System

Abstract

An object of the system according to the embodiment is to efficiently select and provide an image suitable for a business negotiation material or an internal report material.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a selection unit, and a provision unit. The reception unit receives an input from a user. The analysis unit analyzes the input content received by the reception unit. The selection unit selects an image based on the content analyzed by the analysis unit. The providing unit provides the image selected by the selecting unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to efficiently select images suitable for business negotiation materials or internal reports, and has also posed the risk of copyright issues.

[0005] The system according to the embodiment aims to efficiently select and provide images suitable for business negotiation materials and internal reporting materials. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a selecting unit, and a providing unit. The receiving unit receives input from a user. The analyzing unit analyzes the input content received by the receiving unit. The selecting unit selects an image based on the content analyzed by the analyzing unit. The providing unit provides the image selected by the selecting unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently select and provide images suitable for business negotiation materials and internal reporting materials. [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) In an embodiment of the present invention, a document creation support system automatically selects photos and illustrations as candidates when a user simply inputs key points of content they want to include in their document. The system allows users to input key points, and AI analyzes the input and automatically selects appropriate photos and illustrations. The AI ​​selects images from a license-free image database that does not infringe copyright, rather than from the web. For example, the user inputs specific content such as "new product features" or "market analysis results." The system then analyzes the input content using AI and selects appropriate photos and illustrations. For example, for the input "new product features," the system selects images of the new product and related illustrations. Based on the analysis results, the system automatically selects appropriate photos and illustrations from a license-free image database. For example, for the input "market analysis results," the system selects illustrations of graphs and charts. The selected photos and illustrations are presented to the user, who can choose the most appropriate one. This unifies the documents and makes them easier to communicate. This allows the document creation support system to allow users to easily create documents and effectively communicate what they want to say. Furthermore, since there is no risk of infringing copyright, images can be used with peace of mind. For example, the system can be used in a variety of business situations, such as for business negotiation materials and internal reports.

[0029] A material creation support system according to an embodiment includes a reception unit, an analysis unit, a selection unit, and a provision unit. The reception unit receives input from a user. The user input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, a keyboard input interface for receiving text input. The reception unit can also provide a microphone input interface for receiving voice input. The reception unit can also provide a camera interface for receiving image input. For example, the reception unit receives text input from a user and transmits it to the analysis unit. The analysis unit analyzes the input content received by the reception unit using AI. The analysis is performed using, for example, natural language processing technology, but is not limited to, this example. For example, the analysis unit analyzes the input text and extracts information for selecting appropriate photos and illustrations. The analysis unit can also analyze the input images using image analysis technology. The analysis unit can also analyze the input data using data mining technology. For example, the analysis unit extracts keywords from the input text and generates information for selecting appropriate photos or illustrations based on the keywords. The selection unit selects appropriate images based on the content analyzed by the analysis unit. The selection is performed, for example, based on a similarity measure or an algorithm used, but is not limited to such examples. For example, the selection unit selects appropriate photos or illustrations from a license-free image database. The selection unit may also manage a history of previously used images and use this information when selecting. Furthermore, the selection unit may provide information about newly added images to the providing unit so that the latest images can be selected. For example, the selection unit selects images from the license-free image database and transmits them to the providing unit. The providing unit provides the images selected by the selection unit to the user. The provision is performed, for example, by displaying the images on a screen or providing a download link, but is not limited to such examples. For example, the providing unit displays thumbnails of the selected images to allow the user to select the most appropriate one.The providing unit can also learn based on user feedback and improve analysis accuracy. Furthermore, the providing unit can estimate the user's emotions and adjust the display method of the images to be provided based on the estimated user emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. This allows the material creation support system according to the embodiment to automatically select and provide appropriate images based on the content input by the user.

[0030] The selection unit can select an image from a license-free image database. Examples of license-free image databases include, but are not limited to, Pixabay and Unsplash. The selection unit can select an appropriate image from, for example, Pixabay. The selection unit can also select an appropriate image from Unsplash. The selection unit can also select an appropriate image from another license-free image database. For example, the selection unit transmits an image selected from Pixabay to the providing unit. This can avoid the risk of infringing copyright by using license-free images. Some or all of the above-described processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can select an image using an AI model for selecting images from a license-free image database.

[0031] The providing unit presents the selected images to the user, allowing the user to select one. The presentation may be, for example, in a slideshow format or thumbnail display, but is not limited to these examples. The providing unit presents the selected images in a slideshow format, for example. The providing unit may also display the selected images as thumbnails, allowing the user to select the most suitable image. The providing unit may also display a list of the selected images, allowing the user to select from multiple images. For example, the providing unit may display thumbnails of the selected images, allowing the user to click on them to display details. This allows the user to select the most suitable image, thereby achieving uniformity of materials. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may present the images using an AI model for presenting the selected images.

[0032] The analysis unit can learn based on user feedback and improve analysis accuracy. Feedback is collected, for example, by a user survey or by analyzing a usage log, but is not limited to these examples. The analysis unit can collect feedback, for example, by using a user survey. The analysis unit can also collect feedback by analyzing a usage log. The analysis unit can also collect feedback based on the user's operation history. For example, the analysis unit can analyze the history of images selected by the user to improve analysis accuracy. In this way, analysis accuracy is improved by utilizing user feedback. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve analysis accuracy by using an AI model for learning based on feedback.

