System

Abstract

An object of the system according to the embodiment is to easily materialize an image generated by a user.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, a determination unit, and a delivery unit. The reception unit uploads an image from a user. The analysis unit analyzes the image uploaded by the reception unit. The suggestion unit suggests a printing method based on the image analyzed by the analysis unit. The determination unit selects the printing method proposed by the proposal unit and determines the order. The delivery unit delivers the printed matter based on the order determined by the determination 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 techniques have had the problem that the process of actualizing a user-generated image is complicated and difficult to execute easily.

[0005] The system according to the embodiment aims to easily actualize user-generated images. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, a confirmation unit, and a delivery unit. The reception unit uploads images from a user. The analysis unit analyzes the images uploaded by the reception unit. The proposal unit proposes a printing method based on the images analyzed by the analysis unit. The confirmation unit selects the printing method proposed by the proposal unit and confirms the order. The delivery unit delivers printed materials based on the order confirmed by the confirmation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows for easy physicalization of user-generated images. [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 actualization system according to an embodiment of the present invention is a system that actualizes an image created by a user using a generative AI. The image actualization system allows the user to upload an image created by the user using the generative AI, analyzes it, proposes the optimal printing method, confirms the order, and delivers the printed matter. For example, the image actualization system allows the user to upload an image created by the user using the generative AI. The image actualization system then analyzes the uploaded image and proposes the optimal printing method. For example, the system may propose printing methods such as canvas prints, photo books, and posters based on the image resolution and color tone. The image actualization system then allows the user to select from the proposed printing methods and confirm the order. Once the order is confirmed, the image is actualized using a high-quality printing machine and delivered to the address specified by the user. This allows the user to see their favorite images on a daily basis and enjoy them as interior décor. The image actualization system allows the user to see their favorite images on a daily basis and enjoy them as interior décor. For example, by displaying them in a living room or office, they can add color to the space and express their individuality. They can also be used as gifts, making them ideal for special occasions.

[0029] An image actualization system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, a confirmation unit, and a delivery unit. The reception unit receives images from a user. Images uploaded by the user may be in file formats such as, but not limited to, JPEG, PNG, and GIF. The reception unit may upload images via a web interface or a mobile app. The analysis unit analyzes the uploaded images. The analysis unit analyzes, for example, the resolution and color of the images. For example, the analysis unit may evaluate the image resolution using DPI (dots per inch) or the number of pixels. The analysis unit may also evaluate the color of the images using RGB values ​​and color temperature. The proposal unit proposes an optimal printing method based on the image analyzed by the analysis unit. The proposal unit proposes printing methods such as canvas prints, photo books, and posters. For example, the proposal unit may propose a photo book if the image resolution is low and a canvas print if the image resolution is high. The confirmation unit selects the printing method proposed by the proposal unit and confirms the order. The confirmation unit may confirm, for example, a payment method and a shipping address based on the printing method selected by the user. The delivery unit delivers the printed matter based on the order confirmed by the confirmation unit. For example, the delivery unit delivers the printed matter to an address specified by the user. In this way, the image actualization system according to the embodiment can easily actualize an image created by a user using the generation AI, making it possible for the user to see the image on a daily basis.

[0030] The analysis unit can analyze the resolution or color of an image. For example, the analysis unit evaluates the image resolution in terms of DPI (dots per inch) or the number of pixels. For example, the analysis unit checks whether the image resolution is 300 DPI or higher. The analysis unit can also evaluate the color of an image in terms of RGB values ​​or color temperature. For example, the analysis unit checks whether the RGB values ​​are within an appropriate range. By analyzing the image resolution and color, the optimal printing method can be proposed.

[0031] The suggestion unit can suggest printing methods for canvas prints, photo books, and posters. For example, the suggestion unit suggests canvas prints when the image resolution is high. For example, the suggestion unit suggests canvas prints for images with a resolution of 300 DPI or higher. The suggestion unit can also suggest photo books when the image resolution is low. For example, the suggestion unit suggests photo books for images with a resolution of 150 DPI or lower. The suggestion unit can also suggest printing methods for posters. For example, the suggestion unit suggests posters for images with a resolution of 200 DPI or higher. This makes it easier to create actual images by suggesting the optimal printing method to the user.

[0032] The confirmation unit can perform a process to confirm the user's order. For example, the confirmation unit can confirm the payment method and delivery address based on the printing method selected by the user. For example, the confirmation unit can confirm that the user will pay using a credit card, debit card, or electronic money. The confirmation unit can also confirm the delivery address specified by the user. For example, the confirmation unit can confirm that the postal code and address format entered by the user are correct. This allows the user to easily confirm the order.

[0033] The delivery unit can deliver printed materials to an address specified by the user. For example, the delivery unit delivers printed materials to the address specified by the user via a delivery company. For example, the delivery unit delivers printed materials via a delivery company selected by the user (e.g., a post office, a courier service, etc.). The delivery unit can also specify a delivery time. For example, the delivery unit can deliver printed materials according to a delivery time slot desired by the user (e.g., morning, afternoon, night, etc.). Furthermore, the delivery unit can provide a method for tracking the delivery. For example, the delivery unit can provide the user with a delivery tracking number so that the delivery status can be checked in real time. This improves convenience by delivering printed materials to an address specified by the user.

[0034] The reception unit can analyze the user's past upload history and select the optimal upload method. For example, the reception unit preferentially suggests upload methods that the user has frequently used in the past. For example, the reception unit analyzes the user's past upload history and suggests the most frequently used method. The reception unit can also suggest the most efficient upload method from the user's past upload history. For example, the reception unit analyzes the date and time of past uploads and file formats to select the optimal method. The reception unit can also select the optimal method based on upload methods that the user has used successfully in the past. For example, the reception unit analyzes the success rate of past uploads and suggests the method with the highest success rate. In this way, the optimal upload method can be suggested based on the user's past history.

[0035] The reception unit can filter images based on the user's current project or area of ​​interest when uploading images. For example, the reception unit suggests that the user upload only images related to the project currently in progress. For example, the reception unit identifies a project based on the user's input information and past behavior history, and suggests related images. The reception unit can also prioritize uploading related images based on the user's area of ​​interest. For example, the reception unit can analyze the user's past search history and browsing history, and suggest images related to the area of ​​interest. The reception unit can also filter appropriate images according to the progress of the user's project. For example, the reception unit can analyze the progress of the project and prioritize suggesting images related to the project currently in progress. This allows images according to the user's project or area of ​​interest to be uploaded preferentially.

[0036] When uploading an image, the reception unit can select the optimal upload means depending on the user's input method. For example, when the user uses voice input, the reception unit uploads the image using voice recognition technology. For example, the reception unit analyzes the user's voice, converts it into text using voice recognition technology, and uploads the image. Furthermore, when the user uses text input, the reception unit can also upload the image using text analysis technology. For example, the reception unit analyzes the user's text input and uploads related images. Furthermore, when the user uses image input, the reception unit can also upload the image using image recognition technology. For example, the reception unit analyzes the image uploaded by the user, extracts related information, and uploads it. This makes it possible to provide the optimal upload means depending on the user's input method.

[0037] When uploading images, the reception unit can prioritize uploading highly relevant images taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes uploading images related to that area. For example, the reception unit acquires geographical location information based on the user's GPS data or IP address and suggests images related to that area. Furthermore, if the user is traveling, the reception unit can prioritize uploading images related to the user's travel destination. For example, the reception unit suggests related images based on information about the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize uploading images related to the user's home. For example, the reception unit suggests related images based on location information about the user's home. This allows highly relevant images to be uploaded based on the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity when uploading an image and upload related images. For example, the reception unit prioritizes uploading images that the user has shared on social media. For example, the reception unit analyzes the user's social media account and suggests uploading based on the shared images. The reception unit can also analyze the content of the user's posts on social media and upload related images. For example, the reception unit analyzes the content of the user's posts and suggests related images. The reception unit can also upload related images by referring to the activity of the user's friends on social media. For example, the reception unit suggests related images based on images shared by the user's friends. This makes it possible to upload related images based on the user's social media activity.

