system

Abstract

An object of the system according to the embodiment is to efficiently insert an appropriate image or illustration in creating a slide or a PPT.SOLUTION: A system includes a reception part, a generation part, an upload part, and a reflection part. The receiving unit receives input information from a user. The generation unit creates a text, a graph, and numerical data on the slide or the PPT based on the information received by the reception unit. The upload unit uploads the slide or the PPT generated by the generation unit to the cloud. The reflection unit analyzes the content of the slide or the PPT uploaded by the upload unit and reflects the suggestion image in the material.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] With conventional technology, it was difficult to efficiently insert appropriate images and illustrations when creating slides or PowerPoint presentations, which required time and effort.

[0005] The system according to the embodiment aims to efficiently insert appropriate images and illustrations when creating slides and PPTs. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, an upload unit, and a reflection unit. The reception unit receives input information from a user. The generation unit creates text, graphs, and numerical data in slides or PPTs based on the information received by the reception unit. The upload unit uploads the slides or PPTs generated by the generation unit to the cloud. The reflection unit analyzes the content of the slides or PPTs uploaded by the upload unit and reflects the proposed images in the materials. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently insert appropriate images and illustrations when creating slides and PPTs. [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) A document creation support system according to an embodiment of the present invention creates slides and PowerPoint presentations based on user input, uploads them to the cloud, and reflects suggested images. In the document creation support system, AI automatically creates text, graphs, and numerical data based on the information entered by the user. Next, the user specifies the insertion location of the image or illustration by drawing a frame and uploads the data to the cloud. The user then selects the style and color of the image or illustration and issues a command to insert the image. The AI ​​then interprets the content of the document and reflects the suggested image in the document. For example, in a document creation support system, a user inputs the theme and purpose of a presentation and the type of data to be included. Based on this, the AI ​​creates appropriate text, graphs, and numerical data. Next, the user specifies the insertion location of the image or illustration by drawing a frame and uploads the data to the cloud. Security measures such as data encryption and access restrictions are implemented when uploading to the cloud. Next, the user selects the style and color of the image or illustration and issues a command to insert the image. The AI ​​analyzes the content of the document, suggests appropriate images and illustrations, and reflects them in the document. This allows the document creation support system to efficiently create documents. This allows the document creation support system to allow users to create documents efficiently and create and edit documents from anywhere using the cloud. For example, users can access the cloud to create documents while on a business trip or at home. This improves user work efficiency and enables more effective presentations.

[0029] A material creation support system according to an embodiment includes a receiving unit, a generating unit, an uploading unit, and a reflecting unit. The receiving unit receives input information from a user. The input information from the user includes, but is not limited to, the presentation theme, purpose, and type of target data. The receiving unit receives information, for example, by the user entering the information into an input form. The receiving unit can also receive information using voice input or image input. The generating unit creates text, graphs, and numerical data in slides and PPTs based on the information received by the receiving unit. The generating unit automatically generates materials based on the information entered by the user, for example, using AI. For example, the generating unit creates appropriate text, graphs, and numerical data based on the presentation theme and purpose. The generating unit can also display data in an appropriate format based on the type of data specified by the user. The uploading unit uploads the slides and PPTs generated by the generating unit to a cloud. The uploading unit stores the generated materials in the cloud, for example, using a cloud storage service. The uploading unit can implement security measures, such as data encryption and access restrictions, when uploading the materials to the cloud. For example, the upload unit encrypts data using AES encryption and performs password protection and access permission management. The reflection unit analyzes the content of slides or PPTs uploaded by the upload unit and reflects suggested images in the materials. The reflection unit analyzes the content of the materials using, for example, AI and suggests appropriate images or illustrations. For example, the reflection unit reflects suggested images in the materials based on the taste and color of the image or illustration selected by the user. The reflection unit can also insert images or illustrations into framed areas specified by the user. This allows the material creation support system according to the embodiment to efficiently create materials and create and edit materials from anywhere using the cloud. For example, materials can be created by accessing the cloud while on a business trip or from home. This improves user work efficiency and enables more effective presentations.

[0030] The generation unit can create text, graphs, and numerical data based on the theme or purpose of the presentation and the type of target data input by the user. The generation unit, for example, creates appropriate text based on the theme of the presentation input by the user. For example, the generation unit generates text containing business terms based on the theme of a business presentation. The generation unit can also generate text containing educational terms based on the theme of an educational presentation. The generation unit also creates appropriate graphs based on the purpose of the presentation input by the user. For example, the generation unit generates graphs for visualizing sales data. The generation unit can also generate graphs for visualizing customer data. The generation unit also creates appropriate numerical data based on the type of target data input by the user. For example, the generation unit generates numerical data based on survey data. The generation unit can also generate numerical data based on statistical data. In this way, appropriate text, graphs, and numerical data can be generated based on the information input by the user.

[0031] The reflection unit can reflect the proposed image in the document based on the taste or color of the image or illustration selected by the user. The reflection unit reflects the proposed image in the document based on, for example, the taste of the image selected by the user. For example, if a business taste is selected, the reflection unit can suggest an image suitable for a business scene. Furthermore, if a casual taste is selected, the reflection unit can also suggest an image suitable for a casual scene. Furthermore, the reflection unit reflects the proposed image in the document based on the color of the image selected by the user. For example, if a monochrome color is selected, the reflection unit can suggest a monochrome image. Furthermore, if a pastel color is selected, the reflection unit can also suggest a pastel color image. In this way, appropriate images and illustrations can be reflected in the document based on the user's selection.

[0032] The upload unit can implement security measures such as data encryption or access restrictions when uploading to the cloud. The upload unit, for example, encrypts data when uploading to the cloud. For example, the upload unit encrypts data using AES encryption. The upload unit can also implement access restrictions when uploading to the cloud. For example, the upload unit can implement password protection to allow access only to specific users. The upload unit can also manage access rights and set different access rights for each user. This ensures security when uploading to the cloud.

[0033] The reflection unit can insert an image or illustration into a framed location specified by the user. For example, the reflection unit inserts an image into a framed location specified by the user. For example, if the user specifies a frame at a specific position on a slide, the reflection unit inserts an image at that position. Also, if the user specifies a frame at a specific position on a PPT, the reflection unit can insert an illustration at that position. This allows an appropriate image or illustration to be inserted into the location specified by the user.

