system

Abstract

The system according to this embodiment aims to automatically identify areas for improvement in the content and slide design of a presentation and provide advice to the user. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, and a provision unit. The reception unit inputs the content and slides of the user's presentation. The analysis unit analyzes the content and slides of the presentation input by the reception unit. The selection unit identifies areas for improvement based on the results of the analysis by the analysis unit. The provision unit provides advice to the user based on the areas for improvement identified by the selection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the improvement points of the presentation content and slide design are not sufficiently automatically identified and feedback is not provided.

[0005] The system according to the embodiment aims to automatically identify the improvement points of the presentation content and slide design and provide advice to the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a specification unit, and a provision unit. The reception unit inputs the content and slides of the user's presentation. The analysis unit analyzes the content and slides of the presentation entered by the reception unit. The specification unit identifies areas for improvement based on the results of the analysis performed by the analysis unit. The provision unit provides advice to the user based on the areas for improvement identified by the specification unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically identify areas for improvement in the content and slide design of a presentation and provide advice to the user. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The presentation improvement system according to an embodiment of the present invention is a system that uses a generative AI to provide feedback on areas for improvement in the content and slide design of a user's presentation. The presentation improvement system works as follows: the user inputs the content and slides of their presentation into the generative AI. For example, the user uploads a file from a presentation creation application or a PDF file. This information is input into the generative AI. Next, the generative AI automatically analyzes the input presentation content and slides. The generative AI analyzes the presentation's structure, slide design, visual elements, and text content to identify areas for improvement. For example, if the slide layout is visually unclear, the generative AI suggests improvements to the layout. Also, if the text content is redundant, the generative AI suggests simplifying the text. Based on the analysis results, the generative AI provides the user with specific advice. For example, it suggests specific layout changes to improve the slide design, the addition of visual elements, and text revisions. This allows the user to obtain concrete means to make their presentation more attractive and effective. For example, in business presentations, receiving advice from the generative AI can help create more persuasive presentations. Furthermore, in educational settings, receiving feedback from the generative AI can improve students' presentation skills. This allows the presentation improvement system to enhance the quality of the user's presentations.

[0029] The presentation improvement system according to this embodiment comprises a reception unit, an analysis unit, a specification unit, and a provision unit. The reception unit inputs the content and slides of the user's presentation. The content and slides of the user's presentation include, but are not limited to, business presentations, academic presentations, and educational slides. For example, the user can upload files from a presentation material creation application or PDF files to the reception unit. The reception unit also allows the user to directly input text. Furthermore, the reception unit can input the content of the presentation using voice input. For example, the reception unit converts speech into text using speech recognition technology and inputs it as the content of the presentation. The analysis unit analyzes the content and slides of the presentation input by the reception unit using a generation AI. The analysis unit analyzes, for example, the structure of the presentation, the design of the slides, visual elements, and the content of the text. For example, the generation AI analyzes the content of the presentation using a text generation AI (e.g., LLM). The analysis unit can also analyze the design and visual elements of the slides using a multimodal generation AI. For example, the generation AI analyzes the layout, color scheme, and font selection of the slides to identify areas for visual improvement. The Identification Unit identifies areas for improvement based on the results analyzed by the Analysis Unit. For example, the Identification Unit identifies areas for improvement in slide layout or simplification of text. For example, the Generating AI makes specific suggestions for simplifying redundant text. The Identification Unit can also suggest adding visual elements or changing the slide design. The Provisioning Unit provides advice to the user based on the areas for improvement identified by the Identification Unit. For example, the Provisioning Unit suggests specific layout changes, additions of visual elements, or text revisions to improve the slide design. For example, the Provisioning Unit shows the user specific steps for changing the slide layout. The Provisioning Unit can also suggest specific methods for adding visual elements. As a result, the presentation improvement system according to the embodiment can improve the quality of the user's presentation.Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can provide specific advice to the user based on the areas for improvement identified by the generating AI.

[0030] The reception desk inputs the content and slides of the user's presentation. This content and slides may include, but are not limited to, business presentations, academic presentations, or educational slides. The reception desk allows users to upload files from presentation creation applications or PDF files. Users can also directly input text. Furthermore, the reception desk can input presentation content using voice input. For example, the reception desk uses speech recognition technology to convert speech to text and input it as presentation content. Specifically, when a user uploads a file from a presentation creation application, the reception desk analyzes the file's contents and extracts elements such as text, images, and graphs from each slide. Similarly, for PDF files, text and images are analyzed and extracted. When a user directly inputs text, the reception desk analyzes the input text in real time to understand the structure and content of the presentation. In the case of voice input, speech recognition technology is used to convert the user's speech into text and incorporate it as presentation content. The speech recognition technology performs advanced processing such as noise reduction and speaker identification to achieve accurate text conversion. This allows the reception desk to efficiently input presentation data in various formats, enabling smooth processing of the entire system.

[0031] The analysis department uses generative AI to analyze the content and slides of presentations entered by the reception department. For example, the analysis department analyzes the presentation structure, slide design, visual elements, and text content. For instance, the generative AI uses text generation AI (e.g., LLM) to analyze the presentation content. The analysis department can also use multimodal generative AI to analyze slide design and visual elements. Specifically, the generative AI analyzes the text of each slide in the presentation and evaluates the logical structure and consistency of the content. For example, it checks whether the introduction, main content, and conclusion sections are appropriately placed. Regarding slide design, the multimodal generative AI uses image recognition technology to analyze the slide layout, color scheme, font selection, etc., and evaluates the visual balance and consistency. Furthermore, visual elements such as the placement of graphs and charts, and the resolution and quality of images are also analyzed. This allows the analysis department to comprehensively evaluate both the content and design of the presentation and provide foundational data for identifying areas for improvement.