[0033] The selection unit can manage a history of images used in the past and use it when making a selection. The history can be managed using, for example, a database, but is not limited to such an example. For example, the selection unit stores a history of images used in the past in a database. The selection unit can also set a storage period for the history and delete the history after a certain period has passed. The selection unit can also select images based on the history. For example, the selection unit can refer to the history of images used in the past and select images of the same style if the materials are on the same theme. In this way, the accuracy of selection is improved by using the history of past images. Some or all of the above-mentioned processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can select images using an AI model for managing the history of images used in the past.

[0034] The selection unit can provide information about new images added to the providing unit when they are added, allowing the latest image to be selected. New images are added, for example, by periodic database updates or user uploads, but examples are not limited to these. For example, the selection unit can periodically update a license-free image database and add new images. The selection unit can also accept user uploads and add new images to the database. The selection unit can also provide information about new images added to the providing unit when they are added, allowing the latest image to be selected. For example, the selection unit can transmit information about new images added to the providing unit, allowing the providing unit to present the latest images to the user. This allows the latest image to be selected, allowing the latest information to be always provided. Some or all of the above-described processing in the selection unit can be performed, for example, using AI, or can be performed without using AI. For example, the selection unit can use an AI model to provide information about new images added to the providing unit, allowing the latest image to be selected.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display content that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest content that will be used in a specific time period based on the user's past input history. For example, the reception unit stores the user's past input history in a database and analyzes it. This makes it possible to suggest the optimal input method for the user by utilizing the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can suggest the optimal input method using an AI model for analyzing the past input history.

[0036] When receiving input, the reception unit can filter the input content based on the user's current project or area of ​​interest. For example, the reception unit prioritizes receiving content related to the project the user is currently working on. The reception unit can also automatically complete related keywords based on the user's area of ​​interest. The reception unit can also suggest related input content by referring to the user's past project history. For example, the reception unit acquires project information from the user's project management tool and filters the input content. This allows the input content to be filtered based on the user's project or area of ​​interest, thereby providing highly relevant information. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can provide highly relevant information using an AI model for filtering the input content based on project information.

[0037] The reception unit can select a reception means according to the user's input method when receiving input. For example, if the user selects voice input, the reception unit can receive the input content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also preferentially receive keyboard input. Furthermore, if the user selects image input, the reception unit can analyze the input content using image recognition technology. For example, the reception unit provides the optimal reception means according to the user's input method. This improves input efficiency by providing the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can receive the input content using an AI model for receiving input content using voice recognition technology.

[0038] When receiving input, the reception unit can prioritize receiving highly relevant input content by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving content related to that area. Furthermore, when the user is moving, the reception unit can also suggest related content based on the user's current location. Furthermore, when the user is in a specific location, the reception unit can prioritize receiving information related to that location. For example, the reception unit acquires the user's GPS data and filters the input content based on the geographical location information. This makes it possible to provide highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide highly relevant information using an AI model for filtering input content based on GPS data.

[0039] The reception unit may analyze the user's social media activity and receive related input content when receiving input. For example, the reception unit may suggest related input content based on content shared by the user on social media. The reception unit may also analyze the user's social media activity history and automatically complete related keywords. The reception unit may also suggest related input content based on the activities of the user's friends on social media. For example, the reception unit may acquire activity data from the user's social media account and filter the input content. This allows for providing highly relevant information based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may provide highly relevant information using an AI model for filtering input content based on social media activity.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the input interface. The reception unit can also adjust the priority of input content by referring to the user's feedback history. For example, the reception unit stores and analyzes the user's feedback data in a database. This allows the optimal reception method to be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide the optimal reception method by using an AI model for customizing the reception method based on feedback data.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input content. For example, the analysis unit performs a detailed analysis on important content. The analysis unit can also perform a simplified analysis on general content. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. For example, the analysis unit evaluates the importance of the content entered by the user and adjusts the level of detail of the analysis. This makes it possible to provide appropriate information by performing an analysis according to the importance of the input content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide appropriate information using an AI model for adjusting the level of detail of the analysis based on the importance.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input content. For example, the analysis unit can apply a specialized analysis algorithm to technical content. The analysis unit can also apply a simplified analysis algorithm to general content. The analysis unit can also select the optimal analysis algorithm based on the category specified by the user. For example, the analysis unit classifies the category of the input content and applies the corresponding analysis algorithm. This makes it possible to provide highly accurate analysis results by performing analysis according to the category of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide highly accurate analysis results by using an AI model for applying an analysis algorithm according to the category.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit performs a current analysis based on the user's past analysis results. The analysis unit can also analyze the user's past analysis history and optimize the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the user's past feedback. For example, the analysis unit stores the user's past analysis results in a database and analyzes them. This improves the accuracy of the analysis by referring to the past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide highly accurate analysis results by using an AI model for improving the accuracy of the analysis by referring to the past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the input content. For example, the analysis unit prioritizes analysis of urgent content. The analysis unit can also analyze general content with normal priority. The analysis unit can also adjust the priority of analysis based on the submission time specified by the user. For example, the analysis unit evaluates the submission time of the input content and determines the priority of analysis. By setting the priority based on the submission time, urgent content can be processed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use an AI model for determining the priority of analysis based on the submission time to process urgent content preferentially.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input content. For example, the analysis unit prioritizes analysis of highly relevant content. The analysis unit can also postpone analysis of less relevant content. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. For example, the analysis unit evaluates the relevance of the input content and adjusts the order of analysis. This enables efficient analysis by prioritizing analysis of highly relevant content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform efficient analysis using an AI model for adjusting the order of analysis based on relevance.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user is a general user, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented based on the user's level of expertise. For example, the analysis unit can evaluate the user's level of expertise and adjust the way in which the analysis results are presented. This improves understandability by providing analysis results that correspond to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide easy-to-understand analysis results using an AI model for adjusting the use of technical terms in the analysis according to the level of expertise.

