance of past works and generate concise visual concepts based on works of low importance. The generation unit can also generate visual concepts with a moderate level of detail based on past works of moderate importance. For example, the generation unit can evaluate the importance of past works and generate visual concepts with a moderate level of detail based on works of moderate importance. This allows for efficient generation of visual concepts by adjusting the level of detail of the generated visual concepts based on the importance of past works. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can evaluate the importance of past works using AI and adjust the level of detail of the generated visual concepts using AI.

[0049] The generation unit can apply different generation algorithms depending on the art style category during generation. For example, the generation unit can apply an abstract generation algorithm to abstract paintings. For example, the generation unit evaluates the art style category and applies an abstract generation algorithm to abstract paintings. The generation unit can also apply a figurative generation algorithm to figurative paintings. For example, the generation unit evaluates the art style category and applies a figurative generation algorithm to figurative paintings. The generation unit can also apply a generation algorithm specifically for digital art to digital art. For example, the generation unit evaluates the art style category and applies a generation algorithm specifically for digital art. By applying different generation algorithms depending on the art style category, the optimal visual concept can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can evaluate the art style category using AI and apply different generation algorithms using AI.

[0050] The generation unit can determine the generation priority based on the submission dates of past works during the generation process. For example, the generation unit can prioritize generating visual concepts based on the most recent past work. For example, the generation unit can evaluate the submission dates of past works and prioritize generating visual concepts based on the most recent work. The generation unit can also postpone older past works. For example, the generation unit can evaluate the submission dates of past works and postpone older works. The generation unit can also moderately prioritize past works with a moderate submission date. For example, the generation unit can evaluate the submission dates of past works and moderately prioritize works with a moderate submission date. This allows for efficient generation of visual concepts by determining the generation priority based on the submission dates of past works. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can use AI to evaluate the submission dates of past works and use AI to determine the generation priority.

[0051] The generation unit can adjust the generation order based on the relevance of past works during the generation process. For example, the generation unit can preferentially generate visual concepts based on highly relevant past works. For example, the generation unit can evaluate the relevance of past works and preferentially generate visual concepts based on highly relevant works. The generation unit can also postpone past works with low relevance. For example, the generation unit can evaluate the relevance of past works and postpone works with low relevance. The generation unit can also moderately prioritize past works with moderate relevance. For example, the generation unit can evaluate the relevance of past works and moderately prioritize works with moderate relevance. In this way, visual concepts can be efficiently generated by adjusting the generation order based on the relevance of past works. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can evaluate the relevance of past works using AI and adjust the generation order using AI.

[0052] The delivery unit can select the optimal delivery method by referring to the user's past feedback at the time of delivery. For example, the delivery unit can prioritize providing display methods that the user has preferred in the past. For example, the delivery unit can analyze the user's past feedback and prioritize providing preferred display methods. The delivery unit can also exclude display methods that the user has avoided in the past. For example, the delivery unit can analyze the user's past feedback and exclude avoided display methods. The delivery unit can also analyze the user's past feedback and provide the optimal display method. For example, the delivery unit provides the optimal display method based on the user's past feedback. This allows the delivery unit to select the optimal delivery method by referring to the user's past feedback and efficiently deliver the visual concept. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can analyze the user's past feedback using AI and select the optimal delivery method using AI.

[0053] The service provider can customize the content of the offering based on the user's current project at the time of delivery. For example, the service provider may prioritize providing visual concepts related to the project the user is currently working on. For example, the service provider may evaluate the user's current project and prioritize providing relevant visual concepts. The service provider can also provide visual concepts at an appropriate time depending on the progress of the user's project. For example, the service provider may evaluate the progress of the user's project and provide visual concepts at an appropriate time. The service provider can also provide customized visual concepts that match the theme of the user's project. For example, the service provider may evaluate the theme of the user's project and provide customized visual concepts. This allows the service provider to provide highly relevant visual concepts by customizing the content of the offering based on the user's current project. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may use AI to evaluate the user's current project and customize the content of the offering using AI.

[0054] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing visual concepts related to that region. For example, the service provider can prioritize providing visual concepts related to a region based on the user's geographical location information. Also, if the user is traveling, the service provider can prioritize providing visual concepts related to their travel destination. For example, the service provider can prioritize providing visual concepts related to their travel destination based on the user's geographical location information. Furthermore, if the user is participating in a specific event, the service provider can prioritize providing visual concepts related to that event. For example, the service provider can prioritize providing visual concepts related to a specific event based on the user's geographical location information. In this way, by selecting the optimal delivery method considering the user's geographical location information, the service provider can provide the most appropriate visual concepts according to the user's situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can consider the user's geographical location information using AI and select the optimal delivery method using AI.

[0055] The service provider can analyze the user's social media activity and adjust the content of the service at the time of delivery. For example, the service provider can provide visual concepts related to content shared by the user on social media. For example, the service provider can analyze the user's social media activity and provide visual concepts related to the shared content. The service provider can also provide visual concepts related to accounts followed by the user on social media. For example, the service provider can analyze the user's social media activity and provide visual concepts related to the accounts followed. The service provider can also provide visual concepts related to posts that the user has "liked" on social media. For example, the service provider can analyze the user's social media activity and provide visual concepts related to the posts that have been "liked". This allows the service provider to provide highly relevant visual concepts by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can analyze the user's social media activity using AI and adjust the content of the service using AI.

[0056] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can prioritize displaying interface designs that the user has previously preferred. For example, the interface unit can analyze the user's past operation history and prioritize displaying preferred interface designs. The interface unit can also exclude interface designs that the user has previously avoided. For example, the interface unit can analyze the user's past operation history and exclude avoided interface designs. The interface unit can also analyze the user's past operation history and provide the optimal interface design. For example, the interface unit can provide the optimal interface design based on the user's past operation history. This allows the interface unit to efficiently provide the interface by selecting the optimal display method by referring to the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can analyze the user's past operation history using AI and select the optimal display method using AI.

[0057] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. For example, the interface unit can provide a display method optimized for smartphones based on the user's device information. Also, if the user is using a tablet, the interface unit can provide a display method optimized for larger screens. For example, the interface unit can provide a display method optimized for tablets based on the user's device information. Furthermore, if the user is using a smartwatch, the interface unit can provide a concise and highly visible display method. For example, the interface unit can provide a display method optimized for smartwatches based on the user's device information. In this way, by selecting the optimal display method considering the user's device information, the interface unit can provide an optimal interface tailored to the user's situation. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can consider the user's device information using AI and select the optimal display method using AI.

