system

Abstract

The system according to the embodiment aims to create presentation materials efficiently and effectively. [Solution] A system according to an embodiment includes a reception unit, a selection unit, a questioning unit, and a creation unit. The reception unit inputs basic information about a user. The selection unit analyzes the basic information input by the reception unit and selects an appropriate template. The questioning unit asks the user questions based on the template selected by the selection unit. The creation unit creates presentation materials based on the answers obtained by the questioning unit.

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, creating presentation materials took a long time and it was difficult to create materials that communicated effectively.

[0005] The system according to the embodiment aims to create presentation materials efficiently and effectively. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a selection unit, a questioning unit, and a creation unit. The reception unit inputs basic information of a user. The selection unit analyzes the basic information input by the reception unit and selects an appropriate template. The questioning unit asks the user questions based on the template selected by the selection unit. The creation unit creates presentation materials based on the answers obtained by the questioning unit. [Effects of the Invention]

[0007] The system according to the embodiment can create presentation materials efficiently and effectively. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) In an embodiment of the present invention, an automatic presentation creation system uses a generation AI to select an optimal template based on basic information entered by a user and automatically create presentation materials through questions. In this automatic presentation creation system, the user enters basic information such as the purpose and target audience of the presentation, and the generation AI analyzes that information to determine the optimal template. The generation AI then asks the user specific questions, and the presentation materials are automatically created based on the user's answers. The generated presentation materials can be reviewed and revised by the user. For example, if the purpose of the presentation is to "introduce a new product" and the target audience is "management," the user enters that information into the generation AI. This information is then input into the generation AI. The generation AI then analyzes the input basic information and determines the optimal template. For example, for a presentation introducing a new product, a template including items such as product features, market analysis, and competitive comparison is selected. The generation AI then asks the user specific questions such as "What are the main features of the new product?" and "What are the results of the market analysis?" The user simply answers these questions, and the generation AI automatically creates the presentation materials. The generated presentation materials can be reviewed and revised by the user. For example, the user can review the content of the slides created by the generation AI and make any necessary revisions. This significantly reduces the time it takes to create presentation materials, making it easy to create materials that communicate effectively. For example, it can also be used to create malfunction reports, allowing for quick and accurate reporting. This significantly reduces the time it takes for users to create presentation materials, making it easy to create materials that communicate effectively. For example, it can also be used to create malfunction reports, allowing for quick and accurate reporting.

[0029] An automatic presentation creation system according to an embodiment includes a reception unit, a selection unit, a questioning unit, and a creation unit. The reception unit receives basic information from a user, such as the purpose and target audience of the presentation. For example, if the purpose of the presentation is to "introduce a new product" and the target audience is "management," the user enters that information. This information is then input to a generation AI. The selection unit uses the generation AI to analyze the basic information input by the reception unit and select an optimal template. For example, for a presentation introducing a new product, a template including items such as product features, market analysis, and competitive comparison is selected. The questioning unit uses the generation AI to ask specific questions to the user based on the template selected by the selection unit. For example, questions include, "What are the main features of the new product?" and "What are the results of the market analysis?" The creation unit uses the generation AI to create presentation materials based on the answers obtained by the questioning unit. For example, the generation AI automatically creates slides based on the user's answers. This allows the automatic presentation creation system according to an embodiment to significantly reduce the time it takes for users to create presentation materials and easily create compelling materials.

[0030] The selection unit can select a template using a generation AI. The generation AI selects a template using technologies such as GPT-4 (registered trademark) or Gemini. For example, the generation AI analyzes basic information entered by a user and selects the optimal template. For example, in the case of a presentation introducing a new product, the generation AI can select a template that includes items such as product features, market analysis, and competitive comparison. This improves the accuracy of template selection by using the generation AI. Some or all of the above-mentioned processing in the selection unit is performed using the generation AI.

[0031] The questioning unit can ask specific questions to the user using the generation AI. The generation AI, for example, asks customized questions according to the user's needs. For example, the generation AI can ask specific questions such as, "What are the main features of the new product?" or "What are the results of the market analysis?" The generation AI can also adjust the next question based on the user's answer. For example, the generation AI can ask additional questions based on the user's answer to collect more detailed information. In this way, the generation AI can improve the accuracy of the questions posed to the user. Some or all of the above-mentioned processing in the questioning unit is performed using the generation AI.

[0032] The creation unit can create presentation materials using a generation AI. The generation AI, for example, automatically creates slides based on the user's answers. For example, the generation AI generates each slide of the presentation material based on information provided by the user. The generation AI can also automatically adjust the design and layout of the slides. For example, the generation AI optimizes the content of the slides based on the user's answers to create visually appealing presentation materials. In this way, the use of the generation AI improves the accuracy of creating presentation materials. Some or all of the above-mentioned processes in the creation unit are performed using the generation AI.

[0033] The creation unit allows the user to check and modify the presentation materials generated by the generation AI. The generation AI, for example, provides an interface that allows the user to check the generated presentation materials and make modifications as necessary. For example, the generation AI allows the user to check the content of the slides and modify the text and images. The generation AI can also update the presentation materials by reflecting the user's modifications. For example, the generation AI readjusts the design and layout of the slides based on the modifications made by the user. In this way, the user can check and modify the generated presentation materials, improving the quality of the final materials. Some or all of the above-mentioned processes in the creation unit are performed using the generation AI.

[0034] The reception unit can analyze the user's past presentation history and suggest the optimal input format. The reception unit automatically suggests the optimal input format, for example, based on the purpose and target audience of the user's past presentations. For example, the reception unit analyzes the content of presentation materials created by the user in the past and suggests a similar format. The reception unit can also suggest an input format suitable for a specific industry or theme based on the user's past presentation history. For example, the reception unit selects the optimal input format based on the user's past presentation history. This improves input efficiency by suggesting the optimal input format based on the past presentation history. Some or all of the above-mentioned processing in the reception unit is performed using generation AI.