[0047] The selection unit can improve the accuracy of selection based on the interrelationships between images during selection. For example, the selection unit groups related images and selects the most appropriate image. The selection unit can also analyze the interrelationships between images and preferentially select highly related images. The selection unit can also improve the accuracy of selection by taking the interrelationships between images into consideration. For example, the selection unit evaluates co-occurrence relationships between images and selects highly related images. In this way, the accuracy of selection is improved by taking the interrelationships between images into consideration. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can perform highly accurate image selection using an AI model for evaluating the interrelationships between images.

[0048] When selecting, the selection unit can make a selection based on attribute information of the image provider. For example, if the image provider is an expert, the selection unit can preferentially select the image. Furthermore, if the image provider is a general user, the selection unit can also preferentially select general images. Furthermore, the selection unit can select the optimal image by taking into account the attribute information of the image provider. For example, the selection unit evaluates the provider's occupation and past history and selects an appropriate image. In this way, an appropriate image can be selected by taking into account the provider's attribute information. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can select an appropriate image using an AI model for evaluating the provider's attribute information.

[0049] The selection unit can weight the selection based on the frequency of use of the image when making a selection. For example, the selection unit preferentially selects images that are used more frequently. The selection unit can also select images that are used less frequently afterward. The selection unit can also weight the selection based on the frequency of use of the image. For example, the selection unit evaluates the frequency of use of the image and weights it. As a result, appropriate images can be selected by weighting based on the frequency of use. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can select appropriate images using an AI model for weighting the selection based on the frequency of use.

[0050] The selection unit may select based on the geographic distribution of images. For example, if a user inputs content related to a specific region, the selection unit may preferentially select images related to that region. The selection unit may also analyze the geographic distribution of images and preferentially select highly relevant images. The selection unit may also select relevant images based on the user's current location. For example, the selection unit may evaluate the geographic distribution of images and select highly relevant images. This allows highly relevant images to be selected by taking geographic distribution into consideration. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may select highly relevant images using an AI model for selecting images based on geographic distribution.

[0051] The selection unit can improve the accuracy of the selection by referring to literature related to the image during selection. For example, the selection unit can refer to literature related to the image and select the most appropriate image. The selection unit can also analyze literature related to the image and preferentially select highly relevant images. The selection unit can also improve the accuracy of the selection by taking into account literature related to the image. For example, the selection unit evaluates related literature and selects the most appropriate image. As a result, the accuracy of the selection is improved by referring to the related literature. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can perform highly accurate image selection by using an AI model for selecting images by referring to related literature.

[0052] The selection unit can make a selection based on the market value of the image. For example, the selection unit preferentially selects images with high market value. The selection unit can also select images with low market value later. The selection unit can also weight the selection based on the market value of the image. For example, the selection unit evaluates and weights the market value of the image. This makes it possible to select an appropriate image by taking market value into consideration. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can select an appropriate image using an AI model for selecting images based on market value.

[0053] The providing unit can select a display method based on the user's past selection history when providing the display method. The providing unit provides an optimal display method, for example, based on a display method of an image previously selected by the user. The providing unit can also analyze the user's past selection history and optimize the display method. The providing unit can also adjust the display method by referring to the user's past feedback. For example, the providing unit stores and analyzes the user's selection history in a database. This makes it possible to provide an optimal display method by referring to the past selection history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide an optimal display method using an AI model for selecting a display method based on the past selection history.

[0054] The providing unit can customize the display content according to the user's current task when providing the display content. For example, when the user is giving a presentation, the providing unit provides a display method that is optimal for the presentation. Furthermore, when the user is creating a report, the providing unit can also provide a display method that is optimal for the report. Furthermore, the providing unit can customize the display content based on the user's current task. For example, the providing unit acquires task information of the user and customizes the display content. This enables efficient work by providing display content according to the current task. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can support efficient work by using an AI model for customizing the display content based on task information.

[0055] The providing unit can select a display method based on the user's device information when providing the display information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit acquires the user's device information and selects a display method. This improves visibility by providing a display method based on the device information. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can provide a highly visible display method using an AI model for selecting a display method based on device information.

[0056] The providing unit can select a display method based on the user's device information when providing the display information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit acquires the user's device information and selects a display method. This improves visibility by providing a display method based on the device information. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can provide a highly visible display method using an AI model for selecting a display method based on device information.

[0057] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. For example, the providing unit acquires the user's browser settings or OS language settings and makes the display content multilingual. This improves user convenience by providing multilingual display content. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can improve user convenience by using an AI model for making the display content multilingual based on the language setting.

[0058] The providing unit may analyze the user's social media activity and provide related information at the time of providing the information. For example, the providing unit may provide information about places where the user has checked in on social media. The providing unit may also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit may also provide information about related places and events by referring to the activities of the user's friends on social media. For example, the providing unit may obtain activity data from the user's social media account and provide related information. This improves user convenience by providing related information based on social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may improve user convenience by using an AI model for analyzing social media activity and providing related information.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can retrieve additional information from relevant external databases based on the user's input. For example, if the user inputs "market analysis results," the reception unit can retrieve related market reports and statistical data from external databases and send them to the analysis unit. Alternatively, if the user inputs "new product features," the reception unit can retrieve related technical literature and patent information. Furthermore, the reception unit can retrieve related news articles and blog posts based on the user's input and send them to the analysis unit. This improves the accuracy and depth of document creation by providing additional information related to the user's input.