[0034] The reflection unit can analyze the content of the document and suggest appropriate images or illustrations. The reflection unit, for example, analyzes the content of the document using text analysis technology. For example, the reflection unit analyzes the text content of the document and extracts related keywords. The reflection unit can also analyze the content of the document using image analysis technology. For example, the reflection unit analyzes the content of images included in the document and suggests related images. The reflection unit can also analyze the content of the document using data mining technology. For example, the reflection unit analyzes data included in the document and suggests related illustrations. This makes it possible to suggest appropriate images or illustrations based on the content of the document.

[0035] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit, for example, analyzes the user's past input history using data mining technology. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The reception unit can also customize the optimal input method based on information the user has input in the past. For example, the reception unit analyzes information the user has input in the past and suggests the optimal input method. This makes it possible to provide the optimal reception method based on the user's past input history.

[0036] When receiving input information, the reception unit can perform filtering based on the user's current project or field of interest. For example, the reception unit preferentially receives only information related to the user's current project. For example, the reception unit filters and receives information related to a project currently in progress by the user. The reception unit can also filter and receive highly relevant information based on the user's field of interest. For example, the reception unit filters information based on keywords related to the user's field of interest. The reception unit can also filter and receive related information by referring to the user's past project history. For example, the reception unit analyzes the user's past project history and preferentially receives highly relevant information. This makes it possible to preferentially receive information related to the user's current project or field of interest.

[0037] When receiving input information, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit accepts the input information using voice recognition technology. For example, the reception unit converts the user's voice into text data using voice recognition software. Furthermore, if the user selects text input, the reception unit can also accept the input information using text analysis technology. For example, the reception unit analyzes the text entered by the user and extracts appropriate information. Furthermore, if the user selects image input, the reception unit can also accept the input information using image recognition technology. For example, the reception unit analyzes images uploaded by the user and extracts related information. This makes it possible to provide the optimal reception means depending on the user's input method.

[0038] When receiving input information, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving information related to that area. For example, when the user is in a specific city, the reception unit filters and receives information related to that city. Furthermore, when the user is traveling, the reception unit can also prioritize receiving highly relevant information based on the user's current location. For example, the reception unit filters and receives information related to the area to which the user is traveling. Furthermore, when the user is in a specific location, the reception unit can also prioritize receiving information related to that location. For example, when the user is in a specific building, the reception unit filters and receives information related to the building. This makes it possible to prioritize receiving highly relevant information based on the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity and receive related information when receiving input information. The reception unit, for example, receives related information based on information shared by the user on social media. For example, the reception unit analyzes the content of posts shared by the user on social media and filters and receives the related information. The reception unit can also analyze the user's social media activity history and receive related information. For example, the reception unit filters and receives the related information based on the number of likes and followers of the user on social media. The reception unit can also receive related information by referring to the activities of the user's friends on social media. For example, the reception unit filters and receives the related information based on information shared by the user's friends. In this way, it is possible to receive related information based on the user's social media activity.

[0040] The reception unit can customize the reception method based on the user's past feedback when receiving input information. The reception unit, for example, suggests the optimal reception method based on feedback provided by the user in the past. For example, the reception unit analyzes survey results provided by the user in the past and suggests the optimal reception method. The reception unit can also improve the reception method by reflecting the user's past feedback. For example, the reception unit improves the reception method based on user reviews provided by the user in the past. The reception unit can also analyze the user's feedback history and customize the optimal reception method. For example, the reception unit suggests the optimal reception method based on the user's feedback history. In this way, the optimal reception method can be provided based on the user's past feedback.

[0041] The generator can adjust the level of detail of the generated content based on the importance of the presentation during generation. For example, the generator evaluates the importance of the presentation and adjusts the level of detail of the generated content. For example, the generator generates detailed data and graphs for an important presentation. The generator can also generate simple text and graphs that focus on the main points for a brief report. The generator can also generate text and graphs with a moderate level of detail for a presentation of medium importance. This makes it possible to adjust the generated content to an appropriate level of detail depending on the importance of the presentation.

[0042] The generation unit can apply different generation algorithms depending on the category of the presentation during generation. For example, the generation unit applies an appropriate generation algorithm depending on the category of the presentation. For example, in the case of a business presentation, the generation unit applies a business generation algorithm. In addition, in the case of an educational presentation, the generation unit can also apply an education generation algorithm. In addition, in the case of an entertainment presentation, the generation unit can also apply an entertainment generation algorithm. In this way, an appropriate generation algorithm can be applied depending on the category of the presentation.

[0043] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit, for example, analyzes the user's past generation results using data mining technology. For example, the generation unit customizes the generated content by referring to presentations created by the user in the past. The generation unit can also analyze the user's past generation results and optimize the generation algorithm. The generation unit can also improve the accuracy of the generated content based on the user's past feedback. For example, the generation unit analyzes the user's past feedback and improves the generation algorithm. This makes it possible to improve the accuracy of generation based on the user's past generation results.

[0044] The generation unit can determine the priority of the content to be generated based on the submission time of the presentation at the time of generation. The generation unit, for example, evaluates the submission time of the presentation and determines the priority of the content to be generated. For example, if the submission deadline is approaching, the generation unit prioritizes the generation of important data and graphs. Furthermore, if the submission deadline is far away, the generation unit can generate detailed data and graphs. Furthermore, if the submission deadline is medium, the generation unit can generate data and graphs with an appropriate level of detail. This makes it possible to determine the content to be generated with appropriate priority depending on the submission time of the presentation.

[0045] The generation unit can adjust the order of generated content based on the relevance of the presentation during generation. The generation unit, for example, evaluates the relevance of the presentation and adjusts the order of generated content. For example, the generation unit places important information first and arranges the generated content in order of relevance. The generation unit can also postpone less relevant information and arrange important information preferentially. The generation unit can also arrange the generated content in order of relevance in line with the flow of the presentation. This allows the generated content to be arranged in an appropriate order according to the relevance of the presentation.

[0046] The generation unit can adjust the use of technical terms in the generated content according to the user's level of expertise during generation. For example, the generation unit evaluates the user's level of expertise and adjusts the use of technical terms in the generated content. For example, if the user is an expert, the generation unit can provide generated content that uses a lot of technical terms. Also, if the user is a beginner, the generation unit can provide simple generated content that avoids technical terms. Also, the generation unit can adjust the use of appropriate technical terms according to the user's level of expertise. This makes it possible to use appropriate technical terms according to the user's level of expertise.

[0047] The upload unit can determine the upload priority based on the importance of the data when uploading. For example, the upload unit evaluates the importance of the data and determines the upload priority. For example, the upload unit uploads important data first and postpones uploading other data. The upload unit can also adjust the upload order according to the importance of the data. The upload unit can also quickly upload data with high importance and postpone uploading data with low importance. This allows uploading to be performed with appropriate priority according to the importance of the data.