[0058] The management department can select the optimal management method by referring to the user's past usage history when managing subscriptions. For example, the management department can prioritize suggesting subscription plans that the user has preferred in the past. For example, the management department can analyze the user's past usage history and prioritize suggesting preferred subscription plans. The management department can also exclude subscription plans that the user has avoided in the past. For example, the management department can analyze the user's past usage history and exclude avoided subscription plans. The management department can also analyze the user's past usage history and suggest the optimal subscription plan. For example, the management department can suggest the optimal subscription plan based on the user's past usage history. This allows for the selection of the optimal management method and efficient subscription management by referring to the user's past usage history. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can analyze the user's past usage history using AI and select the optimal management method using AI.

[0059] The management department can select the optimal management method when managing subscriptions, taking into account the user's geographical location. For example, if the user is in a specific region, the management department can prioritize suggesting subscription plans related to that region. For example, the management department can prioritize suggesting subscription plans related to a region based on the user's geographical location. Also, if the user is traveling, the management department can prioritize suggesting subscription plans related to the travel destination. For example, the management department can prioritize suggesting subscription plans related to the travel destination based on the user's geographical location. Furthermore, if the user is participating in a specific event, the management department can prioritize suggesting subscription plans related to that event. For example, the management department can prioritize suggesting subscription plans related to a specific event based on the user's geographical location. By selecting the optimal management method while considering the user's geographical location, the management department can provide optimal subscription management tailored to the user's situation. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can consider the user's geographical location using AI and select the optimal management method using AI.

[0060] The management department can analyze users' social media activity and select the optimal management method when managing subscriptions. For example, the management department can propose subscription plans related to content shared by users on social media. For example, the management department can analyze users' social media activity and propose subscription plans related to the content they share. The management department can also propose subscription plans related to accounts that users follow on social media. For example, the management department can analyze users' social media activity and propose subscription plans related to the accounts they follow. The management department can also propose subscription plans related to posts that users "like" on social media. For example, the management department can analyze users' social media activity and propose subscription plans related to the posts they "like". By analyzing users' social media activity, the management department can select the optimal management method and manage subscriptions efficiently. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can analyze users' social media activity using AI and select the optimal management method using AI.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can analyze a user's past submission history and select the optimal collection method. For example, it can analyze the frequency of past submissions and collect data during the time periods with the highest submission frequency. Furthermore, if a user focuses on a specific theme or style, it can prioritize the collection of data related to that theme or style. It can also collect data related to highly-rated works based on the ratings of past submissions. This allows for efficient data collection by analyzing a user's past submission history and selecting the most suitable collection method.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data and a simplified analysis on less important data. It can also perform an analysis with an appropriate level of detail on data of moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, the analysis can be performed efficiently.

[0064] The generation unit can adjust the level of detail of the generated visual concepts based on the importance of past works during the generation process. For example, it can generate detailed visual concepts based on highly important past works, and concise visual concepts based on less important past works. It can also generate visual concepts with a moderate level of detail based on past works of moderate importance. This allows for efficient generation of visual concepts by adjusting the level of detail based on the importance of past works.

[0065] The delivery unit can select the optimal delivery method by referring to past user feedback at the time of delivery. For example, it can prioritize display methods that users have preferred in the past and exclude those that users have avoided in the past. It can also provide the optimal display method based on past user feedback. In this way, by referring to past user feedback, the optimal delivery method can be selected and the visual concept can be delivered efficiently.

[0066] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size; if the user is using a tablet, it can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. By selecting the optimal display method based on the user's device information, it is possible to provide an optimal interface tailored to the user's situation.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects the user's past works and related text data. The user's past works include paintings, photographs, designs, etc. The collection unit collects these works in digital format and also collects related text data such as articles, blogs, and comments written by the user. Step 2: The analysis unit analyzes the collected data to understand the user's style and themes. The analysis unit uses natural language processing AI to extract the meaning of the text data, employing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Step 3: The generation unit generates a new visual concept based on the analysis results obtained by the analysis unit. The generation unit uses image generation AI to analyze past works and art styles, and generates a new visual concept using technologies such as GAN (Generative Adversarial Network) and VAE (Variational Autoencoder). Step 4: The provider provides the generated visual concept to the user. The provider can display the generated visual concept on the user's device or provide it in print.

[0069] (Example of form 2) The Creative Agent System according to an embodiment of the present invention is a platform for professional artists and designers to overcome creative blocks and generate fresh ideas. This system collects the user's past works and related text data, extracts meaning from the data using natural language processing AI, and understands the user's style and themes. Furthermore, it uses image generation AI to analyze past works and art styles and generates new visual concepts based on that analysis. This system provides customized ideas to overcome the user's creative blocks. For example, when a user is working on a new project, it suggests new ideas and concepts based on past works. This allows the user to gain fresh ideas and maintain their creativity. It also provides an intuitive and user-friendly interface, making it easy for users to operate. Furthermore, it is offered on a subscription model, allowing users to access it whenever needed. This mechanism significantly reduces the time spent generating ideas, allowing users to dedicate more time to other creative activities. Moreover, the quality and originality of ideas will dramatically improve, and the market size across the entire creative industry could reach hundreds of billions of yen annually. Thus, the Creative Agent System can overcome users' creative blocks and generate fresh ideas.

[0070] The creative agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's past works and related text data. The user's past works include, but are not limited to, paintings, photographs, and designs. The collection unit collects the user's past works in digital format, for example. The collection unit can also collect related text data. For example, the collection unit collects articles, blogs, and comments written by the user. The analysis unit uses natural language processing AI to extract meaning from the collected data and understand the user's style and themes. The natural language processing AI uses techniques such as morphological analysis, grammatical analysis, and semantic analysis. The analysis unit uses morphological analysis to segment the text data and grammatical analysis to analyze the structure of sentences, for example. The analysis unit can also extract meaning from the text data using semantic analysis. The generation unit uses image generation AI to analyze past works and art styles and generate new visual concepts based on them. Image generation AI may employ technologies such as GAN (Generative Adversarial Network) or VAE (Variational Autoencoder). The generation unit may generate new visual concepts using GAN, for example. The generation unit may also generate visual concepts using VAE. The provision unit provides the generated visual concepts to the user. The provision unit may, for example, display the generated visual concepts on the user's device. The provision unit may also print and provide the generated visual concepts. This allows the creative agent system according to the embodiment to overcome the user's creative block and generate fresh ideas. Some or all of the above-described processes in the collection unit, analysis unit, generation unit, and provision unit may be performed using AI, for example, or without AI. For example, the collection unit may collect the user's past works using AI, the analysis unit may analyze the collected data using AI, the generation unit may generate new visual concepts using AI based on the analysis results, and the provision unit may provide the generated visual concepts using AI.

