System

Abstract

An object of a system according to an embodiment is to efficiently generate an image using image generation AI and realize cost reduction.SOLUTION: A system according to an embodiment includes an image generation unit, a prompt input unit, and a cost reduction unit. The image generation unit generates an image using the image generation AI. The prompt input unit receives an instruction from a user. The cost reduction unit evaluates a cost reduction effect using the image generated by the image generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that selecting images to use in proposals and meeting materials takes time and is costly.

[0005] The system according to the embodiment aims to efficiently generate images using image generation AI, thereby reducing costs. [Means for solving the problem]

[0006] The system according to the embodiment includes an image generation unit, a prompt input unit, and a cost reduction unit. The image generation unit generates an image using an image generation AI. The prompt input unit receives instructions from a user. The cost reduction unit evaluates the cost reduction effect using the image generated by the image generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate images using image generation AI, thereby reducing costs. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The image generation system according to an embodiment of the present invention is a system that can reduce costs by more than 30 million yen per year by limiting the images used in proposals and meeting materials to those generated by the generation AI. As a result, the image generation system can quickly generate images to be used in proposals and meeting materials, and evaluate the cost reduction effect.

[0029] An image generation system according to an embodiment includes an image generation unit, a prompt input unit, and a cost reduction unit. The image generation unit generates an image using an image generation AI. For example, the image generation unit receives a prompt including instructions from a user as input, and generates an image based on the prompt. The generation AI can use technologies such as a generative adversarial network (GAN) or a variational autoencoder (VAE). The prompt input unit receives instructions from a user. For example, the prompt input unit can receive instructions from a user in the form of text input, voice input, gesture input, or the like. The cost reduction unit evaluates cost reduction effects using images generated by the image generation unit. For example, the cost reduction unit evaluates cost reduction effects based on criteria such as time reduction, material cost reduction, and labor cost reduction. This allows the image generation system to quickly generate images to be used in proposals and meeting materials and evaluate cost reduction effects.

[0030] The image generation unit learns the user's past usage history and generates images optimized to the user's preferences. For example, the image generation unit has a generation AI learn the user's past usage history and generate images optimized to the user's preferences. For example, it generates images that match the user's preferences based on the style and theme of images used in the past. Past usage history can be learned by collecting data such as frequency of use, usage time, and usage patterns. In this way, by learning the user's past usage history, it is possible to provide images that better match the user's preferences.

[0031] The image generation unit receives feedback from the user in real time and automatically improves the image based on that feedback. For example, the image generation unit adds a real-time feedback function to the generation AI and automatically improves the image based on user feedback. For example, if the user requests "brighter colors," the image is improved by reflecting that feedback. Feedback can be received in the form of, for example, user ratings, comments, correction requests, etc. This allows the image to be automatically improved based on feedback received in real time.

[0032] Generative AI automatically generates the entire layout and design of proposals and meeting materials. For example, generative AI adds a function to automatically generate the layout of proposals and meeting materials, and generates the entire design simply by the user entering prompts. For example, if you enter "business meeting proposal," the layout and design will be automatically generated. The entire layout and design includes elements such as page composition, color design, and font selection. This makes it possible to automatically generate the entire layout and design of proposals and meeting materials.

[0033] Generative AI works in conjunction with creative tools to generate multimedia content in bulk. For example, generative AI works with video editing software or music generation software to generate videos and images in bulk by simply entering prompts. For example, if you enter "new product introduction video," videos and images will be automatically generated. Creative tools include software such as Adobe Premiere, Final Cut Pro, and Ableton Live. This allows multimedia content to be generated in bulk.

[0034] The generative AI is equipped with an algorithm that optimizes operational costs, further enhancing the cost reduction effect. For example, the generative AI develops an algorithm that optimizes operational costs, further enhancing the cost reduction effect. For example, operational costs can be reduced by improving the processing efficiency of the AI. Operational costs include, for example, server costs, power consumption, and maintenance costs. This allows the operational costs of the generative AI to be optimized, further enhancing the cost reduction effect.

