System

Abstract

An object of a system according to an embodiment is to generate a high-quality presentation in a short time based on information provided by a user.SOLUTION: A system according to an embodiment includes an information analysis unit, a presentation generation unit, and a design generation unit. The information analysis unit analyzes information provided by a user. The presentation generation unit generates a persuasive presentation based on the information analyzed by the information analysis unit. The design generation unit generates a design of the presentation generated by the presentation generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that creating a presentation requires a lot of time and effort, which is a significant burden, especially for individuals who are not good at giving presentations.

[0005] The system according to the embodiment aims to generate a high-quality presentation in a short time based on information provided by a user. [Means for solving the problem]

[0006] A system according to an embodiment includes an information analysis unit, a presentation creation unit, and a design creation unit. The information analysis unit analyzes information provided by a user. The presentation creation unit creates a persuasive presentation based on the information analyzed by the information analysis unit. The design creation unit creates a design for the presentation created by the presentation creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate a high-quality presentation in a short time based on information provided by a user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The presentation support system according to an embodiment of the present invention automatically analyzes information provided by a user, and a generative AI creates and delivers a presentation. This allows the user to save valuable time and enables the creation and delivery of high-quality presentation materials.

[0029] A presentation support system according to an embodiment includes an information analysis unit, a presentation generation unit, and a design generation unit. The information analysis unit analyzes information provided by a user. For example, the user inputs information such as the theme and purpose of the presentation and information about the target audience to the generation AI. The generation AI analyzes this information and generates basic data for constructing the content of the presentation. The presentation generation unit generates a persuasive presentation based on the information analyzed by the information analysis unit. For example, the generation AI selects appropriate vocabulary, creates a logical structure, and generates a visually appealing slide design. This allows a user to create a high-quality presentation in a short amount of time. The design generation unit generates a presentation design. For example, the design generation unit automatically selects slide layouts, colors, fonts, and the like to create visually appealing presentation materials. This allows a user to create professional-looking presentation materials even without design knowledge. As a result, the presentation support system according to an embodiment can generate a high-quality presentation in a short amount of time based on the information provided by the user.

[0030] The information analysis unit analyzes a user's past presentation data and can propose a presentation style optimized for each individual user. For example, the information analysis unit collects data on presentations created by users in the past, and the generation AI analyzes that data. For example, it analyzes the slide structure, vocabulary used, and design trends to identify the user's presentation style. This makes it possible to propose the optimal presentation style based on the user's past data.

[0031] The information analysis unit analyzes the user's tone of voice and speaking style characteristics, and can adjust the content and vocabulary of the presentation based on that. For example, the information analysis unit collects audio data to analyze the user's tone of voice and speaking style characteristics, and the generation AI analyzes that data. For example, it identifies the speaking speed, intonation, and points to emphasize. This allows the content of the presentation to be adjusted based on the user's speaking style.

[0032] The information analysis unit can automatically collect related industry news and trend information in addition to the information provided by the user and reflect it in the presentation. The information analysis unit, for example, builds a system that automatically collects related industry news and trend information in addition to the information provided by the user. For example, it obtains the latest information from news sites and industry reports. This allows the industry news and trend information to be reflected in the presentation.

[0033] The information analysis unit can collect relevant information from a user's social media accounts and incorporate it into the content of a presentation. The information analysis unit, for example, builds a system that automatically collects relevant information from a user's social media accounts. For example, it analyzes the content of posts on Twitter or LinkedIn. This allows information from social media to be incorporated into a presentation.

[0034] The presentation generation unit can refer to data on past successful presentations and incorporate success factors. For example, the presentation generation unit collects data on past successful presentations, and the generation AI analyzes that data. For example, it identifies the structure of successful presentations, the vocabulary used, and design trends. This makes it possible to generate presentation content based on past successes.

[0035] The presentation generation unit can customize the content according to the audience's level of expertise. For example, the presentation generation unit analyzes the audience's level of expertise and generates presentation content accordingly. For example, the frequency of use of technical terms and the level of detail in explanations can be adjusted. This allows the presentation content to be tailored according to the audience's level of expertise.