[0071] The data collection unit collects users' past works and related text data. These past works include, but are not limited to, paintings, photographs, and designs. The unit collects users' past works in digital format, for example. Specifically, it scans paintings and photographs created by users in the past and saves them as digital data. Design data can also be collected in the file format of the design software used by the user. This allows the data collection unit to comprehensively understand the history of the user's creative activities. Furthermore, the data collection unit can also collect related text data. For example, it collects articles, blogs, and comments written by users. This includes texts posted on online platforms, personal notes, and diaries. To collect this text data, the data collection unit can efficiently collect publicly available data from the internet using web scraping techniques and APIs. It also has a function to directly upload text data provided by users. This allows the data collection unit to build a rich dataset for the analysis unit, comprehensively collecting the user's sources of creative inspiration.

[0072] The analysis unit uses natural language processing AI to extract meaning from collected data and understand the user's style and themes. The natural language processing AI employs techniques such as morphological analysis, grammatical analysis, and semantic analysis. Specifically, morphological analysis is used to divide text data into words and identify the part of speech of each word. Grammatical analysis is used to analyze sentence structure and extract grammatical elements such as subject, predicate, and object. Furthermore, semantic analysis is used to extract meaning from text data and understand the user's themes and emotions in their writing. For example, it can identify themes the user is interested in and frequently used expressions from the content of articles and blogs written by the user. The analysis unit integrates these analysis results to reveal the characteristics of the user's creative style and themes. The analysis unit also uses image analysis technology to analyze collected visual data. For example, image recognition technology is used to extract features such as color, composition, and motifs from the user's past works. This allows the analysis unit to understand the user's visual style in detail and build foundational data to provide to the generation unit. Furthermore, the analysis unit organizes the collected data using clustering and classification algorithms to clarify the user's creative tendencies. This allows the analysis unit to comprehensively understand the user's style and themes, providing crucial input for the generation unit to generate new visual concepts.

[0073] The generation unit uses image generation AI to analyze past works and art styles and generate new visual concepts based on that analysis. The image generation AI employs technologies such as GAN (Generative Adversarial Network) and VAE (Variational Autoencoder). Specifically, when generating new visual concepts using GAN, the generation unit uses the user's past works as training data and generates high-quality visual concepts by having the generative network and discriminative network compete. The generative network generates new images, and the discriminative network determines whether the generated images are genuine or fake. By repeating this process, the generative network improves its ability to generate more realistic and creative visual concepts. When generating visual concepts using VAE, the generation unit maps the user's past works into a latent space and samples new visual concepts from that latent space. This allows the generation unit to generate diverse visual concepts based on the user's style and themes. Furthermore, the generation unit also has the function to adjust the generated visual concepts based on user feedback. For example, by providing evaluations and comments on the generated visual concepts, the generation unit can reflect that feedback and improve the next generation process. This allows the generation unit to continuously generate and provide visual concepts that meet the user's needs and preferences.

[0074] The service provider delivers the generated visual concepts to the user. Specifically, it displays the generated visual concepts on the user's device. The service provider can also print and deliver the generated visual concepts. For example, it can display the generated visual concepts on the user's smartphone or tablet, allowing the user to review them immediately. The service provider can also offer a service to print the generated visual concepts in high resolution and mail them to the user. Furthermore, the service provider has a function that allows users to upload the generated visual concepts to an online platform and share them with other users. This allows users to widely publish their creative results and receive feedback from other users. The service provider can also provide an API for directly integrating the generated visual concepts into the user's project. This allows users to easily incorporate the generated visual concepts into their design projects and presentations. In addition, the service provider has a function to customize the generated visual concepts according to the user's preferences. For example, by specifying specific colors and styles, the service provider can generate and deliver a visual concept that meets those requirements. This allows the service provider to offer flexible services that meet users' creative needs and improve user satisfaction.

[0075] The interface unit can provide an intuitive and user-friendly interface. For example, the interface unit can provide an interface that takes ease of operation into consideration. For example, the interface unit can adopt a visually easy-to-understand design so that users can operate it intuitively. The interface unit can also simplify the operation procedure so that users can operate it easily. For example, the interface unit can enable users to complete operations in fewer steps. This makes the system easier to use by providing an interface that users can easily operate. Some or all of the above processing in the interface unit may be performed using AI, for example, or not using AI. For example, the interface unit can analyze the user's operation history using AI and provide an optimal interface.

[0076] The administration department can manage the subscription model. For example, the administration department manages user subscription information. For example, the administration department manages user contract status and pricing structures. The administration department can also manage user subscription renewals and cancellations. For example, the administration department guides users through the necessary procedures when they renew their subscriptions. By managing the subscription model, users can access it whenever they need it. Some or all of the above processes in the administration department may be performed using AI, for example, or not using AI. For example, the administration department can use AI to manage user subscription information and suggest the optimal subscription plan.

[0077] The data collection unit can collect the user's past works and related text data. For example, the data collection unit can collect the user's past works in digital format. For example, the data collection unit can collect paintings, photographs, designs, etc., created by the user. The data collection unit can also collect related text data. For example, the data collection unit can collect articles, blogs, comments, etc., written by the user. By collecting the user's past works and related text data, it is possible to provide data to understand the user's style and themes. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can collect the user's past works using AI and collect related text data using AI.

[0078] The analysis unit can use natural language processing AI to extract meaning from collected data and understand the user's style and themes. For example, the analysis unit can use morphological analysis to divide text data and grammatical analysis to analyze sentence structure. For example, the analysis unit can use morphological analysis to divide text data into words and grammatical analysis to analyze sentence structure. The analysis unit can also use semantic analysis to extract meaning from text data. For example, the analysis unit can use semantic analysis to extract meaning from text data and understand the user's style and themes. In this way, by using natural language processing AI, meaning can be extracted from collected data and the user's style and themes can be understood. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze collected data using AI to understand the user's style and themes.