[0035] The generation AI is equipped with a system that monitors usage and evaluates the cost reduction effect in real time. The generation AI, for example, builds a system that monitors usage in real time and evaluates the cost reduction effect. For example, it records the usage time and number of times generated and calculates the cost reduction effect. This makes it possible to monitor the usage of the generation AI and evaluate the cost reduction effect in real time.

[0036] Generative AI applies the cost-saving effects of image generation to other business processes, such as creating marketing materials and product catalogs. For example, generative AI applies the cost-saving effects of image generation to the creation of marketing materials, thereby reducing costs. For example, images to be used in marketing materials are automatically generated using generative AI. Companies and industries include manufacturing, services, and IT. This allows the cost-saving effects of image generation to be applied to other business processes.

[0037] Generative AI can also expand the cost-saving effects of image generation to other companies and industries, and provide them as consulting services. For example, generative AI can expand the cost-saving effects of image generation to other companies and provide them as consulting services. For example, it can support the image generation of corporate proposals and meeting materials. Consulting services include, for example, technical consulting and business consulting. This allows the cost-saving effects of image generation to be expanded to other companies and industries, and provided as consulting services.

[0038] The generative AI automatically assesses the copyright risk of the images it generates and presents alternatives if the risk is high. For example, the generative AI can add a copyright risk assessment function to automatically assess the copyright risk of generated images. If the risk is high, alternatives can be presented. For example, it can suggest images with a lower risk. Alternatives include, for example, other images, other designs, other layouts, etc. This makes it possible to automatically assess the copyright risk of images it generates and present alternatives if the risk is high.

[0039] Generative AI introduces a filtering algorithm to reduce copyright risks in the generation process. For example, generative AI introduces a filtering algorithm into the generation process to reduce copyright risks. For example, it excludes images that are similar to existing copyrighted works. Filtering algorithms include, for example, rule-based filtering and machine learning-based filtering. This makes it possible to introduce a filtering algorithm to reduce copyright risks in the generation process.

[0040] Generative AI applies copyright risk avoidance measures for image generation to other creative content such as videos and music. For example, generative AI applies copyright risk avoidance measures for image generation to video generation to reduce copyright risks. For example, video scenes and backgrounds are automatically generated by generative AI. Creative content includes, for example, videos, music, artworks, etc. This allows copyright risk avoidance measures for image generation to be applied to other creative content.

[0041] Generative AI also deploys its image generation copyright risk avoidance measures to companies and industries and provides them as consulting services. For example, Generative AI deploys its image generation copyright risk avoidance measures to other companies and provides them as consulting services. For example, it supports image generation for corporate advertisements and marketing materials. External customers include, for example, corporate customers, individual customers, and customers in specific industries. This allows Generative AI to deploy its image generation copyright risk avoidance measures to other companies and industries and provide them as consulting services.

[0042] Generative AI works in conjunction with creative tools to provide an image generation service that provides multimedia content all at once. For example, generative AI works in conjunction with video editing software to provide a service that generates videos and images all at once by simply entering prompts. For example, if you enter "a video introducing a new product," videos and images are automatically generated. Creative tools include software such as Adobe Premiere, Final Cut Pro, and Ableton Live. This allows the image generation service to be linked with other creative tools and provide multimedia content all at once.

[0043] The generative AI receives customer feedback in real time for external sales services and improves the service based on that feedback. For example, the generative AI adds a real-time feedback function to an external sales service and improves the service based on customer feedback. For example, if a customer requests "lighter colors," the generative AI reflects that feedback and improves the service. Feedback can be received in the form of, for example, user ratings, comments, correction requests, etc. This allows the external sales service to receive customer feedback in real time and improve the service.

[0044] Generative AI will also expand its image generation services to other industries, such as the advertising and entertainment industries. For example, Generative AI will expand its image generation services to the advertising industry, providing a service that generates images for advertising. For example, images to be used in advertising campaigns are automatically generated using Generative AI. Industries include, for example, the advertising, entertainment, and manufacturing industries. This will enable the image generation service to be expanded to other industries.

[0045] Generative AI automatically generates planning proposals specialized for different markets and regions. For example, generative AI builds a system that automatically generates planning proposals specialized for different markets and regions. For example, it makes proposals according to market needs and regional characteristics. Markets and regions include, for example, the domestic market, overseas markets, and specific regions. This makes it possible to automatically generate planning proposals specialized for different markets and regions using generative AI.

