System

Abstract

An object of a system according to an embodiment is to shorten the time spent by a salesperson to create materials and to enable the salesperson to concentrate on strategic design.SOLUTION: A system according to an embodiment includes a text input unit, a slide generation unit, a font unification unit, and an image insertion unit. The text input unit inputs text. The slide generation unit generates a slide based on the text input by the text input unit. The font unifying unit unifies a font and a size of the slide generated by the slide generation unit. The image insertion unit inserts an image illustration into the slide generated by the slide generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, salespeople had to spend a lot of time creating documents, which prevented them from concentrating on strategy design.

[0005] The system according to the embodiment aims to reduce the time that salespeople spend creating materials, allowing them to focus on strategic design. [Means for solving the problem]

[0006] The system according to the embodiment includes a text input unit, a slide generation unit, a font unification unit, and an image insertion unit. The text input unit inputs text. The slide generation unit generates slides based on the text input by the text input unit. The font unification unit unifies the font and size of the slides generated by the slide generation unit. The image insertion unit inserts an image illustration into the slide generated by the slide generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the time that salespeople spend creating materials, allowing them to focus on strategic design. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) In the automatic proposal material creation system according to an embodiment of the present invention, a salesperson simply transcribes the content into text, and a generation AI generates slides, standardizes fonts and sizes, and inserts image illustrations. This allows the automatic proposal material creation system to significantly reduce the time required to create materials, allowing salespeople to spend more time designing the issues they need to solve.

[0029] An automatic proposal material creation system according to an embodiment includes a text input unit, a slide generation unit, a font unification unit, and an image insertion unit. A salesperson inputs text into the text input unit. For example, the salesperson inputs the contents of the proposal material in text format. The slide generation unit generates slides based on the text input by the text input unit. For example, the generation AI analyzes the input text and determines an appropriate slide layout and design. The font unification unit unifies the font and size of the slides generated by the slide generation unit. For example, the generation AI uses the same font and size across all slides to create a sense of uniformity. The image insertion unit inserts image illustrations into the slides generated by the slide generation unit. For example, the generation AI automatically inserts image illustrations that correspond to the content of the slide. This significantly reduces the time required to create materials, allowing users to focus on their sales strategies.

[0030] The text input unit can perform grammar checks and corrections on the input text. For example, the generation AI analyzes text input by a salesperson in real time and automatically corrects grammatical errors and spelling mistakes. For example, the instruction "Please create a slide that explains the features of our new product" is corrected to "Please create a slide that explains the features of our new product." The generation AI also checks the grammar of the input text and converts it into more professional expression. For example, the sentence "This product is very good" is corrected to "This product is extremely excellent." The generation AI also automatically checks the grammar of the text input by a salesperson and suggests appropriate corrections. For example, the instruction "Please create a slide that shows the results of your market analysis" is corrected to "Please create a slide that shows the results of your market analysis." This improves the quality of the input text.

[0031] The text input unit can refer to relevant past proposal materials and databases and automatically present reference information. For example, when a salesperson inputs text, the generation AI automatically searches for past proposal materials and presents related information. For example, in response to an instruction such as, "Please create a slide that explains the features of a new product," similar past proposal materials are displayed. The generation AI also references a database and automatically presents reference information related to the input text. For example, in response to an instruction such as, "Please create a slide that shows the results of a market analysis," past market analysis data is displayed. In addition, when a salesperson inputs text, the generation AI automatically references relevant past proposal materials and databases and presents reference information. For example, in response to an instruction such as, "Please create a slide that explains the features of a new product," related information from past proposal materials and databases is displayed. This allows for improved efficiency in document creation by referring to past proposal materials and databases.

[0032] The text input unit uses voice input to convert what a salesperson says into text in real time, which can be used directly to create slides. The text input unit provides, for example, a voice input function that converts what a salesperson says into text in real time, and automatically generates slides based on that text. For example, when a salesperson says, "Please create a slide that explains the features of our new product," the content is converted into text, and slides are generated. Furthermore, the text input unit also uses voice input to convert what a salesperson says into text in real time, and slides are created based on that text. For example, when a salesperson says, "Please create a slide that shows the results of our market analysis," the content is converted into text, and slides are generated. Furthermore, the text input unit provides a voice input function that converts what a salesperson says into text in real time, and automatically generates slides based on that text. For example, when a salesperson says, "Please create a slide that explains the features of our new product," the content is converted into text, and slides are generated. In this way, using voice input eliminates the need for text input and enables slide creation to be expedited.

[0033] The text input unit automatically suggests related visual content as text is entered, thereby enhancing the visual impact of the materials. For example, when a salesperson enters text, the generation AI automatically suggests related visual content. For example, in response to the instruction, "Please create a slide that explains the features of the new product," it suggests product images and videos. The generation AI also automatically suggests related visual content as text is entered, thereby enhancing the visual impact of the materials. For example, in response to the instruction, "Please create a slide that shows the results of a market analysis," it suggests related graphs and charts. The generation AI also automatically suggests related visual content as a salesperson enters text, thereby enhancing the visual impact of the materials. For example, in response to the instruction, "Please create a slide that explains the features of the new product," it suggests product images and videos. In this way, by suggesting visual content, the visual impact of the materials can be enhanced.

[0034] The slide generation unit can refer to past success stories and best practices to propose the optimal slide configuration. In the slide generation unit, for example, the generation AI refers to past success stories and proposes the optimal slide configuration. For example, it proposes a similar slide layout based on past successful proposal materials. The generation AI also refers to best practices and proposes the optimal slide configuration. For example, it proposes the optimal slide configuration based on industry standard slide layouts. The generation AI also refers to past success stories and best practices to propose the optimal slide configuration. For example, it proposes the optimal slide layout based on past success stories. In this way, the optimal slide configuration can be proposed by referring to past success stories and best practices.