[0079] The generation unit can use image generation AI to analyze past works and art styles and generate new visual concepts based on that analysis. For example, the generation unit can use a GAN (Generative Adversarial Network) to generate new visual concepts. Alternatively, the generation unit can use a VAE (Variational Autoencoder) to generate visual concepts. This means that by using image generation AI, it is possible to analyze past works and art styles and generate new visual concepts based on that analysis. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can analyze past works and art styles using AI and generate new visual concepts using AI.

[0080] The service provider can provide the generated visual concept to the user. For example, the service provider can display the generated visual concept on the user's device. For example, the service provider can display the generated visual concept on the user's smartphone or tablet. The service provider can also print and provide the generated visual concept. For example, the service provider can print the generated visual concept on a printer and provide it to the user. By providing the user with the generated visual concept, the user can gain fresh ideas. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the generated visual concept using AI.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection and collect data when the user is relaxed. For example, the data collection unit can monitor the user's emotions in real time and temporarily stop data collection if the user is stressed. Also, if the user is concentrating, the data collection unit can temporarily stop data collection and resume collection after the user has finished their work. For example, the data collection unit can monitor the user's level of concentration and temporarily stop data collection if the user is concentrating. Furthermore, if the user is inspired, the data collection unit can collect data at that moment and use it for later analysis. For example, the data collection unit can monitor the user's emotions and collect data if the user is inspired. This allows for data collection at the optimal time according to the user's state by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to estimate the user's emotions and adjust the timing of data collection using AI.

[0082] The data collection unit can analyze a user's past submission history and select the optimal data collection method. For example, the data collection unit can analyze the frequency of works previously submitted by the user and collect data during the time periods with the highest submission frequency. The data collection unit can also prioritize the collection of data related to a specific theme or style if the user is focused on that theme or style. For example, the data collection unit can analyze a user's past submission history and prioritize the collection of data related to a specific theme or style. The data collection unit can also collect data related to works with high ratings based on the ratings of works previously submitted by the user. For example, the data collection unit can analyze a user's past submission history and prioritize the collection of data related to works with high ratings. This allows the data collection unit to efficiently collect data by analyzing a user's past submission history and selecting the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can analyze a user's past submission history using AI and select the optimal data collection method using AI.

[0083] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can collect only data related to the user's current projects. For example, the data collection unit can prioritize collecting data related to the user's current projects. The data collection unit can also prioritize collecting data related to areas of interest the user has shown interest in. For example, the data collection unit can prioritize collecting data related to the user's areas of interest. The data collection unit can also filter and collect data related to themes the user has shown interest in in the past. For example, the data collection unit can filter data based on the user's past interests and collect relevant data. This allows for the collection of highly relevant data by filtering data based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can filter data using AI based on the user's current projects and areas of interest.

[0084] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit can prioritize collecting data that provides creative inspiration. For example, the data collection unit can monitor the user's emotions in real time and prioritize collecting data that provides creative inspiration when the user is relaxed. Also, if the user is stressed, the data collection unit can prioritize collecting data that has a relaxing effect. For example, the data collection unit can monitor the user's emotions and prioritize collecting data that has a relaxing effect when the user is stressed. Also, if the user is focused, the data collection unit can prioritize collecting data that is useful for the task. For example, the data collection unit can monitor the user's emotions and prioritize collecting data that is useful for the task when the user is focused. In this way, by prioritizing the data to collect based on the user's emotions, it is possible to collect the optimal data according to the user's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to estimate the user's emotions and use AI to determine the priority of the data to be collected.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, the data collection unit can prioritize the collection of data related to the region based on the user's geographical location information. The data collection unit can also prioritize the collection of data related to the travel destination if the user is traveling. For example, the data collection unit can prioritize the collection of data related to the travel destination based on the user's geographical location information. The data collection unit can also prioritize the collection of data related to an event if the user is participating in a specific event. For example, the data collection unit can prioritize the collection of data related to an event based on the user's geographical location information. By collecting highly relevant data while considering the user's geographical location information, the system can provide optimal data tailored to the user's situation. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can consider the user's geographical location information using AI and prioritize the collection of highly relevant data using AI.

[0086] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to content shared by the user on social media. For example, the data collection unit can analyze a user's social media activity and collect data related to shared content. The data collection unit can also collect data related to accounts followed by the user on social media. For example, the data collection unit can analyze a user's social media activity and collect data related to accounts followed. The data collection unit can also collect data related to posts that a user has "liked" on social media. For example, the data collection unit can analyze a user's social media activity and collect relevant data using AI.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, the analysis unit can monitor the user's emotions in real time and provide detailed analysis results if the user is relaxed. Also, if the user is stressed, the analysis unit can provide concise and to-the-point analysis results. For example, the analysis unit can monitor the user's emotions and provide concise and to-the-point analysis results if the user is stressed. Also, if the user is focused, the analysis unit can provide visually easy-to-understand analysis results. For example, the analysis unit can monitor the user's emotions and provide visually easy-to-understand analysis results if the user is focused. In this way, by adjusting the presentation of the analysis based on the user's emotions, it is possible to provide optimal analysis results according to the user's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may use AI to estimate the user's emotions and use AI to adjust the way the analysis is expressed.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can evaluate the importance of the data and perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can evaluate the importance of the data and perform a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with moderate importance. For example, the analysis unit can evaluate the importance of the data and perform an analysis with an appropriate level of detail on data with moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the data, analysis can be performed efficiently. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can evaluate the importance of the data using AI and adjust the level of detail of the analysis using AI.