[0036] The presentation generation unit can automatically generate content that is appropriate for different cultural spheres and regions. The presentation generation unit, for example, builds a system that automatically generates presentation content that is appropriate for different cultural spheres and regions. For example, slides are created that take into account differences in cultural background and language. This makes it possible to generate presentation content that is appropriate for different cultural spheres and regions.

[0037] The presentation generation unit can incorporate the opinions of experts from different industries and fields. For example, the presentation generation unit collects opinions from experts from different industries and fields, and builds a system that generates presentation content based on those opinions. For example, it incorporates the comments and advice of experts. This allows the opinions of experts from different industries and fields to be incorporated into the presentation.

[0038] The design generation unit can automatically apply the user's brand guidelines. The design generation unit, for example, builds a system that automatically applies the user's brand guidelines. For example, the design generation unit can automatically reflect a company logo, color palette, and font style. This allows the user's brand guidelines to be automatically applied to generate a presentation design.

[0039] The design generation unit can select the most effective design using a visual heat map. The design generation unit, for example, builds a system that evaluates the design of presentation materials using a visual heat map. For example, the system analyzes the degree of gaze concentration and attention and selects the optimal design. This allows the most effective design to be selected using a visual heat map.

[0040] The design generation unit can automatically propose different design templates. The design generation unit, for example, builds a system that automatically proposes different design templates. For example, it selects a template according to the user's presentation theme or purpose. This allows different design templates to be automatically proposed.

[0041] The design generation unit can refer to the user's past design history and provide a consistent design. The design generation unit, for example, builds a system that refers to the user's past design history and provides a consistent design. For example, it analyzes the design elements of past presentation materials. This allows the system to refer to the user's past design history and provide a consistent design.

[0042] The presentation generation unit can generate audio that imitates the user's tone of voice and speaking style. The presentation generation unit, for example, builds a system that generates audio that imitates the user's tone of voice and speaking style. For example, the presentation generation unit collects the user's voice data and the generation AI analyzes its characteristics. This makes it possible to generate audio that imitates the user's tone of voice and speaking style.

[0043] The presentation generation unit can automatically translate the content of a presentation into different languages ​​when it is made, making it possible to accommodate an international audience. The presentation generation unit, for example, builds a system that automatically translates the content of a presentation into different languages. For example, it supports multiple languages, such as English, Japanese, and French. This allows automatic translation into different languages ​​and makes it possible to accommodate an international audience.

[0044] The presentation generation unit can automatically select different presentation styles (e.g., conversational or interactive) when giving a presentation. The presentation generation unit, for example, builds a system that automatically selects different presentation styles. For example, it selects a style, such as a conversational or interactive style, depending on the reaction of the audience. This allows different presentation styles to be automatically selected, improving the quality of the presentation.

[0045] The presentation generation unit can automatically adjust a user's schedule and propose an optimal presentation time in situations where remote work or online communication is required. The presentation generation unit, for example, builds a system that automatically adjusts a user's schedule. For example, it works in conjunction with a calendar app to propose an optimal presentation time. This makes it possible to automatically adjust a user's schedule and propose an optimal presentation time in situations where remote work or online communication is required.

[0046] The presentation generation unit can monitor the network status and provide the optimal communication environment in remote work and online communication situations. The presentation generation unit, for example, builds a system that monitors the network status in real time. For example, it analyzes communication speed and connection stability to provide the optimal communication environment. This makes it possible to monitor the network status and provide the optimal communication environment in remote work and online communication situations.

[0047] The presentation generation unit can provide seamless presentations in collaboration with different online conference tools in remote work and online communication settings. The presentation generation unit, for example, builds a system that links with different online conference tools. For example, it links with tools such as Zoom, Microsoft Teams, and Google Meet to provide seamless presentations. This allows for collaboration with different online conference tools to provide seamless presentations.

[0048] The presentation generation unit can automatically generate a presentation optimized for a user's device in remote work or online communication situations. The presentation generation unit, for example, builds a system that automatically generates a presentation optimized for a user's device. For example, it creates slides that match the screen size and resolution of the device. This makes it possible to automatically generate a presentation optimized for the user's device.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The presentation support system can also include a health management unit that monitors the user's health condition. For example, it can measure the user's heart rate and stress level and provide appropriate advice during presentation preparation and delivery. This allows the user to maintain their health and deliver an effective presentation. The health management unit can also collect the user's health data and adjust the presentation schedule and content. For example, if the user is tired, it can suggest taking a break or shortening the presentation time. The health management unit can also suggest relaxing music or breathing techniques based on the user's health condition.

