system

Abstract

The system according to the embodiment aims to express abstract ideas and concepts as concrete images and to make it easy to share images with others. [Solution] A system according to an embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit allows a user to input an abstract idea or concept in text. The analyzing unit analyzes the text input by the receiving unit and extracts related keywords and phrases. The generating unit generates a concrete image based on the keywords and phrases extracted by the analyzing unit. The providing unit provides the image generated by the generating unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to express abstract ideas or concepts as concrete images, making it difficult to share images with others.

[0005] The system according to the embodiment aims to express abstract ideas and concepts as concrete images and to make it easy to share images with others. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit allows a user to input an abstract idea or concept in text. The analyzing unit analyzes the text input by the receiving unit and extracts related keywords and phrases. The generating unit generates a concrete image based on the keywords and phrases extracted by the analyzing unit. The providing unit provides the image generated by the generating unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can express abstract ideas and concepts as concrete images, making it easy to share images with others. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An image generation system according to an embodiment of the present invention utilizes generative AI to convert ideas and abstract concepts into intuitive images. In this image generation system, users input abstract ideas or concepts as text, and the generative AI analyzes the text and extracts relevant keywords and phrases. The generative AI generates concrete and simple images based on the extracted keywords and phrases. The generated images are provided to users and can be shared with others. These images are used for communicating organizational strategies, client proposals, and visualizing technical design documents, enhancing understanding and empathy and promoting effective communication. As a result, they contribute to improving organizational creativity and efficient business execution. For example, when communicating organizational strategies, converting abstract strategic concepts into concrete visuals allows all parties involved to have a consistent understanding. Furthermore, in client proposals, expressing complex technical ideas in simple images can deepen customer understanding. Furthermore, visualizing technical design documents facilitates smooth communication between engineers and enables efficient business execution. Because this service utilizes generative AI, it can flexibly generate images based on the content of the text entered by the user. For example, if a user inputs "new marketing strategy," the generative AI extracts keywords such as "marketing," "strategy," and "new" and generates related images based on them. In this way, abstract ideas and concepts are transformed into concrete visuals, enabling effective communication. This allows the image generation system to transform users' abstract ideas and concepts into concrete images, making it easier to share these images with others.

[0029] An image generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives text input from a user, and the user inputs abstract ideas and concepts. Examples of the text input by the user include, but are not limited to, business ideas, technical concepts, and creative concepts. The reception unit provides an interface for the user to input the text. The analysis unit uses a generation AI to analyze the text input by the reception unit and extract relevant keywords and phrases. The analysis unit uses a text analysis algorithm to analyze the content of the input text. The analysis unit can also extract important keywords and phrases using a keyword extraction method. For example, the analysis unit uses natural language processing technology to analyze the meaning of the text and extract relevant keywords. The generation unit uses a generation AI to generate a specific image based on the keywords and phrases extracted by the analysis unit. The generation unit uses an image generation algorithm to generate an image based on the extracted keywords and phrases. The generation unit can also adjust the type and style of the generated image. For example, the generation unit generates a simple image or a detailed image based on the user's input. The providing unit provides the image generated by the generating unit to the user. For example, the providing unit provides the generated image in a format that the user can download. The providing unit can also generate a sharing link for the generated image. For example, the providing unit provides the generated image in JPEG format or PNG format. This allows the image generation system according to the embodiment to convert a user's abstract ideas or concepts into concrete images, making it easy to share the images with others. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit may input the generated image to the generation AI and cause the generation AI to execute a method for providing the image.

[0030] The providing unit can provide the generated image in a format that the user can download. Downloadable formats include, but are not limited to, JPEG, PNG, and PDF formats. For example, the providing unit can provide the generated image in JPEG format. The providing unit can also provide the generated image in PNG format. The providing unit can also provide the generated image in PDF format. For example, the providing unit can provide the generated image in a high-resolution JPEG format. The providing unit can also provide the generated image in a PNG format with a transparent background. The providing unit can also provide the generated image in a multi-page PDF format. This allows the user to download and use the generated image. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the providing unit can input the generated image into the generation AI and cause the generation AI to select a download format.

[0031] The providing unit can generate a sharing link for the generated image. The sharing link may include, for example, a URL format, whether or not access is restricted, and the like, but is not limited to these examples. The providing unit, for example, generates a URL link for the generated image. The providing unit can also generate an access-restricted link for the generated image. The providing unit can also generate a temporary link for the generated image. For example, the providing unit generates a URL link for the generated image and provides it to a user. The providing unit can also generate an access-restricted link for the generated image so that only specific users can access it. The providing unit can also generate a temporary link for the generated image so that it is accessible only for a certain period of time. This allows a user to easily share the generated image with others. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit may input the sharing link for the generated image to the generation AI and cause the generation AI to generate the link.

[0032] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display ideas and concepts that the user has frequently input in the past as candidates. For example, the reception unit can analyze the user's past input history and extract frequently used keywords and phrases. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. For example, the reception unit can suggest the optimal input method based on the user's past input history. The reception unit can also predict and suggest ideas and concepts to be used during a specific time period based on the user's past input history. For example, the reception unit can analyze the user's past input history in chronological order and extract keywords and phrases that are frequently used during a specific time period. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history into a generation AI and have the generation AI suggest the optimal input method.