[0048] The upload unit can apply different upload methods depending on the data category when uploading. The upload unit applies an appropriate upload method depending on, for example, the data category. For example, the upload unit uploads image data using compression technology. In addition, the upload unit can also upload text data using encryption technology. In addition, the upload unit can also upload video data using streaming technology. This makes it possible to apply an appropriate upload method depending on the data category.

[0049] The upload unit can improve the accuracy of uploading based on the user's past upload history when uploading. The upload unit, for example, analyzes the user's past upload history using data mining technology. For example, the upload unit suggests an optimal upload method by referring to data previously uploaded by the user. The upload unit can also analyze the user's past upload history to improve the accuracy of uploading. The upload unit can also optimize the upload method based on the user's past feedback. For example, the upload unit analyzes the user's past feedback to improve the upload method. This makes it possible to improve the accuracy of uploading based on the user's past upload history.

[0050] The uploading unit can perform uploading based on the geographical distribution of data when uploading. For example, the uploading unit evaluates the geographical distribution of data and performs uploading. For example, when a user is in a specific area, the uploading unit prioritizes uploading data related to that area. Furthermore, when a user is moving, the uploading unit can prioritize uploading highly relevant data based on the user's current location. Furthermore, when a user is in a specific location, the uploading unit can prioritize uploading data related to that location. This allows appropriate uploading to be performed based on the geographical distribution of data.

[0051] The upload unit can improve the accuracy of uploading based on literature related to the data when uploading. For example, the upload unit refers to literature related to the data and proposes an optimal uploading method. For example, the upload unit improves the accuracy of uploading based on the literature related to the data. The upload unit can also analyze literature related to the data and optimize the uploading method. For example, the upload unit improves the uploading method based on the literature related to the data. This makes it possible to improve the accuracy of uploading based on the literature related to the data.

[0052] The uploading unit can perform uploading based on the market value of the data when uploading. For example, the uploading unit evaluates the market value of the data and performs uploading. For example, the uploading unit prioritizes uploading data with high market value and postpones other data. The uploading unit can also adjust the uploading order according to the market value of the data. The uploading unit can also quickly upload data with high market value and postpone data with low market value. This allows appropriate uploading based on the market value of the data.

[0053] The reflection unit can adjust the level of detail of the reflected content based on the importance of the document when reflecting. The reflection unit, for example, evaluates the importance of the document and adjusts the level of detail of the reflected content. For example, the reflection unit reflects detailed images and illustrations in the case of important documents. Furthermore, the reflection unit can reflect simple images and illustrations that highlight the main points in the case of simple reports. Furthermore, the reflection unit can reflect images and illustrations with a moderate level of detail in the case of documents of medium importance. This makes it possible to adjust the reflected content with an appropriate level of detail depending on the importance of the document.

[0054] The reflection unit can apply different reflection algorithms depending on the category of the material when reflecting. The reflection unit applies an appropriate reflection algorithm depending on, for example, the category of the material. For example, in the case of business material, the reflection unit applies a business-oriented reflection algorithm. In addition, in the case of educational material, the reflection unit can also apply an education-oriented reflection algorithm. In addition, in the case of entertainment material, the reflection unit can also apply an entertainment-oriented reflection algorithm. In this way, an appropriate reflection algorithm can be applied depending on the category of the material.

[0055] When reflecting, the reflection unit can improve the accuracy of the reflection based on the user's past reflection results. The reflection unit, for example, analyzes the user's past reflection results using data mining technology. For example, the reflection unit customizes the reflection content by referring to materials created by the user in the past. The reflection unit can also analyze the user's past reflection results and optimize the reflection algorithm. The reflection unit can also improve the accuracy of the reflection content based on the user's past feedback. For example, the reflection unit analyzes the user's past feedback and improves the reflection algorithm. This makes it possible to improve the accuracy of the reflection based on the user's past reflection results.

[0056] At the time of reflection, the reflection unit can determine the priority of the reflection content based on the submission time of the document. The reflection unit, for example, evaluates the submission time of the document and determines the priority of the reflection content. For example, if the submission deadline is approaching, the reflection unit will prioritize reflecting important images and illustrations. Furthermore, if the submission deadline is far away, the reflection unit can also reflect detailed images and illustrations. Furthermore, if the submission deadline is medium, the reflection unit can also reflect images and illustrations with a moderate level of detail. This makes it possible to determine the reflection content with appropriate priority depending on the submission time of the document.

[0057] The reflection unit can adjust the order of the reflected contents based on the relevance of the materials when reflecting. The reflection unit, for example, evaluates the relevance of the materials and adjusts the order of the reflected contents. For example, the reflection unit places important information first and arranges the reflected contents in order of relevance. The reflection unit can also postpone less relevant information and arrange important information preferentially. The reflection unit can also arrange the reflected contents in order of relevance according to the flow of the materials. This makes it possible to arrange the reflected contents in an appropriate order according to the relevance of the materials.

[0058] The reflection unit can adjust the use of technical terms in the reflection content according to the user's level of expertise when reflecting. For example, the reflection unit evaluates the user's level of expertise and adjusts the use of technical terms in the reflection content. For example, if the user is an expert, the reflection unit can provide reflection content that uses a lot of technical terms. Also, if the user is a beginner, the reflection unit can provide simple reflection content that avoids technical terms. Also, the reflection unit can adjust the use of appropriate technical terms according to the user's level of expertise. This makes it possible to use appropriate technical terms according to the user's level of expertise.

[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] When accepting input information from a user, the acceptance unit can analyze the user's past input history and suggest the optimal input method. For example, the acceptance unit can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The acceptance unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. Furthermore, the acceptance unit can customize the optimal input method based on information that the user has previously input. This makes it possible to provide the optimal acceptance method based on the user's past input history.

[0061] The generation unit can create text, graphs, and numerical data based on the theme or purpose of the presentation and the type of target data input by the user. For example, the generation unit can generate text containing business terms based on the theme of a business presentation. The generation unit can also generate text containing educational terms based on the theme of an educational presentation. The generation unit can also generate graphs for visualizing sales data and graphs for visualizing customer data. This makes it possible to generate appropriate text, graphs, and numerical data based on the information input by the user.

[0062] The reflection unit can reflect the suggested image in the document based on the taste or color of the image or illustration selected by the user. For example, if a business taste is selected, an image suitable for a business scene can be suggested. Also, if a casual taste is selected, an image suitable for a casual scene can be suggested. Furthermore, if a monochrome color is selected, a monochrome image can be suggested, and if a pastel color is selected, a pastel color image can be suggested. This allows appropriate images and illustrations to be reflected in the document based on the user's selection.