[0051] The presentation support system can further include a learning analysis unit that analyzes the user's learning history. For example, the content of the presentation can be customized based on the content and skills the user has learned in the past. This makes it possible to generate a presentation that is optimized for the user's knowledge and skills. The learning analysis unit can also collect the user's learning history and adjust the content of the presentation. For example, if the user is knowledgeable in a particular field, it can emphasize information related to that field. The learning analysis unit can also suggest the optimal presentation method based on the user's learning style.

[0052] The presentation support system may further include a creativity support unit to bring out the user's creativity. For example, it may provide a brainstorming tool to help the user come up with new ideas, allowing the user to create more creative presentations. The creativity support unit may also analyze the user's past ideas and projects to suggest new ideas. For example, the user may incorporate elements of successful projects. The creativity support unit may also provide inspiration to bring out the user's creativity. For example, it may present examples of related art and design.

[0053] The presentation support system can also include a time management unit that supports the user's time management. For example, it can automatically adjust the schedule for presentation preparation and presentation. This allows the user to efficiently manage their time and prepare for the presentation. The time management unit can also work with the user's calendar and task management tool to suggest an optimal schedule. For example, it can set priorities for important tasks and ensure that users have time to focus on preparing for the presentation. The time management unit can also monitor the user's progress and send reminders as necessary.

[0054] The presentation support system may further include a feedback collection unit that collects user feedback. For example, after a presentation, feedback from the audience may be automatically collected and analyzed. This allows the user to identify areas for improvement for the next presentation. The feedback collection unit may also collect comments and evaluations from the audience and provide advice on improving the content and style of the presentation. For example, it may identify points that the audience found particularly interesting and areas that need improvement. The feedback collection unit may also suggest training programs to help the user improve their presentation skills.

[0055] The presentation support system may further include a network utilization unit that utilizes the user's network. For example, the user's personal connections and expert networks may be utilized to enhance the content of the presentation. This allows the user to provide more reliable information. The network utilization unit may also collect the user's contact information and social media information to suggest experts and resources related to the presentation. For example, the opinions of experts in a specific field may be incorporated. The network utilization unit may also utilize the user's network to collect feedback to verify the content of the presentation.

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

[0057] Step 1: The information analysis unit analyzes the information provided by the user. For example, the user inputs the theme and purpose of the presentation, information about the target audience, etc. into the generation AI. The generation AI analyzes this information and generates basic data for constructing the content of the presentation. Step 2: The presentation generation unit generates a persuasive presentation based on the information analyzed by the information analysis unit. For example, the generation AI selects appropriate vocabulary, creates a logical structure, and generates visually appealing slide designs. This allows users to obtain high-quality presentations in a short amount of time. Step 3: The design generation unit generates a design for the presentation generated by the presentation generation unit. For example, it automatically performs slide layout, color usage, font selection, etc. to create visually appealing presentation materials. This allows users to create professional-looking presentation materials even without design knowledge.

[0058] (Example 2) The presentation support system according to an embodiment of the present invention automatically analyzes information provided by a user, and a generative AI creates and delivers a presentation. This allows the user to save valuable time and enables the creation and delivery of high-quality presentation materials.

[0059] A presentation support system according to an embodiment includes an information analysis unit, a presentation generation unit, and a design generation unit. The information analysis unit analyzes information provided by a user. For example, the user inputs information such as the theme and purpose of the presentation and information about the target audience to the generation AI. The generation AI analyzes this information and generates basic data for constructing the content of the presentation. The presentation generation unit generates a persuasive presentation based on the information analyzed by the information analysis unit. For example, the generation AI selects appropriate vocabulary, creates a logical structure, and generates a visually appealing slide design. This allows a user to create a high-quality presentation in a short amount of time. The design generation unit generates a presentation design. For example, the design generation unit automatically selects slide layouts, colors, fonts, and the like to create visually appealing presentation materials. This allows a user to create professional-looking presentation materials even without design knowledge. As a result, the presentation support system according to an embodiment can generate a high-quality presentation in a short amount of time based on the information provided by the user.