[0035] When inputting basic information, the reception unit can customize input items based on the user's current project or areas of interest. For example, the reception unit preferentially displays input items related to the user's current project. For example, the reception unit automatically adds related input items based on the user's areas of interest. The reception unit can also customize input items based on themes in which the user has previously shown interest. For example, the reception unit customizes input items based on the user's current project or areas of interest. This improves input efficiency by customizing input items based on the user's current project or areas of interest. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI.

[0036] When inputting basic information, the reception unit can select an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the basic information using voice recognition technology. For example, if the user selects text input, the reception unit provides an interface that supports keyboard input. If the user selects image input, the reception unit can also extract the basic information using image recognition technology. For example, the reception unit selects the optimal input means according to the user's input method. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the reception unit is performed using generation AI.

[0037] When inputting basic information, the reception unit can prioritize inputting highly relevant information taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit causes basic information related to that region to be prioritized. For example, the reception unit causes information related to a related industry or market to be prioritized based on the user's current location. The reception unit can also cause region-specific data to be input based on the user's geographical location information. For example, the reception unit prioritizes inputting highly relevant information taking into account the user's geographical location information. This allows highly relevant information to be input efficiently by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit is performed using a generation AI.

[0038] The reception unit can analyze the user's social media activity and input related information when inputting the basic information. The reception unit, for example, analyzes the content of the user's posts on social media and automatically inputs related basic information. For example, the reception unit inputs related information based on the user's social media activity history. The reception unit can also input related basic information by referring to the activities of the user's friends on social media. For example, the reception unit analyzes the user's social media activity and inputs related information. In this way, related information can be input efficiently by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI.

[0039] The reception unit can customize the input method by reflecting the user's past feedback when inputting basic information. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. For example, the reception unit improves the input interface by reflecting the user's past feedback. The reception unit can also optimize the input procedure by referring to the user's past feedback. For example, the reception unit customizes the input method by reflecting the user's past feedback. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI.

[0040] When selecting a template, the selection unit can adjust the specific content of the template based on the purpose of the presentation. For example, if the purpose of the presentation is to introduce a new product, the selection unit selects a detailed template that includes product features and market analysis. For example, if the purpose of the presentation is a management report, the selection unit selects a template that includes financial data and performance analysis. If the purpose of the presentation is education, the selection unit can also select a template that includes educational content and learning goals. For example, when selecting a template, the selection unit adjusts the level of detail of the template based on the purpose of the presentation. In this way, by adjusting the level of detail of the template based on the purpose of the presentation, the optimal template can be selected. Some or all of the above-mentioned processing in the selection unit is performed using generation AI.

[0041] When selecting a template, the selection unit can apply different selection algorithms depending on the category of the presentation. For example, if the presentation category is technical, the selection unit applies an algorithm that selects a template that emphasizes technical details. For example, if the presentation category is marketing, the selection unit applies an algorithm that selects a template that emphasizes marketing strategy and market analysis. If the presentation category is education, the selection unit can also apply an algorithm that selects a template that emphasizes educational content and learning goals. For example, the selection unit applies different selection algorithms depending on the category of the presentation when selecting a template. This makes it possible to select the optimal template by applying different selection algorithms depending on the category of the presentation. Some or all of the above-mentioned processing in the selection unit is performed using a generation AI.

[0042] When selecting a template, the selection unit can improve the accuracy of the selection by referring to the user's past selection results. The selection unit, for example, suggests an optimal template based on the history of templates selected by the user in the past. For example, the selection unit analyzes the user's past selection results and suggests a template suitable for a similar presentation. The selection unit can also improve the selection algorithm by referring to the user's past selection results. For example, the selection unit improves the accuracy of the selection by referring to the user's past selection results when selecting a template. In this way, by referring to the user's past selection results, the accuracy of the selection is improved. Some or all of the above-mentioned processing in the selection unit is performed using a generation AI.

[0043] When selecting templates, the selection unit can determine the order of templates based on the time of presentation submission. For example, if the presentation submission time is approaching, the selection unit prioritizes selecting templates that can be created quickly. For example, if the presentation submission time is far away, the selection unit prioritizes selecting templates that include detailed information. The selection unit can also automatically determine the priority of optimal templates based on the time of presentation submission. For example, when selecting templates, the selection unit determines the order of templates based on the time of presentation submission. In this way, the optimal template can be selected by prioritizing templates based on the time of presentation submission. Some or all of the above-mentioned processing in the selection unit is performed using a generation AI.

[0044] The selection unit can adjust the order of templates based on the relevance of the presentation when selecting templates. For example, the selection unit preferentially selects templates that are highly relevant to the presentation. For example, the selection unit automatically adjusts the order of templates based on the relevance of the presentation. The selection unit can also analyze the relevance of the presentation and propose the optimal order of templates. For example, the selection unit adjusts the order of templates based on the relevance of the presentation when selecting templates. In this way, by adjusting the order of templates based on the relevance of the presentation, the optimal template can be selected. Some or all of the above-mentioned processing in the selection unit is performed using a generation AI.

[0045] When selecting a template, the selection unit can adjust the terminology of the template according to the user's level of expertise. For example, if the user's level of expertise is high, the selection unit selects a template that uses a lot of technical terminology. For example, if the user's level of expertise is low, the selection unit selects a template that uses fewer technical terminology. The selection unit can also adjust the use of technical terminology in the optimal template based on the user's level of expertise. For example, when selecting a template, the selection unit adjusts the terminology of the template according to the user's level of expertise. This makes it possible to select the optimal template by adjusting the use of technical terminology in the template according to the user's level of expertise. Some or all of the above-mentioned processing in the selection unit is performed using a generation AI.

[0046] The questioning unit can adjust the specific content of the question based on the importance of the presentation when asking a question. For example, if the importance of the presentation is high, the questioning unit asks a question that requests detailed information. For example, if the importance of the presentation is low, the questioning unit asks a brief question. The questioning unit can also automatically adjust the level of detail of the question based on the importance of the presentation. For example, the questioning unit adjusts the specific content of the question based on the importance of the presentation when asking a question. In this way, by adjusting the level of detail of the question based on the importance of the presentation, the most appropriate question can be asked. Some or all of the above-mentioned processing in the questioning unit is performed using a generation AI.