[0046] Generative AI automatically generates visual prototypes of proposed plans, facilitating visual understanding. Generative AI develops systems that automatically generate visual prototypes of proposed plans. For example, they visually display product designs and service flows. Visual prototypes include, for example, 3D models, wireframes, and mockups. This allows for the automatic generation of visual prototypes of proposed plans, facilitating visual understanding.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The image generation system may further include a preference learning unit that learns the user's preferences. For example, the preference learning unit analyzes images and prompts selected by the user in the past to learn the user's preferences. Based on this, the system can preferentially generate images in the user's preferred style or theme. The preference learning unit can also quickly respond to changes in the user's preferences and provide images based on the new preferences. This enables the generation of images optimized to the user's preferences, thereby improving user satisfaction.

[0049] The image generation system can also include a progress management unit that monitors the progress of a user's project. For example, the progress management unit grasps the progress of a user's project in real time and generates images according to the progress. In the early stages of a project, it generates images suitable for idea generation, and in the final stages, it generates images suitable for presentations. The progress management unit can also suggest adjustments to necessary resources and schedules as the project progresses. This makes it possible to generate images according to the project's progress, supporting efficient project management.

[0050] The image generation system may further include an environment management unit that monitors the user's work environment. For example, the environment management unit may measure the brightness and volume of the user's work environment and adjust the image generation process based on this. If the user is working in a quiet environment, the environment management unit may generate images that have a relaxing effect, and if the user is working in a noisy environment, the environment management unit may generate images that enhance concentration. The environment management unit may also automatically adjust the image generation settings in response to changes in the user's work environment. This allows for image generation that is tailored to the user's work environment, providing a more comfortable work environment.

[0051] The image generation system may further include a purpose adaptation unit that generates images according to the purpose of the user's project. For example, the purpose adaptation unit analyzes the purpose of the prompt entered by the user and generates images according to the purpose, such as for presentations, marketing, or education. The purpose adaptation unit may also automatically adjust image generation settings according to the progress of the user's project or changes in the purpose. This makes it possible to generate images according to the purpose of the user's project, thereby providing more effective images.

[0052] The image generation system can also include a schedule management unit that generates images according to the user's project schedule. For example, the schedule management unit grasps the user's project schedule in real time and generates images according to the schedule. In the early stages of the project, it generates images suitable for idea generation, and in the final stages, it generates images suitable for presentations. The schedule management unit can also propose adjustments to the necessary resources and schedule as the project progresses. This makes it possible to generate images according to the project schedule, supporting efficient project management.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The prompt input unit receives an instruction from the user. For example, the prompt input unit can receive the instruction from the user in the form of text input, voice input, gesture input, or the like. Step 2: The image generation unit generates an image using image generation AI. For example, the image generation unit receives a prompt containing instructions from the user as input, and generates an image based on the prompt. The generation AI can use technologies such as GAN (generative adversarial network) and VAE (variational autoencoder). Step 3: The cost reduction unit evaluates the cost reduction effect using the image generated by the image generation unit. For example, the cost reduction unit evaluates the cost reduction effect based on criteria such as time reduction, material cost reduction, and labor cost reduction.

[0055] (Example 2) The image generation system according to an embodiment of the present invention is a system that can reduce costs by more than 30 million yen per year by limiting the images used in proposals and meeting materials to those generated by the generation AI. As a result, the image generation system can quickly generate images to be used in proposals and meeting materials, and evaluate the cost reduction effect.

[0056] An image generation system according to an embodiment includes an image generation unit, a prompt input unit, and a cost reduction unit. The image generation unit generates an image using an image generation AI. For example, the image generation unit receives a prompt including instructions from a user as input, and generates an image based on the prompt. The generation AI can use technologies such as a generative adversarial network (GAN) or a variational autoencoder (VAE). The prompt input unit receives instructions from a user. For example, the prompt input unit can receive instructions from a user in the form of text input, voice input, gesture input, or the like. The cost reduction unit evaluates cost reduction effects using images generated by the image generation unit. For example, the cost reduction unit evaluates cost reduction effects based on criteria such as time reduction, material cost reduction, and labor cost reduction. This allows the image generation system to quickly generate images to be used in proposals and meeting materials and evaluate cost reduction effects.