[0033] The reception unit can provide an appropriate input guide based on the user's current project or area of ​​interest. The reception unit, for example, automatically suggests keywords related to the project the user is currently working on. For example, the reception unit analyzes the user's project information and extracts related keywords. The reception unit can also provide an input guide for related ideas and concepts based on the user's area of ​​interest. For example, the reception unit suggests related keywords and phrases based on the results of a survey on the user's area of ​​interest. The reception unit can also customize the input guide based on topics in which the user has previously shown interest. For example, the reception unit analyzes the user's past input history and suggests keywords and phrases related to topics in which the user has shown interest. This makes it possible to provide an input guide tailored to the user's project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input data on the user's project information and area of ​​interest into the generation AI and have the generation AI provide the input guide.

[0034] The reception unit can prioritize input of related content based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize input of ideas and concepts related to that area. For example, the reception unit can acquire the user's geographical location information and suggest keywords and phrases related to the area. Furthermore, if the user is traveling, the reception unit can also suggest ideas and concepts related to the travel destination. For example, the reception unit can suggest keywords and phrases related to the travel destination based on the user's geographical location information. Furthermore, if the user is participating in a specific event, the reception unit can prioritize input of ideas and concepts related to the event. For example, the reception unit can suggest keywords and phrases related to the event based on the user's geographical location information. This makes it possible to provide optimal content based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest related content.

[0035] The reception unit can analyze the user's social media activity and prompt the user to input related ideas. The reception unit can prompt the user to input related ideas based on, for example, topics frequently mentioned by the user on social media. For example, the reception unit can analyze the user's social media activity and extract frequently mentioned keywords and phrases. The reception unit can also suggest related ideas based on the content of posts from accounts the user follows on social media. For example, the reception unit can analyze the content of posts from the user's followed accounts and suggest related keywords and phrases. The reception unit can also prompt the user to input related ideas based on the activities of groups or communities the user participates in on social media. For example, the reception unit can analyze the activity content of the user's participating groups and suggest related keywords and phrases. This makes it possible to provide optimal ideas based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest related ideas.

[0036] The analysis unit deeply understands the context of the input text and can extract keywords and phrases with higher accuracy. The analysis unit, for example, analyzes the context of the text and extracts related keywords. For example, the analysis unit uses natural language processing technology to analyze the meaning of the text and extract related keywords. The analysis unit can also understand the context of the text and extract important phrases. For example, the analysis unit uses a text analysis algorithm to analyze the context and extract important phrases. The analysis unit can also analyze the context of the text and extract related concepts. For example, the analysis unit uses co-occurrence network analysis to extract related concepts within the text. This allows for a deeper understanding of the context of the text and allows for more accurate extraction of keywords and phrases. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the input text to a generation AI and have the generation AI perform context understanding and keyword extraction.

[0037] The analysis unit can apply different analysis methods depending on the category of the input text. For example, the analysis unit applies an analysis method that prioritizes extracting business terms to business-related text. For example, the analysis unit analyzes text in the business category and extracts business terms. The analysis unit can also apply an analysis method that prioritizes extracting technical terms to technology-related text. For example, the analysis unit analyzes text in the technology category and extracts technical terms. The analysis unit can also apply an analysis method that prioritizes extracting creative expressions to creative-related text. For example, the analysis unit analyzes text in the creative category and extracts creative expressions. This makes it possible to provide an optimal analysis method according to the category of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the category of the input text to the generation AI and have the generation AI select an analysis method.

[0038] The analysis unit can determine the analysis priority based on the submission time of the input text. For example, the analysis unit prioritizes analysis of urgent text and provides results quickly. For example, the analysis unit records the submission time of the input text and evaluates the urgency. The analysis unit can also analyze regular text with normal priority. For example, the analysis unit sets the priority of regular text based on the record of the submission date and time. The analysis unit can also analyze long-term text at a later date. For example, the analysis unit sets the priority of long-term text using a timestamp. This makes it possible to provide optimal analysis priority based on the submission time of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input submission time data of the input text to the generation AI and have the generation AI determine the analysis priority.

[0039] The analysis unit can adjust the order of analysis based on the relevance of the input text. For example, the analysis unit prioritizes analysis of the most relevant text. For example, the analysis unit analyzes the content of the input text and evaluates the relevance. The analysis unit can also postpone analysis of less relevant text. For example, the analysis unit evaluates the similarity of the text and lowers the priority of less relevant text. The analysis unit can also analyze text of medium relevance in the normal order. For example, the analysis unit evaluates thematic similarity and sets the priority of text of medium relevance. This makes it possible to provide an optimal analysis order based on the relevance of the text. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input relevance data of the input text to the generation AI and have the generation AI adjust the analysis order.

[0040] The generation unit can adjust the level of detail of the image based on the importance of the extracted keywords and phrases. The generation unit generates a detailed image based on, for example, important keywords. For example, the generation unit evaluates the importance of the keywords and phrases extracted by the analysis unit and generates a detailed image. The generation unit can also generate a simple image based on keywords of low importance. For example, the generation unit performs keyword frequency analysis and generates a simple image based on keywords of low importance. The generation unit can also generate an image with a moderate level of detail based on keywords of medium importance. For example, the generation unit evaluates the impact and generates an image with a moderate level of detail based on keywords of medium importance. This makes it possible to provide an optimal level of detail for the image based on the importance of the keywords and phrases. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input importance data of the extracted keywords and phrases to the generation AI and cause the generation AI to adjust the level of detail of the image.