[0032] The specific department identifies areas for improvement based on the results analyzed by the analysis department. For example, the specific department identifies areas for improvement in slide layout and text simplification. For instance, the generative AI makes specific suggestions for simplifying redundant text. The specific department can also suggest adding visual elements or changing the slide design. Specifically, the generative AI analyzes each slide of the presentation and suggests removing redundant expressions and unnecessary information, transforming them into concise and clear expressions. For example, it might shorten long bullet points and extract keywords to emphasize key points. Regarding visual elements, it might suggest redesigning the slide layout and rearranging the placement so that important information is visually highlighted. For example, it might review the placement of graphs and charts and suggest color schemes and font choices to ensure visual consistency. Furthermore, the specific department can also suggest adding visual elements tailored to the presentation's theme and purpose. For example, in the case of a business presentation, it might suggest a design incorporating the company logo and brand colors, while in the case of an academic presentation, it might suggest adding graphs and charts to emphasize the reliability of the data. This allows a specific unit to identify concrete areas for improvement in both the content and design of the presentation, enabling them to make effective suggestions to users.

[0033] The service provider provides advice to the user based on the areas for improvement identified by the specific service provider. For example, the service provider may suggest specific layout changes, additions of visual elements, or text revisions to improve the slide design. For instance, the service provider may show the user specific steps for changing the slide layout. The service provider may also suggest specific methods for adding visual elements. Specifically, the service provider provides specific advice to the user based on the areas for improvement identified by the generating AI. For example, as a procedure for changing the slide layout, it may suggest changing the position of text boxes and selecting colors and fonts to highlight important information. As a method for adding visual elements, it may specifically show how to select appropriate images and icons, and how to add graphs and charts. Furthermore, the service provider also provides support to the user when implementing the suggestions. For example, when the user changes the slide layout, it provides guidelines showing specific operating procedures, and when the user adds visual elements, it introduces appropriate resources and tools. In this way, the service provider can provide specific advice and support to the user to improve the quality of their presentations and maximize the effectiveness of the presentation improvement system according to the embodiment.

[0034] The analysis unit can analyze the structure of a presentation, the design of the slides, the visual elements, and the text content. For example, the analysis unit can analyze the structure of a presentation. For example, it can identify components such as the introduction, body, and conclusion, and evaluate the balance of each element. The analysis unit can also analyze the design of the slides. For example, it can evaluate the color scheme, font selection, and layout balance of the slides. Furthermore, the analysis unit can analyze the visual elements. For example, it can evaluate the visual elements such as images, graphs, and icons included in the slides. The analysis unit can also analyze the text content. For example, it can evaluate the keywords, summaries, and detailed explanations of the text. By analyzing the structure of the presentation, the design of the slides, the visual elements, and the text content, more specific areas for improvement can be identified. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can use generative AI to analyze the structure of a presentation and the design of the slides and identify specific areas for improvement.

[0035] The specific unit can identify areas for improvement in the slide layout. For example, the specific unit can analyze the slide layout and propose a visually clearer layout. For example, the specific unit can evaluate the placement of text and images, the use of whitespace, etc., and identify areas for improvement. The specific unit can also propose specific steps for changing the slide layout. For example, the specific unit can make specific suggestions such as left-aligning text, centering images, and using whitespace appropriately. By identifying areas for improvement in the slide layout, a visually clear presentation can be created. Some or all of the above processing in the specific unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the specific unit can analyze the slide layout using a generative AI and identify specific areas for improvement.

[0036] The specific unit can identify areas for text simplification. For example, it can analyze the content of the text and make specific suggestions for removing redundant expressions. For instance, it can suggest specific methods for removing redundant expressions and emphasizing key points. Furthermore, the specific unit can also suggest specific procedures for simplifying the text. For example, it can suggest specific methods for removing redundant expressions and emphasizing key points. By identifying areas for text simplification, redundant content can be reduced, leading to the creation of more effective presentations. Some or all of the above-described processes in the specific unit may be performed using, for example, a generative AI, or without one. For example, the specific unit can analyze the content of the text using a generative AI and identify specific areas for improvement.

[0037] The service provider can suggest specific layout changes to improve the slide design. For example, the service provider can suggest specific steps for changing the slide layout. For example, the service provider can make specific suggestions such as left-aligning text, centering images, and using whitespace appropriately. The service provider can also suggest specific methods for improving the slide design. For example, the service provider can suggest specific methods for improving color schemes, font choices, and layout balance. By suggesting specific layout changes to improve the slide design, it is possible to create a visually appealing presentation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide specific advice to the user based on the improvements identified by the generative AI.

[0038] The service provider can suggest the addition of visual elements. For example, the service provider can suggest specific methods for adding visual elements to slides. For example, the service provider can suggest specific methods for adding visual elements such as new images, graphs, or icons. The service provider can also suggest specific steps for adding visual elements. For example, the service provider can suggest adding a new image to a slide, inserting a graph, or placing an icon. By suggesting the addition of visual elements, the visual appeal of the presentation can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide specific advice to the user based on the improvements identified by the generative AI.

[0039] The service provider can suggest revisions to the text. For example, the service provider can suggest specific methods for revising the content of the text. For example, the service provider can make specific suggestions such as correcting grammar, improving expression, and emphasizing keywords. The service provider can also suggest specific steps for revising the text. For example, the service provider can make specific suggestions such as correcting grammatical errors, removing redundant expressions, and emphasizing key points. By suggesting revisions to the text, it is possible to create presentations that convey a more effective message. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide specific advice to the user based on the areas for improvement identified by the generative AI.