[0057] The image generation unit is equipped with an emotion estimation function that generates images according to the user's emotions. For example, the image generation unit incorporates the emotion estimation function into the generation AI, analyzes the emotions of the prompts entered by the user in real time, and proposes the most appropriate image. For example, if the user requests a "positive meeting atmosphere," it generates an image that matches that emotion. The emotion estimation function can use technologies such as facial expression recognition, voice analysis, and text analysis. This allows the system to generate images according to the user's emotions, thereby providing more appropriate images.

[0058] The image generation unit learns the user's past usage history and generates images optimized to the user's preferences. For example, the image generation unit has a generation AI learn the user's past usage history and generate images optimized to the user's preferences. For example, it generates images that match the user's preferences based on the style and theme of images used in the past. Past usage history can be learned by collecting data such as frequency of use, usage time, and usage patterns. In this way, by learning the user's past usage history, it is possible to provide images that better match the user's preferences.

[0059] The image generation unit receives feedback from the user in real time and automatically improves the image based on that feedback. For example, the image generation unit adds a real-time feedback function to the generation AI and automatically improves the image based on user feedback. For example, if the user requests "brighter colors," the image is improved by reflecting that feedback. Feedback can be received in the form of, for example, user ratings, comments, correction requests, etc. This allows the image to be automatically improved based on feedback received in real time.

[0060] Generative AI automatically generates the entire layout and design of proposals and meeting materials. For example, generative AI adds a function to automatically generate the layout of proposals and meeting materials, and generates the entire design simply by the user entering prompts. For example, if you enter "business meeting proposal," the layout and design will be automatically generated. The entire layout and design includes elements such as page composition, color design, and font selection. This makes it possible to automatically generate the entire layout and design of proposals and meeting materials.

[0061] Generative AI works in conjunction with creative tools to generate multimedia content in bulk. For example, generative AI works with video editing software or music generation software to generate videos and images in bulk by simply entering prompts. For example, if you enter "new product introduction video," videos and images will be automatically generated. Creative tools include software such as Adobe Premiere, Final Cut Pro, and Ableton Live. This allows multimedia content to be generated in bulk.

[0062] The generative AI analyzes the emotion of the prompt in real time and suggests the optimal image based on that emotion. For example, the generative AI is equipped with an emotion estimation function, and analyzes the emotion of the prompt entered by the user in real time and suggests the optimal image. For example, if the user requests a "positive meeting atmosphere," it will generate an image that matches that emotion. The emotion of the prompt can be analyzed using techniques such as emotion analysis algorithms and emotion label definitions. This allows the optimal image to be suggested based on the user's emotion.

[0063] The generative AI is equipped with an algorithm that optimizes operational costs, further enhancing the cost reduction effect. For example, the generative AI develops an algorithm that optimizes operational costs, further enhancing the cost reduction effect. For example, operational costs can be reduced by improving the processing efficiency of the AI. Operational costs include, for example, server costs, power consumption, and maintenance costs. This allows the operational costs of the generative AI to be optimized, further enhancing the cost reduction effect.

[0064] The generation AI is equipped with a system that monitors usage and evaluates the cost reduction effect in real time. The generation AI, for example, builds a system that monitors usage in real time and evaluates the cost reduction effect. For example, it records the usage time and number of times generated and calculates the cost reduction effect. This makes it possible to monitor the usage of the generation AI and evaluate the cost reduction effect in real time.

[0065] Generative AI applies the cost-saving effects of image generation to other business processes, such as creating marketing materials and product catalogs. For example, generative AI applies the cost-saving effects of image generation to the creation of marketing materials, thereby reducing costs. For example, images to be used in marketing materials are automatically generated using generative AI. Companies and industries include manufacturing, services, and IT. This allows the cost-saving effects of image generation to be applied to other business processes.

[0066] Generative AI can also expand the cost-saving effects of image generation to other companies and industries, and provide them as consulting services. For example, generative AI can expand the cost-saving effects of image generation to other companies and provide them as consulting services. For example, it can support the image generation of corporate proposals and meeting materials. Consulting services include, for example, technical consulting and business consulting. This allows the cost-saving effects of image generation to be expanded to other companies and industries, and provided as consulting services.