[0063] The upload unit can implement security measures such as data encryption or access restrictions when uploading to the cloud. For example, data can be encrypted using AES encryption, and password protection and access authority management can be performed. Access restrictions can also be set so that only specific users can access the data. This ensures security when uploading to the cloud.

[0064] The reflection section can insert an image or illustration into a framed location specified by the user. For example, if the user specifies a frame at a specific location on a slide, the image can be inserted at that location. Also, if the user specifies a frame at a specific location on a PowerPoint presentation, an illustration can be inserted at that location. This allows the appropriate image or illustration to be inserted at the location specified by the user.

[0065] The reflection unit can analyze the content of the document and suggest appropriate images or illustrations. For example, it can analyze the text content of the document and extract related keywords. It can also analyze the content of images included in the document and suggest related images. It can also analyze data included in the document and suggest related illustrations. This makes it possible to suggest appropriate images or illustrations based on the content of the document.

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

[0067] Step 1: The reception unit receives input information from the user. The input information from the user includes the presentation theme, purpose, and type of data to be presented. The reception unit receives information by the user entering the information into an input form, and can also receive information using voice input or image input. Step 2: The generation unit creates text, graphs, and numerical data on slides and PPTs based on the information received by the reception unit. The generation unit uses AI to automatically generate materials based on the information entered by the user, creating appropriate text, graphs, and numerical data based on the theme and purpose of the presentation. It can also display data in an appropriate format based on the type of data specified by the user. Step 3: The uploading unit uploads the slides and PPTs generated by the generating unit to the cloud. The uploading unit uses a cloud storage service to store the generated materials in the cloud and can implement security measures such as data encryption and access restrictions. For example, the data can be encrypted using AES encryption, and password protection and access permission management can be implemented. Step 4: The reflection section analyzes the contents of the slides and PPTs uploaded by the upload section and reflects the suggested images in the document. The reflection section uses AI to analyze the contents of the document and suggests appropriate images and illustrations. The suggested images are reflected in the document based on the taste and color of the image or illustration selected by the user, and the image or illustration can also be inserted into a framed area specified by the user.

[0068] (Example 2) A document creation support system according to an embodiment of the present invention creates slides and PowerPoint presentations based on user input, uploads them to the cloud, and reflects suggested images. In the document creation support system, AI automatically creates text, graphs, and numerical data based on the information entered by the user. Next, the user specifies the insertion location of the image or illustration by drawing a frame and uploads the data to the cloud. The user then selects the style and color of the image or illustration and issues a command to insert the image. The AI ​​then interprets the content of the document and reflects the suggested image in the document. For example, in a document creation support system, a user inputs the theme and purpose of a presentation and the type of data to be included. Based on this, the AI ​​creates appropriate text, graphs, and numerical data. Next, the user specifies the insertion location of the image or illustration by drawing a frame and uploads the data to the cloud. Security measures such as data encryption and access restrictions are implemented when uploading to the cloud. Next, the user selects the style and color of the image or illustration and issues a command to insert the image. The AI ​​analyzes the content of the document, suggests appropriate images and illustrations, and reflects them in the document. This allows the document creation support system to efficiently create documents. This allows the document creation support system to allow users to create documents efficiently and create and edit documents from anywhere using the cloud. For example, users can access the cloud to create documents while on a business trip or at home. This improves user work efficiency and enables more effective presentations.

[0069] A material creation support system according to an embodiment includes a receiving unit, a generating unit, an uploading unit, and a reflecting unit. The receiving unit receives input information from a user. The input information from the user includes, but is not limited to, the presentation theme, purpose, and type of target data. The receiving unit receives information, for example, by the user entering the information into an input form. The receiving unit can also receive information using voice input or image input. The generating unit creates text, graphs, and numerical data in slides and PPTs based on the information received by the receiving unit. The generating unit automatically generates materials based on the information entered by the user, for example, using AI. For example, the generating unit creates appropriate text, graphs, and numerical data based on the presentation theme and purpose. The generating unit can also display data in an appropriate format based on the type of data specified by the user. The uploading unit uploads the slides and PPTs generated by the generating unit to a cloud. The uploading unit stores the generated materials in the cloud, for example, using a cloud storage service. The uploading unit can implement security measures, such as data encryption and access restrictions, when uploading the materials to the cloud. For example, the upload unit encrypts data using AES encryption and performs password protection and access permission management. The reflection unit analyzes the content of slides or PPTs uploaded by the upload unit and reflects suggested images in the materials. The reflection unit analyzes the content of the materials using, for example, AI and suggests appropriate images or illustrations. For example, the reflection unit reflects suggested images in the materials based on the taste and color of the image or illustration selected by the user. The reflection unit can also insert images or illustrations into framed areas specified by the user. This allows the material creation support system according to the embodiment to efficiently create materials and create and edit materials from anywhere using the cloud. For example, materials can be created by accessing the cloud while on a business trip or from home. This improves user work efficiency and enables more effective presentations.

[0070] The generation unit can create text, graphs, and numerical data based on the theme or purpose of the presentation and the type of target data input by the user. The generation unit, for example, creates appropriate text based on the theme of the presentation input by the user. For example, the generation unit generates text containing business terms based on the theme of a business presentation. The generation unit can also generate text containing educational terms based on the theme of an educational presentation. The generation unit also creates appropriate graphs based on the purpose of the presentation input by the user. For example, the generation unit generates graphs for visualizing sales data. The generation unit can also generate graphs for visualizing customer data. The generation unit also creates appropriate numerical data based on the type of target data input by the user. For example, the generation unit generates numerical data based on survey data. The generation unit can also generate numerical data based on statistical data. In this way, appropriate text, graphs, and numerical data can be generated based on the information input by the user.

[0071] The reflection unit can reflect the proposed image in the document based on the taste or color of the image or illustration selected by the user. The reflection unit reflects the proposed image in the document based on, for example, the taste of the image selected by the user. For example, if a business taste is selected, the reflection unit can suggest an image suitable for a business scene. Furthermore, if a casual taste is selected, the reflection unit can also suggest an image suitable for a casual scene. Furthermore, the reflection unit reflects the proposed image in the document based on the color of the image selected by the user. For example, if a monochrome color is selected, the reflection unit can suggest a monochrome image. Furthermore, if a pastel color is selected, the reflection unit can also suggest a pastel color image. In this way, appropriate images and illustrations can be reflected in the document based on the user's selection.