[0047] When asking a question, the questioning unit can apply different questioning algorithms depending on the category of the presentation. For example, if the category of the presentation is technology, the questioning unit applies a questioning algorithm that seeks technical details. For example, if the category of the presentation is marketing, the questioning unit applies a questioning algorithm related to marketing strategies. If the category of the presentation is education, the questioning unit can also apply a questioning algorithm related to educational content. For example, the questioning unit applies different questioning algorithms depending on the category of the presentation when asking a question. In this way, by applying different questioning algorithms depending on the category of the presentation, the most appropriate question can be asked. Some or all of the above-mentioned processing in the questioning unit is performed using a generation AI.

[0048] When asking a question, the questioning unit can improve the accuracy of the question by referring to the user's past question results. For example, the questioning unit asks similar questions based on the content of questions answered by the user in the past. For example, the questioning unit analyzes the user's past question results and suggests the most appropriate question. The questioning unit can also improve the question algorithm by referring to the user's past question results. For example, the questioning unit improves the accuracy of the question by referring to the user's past question results. In this way, the accuracy of the question is improved by referring to the user's past question results. Some or all of the above-mentioned processing in the questioning unit is performed using a generation AI.

[0049] The questioning unit can determine the order of questions based on the time of presentation submission when asking a question. For example, if the presentation submission time is approaching, the questioning unit prioritizes important questions. For example, if the presentation submission time is far away, the questioning unit asks detailed questions sequentially. The questioning unit can also automatically determine the priority of questions based on the time of presentation submission when asking a question. For example, the questioning unit determines the order of questions based on the time of presentation submission when asking a question. In this way, by determining the priority of questions based on the time of presentation submission, optimal questions can be asked. Some or all of the above-mentioned processing in the questioning unit is performed using a generation AI.

[0050] The questioning unit can adjust the order of questions based on the relevance of the presentation when asking a question. For example, the questioning unit prioritizes questions that are highly relevant to the presentation. For example, the questioning unit automatically adjusts the order of questions based on the relevance of the presentation. The questioning unit can also analyze the relevance of the presentation and suggest the optimal order of questions. For example, the questioning unit adjusts the order of questions based on the relevance of the presentation when asking a question. In this way, by adjusting the order of questions based on the relevance of the presentation, the optimal questions can be asked. Some or all of the above-mentioned processing in the questioning unit is performed using a generation AI.

[0051] The questioning unit can adjust the terminology of the question according to the user's level of expertise when asking a question. For example, if the user's level of expertise is high, the questioning unit asks a question that uses a lot of technical terms. For example, if the user's level of expertise is low, the questioning unit asks a question that uses fewer technical terms. The questioning unit can also adjust the use of optimal technical terms in the question based on the user's level of expertise. For example, the questioning unit adjusts the terminology of the question according to the user's level of expertise when asking a question. In this way, the optimal question can be asked by adjusting the use of technical terms in the question according to the user's level of expertise. Some or all of the above-mentioned processing in the questioning unit is performed using a generation AI.

[0052] When creating presentation materials, the creation unit can analyze the user's past presentation materials and select an appropriate creation method. The creation unit, for example, suggests an optimal creation method based on the content of presentation materials created by the user in the past. For example, the creation unit analyzes the user's past presentation materials and suggests a creation method suitable for similar presentations. The creation unit can also improve the creation algorithm by referring to the user's past presentation materials. For example, the creation unit analyzes the user's past presentation materials and selects an optimal creation method when creating presentation materials. In this way, the optimal creation method can be selected by analyzing the user's past presentation materials. Some or all of the above-mentioned processing in the creation unit is performed using a generation AI.

[0053] When creating presentation materials, the creation unit can customize the content of the materials based on the user's current project and areas of interest. For example, the creation unit prioritizes including content related to the user's current project. For example, the creation unit automatically adds relevant information based on the user's areas of interest. The creation unit can also customize the content of the materials based on topics in which the user has previously shown interest. For example, the creation unit customizes the content of the materials based on the user's current project and areas of interest when creating presentation materials. This allows the creation of optimal presentation materials by customizing the content of the materials based on the user's current project and areas of interest. Some or all of the above-mentioned processing in the creation unit is performed using a generation AI.

[0054] The creation unit can improve the method of creating materials by reflecting user feedback when creating presentation materials. The creation unit, for example, suggests an optimal creation method based on feedback provided by the user in the past. For example, the creation unit improves the material creation interface by reflecting the user's past feedback. The creation unit can also optimize the creation procedure by referring to the user's past feedback. For example, the creation unit improves the method of creating materials by reflecting user feedback when creating presentation materials. In this way, the method of creating materials can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the creation unit is performed using a generation AI.

[0055] When creating presentation materials, the creation unit can select an appropriate material creation method by taking into account the user's geographic location information. For example, if the user is in a specific region, the creation unit prioritizes including information related to that region. For example, the creation unit prioritizes including information on related industries and markets based on the user's current location. The creation unit can also include region-specific data based on the user's geographic location information. For example, when creating presentation materials, the creation unit selects the optimal material creation method by taking into account the user's geographic location information. This makes it possible to select the optimal material creation method by taking into account the user's geographic location information. Some or all of the above-mentioned processing in the creation unit is performed using a generation AI.

[0056] When creating presentation materials, the creation unit can analyze the user's social media activity and suggest ways to create the materials. The creation unit, for example, analyzes the content of the user's social media posts and automatically includes relevant information. For example, the creation unit includes relevant information based on the user's social media activity history. The creation unit can also include relevant information by referring to the activity of the user's friends on social media. For example, when creating presentation materials, the creation unit analyzes the user's social media activity and suggests ways to create the materials. In this way, by analyzing the user's social media activity, it is possible to suggest the optimal way to create the materials. Some or all of the above-mentioned processing in the creation unit is performed using a generation AI.