[0089] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing (NLP) algorithm to text data. For example, the analysis unit can apply a natural language processing algorithm to text data and analyze the data. The analysis unit can also apply an image analysis algorithm to image data. For example, the analysis unit can apply an image analysis algorithm to image data and analyze the data. The analysis unit can also apply a speech analysis algorithm to speech data. For example, the analysis unit can apply a speech analysis algorithm to speech data and analyze the data. By applying different analysis algorithms depending on the data category, the analysis unit can provide optimal analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply different analysis algorithms using AI depending on the data category.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, the analysis unit can monitor the user's emotions in real time and provide detailed analysis results if the user is relaxed. Also, if the user is stressed, the analysis unit can provide concise and to-the-point analysis results. For example, the analysis unit can monitor the user's emotions and provide concise and to-the-point analysis results if the user is stressed. Also, if the user is focused, the analysis unit can provide visually easy-to-understand analysis results. For example, the analysis unit can monitor the user's emotions and provide visually easy-to-understand analysis results if the user is focused. By adjusting the length of the analysis based on the user's emotions, it is possible to provide optimal analysis results according to the user's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to estimate the user's emotions and adjust the length of the analysis using AI.

[0091] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may evaluate the data submission date and prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. For example, the analysis unit may evaluate the data submission date and postpone the analysis of older data. The analysis unit may also moderately prioritize data with a medium submission date. For example, the analysis unit may evaluate the data submission date and moderately prioritize data with a medium submission date. This allows for efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit may use AI to evaluate the data submission date and use AI to determine the priority of analysis.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can evaluate the relevance of the data and prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can evaluate the relevance of the data and postpone the analysis of less relevant data. The analysis unit can also moderately prioritize data with a moderate level of relevance. For example, the analysis unit can evaluate the relevance of the data and moderately prioritize data with a moderate level of relevance. In this way, by adjusting the order of analysis based on the relevance of the data, analysis can be performed efficiently. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can evaluate the relevance of the data using AI and adjust the order of analysis using AI.

[0093] The generation unit can estimate the user's emotions and adjust the way the generated visual concept is expressed based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a visual concept with soft colors. For example, the generation unit can monitor the user's emotions in real time and generate a visual concept with soft colors if the user is relaxed. Also, if the user is stressed, the generation unit can generate a simple and highly visible visual concept. For example, the generation unit can monitor the user's emotions and generate a simple and highly visible visual concept if the user is stressed. Also, if the user is focused, the generation unit can generate a detailed and complex visual concept. For example, the generation unit can monitor the user's emotions and generate a detailed and complex visual concept if the user is focused. In this way, by adjusting the way the generated visual concept is expressed based on the user's emotions, it is possible to provide an optimal visual concept according to the user's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may use AI to estimate the user's emotions and use AI to adjust the way the generated visual concept is expressed.

[0094] The generation unit can adjust the level of detail of the generated visual concepts based on the importance of past works during the generation process. For example, the generation unit can generate detailed visual concepts based on past works of high importance. For example, the generation unit can evaluate the importance of past works and generate detailed visual concepts based on works of high importance. The generation unit can also generate concise visual concepts based on past works of low importance. For example, the generation unit can evaluate the importance of past works and generate concise visual concepts based on works of low importance. The generation unit can also generate visual concepts with a moderate level of detail based on past works of moderate importance. For example, the generation unit can evaluate the importance of past works and generate visual concepts with a moderate level of detail based on works of moderate importance. This allows for efficient generation of visual concepts by adjusting the level of detail of the generated visual concepts based on the importance of past works. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can evaluate the importance of past works using AI and adjust the level of detail of the generated visual concepts using AI.

[0095] The generation unit can apply different generation algorithms depending on the art style category during generation. For example, the generation unit can apply an abstract generation algorithm to abstract paintings. For example, the generation unit evaluates the art style category and applies an abstract generation algorithm to abstract paintings. The generation unit can also apply a figurative generation algorithm to figurative paintings. For example, the generation unit evaluates the art style category and applies a figurative generation algorithm to figurative paintings. The generation unit can also apply a generation algorithm specifically for digital art to digital art. For example, the generation unit evaluates the art style category and applies a generation algorithm specifically for digital art. By applying different generation algorithms depending on the art style category, the optimal visual concept can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can evaluate the art style category using AI and apply different generation algorithms using AI.

[0096] The generation unit can estimate the user's emotions and adjust the length of the visual concepts it generates based on those emotions. For example, if the user is relaxed, the generation unit can generate longer visual concepts. For example, the generation unit can monitor the user's emotions in real time and generate longer visual concepts when the user is relaxed. The generation unit can also generate shorter, more concise visual concepts when the user is stressed. For example, the generation unit can monitor the user's emotions and generate shorter, more concise visual concepts when the user is stressed. The generation unit can also generate more detailed, longer visual concepts when the user is focused. For example, the generation unit can monitor the user's emotions and generate more detailed, longer visual concepts when the user is focused. By adjusting the length of the visual concepts generated based on the user's emotions, it is possible to provide optimal visual concepts tailored to the user's state. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generation AI. Generation AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may use AI to estimate the user's emotions and use AI to adjust the length of the generated visual concept.

[0097] The generation unit can determine the generation priority based on the submission dates of past works during the generation process. For example, the generation unit can prioritize generating visual concepts based on the most recent past work. For example, the generation unit can evaluate the submission dates of past works and prioritize generating visual concepts based on the most recent work. The generation unit can also postpone older past works. For example, the generation unit can evaluate the submission dates of past works and postpone older works. The generation unit can also moderately prioritize past works with a moderate submission date. For example, the generation unit can evaluate the submission dates of past works and moderately prioritize works with a moderate submission date. This allows for efficient generation of visual concepts by determining the generation priority based on the submission dates of past works. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can use AI to evaluate the submission dates of past works and use AI to determine the generation priority.

[0098] The generation unit can adjust the generation order based on the relevance of past works during the generation process. For example, the generation unit can preferentially generate visual concepts based on highly relevant past works. For example, the generation unit can evaluate the relevance of past works and preferentially generate visual concepts based on highly relevant works. The generation unit can also postpone past works with low relevance. For example, the generation unit can evaluate the relevance of past works and postpone works with low relevance. The generation unit can also moderately prioritize past works with moderate relevance. For example, the generation unit can evaluate the relevance of past works and moderately prioritize works with moderate relevance. In this way, visual concepts can be efficiently generated by adjusting the generation order based on the relevance of past works. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can evaluate the relevance of past works using AI and adjust the generation order using AI.