[0072] The upload unit can implement security measures such as data encryption or access restrictions when uploading to the cloud. The upload unit, for example, encrypts data when uploading to the cloud. For example, the upload unit encrypts data using AES encryption. The upload unit can also implement access restrictions when uploading to the cloud. For example, the upload unit can implement password protection to allow access only to specific users. The upload unit can also manage access rights and set different access rights for each user. This ensures security when uploading to the cloud.

[0073] The reflection unit can insert an image or illustration into a framed location specified by the user. For example, the reflection unit inserts an image into a framed location specified by the user. For example, if the user specifies a frame at a specific position on a slide, the reflection unit inserts an image at that position. Also, if the user specifies a frame at a specific position on a PPT, the reflection unit can insert an illustration at that position. This allows an appropriate image or illustration to be inserted into the location specified by the user.

[0074] The reflection unit can analyze the content of the document and suggest appropriate images or illustrations. The reflection unit, for example, analyzes the content of the document using text analysis technology. For example, the reflection unit analyzes the text content of the document and extracts related keywords. The reflection unit can also analyze the content of the document using image analysis technology. For example, the reflection unit analyzes the content of images included in the document and suggests related images. The reflection unit can also analyze the content of the document using data mining technology. For example, the reflection unit analyzes data included in the document and suggests related illustrations. This makes it possible to suggest appropriate images or illustrations based on the content of the document.

[0075] The reception unit can estimate the user's emotions and adjust the timing of receiving input information based on the estimated user emotions. The reception unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit records the user's voice and estimates the emotion using a voice analysis algorithm. The reception unit can also estimate the user's emotions using text analysis technology. For example, the reception unit analyzes text entered by the user and estimates the emotion. This allows input information to be received at an appropriate time depending on the user's emotions. For example, if the user is feeling stressed, the timing of receiving input information can be delayed to provide time for relaxation. If the user is relaxed, the timing of receiving input information can be immediately accepted to promote smooth operation. If the user is in a hurry, the timing of receiving input information can be accelerated to provide a prompt response. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0076] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit, for example, analyzes the user's past input history using data mining technology. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. The reception unit can also customize the optimal input method based on information the user has input in the past. For example, the reception unit analyzes information the user has input in the past and suggests the optimal input method. This makes it possible to provide the optimal reception method based on the user's past input history.

[0077] When receiving input information, the reception unit can perform filtering based on the user's current project or field of interest. For example, the reception unit preferentially receives only information related to the user's current project. For example, the reception unit filters and receives information related to a project currently in progress by the user. The reception unit can also filter and receive highly relevant information based on the user's field of interest. For example, the reception unit filters information based on keywords related to the user's field of interest. The reception unit can also filter and receive related information by referring to the user's past project history. For example, the reception unit analyzes the user's past project history and preferentially receives highly relevant information. This makes it possible to preferentially receive information related to the user's current project or field of interest.

[0078] When receiving input information, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit accepts the input information using voice recognition technology. For example, the reception unit converts the user's voice into text data using voice recognition software. Furthermore, if the user selects text input, the reception unit can also accept the input information using text analysis technology. For example, the reception unit analyzes the text entered by the user and extracts appropriate information. Furthermore, if the user selects image input, the reception unit can also accept the input information using image recognition technology. For example, the reception unit analyzes images uploaded by the user and extracts related information. This makes it possible to provide the optimal reception means depending on the user's input method.

[0079] The reception unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions. The reception unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit records the user's voice and estimates the emotion using a voice analysis algorithm. The reception unit can also estimate the user's emotions using text analysis technology. For example, the reception unit analyzes text entered by the user and estimates the emotion. This allows the priority of information to be determined according to the user's emotions. For example, if the user is feeling stressed, less important information is postponed and more important information is prioritized. If the user is relaxed, all information is accepted equally. If the user is in a hurry, more urgent information is prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0080] When receiving input information, the reception unit can prioritize receiving highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving information related to that area. For example, when the user is in a specific city, the reception unit filters and receives information related to that city. Furthermore, when the user is traveling, the reception unit can also prioritize receiving highly relevant information based on the user's current location. For example, the reception unit filters and receives information related to the area to which the user is traveling. Furthermore, when the user is in a specific location, the reception unit can also prioritize receiving information related to that location. For example, when the user is in a specific building, the reception unit filters and receives information related to the building. This makes it possible to prioritize receiving highly relevant information based on the user's geographical location information.

[0081] The reception unit can analyze the user's social media activity and receive related information when receiving input information. The reception unit, for example, receives related information based on information shared by the user on social media. For example, the reception unit analyzes the content of posts shared by the user on social media and filters and receives the related information. The reception unit can also analyze the user's social media activity history and receive related information. For example, the reception unit filters and receives the related information based on the number of likes and followers of the user on social media. The reception unit can also receive related information by referring to the activities of the user's friends on social media. For example, the reception unit filters and receives the related information based on information shared by the user's friends. In this way, it is possible to receive related information based on the user's social media activity.

[0082] The reception unit can customize the reception method based on the user's past feedback when receiving input information. The reception unit, for example, suggests the optimal reception method based on feedback provided by the user in the past. For example, the reception unit analyzes survey results provided by the user in the past and suggests the optimal reception method. The reception unit can also improve the reception method by reflecting the user's past feedback. For example, the reception unit improves the reception method based on user reviews provided by the user in the past. The reception unit can also analyze the user's feedback history and customize the optimal reception method. For example, the reception unit suggests the optimal reception method based on the user's feedback history. In this way, the optimal reception method can be provided based on the user's past feedback.

[0083] The generation unit can estimate the user's emotion and adjust the expression method of the generated text or graph based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit records the user's voice and estimates the emotion using a voice analysis algorithm. The generation unit can also estimate the user's emotion using text analysis technology. For example, the generation unit analyzes text entered by the user and estimates the emotion. This allows the generation unit to generate text or graphs in an appropriate expression method depending on the user's emotion. For example, if the user is relaxed, text or graphs with soft colors are generated. If the user is in a hurry, simple text or graphs with high visibility are generated. If the user is excited, text or graphs with visually stimulating effects are generated. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0084] The generator can adjust the level of detail of the generated content based on the importance of the presentation during generation. For example, the generator evaluates the importance of the presentation and adjusts the level of detail of the generated content. For example, the generator generates detailed data and graphs for an important presentation. The generator can also generate simple text and graphs that focus on the main points for a brief report. The generator can also generate text and graphs with a moderate level of detail for a presentation of medium importance. This makes it possible to adjust the generated content to an appropriate level of detail depending on the importance of the presentation.