[0057] The creation unit can customize the method of creating presentation materials by reflecting the user's past feedback. The creation unit, for example, suggests the optimal creation method based on feedback provided by the user in the past. For example, the creation unit improves the document creation interface by reflecting the user's past feedback. The creation unit can also optimize the creation procedure by referring to the user's past feedback. For example, the creation unit customizes the method of creating presentation materials by reflecting the user's past feedback when creating presentation materials. In this way, the method of creating materials can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processes in the creation unit are performed using a generation AI.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can analyze the evaluations of the user's past presentation materials and prioritize the incorporation of highly rated elements. For example, it can automatically suggest slide layouts and designs that received high evaluations among the presentation materials the user has created in the past. The reception unit can also select the optimal template based on the evaluations of the user's past presentation materials. This allows the user to create more effective presentation materials by taking advantage of past success stories.

[0060] The questioning unit can analyze the user's answer history and optimize the order and content of questions based on past answers. For example, it can prioritize questions to which the user has previously provided detailed answers. It can also avoid questions that the user has previously found difficult to answer. This makes it possible to utilize the user's answer history to achieve a smoother questioning process.

[0061] The creation unit analyzes the revision history of the user's past presentation materials and can automatically improve the parts that have been frequently revised. For example, it can automatically optimize the layout and content of slides that the user has frequently revised in the past. This makes it possible to create more complete presentation materials by utilizing the user's past revision history.

[0062] The selection unit can prioritize the selection of highly rated templates based on the user's evaluations of past presentation materials. For example, it can automatically suggest templates of presentation materials that have received high evaluations in the past. This allows the user to create more effective presentation materials by taking advantage of the user's past success stories.

[0063] The creation unit can dynamically adjust the content of presentation materials taking into account the progress of the user's current project. For example, if the project is in the early stages, it can prioritize including information about plans and goals. If the project is underway, it can also emphasize information about progress and results. This makes it possible to create optimal presentation materials according to the progress of the project.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The user enters basic information such as the purpose and target audience of the presentation into the reception unit. For example, if the purpose of the presentation is to "introduce a new product" and the target audience is "management," the user enters that information. This information is then input into the generation AI. Step 2: The selection unit uses the generation AI to analyze the basic information entered by the reception unit and select the optimal template. For example, in the case of a presentation introducing a new product, a template including items such as product features, market analysis, and competitive comparisons will be selected. Step 3: The questioning unit uses the generation AI to ask specific questions to the user based on the templates selected by the selection unit, such as "What are the main features of the new product?" or "What are the results of your market analysis?" Step 4: The creation unit uses the generation AI to create presentation materials based on the answers obtained by the questioning unit. For example, the generation AI automatically creates slides based on the user's answers.

[0066] (Example 2) In an embodiment of the present invention, an automatic presentation creation system uses a generation AI to select an optimal template based on basic information entered by a user and automatically create presentation materials through questions. In this automatic presentation creation system, the user enters basic information such as the purpose and target audience of the presentation, and the generation AI analyzes that information to determine the optimal template. The generation AI then asks the user specific questions, and the presentation materials are automatically created based on the user's answers. The generated presentation materials can be reviewed and revised by the user. For example, if the purpose of the presentation is to "introduce a new product" and the target audience is "management," the user enters that information into the generation AI. This information is then input into the generation AI. The generation AI then analyzes the input basic information and determines the optimal template. For example, for a presentation introducing a new product, a template including items such as product features, market analysis, and competitive comparison is selected. The generation AI then asks the user specific questions such as "What are the main features of the new product?" and "What are the results of the market analysis?" The user simply answers these questions, and the generation AI automatically creates the presentation materials. The generated presentation materials can be reviewed and revised by the user. For example, the user can review the content of the slides created by the generation AI and make any necessary revisions. This significantly reduces the time it takes to create presentation materials, making it easy to create materials that communicate effectively. For example, it can also be used to create malfunction reports, allowing for quick and accurate reporting. This significantly reduces the time it takes for users to create presentation materials, making it easy to create materials that communicate effectively. For example, it can also be used to create malfunction reports, allowing for quick and accurate reporting.

[0067] An automatic presentation creation system according to an embodiment includes a reception unit, a selection unit, a questioning unit, and a creation unit. The reception unit receives basic information from a user, such as the purpose and target audience of the presentation. For example, if the purpose of the presentation is to "introduce a new product" and the target audience is "management," the user enters that information. This information is then input to a generation AI. The selection unit uses the generation AI to analyze the basic information input by the reception unit and select an optimal template. For example, for a presentation introducing a new product, a template including items such as product features, market analysis, and competitive comparison is selected. The questioning unit uses the generation AI to ask specific questions to the user based on the template selected by the selection unit. For example, questions include, "What are the main features of the new product?" and "What are the results of the market analysis?" The creation unit uses the generation AI to create presentation materials based on the answers obtained by the questioning unit. For example, the generation AI automatically creates slides based on the user's answers. This allows the automatic presentation creation system according to an embodiment to significantly reduce the time it takes for users to create presentation materials and easily create compelling materials.

[0068] The selection unit can select a template using a generation AI. The generation AI selects a template using technologies such as GPT-4 and Gemini. For example, the generation AI analyzes basic information entered by the user and selects the optimal template. For example, in the case of a presentation introducing a new product, the generation AI can select a template that includes items such as product features, market analysis, and competitive comparison. As a result, the use of the generation AI improves the accuracy of template selection. Some or all of the above-mentioned processing in the selection unit is performed using the generation AI.

[0069] The questioning unit can ask specific questions to the user using the generation AI. The generation AI, for example, asks customized questions according to the user's needs. For example, the generation AI can ask specific questions such as, "What are the main features of the new product?" or "What are the results of the market analysis?" The generation AI can also adjust the next question based on the user's answer. For example, the generation AI can ask additional questions based on the user's answer to collect more detailed information. In this way, the generation AI can improve the accuracy of the questions posed to the user. Some or all of the above-mentioned processing in the questioning unit is performed using the generation AI.