[0061] The analysis unit can analyze related video content and select an appropriate thumbnail image based on the user's input. For example, if the user inputs "features of a new product," the analysis unit can analyze related product introduction videos and select the most appropriate thumbnail image. Also, if the user inputs "market analysis results," the analysis unit can analyze related market analysis videos and select appropriate thumbnail images of graphs and charts. Furthermore, the analysis unit can analyze related educational videos and tutorial videos based on the user's input and select appropriate thumbnail images. This allows the use of video content to improve the visual appeal of materials.

[0062] The analysis unit can analyze related audio content and select appropriate audio files based on user input. For example, if a user inputs "features of a new product," the analysis unit can analyze audio files introducing the related product and select appropriate portions. Alternatively, if a user inputs "market analysis results," the analysis unit can analyze audio files of related market analysis and select appropriate portions. Furthermore, the analysis unit can analyze audio files of related podcasts and interviews based on user input and select appropriate portions. This allows the utilization of audio content to increase the diversity and depth of materials.

[0063] The providing unit can generate and provide related infographics based on the user's input. For example, if the user inputs "market analysis results," the providing unit can generate and provide an infographic based on the related data. Also, if the user inputs "new product features," the providing unit can generate an infographic based on the related information. Furthermore, based on the content entered by the user, it can generate infographics that visually represent related statistical data and research results. This provides visually easy-to-understand information, improving the ease of understanding of the materials.

[0064] The reception unit can retrieve files from relevant cloud storage based on the user's input and send them to the analysis unit. For example, if a user inputs "features of a new product," the reception unit can retrieve related technical documents and blueprints from cloud storage and send them to the analysis unit. Also, if a user inputs "market analysis results," the reception unit can retrieve related data sets and reports from cloud storage. Furthermore, based on the user's input, the reception unit can retrieve related presentation materials and reports from cloud storage. This allows the use of cloud storage to improve the efficiency of document creation.

[0065] The selection unit can select relevant 3D models based on the user's input and send them to the provision unit. For example, if the user inputs "new product features," the selection unit can select relevant 3D models and send them to the provision unit. Also, if the user inputs "market analysis results," the selection unit can select relevant 3D graphs and charts. Furthermore, the selection unit can select relevant architectural or mechanical models based on the user's input. This allows the use of 3D models to improve the visual depth and ease of understanding of the materials.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives input from a user. The input from a user includes text input, voice input, image input, etc. For example, the reception unit provides a keyboard input interface for receiving text input, a microphone input interface for receiving voice input, and a camera interface for receiving image input. Step 2: The analysis unit analyzes the input content received by the reception unit. The analysis is performed using, for example, natural language processing technology, image analysis technology, or data mining technology. The analysis unit extracts keywords from the input text and generates information for selecting appropriate photos and illustrations based on those keywords. Step 3: The selection unit selects appropriate images based on the content analyzed by the analysis unit. The selection is based on a similarity measure and the algorithm used. For example, the selection unit selects appropriate photos or illustrations from a license-free image database. The selection unit can also manage a history of previously used images and use this information when making a selection. Step 4: The providing unit provides the images selected by the selecting unit to the user. The images are provided by a method such as displaying them on the screen or providing a download link. For example, the providing unit may display thumbnails of the selected images so that the user can select the most suitable one. The providing unit may also learn from user feedback and improve the accuracy of the analysis.

[0068] (Example 2) In an embodiment of the present invention, a document creation support system automatically selects photos and illustrations as candidates when a user simply inputs key points of content they want to include in their document. The system allows users to input key points, and AI analyzes the input and automatically selects appropriate photos and illustrations. The AI ​​selects images from a license-free image database that does not infringe copyright, rather than from the web. For example, the user inputs specific content such as "new product features" or "market analysis results." The system then analyzes the input content using AI and selects appropriate photos and illustrations. For example, for the input "new product features," the system selects images of the new product and related illustrations. Based on the analysis results, the system automatically selects appropriate photos and illustrations from a license-free image database. For example, for the input "market analysis results," the system selects illustrations of graphs and charts. The selected photos and illustrations are presented to the user, who can choose the most appropriate one. This unifies the documents and makes them easier to communicate. This allows the document creation support system to allow users to easily create documents and effectively communicate what they want to say. Furthermore, since there is no risk of infringing copyright, images can be used with peace of mind. For example, the system can be used in a variety of business situations, such as for business negotiation materials and internal reports.

[0069] A material creation support system according to an embodiment includes a reception unit, an analysis unit, a selection unit, and a provision unit. The reception unit receives input from a user. The user input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, a keyboard input interface for receiving text input. The reception unit can also provide a microphone input interface for receiving voice input. The reception unit can also provide a camera interface for receiving image input. For example, the reception unit receives text input from a user and transmits it to the analysis unit. The analysis unit analyzes the input content received by the reception unit using AI. The analysis is performed using, for example, natural language processing technology, but is not limited to, this example. For example, the analysis unit analyzes the input text and extracts information for selecting appropriate photos and illustrations. The analysis unit can also analyze the input images using image analysis technology. The analysis unit can also analyze the input data using data mining technology. For example, the analysis unit extracts keywords from the input text and generates information for selecting appropriate photos or illustrations based on the keywords. The selection unit selects appropriate images based on the content analyzed by the analysis unit. The selection is performed, for example, based on a similarity measure or an algorithm used, but is not limited to such examples. For example, the selection unit selects appropriate photos or illustrations from a license-free image database. The selection unit may also manage a history of previously used images and use this information when selecting. Furthermore, the selection unit may provide information about newly added images to the providing unit so that the latest images can be selected. For example, the selection unit selects images from the license-free image database and transmits them to the providing unit. The providing unit provides the images selected by the selection unit to the user. The provision is performed, for example, by displaying the images on a screen or providing a download link, but is not limited to such examples. For example, the providing unit displays thumbnails of the selected images to allow the user to select the most appropriate one.The providing unit can also learn based on user feedback and improve analysis accuracy. Furthermore, the providing unit can estimate the user's emotions and adjust the display method of the images to be provided based on the estimated user emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. This allows the material creation support system according to the embodiment to automatically select and provide appropriate images based on the content input by the user.