[0067] The emotion estimation function evaluates the cost reduction effect based on the user's emotion and proposes the optimal cost reduction measure. The emotion estimation function, for example, builds a system that evaluates the cost reduction effect based on the user's emotion. For example, the optimal cost reduction measure is proposed based on the user's emotion score. Cost reduction measures include, for example, process improvement, resource optimization, outsourcing, etc. This makes it possible to evaluate the cost reduction effect based on the user's emotion and propose the optimal cost reduction measure.

[0068] The generative AI incorporates an emotion estimation function and proposes copyright risk avoidance measures according to the user's emotions. For example, if the user desires a "sense of security," the generative AI will provide copyright risk avoidance measures that match that emotion. For example, it will prioritize suggesting images with low risk. Copyright risks include, for example, the possibility of copyright infringement and license confirmation. This makes it possible to propose copyright risk avoidance measures according to the user's emotions.

[0069] The generative AI automatically assesses the copyright risk of the images it generates and presents alternatives if the risk is high. For example, the generative AI can add a copyright risk assessment function to automatically assess the copyright risk of generated images. If the risk is high, alternatives can be presented. For example, it can suggest images with a lower risk. Alternatives include, for example, other images, other designs, other layouts, etc. This makes it possible to automatically assess the copyright risk of images it generates and present alternatives if the risk is high.

[0070] Generative AI introduces a filtering algorithm to reduce copyright risks in the generation process. For example, generative AI introduces a filtering algorithm into the generation process to reduce copyright risks. For example, it excludes images that are similar to existing copyrighted works. Filtering algorithms include, for example, rule-based filtering and machine learning-based filtering. This makes it possible to introduce a filtering algorithm to reduce copyright risks in the generation process.

[0071] Generative AI applies copyright risk avoidance measures for image generation to other creative content such as videos and music. For example, generative AI applies copyright risk avoidance measures for image generation to video generation to reduce copyright risks. For example, video scenes and backgrounds are automatically generated by generative AI. Creative content includes, for example, videos, music, artworks, etc. This allows copyright risk avoidance measures for image generation to be applied to other creative content.

[0072] Generative AI also deploys its image generation copyright risk avoidance measures to companies and industries and provides them as consulting services. For example, Generative AI deploys its image generation copyright risk avoidance measures to other companies and provides them as consulting services. For example, it supports image generation for corporate advertisements and marketing materials. External customers include, for example, corporate customers, individual customers, and customers in specific industries. This allows Generative AI to deploy its image generation copyright risk avoidance measures to other companies and industries and provide them as consulting services.

[0073] The emotion estimation function proposes copyright risk avoidance measures based on the user's emotions and provides the optimal risk avoidance measures. The emotion estimation function, for example, builds a system that proposes copyright risk avoidance measures based on the user's emotions. For example, it provides the optimal risk avoidance measures based on the user's emotion score. The risk avoidance measures include, for example, presenting alternatives and methods for reducing risk. This makes it possible to propose copyright risk avoidance measures based on the user's emotions and provide the optimal risk avoidance measures.

[0074] Generative AI incorporates an emotion estimation function and provides an image generation service that corresponds to the emotions of external customers. For example, if an external customer requests a "creative image," generative AI incorporates an emotion estimation function and provides a service that generates an image that matches that emotion. For example, it generates an image based on the customer's emotions. Image generation services include, for example, online services and customization services. This makes it possible to provide an image generation service that corresponds to the emotions of external customers.

[0075] Generative AI works in conjunction with creative tools to provide an image generation service that provides multimedia content all at once. For example, generative AI works in conjunction with video editing software to provide a service that generates videos and images all at once by simply entering prompts. For example, if you enter "a video introducing a new product," videos and images are automatically generated. Creative tools include software such as Adobe Premiere, Final Cut Pro, and Ableton Live. This allows the image generation service to be linked with other creative tools and provide multimedia content all at once.