[0041] The generation unit can apply different image generation algorithms depending on the category of the extracted keywords or phrases. For example, the generation unit applies a business-oriented image generation algorithm to business-related keywords. For example, the generation unit analyzes keywords in the business category and applies a business-oriented image generation algorithm. The generation unit can also apply a technology-oriented image generation algorithm to technology-related keywords. For example, the generation unit analyzes keywords in the technology category and applies a technology-oriented image generation algorithm. The generation unit can also apply a creative-oriented image generation algorithm to creative-related keywords. For example, the generation unit analyzes keywords in the creative category and applies a creative-oriented image generation algorithm. This makes it possible to provide an optimal image generation algorithm according to the category of the keywords or phrases. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input category data of the extracted keywords or phrases into the generation AI and cause the generation AI to select an image generation algorithm.

[0042] The generation unit can determine the priority of image generation based on the submission time of the extracted keywords or phrases. The generation unit, for example, prioritizes image generation based on urgent keywords. For example, the generation unit records the submission time of the extracted keywords or phrases and evaluates the urgency. The generation unit can also generate images with normal priority based on periodic keywords. For example, the generation unit sets the priority of periodic keywords based on the record of the submission date and time. The generation unit can also generate images at a later date based on long-term keywords. For example, the generation unit sets the priority of long-term keywords using timestamps. This makes it possible to provide an optimal priority for image generation based on the submission time of the keywords or phrases. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input submission time data of the extracted keywords or phrases into the generation AI and have the generation AI determine the priority of image generation.

[0043] The generation unit can adjust the order of image generation based on the relevance of the extracted keywords and phrases. The generation unit, for example, prioritizes image generation based on the most relevant keywords. For example, the generation unit analyzes the content of the extracted keywords and phrases and evaluates their relevance. The generation unit can also generate images later based on keywords with low relevance. For example, the generation unit evaluates the similarity of keywords and lowers the priority of keywords with low relevance. The generation unit can also generate images in a normal order based on keywords with medium relevance. For example, the generation unit evaluates the degree of theme agreement and prioritizes keywords with medium relevance. This makes it possible to provide an optimal order of image generation based on the relevance of keywords and phrases. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input relevance data of the extracted keywords and phrases into the generation AI and cause the generation AI to adjust the order of image generation.

[0044] The providing unit can select the optimal providing method by referring to the past usage history of the generated image. The providing unit, for example, prioritizes providing a display method that the user frequently used in the past. For example, the providing unit analyzes the past usage history of the generated image and extracts a frequently used display method. The providing unit can also suggest the optimal display method based on the user's past usage history. For example, the providing unit evaluates usage frequency and suggests the optimal display method. The providing unit can also provide a customized display method based on the display method the user used in the past. For example, the providing unit analyzes an access log and provides a customized display method. This makes it possible to provide the optimal image providing method based on the past usage history. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input past usage history data of the generated image into the generation AI and have the generation AI select the providing method.

[0045] The providing unit can apply different providing methods depending on the category of the generated image. For example, the providing unit applies a business-oriented providing method to business-related images. For example, the providing unit analyzes images in the business category and applies the business-oriented providing method. The providing unit can also apply a technology-oriented providing method to technology-related images. For example, the providing unit analyzes images in the technology category and applies the technology-oriented providing method. The providing unit can also apply a creative-oriented providing method to creative-related images. For example, the providing unit analyzes images in the creative category and applies the creative-oriented providing method. This makes it possible to provide an optimal providing method according to the image category. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input category data of the generated image to the generation AI and cause the generation AI to select a providing method.

[0046] The providing unit can determine the priority of provision based on the submission time of the generated images. For example, the providing unit provides urgent images with priority so that they can be downloaded quickly. For example, the providing unit records the submission time of the generated images and evaluates the urgency. The providing unit can also provide regular images with normal priority. For example, the providing unit sets the priority of regular images based on the record of the submission date and time. The providing unit can also provide long-term images at a later date. For example, the providing unit sets the priority of long-term images using a timestamp. This makes it possible to provide an optimal provision priority based on the submission time of the images. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input submission time data of the generated images to the generation AI and have the generation AI determine the provision priority.

[0047] The providing unit can adjust the order of providing based on the relevance of the generated images. The providing unit, for example, prioritizes providing the most relevant images. For example, the providing unit analyzes the content of the generated images and evaluates their relevance. The providing unit can also provide images with low relevance later. For example, the providing unit evaluates the similarity of the images and lowers the priority of images with low relevance. The providing unit can also provide images with medium relevance in the normal order. For example, the providing unit evaluates the degree of theme agreement and sets the priority of images with medium relevance. This makes it possible to provide an optimal order of providing based on the relevance of the images. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input relevance data of the generated images to the generation AI and cause the generation AI to adjust the order of providing.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The reception unit can automatically search for related past projects and documents based on the user's input and provide them as reference materials. For example, when a user inputs an idea for a new project, the reception unit searches for similar past projects and presents the results to the user. The reception unit can also search for past technical documents related to the technical concept input by the user and provide them as reference materials. Furthermore, the reception unit can automatically search for market research reports and competitive analyses related to the business idea input by the user and provide them to the user. This allows users to utilize past knowledge to generate ideas and progress projects more effectively.