[0070] The selection unit can select an image from a license-free image database. Examples of license-free image databases include, but are not limited to, Pixabay and Unsplash. The selection unit can select an appropriate image from, for example, Pixabay. The selection unit can also select an appropriate image from Unsplash. The selection unit can also select an appropriate image from another license-free image database. For example, the selection unit transmits an image selected from Pixabay to the providing unit. This can avoid the risk of infringing copyright by using license-free images. Some or all of the above-described processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can select an image using an AI model for selecting images from a license-free image database.

[0071] The providing unit presents the selected images to the user, allowing the user to select one. The presentation may be, for example, in a slideshow format or thumbnail display, but is not limited to these examples. The providing unit presents the selected images in a slideshow format, for example. The providing unit may also display the selected images as thumbnails, allowing the user to select the most suitable image. The providing unit may also display a list of the selected images, allowing the user to select from multiple images. For example, the providing unit may display thumbnails of the selected images, allowing the user to click on them to display details. This allows the user to select the most suitable image, thereby achieving uniformity of materials. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may present the images using an AI model for presenting the selected images.

[0072] The analysis unit can learn based on user feedback and improve analysis accuracy. Feedback is collected, for example, by a user survey or by analyzing a usage log, but is not limited to these examples. The analysis unit can collect feedback, for example, by using a user survey. The analysis unit can also collect feedback by analyzing a usage log. The analysis unit can also collect feedback based on the user's operation history. For example, the analysis unit can analyze the history of images selected by the user to improve analysis accuracy. In this way, analysis accuracy is improved by utilizing user feedback. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve analysis accuracy by using an AI model for learning based on feedback.

[0073] The selection unit can manage a history of images used in the past and use it when making a selection. The history can be managed using, for example, a database, but is not limited to such an example. For example, the selection unit stores a history of images used in the past in a database. The selection unit can also set a storage period for the history and delete the history after a certain period has passed. The selection unit can also select images based on the history. For example, the selection unit can refer to the history of images used in the past and select images of the same style if the materials are on the same theme. In this way, the accuracy of selection is improved by using the history of past images. Some or all of the above-mentioned processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can select images using an AI model for managing the history of images used in the past.

[0074] The selection unit can provide information about new images added to the providing unit when they are added, allowing the latest image to be selected. New images are added, for example, by periodic database updates or user uploads, but examples are not limited to these. For example, the selection unit can periodically update a license-free image database and add new images. The selection unit can also accept user uploads and add new images to the database. The selection unit can also provide information about new images added to the providing unit when they are added, allowing the latest image to be selected. For example, the selection unit can transmit information about new images added to the providing unit, allowing the providing unit to present the latest images to the user. This allows the latest image to be selected, allowing the latest information to be always provided. Some or all of the above-described processing in the selection unit can be performed, for example, using AI, or can be performed without using AI. For example, the selection unit can use an AI model to provide information about new images added to the providing unit, allowing the latest image to be selected.

[0075] The reception unit can estimate the user's emotions and adjust the input reception interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept input content. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This improves ease of input by providing an interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input image data of the user captured with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0076] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display content that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest content that will be used in a specific time period based on the user's past input history. For example, the reception unit stores the user's past input history in a database and analyzes it. This makes it possible to suggest the optimal input method for the user by utilizing the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can suggest the optimal input method using an AI model for analyzing the past input history.

[0077] When receiving input, the reception unit can filter the input content based on the user's current project or area of ​​interest. For example, the reception unit prioritizes receiving content related to the project the user is currently working on. The reception unit can also automatically complete related keywords based on the user's area of ​​interest. The reception unit can also suggest related input content by referring to the user's past project history. For example, the reception unit acquires project information from the user's project management tool and filters the input content. This allows the input content to be filtered based on the user's project or area of ​​interest, thereby providing highly relevant information. Some or all of the above-described processing by the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can provide highly relevant information using an AI model for filtering the input content based on project information.

[0078] The reception unit can select a reception means according to the user's input method when receiving input. For example, if the user selects voice input, the reception unit can receive the input content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also preferentially receive keyboard input. Furthermore, if the user selects image input, the reception unit can analyze the input content using image recognition technology. For example, the reception unit provides the optimal reception means according to the user's input method. This improves input efficiency by providing the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can receive the input content using an AI model for receiving input content using voice recognition technology.

[0079] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize important content. Furthermore, if the user is relaxed, the reception unit can prioritize detailed content. Furthermore, if the user is in a hurry, the reception unit can prioritize content that requires prompt processing. For example, the reception unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. This allows important content to be prioritized by setting priorities according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input image data of the user captured with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0080] When receiving input, the reception unit can prioritize receiving highly relevant input content by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving content related to that area. Furthermore, when the user is moving, the reception unit can also suggest related content based on the user's current location. Furthermore, when the user is in a specific location, the reception unit can prioritize receiving information related to that location. For example, the reception unit acquires the user's GPS data and filters the input content based on the geographical location information. This makes it possible to provide highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide highly relevant information using an AI model for filtering input content based on GPS data.