[0039] The reception unit can customize the upload method by reflecting the user's past feedback when uploading an image. The reception unit, for example, suggests the optimal upload method based on feedback provided by the user in the past. For example, the reception unit analyzes the user's past feedback and suggests the method with the highest rating. The reception unit can also improve the upload procedure based on the user's past feedback. For example, the reception unit improves the upload interface based on the user's feedback. The reception unit can also customize the upload interface by reflecting the user's feedback. For example, the reception unit changes the interface settings based on the user's preferences. This makes it possible to provide the optimal upload method based on the user's past feedback.

[0040] When analyzing an image, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. For example, the analysis unit performs a detailed analysis on an image with a high level of importance. For example, the analysis unit evaluates the importance of an image based on a user's evaluation or an analysis result, and performs a detailed analysis on an image with a high level of importance. The analysis unit can also perform a concise analysis on an image with a low level of importance. For example, the analysis unit provides a concise analysis result for an image with a low level of importance. The analysis unit can also determine the priority of the analysis according to the importance of the image. For example, the analysis unit prioritizes the analysis of images with a high level of importance. This makes it possible to adjust the level of detail of the analysis according to the importance of the image.

[0041] When analyzing an image, the analysis unit can apply different analysis algorithms depending on the category of the image. For example, the analysis unit applies a color analysis algorithm to a landscape image. For example, the analysis unit performs color analysis on the landscape image to evaluate color balance and contrast. The analysis unit can also apply a face recognition algorithm to a portrait image. For example, the analysis unit performs face recognition on the portrait image to evaluate facial features and expressions. The analysis unit can also apply a style analysis algorithm to an art image. For example, the analysis unit performs style analysis on the art image to evaluate the features and style of the art. This allows the optimal analysis algorithm to be applied depending on the image category.

[0042] When analyzing an image, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis data and optimizes the parameters of the algorithm. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit reduces the error rate of the analysis based on the past analysis results. The analysis unit can also determine the priority of the analysis by referring to the user's past analysis results. For example, the analysis unit prioritizes analyses with higher importance based on the past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0043] When analyzing images, the analysis unit can determine the priority of analysis based on the time of submission of the images. The analysis unit, for example, prioritizes analyzing the most recent images. For example, the analysis unit prioritizes the most recent images based on the submission date and time of the images. The analysis unit can also postpone images that have been submitted earlier. For example, the analysis unit postpones images that have been submitted earlier. The analysis unit can also determine the priority of analysis based on the time of submission. For example, the analysis unit prioritizes analyzing images that have been submitted more recently. This makes it possible to determine the priority of analysis based on the time of submission of the images.

[0044] When analyzing images, the analysis unit can adjust the order of analysis based on the relevance of the images. The analysis unit, for example, prioritizes analyzing highly relevant images. For example, the analysis unit evaluates the relevance based on the similarity of the image content or related keywords, and prioritizes highly relevant images. The analysis unit can also postpone images with low relevance. For example, the analysis unit postpones images with low relevance. The analysis unit can also adjust the order of analysis based on the relevance of the images. For example, the analysis unit prioritizes analyzing highly relevant images. This makes it possible to adjust the order of analysis based on the relevance of the images.

[0045] When analyzing an image, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses detailed technical terminology for users with high levels of expertise. For example, the analysis unit evaluates the user's level of expertise based on the user's occupation and past usage history and uses technical terminology accordingly. The analysis unit can also use concise terminology for users with low levels of expertise. For example, the analysis unit avoids technical terminology and uses concise expressions for users with low levels of expertise. The analysis unit can also adjust the technical terminology used in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0046] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the image. For example, the suggestion unit makes a detailed proposal for an image with high importance. For example, the suggestion unit evaluates the importance of an image based on a user's evaluation or an analysis result, and makes a detailed proposal for an image with high importance. The suggestion unit can also make a concise proposal for an image with low importance. For example, the suggestion unit provides a concise proposal for an image with low importance. The suggestion unit can also determine the priority of the proposal according to the importance of the image. For example, the suggestion unit preferentially suggests images with high importance. This makes it possible to adjust the level of detail of the proposal according to the importance of the image.

[0047] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the image. For example, the suggestion unit applies a color suggestion algorithm to a landscape image. For example, the suggestion unit makes color suggestions for the landscape image and evaluates color balance and contrast. The suggestion unit can also apply a face recognition suggestion algorithm to a portrait image. For example, the suggestion unit performs face recognition on the portrait image and evaluates facial features and expressions. The suggestion unit can also apply a style suggestion algorithm to an art image. For example, the suggestion unit makes style suggestions for the art image and evaluates the features and style of the art. This makes it possible to apply the optimal suggestion algorithm depending on the image category.

[0048] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, adjusts the proposal algorithm based on the user's past proposal results. For example, the suggestion unit analyzes the user's past proposal data and optimizes algorithm parameters. The suggestion unit can also improve the accuracy of the proposal based on the user's past proposal results. For example, the suggestion unit reduces the error rate of the proposal based on the past proposal results. The suggestion unit can also determine the priority of the proposal by referring to the user's past proposal results. For example, the suggestion unit prioritizes proposals with higher importance based on the past proposal results. This makes it possible to improve the accuracy of the proposal based on the user's past proposal results.

[0049] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the image. For example, the suggestion unit preferentially suggests the most recent image. For example, the suggestion unit prioritizes the most recent image based on the submission date and time of the image. The suggestion unit can also postpone images that have been submitted earlier. For example, the suggestion unit postpones images that have been submitted earlier. The suggestion unit can also determine the priority of the proposal based on the time of submission. For example, the suggestion unit preferentially suggests images that have been submitted more recently. This makes it possible to determine the priority of the proposal based on the time of submission of the image.

[0050] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the images. For example, the suggestion unit preferentially suggests highly relevant images. For example, the suggestion unit evaluates the relevance based on the similarity of the content of the images or related keywords, and prioritizes highly relevant images. The suggestion unit can also postpone images with low relevance. For example, the suggestion unit postpones images with low relevance. The suggestion unit can also adjust the order of suggestions based on the relevance of the images. For example, the suggestion unit preferentially suggests images with high relevance. This makes it possible to adjust the order of suggestions based on the relevance of the images.

[0051] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit uses detailed technical terminology for a user with high level of expertise. For example, the suggestion unit evaluates the level of expertise based on the user's occupation and past usage history and uses technical terminology. The suggestion unit can also use concise terminology for a user with low level of expertise. For example, the suggestion unit avoids technical terminology and uses concise expressions for a user with low level of expertise. The suggestion unit can also adjust the technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit adjusts the way the suggestion result is expressed according to the user's level of expertise. This makes it possible to provide optimal suggestions according to the user's level of expertise.

[0052] When an order is confirmed, the confirmation unit can analyze the user's past order history and select the optimal confirmation method. For example, the confirmation unit prioritizes and suggests confirmation methods that the user has used in the past. For example, the confirmation unit analyzes the user's past order history and suggests the most frequently used method. The confirmation unit can also suggest the most efficient confirmation method from the user's past order history. For example, the confirmation unit analyzes past order dates and order contents to select the optimal method. The confirmation unit can also select the optimal method based on confirmation methods that have been successful for the user in the past. For example, the confirmation unit analyzes past order success rates and suggests the method with the highest success rate. This makes it possible to provide the optimal confirmation method based on the user's past order history.

[0053] The confirmation unit can customize the confirmation procedure based on the user's current living situation when confirming an order. For example, if the user is busy, the confirmation unit provides a simple confirmation procedure. For example, if the confirmation unit determines that the user is busy based on the user's lifestyle or daily behavioral patterns, the confirmation unit provides a simple confirmation procedure. The confirmation unit can also provide a detailed confirmation procedure if the user is relaxed. For example, if the confirmation unit determines that the user is relaxed based on the user's behavioral patterns, the confirmation unit can also customize the confirmation procedure according to the user's living situation. For example, the confirmation unit changes the settings of the confirmation procedure based on the user's preferences. This makes it possible to provide an optimal confirmation procedure according to the user's living situation.