[0076] The generative AI receives customer feedback in real time for external sales services and improves the service based on that feedback. For example, the generative AI adds a real-time feedback function to an external sales service and improves the service based on customer feedback. For example, if a customer requests "lighter colors," the generative AI reflects that feedback and improves the service. Feedback can be received in the form of, for example, user ratings, comments, correction requests, etc. This allows the external sales service to receive customer feedback in real time and improve the service.

[0077] Generative AI will also expand its image generation services to other industries, such as the advertising and entertainment industries. For example, Generative AI will expand its image generation services to the advertising industry, providing a service that generates images for advertising. For example, images to be used in advertising campaigns are automatically generated using Generative AI. Industries include, for example, the advertising, entertainment, and manufacturing industries. This will enable the image generation service to be expanded to other industries.

[0078] The emotion estimation function provides an image generation service based on the emotions of external customers, thereby improving customer satisfaction. The emotion estimation function, for example, builds a system that provides an image generation service based on the emotions of external customers. For example, an optimal image is generated based on the customer's emotion score. This makes it possible to provide an image generation service based on the emotions of external customers, thereby improving customer satisfaction.

[0079] Generative AI automatically generates planning proposals specialized for different markets and regions. For example, generative AI builds a system that automatically generates planning proposals specialized for different markets and regions. For example, it makes proposals according to market needs and regional characteristics. Markets and regions include, for example, the domestic market, overseas markets, and specific regions. This makes it possible to automatically generate planning proposals specialized for different markets and regions using generative AI.

[0080] Generative AI automatically generates visual prototypes of proposed plans, facilitating visual understanding. Generative AI develops systems that automatically generate visual prototypes of proposed plans. For example, they visually display product designs and service flows. Visual prototypes include, for example, 3D models, wireframes, and mockups. This allows for the automatic generation of visual prototypes of proposed plans, facilitating visual understanding.

[0081] The emotion estimation function monitors users' emotional reactions to new project proposals and continuously improves the quality of proposals. For example, we will develop a system that monitors users' emotional reactions to new project proposals in real time. For example, we will analyze the user's facial expressions and voice and calculate an emotion score. This will allow us to use the emotion estimation function to monitor users' emotional reactions to new project proposals and continuously improve the quality of proposals.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The image generation system may further include a health management unit that monitors the user's health condition. For example, the health management unit may measure the user's heart rate and stress level and adjust the image generation process based on this. If the user is in a high-stress state, it may generate an image with a relaxing effect. The health management unit may also record the user's health data over the long term and optimize image generation in response to changes in the user's health condition. This allows for image generation that is tailored to the user's health condition, enabling a more personalized service to be provided.

[0084] The image generation system may further include a preference learning unit that learns the user's preferences. For example, the preference learning unit analyzes images and prompts selected by the user in the past to learn the user's preferences. Based on this, the system can preferentially generate images in the user's preferred style or theme. The preference learning unit can also quickly respond to changes in the user's preferences and provide images based on the new preferences. This enables the generation of images optimized to the user's preferences, thereby improving user satisfaction.

[0085] The image generation system may further include an emotion adjustment unit that estimates the user's emotion and adjusts the color tone and composition of the image based on the estimated emotion. For example, the emotion adjustment unit analyzes the emotion of the prompt input by the user and generates an image with bright color tones if the emotion is positive, and generates an image with muted color tones if the emotion is negative. The emotion adjustment unit may also change the composition of the image according to the user's emotion to provide an image that better matches the emotion. This makes it possible to generate an image according to the user's emotion, thereby providing a more appropriate image.

[0086] The image generation system can also include a progress management unit that monitors the progress of a user's project. For example, the progress management unit grasps the progress of a user's project in real time and generates images according to the progress. In the early stages of a project, it generates images suitable for idea generation, and in the final stages, it generates images suitable for presentations. The progress management unit can also suggest adjustments to necessary resources and schedules as the project progresses. This makes it possible to generate images according to the project's progress, supporting efficient project management.

[0087] The image generation system may further include a theme suggestion unit that estimates the user's emotion and suggests an image theme or style based on the estimated emotion. For example, the theme suggestion unit analyzes the emotion of the prompt entered by the user, and suggests a bright and cheerful themed image if the emotion is positive, and suggests a calm themed image if the emotion is negative. The theme suggestion unit may also change the style of the image according to the user's emotion to provide an image that better matches the emotion. This makes it possible to generate images with themes and styles that correspond to the user's emotion, thereby providing more appropriate images.