[0050] The providing unit can collect feedback from users on the generated images and improve the images based on that feedback. For example, the providing unit can provide an interface that allows users to input ratings and comments on the generated images. The providing unit can also analyze user feedback and extract common areas for improvement. Furthermore, the providing unit can instruct the generation AI to regenerate the image based on user feedback and provide the improved image again. This makes it possible to provide images that better meet the user's needs.

[0051] The providing unit may have a function of directly transmitting the generated image to the user's device. For example, the providing unit may directly transmit the generated image to the user's smartphone or tablet. The providing unit may also automatically save the generated image in the user's cloud storage. Furthermore, the providing unit may also transmit the generated image to an email address specified by the user. This allows the user to use the generated image quickly and conveniently.

[0052] The reception unit can analyze the user's past input history and suggest the most suitable input template. For example, it can automatically display input formats that the user has frequently used in the past as candidates. The reception unit can also suggest related templates based on the user's past input content. Furthermore, the reception unit can predict and suggest templates suitable for specific projects or tasks from the user's past input history. This allows the user to input data efficiently.

[0053] The reception unit can provide relevant news and trending information based on the user's current project or areas of interest. For example, the reception unit can automatically display the latest news articles related to the project the user is currently working on. The reception unit can also provide relevant trending information and industry reports based on the user's areas of interest. Furthermore, the reception unit can provide the latest information based on topics in which the user has previously shown interest. This allows the user to utilize the latest information to generate ideas and progress projects more effectively.

[0054] The reception unit can provide information about related events and seminars based on the user's geographical location information. For example, if the user is in a specific area, information about events and seminars held in that area is automatically displayed. In addition, if the user is traveling, the reception unit can provide information about events and seminars held at the user's travel destination. Furthermore, if the user is interested in a specific industry, the reception unit can provide information about events and seminars related to that industry. This allows the user to obtain the most appropriate information based on their geographical location information.

[0055] The reception unit can analyze the user's social media activity and automatically suggest related content. For example, it can suggest related news articles or blog posts based on topics that the user frequently mentions on social media. The reception unit can also suggest related content based on the content posted by accounts the user follows. Furthermore, the reception unit can also suggest related content based on the activities of groups or communities in which the user participates. This allows the user to obtain optimal information based on their social media activity.

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

[0057] Step 1: The user inputs abstract ideas or concepts in text form into the reception unit. The text input by the user can include business ideas, technical concepts, creative concepts, etc. The reception unit provides an interface for the user to input the text. Step 2: The analysis unit uses the generation AI to analyze the text entered by the reception unit and extract relevant keywords and phrases. The analysis unit uses text analysis algorithms and natural language processing technology to analyze the content of the entered text and extract important keywords and phrases. Step 3: The generator uses a generation AI to generate specific images based on the keywords and phrases extracted by the analyzer. The generator uses an image generation algorithm to generate images based on the extracted keywords and phrases, and can also adjust the type and style of the generated images. Step 4: The providing unit provides the image generated by the generating unit to the user. The providing unit provides the generated image in a format that the user can download, and can also generate a sharing link for the generated image. For example, the providing unit provides the generated image in JPEG format or PNG format.

[0058] (Example 2) An image generation system according to an embodiment of the present invention utilizes generative AI to convert ideas and abstract concepts into intuitive images. In this image generation system, users input abstract ideas or concepts as text, and the generative AI analyzes the text and extracts relevant keywords and phrases. The generative AI generates concrete and simple images based on the extracted keywords and phrases. The generated images are provided to users and can be shared with others. These images are used for communicating organizational strategies, client proposals, and visualizing technical design documents, enhancing understanding and empathy and promoting effective communication. As a result, they contribute to improving organizational creativity and efficient business execution. For example, when communicating organizational strategies, converting abstract strategic concepts into concrete visuals allows all parties involved to have a consistent understanding. Furthermore, in client proposals, expressing complex technical ideas in simple images can deepen customer understanding. Furthermore, visualizing technical design documents facilitates smooth communication between engineers and enables efficient business execution. Because this service utilizes generative AI, it can flexibly generate images based on the content of the text entered by the user. For example, if a user inputs "new marketing strategy," the generative AI extracts keywords such as "marketing," "strategy," and "new" and generates related images based on them. In this way, abstract ideas and concepts are transformed into concrete visuals, enabling effective communication. This allows the image generation system to transform users' abstract ideas and concepts into concrete images, making it easier to share these images with others.

[0059] An image generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives text input from a user, and the user inputs abstract ideas and concepts. Examples of the text input by the user include, but are not limited to, business ideas, technical concepts, and creative concepts. The reception unit provides an interface for the user to input the text. The analysis unit uses a generation AI to analyze the text input by the reception unit and extract relevant keywords and phrases. The analysis unit uses a text analysis algorithm to analyze the content of the input text. The analysis unit can also extract important keywords and phrases using a keyword extraction method. For example, the analysis unit uses natural language processing technology to analyze the meaning of the text and extract relevant keywords. The generation unit uses a generation AI to generate a specific image based on the keywords and phrases extracted by the analysis unit. The generation unit uses an image generation algorithm to generate an image based on the extracted keywords and phrases. The generation unit can also adjust the type and style of the generated image. For example, the generation unit generates a simple image or a detailed image based on the user's input. The providing unit provides the image generated by the generating unit to the user. For example, the providing unit provides the generated image in a format that the user can download. The providing unit can also generate a sharing link for the generated image. For example, the providing unit provides the generated image in JPEG format or PNG format. This allows the image generation system according to the embodiment to convert a user's abstract ideas or concepts into concrete images, making it easy to share the images with others. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit may input the generated image to the generation AI and cause the generation AI to execute a method for providing the image.