[0035] The slide generation unit can evaluate the reliability of data when generating slides and use only highly reliable information. For example, when the generation AI generates slides, the slide generation unit automatically evaluates the reliability of input data and uses only highly reliable information. For example, it excludes low-reliability information. The generation AI can also evaluate the reliability of data and generate slides using only highly reliable information. For example, it can prioritize the use of highly reliable data sources. The generation AI can also automatically evaluate the reliability of data when generating slides and use only highly reliable information. For example, it can exclude low-reliability information. This can improve the reliability of materials by using only highly reliable information.

[0036] The slide generation unit can automatically apply design templates for different industries or fields to provide a variety of visual styles. For example, when the generation AI generates slides, the slide generation unit automatically applies design templates for different industries to provide a variety of visual styles. For example, templates for the medical industry or templates for the IT industry are used. The slide generation unit also automatically applies design templates for different fields to diversify the visual styles of slides. For example, templates for the education or entertainment field are used. The generation AI also automatically applies design templates for different industries or fields to provide a variety of visual styles when generating slides. For example, templates for the financial industry or templates for the manufacturing industry are used. In this way, a variety of visual styles can be provided by applying design templates for different industries or fields.

[0037] The slide generation unit can automatically insert related infographics and interactive elements when generating slides to enhance visual appeal. For example, when the generation AI generates slides, the slide generation unit automatically inserts related infographics to enhance visual appeal. For example, graphs and charts for visualizing data are inserted. Furthermore, when generating slides, the generation AI automatically inserts interactive elements to enhance visual appeal. For example, an interactive graph that displays detailed information when clicked is inserted. Furthermore, when the generation AI generates slides, the generation AI automatically inserts related infographics and interactive elements to enhance visual appeal. For example, an animation for visualizing data is inserted. In this way, inserting infographics and interactive elements can enhance visual appeal.

[0038] The font unification unit may use an algorithm that optimizes visual readability and aesthetic beauty when unifying fonts and sizes. For example, the font unification unit may use an algorithm that optimizes visual readability and aesthetic beauty when unifying fonts and sizes. For example, it may select a font that is easy to read and an appropriate size. Furthermore, when unifying fonts and sizes, it may use an algorithm that optimizes visual readability and aesthetic beauty. For example, it may select a visually balanced font and size. Furthermore, when unifying fonts and sizes, it may use an algorithm that optimizes visual readability and aesthetic beauty. For example, it may select a visually appealing font and size. This may improve the quality of the material by optimizing visual readability and aesthetic beauty.

[0039] The font unification unit not only unifies fonts and sizes, but also automatically adjusts the consistency of colors and layouts, thereby improving overall design quality. For example, the generation AI automatically selects colors and layouts that have a consistent look. In addition to unifying fonts and sizes, the generation AI automatically adjusts the consistency of colors and layouts, thereby improving overall design quality. For example, the generation AI automatically selects colors and layouts that are visually balanced. In addition to unifying fonts and sizes, the generation AI automatically adjusts the consistency of colors and layouts, thereby improving overall design quality. For example, the generation AI automatically selects colors and layouts that are visually appealing. In this way, the generation AI automatically adjusts the consistency of colors and layouts, thereby improving overall design quality.

[0040] The font unification unit can automatically apply customization settings based on a company's brand guidelines. For example, when the generation AI unifies fonts and sizes, the font unification unit automatically applies customization settings based on the company's brand guidelines. For example, the company's logo and color palette are used. Furthermore, when unifying fonts and sizes, the font unification unit automatically applies customization settings based on the company's brand guidelines. For example, the company's font style and size are used. Furthermore, when the generation AI unifies fonts and sizes, the font unification unit automatically applies customization settings based on the company's brand guidelines. For example, the company's design template is used. In this way, brand consistency can be maintained by applying customization settings based on the company's brand guidelines.

[0041] The font unification unit not only unifies fonts and sizes, but also automatically sets slide animation effects and transitions, thereby enhancing the dynamic effects of presentations. For example, the generation AI may unify fonts and sizes, automatically set slide animation effects and transitions, thereby enhancing the dynamic effects of presentations. For example, it may set slide transition effects. In addition to unifying fonts and sizes, it may automatically set slide animation effects and transitions, thereby enhancing the dynamic effects of presentations. For example, it may set a text fade-in effect. In addition to unifying fonts and sizes, the generation AI may automatically set slide animation effects and transitions, thereby enhancing the dynamic effects of presentations. For example, it may set an image zoom-in effect. This automatically setting animation effects and transitions enhances the dynamic effects of presentations.

[0042] The image insertion unit automatically checks the copyright and license information of images, and can use only appropriate images. For example, when the generation AI inserts an image illustration, the image insertion unit automatically checks the copyright and license information of images, and can use only appropriate images. For example, it prioritizes the use of images with free licenses. It also automatically checks the copyright and license information of images, and can use only appropriate images. For example, it uses copyright-free images. When the generation AI inserts an image illustration, it automatically checks the copyright and license information of images, and can use only appropriate images. For example, it uses images with clear licenses. This allows the copyright and license information of images to be checked, and can use only appropriate images.

[0043] In addition to inserting an image illustration, the image insertion unit automatically generates a caption and description for the image, thereby aiding visual understanding. For example, when the generation AI inserts an image illustration, the image insertion unit automatically generates a caption and description for the image, thereby aiding visual understanding. For example, it generates a caption such as "Features of a new product" for a product image. In addition to inserting an image illustration, the generation AI automatically generates a caption and description for the image, thereby aiding visual understanding. For example, it generates a description such as "Changes in market share" for a market analysis graph. In addition, when the generation AI inserts an image illustration, it automatically generates a caption and description for the image, thereby aiding visual understanding. For example, it generates a caption such as "Features of a new product" for a product image. In this way, generating a caption and description for the image can aid visual understanding.