[0070] The creation unit can create presentation materials using a generation AI. The generation AI, for example, automatically creates slides based on the user's answers. For example, the generation AI generates each slide of the presentation material based on information provided by the user. The generation AI can also automatically adjust the design and layout of the slides. For example, the generation AI optimizes the content of the slides based on the user's answers to create visually appealing presentation materials. In this way, the use of the generation AI improves the accuracy of creating presentation materials. Some or all of the above-mentioned processes in the creation unit are performed using the generation AI.

[0071] The creation unit allows the user to check and modify the presentation materials generated by the generation AI. The generation AI, for example, provides an interface that allows the user to check the generated presentation materials and make modifications as necessary. For example, the generation AI allows the user to check the content of the slides and modify the text and images. The generation AI can also update the presentation materials by reflecting the user's modifications. For example, the generation AI readjusts the design and layout of the slides based on the modifications made by the user. In this way, the user can check and modify the generated presentation materials, improving the quality of the final materials. Some or all of the above-mentioned processes in the creation unit are performed using the generation AI.

[0072] The reception unit can estimate the user's emotions and adjust the input method for basic information based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. For example, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input and enable quick input of basic information. For example, the reception unit can estimate the user's emotions and adjust the input method based on the estimated emotions. This reduces the user's stress by adjusting the input method according to the user's emotions, enabling efficient input. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit is performed using the generation AI.

[0073] The reception unit can analyze the user's past presentation history and suggest the optimal input format. The reception unit automatically suggests the optimal input format, for example, based on the purpose and target audience of the user's past presentations. For example, the reception unit analyzes the content of presentation materials created by the user in the past and suggests a similar format. The reception unit can also suggest an input format suitable for a specific industry or theme based on the user's past presentation history. For example, the reception unit selects the optimal input format based on the user's past presentation history. This improves input efficiency by suggesting the optimal input format based on the past presentation history. Some or all of the above-mentioned processing in the reception unit is performed using generation AI.

[0074] When inputting basic information, the reception unit can customize input items based on the user's current project or areas of interest. For example, the reception unit preferentially displays input items related to the user's current project. For example, the reception unit automatically adds related input items based on the user's areas of interest. The reception unit can also customize input items based on themes in which the user has previously shown interest. For example, the reception unit customizes input items based on the user's current project or areas of interest. This improves input efficiency by customizing input items based on the user's current project or areas of interest. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI.

[0075] When inputting basic information, the reception unit can select an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the basic information using voice recognition technology. For example, if the user selects text input, the reception unit provides an interface that supports keyboard input. If the user selects image input, the reception unit can also extract the basic information using image recognition technology. For example, the reception unit selects the optimal input means according to the user's input method. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-mentioned processing in the reception unit is performed using generation AI.

[0076] The reception unit can estimate the user's emotions and determine the priority of the basic information to be input based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize input of the most important basic information. For example, if the user is relaxed, the reception unit can prioritize input of detailed basic information. For example, if the user is in a hurry, the reception unit can prioritize input of only the minimum necessary basic information. For example, the reception unit can estimate the user's emotions and determine the priority of the basic information to be input based on the estimated emotions. This allows efficient input by determining the priority of the basic information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit is performed using the generation AI.

[0077] When inputting basic information, the reception unit can prioritize inputting highly relevant information taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit causes basic information related to that region to be prioritized. For example, the reception unit causes information related to a related industry or market to be prioritized based on the user's current location. The reception unit can also cause region-specific data to be input based on the user's geographical location information. For example, the reception unit prioritizes inputting highly relevant information taking into account the user's geographical location information. This allows highly relevant information to be input efficiently by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit is performed using a generation AI.

[0078] The reception unit can analyze the user's social media activity and input related information when inputting the basic information. The reception unit, for example, analyzes the content of the user's posts on social media and automatically inputs related basic information. For example, the reception unit inputs related information based on the user's social media activity history. The reception unit can also input related basic information by referring to the activities of the user's friends on social media. For example, the reception unit analyzes the user's social media activity and inputs related information. In this way, related information can be input efficiently by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI.

[0079] The reception unit can customize the input method by reflecting the user's past feedback when inputting basic information. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. For example, the reception unit improves the input interface by reflecting the user's past feedback. The reception unit can also optimize the input procedure by referring to the user's past feedback. For example, the reception unit customizes the input method by reflecting the user's past feedback. In this way, the input method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the reception unit is performed using a generation AI.

[0080] The selection unit can estimate the user's emotion and adjust the template selection criteria based on the estimated user's emotion. For example, if the user is nervous, the selection unit selects a simple and easy-to-understand template. For example, if the user is relaxed, the selection unit selects a template that includes detailed information. If the user is in a hurry, the selection unit can also select a template that can be created quickly. For example, the selection unit can estimate the user's emotion and adjust the template selection criteria based on the estimated emotion. This allows the optimal template to be selected by adjusting the template selection criteria according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the selection unit is performed using the generation AI.

[0081] When selecting a template, the selection unit can adjust the specific content of the template based on the purpose of the presentation. For example, if the purpose of the presentation is to introduce a new product, the selection unit selects a detailed template that includes product features and market analysis. For example, if the purpose of the presentation is a management report, the selection unit selects a template that includes financial data and performance analysis. If the purpose of the presentation is education, the selection unit can also select a template that includes educational content and learning goals. For example, when selecting a template, the selection unit adjusts the level of detail of the template based on the purpose of the presentation. In this way, by adjusting the level of detail of the template based on the purpose of the presentation, the optimal template can be selected. Some or all of the above-mentioned processing in the selection unit is performed using generation AI.

[0082] When selecting a template, the selection unit can apply different selection algorithms depending on the category of the presentation. For example, if the presentation category is technical, the selection unit applies an algorithm that selects a template that emphasizes technical details. For example, if the presentation category is marketing, the selection unit applies an algorithm that selects a template that emphasizes marketing strategy and market analysis. If the presentation category is education, the selection unit can also apply an algorithm that selects a template that emphasizes educational content and learning goals. For example, the selection unit applies different selection algorithms depending on the category of the presentation when selecting a template. This makes it possible to select the optimal template by applying different selection algorithms depending on the category of the presentation. Some or all of the above-mentioned processing in the selection unit is performed using a generation AI.