[0060] The information analysis unit analyzes a user's past presentation data and can propose a presentation style optimized for each individual user. For example, the information analysis unit collects data on presentations created by users in the past, and the generation AI analyzes that data. For example, it analyzes the slide structure, vocabulary used, and design trends to identify the user's presentation style. This makes it possible to propose the optimal presentation style based on the user's past data.

[0061] The information analysis unit analyzes the user's tone of voice and speaking style characteristics, and can adjust the content and vocabulary of the presentation based on that. For example, the information analysis unit collects audio data to analyze the user's tone of voice and speaking style characteristics, and the generation AI analyzes that data. For example, it identifies the speaking speed, intonation, and points to emphasize. This allows the content of the presentation to be adjusted based on the user's speaking style.

[0062] The information analysis unit can use the emotion estimation function to analyze the emotions a user has toward a presentation and provide information for eliciting positive emotions. The information analysis unit, for example, uses the emotion estimation function to analyze the emotions a user has toward a presentation in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to analyze the user's emotions and provide information for eliciting positive emotions.

[0063] The information analysis unit can automatically collect related industry news and trend information in addition to the information provided by the user and reflect it in the presentation. The information analysis unit, for example, builds a system that automatically collects related industry news and trend information in addition to the information provided by the user. For example, it obtains the latest information from news sites and industry reports. This allows the industry news and trend information to be reflected in the presentation.

[0064] The information analysis unit can collect relevant information from a user's social media accounts and incorporate it into the content of a presentation. The information analysis unit, for example, builds a system that automatically collects relevant information from a user's social media accounts. For example, it analyzes the content of posts on Twitter or LinkedIn. This allows information from social media to be incorporated into a presentation.

[0065] The information analysis unit uses the emotion estimation function to provide real-time emotional feedback for information input by a user, and can adjust the content of a presentation. The information analysis unit, for example, uses the emotion estimation function to build a system that provides real-time emotional feedback for information input by a user. For example, the information analysis unit analyzes the user's facial expressions and voice and displays an emotion score. This allows real-time emotional feedback for the user's input information to be provided, and the content of a presentation to be adjusted.

[0066] The presentation generation unit can refer to data on past successful presentations and incorporate success factors. For example, the presentation generation unit collects data on past successful presentations, and the generation AI analyzes that data. For example, it identifies the structure of successful presentations, the vocabulary used, and design trends. This makes it possible to generate presentation content based on past successes.

[0067] The presentation generation unit can customize the content according to the audience's level of expertise. For example, the presentation generation unit analyzes the audience's level of expertise and generates presentation content accordingly. For example, the frequency of use of technical terms and the level of detail in explanations can be adjusted. This allows the presentation content to be tailored according to the audience's level of expertise.

[0068] The presentation generation unit can use the emotion estimation function to predict the audience's emotional response and generate content that will elicit a positive response. The presentation generation unit, for example, uses the emotion estimation function to build a system that predicts the audience's emotional response. For example, it analyzes the audience's response based on past data and calculates an emotion score. This makes it possible to predict the audience's emotional response and generate content that will elicit a positive response.

[0069] The presentation generation unit can automatically generate content that is appropriate for different cultural spheres and regions. The presentation generation unit, for example, builds a system that automatically generates presentation content that is appropriate for different cultural spheres and regions. For example, slides are created that take into account differences in cultural background and language. This makes it possible to generate presentation content that is appropriate for different cultural spheres and regions.

[0070] The presentation generation unit can incorporate the opinions of experts from different industries and fields. For example, the presentation generation unit collects opinions from experts from different industries and fields, and builds a system that generates presentation content based on those opinions. For example, it incorporates the comments and advice of experts. This allows the opinions of experts from different industries and fields to be incorporated into the presentation.

[0071] The presentation generation unit can use the emotion estimation function to collect real-time emotional feedback on the content of the presentation and adjust the content. The presentation generation unit, for example, uses the emotion estimation function to build a system that collects real-time emotional feedback on the content of the presentation. For example, it analyzes the audience's facial expressions and voices and displays an emotion score. This allows the content of the presentation to be adjusted based on the real-time emotional feedback.