[0044] The image insertion unit can automatically integrate custom images and company logos provided by the user. For example, when the generation AI inserts an image illustration, the image insertion unit automatically integrates custom images and company logos provided by the user. For example, it inserts a company logo into the header of the slide. It also automatically integrates custom images and company logos provided by the user and reflects them in the slide design. For example, it uses a product image as the main visual of the slide. It also automatically integrates custom images and company logos provided by the user when the generation AI inserts an image illustration. For example, it inserts a company logo into the footer of the slide. This allows for increased customization of materials by integrating custom images and company logos provided by the user.

[0045] The image insertion unit can automatically insert related video clips and animations in addition to inserting image illustrations to enhance the visual impact. For example, the generation AI can automatically insert related video clips in addition to inserting image illustrations to enhance the visual impact. For example, a product demonstration video can be inserted into a slide. Furthermore, in addition to inserting image illustrations, the generation AI can automatically insert animations to enhance the visual impact. For example, an animation showing data fluctuations can be inserted into a slide. Furthermore, in addition to inserting image illustrations, the generation AI can automatically insert related video clips and animations to enhance the visual impact. For example, an animation showing how to use a product can be inserted into a slide. In this way, by inserting video clips and animations, the visual impact can be enhanced.

[0046] Furthermore, the automatic proposal document creation system monitors the progress of document creation in real time and proposes the optimal work flow. In the automatic proposal document creation system, for example, the generation AI monitors the progress of document creation in real time and proposes the optimal work flow. For example, it automatically sets work priorities. The progress of document creation is also monitored in real time and the generation AI proposes the optimal work flow. For example, it presents steps to maximize work efficiency. The generation AI monitors the progress of document creation in real time and proposes the optimal work flow. For example, it automatically presents the next step depending on the progress of the work. In this way, work efficiency can be maximized by monitoring the progress of document creation and proposing the optimal work flow.

[0047] Furthermore, the automatic proposal material creation system automatically records progress at each step of document creation so that it can be referenced later. In the automatic proposal material creation system, for example, the generation AI automatically records progress at each step of document creation so that it can be referenced later. For example, the work history is automatically saved. Also, at each step of document creation, the generation AI automatically records progress so that it can be referenced later. For example, the work progress is displayed on a timeline. Also, at each step of document creation, the generation AI automatically records progress so that it can be referenced later. For example, the work history is automatically saved. This makes it possible to manage the work history by recording the progress of document creation so that it can be referenced later.

[0048] Furthermore, the automatic proposal material creation system supports collaboration with other team members in real time when creating materials, thereby increasing the efficiency of collaborative work. For example, the automatic proposal material creation system supports collaboration with other team members in real time when creating materials, by providing a collaborative editing function, for example. The generation AI also supports collaboration with other team members in real time when creating materials, by providing comments and feedback in real time, for example. The generation AI also supports collaboration with other team members in real time when creating materials, by providing a collaborative editing function, for example. This supports collaboration with other team members, thereby increasing the efficiency of collaborative work.

[0049] Furthermore, the automatic proposal material creation system improves overall work efficiency by visualizing the progress of document creation and linking with a project management tool. In the automatic proposal material creation system, for example, the generation AI visualizes the progress of document creation and links with a project management tool. For example, the progress is displayed in a Gantt chart. Also, by visualizing the progress of document creation and linking with a project management tool, overall work efficiency is improved. For example, the progress is displayed in a dashboard. Also, the generation AI visualizes the progress of document creation and links with a project management tool. For example, the progress is displayed in a Gantt chart. In this way, by visualizing the progress of document creation and linking with a project management tool, overall work efficiency can be improved.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The automated proposal material creation system can further include a legal information provider that automatically provides relevant legal information based on user input. For example, when a salesperson creates a slide to explain the features of a new product, the generation AI automatically presents relevant patent and trademark information. The legal information provider can also automatically present relevant legal risks and points to note based on the input text. For example, it could present legal risks related to the launch of a new product. This allows salespeople to create materials while taking legal risks into consideration.

[0052] The automated proposal creation system can further include a market data provider that automatically provides relevant market data based on user input. For example, when a salesperson creates a slide showing the results of a market analysis, the generation AI automatically presents the latest market data and trend information. The market data provider can also automatically present relevant market forecasts and competitive analysis based on the input text. For example, it could present the market share and growth forecasts of competitors. This allows salespeople to create materials based on the latest market information.

[0053] The automated proposal creation system can further include a technical information provider that automatically provides relevant technical information based on user input. For example, when a salesperson creates a slide to explain the technical features of a new product, the generation AI automatically presents related technical literature and research results. The technical information provider can also automatically present relevant technical risks and challenges based on the input text. For example, it could present the technical risks associated with introducing new technology. This allows salespeople to create documents with technical backing.

[0054] The automated proposal creation system can further include a financial information provider that automatically provides relevant financial information based on user input. For example, when a salesperson creates a slide to explain the financial impact of a new product, the generative AI automatically presents relevant financial data and forecasts. The financial information provider can also automatically present relevant financial risks and investment benefits based on the input text. For example, it can present the investment benefits associated with launching a new product. This allows salespeople to create materials from a financial perspective.

[0055] The automated proposal material creation system can further include an environmental information provider that automatically provides relevant environmental information based on user input. For example, when a salesperson creates a slide to explain the environmental impact of a new product, the generation AI automatically presents relevant environmental data and regulatory information. The environmental information provider can also automatically present information on relevant environmental risks and sustainability based on the input text. For example, it could present the environmental impact of the new product's manufacturing process. This allows salespeople to create materials from an environmental perspective.