[0088] The image generation system may further include an environment management unit that monitors the user's work environment. For example, the environment management unit may measure the brightness and volume of the user's work environment and adjust the image generation process based on this. If the user is working in a quiet environment, the environment management unit may generate images that have a relaxing effect, and if the user is working in a noisy environment, the environment management unit may generate images that enhance concentration. The environment management unit may also automatically adjust the image generation settings in response to changes in the user's work environment. This allows for image generation that is tailored to the user's work environment, providing a more comfortable work environment.

[0089] The image generation system may further include a filtering unit that estimates the user's emotion and filters images based on the estimated emotion. For example, the filtering unit may analyze the emotion of the prompt input by the user and prioritize displaying images with bright colors if the emotion is positive, and images with muted colors if the emotion is negative. The filtering unit may also narrow down image options according to the user's emotion and provide images that better match the emotion. This enables filtering of images according to the user's emotion, thereby providing more appropriate images.

[0090] The image generation system may further include a purpose adaptation unit that generates images according to the purpose of the user's project. For example, the purpose adaptation unit analyzes the purpose of the prompt entered by the user and generates images according to the purpose, such as for presentations, marketing, or education. The purpose adaptation unit may also automatically adjust image generation settings according to the progress of the user's project or changes in the purpose. This makes it possible to generate images according to the purpose of the user's project, thereby providing more effective images.

[0091] The image generation system may further include a layout adjustment unit that estimates the user's emotion and adjusts the arrangement and layout of images based on the estimated emotion. For example, the layout adjustment unit analyzes the emotion of the prompt entered by the user and proposes an open layout if the emotion is positive and a calm layout if the emotion is negative. The layout adjustment unit can also change the arrangement of images according to the user's emotion and provide a layout that better matches the emotion. This enables the arrangement and layout of images to be tailored to the user's emotion, thereby providing more appropriate images.

[0092] The image generation system can also include a schedule management unit that generates images according to the user's project schedule. For example, the schedule management unit grasps the user's project schedule in real time and generates images according to the schedule. In the early stages of the project, it generates images suitable for idea generation, and in the final stages, it generates images suitable for presentations. The schedule management unit can also propose adjustments to the necessary resources and schedule as the project progresses. This makes it possible to generate images according to the project schedule, supporting efficient project management.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The prompt input unit receives an instruction from the user. For example, the prompt input unit can receive the instruction from the user in the form of text input, voice input, gesture input, or the like. Step 2: The image generation unit generates an image using image generation AI. For example, the image generation unit receives a prompt containing instructions from the user as input, and generates an image based on the prompt. The generation AI can use technologies such as GAN (generative adversarial network) and VAE (variational autoencoder). Step 3: The cost reduction unit evaluates the cost reduction effect using the image generated by the image generation unit. For example, the cost reduction unit evaluates the cost reduction effect based on criteria such as time reduction, material cost reduction, and labor cost reduction.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image generation unit that generates an image using an image generation AI; a prompt input unit for receiving instructions from a user; a cost reduction unit that evaluates the cost reduction effect using the image generated by the image generation unit. A system characterized by:

2. The image generation unit Emotion estimation function that generates an image according to the user's emotion 2. The system of claim 1.

3. The generated AI is Automatically generate the layout and overall design of proposals and meeting materials 2. The system of claim 1.

4. The generated AI is Equipped with an algorithm that optimizes operational costs, further enhancing the cost reduction effect.

2. The system of claim 1.

5. The generated AI is Incorporating an emotion estimation function to propose ways to avoid copyright risks based on the user's emotions 2. The system of claim 1.

6. The generated AI is Incorporating emotion estimation functionality to provide image generation services that respond to the emotions of external customers 2. The system of claim 1.

7. The idea is Combining ideas from different industries and applications to discover new market needs 2. The system of claim 1.

8. The emotion estimation function is When proposing a new project, a personalized proposal is made based on the user's emotions.

2. The system of claim 1.

Images