[0072] The design generation unit can automatically apply the user's brand guidelines. The design generation unit, for example, builds a system that automatically applies the user's brand guidelines. For example, the design generation unit can automatically reflect a company logo, color palette, and font style. This allows the user's brand guidelines to be automatically applied to generate a presentation design.

[0073] The design generation unit can select the most effective design using a visual heat map. The design generation unit, for example, builds a system that evaluates the design of presentation materials using a visual heat map. For example, the system analyzes the degree of gaze concentration and attention and selects the optimal design. This allows the most effective design to be selected using a visual heat map.

[0074] The design generation unit can use the emotion estimation function to generate designs that visually evoke positive emotions. The design generation unit, for example, builds a system that uses the emotion estimation function to generate designs that visually evoke positive emotions. For example, it incorporates design elements based on color psychology. This makes it possible to generate designs that visually evoke positive emotions.

[0075] The design generation unit can automatically propose different design templates. The design generation unit, for example, builds a system that automatically proposes different design templates. For example, it selects a template according to the user's presentation theme or purpose. This allows different design templates to be automatically proposed.

[0076] The design generation unit can refer to the user's past design history and provide a consistent design. The design generation unit, for example, builds a system that refers to the user's past design history and provides a consistent design. For example, it analyzes the design elements of past presentation materials. This allows the system to refer to the user's past design history and provide a consistent design.

[0077] The design generation unit can use the emotion estimation function to collect real-time emotional feedback on the design and adjust the design. The design generation unit, for example, builds a system that uses the emotion estimation function to collect real-time emotional feedback on the design. For example, the design generation unit analyzes the user's facial expressions and voice and displays an emotion score. This allows the design to be adjusted based on the real-time emotional feedback.

[0078] The presentation generation unit can generate audio that imitates the user's tone of voice and speaking style. The presentation generation unit, for example, builds a system that generates audio that imitates the user's tone of voice and speaking style. For example, the presentation generation unit collects the user's voice data and the generation AI analyzes its characteristics. This makes it possible to generate audio that imitates the user's tone of voice and speaking style.

[0079] The presentation generation unit can analyze the audience's real-time reactions and dynamically adjust the content of the presentation. The presentation generation unit, for example, builds a system that analyzes the audience's real-time reactions. For example, it analyzes the audience's facial expressions and voices and calculates an emotion score. This allows the presentation content to be dynamically adjusted based on the audience's real-time reactions.

[0080] The presentation generation unit can use the emotion estimation function to predict the audience's emotional response and make a presentation that will elicit a positive response. The presentation generation unit, for example, uses the emotion estimation function to build a system that predicts the audience's emotional response. For example, the system analyzes the audience's response based on past data and calculates an emotion score. This makes it possible to predict the audience's emotional response and make a presentation that will elicit a positive response.

[0081] The presentation generation unit can automatically translate the content of a presentation into different languages ​​when it is made, making it possible to accommodate an international audience. The presentation generation unit, for example, builds a system that automatically translates the content of a presentation into different languages. For example, it supports multiple languages, such as English, Japanese, and French. This allows automatic translation into different languages ​​and makes it possible to accommodate an international audience.

[0082] The presentation generation unit can automatically select different presentation styles (e.g., conversational or interactive) when giving a presentation. The presentation generation unit, for example, builds a system that automatically selects different presentation styles. For example, it selects a style, such as a conversational or interactive style, depending on the reaction of the audience. This allows different presentation styles to be automatically selected, improving the quality of the presentation.

[0083] The presentation generation unit can use the emotion estimation function to collect real-time emotional feedback on the presentation and adjust the presentation style. The presentation generation unit, for example, uses the emotion estimation function to build a system that collects real-time emotional feedback on the presentation. For example, it analyzes the audience's facial expressions and voices and displays an emotion score. This allows the presentation style to be adjusted based on the real-time emotional feedback.

[0084] The presentation generation unit can automatically adjust a user's schedule and propose an optimal presentation time in situations where remote work or online communication is required. The presentation generation unit, for example, builds a system that automatically adjusts a user's schedule. For example, it works in conjunction with a calendar app to propose an optimal presentation time. This makes it possible to automatically adjust a user's schedule and propose an optimal presentation time in situations where remote work or online communication is required.