[0099] The service provider can estimate the user's emotions and adjust the display method of the visual concept based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a display method with soft colors. For example, the service provider can monitor the user's emotions in real time and provide a display method with soft colors when the user is relaxed. The service provider can also provide a simple and highly visible display method when the user is stressed. For example, the service provider can monitor the user's emotions and provide a simple and highly visible display method when the user is stressed. The service provider can also provide a detailed and complex display method when the user is focused. For example, the service provider can monitor the user's emotions and provide a detailed and complex display method when the user is focused. By adjusting the display method of the visual concept based on the user's emotions, the service provider can provide the optimal display method according to the user's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit may use AI to estimate the user's emotions and use AI to adjust the way the provided visual concept is displayed.

[0100] The delivery unit can select the optimal delivery method by referring to the user's past feedback at the time of delivery. For example, the delivery unit can prioritize providing display methods that the user has preferred in the past. For example, the delivery unit can analyze the user's past feedback and prioritize providing preferred display methods. The delivery unit can also exclude display methods that the user has avoided in the past. For example, the delivery unit can analyze the user's past feedback and exclude avoided display methods. The delivery unit can also analyze the user's past feedback and provide the optimal display method. For example, the delivery unit provides the optimal display method based on the user's past feedback. This allows the delivery unit to select the optimal delivery method by referring to the user's past feedback and efficiently deliver the visual concept. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can analyze the user's past feedback using AI and select the optimal delivery method using AI.

[0101] The service provider can customize the content of the offering based on the user's current project at the time of delivery. For example, the service provider may prioritize providing visual concepts related to the project the user is currently working on. For example, the service provider may evaluate the user's current project and prioritize providing relevant visual concepts. The service provider can also provide visual concepts at an appropriate time depending on the progress of the user's project. For example, the service provider may evaluate the progress of the user's project and provide visual concepts at an appropriate time. The service provider can also provide customized visual concepts that match the theme of the user's project. For example, the service provider may evaluate the theme of the user's project and provide customized visual concepts. This allows the service provider to provide highly relevant visual concepts by customizing the content of the offering based on the user's current project. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may use AI to evaluate the user's current project and customize the content of the offering using AI.

[0102] The service provider can estimate the user's emotions and prioritize the visual concepts to be provided based on those emotions. For example, if the user is relaxed, the service provider can prioritize providing visual concepts that inspire creativity. For example, the service provider can monitor the user's emotions in real time and prioritize providing visual concepts that inspire creativity when the user is relaxed. Also, if the user is stressed, the service provider can prioritize providing visual concepts that have a relaxing effect. For example, the service provider can monitor the user's emotions and prioritize providing visual concepts that have a relaxing effect when the user is stressed. Also, if the user is focused, the service provider can prioritize providing visual concepts that are helpful for their work. For example, the service provider can monitor the user's emotions and prioritize providing visual concepts that are helpful for their work when the user is focused. In this way, by prioritizing the visual concepts to be provided based on the user's emotions, the service provider can provide the optimal visual concepts according to the user's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit may use AI to estimate the user's emotions and use AI to determine the priority of the visual concepts to be offered.

[0103] The service provider can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing visual concepts related to that region. For example, the service provider can prioritize providing visual concepts related to a region based on the user's geographical location information. Also, if the user is traveling, the service provider can prioritize providing visual concepts related to their travel destination. For example, the service provider can prioritize providing visual concepts related to their travel destination based on the user's geographical location information. Furthermore, if the user is participating in a specific event, the service provider can prioritize providing visual concepts related to that event. For example, the service provider can prioritize providing visual concepts related to a specific event based on the user's geographical location information. In this way, by selecting the optimal delivery method considering the user's geographical location information, the service provider can provide the most appropriate visual concepts according to the user's situation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can consider the user's geographical location information using AI and select the optimal delivery method using AI.

[0104] The service provider can analyze the user's social media activity and adjust the content of the service at the time of delivery. For example, the service provider can provide visual concepts related to content shared by the user on social media. For example, the service provider can analyze the user's social media activity and provide visual concepts related to the shared content. The service provider can also provide visual concepts related to accounts followed by the user on social media. For example, the service provider can analyze the user's social media activity and provide visual concepts related to the accounts followed. The service provider can also provide visual concepts related to posts that the user has "liked" on social media. For example, the service provider can analyze the user's social media activity and provide visual concepts related to the posts that have been "liked". This allows the service provider to provide highly relevant visual concepts by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can analyze the user's social media activity using AI and adjust the content of the service using AI.

[0105] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is tense, the interface unit can provide an interface with calming colors to reduce visual stress. For example, the interface unit can monitor the user's emotions in real time and provide an interface with calming colors if the user is tense. Also, if the user is enjoying themselves, the interface unit can provide an interface with bright colors to make the input process more enjoyable. For example, the interface unit can monitor the user's emotions and provide an interface with bright colors if the user is enjoying themselves. Furthermore, if the user is tired, the interface unit can provide a simple and highly visible interface. For example, the interface unit can monitor the user's emotions and provide a simple and highly visible interface if the user is tired. In this way, by adjusting the interface display method based on the user's emotions, it is possible to provide an optimal interface according to the user's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can use AI to estimate the user's emotions and adjust the interface display method using AI.

[0106] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can prioritize displaying interface designs that the user has previously preferred. For example, the interface unit can analyze the user's past operation history and prioritize displaying preferred interface designs. The interface unit can also exclude interface designs that the user has previously avoided. For example, the interface unit can analyze the user's past operation history and exclude avoided interface designs. The interface unit can also analyze the user's past operation history and provide the optimal interface design. For example, the interface unit can provide the optimal interface design based on the user's past operation history. This allows the interface unit to efficiently provide the interface by selecting the optimal display method by referring to the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can analyze the user's past operation history using AI and select the optimal display method using AI.

[0107] The interface unit can estimate the user's emotions and adjust the interface's operation procedures based on the estimated emotions. For example, if the user is tense, the interface unit can simplify the operation procedures to reduce stress. For example, the interface unit can monitor the user's emotions in real time and simplify the operation procedures if the user is tense. Also, if the user is enjoying themselves, the interface unit can make the operation procedures more detailed to increase enjoyment. For example, the interface unit can monitor the user's emotions and make the operation procedures more detailed if the user is enjoying themselves. Furthermore, if the user is tired, the interface unit can minimize the operation procedures to make operation easier. For example, the interface unit can monitor the user's emotions and minimize the operation procedures if the user is tired. In this way, by adjusting the interface's operation procedures based on the user's emotions, it is possible to provide the optimal operation procedures according to the user's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can use AI to estimate the user's emotions and adjust the interface operation procedures using AI.