[0040] The reception desk can analyze the user's past presentation history and select the optimal input method. For example, the reception desk can prioritize suggesting file formats the user has used in the past. It can also automatically select templates that the user has preferred to use in the past. Furthermore, the reception desk can analyze the user's past input methods (voice, text, etc.) and suggest the optimal method. This allows for efficient input by suggesting the optimal input method through analysis of the user's past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past presentation history into a generating AI and have the generating AI select the optimal input method.

[0041] The reception system can filter presentations based on the user's current projects and areas of interest when they are entered. For example, the reception system can prioritize presentations related to the user's current projects. The reception system can also automatically select relevant slides and content based on the user's areas of interest. Furthermore, the reception system can suggest appropriate input content according to the progress of the user's projects. This allows for the priority of entering highly relevant presentations by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input information about the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0042] The reception desk can prioritize inputting presentations that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize inputting presentations related to that region. Furthermore, if the user is on a business trip, the reception desk can prioritize inputting presentations related to the destination. Additionally, if the user is at home, the reception desk can prioritize inputting presentations related to their work at home. This allows for the prioritization of highly relevant presentations by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For instance, the reception desk could input the user's geographical location into a generating AI and have the generating AI select highly relevant presentations.

[0043] The reception desk can analyze a user's social media activity and input relevant presentations when inputting presentations. For example, the reception desk can input relevant presentations based on what the user has shared on social media. It can also input relevant presentations based on topics the user follows on social media. Furthermore, the reception desk can analyze the user's social media activity history and input relevant presentations. This allows for efficient input of relevant presentations by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on the user's social media activity into a generating AI and have the generating AI select relevant presentations.

[0044] The analysis unit can adjust the level of detail of its analysis based on the importance of the presentations. For example, it can perform a detailed analysis on presentations of high importance, and a concise analysis on presentations of low importance. Furthermore, it can adjust the depth of its analysis according to the importance of the presentations. This allows for efficient analysis by adjusting the level of detail based on the importance of the presentations. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to evaluate the importance of presentations and adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the presentation category during analysis. For example, the analysis unit can apply a business-oriented analysis algorithm to business presentations. It can also apply an educational-oriented analysis algorithm to educational presentations. Furthermore, it can apply a technical-oriented analysis algorithm to technical presentations. This allows for more appropriate analysis results by applying different analysis algorithms depending on the presentation category. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to identify the presentation category and then apply the appropriate analysis algorithm.

[0046] The analysis unit can prioritize analyses based on the submission dates of presentations. For example, the analysis unit might prioritize analyzing presentations with approaching deadlines. It can also postpone analyzing presentations with later deadlines. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission dates. This enables efficient analysis by prioritizing analyses based on the presentation submission dates. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not. For example, the analysis unit can use generative AI to evaluate the submission dates of presentations and determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the presentations during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant presentations. It can also postpone the analysis of less relevant presentations. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the presentations. This enables efficient analysis by adjusting the order of analysis based on the relevance of the presentations. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to evaluate the relevance of presentations and adjust the order of analysis.

[0048] The identification unit can improve the accuracy of identifying areas for improvement by considering the interrelationships of the presentation. For example, the identification unit can identify areas for improvement by considering the relationships between slides in the presentation. It can also identify areas for improvement by considering the overall structure of the presentation. Furthermore, it can identify areas for improvement by considering the consistency of the content of the presentation. In this way, the accuracy of identifying areas can be improved by considering the interrelationships of the presentation. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can evaluate the interrelationships of the presentation using a generative AI and identify areas for improvement.

[0049] The identification unit can identify areas for improvement by considering the attribute information of the presentation presenter. For example, the identification unit can identify appropriate areas for improvement based on the presenter's area of ​​expertise. It can also identify appropriate areas for improvement based on the presenter's years of experience. Furthermore, it can identify appropriate areas for improvement based on the presenter's past presentation history. This allows for the identification of more appropriate areas for improvement by considering the attribute information of the presentation presenter. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can evaluate the presenter's attribute information using a generative AI and identify areas for improvement.

[0050] The identification unit can identify areas for improvement by considering the geographical distribution of the presentation. For example, the identification unit can identify appropriate areas for improvement based on the target region of the presentation. The identification unit can also identify areas for improvement by considering the geographical perspective of the presentation. Furthermore, the identification unit can identify areas for improvement by considering region-specific elements of the presentation. This allows for the identification of more appropriate areas for improvement by considering the geographical distribution of the presentation. Some or all of the above processing in the identification unit may be performed using, for example, generative AI, or without generative AI. For example, the identification unit can evaluate the geographical distribution of the presentation using generative AI and identify areas for improvement.

[0051] The identification unit can improve the accuracy of its identification of areas for improvement by referring to relevant literature on the presentation. For example, the identification unit can identify areas for improvement by referring to literature related to the content of the presentation. It can also identify areas for improvement by referring to the latest research related to the topic of the presentation. Furthermore, the identification unit can identify areas for improvement by referring to best practices in the field of presentations. This improves the accuracy of identification by referring to relevant literature on the presentation. Some or all of the above processing in the identification unit may be performed using, for example, generative AI, or not using generative AI. For example, the identification unit can search for relevant literature and identify areas for improvement using generative AI.

[0052] The service provider can adjust the level of detail in the advice given based on the importance of the presentation. For example, it can provide detailed advice for high-importance presentations and concise advice for low-importance presentations. Furthermore, it can adjust the depth of the advice according to the importance of the presentation. This allows for efficient advice by adjusting the level of detail based on the importance of the presentation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use generative AI to evaluate the importance of a presentation and adjust the level of detail in the advice.