[0060] The providing unit can provide the generated image in a format that the user can download. Downloadable formats include, but are not limited to, JPEG, PNG, and PDF formats. For example, the providing unit can provide the generated image in JPEG format. The providing unit can also provide the generated image in PNG format. The providing unit can also provide the generated image in PDF format. For example, the providing unit can provide the generated image in a high-resolution JPEG format. The providing unit can also provide the generated image in a PNG format with a transparent background. The providing unit can also provide the generated image in a multi-page PDF format. This allows the user to download and use the generated image. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI or can be performed without using a generation AI. For example, the providing unit can input the generated image into the generation AI and cause the generation AI to select a download format.

[0061] The providing unit can generate a sharing link for the generated image. The sharing link may include, for example, a URL format, whether or not access is restricted, and the like, but is not limited to these examples. The providing unit, for example, generates a URL link for the generated image. The providing unit can also generate an access-restricted link for the generated image. The providing unit can also generate a temporary link for the generated image. For example, the providing unit generates a URL link for the generated image and provides it to a user. The providing unit can also generate an access-restricted link for the generated image so that only specific users can access it. The providing unit can also generate a temporary link for the generated image so that it is accessible only for a certain period of time. This allows a user to easily share the generated image with others. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit may input the sharing link for the generated image to the generation AI and cause the generation AI to generate the link.

[0062] The reception unit can estimate the user's emotion and dynamically change the design of the input interface based on the estimated user emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide an optimal input interface according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the reception unit can be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit can input the user's emotional data into the generation AI and cause the generation AI to change the design of the input interface.

[0063] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display ideas and concepts that the user has frequently input in the past as candidates. For example, the reception unit can analyze the user's past input history and extract frequently used keywords and phrases. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. For example, the reception unit can suggest the optimal input method based on the user's past input history. The reception unit can also predict and suggest ideas and concepts to be used during a specific time period based on the user's past input history. For example, the reception unit can analyze the user's past input history in chronological order and extract keywords and phrases that are frequently used during a specific time period. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history into a generation AI and have the generation AI suggest the optimal input method.

[0064] The reception unit can provide an appropriate input guide based on the user's current project or area of ​​interest. The reception unit, for example, automatically suggests keywords related to the project the user is currently working on. For example, the reception unit analyzes the user's project information and extracts related keywords. The reception unit can also provide an input guide for related ideas and concepts based on the user's area of ​​interest. For example, the reception unit suggests related keywords and phrases based on the results of a survey on the user's area of ​​interest. The reception unit can also customize the input guide based on topics in which the user has previously shown interest. For example, the reception unit analyzes the user's past input history and suggests keywords and phrases related to topics in which the user has shown interest. This makes it possible to provide an input guide tailored to the user's project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input data on the user's project information and area of ​​interest into the generation AI and have the generation AI provide the input guide.

[0065] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize displaying important input items and postpone other items. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Alternatively, if the user is relaxed, the reception unit can display all input items equally to allow free input. For example, the reception unit can record the user's voice and estimate the emotion using voice analysis technology. Alternatively, if the user is in a hurry, the reception unit can display the most important input items first to allow quick input. For example, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for optimal prioritization of input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit may input user emotion data into the generation AI and have the generation AI determine the priority of the input content.

[0066] The reception unit can prioritize input of related content based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize input of ideas and concepts related to that area. For example, the reception unit can acquire the user's geographical location information and suggest keywords and phrases related to the area. Furthermore, if the user is traveling, the reception unit can also suggest ideas and concepts related to the travel destination. For example, the reception unit can suggest keywords and phrases related to the travel destination based on the user's geographical location information. Furthermore, if the user is participating in a specific event, the reception unit can prioritize input of ideas and concepts related to the event. For example, the reception unit can suggest keywords and phrases related to the event based on the user's geographical location information. This makes it possible to provide optimal content based on the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest related content.

[0067] The reception unit can analyze the user's social media activity and prompt the user to input related ideas. The reception unit can prompt the user to input related ideas based on, for example, topics frequently mentioned by the user on social media. For example, the reception unit can analyze the user's social media activity and extract frequently mentioned keywords and phrases. The reception unit can also suggest related ideas based on the content of posts from accounts the user follows on social media. For example, the reception unit can analyze the content of posts from the user's followed accounts and suggest related keywords and phrases. The reception unit can also prompt the user to input related ideas based on the activities of groups or communities the user participates in on social media. For example, the reception unit can analyze the activity content of the user's participating groups and suggest related keywords and phrases. This makes it possible to provide optimal ideas based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest related ideas.