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

[0057] Step 1: In the text input section, a salesperson inputs text. For example, the salesperson inputs the contents of a proposal document in text format. Step 2: The slide generator generates slides based on the text entered by the text input unit. For example, the generation AI analyzes the entered text and determines the appropriate slide layout and design. Step 3: The font unification unit unifies the font and size of the slides generated by the slide generation unit. For example, the generation AI uses the same font and size on all slides to create a uniform look. Step 4: The image insertion unit inserts image illustrations into the slides generated by the slide generation unit. For example, the generation AI automatically inserts image illustrations according to the content of the slides.

[0058] (Example 2) In the automatic proposal material creation system according to an embodiment of the present invention, a salesperson simply transcribes the content into text, and a generation AI generates slides, standardizes fonts and sizes, and inserts image illustrations. This allows the automatic proposal material creation system to significantly reduce the time required to create materials, allowing salespeople to spend more time designing the issues they need to solve.

[0059] An automatic proposal material creation system according to an embodiment includes a text input unit, a slide generation unit, a font unification unit, and an image insertion unit. A salesperson inputs text into the text input unit. For example, the salesperson inputs the contents of the proposal material in text format. The slide generation unit generates slides based on the text input by the text input unit. For example, the generation AI analyzes the input text and determines an appropriate slide layout and design. The font unification unit unifies the font and size of the slides generated by the slide generation unit. For example, the generation AI uses the same font and size across all slides to create a sense of uniformity. The image insertion unit inserts image illustrations into the slides generated by the slide generation unit. For example, the generation AI automatically inserts image illustrations that correspond to the content of the slide. This significantly reduces the time required to create materials, allowing users to focus on their sales strategies.

[0060] The text input unit can perform grammar checks and corrections on the input text. For example, the generation AI analyzes text input by a salesperson in real time and automatically corrects grammatical errors and spelling mistakes. For example, the instruction "Please create a slide that explains the features of our new product" is corrected to "Please create a slide that explains the features of our new product." The generation AI also checks the grammar of the input text and converts it into more professional expression. For example, the sentence "This product is very good" is corrected to "This product is extremely excellent." The generation AI also automatically checks the grammar of the text input by a salesperson and suggests appropriate corrections. For example, the instruction "Please create a slide that shows the results of your market analysis" is corrected to "Please create a slide that shows the results of your market analysis." This improves the quality of the input text.

[0061] The text input unit can refer to relevant past proposal materials and databases and automatically present reference information. For example, when a salesperson inputs text, the generation AI automatically searches for past proposal materials and presents related information. For example, in response to an instruction such as, "Please create a slide that explains the features of a new product," similar past proposal materials are displayed. The generation AI also references a database and automatically presents reference information related to the input text. For example, in response to an instruction such as, "Please create a slide that shows the results of a market analysis," past market analysis data is displayed. In addition, when a salesperson inputs text, the generation AI automatically references relevant past proposal materials and databases and presents reference information. For example, in response to an instruction such as, "Please create a slide that explains the features of a new product," related information from past proposal materials and databases is displayed. This allows for improved efficiency in document creation by referring to past proposal materials and databases.

[0062] The text input unit can use the emotion estimation function to analyze the emotional tone of the input text and make suggestions to convert it to a positive expression. For example, the text input unit uses the emotion estimation function to analyze the emotional tone of text input by a salesperson and make suggestions to convert it to a positive expression. For example, the generation AI can suggest, for a sentence such as "This product has problems," that it becomes "This product has room for improvement." The generation AI can also analyze the emotional tone of the input text and make suggestions to convert negative expressions to positive expressions. For example, the generation AI can suggest, for a sentence such as "The market analysis results are poor," that it becomes "The market analysis results have room for improvement." The generation AI can also analyze the emotional tone of text input by a salesperson and make suggestions to convert it to a positive expression. For example, the generation AI can suggest, for a sentence such as "This product is not very good," that it becomes "This product has room for improvement." This can convert the emotional tone of the input text to a positive one, thereby improving the impression of the document.

[0063] The text input unit uses voice input to convert what a salesperson says into text in real time, which can be used directly to create slides. The text input unit provides, for example, a voice input function that converts what a salesperson says into text in real time, and automatically generates slides based on that text. For example, when a salesperson says, "Please create a slide that explains the features of our new product," the content is converted into text, and slides are generated. Furthermore, the text input unit also uses voice input to convert what a salesperson says into text in real time, and slides are created based on that text. For example, when a salesperson says, "Please create a slide that shows the results of our market analysis," the content is converted into text, and slides are generated. Furthermore, the text input unit provides a voice input function that converts what a salesperson says into text in real time, and automatically generates slides based on that text. For example, when a salesperson says, "Please create a slide that explains the features of our new product," the content is converted into text, and slides are generated. In this way, using voice input eliminates the need for text input and enables slide creation to be expedited.

[0064] The text input unit automatically suggests related visual content as text is entered, thereby enhancing the visual impact of the materials. For example, when a salesperson enters text, the generation AI automatically suggests related visual content. For example, in response to the instruction, "Please create a slide that explains the features of the new product," it suggests product images and videos. The generation AI also automatically suggests related visual content as text is entered, thereby enhancing the visual impact of the materials. For example, in response to the instruction, "Please create a slide that shows the results of a market analysis," it suggests related graphs and charts. The generation AI also automatically suggests related visual content as a salesperson enters text, thereby enhancing the visual impact of the materials. For example, in response to the instruction, "Please create a slide that explains the features of the new product," it suggests product images and videos. In this way, by suggesting visual content, the visual impact of the materials can be enhanced.