[0053] The service provider can apply different advice algorithms depending on the presentation category when providing advice. For example, it can apply a business-oriented advice algorithm to business presentations. It can also apply an educational-oriented advice algorithm to educational presentations. Furthermore, it can apply a technical-oriented advice algorithm to technical presentations. By applying different advice algorithms depending on the presentation category, it can provide more appropriate advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can identify the presentation category using generative AI and apply an appropriate advice algorithm.

[0054] The service provider can prioritize advice based on the presentation submission deadlines. For example, it may prioritize advice for presentations with approaching deadlines and postpone advice for presentations with later deadlines. Furthermore, the service provider can dynamically adjust the priority of advice based on the submission timing. This enables efficient advice delivery by prioritizing advice based on the presentation submission timing. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can use generative AI to evaluate presentation submission timings and determine the priority of advice.

[0055] The advice delivery unit can adjust the order of advice based on the relevance of the presentations when providing advice. For example, the unit will prioritize advice on highly relevant presentations. It can also postpone advice on less relevant presentations. Furthermore, the unit can dynamically adjust the order of advice according to the relevance of the presentations. This enables efficient advice delivery by adjusting the order of advice based on the relevance of the presentations. Some or all of the above processing in the advice delivery unit may be performed using AI, for example, or not using AI. For example, the advice delivery unit can evaluate the relevance of presentations using generative AI and adjust the order of advice.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The reception desk can analyze a user's past presentation history when inputting presentation content and slides, and select the optimal input method. For example, it can prioritize suggesting file formats the user has used in the past. The reception desk can also automatically select templates that the user has preferred to use in the past. Furthermore, the reception desk can analyze the user's past input methods (voice, text, etc.) and suggest the optimal method. In this way, by analyzing the user's past history, it suggests the optimal input method and enables efficient input.

[0058] The specific component can improve the accuracy of identifying areas for improvement in slide layout by considering the interrelationships within the presentation. For example, it can identify areas for improvement by considering the relationships between slides in the presentation. It can also identify areas for improvement by considering the overall structure of the presentation. Furthermore, it can identify areas for improvement by considering the consistency of the presentation's content. In this way, by considering the interrelationships within the presentation, the accuracy of identifying areas for improvement can be improved.

[0059] The service provider can prioritize advice based on the presentation submission deadline when proposing specific layout changes to improve slide design. For example, presentations with approaching deadlines will receive priority advice, while those with later deadlines can be postponed. Furthermore, the priority of advice can be dynamically adjusted according to the submission timing. This allows for efficient advice delivery by prioritizing advice based on the presentation submission date.

[0060] The service provider can apply different advice algorithms depending on the presentation category when suggesting text revisions. For example, a business-oriented advice algorithm can be applied to business presentations. Similarly, an educational-oriented advice algorithm can be applied to educational presentations. Furthermore, a technical-oriented advice algorithm can be applied to technical presentations. This allows for more appropriate advice to be provided by applying different advice algorithms depending on the presentation category.

[0061] The reception desk can prioritize the input of presentations that are most relevant to the user's geographical location when they are entering their presentations. For example, if the user is in a specific region, presentations related to that region will be prioritized. Similarly, if the user is on a business trip, presentations related to their destination will be prioritized. Furthermore, if the user is at home, presentations related to their work at home will be prioritized. This allows the system to prioritize the input of presentations that are most relevant to the user's geographical location.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk inputs the user's presentation content and slides. Users can upload files from presentation creation applications or PDF files, or input text directly. Furthermore, they can input presentation content using voice input, with speech recognition technology converting the speech to text. Step 2: The analysis department uses generative AI to analyze the presentation content and slides entered by the reception department. Specifically, it analyzes the presentation structure, slide design, visual elements, and text content. For example, it uses text generation AI (LLM) to analyze the presentation content and multimodal generation AI to analyze the slide design and visual elements. Step 3: The specific team identifies areas for improvement based on the analysis conducted by the analysis team. For example, they might identify areas for improvement in slide layout or text simplification, and make specific suggestions for shortening redundant text. They might also suggest adding visual elements or changing the slide design. Step 4: The providing team provides advice to the user based on the areas for improvement identified by the specific team. Specifically, they suggest concrete layout changes, additions of visual elements, and text revisions to improve the slide design. For example, they might provide specific steps for changing the slide layout or specific methods for adding visual elements.

[0064] (Example of form 2) The presentation improvement system according to an embodiment of the present invention is a system that uses a generative AI to provide feedback on areas for improvement in the content and slide design of a user's presentation. The presentation improvement system works as follows: the user inputs the content and slides of their presentation into the generative AI. For example, the user uploads a file from a presentation creation application or a PDF file. This information is input into the generative AI. Next, the generative AI automatically analyzes the input presentation content and slides. The generative AI analyzes the presentation's structure, slide design, visual elements, and text content to identify areas for improvement. For example, if the slide layout is visually unclear, the generative AI suggests improvements to the layout. Also, if the text content is redundant, the generative AI suggests simplifying the text. Based on the analysis results, the generative AI provides the user with specific advice. For example, it suggests specific layout changes to improve the slide design, the addition of visual elements, and text revisions. This allows the user to obtain concrete means to make their presentation more attractive and effective. For example, in business presentations, receiving advice from the generative AI can help create more persuasive presentations. Furthermore, in educational settings, receiving feedback from the generative AI can improve students' presentation skills. This allows the presentation improvement system to enhance the quality of the user's presentations.