[0068] The analysis unit can estimate the user's emotions and dynamically adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis and extracts more keywords and phrases. For example, the analysis unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and prioritize extracting the most important keywords and phrases. For example, the analysis unit records the user's voice and estimates their emotions using voice analysis technology. Furthermore, if the user is excited, the analysis unit can prioritize extracting visually stimulating keywords and phrases. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates their emotions using an emotion estimation algorithm. This allows for providing an optimal analysis algorithm based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, 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 analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0069] The analysis unit deeply understands the context of the input text and can extract keywords and phrases with higher accuracy. The analysis unit, for example, analyzes the context of the text and extracts related keywords. For example, the analysis unit uses natural language processing technology to analyze the meaning of the text and extract related keywords. The analysis unit can also understand the context of the text and extract important phrases. For example, the analysis unit uses a text analysis algorithm to analyze the context and extract important phrases. The analysis unit can also analyze the context of the text and extract related concepts. For example, the analysis unit uses co-occurrence network analysis to extract related concepts within the text. This allows for a deeper understanding of the context of the text and allows for more accurate extraction of keywords and phrases. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the input text to a generation AI and have the generation AI perform context understanding and keyword extraction.

[0070] The analysis unit can apply different analysis methods depending on the category of the input text. For example, the analysis unit applies an analysis method that prioritizes extracting business terms to business-related text. For example, the analysis unit analyzes text in the business category and extracts business terms. The analysis unit can also apply an analysis method that prioritizes extracting technical terms to technology-related text. For example, the analysis unit analyzes text in the technology category and extracts technical terms. The analysis unit can also apply an analysis method that prioritizes extracting creative expressions to creative-related text. For example, the analysis unit analyzes text in the creative category and extracts creative expressions. This makes it possible to provide an optimal analysis method according to the category of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the category of the input text to the generation AI and have the generation AI select an analysis method.

[0071] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, the analysis unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the key points. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows for providing an optimal display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0072] The analysis unit can determine the analysis priority based on the submission time of the input text. For example, the analysis unit prioritizes analysis of urgent text and provides results quickly. For example, the analysis unit records the submission time of the input text and evaluates the urgency. The analysis unit can also analyze regular text with normal priority. For example, the analysis unit sets the priority of regular text based on the record of the submission date and time. The analysis unit can also analyze long-term text at a later date. For example, the analysis unit sets the priority of long-term text using a timestamp. This makes it possible to provide optimal analysis priority based on the submission time of the text. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input submission time data of the input text to the generation AI and have the generation AI determine the analysis priority.

[0073] The analysis unit can adjust the order of analysis based on the relevance of the input text. For example, the analysis unit prioritizes analysis of the most relevant text. For example, the analysis unit analyzes the content of the input text and evaluates the relevance. The analysis unit can also postpone analysis of less relevant text. For example, the analysis unit evaluates the similarity of the text and lowers the priority of less relevant text. The analysis unit can also analyze text of medium relevance in the normal order. For example, the analysis unit evaluates thematic similarity and sets the priority of text of medium relevance. This makes it possible to provide an optimal analysis order based on the relevance of the text. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input relevance data of the input text to the generation AI and have the generation AI adjust the analysis order.

[0074] The generation unit can estimate the user's emotions and adjust the style of the generated image based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates an image with soft colors. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is in a hurry, the generation unit can generate a simple, intuitive image. For example, the generation unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is excited, the generation unit can generate a visually stimulating image. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the optimal image style to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the style of the image.

[0075] The generation unit can adjust the level of detail of the image based on the importance of the extracted keywords and phrases. The generation unit generates a detailed image based on, for example, important keywords. For example, the generation unit evaluates the importance of the keywords and phrases extracted by the analysis unit and generates a detailed image. The generation unit can also generate a simple image based on keywords of low importance. For example, the generation unit performs keyword frequency analysis and generates a simple image based on keywords of low importance. The generation unit can also generate an image with a moderate level of detail based on keywords of medium importance. For example, the generation unit evaluates the impact and generates an image with a moderate level of detail based on keywords of medium importance. This makes it possible to provide an optimal level of detail for the image based on the importance of the keywords and phrases. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input importance data of the extracted keywords and phrases to the generation AI and cause the generation AI to adjust the level of detail of the image.

[0076] The generation unit can apply different image generation algorithms depending on the category of the extracted keywords or phrases. For example, the generation unit applies a business-oriented image generation algorithm to business-related keywords. For example, the generation unit analyzes keywords in the business category and applies a business-oriented image generation algorithm. The generation unit can also apply a technology-oriented image generation algorithm to technology-related keywords. For example, the generation unit analyzes keywords in the technology category and applies a technology-oriented image generation algorithm. The generation unit can also apply a creative-oriented image generation algorithm to creative-related keywords. For example, the generation unit analyzes keywords in the creative category and applies a creative-oriented image generation algorithm. This makes it possible to provide an optimal image generation algorithm according to the category of the keywords or phrases. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input category data of the extracted keywords or phrases into the generation AI and cause the generation AI to select an image generation algorithm.

[0077] The generation unit can estimate the user's emotion and adjust the size of the generated image based on the estimated user emotion. For example, if the user is relaxed, the generation unit generates a large image. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also generate a small image if the user is in a hurry. For example, the generation unit records the user's voice and estimates the emotion using voice analysis technology. The generation unit can also generate a visually stimulating image if the user is excited. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows for providing an optimal image size according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the size of the image.