[0054] The confirmation unit can improve the confirmation method by reflecting user feedback when confirming an order. For example, the confirmation unit suggests an optimal confirmation method based on feedback provided by the user in the past. For example, the confirmation unit analyzes the user's past feedback and suggests the method with the highest rating. The confirmation unit can also improve the confirmation procedure based on the user's past feedback. For example, the confirmation unit improves the confirmation interface based on the user's feedback. The confirmation unit can also customize the confirmation interface by reflecting the user's feedback. For example, the confirmation unit changes the interface settings based on the user's preferences. This makes it possible to provide an optimal confirmation method based on the user's feedback.

[0055] When confirming an order, the confirmation unit can select the optimal confirmation method taking into account the user's geographical location information. For example, if the user is in a specific area, the confirmation unit prioritizes suggesting a confirmation method related to that area. For example, the confirmation unit obtains geographical location information based on the user's GPS data or IP address and suggests a confirmation method related to that area. Furthermore, if the user is traveling, the confirmation unit can prioritize suggesting a confirmation method related to the user's travel destination. For example, the confirmation unit can suggest a related confirmation method based on information about the user's travel destination. Furthermore, if the user is at home, the confirmation unit can prioritize suggesting a confirmation method related to the user's home. For example, the confirmation unit suggests a related confirmation method based on location information about the user's home. This makes it possible to provide the optimal confirmation method based on the user's geographical location information.

[0056] The confirmation unit can analyze the user's social media activity and suggest a confirmation method when confirming an order. For example, the confirmation unit prioritizes confirming order details shared by the user on social media. For example, the confirmation unit analyzes the user's social media account and suggests confirmation based on the shared order details. The confirmation unit can also analyze the user's social media posts and suggest related confirmation methods. For example, the confirmation unit analyzes the user's posts and suggests related confirmation methods. The confirmation unit can also suggest related confirmation methods by referring to the activities of the user's friends on social media. For example, the confirmation unit suggests related confirmation methods based on the order details shared by the user's friends. This makes it possible to provide an optimal confirmation method based on the user's social media activity.

[0057] The confirmation unit can customize the confirmation method by reflecting the user's past feedback when confirming an order. The confirmation unit, for example, suggests an optimal confirmation method based on feedback provided by the user in the past. For example, the confirmation unit analyzes the user's past feedback and suggests the method with the highest rating. The confirmation unit can also improve the confirmation procedure based on the user's past feedback. For example, the confirmation unit improves the confirmation interface based on the user's feedback. The confirmation unit can also customize the confirmation interface by reflecting the user's feedback. For example, the confirmation unit changes the interface settings based on the user's preferences. This makes it possible to provide an optimal confirmation method based on the user's past feedback.

[0058] At the time of delivery, the delivery unit can analyze the user's past delivery history and select the optimal delivery method. For example, the delivery unit prioritizes suggesting delivery methods that the user has used in the past. For example, the delivery unit analyzes the user's past delivery history and suggests the most frequently used method. The delivery unit can also suggest the most efficient delivery method from the user's past delivery history. For example, the delivery unit analyzes past delivery dates and delivery contents to select the optimal method. The delivery unit can also select the optimal method based on delivery methods that have been successful for the user in the past. For example, the delivery unit analyzes past delivery success rates and suggests the method with the highest success rate. This makes it possible to provide the optimal delivery method based on the user's past delivery history.

[0059] The delivery unit can customize the delivery means based on the user's current living situation at the time of delivery. For example, if the user is busy, the delivery unit suggests quick delivery. For example, the delivery unit suggests quick delivery when it determines that the user is busy based on the user's lifestyle and daily behavior patterns. The delivery unit can also suggest regular delivery when the user is relaxed. For example, the delivery unit suggests regular delivery when it determines that the user is relaxed based on the user's behavior patterns. The delivery unit can also customize the delivery means according to the user's living situation. For example, the delivery unit changes the delivery means settings based on the user's preferences. This makes it possible to provide the optimal delivery means according to the user's living situation.

[0060] The delivery unit can improve the delivery method by reflecting user feedback at the time of delivery. The delivery unit, for example, proposes the optimal delivery method based on feedback provided by the user in the past. For example, the delivery unit analyzes the user's past feedback and proposes the method with the highest rating. The delivery unit can also improve the delivery procedure based on the user's past feedback. For example, the delivery unit improves the delivery interface based on the user's feedback. The delivery unit can also customize the delivery interface by reflecting user feedback. For example, the delivery unit changes the interface settings based on the user's preferences. This makes it possible to provide the optimal delivery method based on the user's feedback.

[0061] The delivery unit can select the optimal delivery method at the time of delivery by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the delivery unit prioritizes suggesting delivery methods related to that area. For example, the delivery unit obtains geographical location information based on the user's GPS data or IP address and suggests delivery methods related to that area. Furthermore, if the user is traveling, the delivery unit can prioritize suggesting delivery methods related to the user's travel destination. For example, the delivery unit suggests relevant delivery methods based on information about the user's travel destination. Furthermore, if the user is at home, the delivery unit can prioritize suggesting delivery methods related to the user's home. For example, the delivery unit suggests relevant delivery methods based on location information about the user's home. This makes it possible to provide the optimal delivery method based on the user's geographical location information.

[0062] The delivery unit can analyze the user's social media activity and suggest a delivery method at the time of delivery. For example, the delivery unit prioritizes delivery content shared by the user on social media. For example, the delivery unit analyzes the user's social media account and suggests delivery based on the shared delivery content. The delivery unit can also analyze the content posted by the user on social media and suggest a related delivery method. For example, the delivery unit analyzes the content posted by the user and suggests a related delivery method. The delivery unit can also suggest a related delivery method by referring to the activity of the user's friends on social media. For example, the delivery unit suggests a related delivery method based on delivery content shared by the user's friends. This makes it possible to provide the optimal delivery method based on the user's social media activity.

[0063] The delivery unit can customize the delivery method by reflecting the user's past feedback at the time of delivery. The delivery unit, for example, suggests the optimal delivery method based on feedback provided by the user in the past. For example, the delivery unit analyzes the user's past feedback and suggests the method with the highest rating. The delivery unit can also improve the delivery procedure based on the user's past feedback. For example, the delivery unit improves the delivery interface based on the user's feedback. The delivery unit can also customize the delivery interface by reflecting the user's feedback. For example, the delivery unit changes the interface settings based on the user's preferences. This makes it possible to provide the optimal delivery method based on the user's past feedback.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The reception unit can analyze the user's past upload history and select the optimal upload method. For example, the reception unit can prioritize and suggest upload methods that the user has used frequently in the past. For example, the reception unit can analyze the user's past upload history and suggest the most frequently used method. The reception unit can also suggest the most efficient upload method from the user's past upload history. For example, the reception unit can analyze the date and time of past uploads and file formats to select the optimal method. The reception unit can also select the optimal method based on upload methods that the user has used successfully in the past. For example, the reception unit can analyze the past upload success rate and suggest the method with the highest success rate. In this way, the optimal upload method can be suggested based on the user's past history.

[0066] When analyzing an image, the analysis unit can apply different analysis algorithms depending on the category of the image. For example, a color analysis algorithm can be applied to a landscape image. For example, the analysis unit can perform color analysis on the landscape image to evaluate color balance and contrast. The analysis unit can also apply a face recognition algorithm to a portrait image. For example, the analysis unit can perform face recognition on the portrait image to evaluate facial features and expressions. The analysis unit can also apply a style analysis algorithm to an art image. For example, the analysis unit can perform style analysis on the art image to evaluate the features and style of the art. This allows the optimal analysis algorithm to be applied depending on the image category.

[0067] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the image. For example, a detailed suggestion is made for an image with high importance. For example, the suggestion unit evaluates the importance of an image based on a user's evaluation or an analysis result, and makes a detailed suggestion for an image with high importance. The suggestion unit can also make a concise suggestion for an image with low importance. For example, the suggestion unit provides a concise suggestion for an image with low importance. The suggestion unit can also determine the priority of the suggestion according to the importance of the image. For example, the suggestion unit preferentially suggests images with high importance. This makes it possible to adjust the level of detail of the suggestion according to the importance of the image.