[0065] The presentation improvement system according to this embodiment comprises a reception unit, an analysis unit, a specification unit, and a provision unit. The reception unit inputs the content and slides of the user's presentation. The content and slides of the user's presentation include, but are not limited to, business presentations, academic presentations, and educational slides. For example, the user can upload files from a presentation material creation application or PDF files to the reception unit. The reception unit also allows the user to directly input text. Furthermore, the reception unit can input the content of the presentation using voice input. For example, the reception unit converts speech into text using speech recognition technology and inputs it as the content of the presentation. The analysis unit analyzes the content and slides of the presentation input by the reception unit using a generation AI. The analysis unit analyzes, for example, the structure of the presentation, the design of the slides, visual elements, and the content of the text. For example, the generation AI analyzes the content of the presentation using a text generation AI (e.g., LLM). The analysis unit can also analyze the design and visual elements of the slides using a multimodal generation AI. For example, the generation AI analyzes the layout, color scheme, and font selection of the slides to identify areas for visual improvement. The Identification Unit identifies areas for improvement based on the results analyzed by the Analysis Unit. For example, the Identification Unit identifies areas for improvement in slide layout or simplification of text. For example, the Generating AI makes specific suggestions for simplifying redundant text. The Identification Unit can also suggest adding visual elements or changing the slide design. The Provisioning Unit provides advice to the user based on the areas for improvement identified by the Identification Unit. For example, the Provisioning Unit suggests specific layout changes, additions of visual elements, or text revisions to improve the slide design. For example, the Provisioning Unit shows the user specific steps for changing the slide layout. The Provisioning Unit can also suggest specific methods for adding visual elements. As a result, the presentation improvement system according to the embodiment can improve the quality of the user's presentation.Some or all of the processing described above in the service provision unit may be performed using AI, for example, or without AI. For example, the service provision unit can provide specific advice to the user based on the areas for improvement identified by the generating AI.

[0066] The reception desk inputs the content and slides of the user's presentation. This content and slides may include, but are not limited to, business presentations, academic presentations, or educational slides. The reception desk allows users to upload files from presentation creation applications or PDF files. Users can also directly input text. Furthermore, the reception desk can input presentation content using voice input. For example, the reception desk uses speech recognition technology to convert speech to text and input it as presentation content. Specifically, when a user uploads a file from a presentation creation application, the reception desk analyzes the file's contents and extracts elements such as text, images, and graphs from each slide. Similarly, for PDF files, text and images are analyzed and extracted. When a user directly inputs text, the reception desk analyzes the input text in real time to understand the structure and content of the presentation. In the case of voice input, speech recognition technology is used to convert the user's speech into text and incorporate it as presentation content. The speech recognition technology performs advanced processing such as noise reduction and speaker identification to achieve accurate text conversion. This allows the reception desk to efficiently input presentation data in various formats, enabling smooth processing of the entire system.

[0067] The analysis department uses generative AI to analyze the content and slides of presentations entered by the reception department. For example, the analysis department analyzes the presentation structure, slide design, visual elements, and text content. For instance, the generative AI uses text generation AI (e.g., LLM) to analyze the presentation content. The analysis department can also use multimodal generative AI to analyze slide design and visual elements. Specifically, the generative AI analyzes the text of each slide in the presentation and evaluates the logical structure and consistency of the content. For example, it checks whether the introduction, main content, and conclusion sections are appropriately placed. Regarding slide design, the multimodal generative AI uses image recognition technology to analyze the slide layout, color scheme, font selection, etc., and evaluates the visual balance and consistency. Furthermore, visual elements such as the placement of graphs and charts, and the resolution and quality of images are also analyzed. This allows the analysis department to comprehensively evaluate both the content and design of the presentation and provide foundational data for identifying areas for improvement.

[0068] The specific department identifies areas for improvement based on the results analyzed by the analysis department. For example, the specific department identifies areas for improvement in slide layout and text simplification. For instance, the generative AI makes specific suggestions for simplifying redundant text. The specific department can also suggest adding visual elements or changing the slide design. Specifically, the generative AI analyzes each slide of the presentation and suggests removing redundant expressions and unnecessary information, transforming them into concise and clear expressions. For example, it might shorten long bullet points and extract keywords to emphasize key points. Regarding visual elements, it might suggest redesigning the slide layout and rearranging the placement so that important information is visually highlighted. For example, it might review the placement of graphs and charts and suggest color schemes and font choices to ensure visual consistency. Furthermore, the specific department can also suggest adding visual elements tailored to the presentation's theme and purpose. For example, in the case of a business presentation, it might suggest a design incorporating the company logo and brand colors, while in the case of an academic presentation, it might suggest adding graphs and charts to emphasize the reliability of the data. This allows a specific unit to identify concrete areas for improvement in both the content and design of the presentation, enabling them to make effective suggestions to users.

[0069] The service provider provides advice to the user based on the areas for improvement identified by the specific service provider. For example, the service provider may suggest specific layout changes, additions of visual elements, or text revisions to improve the slide design. For instance, the service provider may show the user specific steps for changing the slide layout. The service provider may also suggest specific methods for adding visual elements. Specifically, the service provider provides specific advice to the user based on the areas for improvement identified by the generating AI. For example, as a procedure for changing the slide layout, it may suggest changing the position of text boxes and selecting colors and fonts to highlight important information. As a method for adding visual elements, it may specifically show how to select appropriate images and icons, and how to add graphs and charts. Furthermore, the service provider also provides support to the user when implementing the suggestions. For example, when the user changes the slide layout, it provides guidelines showing specific operating procedures, and when the user adds visual elements, it introduces appropriate resources and tools. In this way, the service provider can provide specific advice and support to the user to improve the quality of their presentations and maximize the effectiveness of the presentation improvement system according to the embodiment.