[0065] The text input unit can use an emotion estimation function to analyze the emotions of a salesperson when they are entering text, and provide an interface for reducing stress. For example, when a salesperson enters text, the generation AI can use the emotion estimation function to analyze the emotions in real time, and provide an interface for reducing stress. For example, relaxing music can be played while the salesperson is entering text. The generation AI can also analyze the emotions of a salesperson in real time, and provide an interface for reducing stress. For example, a relaxing background image can be displayed while the salesperson is entering text. The generation AI can also analyze the emotions of a salesperson when they are entering text, and provide an interface for reducing stress. For example, a relaxing message can be displayed while the salesperson is entering text. This can reduce stress for salespeople and provide a comfortable working environment.

[0066] The slide generation unit can refer to past success stories and best practices to propose the optimal slide configuration. In the slide generation unit, for example, the generation AI refers to past success stories and proposes the optimal slide configuration. For example, it proposes a similar slide layout based on past successful proposal materials. The generation AI also refers to best practices and proposes the optimal slide configuration. For example, it proposes the optimal slide configuration based on industry standard slide layouts. The generation AI also refers to past success stories and best practices to propose the optimal slide configuration. For example, it proposes the optimal slide layout based on past success stories. In this way, the optimal slide configuration can be proposed by referring to past success stories and best practices.

[0067] The slide generation unit can evaluate the reliability of data when generating slides and use only highly reliable information. For example, when the generation AI generates slides, the slide generation unit automatically evaluates the reliability of input data and uses only highly reliable information. For example, it excludes low-reliability information. The generation AI can also evaluate the reliability of data and generate slides using only highly reliable information. For example, it can prioritize the use of highly reliable data sources. The generation AI can also automatically evaluate the reliability of data when generating slides and use only highly reliable information. For example, it can exclude low-reliability information. This can improve the reliability of materials by using only highly reliable information.

[0068] The slide generation unit can use the emotion estimation function to predict the emotional impact that the slide content will have on the viewer and select the optimal expression. In the slide generation unit, for example, the generation AI uses the emotion estimation function to predict the emotional impact that the slide content will have on the viewer and select the optimal expression. For example, it selects an expression that elicits positive emotions. In addition, the emotion estimation function is used to predict the emotional impact that the slide content will have on the viewer and select the optimal expression. For example, it selects colors and fonts that will elicit the viewer's emotions. In addition, the generation AI uses the emotion estimation function to predict the emotional impact that the slide content will have on the viewer and select the optimal expression. For example, it selects images and graphs that will elicit the viewer's emotions. In this way, the effectiveness of the presentation can be enhanced by predicting the emotional impact on the viewer and selecting the optimal expression.

[0069] The slide generation unit can automatically apply design templates for different industries or fields to provide a variety of visual styles. For example, when the generation AI generates slides, the slide generation unit automatically applies design templates for different industries to provide a variety of visual styles. For example, templates for the medical industry or templates for the IT industry are used. The slide generation unit also automatically applies design templates for different fields to diversify the visual styles of slides. For example, templates for the education or entertainment field are used. The generation AI also automatically applies design templates for different industries or fields to provide a variety of visual styles when generating slides. For example, templates for the financial industry or templates for the manufacturing industry are used. In this way, a variety of visual styles can be provided by applying design templates for different industries or fields.

[0070] The slide generation unit can automatically insert related infographics and interactive elements when generating slides to enhance visual appeal. For example, when the generation AI generates slides, the slide generation unit automatically inserts related infographics to enhance visual appeal. For example, graphs and charts for visualizing data are inserted. Furthermore, when generating slides, the generation AI automatically inserts interactive elements to enhance visual appeal. For example, an interactive graph that displays detailed information when clicked is inserted. Furthermore, when the generation AI generates slides, the generation AI automatically inserts related infographics and interactive elements to enhance visual appeal. For example, an animation for visualizing data is inserted. In this way, inserting infographics and interactive elements can enhance visual appeal.

[0071] The slide generation unit can use the emotion estimation function to monitor the emotional response that the slide content has on the viewer in real time and modify the slide as necessary. For example, the generation AI in the slide generation unit uses the emotion estimation function to monitor the emotional response that the slide content has on the viewer in real time and modify the slide as necessary. For example, if the viewer's emotion is negative, the slide content is changed. The emotion estimation function can also be used to monitor the emotional response that the slide content has on the viewer in real time and modify the slide as necessary. For example, if the viewer's emotion is positive, the slide content is emphasized. The generation AI can also use the emotion estimation function to monitor the emotional response that the slide content has on the viewer in real time and modify the slide as necessary. For example, the slide content is adjusted if the viewer's emotion changes. In this way, the effectiveness of the presentation can be maximized by monitoring the viewer's emotional response in real time and modifying the slide.

[0072] The font unification unit may use an algorithm that optimizes visual readability and aesthetic beauty when unifying fonts and sizes. For example, the font unification unit may use an algorithm that optimizes visual readability and aesthetic beauty when unifying fonts and sizes. For example, it may select a font that is easy to read and an appropriate size. Furthermore, when unifying fonts and sizes, it may use an algorithm that optimizes visual readability and aesthetic beauty. For example, it may select a visually balanced font and size. Furthermore, when unifying fonts and sizes, it may use an algorithm that optimizes visual readability and aesthetic beauty. For example, it may select a visually appealing font and size. This may improve the quality of the material by optimizing visual readability and aesthetic beauty.