[0081] The reception unit may analyze the user's social media activity and receive related input content when receiving input. For example, the reception unit may suggest related input content based on content shared by the user on social media. The reception unit may also analyze the user's social media activity history and automatically complete related keywords. The reception unit may also suggest related input content based on the activities of the user's friends on social media. For example, the reception unit may acquire activity data from the user's social media account and filter the input content. This allows for providing highly relevant information based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may provide highly relevant information using an AI model for filtering input content based on social media activity.

[0082] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the input interface. The reception unit can also adjust the priority of input content by referring to the user's feedback history. For example, the reception unit stores and analyzes the user's feedback data in a database. This allows the optimal reception method to be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can provide the optimal reception method by using an AI model for customizing the reception method based on feedback data.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. This improves understandability by providing an analysis result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the user captured with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input content. For example, the analysis unit performs a detailed analysis on important content. The analysis unit can also perform a simplified analysis on general content. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. For example, the analysis unit evaluates the importance of the content entered by the user and adjusts the level of detail of the analysis. This makes it possible to provide appropriate information by performing an analysis according to the importance of the input content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide appropriate information using an AI model for adjusting the level of detail of the analysis based on the importance.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input content. For example, the analysis unit can apply a specialized analysis algorithm to technical content. The analysis unit can also apply a simplified analysis algorithm to general content. The analysis unit can also select the optimal analysis algorithm based on the category specified by the user. For example, the analysis unit classifies the category of the input content and applies the corresponding analysis algorithm. This makes it possible to provide highly accurate analysis results by performing analysis according to the category of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide highly accurate analysis results by using an AI model for applying an analysis algorithm according to the category.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit performs a current analysis based on the user's past analysis results. The analysis unit can also analyze the user's past analysis history and optimize the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the user's past feedback. For example, the analysis unit stores the user's past analysis results in a database and analyzes them. This improves the accuracy of the analysis by referring to the past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide highly accurate analysis results by using an AI model for improving the accuracy of the analysis by referring to the past analysis results.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This improves comprehension by providing the length of the analysis according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of the user captured with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0088] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the input content. For example, the analysis unit prioritizes analysis of urgent content. The analysis unit can also analyze general content with normal priority. The analysis unit can also adjust the priority of analysis based on the submission time specified by the user. For example, the analysis unit evaluates the submission time of the input content and determines the priority of analysis. By setting the priority based on the submission time, urgent content can be processed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use an AI model for determining the priority of analysis based on the submission time to process urgent content preferentially.

[0089] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input content. For example, the analysis unit prioritizes analysis of highly relevant content. The analysis unit can also postpone analysis of less relevant content. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. For example, the analysis unit evaluates the relevance of the input content and adjusts the order of analysis. This enables efficient analysis by prioritizing analysis of highly relevant content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform efficient analysis using an AI model for adjusting the order of analysis based on relevance.

[0090] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user is a general user, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented based on the user's level of expertise. For example, the analysis unit can evaluate the user's level of expertise and adjust the way in which the analysis results are presented. This improves understandability by providing analysis results that correspond to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide easy-to-understand analysis results using an AI model for adjusting the use of technical terms in the analysis according to the level of expertise.

[0091] The selection unit can estimate the user's emotions and determine the priority of images to select based on the estimated user emotions. For example, if the user is nervous, the selection unit can prioritize simple, highly visible images. Furthermore, if the user is relaxed, the selection unit can prioritize images containing detailed information. Furthermore, if the user is in a hurry, the selection unit can prioritize images that capture the main points. For example, the selection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. This allows for the provision of appropriate images by preferentially selecting images that correspond to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit can be performed using, for example, AI, or without AI. For example, the selection unit can input image data of the user captured with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0092] The selection unit can improve the accuracy of selection based on the interrelationships between images during selection. For example, the selection unit groups related images and selects the most appropriate image. The selection unit can also analyze the interrelationships between images and preferentially select highly related images. The selection unit can also improve the accuracy of selection by taking the interrelationships between images into consideration. For example, the selection unit evaluates co-occurrence relationships between images and selects highly related images. In this way, the accuracy of selection is improved by taking the interrelationships between images into consideration. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can perform highly accurate image selection using an AI model for evaluating the interrelationships between images.

[0093] When selecting, the selection unit can make a selection based on attribute information of the image provider. For example, if the image provider is an expert, the selection unit can preferentially select the image. Furthermore, if the image provider is a general user, the selection unit can also preferentially select general images. Furthermore, the selection unit can select the optimal image by taking into account the attribute information of the image provider. For example, the selection unit evaluates the provider's occupation and past history and selects an appropriate image. In this way, an appropriate image can be selected by taking into account the provider's attribute information. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can select an appropriate image using an AI model for evaluating the provider's attribute information.

[0094] The selection unit can weight the selection based on the frequency of use of the image when making a selection. For example, the selection unit preferentially selects images that are used more frequently. The selection unit can also select images that are used less frequently afterward. The selection unit can also weight the selection based on the frequency of use of the image. For example, the selection unit evaluates the frequency of use of the image and weights it. As a result, appropriate images can be selected by weighting based on the frequency of use. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can select appropriate images using an AI model for weighting the selection based on the frequency of use.