[0070] The analysis unit can analyze the structure of a presentation, the design of the slides, the visual elements, and the text content. For example, the analysis unit can analyze the structure of a presentation. For example, it can identify components such as the introduction, body, and conclusion, and evaluate the balance of each element. The analysis unit can also analyze the design of the slides. For example, it can evaluate the color scheme, font selection, and layout balance of the slides. Furthermore, the analysis unit can analyze the visual elements. For example, it can evaluate the visual elements such as images, graphs, and icons included in the slides. The analysis unit can also analyze the text content. For example, it can evaluate the keywords, summaries, and detailed explanations of the text. By analyzing the structure of the presentation, the design of the slides, the visual elements, and the text content, more specific areas for improvement can be identified. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can use generative AI to analyze the structure of a presentation and the design of the slides and identify specific areas for improvement.

[0071] The specific unit can identify areas for improvement in the slide layout. For example, the specific unit can analyze the slide layout and propose a visually clearer layout. For example, the specific unit can evaluate the placement of text and images, the use of whitespace, etc., and identify areas for improvement. The specific unit can also propose specific steps for changing the slide layout. For example, the specific unit can make specific suggestions such as left-aligning text, centering images, and using whitespace appropriately. By identifying areas for improvement in the slide layout, a visually clear presentation can be created. Some or all of the above processing in the specific unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the specific unit can analyze the slide layout using a generative AI and identify specific areas for improvement.

[0072] The specific unit can identify areas for text simplification. For example, it can analyze the content of the text and make specific suggestions for removing redundant expressions. For instance, it can suggest specific methods for removing redundant expressions and emphasizing key points. Furthermore, the specific unit can also suggest specific procedures for simplifying the text. For example, it can suggest specific methods for removing redundant expressions and emphasizing key points. By identifying areas for text simplification, redundant content can be reduced, leading to the creation of more effective presentations. Some or all of the above-described processes in the specific unit may be performed using, for example, a generative AI, or without one. For example, the specific unit can analyze the content of the text using a generative AI and identify specific areas for improvement.

[0073] The service provider can suggest specific layout changes to improve the slide design. For example, the service provider can suggest specific steps for changing the slide layout. For example, the service provider can make specific suggestions such as left-aligning text, centering images, and using whitespace appropriately. The service provider can also suggest specific methods for improving the slide design. For example, the service provider can suggest specific methods for improving color schemes, font choices, and layout balance. By suggesting specific layout changes to improve the slide design, it is possible to create a visually appealing presentation. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide specific advice to the user based on the improvements identified by the generative AI.

[0074] The service provider can suggest the addition of visual elements. For example, the service provider can suggest specific methods for adding visual elements to slides. For example, the service provider can suggest specific methods for adding visual elements such as new images, graphs, or icons. The service provider can also suggest specific steps for adding visual elements. For example, the service provider can suggest adding a new image to a slide, inserting a graph, or placing an icon. By suggesting the addition of visual elements, the visual appeal of the presentation can be improved. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide specific advice to the user based on the improvements identified by the generative AI.

[0075] The service provider can suggest revisions to the text. For example, the service provider can suggest specific methods for revising the content of the text. For example, the service provider can make specific suggestions such as correcting grammar, improving expression, and emphasizing keywords. The service provider can also suggest specific steps for revising the text. For example, the service provider can make specific suggestions such as correcting grammatical errors, removing redundant expressions, and emphasizing key points. By suggesting revisions to the text, it is possible to create presentations that convey a more effective message. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide specific advice to the user based on the areas for improvement identified by the generative AI.

[0076] The reception desk can estimate the user's emotions and adjust the timing of presentation input based on the estimated emotions. For example, if the user is nervous, the reception desk can delay the input timing to help them relax. Conversely, if the user is in a hurry, the reception desk can speed up the timing to encourage quick input. Furthermore, if the user is focused, the reception desk can prompt input at the optimal time. This allows the user to input the presentation in the best possible state by adjusting the input timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user image data captured by a camera into a generative AI and have the generative AI perform emotion estimation.

[0077] The reception desk can analyze the user's past presentation history and select the optimal input method. For example, the reception desk can prioritize suggesting file formats the user has used in the past. It can also automatically select templates that the user has preferred to use in the past. Furthermore, the reception desk can analyze the user's past input methods (voice, text, etc.) and suggest the optimal method. This allows for efficient input by suggesting the optimal input method through analysis of the user's past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past presentation history into a generating AI and have the generating AI select the optimal input method.

[0078] The reception system can filter presentations based on the user's current projects and areas of interest when they are entered. For example, the reception system can prioritize presentations related to the user's current projects. The reception system can also automatically select relevant slides and content based on the user's areas of interest. Furthermore, the reception system can suggest appropriate input content according to the progress of the user's projects. This allows for the priority of entering highly relevant presentations by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input information about the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0079] The reception desk can estimate the user's emotions and determine the priority of presentations to input based on the estimated emotions. For example, if the user is stressed, the reception desk may postpone less important presentations. Conversely, if the user is relaxed, the reception desk may prioritize more important presentations. Furthermore, if the user is in a hurry, the reception desk may input the most important presentation first. This allows for input in the order best suited to the user's state by prioritizing presentations according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user image data captured by a camera into a generative AI and have the generative AI perform the user's emotion estimation.

[0080] The reception desk can prioritize inputting presentations that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize inputting presentations related to that region. Furthermore, if the user is on a business trip, the reception desk can prioritize inputting presentations related to the destination. Additionally, if the user is at home, the reception desk can prioritize inputting presentations related to their work at home. This allows for the prioritization of highly relevant presentations by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For instance, the reception desk could input the user's geographical location into a generating AI and have the generating AI select highly relevant presentations.