[0073] The font unification unit not only unifies fonts and sizes, but also automatically adjusts the consistency of colors and layouts, thereby improving overall design quality. For example, the generation AI automatically selects colors and layouts that have a consistent look. In addition to unifying fonts and sizes, the generation AI automatically adjusts the consistency of colors and layouts, thereby improving overall design quality. For example, the generation AI automatically selects colors and layouts that are visually balanced. In addition to unifying fonts and sizes, the generation AI automatically adjusts the consistency of colors and layouts, thereby improving overall design quality. For example, the generation AI automatically selects colors and layouts that are visually appealing. In this way, the generation AI automatically adjusts the consistency of colors and layouts, thereby improving overall design quality.

[0074] The font unification unit can use the emotion estimation function to select the font and size that will have the most positive impact on the viewer's emotions. In the font unification unit, for example, the generation AI uses the emotion estimation function to select the font and size that will have the most positive impact on the viewer's emotions. For example, the optimal font and size is selected based on the viewer's emotion score. The emotion estimation function is also used to select the font and size that will have the most positive impact on the viewer's emotions. For example, the optimal font and size is selected based on the viewer's emotional response. The generation AI also uses the emotion estimation function to select the font and size that will have the most positive impact on the viewer's emotions. For example, the optimal font and size is selected based on the viewer's emotional data. In this way, the effectiveness of the presentation can be enhanced by selecting the font and size that will have the most positive impact on the viewer's emotions.

[0075] The font unification unit can automatically apply customization settings based on a company's brand guidelines. For example, when the generation AI unifies fonts and sizes, the font unification unit automatically applies customization settings based on the company's brand guidelines. For example, the company's logo and color palette are used. Furthermore, when unifying fonts and sizes, the font unification unit automatically applies customization settings based on the company's brand guidelines. For example, the company's font style and size are used. Furthermore, when the generation AI unifies fonts and sizes, the font unification unit automatically applies customization settings based on the company's brand guidelines. For example, the company's design template is used. In this way, brand consistency can be maintained by applying customization settings based on the company's brand guidelines.

[0076] The font unification unit not only unifies fonts and sizes, but also automatically sets slide animation effects and transitions, thereby enhancing the dynamic effects of presentations. For example, the generation AI may unify fonts and sizes, automatically set slide animation effects and transitions, thereby enhancing the dynamic effects of presentations. For example, it may set slide transition effects. In addition to unifying fonts and sizes, it may automatically set slide animation effects and transitions, thereby enhancing the dynamic effects of presentations. For example, it may set a text fade-in effect. In addition to unifying fonts and sizes, the generation AI may automatically set slide animation effects and transitions, thereby enhancing the dynamic effects of presentations. For example, it may set an image zoom-in effect. This automatically setting animation effects and transitions enhances the dynamic effects of presentations.

[0077] The font unification unit can use the emotion estimation function to analyze the viewer's emotional response in real time and continuously adjust the optimal font and size. In the font unification unit, for example, the generation AI uses the emotion estimation function to analyze the viewer's emotional response in real time and continuously adjust the optimal font and size. For example, adjusting the font and size based on the viewer's emotion score. In addition, the emotion estimation function is used to analyze the viewer's emotional response in real time and continuously adjust the optimal font and size. For example, adjusting the font and size based on the viewer's emotional response. In addition, the generation AI uses the emotion estimation function to analyze the viewer's emotional response in real time and continuously adjust the optimal font and size. For example, adjusting the font and size based on the viewer's emotion data. In this way, the effectiveness of the presentation can be maximized by analyzing the viewer's emotional response in real time and continuously adjusting the optimal font and size.

[0078] The image insertion unit automatically checks the copyright and license information of images, and can use only appropriate images. For example, when the generation AI inserts an image illustration, the image insertion unit automatically checks the copyright and license information of images, and can use only appropriate images. For example, it prioritizes the use of images with free licenses. It also automatically checks the copyright and license information of images, and can use only appropriate images. For example, it uses copyright-free images. When the generation AI inserts an image illustration, it automatically checks the copyright and license information of images, and can use only appropriate images. For example, it uses images with clear licenses. This allows the copyright and license information of images to be checked, and can use only appropriate images.

[0079] In addition to inserting an image illustration, the image insertion unit automatically generates a caption and description for the image, thereby aiding visual understanding. For example, when the generation AI inserts an image illustration, the image insertion unit automatically generates a caption and description for the image, thereby aiding visual understanding. For example, it generates a caption such as "Features of a new product" for a product image. In addition to inserting an image illustration, the generation AI automatically generates a caption and description for the image, thereby aiding visual understanding. For example, it generates a description such as "Changes in market share" for a market analysis graph. In addition, when the generation AI inserts an image illustration, it automatically generates a caption and description for the image, thereby aiding visual understanding. For example, it generates a caption such as "Features of a new product" for a product image. In this way, generating a caption and description for the image can aid visual understanding.

[0080] The image insertion unit can use the emotion estimation function to select the image illustration that will have the most positive impact on the viewer's emotions. In the image insertion unit, for example, the generation AI uses the emotion estimation function to select the image illustration that will have the most positive impact on the viewer's emotions. For example, the optimal image illustration is selected based on the viewer's emotion score. In addition, the emotion estimation function is used to select the image illustration that will have the most positive impact on the viewer's emotions. For example, the optimal image illustration is selected based on the viewer's emotional response. In addition, the generation AI uses the emotion estimation function to select the image illustration that will have the most positive impact on the viewer's emotions. For example, the optimal image illustration is selected based on the viewer's emotion data. In this way, the effectiveness of the presentation can be enhanced by selecting the image illustration that will have the most positive impact on the viewer's emotions.