[0083] When selecting a template, the selection unit can improve the accuracy of the selection by referring to the user's past selection results. The selection unit, for example, suggests an optimal template based on the history of templates selected by the user in the past. For example, the selection unit analyzes the user's past selection results and suggests a template suitable for a similar presentation. The selection unit can also improve the selection algorithm by referring to the user's past selection results. For example, the selection unit improves the accuracy of the selection by referring to the user's past selection results when selecting a template. In this way, by referring to the user's past selection results, the accuracy of the selection is improved. Some or all of the above-mentioned processing in the selection unit is performed using a generation AI.

[0084] The selection unit can estimate the user's emotion and adjust the length of the template based on the estimated user's emotion. For example, if the user is nervous, the selection unit selects a short, to-the-point template. For example, if the user is relaxed, the selection unit selects a longer template containing detailed information. If the user is in a hurry, the selection unit can also select a short template that can be created quickly. For example, the selection unit can estimate the user's emotion and adjust the length of the template based on the estimated emotion. This allows the optimal template to be selected by adjusting the template length according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the selection unit is performed using the generation AI.

[0085] When selecting templates, the selection unit can determine the order of templates based on the time of presentation submission. For example, if the presentation submission time is approaching, the selection unit prioritizes selecting templates that can be created quickly. For example, if the presentation submission time is far away, the selection unit prioritizes selecting templates that include detailed information. The selection unit can also automatically determine the priority of optimal templates based on the time of presentation submission. For example, when selecting templates, the selection unit determines the order of templates based on the time of presentation submission. In this way, the optimal template can be selected by prioritizing templates based on the time of presentation submission. Some or all of the above-mentioned processing in the selection unit is performed using a generation AI.

[0086] The selection unit can adjust the order of templates based on the relevance of the presentation when selecting templates. For example, the selection unit preferentially selects templates that are highly relevant to the presentation. For example, the selection unit automatically adjusts the order of templates based on the relevance of the presentation. The selection unit can also analyze the relevance of the presentation and propose the optimal order of templates. For example, the selection unit adjusts the order of templates based on the relevance of the presentation when selecting templates. In this way, by adjusting the order of templates based on the relevance of the presentation, the optimal template can be selected. Some or all of the above-mentioned processing in the selection unit is performed using a generation AI.

[0087] When selecting a template, the selection unit can adjust the terminology of the template according to the user's level of expertise. For example, if the user's level of expertise is high, the selection unit selects a template that uses a lot of technical terminology. For example, if the user's level of expertise is low, the selection unit selects a template that uses fewer technical terminology. The selection unit can also adjust the use of technical terminology in the optimal template based on the user's level of expertise. For example, when selecting a template, the selection unit adjusts the terminology of the template according to the user's level of expertise. This makes it possible to select the optimal template by adjusting the use of technical terminology in the template according to the user's level of expertise. Some or all of the above-mentioned processing in the selection unit is performed using a generation AI.

[0088] The questioning unit can estimate the user's emotions and adjust the way the question is phrased based on the estimated user emotions. For example, if the user is nervous, the questioning unit asks a simple and easy-to-understand question. For example, if the user is relaxed, the questioning unit asks a question that seeks detailed information. If the user is in a hurry, the questioning unit can also ask a question that can be answered quickly. For example, the questioning unit estimates the user's emotions and adjusts the way the question is phrased based on the estimated emotions. This allows the optimal question to be posed by adjusting the way the question is phrased according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the questioning unit is performed using the generation AI.

[0089] The questioning unit can adjust the specific content of the question based on the importance of the presentation when asking a question. For example, if the importance of the presentation is high, the questioning unit asks a question that requests detailed information. For example, if the importance of the presentation is low, the questioning unit asks a brief question. The questioning unit can also automatically adjust the level of detail of the question based on the importance of the presentation. For example, the questioning unit adjusts the specific content of the question based on the importance of the presentation when asking a question. In this way, by adjusting the level of detail of the question based on the importance of the presentation, the most appropriate question can be asked. Some or all of the above-mentioned processing in the questioning unit is performed using a generation AI.

[0090] When asking a question, the questioning unit can apply different questioning algorithms depending on the category of the presentation. For example, if the category of the presentation is technology, the questioning unit applies a questioning algorithm that seeks technical details. For example, if the category of the presentation is marketing, the questioning unit applies a questioning algorithm related to marketing strategies. If the category of the presentation is education, the questioning unit can also apply a questioning algorithm related to educational content. For example, the questioning unit applies different questioning algorithms depending on the category of the presentation when asking a question. In this way, by applying different questioning algorithms depending on the category of the presentation, the most appropriate question can be asked. Some or all of the above-mentioned processing in the questioning unit is performed using a generation AI.

[0091] When asking a question, the questioning unit can improve the accuracy of the question by referring to the user's past question results. For example, the questioning unit asks similar questions based on the content of questions answered by the user in the past. For example, the questioning unit analyzes the user's past question results and suggests the most appropriate question. The questioning unit can also improve the question algorithm by referring to the user's past question results. For example, the questioning unit improves the accuracy of the question by referring to the user's past question results. In this way, the accuracy of the question is improved by referring to the user's past question results. Some or all of the above-mentioned processing in the questioning unit is performed using a generation AI.

[0092] The questioning unit can estimate the user's emotions and adjust the length of the questions based on the estimated user emotions. For example, if the user is nervous, the questioning unit asks short, to-the-point questions. For example, if the user is relaxed, the questioning unit asks longer questions seeking detailed information. If the user is in a hurry, the questioning unit can also ask short questions that can be answered quickly. For example, the questioning unit estimates the user's emotions and adjusts the length of the questions based on the estimated emotions. This allows the optimal questions to be asked by adjusting the length of the questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the questioning unit is performed using the generation AI.