[0068] When an order is confirmed, the confirmation unit can analyze the user's past order history and select the optimal confirmation method. For example, the confirmation unit can prioritize and suggest confirmation methods that the user has used in the past. For example, the confirmation unit can analyze the user's past order history and suggest the most frequently used method. The confirmation unit can also suggest the most efficient confirmation method from the user's past order history. For example, the confirmation unit can analyze past order dates and order contents to select the optimal method. The confirmation unit can also select the optimal method based on confirmation methods that have been successful for the user in the past. For example, the confirmation unit can analyze past order success rates and suggest the method with the highest success rate. This makes it possible to provide the optimal confirmation method based on the user's past order history.

[0069] At the time of delivery, the delivery unit can analyze the user's past delivery history and select the optimal delivery method. For example, the delivery unit prioritizes the suggestion of delivery methods that the user has used in the past. For example, the delivery unit analyzes the user's past delivery history and suggests the most frequently used method. The delivery unit can also suggest the most efficient delivery method from the user's past delivery history. For example, the delivery unit analyzes past delivery dates and delivery contents to select the optimal method. The delivery unit can also select the optimal method based on delivery methods that have been successful for the user in the past. For example, the delivery unit analyzes past delivery success rates and suggests the method with the highest success rate. This makes it possible to provide the optimal delivery method based on the user's past delivery history.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The reception unit receives an image from the user. The image uploaded by the user may be in a file format such as, but not limited to, JPEG, PNG, or GIF. The reception unit may upload the image through, for example, a web interface or a mobile app. Step 2: The analysis unit analyzes the uploaded image. For example, the analysis unit analyzes the image's resolution and color. Specifically, the image resolution can be evaluated in terms of DPI (dots per inch) or pixel count, and the color can be evaluated in terms of RGB values ​​or color temperature. Step 3: The suggestion unit suggests the optimal printing method based on the image analyzed by the analysis unit. The suggestion unit suggests printing methods such as canvas print, photo book, poster, etc. If the image resolution is low, the suggestion unit can suggest a photo book, and if the image resolution is high, the suggestion unit can suggest a canvas print. Step 4: The confirmation unit selects the printing method proposed by the suggestion unit and confirms the order. The confirmation unit confirms the payment method and delivery address based on the printing method selected by the user. Step 5: The delivery unit delivers the printed matter based on the order confirmed by the confirmation unit. The delivery unit delivers the printed matter to the address specified by the user.

[0072] (Example 2) An image actualization system according to an embodiment of the present invention is a system that actualizes an image created by a user using a generative AI. The image actualization system allows the user to upload an image created by the user using the generative AI, analyzes it, proposes the optimal printing method, confirms the order, and delivers the printed matter. For example, the image actualization system allows the user to upload an image created by the user using the generative AI. The image actualization system then analyzes the uploaded image and proposes the optimal printing method. For example, the system may propose printing methods such as canvas prints, photo books, and posters based on the image resolution and color tone. The image actualization system then allows the user to select from the proposed printing methods and confirm the order. Once the order is confirmed, the image is actualized using a high-quality printing machine and delivered to the address specified by the user. This allows the user to see their favorite images on a daily basis and enjoy them as interior décor. The image actualization system allows the user to see their favorite images on a daily basis and enjoy them as interior décor. For example, by displaying them in a living room or office, they can add color to the space and express their individuality. They can also be used as gifts, making them ideal for special occasions.

[0073] An image actualization system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, a confirmation unit, and a delivery unit. The reception unit receives images from a user. Images uploaded by the user may be in file formats such as, but not limited to, JPEG, PNG, and GIF. The reception unit may upload images via a web interface or a mobile app. The analysis unit analyzes the uploaded images. The analysis unit analyzes, for example, the resolution and color of the images. For example, the analysis unit may evaluate the image resolution using DPI (dots per inch) or the number of pixels. The analysis unit may also evaluate the color of the images using RGB values ​​and color temperature. The proposal unit proposes an optimal printing method based on the image analyzed by the analysis unit. The proposal unit proposes printing methods such as canvas prints, photo books, and posters. For example, the proposal unit may propose a photo book if the image resolution is low and a canvas print if the image resolution is high. The confirmation unit selects the printing method proposed by the proposal unit and confirms the order. The confirmation unit may confirm, for example, a payment method and a shipping address based on the printing method selected by the user. The delivery unit delivers the printed matter based on the order confirmed by the confirmation unit. For example, the delivery unit delivers the printed matter to an address specified by the user. In this way, the image actualization system according to the embodiment can easily actualize an image created by a user using the generation AI, making it possible for the user to see the image on a daily basis.

[0074] The analysis unit can analyze the resolution or color of an image. For example, the analysis unit evaluates the image resolution in terms of DPI (dots per inch) or the number of pixels. For example, the analysis unit checks whether the image resolution is 300 DPI or higher. The analysis unit can also evaluate the color of an image in terms of RGB values ​​or color temperature. For example, the analysis unit checks whether the RGB values ​​are within an appropriate range. By analyzing the image resolution and color, the optimal printing method can be proposed.

[0075] The suggestion unit can suggest printing methods for canvas prints, photo books, and posters. For example, the suggestion unit suggests canvas prints when the image resolution is high. For example, the suggestion unit suggests canvas prints for images with a resolution of 300 DPI or higher. The suggestion unit can also suggest photo books when the image resolution is low. For example, the suggestion unit suggests photo books for images with a resolution of 150 DPI or lower. The suggestion unit can also suggest printing methods for posters. For example, the suggestion unit suggests posters for images with a resolution of 200 DPI or higher. This makes it easier to create actual images by suggesting the optimal printing method to the user.

[0076] The confirmation unit can perform a process to confirm the user's order. For example, the confirmation unit can confirm the payment method and delivery address based on the printing method selected by the user. For example, the confirmation unit can confirm that the user will pay using a credit card, debit card, or electronic money. The confirmation unit can also confirm the delivery address specified by the user. For example, the confirmation unit can confirm that the postal code and address format entered by the user are correct. This allows the user to easily confirm the order.

[0077] The delivery unit can deliver printed materials to an address specified by the user. For example, the delivery unit delivers printed materials to the address specified by the user via a delivery company. For example, the delivery unit delivers printed materials via a delivery company selected by the user (e.g., a post office, a courier service, etc.). The delivery unit can also specify a delivery time. For example, the delivery unit can deliver printed materials according to a delivery time slot desired by the user (e.g., morning, afternoon, night, etc.). Furthermore, the delivery unit can provide a method for tracking the delivery. For example, the delivery unit can provide the user with a delivery tracking number so that the delivery status can be checked in real time. This improves convenience by delivering printed materials to an address specified by the user.

[0078] The reception unit can analyze the user's emotions and adjust the timing of image uploads based on the analyzed user emotions. For example, if the user is relaxed, the reception unit sends a notification prompting the user to upload an image. For example, the reception unit analyzes the user's facial expressions using a camera and sends a notification if it determines that the user is relaxed. The reception unit can also suggest postponing uploading if the user is feeling stressed. For example, the reception unit analyzes the user's voice and makes a suggestion if it determines that the user is feeling stressed. The reception unit can also send a notification prompting the user to upload immediately if the user is excited. For example, the reception unit analyzes the user's biometric data (heart rate and electrodermal activity) and sends a notification if it determines that the user is excited. This allows images to be uploaded at the optimal timing depending on the user's emotions. Emotion estimation is realized 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.

[0079] The reception unit can analyze the user's past upload history and select the optimal upload method. For example, the reception unit preferentially suggests upload methods that the user has frequently used in the past. For example, the reception unit analyzes the user's past upload history and suggests the most frequently used method. The reception unit can also suggest the most efficient upload method from the user's past upload history. For example, the reception unit analyzes the date and time of past uploads and file formats to select the optimal method. The reception unit can also select the optimal method based on upload methods that the user has used successfully in the past. For example, the reception unit analyzes the success rate of past uploads and suggests the method with the highest success rate. In this way, the optimal upload method can be suggested based on the user's past history.