[0078] The generation unit can determine the priority of image generation based on the submission time of the extracted keywords or phrases. The generation unit, for example, prioritizes image generation based on urgent keywords. For example, the generation unit records the submission time of the extracted keywords or phrases and evaluates the urgency. The generation unit can also generate images with normal priority based on periodic keywords. For example, the generation unit sets the priority of periodic keywords based on the record of the submission date and time. The generation unit can also generate images at a later date based on long-term keywords. For example, the generation unit sets the priority of long-term keywords using timestamps. This makes it possible to provide an optimal priority for image generation based on the submission time of the keywords or phrases. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input submission time data of the extracted keywords or phrases into the generation AI and have the generation AI determine the priority of image generation.

[0079] The generation unit can adjust the order of image generation based on the relevance of the extracted keywords and phrases. The generation unit, for example, prioritizes image generation based on the most relevant keywords. For example, the generation unit analyzes the content of the extracted keywords and phrases and evaluates their relevance. The generation unit can also generate images later based on keywords with low relevance. For example, the generation unit evaluates the similarity of keywords and lowers the priority of keywords with low relevance. The generation unit can also generate images in a normal order based on keywords with medium relevance. For example, the generation unit evaluates the degree of theme agreement and prioritizes keywords with medium relevance. This makes it possible to provide an optimal order of image generation based on the relevance of keywords and phrases. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input relevance data of the extracted keywords and phrases into the generation AI and cause the generation AI to adjust the order of image generation.

[0080] The providing unit can estimate the user's emotions and adjust the display method of the images provided based on the estimated user emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the providing unit can provide a display method that includes detailed information. For example, the providing unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This makes it possible to provide an optimal image display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to adjust the image display method.

[0081] The providing unit can select the optimal providing method by referring to the past usage history of the generated image. The providing unit, for example, prioritizes providing a display method that the user frequently used in the past. For example, the providing unit analyzes the past usage history of the generated image and extracts a frequently used display method. The providing unit can also suggest the optimal display method based on the user's past usage history. For example, the providing unit evaluates usage frequency and suggests the optimal display method. The providing unit can also provide a customized display method based on the display method the user used in the past. For example, the providing unit analyzes an access log and provides a customized display method. This makes it possible to provide the optimal image providing method based on the past usage history. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input past usage history data of the generated image into the generation AI and have the generation AI select the providing method.

[0082] The providing unit can apply different providing methods depending on the category of the generated image. For example, the providing unit applies a business-oriented providing method to business-related images. For example, the providing unit analyzes images in the business category and applies the business-oriented providing method. The providing unit can also apply a technology-oriented providing method to technology-related images. For example, the providing unit analyzes images in the technology category and applies the technology-oriented providing method. The providing unit can also apply a creative-oriented providing method to creative-related images. For example, the providing unit analyzes images in the creative category and applies the creative-oriented providing method. This makes it possible to provide an optimal providing method according to the image category. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input category data of the generated image to the generation AI and cause the generation AI to select a providing method.

[0083] The providing unit can estimate the user's emotions and adjust the download format of the images provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide a simple and easy-to-use download format. For example, the providing unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the providing unit can provide multiple download formats to increase options. For example, the providing unit can record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the providing unit can provide a format that allows for quick downloads. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the optimal download format to be provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to adjust the download format.

[0084] The providing unit can determine the priority of provision based on the submission time of the generated images. For example, the providing unit provides urgent images with priority so that they can be downloaded quickly. For example, the providing unit records the submission time of the generated images and evaluates the urgency. The providing unit can also provide regular images with normal priority. For example, the providing unit sets the priority of regular images based on the record of the submission date and time. The providing unit can also provide long-term images at a later date. For example, the providing unit sets the priority of long-term images using a timestamp. This makes it possible to provide an optimal provision priority based on the submission time of the images. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit can input submission time data of the generated images to the generation AI and have the generation AI determine the provision priority.

[0085] The providing unit can adjust the order of providing based on the relevance of the generated images. The providing unit, for example, prioritizes providing the most relevant images. For example, the providing unit analyzes the content of the generated images and evaluates their relevance. The providing unit can also provide images with low relevance later. For example, the providing unit evaluates the similarity of the images and lowers the priority of images with low relevance. The providing unit can also provide images with medium relevance in the normal order. For example, the providing unit evaluates the degree of theme agreement and sets the priority of images with medium relevance. This makes it possible to provide an optimal order of providing based on the relevance of the images. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input relevance data of the generated images to the generation AI and cause the generation AI to adjust the order of providing. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and provides an interface for a user to input text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input text using a generation AI and extracts related keywords and phrases. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a specific image based on the extracted keywords and phrases. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated image to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and provides an interface for a user to input text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input text using a generation AI and extracts related keywords and phrases. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a specific image based on the extracted keywords and phrases. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated image to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and provides an interface for the user to input text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input text using a generation AI and extracts related keywords and phrases. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a specific image based on the extracted keywords and phrases. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and provides the generated image to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and provides an interface for a user to input text. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input text using a generation AI and extracts related keywords and phrases. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a specific image based on the extracted keywords and phrases. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated image to the user.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The reception unit can automatically search for related past projects and documents based on the user's input and provide them as reference materials. For example, when a user inputs an idea for a new project, the reception unit searches for similar past projects and presents the results to the user. The reception unit can also search for past technical documents related to the technical concept input by the user and provide them as reference materials. Furthermore, the reception unit can automatically search for market research reports and competitive analyses related to the business idea input by the user and provide them to the user. This allows users to utilize past knowledge to generate ideas and progress projects more effectively.