[0108] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. For example, the interface unit can provide a display method optimized for smartphones based on the user's device information. Also, if the user is using a tablet, the interface unit can provide a display method optimized for larger screens. For example, the interface unit can provide a display method optimized for tablets based on the user's device information. Furthermore, if the user is using a smartwatch, the interface unit can provide a concise and highly visible display method. For example, the interface unit can provide a display method optimized for smartwatches based on the user's device information. In this way, by selecting the optimal display method considering the user's device information, the interface unit can provide an optimal interface tailored to the user's situation. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can consider the user's device information using AI and select the optimal display method using AI.

[0109] The management department can estimate the user's emotions and adjust subscription management methods based on the estimated emotions. For example, if the user is relaxed, the management department can provide detailed subscription information. For example, the management department can monitor the user's emotions in real time and provide detailed subscription information if the user is relaxed. Also, if the user is stressed, the management department can provide concise and to-the-point subscription information. For example, the management department can monitor the user's emotions and provide concise and to-the-point subscription information if the user is stressed. Also, if the user is focused, the management department can provide visually easy-to-understand subscription information. For example, the management department can monitor the user's emotions and provide visually easy-to-understand subscription information if the user is focused. In this way, by adjusting subscription management methods based on the user's emotions, it is possible to provide optimal subscription management according to the user's state. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the management department may be performed using AI, for example, or not using AI. For example, the management department may use AI to estimate user emotions and use AI to adjust subscription management methods.

[0110] The management department can select the optimal management method by referring to the user's past usage history when managing subscriptions. For example, the management department can prioritize suggesting subscription plans that the user has preferred in the past. For example, the management department can analyze the user's past usage history and prioritize suggesting preferred subscription plans. The management department can also exclude subscription plans that the user has avoided in the past. For example, the management department can analyze the user's past usage history and exclude avoided subscription plans. The management department can also analyze the user's past usage history and suggest the optimal subscription plan. For example, the management department can suggest the optimal subscription plan based on the user's past usage history. This allows for the selection of the optimal management method and efficient subscription management by referring to the user's past usage history. Some or all of the above processes in the management department may be performed using AI, or not. For example, the management department can analyze the user's past usage history using AI and select the optimal management method using AI.

[0111] The management department can estimate the user's emotions and prioritize subscriptions based on those emotions. For example, if the user is relaxed, the management department can provide detailed subscription information. For example, the management department can monitor the user's emotions in real time and provide detailed subscription information if the user is relaxed. The management department can also provide concise and to-the-point subscription information if the user is stressed. For example, the management department can monitor the user's emotions and provide concise and to-the-point subscription information if the user is stressed. The management department can also provide visually easy-to-understand subscription information if the user is focused. For example, the management department can monitor the user's emotions and provide visually easy-to-understand subscription information if the user is focused. By prioritizing subscriptions based on the user's emotions, it is possible to provide optimal subscription management tailored to the user's state. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the management department may be performed using AI, for example, or not using AI. For example, the management department may use AI to estimate user emotions and use AI to determine subscription priorities.

[0112] The management department can select the optimal management method when managing subscriptions, taking into account the user's geographical location. For example, if the user is in a specific region, the management department can prioritize suggesting subscription plans related to that region. For example, the management department can prioritize suggesting subscription plans related to a region based on the user's geographical location. Also, if the user is traveling, the management department can prioritize suggesting subscription plans related to the travel destination. For example, the management department can prioritize suggesting subscription plans related to the travel destination based on the user's geographical location. Furthermore, if the user is participating in a specific event, the management department can prioritize suggesting subscription plans related to that event. For example, the management department can prioritize suggesting subscription plans related to a specific event based on the user's geographical location. By selecting the optimal management method while considering the user's geographical location, the management department can provide optimal subscription management tailored to the user's situation. Some or all of the above processing in the management department may be performed using AI, for example, or not. For example, the management department can consider the user's geographical location using AI and select the optimal management method using AI.

[0113] The management department can analyze users' social media activity and select the optimal management method when managing subscriptions. For example, the management department can propose subscription plans related to content shared by users on social media. For example, the management department can analyze users' social media activity and propose subscription plans related to the content they share. The management department can also propose subscription plans related to accounts that users follow on social media. For example, the management department can analyze users' social media activity and propose subscription plans related to the accounts they follow. The management department can also propose subscription plans related to posts that users "like" on social media. For example, the management department can analyze users' social media activity and propose subscription plans related to the posts they "like". By analyzing users' social media activity, the management department can select the optimal management method and manage subscriptions efficiently. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can analyze users' social media activity using AI and select the optimal management method using AI.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on those emotions. For example, if the user is relaxed, it can perform a detailed analysis; if the user is stressed, it can perform a concise analysis. Furthermore, if the user is focused, it can provide visually easy-to-understand analysis results. By adjusting the depth of the analysis based on the user's emotions, it can provide optimal analysis results tailored to the user's state.

[0116] The generation unit can estimate the user's emotions and adjust the color scheme of the generated visual concept based on those emotions. For example, if the user is relaxed, it can generate a visual concept with soft colors; if the user is stressed, it can generate a simple and highly visible visual concept. Furthermore, if the user is focused, it can generate a detailed and complex visual concept. By adjusting the color scheme of the generated visual concept based on the user's emotions, it can provide the optimal visual concept according to the user's state.

[0117] The service provider can estimate the user's emotions and adjust the display method of the visual concept based on those emotions. For example, if the user is relaxed, it can provide a display method with soft colors; if the user is stressed, it can provide a simple and highly visible display method. Furthermore, if the user is focused, it can provide a detailed and complex display method. By adjusting the display method of the visual concept based on the user's emotions, the service provider can offer the optimal display method tailored to the user's state.

[0118] The interface unit can estimate the user's emotions and adjust the interface's operation procedures based on those emotions. For example, if the user is tense, the operation procedures can be simplified to reduce stress. If the user is enjoying themselves, the operation procedures can be made more detailed to enhance their enjoyment. Furthermore, if the user is tired, the operation procedures can be minimized to make the operation easier. In this way, by adjusting the interface's operation procedures based on the user's emotions, the system can provide the optimal operation procedures tailored to the user's state.