[0081] The reception desk can analyze a user's social media activity and input relevant presentations when inputting presentations. For example, the reception desk can input relevant presentations based on what the user has shared on social media. It can also input relevant presentations based on topics the user follows on social media. Furthermore, the reception desk can analyze the user's social media activity history and input relevant presentations. This allows for efficient input of relevant presentations by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on the user's social media activity into a generating AI and have the generating AI select relevant presentations.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. In this way, by adjusting the presentation of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0083] The analysis unit can adjust the level of detail of its analysis based on the importance of the presentations. For example, it can perform a detailed analysis on presentations of high importance, and a concise analysis on presentations of low importance. Furthermore, it can adjust the depth of its analysis according to the importance of the presentations. This allows for efficient analysis by adjusting the level of detail based on the importance of the presentations. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to evaluate the importance of presentations and adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the presentation category during analysis. For example, the analysis unit can apply a business-oriented analysis algorithm to business presentations. It can also apply an educational-oriented analysis algorithm to educational presentations. Furthermore, it can apply a technical-oriented analysis algorithm to technical presentations. This allows for more appropriate analysis results by applying different analysis algorithms depending on the presentation category. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to identify the presentation category and then apply the appropriate analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a longer analysis with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the user's emotion estimation.

[0086] The analysis unit can prioritize analyses based on the submission dates of presentations. For example, the analysis unit might prioritize analyzing presentations with approaching deadlines. It can also postpone analyzing presentations with later deadlines. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the submission dates. This enables efficient analysis by prioritizing analyses based on the presentation submission dates. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not. For example, the analysis unit can use generative AI to evaluate the submission dates of presentations and determine the analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the presentations during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant presentations. It can also postpone the analysis of less relevant presentations. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the presentations. This enables efficient analysis by adjusting the order of analysis based on the relevance of the presentations. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to evaluate the relevance of presentations and adjust the order of analysis.

[0088] The identification unit can estimate the user's emotions and adjust the method of identifying areas for improvement based on the estimated emotions. For example, if the user is relaxed, the identification unit can provide detailed areas for improvement. If the user is in a hurry, the identification unit can also provide concise areas for improvement. Furthermore, if the user is excited, the identification unit can provide areas for improvement with visually stimulating effects. By adjusting the method of identifying areas for improvement according to the user's emotions, the system can provide the most suitable areas for improvement for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0089] The identification unit can improve the accuracy of identifying areas for improvement by considering the interrelationships of the presentation. For example, the identification unit can identify areas for improvement by considering the relationships between slides in the presentation. It can also identify areas for improvement by considering the overall structure of the presentation. Furthermore, it can identify areas for improvement by considering the consistency of the content of the presentation. In this way, the accuracy of identifying areas can be improved by considering the interrelationships of the presentation. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can evaluate the interrelationships of the presentation using a generative AI and identify areas for improvement.

[0090] The identification unit can identify areas for improvement by considering the attribute information of the presentation presenter. For example, the identification unit can identify appropriate areas for improvement based on the presenter's area of ​​expertise. It can also identify appropriate areas for improvement based on the presenter's years of experience. Furthermore, it can identify appropriate areas for improvement based on the presenter's past presentation history. This allows for the identification of more appropriate areas for improvement by considering the attribute information of the presentation presenter. Some or all of the above processing in the identification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the identification unit can evaluate the presenter's attribute information using a generative AI and identify areas for improvement.

[0091] The specific unit can estimate the user's emotions and adjust the display method of improvement based on the estimated user emotions. For example, if the user is nervous, the specific unit can provide a simple and highly visible display method. If the user is relaxed, the specific unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the specific unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of improvement according to the user's emotions, the optimal display method can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the specific unit may be performed using AI, for example, or without AI. For example, the specific unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0092] The identification unit can identify areas for improvement by considering the geographical distribution of the presentation. For example, the identification unit can identify appropriate areas for improvement based on the target region of the presentation. The identification unit can also identify areas for improvement by considering the geographical perspective of the presentation. Furthermore, the identification unit can identify areas for improvement by considering region-specific elements of the presentation. This allows for the identification of more appropriate areas for improvement by considering the geographical distribution of the presentation. Some or all of the above processing in the identification unit may be performed using, for example, generative AI, or without generative AI. For example, the identification unit can evaluate the geographical distribution of the presentation using generative AI and identify areas for improvement.

[0093] The identification unit can improve the accuracy of its identification of areas for improvement by referring to relevant literature on the presentation. For example, the identification unit can identify areas for improvement by referring to literature related to the content of the presentation. It can also identify areas for improvement by referring to the latest research related to the topic of the presentation. Furthermore, the identification unit can identify areas for improvement by referring to best practices in the field of presentations. This improves the accuracy of identification by referring to relevant literature on the presentation. Some or all of the above processing in the identification unit may be performed using, for example, generative AI, or not using generative AI. For example, the identification unit can search for relevant literature and identify areas for improvement using generative AI.

[0094] The service provider can estimate the user's emotions and adjust the way advice is presented based on the estimated emotions. For example, if the user is nervous, the service provider can provide advice in a calm tone. If the user is relaxed, the service provider can also provide advice in a cheerful tone. Furthermore, if the user is in a hurry, the service provider can provide quick and concise advice. In this way, by adjusting the way advice is presented according to the user's emotions, the service provider can provide the most appropriate advice for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0095] The service provider can adjust the level of detail in the advice given based on the importance of the presentation. For example, it can provide detailed advice for high-importance presentations and concise advice for low-importance presentations. Furthermore, it can adjust the depth of the advice according to the importance of the presentation. This allows for efficient advice by adjusting the level of detail based on the importance of the presentation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use generative AI to evaluate the importance of a presentation and adjust the level of detail in the advice.