[0095] The selection unit can estimate the user's emotion and adjust the display method of the selected image based on the estimated user emotion. For example, when the user is nervous, the selection unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the selection unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the selection unit can provide a display method that focuses on the main points. For example, the selection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. This improves visibility by providing a display method that corresponds to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI. For example, the selection unit may input image data of the user captured with a camera into the generation AI and cause the generation AI to estimate the user's emotion.

[0096] The selection unit may select based on the geographic distribution of images. For example, if a user inputs content related to a specific region, the selection unit may preferentially select images related to that region. The selection unit may also analyze the geographic distribution of images and preferentially select highly relevant images. The selection unit may also select relevant images based on the user's current location. For example, the selection unit may evaluate the geographic distribution of images and select highly relevant images. This allows highly relevant images to be selected by taking geographic distribution into consideration. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit may select highly relevant images using an AI model for selecting images based on geographic distribution.

[0097] The selection unit can improve the accuracy of the selection by referring to literature related to the image during selection. For example, the selection unit can refer to literature related to the image and select the most appropriate image. The selection unit can also analyze literature related to the image and preferentially select highly relevant images. The selection unit can also improve the accuracy of the selection by taking into account literature related to the image. For example, the selection unit evaluates related literature and selects the most appropriate image. As a result, the accuracy of the selection is improved by referring to the related literature. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can perform highly accurate image selection by using an AI model for selecting images by referring to related literature.

[0098] The selection unit can make a selection based on the market value of the image. For example, the selection unit preferentially selects images with high market value. The selection unit can also select images with low market value later. The selection unit can also weight the selection based on the market value of the image. For example, the selection unit evaluates and weights the market value of the image. This makes it possible to select an appropriate image by taking market value into consideration. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can select an appropriate image using an AI model for selecting images based on market value.

[0099] The providing unit can estimate the user's emotions and adjust the display method of the images to be provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. This improves visibility by providing a display method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input image data of the user captured with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0100] The providing unit can select a display method based on the user's past selection history when providing the display method. The providing unit provides an optimal display method, for example, based on a display method of an image previously selected by the user. The providing unit can also analyze the user's past selection history and optimize the display method. The providing unit can also adjust the display method by referring to the user's past feedback. For example, the providing unit stores and analyzes the user's selection history in a database. This makes it possible to provide an optimal display method by referring to the past selection history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide an optimal display method using an AI model for selecting a display method based on the past selection history.

[0101] The providing unit can customize the display content according to the user's current task when providing the display content. For example, when the user is giving a presentation, the providing unit provides a display method that is optimal for the presentation. Furthermore, when the user is creating a report, the providing unit can also provide a display method that is optimal for the report. Furthermore, the providing unit can customize the display content based on the user's current task. For example, the providing unit acquires task information of the user and customizes the display content. This enables efficient work by providing display content according to the current task. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can support efficient work by using an AI model for customizing the display content based on task information.

[0102] The providing unit can select a display method based on the user's device information when providing the display information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit acquires the user's device information and selects a display method. This improves visibility by providing a display method based on the device information. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can provide a highly visible display method using an AI model for selecting a display method based on device information.

[0103] The providing unit can estimate the user's emotions and adjust the operation procedures of the images to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple and intuitive operation procedures. Furthermore, if the user is relaxed, the providing unit can also provide detailed operation procedures. Furthermore, if the user is in a hurry, the providing unit can also provide procedures that allow for quick operation. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. This improves ease of operation by providing operation procedures according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input image data of the user captured with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0104] The providing unit can select a display method based on the user's device information when providing the display information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit acquires the user's device information and selects a display method. This improves visibility by providing a display method based on the device information. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can provide a highly visible display method using an AI model for selecting a display method based on device information.

[0105] The providing unit can make the display content multilingual according to the user's language setting when providing the content. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. For example, the providing unit acquires the user's browser settings or OS language settings and makes the display content multilingual. This improves user convenience by providing multilingual display content. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can improve user convenience by using an AI model for making the display content multilingual based on the language setting.

[0106] The providing unit may analyze the user's social media activity and provide related information at the time of providing the information. For example, the providing unit may provide information about places where the user has checked in on social media. The providing unit may also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit may also provide information about related places and events by referring to the activities of the user's friends on social media. For example, the providing unit may obtain activity data from the user's social media account and provide related information. This improves user convenience by providing related information based on social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may improve user convenience by using an AI model for analyzing social media activity and providing related information. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, selection unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives input from a user using a keyboard input interface or a microphone input interface of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the input content using natural language processing technology. The selection unit is implemented by the specific processing unit 290 of the data processing device 12 and selects appropriate photos or illustrations from a license-free image database. The provision unit provides the selected image to the user using the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, selection unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice input from the user using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input content using natural language processing technology. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects appropriate photos or illustrations from a license-free image database. The provision unit provides the selected image to the user using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives voice input from the user using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input content using natural language processing technology. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects appropriate photos or illustrations from a license-free image database. The provision unit provides the selected image to the user using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input from the user using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input content using natural language processing technology. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects appropriate photos or illustrations from a license-free image database. The provision unit provides the selected image to the user using the display of the robot 414.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can retrieve additional information from relevant external databases based on the user's input. For example, if the user inputs "market analysis results," the reception unit can retrieve related market reports and statistical data from external databases and send them to the analysis unit. Alternatively, if the user inputs "new product features," the reception unit can retrieve related technical literature and patent information. Furthermore, the reception unit can retrieve related news articles and blog posts based on the user's input and send them to the analysis unit. This improves the accuracy and depth of document creation by providing additional information related to the user's input.