[0081] The image insertion unit can automatically integrate custom images and company logos provided by the user. For example, when the generation AI inserts an image illustration, the image insertion unit automatically integrates custom images and company logos provided by the user. For example, it inserts a company logo into the header of the slide. It also automatically integrates custom images and company logos provided by the user and reflects them in the slide design. For example, it uses a product image as the main visual of the slide. It also automatically integrates custom images and company logos provided by the user when the generation AI inserts an image illustration. For example, it inserts a company logo into the footer of the slide. This allows for increased customization of materials by integrating custom images and company logos provided by the user.

[0082] The image insertion unit can automatically insert related video clips and animations in addition to inserting image illustrations to enhance the visual impact. For example, the generation AI can automatically insert related video clips in addition to inserting image illustrations to enhance the visual impact. For example, a product demonstration video can be inserted into a slide. Furthermore, in addition to inserting image illustrations, the generation AI can automatically insert animations to enhance the visual impact. For example, an animation showing data fluctuations can be inserted into a slide. Furthermore, in addition to inserting image illustrations, the generation AI can automatically insert related video clips and animations to enhance the visual impact. For example, an animation showing how to use a product can be inserted into a slide. In this way, by inserting video clips and animations, the visual impact can be enhanced.

[0083] The image insertion unit can use the emotion estimation function to analyze the viewer's emotional response in real time and continuously adjust the optimal image illustration. In the image insertion unit, for example, the generation AI uses the emotion estimation function to analyze the viewer's emotional response in real time and continuously adjust the optimal image illustration. For example, the image illustration is changed based on the viewer's emotion score. Also, the emotion estimation function is used to analyze the viewer's emotional response in real time and continuously adjust the optimal image illustration. For example, the image illustration is changed based on the viewer's emotional response. Also, the generation AI uses the emotion estimation function to analyze the viewer's emotional response in real time and continuously adjust the optimal image illustration. For example, the image illustration is changed based on the viewer's emotion data. In this way, the effectiveness of the presentation can be maximized by analyzing the viewer's emotional response in real time and continuously adjusting the optimal image illustration.

[0084] Furthermore, the automatic proposal document creation system monitors the progress of document creation in real time and proposes the optimal work flow. In the automatic proposal document creation system, for example, the generation AI monitors the progress of document creation in real time and proposes the optimal work flow. For example, it automatically sets work priorities. The progress of document creation is also monitored in real time and the generation AI proposes the optimal work flow. For example, it presents steps to maximize work efficiency. The generation AI monitors the progress of document creation in real time and proposes the optimal work flow. For example, it automatically presents the next step depending on the progress of the work. In this way, work efficiency can be maximized by monitoring the progress of document creation and proposing the optimal work flow.

[0085] Furthermore, the automatic proposal material creation system automatically records progress at each step of document creation so that it can be referenced later. In the automatic proposal material creation system, for example, the generation AI automatically records progress at each step of document creation so that it can be referenced later. For example, the work history is automatically saved. Also, at each step of document creation, the generation AI automatically records progress so that it can be referenced later. For example, the work progress is displayed on a timeline. Also, at each step of document creation, the generation AI automatically records progress so that it can be referenced later. For example, the work history is automatically saved. This makes it possible to manage the work history by recording the progress of document creation so that it can be referenced later.

[0086] Furthermore, the automatic proposal material creation system uses an emotion estimation function to analyze the stress level of a user while creating a document and provides a relaxing environment. In the automatic proposal material creation system, for example, the generation AI uses the emotion estimation function to analyze the stress level of a user while creating a document and provides a relaxing environment. For example, relaxing music is played. The emotion estimation function is also used to analyze the stress level of a user while creating a document and provides a relaxing environment. For example, a relaxing background image is displayed. The generation AI also uses the emotion estimation function to analyze the stress level of a user while creating a document and provides a relaxing environment. For example, a relaxing message is displayed. In this way, work efficiency can be improved by analyzing the user's stress level and providing a relaxing environment.

[0087] Furthermore, the automatic proposal material creation system supports collaboration with other team members in real time when creating materials, thereby increasing the efficiency of collaborative work. For example, the automatic proposal material creation system supports collaboration with other team members in real time when creating materials, by providing a collaborative editing function, for example. The generation AI also supports collaboration with other team members in real time when creating materials, by providing comments and feedback in real time, for example. The generation AI also supports collaboration with other team members in real time when creating materials, by providing a collaborative editing function, for example. This supports collaboration with other team members, thereby increasing the efficiency of collaborative work.

[0088] Furthermore, the automatic proposal material creation system improves overall work efficiency by visualizing the progress of document creation and linking with a project management tool. In the automatic proposal material creation system, for example, the generation AI visualizes the progress of document creation and links with a project management tool. For example, the progress is displayed in a Gantt chart. Also, by visualizing the progress of document creation and linking with a project management tool, overall work efficiency is improved. For example, the progress is displayed in a dashboard. Also, the generation AI visualizes the progress of document creation and links with a project management tool. For example, the progress is displayed in a Gantt chart. In this way, by visualizing the progress of document creation and linking with a project management tool, overall work efficiency can be improved.

[0089] Furthermore, the automatic proposal document creation system uses an emotion estimation function to monitor the emotional reactions of a user while creating a document in real time, and provides an optimal work environment. In the automatic proposal document creation system, for example, the generation AI uses the emotion estimation function to monitor the emotional reactions of a user while creating a document in real time, and provides an optimal work environment. For example, the work environment is adjusted based on the user's emotion score. In addition, the emotion estimation function is used to monitor the emotional reactions of a user while creating a document in real time, and provides an optimal work environment. For example, the work environment is adjusted based on the user's emotional reactions. In addition, the generation AI uses the emotion estimation function to monitor the emotional reactions of a user while creating a document in real time, and provides an optimal work environment. For example, the work environment is adjusted based on the user's emotion data. In this way, work efficiency can be improved by monitoring the user's emotional reactions in real time and providing an optimal work environment.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The automated proposal material creation system can further include a legal information provider that automatically provides relevant legal information based on user input. For example, when a salesperson creates a slide to explain the features of a new product, the generation AI automatically presents relevant patent and trademark information. The legal information provider can also automatically present relevant legal risks and points to note based on the input text. For example, it could present legal risks related to the launch of a new product. This allows salespeople to create materials while taking legal risks into consideration.