[0080] The reception unit can filter images based on the user's current project or area of ​​interest when uploading images. For example, the reception unit suggests that the user upload only images related to the project currently in progress. For example, the reception unit identifies a project based on the user's input information and past behavior history, and suggests related images. The reception unit can also prioritize uploading related images based on the user's area of ​​interest. For example, the reception unit can analyze the user's past search history and browsing history, and suggest images related to the area of ​​interest. The reception unit can also filter appropriate images according to the progress of the user's project. For example, the reception unit can analyze the progress of the project and prioritize suggesting images related to the project currently in progress. This allows images according to the user's project or area of ​​interest to be uploaded preferentially.

[0081] When uploading an image, the reception unit can select the optimal upload means depending on the user's input method. For example, when the user uses voice input, the reception unit uploads the image using voice recognition technology. For example, the reception unit analyzes the user's voice, converts it into text using voice recognition technology, and uploads the image. Furthermore, when the user uses text input, the reception unit can also upload the image using text analysis technology. For example, the reception unit analyzes the user's text input and uploads related images. Furthermore, when the user uses image input, the reception unit can also upload the image using image recognition technology. For example, the reception unit analyzes the image uploaded by the user, extracts related information, and uploads it. This makes it possible to provide the optimal upload means depending on the user's input method.

[0082] The reception unit can estimate the user's emotions and determine the priority of images to be uploaded based on the estimated user emotions. For example, if the user is relaxed, the reception unit prioritizes uploading images with lower importance. For example, the reception unit analyzes the user's facial expressions using a camera and prioritizes images with lower importance if it determines that the user is relaxed. The reception unit can also prioritize uploading images with higher importance if the user is stressed. For example, the reception unit analyzes the user's voice and prioritizes images with higher importance if it determines that the user is stressed. The reception unit can also prioritize uploading the most recent images if the user is excited. For example, the reception unit analyzes the user's biometric data (heart rate and electrodermal activity) and prioritizes the most recent images if it determines that the user is excited. This allows the priority of images to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0083] When uploading images, the reception unit can prioritize uploading highly relevant images taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes uploading images related to that area. For example, the reception unit acquires geographical location information based on the user's GPS data or IP address and suggests images related to that area. Furthermore, if the user is traveling, the reception unit can prioritize uploading images related to the user's travel destination. For example, the reception unit suggests related images based on information about the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize uploading images related to the user's home. For example, the reception unit suggests related images based on location information about the user's home. This allows highly relevant images to be uploaded based on the user's geographical location information.

[0084] The reception unit can analyze the user's social media activity when uploading an image and upload related images. For example, the reception unit prioritizes uploading images that the user has shared on social media. For example, the reception unit analyzes the user's social media account and suggests uploading based on the shared images. The reception unit can also analyze the content of the user's posts on social media and upload related images. For example, the reception unit analyzes the content of the user's posts and suggests related images. The reception unit can also upload related images by referring to the activity of the user's friends on social media. For example, the reception unit suggests related images based on images shared by the user's friends. This makes it possible to upload related images based on the user's social media activity.

[0085] The reception unit can customize the upload method by reflecting the user's past feedback when uploading an image. The reception unit, for example, suggests the optimal upload method based on feedback provided by the user in the past. For example, the reception unit analyzes the user's past feedback and suggests the method with the highest rating. The reception unit can also improve the upload procedure based on the user's past feedback. For example, the reception unit improves the upload interface based on the user's feedback. The reception unit can also customize the upload interface by reflecting the user's feedback. For example, the reception unit changes the interface settings based on the user's preferences. This makes it possible to provide the optimal upload method based on the user's past feedback.

[0086] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, the analysis unit provides detailed analysis results when the user is relaxed. For example, the analysis unit analyzes the user's facial expressions using a camera and provides detailed analysis results when it determines that the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. For example, the analysis unit analyzes the user's voice and provides concise analysis results when it determines that the user is stressed. The analysis unit can also provide visually appealing analysis results when the user is excited. For example, the analysis unit analyzes the user's biometric data (heart rate and electrodermal activity) and provides visually appealing analysis results when it determines that the user is excited. This allows the system to provide optimal analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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.

[0087] When analyzing an image, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. For example, the analysis unit performs a detailed analysis on an image with a high level of importance. For example, the analysis unit evaluates the importance of an image based on a user's evaluation or an analysis result, and performs a detailed analysis on an image with a high level of importance. The analysis unit can also perform a concise analysis on an image with a low level of importance. For example, the analysis unit provides a concise analysis result for an image with a low level of importance. The analysis unit can also determine the priority of the analysis according to the importance of the image. For example, the analysis unit prioritizes the analysis of images with a high level of importance. This makes it possible to adjust the level of detail of the analysis according to the importance of the image.

[0088] When analyzing an image, the analysis unit can apply different analysis algorithms depending on the category of the image. For example, the analysis unit applies a color analysis algorithm to a landscape image. For example, the analysis unit performs color analysis on the landscape image to evaluate color balance and contrast. The analysis unit can also apply a face recognition algorithm to a portrait image. For example, the analysis unit performs face recognition on the portrait image to evaluate facial features and expressions. The analysis unit can also apply a style analysis algorithm to an art image. For example, the analysis unit performs style analysis on the art image to evaluate the features and style of the art. This allows the optimal analysis algorithm to be applied depending on the image category.

[0089] When analyzing an image, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. For example, the analysis unit analyzes the user's past analysis data and optimizes the parameters of the algorithm. The analysis unit can also improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit reduces the error rate of the analysis based on the past analysis results. The analysis unit can also determine the priority of the analysis by referring to the user's past analysis results. For example, the analysis unit prioritizes analyses with higher importance based on the past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit provides a detailed analysis, for example, when the user is relaxed. For example, the analysis unit analyzes the user's facial expressions using a camera and provides a detailed analysis result when it determines that the user is relaxed. The analysis unit can also provide a concise analysis when the user is stressed. For example, the analysis unit analyzes the user's voice and provides a concise analysis result when it determines that the user is stressed. The analysis unit can also provide a visually appealing analysis when the user is excited. For example, the analysis unit analyzes the user's biometric data (heart rate and electrodermal activity) and provides a visually appealing analysis result when it determines that the user is excited. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] When analyzing images, the analysis unit can determine the priority of analysis based on the time of submission of the images. The analysis unit, for example, prioritizes analyzing the most recent images. For example, the analysis unit prioritizes the most recent images based on the submission date and time of the images. The analysis unit can also postpone images that have been submitted earlier. For example, the analysis unit postpones images that have been submitted earlier. The analysis unit can also determine the priority of analysis based on the time of submission. For example, the analysis unit prioritizes analyzing images that have been submitted more recently. This makes it possible to determine the priority of analysis based on the time of submission of the images.

[0092] When analyzing images, the analysis unit can adjust the order of analysis based on the relevance of the images. The analysis unit, for example, prioritizes analyzing highly relevant images. For example, the analysis unit evaluates the relevance based on the similarity of the image content or related keywords, and prioritizes highly relevant images. The analysis unit can also postpone images with low relevance. For example, the analysis unit postpones images with low relevance. The analysis unit can also adjust the order of analysis based on the relevance of the images. For example, the analysis unit prioritizes analyzing highly relevant images. This makes it possible to adjust the order of analysis based on the relevance of the images.

[0093] When analyzing an image, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, the analysis unit uses detailed technical terminology for users with high levels of expertise. For example, the analysis unit evaluates the user's level of expertise based on the user's occupation and past usage history and uses technical terminology accordingly. The analysis unit can also use concise terminology for users with low levels of expertise. For example, the analysis unit avoids technical terminology and uses concise expressions for users with low levels of expertise. The analysis unit can also adjust the technical terminology used in the analysis according to the user's level of expertise. For example, the analysis unit adjusts the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide optimal analysis results according to the user's level of expertise.

[0094] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, the suggestion unit provides detailed suggestions when the user is relaxed. For example, the suggestion unit analyzes the user's facial expressions using a camera and provides detailed suggestions when it determines that the user is relaxed. The suggestion unit can also provide concise suggestions when the user is stressed. For example, the suggestion unit analyzes the user's voice and provides concise suggestions when it determines that the user is stressed. The suggestion unit can also provide visually appealing suggestions when the user is excited. For example, the suggestion unit analyzes the user's biometric data (heart rate and electrodermal activity) and provides visually appealing suggestions when it determines that the user is excited. This allows optimal suggestions to be provided according to 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.