[0109] The analysis unit can analyze related video content and select an appropriate thumbnail image based on the user's input. For example, if the user inputs "features of a new product," the analysis unit can analyze related product introduction videos and select the most appropriate thumbnail image. Also, if the user inputs "market analysis results," the analysis unit can analyze related market analysis videos and select appropriate thumbnail images of graphs and charts. Furthermore, the analysis unit can analyze related educational videos and tutorial videos based on the user's input and select appropriate thumbnail images. This allows the use of video content to improve the visual appeal of materials.

[0110] The providing unit can estimate the user's emotion and adjust the color tone of the image to be provided based on the estimated user's emotion. For example, if the user is nervous, the providing unit can provide an image with calm color tones. If the user is relaxed, the providing unit can provide an image with bright and vivid color tones. Furthermore, if the user is excited, the providing unit can provide an image with stimulating color tones. This improves the visual effect by providing an image with color tones that correspond to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0111] The analysis unit can analyze related audio content and select appropriate audio files based on user input. For example, if a user inputs "features of a new product," the analysis unit can analyze audio files introducing the related product and select appropriate portions. Alternatively, if a user inputs "market analysis results," the analysis unit can analyze audio files of related market analysis and select appropriate portions. Furthermore, the analysis unit can analyze audio files of related podcasts and interviews based on user input and select appropriate portions. This allows the utilization of audio content to increase the diversity and depth of materials.

[0112] The selection unit can estimate the user's emotion and adjust the size of the selected image based on the estimated user emotion. For example, if the user is nervous, the selection unit can prioritize small, simple images. Alternatively, if the user is relaxed, the selection unit can prioritize large, detailed images. Furthermore, if the user is in a hurry, the selection unit can prioritize medium-sized images that capture the main points. This improves visibility and comprehension by providing image sizes that correspond to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The providing unit can generate and provide related infographics based on the user's input. For example, if the user inputs "market analysis results," the providing unit can generate and provide an infographic based on the related data. Also, if the user inputs "new product features," the providing unit can generate an infographic based on the related information. Furthermore, based on the content entered by the user, it can generate infographics that visually represent related statistical data and research results. This provides visually easy-to-understand information, improving the ease of understanding of the materials.

[0114] The analysis unit can estimate the user's emotions and adjust the speed of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can quickly perform the analysis and provide the results. Alternatively, if the user is relaxed, the analysis unit can perform a detailed analysis and provide the results over time. Furthermore, if the user is nervous, the analysis unit can perform the analysis at a medium speed and provide the results. This enables efficient information provision by providing an analysis speed that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] The reception unit can retrieve files from relevant cloud storage based on the user's input and send them to the analysis unit. For example, if a user inputs "features of a new product," the reception unit can retrieve related technical documents and blueprints from cloud storage and send them to the analysis unit. Also, if a user inputs "market analysis results," the reception unit can retrieve related data sets and reports from cloud storage. Furthermore, based on the user's input, the reception unit can retrieve related presentation materials and reports from cloud storage. This allows the use of cloud storage to improve the efficiency of document creation.

[0116] The providing unit can estimate the user's emotions and adjust the animation effect of the images provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple and calming animation effect. If the user is relaxed, the providing unit can provide a dynamic animation effect. Furthermore, if the user is excited, the providing unit can provide a visually stimulating animation effect. This improves visual appeal by providing animation effects according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0117] The selection unit can select relevant 3D models based on the user's input and send them to the provision unit. For example, if the user inputs "new product features," the selection unit can select relevant 3D models and send them to the provision unit. Also, if the user inputs "market analysis results," the selection unit can select relevant 3D graphs and charts. Furthermore, the selection unit can select relevant architectural or mechanical models based on the user's input. This allows the use of 3D models to improve the visual depth and ease of understanding of the materials.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives input from a user. The input from a user includes text input, voice input, image input, etc. For example, the reception unit provides a keyboard input interface for receiving text input, a microphone input interface for receiving voice input, and a camera interface for receiving image input. Step 2: The analysis unit analyzes the input content received by the reception unit. The analysis is performed using, for example, natural language processing technology, image analysis technology, or data mining technology. The analysis unit extracts keywords from the input text and generates information for selecting appropriate photos and illustrations based on those keywords. Step 3: The selection unit selects appropriate images based on the content analyzed by the analysis unit. The selection is based on a similarity measure and the algorithm used. For example, the selection unit selects appropriate photos or illustrations from a license-free image database. The selection unit can also manage a history of previously used images and use this information when making a selection. Step 4: The providing unit provides the images selected by the selecting unit to the user. The images are provided by a method such as displaying them on the screen or providing a download link. For example, the providing unit may display thumbnails of the selected images so that the user can select the most suitable one. The providing unit may also learn from user feedback and improve the accuracy of the analysis.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; an analysis unit that analyzes the input content received by the reception unit; a selection unit that selects an image based on the content analyzed by the analysis unit; a providing unit that provides the image selected by the selecting unit to a user. A system characterized by:

2. The selection unit Choose images from our license-free image database 2. The system of claim 1.

3. The providing unit Presents the user with a selection of images to choose from 2. The system of claim 1.

4. The analysis unit Learns from user feedback and improves analysis accuracy 2. The system of claim 1.

5. The selection unit Manage the history of previously used images and use it when selecting 2. The system of claim 1.

6. The selection unit When new images are added, the information is provided to the provider, allowing them to select the latest images.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the input reception interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests input methods 2. The system of claim 1.

Images