[0085] The presentation generation unit can monitor the network status and provide the optimal communication environment in remote work and online communication situations. The presentation generation unit, for example, builds a system that monitors the network status in real time. For example, it analyzes communication speed and connection stability to provide the optimal communication environment. This makes it possible to monitor the network status and provide the optimal communication environment in remote work and online communication situations.

[0086] The presentation generation unit can use the emotion estimation function to analyze the emotions of users during online communication and provide support for eliciting positive emotions. The presentation generation unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of users during online communication in real time. For example, the presentation generation unit analyzes the user's facial expressions and voice and displays an emotion score. This allows the presentation generation unit to analyze the emotions of users during online communication and provide support for eliciting positive emotions.

[0087] The presentation generation unit can provide seamless presentations in collaboration with different online conference tools in remote work and online communication settings. The presentation generation unit, for example, builds a system that links with different online conference tools. For example, it links with tools such as Zoom, Microsoft Teams, and Google Meet to provide seamless presentations. This allows for collaboration with different online conference tools to provide seamless presentations.

[0088] The presentation generation unit can automatically generate a presentation optimized for a user's device in remote work or online communication situations. The presentation generation unit, for example, builds a system that automatically generates a presentation optimized for a user's device. For example, it creates slides that match the screen size and resolution of the device. This makes it possible to automatically generate a presentation optimized for the user's device.

[0089] The presentation generation unit can use the emotion estimation function to monitor the emotional reactions of users during online communication in real time and propose an optimal communication method. The presentation generation unit, for example, uses the emotion estimation function to build a system that monitors the emotional reactions of users during online communication in real time. For example, the system analyzes the user's facial expressions and voice and displays an emotion score. This makes it possible to monitor the emotional reactions of users during online communication in real time and propose an optimal communication method.

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

[0091] The presentation support system can also include a health management unit that monitors the user's health condition. For example, it can measure the user's heart rate and stress level and provide appropriate advice during presentation preparation and delivery. This allows the user to maintain their health and deliver an effective presentation. The health management unit can also collect the user's health data and adjust the presentation schedule and content. For example, if the user is tired, it can suggest taking a break or shortening the presentation time. The health management unit can also suggest relaxing music or breathing techniques based on the user's health condition.

[0092] The presentation support system can further include a learning analysis unit that analyzes the user's learning history. For example, the content of the presentation can be customized based on the content and skills the user has learned in the past. This makes it possible to generate a presentation that is optimized for the user's knowledge and skills. The learning analysis unit can also collect the user's learning history and adjust the content of the presentation. For example, if the user is knowledgeable in a particular field, it can emphasize information related to that field. The learning analysis unit can also suggest the optimal presentation method based on the user's learning style.

[0093] The presentation support system can further include an emotion adjustment unit that estimates the user's emotions and adjusts the content of the presentation. For example, if the user is nervous, the emotion adjustment unit can provide advice on how to relax or simplify the content of the presentation. This allows the user to give a presentation with confidence. The emotion adjustment unit can also analyze the user's facial expressions and voice and calculate an emotion score. For example, if the user has positive emotions, the emotion adjustment unit can provide information to further bring out those emotions. The emotion adjustment unit can also adjust the tone and style of the presentation based on the user's emotions.

[0094] The presentation support system may further include a creativity support unit to bring out the user's creativity. For example, it may provide a brainstorming tool to help the user come up with new ideas, allowing the user to create more creative presentations. The creativity support unit may also analyze the user's past ideas and projects to suggest new ideas. For example, the user may incorporate elements of successful projects. The creativity support unit may also provide inspiration to bring out the user's creativity. For example, it may present examples of related art and design.

[0095] The presentation support system can further include a design adjustment unit that estimates the user's emotions and adjusts the design of the presentation. For example, if the user is feeling stressed, a visually relaxing design can be suggested. This allows the user to relax while preparing for the presentation. The design adjustment unit can also analyze the user's facial expressions and voice and calculate an emotion score. For example, if the user is feeling positive, a design that further elicits that emotion can be suggested. The design adjustment unit can also adjust the color usage and layout of the presentation based on the user's emotions.