[0095] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the image. For example, the suggestion unit makes a detailed proposal for an image with high importance. For example, the suggestion unit evaluates the importance of an image based on a user's evaluation or an analysis result, and makes a detailed proposal for an image with high importance. The suggestion unit can also make a concise proposal for an image with low importance. For example, the suggestion unit provides a concise proposal for an image with low importance. The suggestion unit can also determine the priority of the proposal according to the importance of the image. For example, the suggestion unit preferentially suggests images with high importance. This makes it possible to adjust the level of detail of the proposal according to the importance of the image.

[0096] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the image. For example, the suggestion unit applies a color suggestion algorithm to a landscape image. For example, the suggestion unit makes color suggestions for the landscape image and evaluates color balance and contrast. The suggestion unit can also apply a face recognition suggestion algorithm to a portrait image. For example, the suggestion unit performs face recognition on the portrait image and evaluates facial features and expressions. The suggestion unit can also apply a style suggestion algorithm to an art image. For example, the suggestion unit makes style suggestions for the art image and evaluates the features and style of the art. This makes it possible to apply the optimal suggestion algorithm depending on the image category.

[0097] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, adjusts the proposal algorithm based on the user's past proposal results. For example, the suggestion unit analyzes the user's past proposal data and optimizes algorithm parameters. The suggestion unit can also improve the accuracy of the proposal based on the user's past proposal results. For example, the suggestion unit reduces the error rate of the proposal based on the past proposal results. The suggestion unit can also determine the priority of the proposal by referring to the user's past proposal results. For example, the suggestion unit prioritizes proposals with higher importance based on the past proposal results. This makes it possible to improve the accuracy of the proposal based on the user's past proposal results.

[0098] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, the suggestion unit provides detailed suggestions when the user is relaxed. For example, the suggestion unit analyzes the user's facial expressions using a camera and provides detailed suggestions when it determines that the user is relaxed. The suggestion unit can also provide concise suggestions when the user is stressed. For example, the suggestion unit analyzes the user's voice and provides concise suggestions when it determines that the user is stressed. The suggestion unit can also provide visually appealing suggestions when the user is excited. For example, the suggestion unit analyzes the user's biometric data (heart rate and electrodermal activity) and provides visually appealing suggestions when it determines that the user is excited. This allows the length of the suggestions to be adjusted according to 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.

[0099] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the image. For example, the suggestion unit preferentially suggests the most recent image. For example, the suggestion unit prioritizes the most recent image based on the submission date and time of the image. The suggestion unit can also postpone images that have been submitted earlier. For example, the suggestion unit postpones images that have been submitted earlier. The suggestion unit can also determine the priority of the proposal based on the time of submission. For example, the suggestion unit preferentially suggests images that have been submitted more recently. This makes it possible to determine the priority of the proposal based on the time of submission of the image.

[0100] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the images. For example, the suggestion unit preferentially suggests highly relevant images. For example, the suggestion unit evaluates the relevance based on the similarity of the content of the images or related keywords, and prioritizes highly relevant images. The suggestion unit can also postpone images with low relevance. For example, the suggestion unit postpones images with low relevance. The suggestion unit can also adjust the order of suggestions based on the relevance of the images. For example, the suggestion unit preferentially suggests images with high relevance. This makes it possible to adjust the order of suggestions based on the relevance of the images.

[0101] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit uses detailed technical terminology for a user with high level of expertise. For example, the suggestion unit evaluates the level of expertise based on the user's occupation and past usage history and uses technical terminology. The suggestion unit can also use concise terminology for a user with low level of expertise. For example, the suggestion unit avoids technical terminology and uses concise expressions for a user with low level of expertise. The suggestion unit can also adjust the technical terminology in the suggestion according to the user's level of expertise. For example, the suggestion unit adjusts the way the suggestion result is expressed according to the user's level of expertise. This makes it possible to provide optimal suggestions according to the user's level of expertise.

[0102] The confirmation unit can estimate the user's emotions and adjust the order confirmation method based on the estimated user emotions. For example, the confirmation unit provides detailed confirmation instructions when the user is relaxed. For example, the confirmation unit analyzes the user's facial expressions using a camera and provides detailed confirmation instructions when it determines that the user is relaxed. The confirmation unit can also provide concise confirmation instructions when the user is stressed. For example, the confirmation unit analyzes the user's voice and provides concise confirmation instructions when it determines that the user is stressed. The confirmation unit can also provide visually appealing confirmation instructions when the user is excited. For example, the confirmation unit analyzes the user's biometric data (heart rate and electrodermal activity) and provides visually appealing confirmation instructions when it determines that the user is excited. This makes it possible to provide an optimal order confirmation method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] When an order is confirmed, the confirmation unit can analyze the user's past order history and select the optimal confirmation method. For example, the confirmation unit prioritizes and suggests confirmation methods that the user has used in the past. For example, the confirmation unit analyzes the user's past order history and suggests the most frequently used method. The confirmation unit can also suggest the most efficient confirmation method from the user's past order history. For example, the confirmation unit analyzes past order dates and order contents to select the optimal method. The confirmation unit can also select the optimal method based on confirmation methods that have been successful for the user in the past. For example, the confirmation unit analyzes past order success rates and suggests the method with the highest success rate. This makes it possible to provide the optimal confirmation method based on the user's past order history.

[0104] The confirmation unit can customize the confirmation procedure based on the user's current living situation when confirming an order. For example, if the user is busy, the confirmation unit provides a simple confirmation procedure. For example, if the confirmation unit determines that the user is busy based on the user's lifestyle or daily behavioral patterns, the confirmation unit provides a simple confirmation procedure. The confirmation unit can also provide a detailed confirmation procedure if the user is relaxed. For example, if the confirmation unit determines that the user is relaxed based on the user's behavioral patterns, the confirmation unit can also customize the confirmation procedure according to the user's living situation. For example, the confirmation unit changes the settings of the confirmation procedure based on the user's preferences. This makes it possible to provide an optimal confirmation procedure according to the user's living situation.

[0105] The confirmation unit can improve the confirmation method by reflecting user feedback when confirming an order. For example, the confirmation unit suggests an optimal confirmation method based on feedback provided by the user in the past. For example, the confirmation unit analyzes the user's past feedback and suggests the method with the highest rating. The confirmation unit can also improve the confirmation procedure based on the user's past feedback. For example, the confirmation unit improves the confirmation interface based on the user's feedback. The confirmation unit can also customize the confirmation interface by reflecting the user's feedback. For example, the confirmation unit changes the interface settings based on the user's preferences. This makes it possible to provide an optimal confirmation method based on the user's feedback.

[0106] The confirmation unit can estimate the user's emotions and determine the priority of order confirmation based on the estimated user emotions. For example, if the user is relaxed, the confirmation unit prioritizes orders of lower importance. For example, the confirmation unit analyzes the user's facial expressions using a camera and prioritizes orders of lower importance if it determines that the user is relaxed. The confirmation unit can also prioritize orders of higher importance if the user is stressed. For example, the confirmation unit analyzes the user's voice and prioritizes orders of higher importance if it determines that the user is stressed. The confirmation unit can also prioritize the most recent order if the user is excited. For example, the confirmation unit analyzes the user's biometric data (heart rate and electrodermal activity) and prioritizes the most recent order if it determines that the user is excited. This allows the priority of order confirmation to be determined according to the user's emotions. Emotion estimation is realized 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.

[0107] When confirming an order, the confirmation unit can select the optimal confirmation method taking into account the user's geographical location information. For example, if the user is in a specific area, the confirmation unit prioritizes suggesting a confirmation method related to that area. For example, the confirmation unit obtains geographical location information based on the user's GPS data or IP address and suggests a confirmation method related to that area. Furthermore, if the user is traveling, the confirmation unit can prioritize suggesting a confirmation method related to the user's travel destination. For example, the confirmation unit can suggest a related confirmation method based on information about the user's travel destination. Furthermore, if the user is at home, the confirmation unit can prioritize suggesting a confirmation method related to the user's home. For example, the confirmation unit suggests a related confirmation method based on location information about the user's home. This makes it possible to provide the optimal confirmation method based on the user's geographical location information.