[0119] The management department can estimate the user's emotions and adjust subscription management methods based on those estimates. For example, if the user is relaxed, detailed subscription information can be provided; if the user is stressed, concise and to-the-point subscription information can be provided. Furthermore, if the user is focused, visually easy-to-understand subscription information can be provided. This allows for optimal subscription management tailored to the user's state by adjusting subscription management methods based on their emotions.

[0120] The data collection unit can analyze a user's past submission history and select the optimal collection method. For example, it can analyze the frequency of past submissions and collect data during the time periods with the highest submission frequency. Furthermore, if a user focuses on a specific theme or style, it can prioritize the collection of data related to that theme or style. It can also collect data related to highly-rated works based on the ratings of past submissions. This allows for efficient data collection by analyzing a user's past submission history and selecting the most suitable collection method.

[0121] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data and a simplified analysis on less important data. It can also perform an analysis with an appropriate level of detail on data of moderate importance. By adjusting the level of detail of the analysis based on the importance of the data, the analysis can be performed efficiently.

[0122] The generation unit can adjust the level of detail of the generated visual concepts based on the importance of past works during the generation process. For example, it can generate detailed visual concepts based on highly important past works, and concise visual concepts based on less important past works. It can also generate visual concepts with a moderate level of detail based on past works of moderate importance. This allows for efficient generation of visual concepts by adjusting the level of detail based on the importance of past works.

[0123] The delivery unit can select the optimal delivery method by referring to past user feedback at the time of delivery. For example, it can prioritize display methods that users have preferred in the past and exclude those that users have avoided in the past. It can also provide the optimal display method based on past user feedback. In this way, by referring to past user feedback, the optimal delivery method can be selected and the visual concept can be delivered efficiently.

[0124] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size; if the user is using a tablet, it can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. By selecting the optimal display method based on the user's device information, it is possible to provide an optimal interface tailored to the user's situation.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The collection unit collects the user's past works and related text data. The user's past works include paintings, photographs, designs, etc. The collection unit collects these works in digital format and also collects related text data such as articles, blogs, and comments written by the user. Step 2: The analysis unit analyzes the collected data to understand the user's style and themes. The analysis unit uses natural language processing AI to extract the meaning of the text data, employing techniques such as morphological analysis, grammatical analysis, and semantic analysis. Step 3: The generation unit generates a new visual concept based on the analysis results obtained by the analysis unit. The generation unit uses image generation AI to analyze past works and art styles, and generates a new visual concept using technologies such as GAN (Generative Adversarial Network) and VAE (Variational Autoencoder). Step 4: The provider provides the generated visual concept to the user. The provider can display the generated visual concept on the user's device or provide it in print.

[0127] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0128] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0129] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's past works and related text data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and uses natural language processing AI to extract meaning from the collected data and understand the user's style and themes. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and uses image generation AI to generate a new visual concept. The provision unit is implemented in the control unit 46A of the smart device 14, and displays the generated visual concept on the user's device. The provision unit can also print and provide the generated visual concept. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's past works and related text data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which extracts meaning from the collected data using natural language processing AI to understand the user's style and themes. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates a new visual concept using image generation AI. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which displays the generated visual concept on the user's device. The provision unit can also print and provide the generated visual concept. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's past works and related text data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which extracts meaning from the collected data using natural language processing AI to understand the user's style and themes. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates a new visual concept using image generation AI. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, which displays the generated visual concept on the user's device. The provision unit can also print and provide the generated visual concept. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0171] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0172] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0173] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0174] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0175] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0176] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0178] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0179] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's past works and related text data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which extracts meaning from the collected data using natural language processing AI to understand the user's style and themes. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, which generates a new visual concept using image generation AI. The provision unit is implemented in the control unit 46A of the robot 414, which displays the generated visual concept on the user's device. The provision unit can also print and provide the generated visual concept. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0180] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0182] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0183] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0184] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0188] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0189] 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.

[0190] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0191] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0192] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0193] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0195] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0196] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0197] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0198] (Note 1) A collection unit that collects the user's past works and related text data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand the user's style and themes, A generation unit that generates a new visual concept based on the analysis results obtained by the aforementioned analysis unit, The system comprises a providing unit that provides the visual concept generated by the generation unit. A system characterized by the following features. (Note 2) It features an interface section that provides an intuitive and easy-to-use interface. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has an administrative department that manages the subscription model. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect the user's past works and related text data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Using natural language processing AI, we extract meaning from collected data and understand the user's style and themes. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Using image generation AI, we analyze past works and art styles and generate new visual concepts based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Provide the generated visual concept to the user. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past submission history to select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts how the generated visual concepts are represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, adjust the level of detail based on the importance of past works. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, different generation algorithms are applied depending on the art style category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts the length of the visual concepts generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the generation priority is determined based on the submission dates of past works. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the generation order is adjusted based on the relevance of past works. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the visual concepts are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, we will refer to past user feedback to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, customize the content based on the user's current project. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the visual concepts to be presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and adjust the content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 32) The interface unit is It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The interface unit is When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The interface unit is It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The interface unit is When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned management department, It estimates user sentiment and adjusts subscription management based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned management department, When managing subscriptions, the system selects the optimal management method by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned management department, It estimates user sentiment and prioritizes subscriptions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned management department, When managing subscriptions, the optimal management method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned management department, When managing subscriptions, analyze users' social media activity to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects the user's past works and related text data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand the user's style and themes, A generation unit that generates a new visual concept based on the analysis results obtained by the aforementioned analysis unit, The system comprises a providing unit that provides the visual concept generated by the generation unit. A system characterized by the following features.

2. It features an interface section that provides an intuitive and easy-to-use interface. The system according to feature 1.

3. It has an administrative department that manages the subscription model. The system according to feature 1.

4. The aforementioned collection unit is Collect the user's past works and related text data. The system according to feature 1.

5. The aforementioned analysis unit, Using natural language processing AI, we extract meaning from collected data and understand the user's style and themes. The system according to feature 1.

6. The generating unit is Using image generation AI, we analyze past works and art styles and generate new visual concepts based on that analysis. The system according to feature 1.

7. The aforementioned supply unit is, Provide the generated visual concept to the user. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past submission history to select the optimal collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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