[0096] The presentation support system can also include a time management unit that supports the user's time management. For example, it can automatically adjust the schedule for presentation preparation and presentation. This allows the user to efficiently manage their time and prepare for the presentation. The time management unit can also work with the user's calendar and task management tool to suggest an optimal schedule. For example, it can set priorities for important tasks and ensure that users have time to focus on preparing for the presentation. The time management unit can also monitor the user's progress and send reminders as necessary.

[0097] The presentation support system can further include a progress support unit that estimates the user's emotions and supports the progress of the presentation. For example, if the user is nervous, the progress support unit can provide advice on how to relax and support the smooth progress of the presentation. This allows the user to proceed with the presentation with confidence. The progress support unit can also analyze the user's facial expressions and voice and calculate an emotion score. For example, if the user has positive emotions, the progress support unit can provide support to further bring out those emotions. The progress support unit can also adjust the way the presentation proceeds based on the user's emotions.

[0098] The presentation support system may further include a feedback collection unit that collects user feedback. For example, after a presentation, feedback from the audience may be automatically collected and analyzed. This allows the user to identify areas for improvement for the next presentation. The feedback collection unit may also collect comments and evaluations from the audience and provide advice on improving the content and style of the presentation. For example, it may identify points that the audience found particularly interesting and areas that need improvement. The feedback collection unit may also suggest training programs to help the user improve their presentation skills.

[0099] The presentation support system can further include a practice support unit that estimates the user's emotions and supports the user in practicing the presentation. For example, if the user is nervous, the practice support unit can provide advice on how to relax and support the user in practicing the presentation smoothly. This allows the user to practice the presentation with confidence. The practice support unit can also analyze the user's facial expressions and voice and calculate an emotion score. For example, if the user has positive emotions, the practice support unit can provide support to further bring out those emotions. The practice support unit can also adjust the presentation practice method based on the user's emotions.

[0100] The presentation support system may further include a network utilization unit that utilizes the user's network. For example, the user's personal connections and expert networks may be utilized to enhance the content of the presentation. This allows the user to provide more reliable information. The network utilization unit may also collect the user's contact information and social media information to suggest experts and resources related to the presentation. For example, the opinions of experts in a specific field may be incorporated. The network utilization unit may also utilize the user's network to collect feedback to verify the content of the presentation.

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

[0102] Step 1: The information analysis unit analyzes the information provided by the user. For example, the user inputs the theme and purpose of the presentation, information about the target audience, etc. into the generation AI. The generation AI analyzes this information and generates basic data for constructing the content of the presentation. Step 2: The presentation generation unit generates a persuasive presentation based on the information analyzed by the information analysis unit. For example, the generation AI selects appropriate vocabulary, creates a logical structure, and generates visually appealing slide designs. This allows users to obtain high-quality presentations in a short amount of time. Step 3: The design generation unit generates a design for the presentation generated by the presentation generation unit. For example, it automatically performs slide layout, color usage, font selection, etc. to create visually appealing presentation materials. This allows users to create professional-looking presentation materials even without design knowledge.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information analysis unit that analyzes information provided by a user; a presentation generation unit that generates a persuasive presentation based on the information analyzed by the information analysis unit; a design generation unit that generates a design for the presentation generated by the presentation generation unit; A system characterized by:

2. The information analysis unit Using the emotion estimation function, the emotion that the user feels toward the presentation is analyzed, and information is provided to elicit positive emotions.

2. The system of claim 1.

3. The information analysis unit Using emotion estimation functionality, the user is provided with real-time emotional feedback on the information they input, and the presentation content is adjusted accordingly.

2. The system of claim 1.

4. The presentation generation unit Use emotion estimation to predict audience emotional responses and generate content to elicit positive reactions 2. The system of claim 1.

5. The design generation unit Using emotion estimation to generate designs that visually evoke positive emotions 2. The system of claim 1.

6. The presentation generation unit Analyze real-time audience responses and dynamically adjust presentation content 2. The system of claim 1.

7. The presentation generation unit Using emotion estimation function, the system analyzes the user's emotions during online communication and provides support to bring out positive emotions.

2. The system of claim 1.

8. The presentation generation unit Using emotion estimation function, the emotional reactions of the user during online communication are monitored in real time, and the optimal communication method is suggested.

2. The system of claim 1.

Images