[0108] The confirmation unit can analyze the user's social media activity and suggest a confirmation method when confirming an order. For example, the confirmation unit prioritizes confirming order details shared by the user on social media. For example, the confirmation unit analyzes the user's social media account and suggests confirmation based on the shared order details. The confirmation unit can also analyze the user's social media posts and suggest related confirmation methods. For example, the confirmation unit analyzes the user's posts and suggests related confirmation methods. The confirmation unit can also suggest related confirmation methods by referring to the activities of the user's friends on social media. For example, the confirmation unit suggests related confirmation methods based on the order details shared by the user's friends. This makes it possible to provide an optimal confirmation method based on the user's social media activity.

[0109] The confirmation unit can customize the confirmation method by reflecting the user's past feedback when confirming an order. The confirmation unit, for example, suggests an optimal confirmation method based on feedback provided by the user in the past. For example, the confirmation unit analyzes the user's past feedback and suggests the method with the highest rating. The confirmation unit can also improve the confirmation procedure based on the user's past feedback. For example, the confirmation unit improves the confirmation interface based on the user's feedback. The confirmation unit can also customize the confirmation interface by reflecting the user's feedback. For example, the confirmation unit changes the interface settings based on the user's preferences. This makes it possible to provide an optimal confirmation method based on the user's past feedback.

[0110] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated user emotions. For example, the delivery unit suggests regular delivery if the user is relaxed. For example, the delivery unit analyzes the user's facial expressions using a camera and suggests regular delivery if it determines that the user is relaxed. The delivery unit can also suggest expedited delivery if the user is stressed. For example, the delivery unit analyzes the user's voice and suggests expedited delivery if it determines that the user is stressed. The delivery unit can also suggest special delivery options if the user is excited. For example, the delivery unit analyzes the user's biometric data (heart rate and electrodermal activity) and suggests special delivery options if it determines that the user is excited. This makes it possible to provide the optimal delivery method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] At the time of delivery, the delivery unit can analyze the user's past delivery history and select the optimal delivery method. For example, the delivery unit prioritizes suggesting delivery methods that the user has used in the past. For example, the delivery unit analyzes the user's past delivery history and suggests the most frequently used method. The delivery unit can also suggest the most efficient delivery method from the user's past delivery history. For example, the delivery unit analyzes past delivery dates and delivery contents to select the optimal method. The delivery unit can also select the optimal method based on delivery methods that have been successful for the user in the past. For example, the delivery unit analyzes past delivery success rates and suggests the method with the highest success rate. This makes it possible to provide the optimal delivery method based on the user's past delivery history.

[0112] The delivery unit can customize the delivery means based on the user's current living situation at the time of delivery. For example, if the user is busy, the delivery unit suggests quick delivery. For example, the delivery unit suggests quick delivery when it determines that the user is busy based on the user's lifestyle and daily behavior patterns. The delivery unit can also suggest regular delivery when the user is relaxed. For example, the delivery unit suggests regular delivery when it determines that the user is relaxed based on the user's behavior patterns. The delivery unit can also customize the delivery means according to the user's living situation. For example, the delivery unit changes the delivery means settings based on the user's preferences. This makes it possible to provide the optimal delivery means according to the user's living situation.

[0113] The delivery unit can improve the delivery method by reflecting user feedback at the time of delivery. The delivery unit, for example, proposes the optimal delivery method based on feedback provided by the user in the past. For example, the delivery unit analyzes the user's past feedback and proposes the method with the highest rating. The delivery unit can also improve the delivery procedure based on the user's past feedback. For example, the delivery unit improves the delivery interface based on the user's feedback. The delivery unit can also customize the delivery interface by reflecting user feedback. For example, the delivery unit changes the interface settings based on the user's preferences. This makes it possible to provide the optimal delivery method based on the user's feedback.

[0114] The delivery unit can estimate the user's emotions and determine delivery priorities based on the estimated user emotions. For example, if the user is relaxed, the delivery unit prioritizes deliveries of lower importance. For example, the delivery unit analyzes the user's facial expressions using a camera and prioritizes deliveries of lower importance if it determines that the user is relaxed. The delivery unit can also prioritize deliveries of higher importance if the user is stressed. For example, the delivery unit analyzes the user's voice and prioritizes deliveries of higher importance if it determines that the user is stressed. The delivery unit can also prioritize the most recent deliveries if the user is excited. For example, the delivery unit analyzes the user's biometric data (heart rate and electrodermal activity) and prioritizes the most recent deliveries if it determines that the user is excited. This allows delivery priorities to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] The delivery unit can select the optimal delivery method at the time of delivery by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the delivery unit prioritizes suggesting delivery methods related to that area. For example, the delivery unit obtains geographical location information based on the user's GPS data or IP address and suggests delivery methods related to that area. Furthermore, if the user is traveling, the delivery unit can prioritize suggesting delivery methods related to the user's travel destination. For example, the delivery unit suggests relevant delivery methods based on information about the user's travel destination. Furthermore, if the user is at home, the delivery unit can prioritize suggesting delivery methods related to the user's home. For example, the delivery unit suggests relevant delivery methods based on location information about the user's home. This makes it possible to provide the optimal delivery method based on the user's geographical location information.

[0116] The delivery unit can analyze the user's social media activity and suggest a delivery method at the time of delivery. For example, the delivery unit prioritizes delivery content shared by the user on social media. For example, the delivery unit analyzes the user's social media account and suggests delivery based on the shared delivery content. The delivery unit can also analyze the content posted by the user on social media and suggest a related delivery method. For example, the delivery unit analyzes the content posted by the user and suggests a related delivery method. The delivery unit can also suggest a related delivery method by referring to the activity of the user's friends on social media. For example, the delivery unit suggests a related delivery method based on delivery content shared by the user's friends. This makes it possible to provide the optimal delivery method based on the user's social media activity.

[0117] The delivery unit can customize the delivery method by reflecting the user's past feedback at the time of delivery. The delivery unit, for example, suggests the optimal delivery method based on feedback provided by the user in the past. For example, the delivery unit analyzes the user's past feedback and suggests the method with the highest rating. The delivery unit can also improve the delivery procedure based on the user's past feedback. For example, the delivery unit improves the delivery interface based on the user's feedback. The delivery unit can also customize the delivery interface by reflecting the user's feedback. For example, the delivery unit changes the interface settings based on the user's preferences. This makes it possible to provide the optimal delivery method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, determination unit, and delivery 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 uploads an image through the reception device 38 of the smart device 14. The analysis unit analyzes the resolution and color of the image using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the optimal printing method using the specific processing unit 290 of the data processing device 12. The determination unit confirms the order using the control unit 46A of the smart device 14. The delivery unit manages the delivery of printed materials using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, determination unit, and delivery 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 uploads an image by voice command through the microphone 238 of the smart glasses 214. The analysis unit analyzes the resolution and color of the image using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the optimal printing method using the specific processing unit 290 of the data processing device 12. The determination unit confirms the order using the control unit 46A of the smart glasses 214. The delivery unit manages the delivery of the printed matter using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, determination unit, and delivery unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit uploads an image by voice command through the microphone 238 of the headset terminal 314. The analysis unit analyzes the resolution and color of the image using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the optimal printing method using the specific processing unit 290 of the data processing device 12. The determination unit confirms the order using the control unit 46A of the headset terminal 314. The delivery unit manages the delivery of printed materials using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, determination unit, and delivery unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit uploads an image by voice command through the microphone 238 of the robot 414. The analysis unit analyzes the resolution and color of the image using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the optimal printing method using the specific processing unit 290 of the data processing device 12. The determination unit confirms the order using the control unit 46A of the robot 414. The delivery unit manages the delivery of printed materials using the specific processing unit 290 of the data processing device 12.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The reception unit can analyze the user's past upload history and select the optimal upload method. For example, the reception unit can prioritize and suggest upload methods that the user has used frequently in the past. For example, the reception unit can analyze the user's past upload history and suggest the most frequently used method. The reception unit can also suggest the most efficient upload method from the user's past upload history. For example, the reception unit can analyze the date and time of past uploads and file formats to select the optimal method. The reception unit can also select the optimal method based on upload methods that the user has used successfully in the past. For example, the reception unit can analyze the past upload success rate and suggest the method with the highest success rate. In this way, the optimal upload method can be suggested based on the user's past history.