[0085] The generation unit can apply different generation algorithms depending on the category of the presentation during generation. For example, the generation unit applies an appropriate generation algorithm depending on the category of the presentation. For example, in the case of a business presentation, the generation unit applies a business generation algorithm. In addition, in the case of an educational presentation, the generation unit can also apply an education generation algorithm. In addition, in the case of an entertainment presentation, the generation unit can also apply an entertainment generation algorithm. In this way, an appropriate generation algorithm can be applied depending on the category of the presentation.

[0086] During generation, the generation unit can improve the accuracy of generation based on the user's past generation results. The generation unit, for example, analyzes the user's past generation results using data mining technology. For example, the generation unit customizes the generated content by referring to presentations created by the user in the past. The generation unit can also analyze the user's past generation results and optimize the generation algorithm. The generation unit can also improve the accuracy of the generated content based on the user's past feedback. For example, the generation unit analyzes the user's past feedback and improves the generation algorithm. This makes it possible to improve the accuracy of generation based on the user's past generation results.

[0087] The generation unit can estimate the user's emotion and adjust the length of the generated text or graph based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion using facial expression recognition technology. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit records the user's voice and estimates the emotion using a voice analysis algorithm. The generation unit can also estimate the user's emotion using text analysis technology. For example, the generation unit analyzes text entered by the user and estimates the emotion. This allows text and graphs to be generated with an appropriate length depending on the user's emotion. For example, if the user is in a hurry, short, concise text and graphs can be generated. If the user is relaxed, longer text and graphs with detailed explanations can be generated. If the user is excited, text and graphs with visually stimulating effects can be generated. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] The generation unit can determine the priority of the content to be generated based on the submission time of the presentation at the time of generation. The generation unit, for example, evaluates the submission time of the presentation and determines the priority of the content to be generated. For example, if the submission deadline is approaching, the generation unit prioritizes the generation of important data and graphs. Furthermore, if the submission deadline is far away, the generation unit can generate detailed data and graphs. Furthermore, if the submission deadline is medium, the generation unit can generate data and graphs with an appropriate level of detail. This makes it possible to determine the content to be generated with appropriate priority depending on the submission time of the presentation.

[0089] The generation unit can adjust the order of generated content based on the relevance of the presentation during generation. The generation unit, for example, evaluates the relevance of the presentation and adjusts the order of generated content. For example, the generation unit places important information first and arranges the generated content in order of relevance. The generation unit can also postpone less relevant information and arrange important information preferentially. The generation unit can also arrange the generated content in order of relevance in line with the flow of the presentation. This allows the generated content to be arranged in an appropriate order according to the relevance of the presentation.

[0090] The generation unit can adjust the use of technical terms in the generated content according to the user's level of expertise during generation. For example, the generation unit evaluates the user's level of expertise and adjusts the use of technical terms in the generated content. For example, if the user is an expert, the generation unit can provide generated content that uses a lot of technical terms. Also, if the user is a beginner, the generation unit can provide simple generated content that avoids technical terms. Also, the generation unit can adjust the use of appropriate technical terms according to the user's level of expertise. This makes it possible to use appropriate technical terms according to the user's level of expertise.

[0091] The upload unit can estimate the user's emotions and adjust the timing of uploading based on the estimated user emotions. The upload unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the upload unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The upload unit can also estimate the user's emotions using voice analysis technology. For example, the upload unit records the user's voice and estimates the emotion using a voice analysis algorithm. The upload unit can also estimate the user's emotions using text analysis technology. For example, the upload unit analyzes text entered by the user and estimates the emotion. This allows uploading to be performed at an appropriate time depending on the user's emotions. For example, if the user is feeling stressed, the upload timing can be delayed to provide time for relaxation. If the user is relaxed, the upload can be performed immediately to promote smooth operation. If the user is in a hurry, the upload timing can be accelerated to respond quickly. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0092] The upload unit can determine the upload priority based on the importance of the data when uploading. For example, the upload unit evaluates the importance of the data and determines the upload priority. For example, the upload unit uploads important data first and postpones uploading other data. The upload unit can also adjust the upload order according to the importance of the data. The upload unit can also quickly upload data with high importance and postpone uploading data with low importance. This allows uploading to be performed with appropriate priority according to the importance of the data.

[0093] The upload unit can apply different upload methods depending on the data category when uploading. The upload unit applies an appropriate upload method depending on, for example, the data category. For example, the upload unit uploads image data using compression technology. In addition, the upload unit can also upload text data using encryption technology. In addition, the upload unit can also upload video data using streaming technology. This makes it possible to apply an appropriate upload method depending on the data category.

[0094] The upload unit can improve the accuracy of uploading based on the user's past upload history when uploading. The upload unit, for example, analyzes the user's past upload history using data mining technology. For example, the upload unit suggests an optimal upload method by referring to data previously uploaded by the user. The upload unit can also analyze the user's past upload history to improve the accuracy of uploading. The upload unit can also optimize the upload method based on the user's past feedback. For example, the upload unit analyzes the user's past feedback to improve the upload method. This makes it possible to improve the accuracy of uploading based on the user's past upload history.

[0095] The upload unit can estimate a user's emotions and determine the priority of data to be uploaded based on the estimated user's emotions. The upload unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the upload unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The upload unit can also estimate the user's emotions using voice analysis technology. For example, the upload unit records the user's voice and estimates the emotion using a voice analysis algorithm. The upload unit can also estimate the user's emotions using text analysis technology. For example, the upload unit analyzes text entered by the user and estimates the emotion. This allows data to be uploaded in an appropriate priority order depending on the user's emotions. For example, if the user is feeling stressed, less important data is postponed and more important data is uploaded first. If the user is relaxed, all data is uploaded equally. If the user is in a hurry, more urgent data is uploaded first. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0096] The uploading unit can perform uploading based on the geographical distribution of data when uploading. For example, the uploading unit evaluates the geographical distribution of data and performs uploading. For example, when a user is in a specific area, the uploading unit prioritizes uploading data related to that area. Furthermore, when a user is moving, the uploading unit can prioritize uploading highly relevant data based on the user's current location. Furthermore, when a user is in a specific location, the uploading unit can prioritize uploading data related to that location. This allows appropriate uploading to be performed based on the geographical distribution of data.

[0097] The upload unit can improve the accuracy of uploading based on literature related to the data when uploading. For example, the upload unit refers to literature related to the data and proposes an optimal uploading method. For example, the upload unit improves the accuracy of uploading based on the literature related to the data. The upload unit can also analyze literature related to the data and optimize the uploading method. For example, the upload unit improves the uploading method based on the literature related to the data. This makes it possible to improve the accuracy of uploading based on the literature related to the data.