[0093] The questioning unit can determine the order of questions based on the time of presentation submission when asking a question. For example, if the presentation submission time is approaching, the questioning unit prioritizes important questions. For example, if the presentation submission time is far away, the questioning unit asks detailed questions sequentially. The questioning unit can also automatically determine the priority of questions based on the time of presentation submission when asking a question. For example, the questioning unit determines the order of questions based on the time of presentation submission when asking a question. In this way, by determining the priority of questions based on the time of presentation submission, optimal questions can be asked. Some or all of the above-mentioned processing in the questioning unit is performed using a generation AI.

[0094] The questioning unit can adjust the order of questions based on the relevance of the presentation when asking a question. For example, the questioning unit prioritizes questions that are highly relevant to the presentation. For example, the questioning unit automatically adjusts the order of questions based on the relevance of the presentation. The questioning unit can also analyze the relevance of the presentation and suggest the optimal order of questions. For example, the questioning unit adjusts the order of questions based on the relevance of the presentation when asking a question. In this way, by adjusting the order of questions based on the relevance of the presentation, the optimal questions can be asked. Some or all of the above-mentioned processing in the questioning unit is performed using a generation AI.

[0095] The questioning unit can adjust the terminology of the question according to the user's level of expertise when asking a question. For example, if the user's level of expertise is high, the questioning unit asks a question that uses a lot of technical terms. For example, if the user's level of expertise is low, the questioning unit asks a question that uses fewer technical terms. The questioning unit can also adjust the use of optimal technical terms in the question based on the user's level of expertise. For example, the questioning unit adjusts the terminology of the question according to the user's level of expertise when asking a question. In this way, the optimal question can be asked by adjusting the use of technical terms in the question according to the user's level of expertise. Some or all of the above-mentioned processing in the questioning unit is performed using a generation AI.

[0096] The creation unit can estimate the user's emotions and adjust the creation method of the presentation materials based on the estimated user emotions. For example, if the user is nervous, the creation unit creates simple and easy-to-understand presentation materials. For example, if the user is relaxed, the creation unit creates presentation materials that include detailed information. If the user is in a hurry, the creation unit can also create presentation materials that can be created quickly. For example, the creation unit can estimate the user's emotions and adjust the creation method of the presentation materials based on the estimated emotions. This allows the creation of optimal presentation materials by adjusting the creation method of the presentation materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the creation unit is performed using the generation AI.

[0097] When creating presentation materials, the creation unit can analyze the user's past presentation materials and select an appropriate creation method. The creation unit, for example, suggests an optimal creation method based on the content of presentation materials created by the user in the past. For example, the creation unit analyzes the user's past presentation materials and suggests a creation method suitable for similar presentations. The creation unit can also improve the creation algorithm by referring to the user's past presentation materials. For example, the creation unit analyzes the user's past presentation materials and selects an optimal creation method when creating presentation materials. In this way, the optimal creation method can be selected by analyzing the user's past presentation materials. Some or all of the above-mentioned processing in the creation unit is performed using a generation AI.

[0098] When creating presentation materials, the creation unit can customize the content of the materials based on the user's current project and areas of interest. For example, the creation unit prioritizes including content related to the user's current project. For example, the creation unit automatically adds relevant information based on the user's areas of interest. The creation unit can also customize the content of the materials based on topics in which the user has previously shown interest. For example, the creation unit customizes the content of the materials based on the user's current project and areas of interest when creating presentation materials. This allows the creation of optimal presentation materials by customizing the content of the materials based on the user's current project and areas of interest. Some or all of the above-mentioned processing in the creation unit is performed using a generation AI.

[0099] The creation unit can improve the method of creating materials by reflecting user feedback when creating presentation materials. The creation unit, for example, suggests an optimal creation method based on feedback provided by the user in the past. For example, the creation unit improves the material creation interface by reflecting the user's past feedback. The creation unit can also optimize the creation procedure by referring to the user's past feedback. For example, the creation unit improves the method of creating materials by reflecting user feedback when creating presentation materials. In this way, the method of creating materials can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the creation unit is performed using a generation AI.

[0100] The creation unit can estimate the user's emotions and prioritize presentation materials based on the estimated user emotions. For example, if the user is nervous, the creation unit prioritizes creating the most important content. For example, if the user is relaxed, the creation unit sequentially creates detailed information. If the user is in a hurry, the creation unit can also prioritize creating only the minimum necessary content. For example, the creation unit can estimate the user's emotions and prioritize presentation materials based on the estimated emotions. This allows optimal presentation materials to be created by prioritizing presentation materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the creation unit is performed using the generation AI.

[0101] When creating presentation materials, the creation unit can select an appropriate material creation method by taking into account the user's geographic location information. For example, if the user is in a specific region, the creation unit prioritizes including information related to that region. For example, the creation unit prioritizes including information on related industries and markets based on the user's current location. The creation unit can also include region-specific data based on the user's geographic location information. For example, when creating presentation materials, the creation unit selects the optimal material creation method by taking into account the user's geographic location information. This makes it possible to select the optimal material creation method by taking into account the user's geographic location information. Some or all of the above-mentioned processing in the creation unit is performed using a generation AI.

[0102] When creating presentation materials, the creation unit can analyze the user's social media activity and suggest ways to create the materials. The creation unit, for example, analyzes the content of the user's social media posts and automatically includes relevant information. For example, the creation unit includes relevant information based on the user's social media activity history. The creation unit can also include relevant information by referring to the activity of the user's friends on social media. For example, when creating presentation materials, the creation unit analyzes the user's social media activity and suggests ways to create the materials. In this way, by analyzing the user's social media activity, it is possible to suggest the optimal way to create the materials. Some or all of the above-mentioned processing in the creation unit is performed using a generation AI.