[0088] The providing unit can collect feedback from users on the generated images and improve the images based on that feedback. For example, the providing unit can provide an interface that allows users to input ratings and comments on the generated images. The providing unit can also analyze user feedback and extract common areas for improvement. Furthermore, the providing unit can instruct the generation AI to regenerate the image based on user feedback and provide the improved image again. This makes it possible to provide images that better meet the user's needs.

[0089] The providing unit may have a function of directly transmitting the generated image to the user's device. For example, the providing unit may directly transmit the generated image to the user's smartphone or tablet. The providing unit may also automatically save the generated image in the user's cloud storage. Furthermore, the providing unit may also transmit the generated image to an email address specified by the user. This allows the user to use the generated image quickly and conveniently.

[0090] The reception unit can estimate the user's emotions and dynamically change the confirmation message for the input content based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can display the confirmation message in gentle language. If the user is excited, the reception unit can also display the confirmation message in energetic language. Furthermore, if the user is relaxed, the reception unit can also display the confirmation message in calm language. This enables optimal communication according to the user's emotions.

[0091] The reception unit can analyze the user's past input history and suggest the most suitable input template. For example, it can automatically display input formats that the user has frequently used in the past as candidates. The reception unit can also suggest related templates based on the user's past input content. Furthermore, the reception unit can predict and suggest templates suitable for specific projects or tasks from the user's past input history. This allows the user to input data efficiently.

[0092] The reception unit can provide relevant news and trending information based on the user's current project or areas of interest. For example, the reception unit can automatically display the latest news articles related to the project the user is currently working on. The reception unit can also provide relevant trending information and industry reports based on the user's areas of interest. Furthermore, the reception unit can provide the latest information based on topics in which the user has previously shown interest. This allows the user to utilize the latest information to generate ideas and progress projects more effectively.

[0093] The reception unit can estimate the user's emotions and provide an automatic completion function for input content based on the estimated user's emotions. For example, if the user is nervous, the reception unit can automatically complete simple input content. If the user is relaxed, the reception unit can also automatically complete detailed input content. Furthermore, if the user is in a hurry, the reception unit can also prioritize completion of the most important input content. This enables optimal input support according to the user's emotions.

[0094] The reception unit can provide information about related events and seminars based on the user's geographical location information. For example, if the user is in a specific area, information about events and seminars held in that area is automatically displayed. In addition, if the user is traveling, the reception unit can provide information about events and seminars held at the user's travel destination. Furthermore, if the user is interested in a specific industry, the reception unit can provide information about events and seminars related to that industry. This allows the user to obtain the most appropriate information based on their geographical location information.

[0095] The reception unit can analyze the user's social media activity and automatically suggest related content. For example, it can suggest related news articles or blog posts based on topics that the user frequently mentions on social media. The reception unit can also suggest related content based on the content posted by accounts the user follows. Furthermore, the reception unit can also suggest related content based on the activities of groups or communities in which the user participates. This allows the user to obtain optimal information based on their social media activity.

[0096] The analysis unit can estimate the user's emotions and adjust the method of feedback of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible feedback. If the user is relaxed, the analysis unit can also provide feedback including detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide feedback that focuses on the main points. This makes it possible to provide optimal feedback according to the user's emotions.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The user inputs abstract ideas or concepts in text form into the reception unit. The text input by the user can include business ideas, technical concepts, creative concepts, etc. The reception unit provides an interface for the user to input the text. Step 2: The analysis unit uses the generation AI to analyze the text entered by the reception unit and extract relevant keywords and phrases. The analysis unit uses text analysis algorithms and natural language processing technology to analyze the content of the entered text and extract important keywords and phrases. Step 3: The generator uses a generation AI to generate specific images based on the keywords and phrases extracted by the analyzer. The generator uses an image generation algorithm to generate images based on the extracted keywords and phrases, and can also adjust the type and style of the generated images. Step 4: The providing unit provides the image generated by the generating unit to the user. The providing unit provides the generated image in a format that the user can download, and can also generate a sharing link for the generated image. For example, the providing unit provides the generated image in JPEG format or PNG format.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

[0170] [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a receiving section where a user inputs an abstract idea or concept in text; an analysis unit that analyzes the text input by the reception unit and extracts related keywords and phrases; a generation unit that generates a specific image based on the keywords and phrases extracted by the analysis unit; a providing unit that provides the image generated by the generating unit to a user. A system characterized by:

2. The providing unit Provide the generated image in a format that users can download 2. The system of claim 1.

3. The providing unit Generate a shareable link for the generated image The system of claim 1 .

4. The reception unit Estimate user emotions and dynamically change the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

5. The reception unit Analyzes the user's past input history and suggests appropriate input methods The system of claim 1 .

6. The reception unit Providing appropriate input guidance based on the user's current project or area of ​​interest 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and prioritize appropriate input content based on the estimated user emotions. The system of claim 1 .

8. The reception unit Prioritize relevant content based on the user's geographic location 2. The system of claim 1.

9. The reception unit Analyze your social media activity and prompt you to enter relevant ideas 2. The system of claim 1.

10. The analysis unit Estimate user emotions and dynamically adjust appropriate analysis algorithms based on the estimated user emotions. The system of claim 1 .

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