[0098] The uploading unit can perform uploading based on the market value of the data when uploading. For example, the uploading unit evaluates the market value of the data and performs uploading. For example, the uploading unit prioritizes uploading data with high market value and postpones other data. The uploading unit can also adjust the uploading order according to the market value of the data. The uploading unit can also quickly upload data with high market value and postpone data with low market value. This allows appropriate uploading based on the market value of the data.

[0099] The reflection unit can estimate the user's emotions and adjust the expression method of the reflected image or illustration based on the estimated user's emotions. The reflection unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the reflection unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The reflection unit can also estimate the user's emotions using voice analysis technology. For example, the reflection unit records the user's voice and estimates the emotions using a voice analysis algorithm. The reflection unit can also estimate the user's emotions using text analysis technology. For example, the reflection unit analyzes text entered by the user and estimates the emotions. This allows images and illustrations to be reflected in an appropriate expression method depending on the user's emotions. For example, if the user is relaxed, images and illustrations with soft colors are reflected. If the user is in a hurry, simple images and illustrations with high visibility are reflected. If the user is excited, images and illustrations with visually stimulating effects are reflected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] The reflection unit can adjust the level of detail of the reflected content based on the importance of the document when reflecting. The reflection unit, for example, evaluates the importance of the document and adjusts the level of detail of the reflected content. For example, the reflection unit reflects detailed images and illustrations in the case of important documents. Furthermore, the reflection unit can reflect simple images and illustrations that highlight the main points in the case of simple reports. Furthermore, the reflection unit can reflect images and illustrations with a moderate level of detail in the case of documents of medium importance. This makes it possible to adjust the reflected content with an appropriate level of detail depending on the importance of the document.

[0101] The reflection unit can apply different reflection algorithms depending on the category of the material when reflecting. The reflection unit applies an appropriate reflection algorithm depending on, for example, the category of the material. For example, in the case of business material, the reflection unit applies a business-oriented reflection algorithm. In addition, in the case of educational material, the reflection unit can also apply an education-oriented reflection algorithm. In addition, in the case of entertainment material, the reflection unit can also apply an entertainment-oriented reflection algorithm. In this way, an appropriate reflection algorithm can be applied depending on the category of the material.

[0102] When reflecting, the reflection unit can improve the accuracy of the reflection based on the user's past reflection results. The reflection unit, for example, analyzes the user's past reflection results using data mining technology. For example, the reflection unit customizes the reflection content by referring to materials created by the user in the past. The reflection unit can also analyze the user's past reflection results and optimize the reflection algorithm. The reflection unit can also improve the accuracy of the reflection content based on the user's past feedback. For example, the reflection unit analyzes the user's past feedback and improves the reflection algorithm. This makes it possible to improve the accuracy of the reflection based on the user's past reflection results.

[0103] The reflection unit can estimate the user's emotions and adjust the length of the images or illustrations to be reflected based on the estimated user emotions. The reflection unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the reflection unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The reflection unit can also estimate the user's emotions using voice analysis technology. For example, the reflection unit records the user's voice and estimates the emotions using a voice analysis algorithm. The reflection unit can also estimate the user's emotions using text analysis technology. For example, the reflection unit analyzes text entered by the user and estimates the emotions. This allows images and illustrations to be reflected at an appropriate length depending on the user's emotions. For example, if the user is in a hurry, short, to-the-point images and illustrations are reflected. If the user is relaxed, longer images and illustrations with detailed explanations are reflected. If the user is excited, images and illustrations with visually stimulating effects are reflected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0104] At the time of reflection, the reflection unit can determine the priority of the reflection content based on the submission time of the document. The reflection unit, for example, evaluates the submission time of the document and determines the priority of the reflection content. For example, if the submission deadline is approaching, the reflection unit will prioritize reflecting important images and illustrations. Furthermore, if the submission deadline is far away, the reflection unit can also reflect detailed images and illustrations. Furthermore, if the submission deadline is medium, the reflection unit can also reflect images and illustrations with a moderate level of detail. This makes it possible to determine the reflection content with appropriate priority depending on the submission time of the document.

[0105] The reflection unit can adjust the order of the reflected contents based on the relevance of the materials when reflecting. The reflection unit, for example, evaluates the relevance of the materials and adjusts the order of the reflected contents. For example, the reflection unit places important information first and arranges the reflected contents in order of relevance. The reflection unit can also postpone less relevant information and arrange important information preferentially. The reflection unit can also arrange the reflected contents in order of relevance according to the flow of the materials. This makes it possible to arrange the reflected contents in an appropriate order according to the relevance of the materials.

[0106] The reflection unit can adjust the use of technical terms in the reflection content according to the user's level of expertise when reflecting. For example, the reflection unit evaluates the user's level of expertise and adjusts the use of technical terms in the reflection content. For example, if the user is an expert, the reflection unit can provide reflection content that uses a lot of technical terms. Also, if the user is a beginner, the reflection unit can provide simple reflection content that avoids technical terms. Also, the reflection unit can adjust the use of appropriate technical terms according to the user's level of expertise. This makes it possible to use appropriate technical terms according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, upload unit, and reflection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives information by a user entering information into an input form. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and automatically generates materials based on the information entered by the user using AI. The upload unit uploads the materials to the cloud via the communication I / F 26 of the data processing device 12. The reflection unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the contents of the materials and suggests appropriate images and illustrations. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, upload unit, and reflection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and receives information using voice input from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and automatically generates materials based on information input by the user using AI. The upload unit uploads the materials to the cloud via the communication I / F 26 of the data processing device 12. The reflection unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the materials and suggests appropriate images and illustrations. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, upload unit, and reflection unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, and receives information using voice input from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and automatically generates materials based on information input by the user using AI. The upload unit uploads the materials to the cloud via the communication I / F 26 of the data processing device 12. The reflection unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the materials and suggests appropriate images and illustrations. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, upload unit, and reflection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and receives information using voice input from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and automatically generates materials based on information input by the user using AI. The upload unit uploads the materials to the cloud via the communication I / F 26 of the data processing device 12. The reflection unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the contents of the materials and suggests appropriate images and illustrations.

[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] When accepting input information from a user, the acceptance unit can analyze the user's past input history and suggest the optimal input method. For example, the acceptance unit can preferentially suggest input methods (voice, text, etc.) that the user has frequently used in the past. The acceptance unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. Furthermore, the acceptance unit can customize the optimal input method based on information that the user has previously input. This makes it possible to provide the optimal acceptance method based on the user's past input history.