[0120] When analyzing an image, the analysis unit can apply different analysis algorithms depending on the category of the image. For example, a color analysis algorithm can be applied to a landscape image. For example, the analysis unit can perform color analysis on the landscape image to evaluate color balance and contrast. The analysis unit can also apply a face recognition algorithm to a portrait image. For example, the analysis unit can perform face recognition on the portrait image to evaluate facial features and expressions. The analysis unit can also apply a style analysis algorithm to an art image. For example, the analysis unit can perform style analysis on the art image to evaluate the features and style of the art. This allows the optimal analysis algorithm to be applied depending on the image category.

[0121] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the image. For example, a detailed suggestion is made for an image with high importance. For example, the suggestion unit evaluates the importance of an image based on a user's evaluation or an analysis result, and makes a detailed suggestion for an image with high importance. The suggestion unit can also make a concise suggestion for an image with low importance. For example, the suggestion unit provides a concise suggestion for an image with low importance. The suggestion unit can also determine the priority of the suggestion according to the importance of the image. For example, the suggestion unit preferentially suggests images with high importance. This makes it possible to adjust the level of detail of the suggestion according to the importance of the image.

[0122] When an order is confirmed, the confirmation unit can analyze the user's past order history and select the optimal confirmation method. For example, the confirmation unit can prioritize and suggest confirmation methods that the user has used in the past. For example, the confirmation unit can analyze the user's past order history and suggest the most frequently used method. The confirmation unit can also suggest the most efficient confirmation method from the user's past order history. For example, the confirmation unit can analyze past order dates and order contents to select the optimal method. The confirmation unit can also select the optimal method based on confirmation methods that have been successful for the user in the past. For example, the confirmation unit can analyze past order success rates and suggest the method with the highest success rate. This makes it possible to provide the optimal confirmation method based on the user's past order history.

[0123] At the time of delivery, the delivery unit can analyze the user's past delivery history and select the optimal delivery method. For example, the delivery unit prioritizes the suggestion of delivery methods that the user has used in the past. For example, the delivery unit analyzes the user's past delivery history and suggests the most frequently used method. The delivery unit can also suggest the most efficient delivery method from the user's past delivery history. For example, the delivery unit analyzes past delivery dates and delivery contents to select the optimal method. The delivery unit can also select the optimal method based on delivery methods that have been successful for the user in the past. For example, the delivery unit analyzes past delivery success rates and suggests the method with the highest success rate. This makes it possible to provide the optimal delivery method based on the user's past delivery history.

[0124] The reception unit can analyze the user's emotions and adjust the timing of image uploads based on the analyzed user emotions. For example, if the user is relaxed, it sends a notification encouraging the user to upload images. For example, the reception unit analyzes the user's facial expressions using a camera and sends a notification if it determines that the user is relaxed. The reception unit can also suggest postponing uploading if the user is feeling stressed. For example, the reception unit analyzes the user's voice and makes a suggestion if it determines that the user is feeling stressed. The reception unit can also send a notification encouraging the user to upload immediately if the user is excited. For example, the reception unit analyzes the user's biometric data (heart rate and electrodermal activity) and sends a notification if it determines that the user is excited. This allows images to be uploaded at the optimal timing depending on the user's emotions. Emotion estimation is realized 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.

[0125] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis result is provided. For example, the analysis unit analyzes the user's facial expressions using a camera and provides a detailed analysis result if it determines that the user is relaxed. The analysis unit can also provide a concise analysis result if the user is stressed. For example, the analysis unit analyzes the user's voice and provides a concise analysis result if it determines that the user is stressed. The analysis unit can also provide a visually appealing analysis result if the user is excited. For example, the analysis unit analyzes the user's biometric data (heart rate and electrodermal activity) and provides a visually appealing analysis result if it determines that the user is excited. This allows the system to provide optimal analysis results 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.

[0126] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is relaxed, detailed suggestions are provided. For example, the suggestion unit analyzes the user's facial expressions using a camera and provides detailed suggestions if it determines that the user is relaxed. The suggestion unit can also provide concise suggestions if the user is stressed. For example, the suggestion unit analyzes the user's voice and provides concise suggestions if it determines that the user is stressed. The suggestion unit can also provide visually appealing suggestions if the user is excited. For example, the suggestion unit analyzes the user's biometric data (heart rate and electrodermal activity) and provides visually appealing suggestions if it determines that the user is excited. This allows optimal suggestions to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0127] The confirmation unit can estimate the user's emotions and adjust the order confirmation method based on the estimated user emotions. For example, if the user is relaxed, detailed confirmation instructions are provided. For example, the confirmation unit analyzes the user's facial expressions using a camera and provides detailed confirmation instructions if it determines that the user is relaxed. The confirmation unit can also provide concise confirmation instructions if the user is stressed. For example, the confirmation unit analyzes the user's voice and provides concise confirmation instructions if it determines that the user is stressed. The confirmation unit can also provide visually appealing confirmation instructions if the user is excited. For example, the confirmation unit analyzes the user's biometric data (heart rate and electrodermal activity) and provides visually appealing confirmation instructions if it determines that the user is excited. This makes it possible to provide an optimal order confirmation method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0128] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated user emotions. For example, if the user is relaxed, it suggests regular delivery. For example, the delivery unit analyzes the user's facial expressions using a camera and suggests regular delivery if it determines that the user is relaxed. The delivery unit can also suggest expedited delivery if the user is stressed. For example, the delivery unit analyzes the user's voice and suggests expedited delivery if it determines that the user is stressed. The delivery unit can also suggest special delivery options if the user is excited. For example, the delivery unit analyzes the user's biometric data (heart rate and electrodermal activity) and suggests special delivery options if it determines that the user is excited. This makes it possible to provide the optimal delivery method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The reception unit receives an image from the user. The image uploaded by the user may be in a file format such as, but not limited to, JPEG, PNG, or GIF. The reception unit may upload the image through, for example, a web interface or a mobile app. Step 2: The analysis unit analyzes the uploaded image. For example, the analysis unit analyzes the image's resolution and color. Specifically, the image resolution can be evaluated in terms of DPI (dots per inch) or pixel count, and the color can be evaluated in terms of RGB values ​​or color temperature. Step 3: The suggestion unit suggests the optimal printing method based on the image analyzed by the analysis unit. The suggestion unit suggests printing methods such as canvas print, photo book, poster, etc. If the image resolution is low, the suggestion unit can suggest a photo book, and if the image resolution is high, the suggestion unit can suggest a canvas print. Step 4: The confirmation unit selects the printing method proposed by the suggestion unit and confirms the order. The confirmation unit confirms the payment method and delivery address based on the printing method selected by the user. Step 5: The delivery unit delivers the printed matter based on the order confirmed by the confirmation unit. The delivery unit delivers the printed matter to the address specified by the user.

[0131] 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.

[0132] 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 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.

[0133] 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.

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

[0136] 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.

[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 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.

[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 (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).

[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] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for uploading images from users; an analysis unit that analyzes the image uploaded by the reception unit; a proposal unit that proposes a printing method based on the image analyzed by the analysis unit; a confirmation unit that selects the printing method proposed by the proposal unit and confirms the order; a delivery unit that delivers printed materials based on the order confirmed by the confirmation unit. A system characterized by:

2. The analysis unit Analyze image resolution or color 2. The system of claim 1.

3. The proposal unit How to print canvas prints, photo books, and posters 2. The system of claim 1.

4. The determination unit Process to confirm user order 2. The system of claim 1.

5. The delivery unit Deliver printed materials to a user-specified address 2. The system of claim 1.

6. The reception unit Analyze user emotions and adjust the timing of image uploads based on the analyzed user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's upload history and select the optimal upload method 2. The system of claim 1.

8. The reception unit Filter images as they are uploaded based on the user's current project or area of ​​interest 2. The system of claim 1.

Images