[0092] The automated proposal creation system can further include a market data provider that automatically provides relevant market data based on user input. For example, when a salesperson creates a slide showing the results of a market analysis, the generation AI automatically presents the latest market data and trend information. The market data provider can also automatically present relevant market forecasts and competitive analysis based on the input text. For example, it could present the market share and growth forecasts of competitors. This allows salespeople to create materials based on the latest market information.

[0093] The automated proposal creation system can further include a technical information provider that automatically provides relevant technical information based on user input. For example, when a salesperson creates a slide to explain the technical features of a new product, the generation AI automatically presents related technical literature and research results. The technical information provider can also automatically present relevant technical risks and challenges based on the input text. For example, it could present the technical risks associated with introducing new technology. This allows salespeople to create documents with technical backing.

[0094] The automated proposal creation system can further include a financial information provider that automatically provides relevant financial information based on user input. For example, when a salesperson creates a slide to explain the financial impact of a new product, the generative AI automatically presents relevant financial data and forecasts. The financial information provider can also automatically present relevant financial risks and investment benefits based on the input text. For example, it can present the investment benefits associated with launching a new product. This allows salespeople to create materials from a financial perspective.

[0095] The automated proposal material creation system can further include an environmental information provider that automatically provides relevant environmental information based on user input. For example, when a salesperson creates a slide to explain the environmental impact of a new product, the generation AI automatically presents relevant environmental data and regulatory information. The environmental information provider can also automatically present information on relevant environmental risks and sustainability based on the input text. For example, it could present the environmental impact of the new product's manufacturing process. This allows salespeople to create materials from an environmental perspective.

[0096] The automatic proposal material creation system can further include a timing suggestion unit that estimates the user's emotions and suggests the optimal timing for the presentation based on the estimated emotions. For example, when a salesperson gives a presentation, the generation AI analyzes the user's emotions in real time and suggests switching slides at the optimal timing. The timing suggestion unit can also adjust the speed of the presentation based on the user's emotions. For example, if the user is nervous, it will suggest a slower pace. This can maximize the effectiveness of the presentation.

[0097] The automatic proposal material creation system can further include a feedback providing unit that estimates the user's emotions and provides optimal feedback based on the estimated emotions. For example, when a salesperson gives a presentation, the generation AI analyzes the user's emotions in real time and provides appropriate feedback. The feedback providing unit can also suggest areas for improving the presentation based on the user's emotions. For example, if the user is feeling anxious, it can provide advice on how to relax. This can improve the quality of the presentation.

[0098] The automatic proposal material creation system can further include a design suggestion unit that estimates the user's emotions and proposes optimal slide designs based on the estimated emotions. For example, when a salesperson gives a presentation, the generation AI analyzes the user's emotions in real time and proposes optimal slide designs. The design suggestion unit can also adjust the colors and fonts of slides based on the user's emotions. For example, if the user is relaxed, it will propose slides with calm colors. This maximizes the visual impact of the presentation.

[0099] The automatic proposal material creation system can further include a content suggestion unit that estimates the user's emotions and suggests optimal presentation content based on the estimated emotions. For example, when a salesperson gives a presentation, the generation AI analyzes the user's emotions in real time and suggests optimal presentation content. The content suggestion unit can also adjust the presentation topic and examples based on the user's emotions. For example, if the user is excited, it will suggest specific success stories. This can maximize the effectiveness of the presentation.

[0100] The automatic proposal material creation system can further include a structure suggestion unit that estimates the user's emotions and proposes the optimal presentation structure based on the estimated emotions. For example, when a salesperson gives a presentation, the generation AI analyzes the user's emotions in real time and proposes the optimal presentation structure. The structure suggestion unit can also adjust the order and content of the presentation based on the user's emotions. For example, if the user is nervous, it can suggest an introductory section that will help them relax. This maximizes the effectiveness of the presentation.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: In the text input section, a salesperson inputs text. For example, the salesperson inputs the contents of a proposal document in text format. Step 2: The slide generator generates slides based on the text entered by the text input unit. For example, the generation AI analyzes the entered text and determines the appropriate slide layout and design. Step 3: The font unification unit unifies the font and size of the slides generated by the slide generation unit. For example, the generation AI uses the same font and size on all slides to create a uniform look. Step 4: The image insertion unit inserts image illustrations into the slides generated by the slide generation unit. For example, the generation AI automatically inserts image illustrations according to the content of the slides.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0170] 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 text input section for inputting text; a slide generator that generates slides based on the text input by the text input unit; a font unification unit that unifies the font and size of the slides generated by the slide generation unit; an image inserting unit that inserts an image illustration into the slide generated by the slide generating unit; A system characterized by:

2. The text input unit Using voice input, what a salesperson says is converted into text in real time and used to create slides.

2. The system of claim 1.

3. The slide generation unit Referencing past success stories and best practices, we suggest the optimal slide configuration 2. The system of claim 1.

4. The font unification unit Use algorithms that optimize visual readability and aesthetics when standardizing fonts and sizes 2. The system of claim 1.

5. The image insertion unit Automatically check image copyright and license information and use only appropriate images 2. The system of claim 1.

6. The text input unit Analyzes the emotional tone of the text you type and makes suggestions to convert it into positive expressions 2. The system of claim 1.

Images