[0109] The generation unit can create text, graphs, and numerical data based on the theme or purpose of the presentation and the type of target data input by the user. For example, the generation unit can generate text containing business terms based on the theme of a business presentation. The generation unit can also generate text containing educational terms based on the theme of an educational presentation. The generation unit can also generate graphs for visualizing sales data and graphs for visualizing customer data. This makes it possible to generate appropriate text, graphs, and numerical data based on the information input by the user.

[0110] The reflection unit can reflect the suggested image in the document based on the taste or color of the image or illustration selected by the user. For example, if a business taste is selected, an image suitable for a business scene can be suggested. Also, if a casual taste is selected, an image suitable for a casual scene can be suggested. Furthermore, if a monochrome color is selected, a monochrome image can be suggested, and if a pastel color is selected, a pastel color image can be suggested. This allows appropriate images and illustrations to be reflected in the document based on the user's selection.

[0111] The upload unit can implement security measures such as data encryption or access restrictions when uploading to the cloud. For example, data can be encrypted using AES encryption, and password protection and access authority management can be performed. Access restrictions can also be set so that only specific users can access the data. This ensures security when uploading to the cloud.

[0112] The reflection section can insert an image or illustration into a framed location specified by the user. For example, if the user specifies a frame at a specific location on a slide, the image can be inserted at that location. Also, if the user specifies a frame at a specific location on a PowerPoint presentation, an illustration can be inserted at that location. This allows the appropriate image or illustration to be inserted at the location specified by the user.

[0113] The reflection unit can analyze the content of the document and suggest appropriate images or illustrations. For example, it can analyze the text content of the document and extract related keywords. It can also analyze the content of images included in the document and suggest related images. It can also analyze data included in the document and suggest related illustrations. This makes it possible to suggest appropriate images or illustrations based on the content of the document.

[0114] The reception unit can estimate the user's emotions and adjust the timing of receiving input information based on the estimated user emotions. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. It can also record the user's voice and estimate the emotion using a voice analysis algorithm. It can also analyze text entered by the user to estimate the emotion. This allows the reception of input information at an appropriate timing depending on the user's emotions. For example, if the user is feeling stressed, the reception timing of input information can be delayed to provide time for relaxation. If the user is relaxed, the reception timing of input information can be immediately accepted to promote smooth operation. If the user is in a hurry, the reception timing of input information can be accelerated to provide a prompt response.

[0115] The generation unit can estimate the user's emotion and adjust the expression method of the generated text or graph based on the estimated user's emotion. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using a facial expression recognition algorithm. Alternatively, the user's voice can be recorded and the emotion can be estimated using a voice analysis algorithm. Furthermore, the emotion can be estimated by analyzing text entered by the user. This makes it possible to generate text or graphs in an appropriate expression method according to the user's emotion. For example, if the user is relaxed, text or graphs with soft colors can be generated. If the user is in a hurry, simple text or graphs with high visibility can be generated. If the user is excited, text or graphs with visually stimulating effects can be generated.

[0116] The reflection unit can estimate the user's emotions and adjust the expression method of the reflected image or illustration based on the estimated user's emotions. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using a facial expression recognition algorithm. The user's voice can also be recorded and the emotion can be estimated using a voice analysis algorithm. Furthermore, the emotion can be estimated by analyzing text entered by the user. This makes it possible to reflect images and illustrations in an appropriate expression method depending on the user's emotions. For example, if the user is relaxed, images and illustrations with soft colors can be reflected. If the user is in a hurry, simple images and illustrations with high visibility can be reflected. If the user is excited, images and illustrations with visually stimulating effects can be reflected.

[0117] The upload unit can estimate the user's emotions and adjust the timing of uploading based on the estimated user emotions. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using a facial expression recognition algorithm. The user's voice can also be recorded and the emotion can be estimated using a voice analysis algorithm. Furthermore, the emotion can be estimated by analyzing text entered by the user. This allows uploading to be performed at an appropriate time depending on the user's emotions. For example, if the user is feeling stressed, the upload timing can be delayed to provide time for relaxation. If the user is relaxed, the upload can be performed immediately to promote smooth operation. If the user is in a hurry, the upload timing can be accelerated to provide a prompt response.

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

[0119] Step 1: The reception unit receives input information from the user. The input information from the user includes the presentation theme, purpose, and type of data to be presented. The reception unit receives information by the user entering the information into an input form, and can also receive information using voice input or image input. Step 2: The generation unit creates text, graphs, and numerical data on slides and PPTs based on the information received by the reception unit. The generation unit uses AI to automatically generate materials based on the information entered by the user, creating appropriate text, graphs, and numerical data based on the theme and purpose of the presentation. It can also display data in an appropriate format based on the type of data specified by the user. Step 3: The uploading unit uploads the slides and PPTs generated by the generating unit to the cloud. The uploading unit uses a cloud storage service to store the generated materials in the cloud and can implement security measures such as data encryption and access restrictions. For example, the data can be encrypted using AES encryption, and password protection and access permission management can be implemented. Step 4: The reflection section analyzes the contents of the slides and PPTs uploaded by the upload section and reflects the suggested images in the document. The reflection section uses AI to analyze the contents of the document and suggests appropriate images and illustrations. The suggested images are reflected in the document based on the taste and color of the image or illustration selected by the user, and the image or illustration can also be inserted into a framed area specified by the user.

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

[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 AI 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, 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 information from a user; a generating unit that generates text, graphs, and numerical data on slides or PPTs based on the information received by the receiving unit; an uploading unit that uploads the slides or PPTs generated by the generating unit to a cloud; a reflection unit that analyzes the content of the slide or PPT uploaded by the upload unit and reflects the proposed image in the document. A system characterized by:

2. The generation unit Create text, graphs, and numerical data based on the presentation topic or purpose and type of data you provide.

2. The system of claim 1.

3. The reflection unit Reflecting the proposed image in the document based on the taste or color of the image or illustration selected by the user 2. The system of claim 1.

4. The upload unit Implement security measures such as encrypting data or restricting access when uploading to the cloud 2. The system of claim 1.

5. The reflection unit Insert an image or illustration into a framed area specified by the user 2. The system of claim 1.

6. The reflection unit Analyze the content of the material and suggest appropriate images or illustrations 2. The system of claim 1.

7. The reception unit Estimates user emotions and adjusts the timing of accepting input information based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past input history and select the optimal reception method 2. The system of claim 1.

Images