[0103] The creation unit can customize the method of creating presentation materials by reflecting the user's past feedback. The creation unit, for example, suggests the optimal creation method based on feedback provided by the user in the past. For example, the creation unit improves the document creation interface by reflecting the user's past feedback. The creation unit can also optimize the creation procedure by referring to the user's past feedback. For example, the creation unit customizes the method of creating presentation materials by reflecting the user's past feedback when creating presentation materials. In this way, the method of creating materials can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processes in the creation unit are performed using a generation AI. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, selection unit, question unit, and creation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and the user inputs basic information such as the purpose and target audience of the presentation. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input basic information using a generation AI and selects the optimal template. The question unit is realized, for example, by the control unit 46A of the smart device 14, and asks the user specific questions based on the selected template. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates presentation materials based on the answers obtained by the question unit. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, selection unit, question unit, and creation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and the user inputs basic information such as the purpose and target audience of the presentation. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input basic information using a generation AI and selects an optimal template. The question unit is realized, for example, by the control unit 46A of the smart glasses 214, and asks the user specific questions based on the selected template. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates presentation materials based on the answers obtained by the question unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, selection unit, question unit, and creation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, and the user inputs basic information such as the purpose of the presentation and the target audience. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input basic information using a generation AI and selects an optimal template. The question unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and asks the user specific questions based on the selected template. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates presentation materials based on the answers obtained by the question unit. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, selection unit, question unit, and creation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and the user inputs basic information such as the purpose and target audience of the presentation. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input basic information using a generation AI and selects an optimal template. The question unit is realized, for example, by the control unit 46A of the robot 414, and asks the user specific questions based on the selected template. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and creates presentation materials based on the answers obtained by the question unit.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can analyze the evaluations of the user's past presentation materials and prioritize the incorporation of highly rated elements. For example, it can automatically suggest slide layouts and designs that received high evaluations among the presentation materials the user has created in the past. The reception unit can also select the optimal template based on the evaluations of the user's past presentation materials. This allows the user to create more effective presentation materials by taking advantage of past success stories.

[0106] The selection unit can estimate the user's emotions and adjust the color and design of the template based on the estimated user's emotions. For example, if the user is relaxed, a template with calm colors can be selected. If the user is nervous, a design with less visual stimulation can be selected. This makes it possible to maximize the effectiveness of presentation materials by making visual adjustments according to the user's emotions.

[0107] The questioning unit can analyze the user's answer history and optimize the order and content of questions based on past answers. For example, it can prioritize questions to which the user has previously provided detailed answers. It can also avoid questions that the user has previously found difficult to answer. This makes it possible to utilize the user's answer history to achieve a smoother questioning process.

[0108] The creation unit can estimate the user's emotions and adjust the font size and style of the presentation materials based on the estimated user emotions. For example, if the user is nervous, a large, easy-to-read font can be used. If the user is relaxed, a font with a high design quality can be used. In this way, by adjusting the font according to the user's emotions, the visibility and design quality of the presentation materials can be improved.

[0109] The creation unit analyzes the revision history of the user's past presentation materials and can automatically improve the parts that have been frequently revised. For example, it can automatically optimize the layout and content of slides that the user has frequently revised in the past. This makes it possible to create more complete presentation materials by utilizing the user's past revision history.

[0110] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided. If the user is feeling relaxed, detailed customization options can also be provided. This allows the interface to be adjusted according to the user's emotions, thereby improving the efficiency and comfort of input work.

[0111] The selection unit can prioritize the selection of highly rated templates based on the user's evaluations of past presentation materials. For example, it can automatically suggest templates of presentation materials that have received high evaluations in the past. This allows the user to create more effective presentation materials by taking advantage of the user's past success stories.

[0112] The questioning unit can estimate the user's emotions and adjust the timing of questions based on the estimated user's emotions. For example, if the user is relaxed, the timing of asking detailed questions can be adjusted. If the user is nervous, the questioning unit can start with simple questions. In this way, by adjusting the timing of questions according to the user's emotions, more effective information collection can be achieved.

[0113] The creation unit can dynamically adjust the content of presentation materials taking into account the progress of the user's current project. For example, if the project is in the early stages, it can prioritize including information about plans and goals. If the project is underway, it can also emphasize information about progress and results. This makes it possible to create optimal presentation materials according to the progress of the project.

[0114] The creation unit can estimate the user's emotions and adjust the animation effects of the presentation materials based on the estimated user emotions. For example, if the user is nervous, a simple and unobtrusive animation can be used. If the user is relaxed, a visually appealing animation can be used. In this way, the visual appeal of the presentation materials can be maximized by adjusting the animation effects according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The user enters basic information such as the purpose and target audience of the presentation into the reception unit. For example, if the purpose of the presentation is to "introduce a new product" and the target audience is "management," the user enters that information. This information is then input into the generation AI. Step 2: The selection unit uses the generation AI to analyze the basic information entered by the reception unit and select the optimal template. For example, in the case of a presentation introducing a new product, a template including items such as product features, market analysis, and competitive comparisons will be selected. Step 3: The questioning unit uses the generation AI to ask specific questions to the user based on the templates selected by the selection unit, such as "What are the main features of the new product?" or "What are the results of your market analysis?" Step 4: The creation unit uses the generation AI to create presentation materials based on the answers obtained by the questioning unit. For example, the generation AI automatically creates slides based on the user's answers.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[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 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.

[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. 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 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.

[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 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting basic information of a user; a selection unit that analyzes the basic information input by the reception unit and selects an appropriate template; a questioning unit that asks a question to a user based on the template selected by the selection unit; a creation unit that creates presentation materials based on the answers obtained by the question unit. A system characterized by:

2. The selection unit Select a template using generative AI 2. The system of claim 1.

3. The interrogation unit Generative AI asks specific questions to the user 2. The system of claim 1.

4. The creation unit Creating presentation materials using generative AI 2. The system of claim 1.

5. The creation unit Users can check and edit the presentation materials generated by the AI.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the input method for basic information based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyzes the user's past presentation history and suggests appropriate input formats 2. The system of claim 1.

8. The reception unit When entering basic information, customize the input fields based on the user's current projects and interests 2. The system of claim 1.

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