[0096] The service provider can apply different advice algorithms depending on the presentation category when providing advice. For example, it can apply a business-oriented advice algorithm to business presentations. It can also apply an educational-oriented advice algorithm to educational presentations. Furthermore, it can apply a technical-oriented advice algorithm to technical presentations. By applying different advice algorithms depending on the presentation category, it can provide more appropriate advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can identify the presentation category using generative AI and apply an appropriate advice algorithm.

[0097] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise advice. If the user is relaxed, the service provider can also provide longer advice with detailed explanations. Furthermore, if the user is excited, the service provider can provide advice with visually stimulating effects. By adjusting the length of the advice according to the user's emotions, the service provider can provide the most appropriate advice for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user image data captured by a camera into a generative AI and have the generative AI perform the user's emotion estimation.

[0098] The service provider can prioritize advice based on the presentation submission deadlines. For example, it may prioritize advice for presentations with approaching deadlines and postpone advice for presentations with later deadlines. Furthermore, the service provider can dynamically adjust the priority of advice based on the submission timing. This enables efficient advice delivery by prioritizing advice based on the presentation submission timing. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can use generative AI to evaluate presentation submission timings and determine the priority of advice.

[0099] The advice delivery unit can adjust the order of advice based on the relevance of the presentations when providing advice. For example, the unit will prioritize advice on highly relevant presentations. It can also postpone advice on less relevant presentations. Furthermore, the unit can dynamically adjust the order of advice according to the relevance of the presentations. This enables efficient advice delivery by adjusting the order of advice based on the relevance of the presentations. Some or all of the above processing in the advice delivery unit may be performed using AI, for example, or not using AI. For example, the advice delivery unit can evaluate the relevance of presentations using generative AI and adjust the order of advice.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception desk can analyze a user's past presentation history when inputting presentation content and slides, and select the optimal input method. For example, it can prioritize suggesting file formats the user has used in the past. The reception desk can also automatically select templates that the user has preferred to use in the past. Furthermore, the reception desk can analyze the user's past input methods (voice, text, etc.) and suggest the optimal method. In this way, by analyzing the user's past history, it suggests the optimal input method and enables efficient input.

[0102] The analysis unit can estimate user emotions when analyzing the presentation structure, slide design, visual elements, and text content, and adjust the presentation of the analysis based on the estimated user emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, it can provide analysis results with visually stimulating effects. In this way, by adjusting the presentation of the analysis according to the user's emotions, it can provide the most optimal analysis results for the user.

[0103] The specific component can improve the accuracy of identifying areas for improvement in slide layout by considering the interrelationships within the presentation. For example, it can identify areas for improvement by considering the relationships between slides in the presentation. It can also identify areas for improvement by considering the overall structure of the presentation. Furthermore, it can identify areas for improvement by considering the consistency of the presentation's content. In this way, by considering the interrelationships within the presentation, the accuracy of identifying areas for improvement can be improved.

[0104] The identification unit can estimate the user's emotions when identifying areas for text simplification and adjust the method of identifying improvements based on those emotions. For example, if the user is relaxed, it can provide detailed improvements. If the user is in a hurry, it can provide concise improvements that get straight to the point. Furthermore, if the user is excited, it can provide improvements with visually stimulating effects. By adjusting the method of identifying improvements according to the user's emotions, it is possible to provide the most suitable improvements for the user.

[0105] The service provider can prioritize advice based on the presentation submission deadline when proposing specific layout changes to improve slide design. For example, presentations with approaching deadlines will receive priority advice, while those with later deadlines can be postponed. Furthermore, the priority of advice can be dynamically adjusted according to the submission timing. This allows for efficient advice delivery by prioritizing advice based on the presentation submission date.

[0106] When suggesting the addition of visual elements, the service provider can estimate the user's emotions and adjust the way advice is presented based on those emotions. For example, if the user is nervous, advice can be presented in a calm tone. If the user is relaxed, advice can be presented in a bright tone. Furthermore, if the user is in a hurry, quick and concise advice can be presented. By adjusting the way advice is presented according to the user's emotions, the service provider can deliver the most appropriate advice to the user.

[0107] The service provider can apply different advice algorithms depending on the presentation category when suggesting text revisions. For example, a business-oriented advice algorithm can be applied to business presentations. Similarly, an educational-oriented advice algorithm can be applied to educational presentations. Furthermore, a technical-oriented advice algorithm can be applied to technical presentations. This allows for more appropriate advice to be provided by applying different advice algorithms depending on the presentation category.

[0108] The reception desk can estimate the user's emotions and adjust the timing of presentation input based on those estimates. For example, if the user is nervous, the input timing can be delayed to help them relax. Conversely, if the user is in a hurry, the timing can be sped up to encourage quick input. Furthermore, if the user is focused, input can be prompted at the optimal time. By adjusting the input timing according to the user's emotions, the system ensures that the user can input their presentation in the most optimal state.

[0109] The reception desk can prioritize the input of presentations that are most relevant to the user's geographical location when they are entering their presentations. For example, if the user is in a specific region, presentations related to that region will be prioritized. Similarly, if the user is on a business trip, presentations related to their destination will be prioritized. Furthermore, if the user is at home, presentations related to their work at home will be prioritized. This allows the system to prioritize the input of presentations that are most relevant to the user's geographical location.

[0110] A specific unit can estimate the user's emotions and adjust the display method of improvement points based on those emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of improvement points according to the user's emotions, the optimal display method can be provided for the user.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk inputs the user's presentation content and slides. Users can upload files from presentation creation applications or PDF files, or input text directly. Furthermore, they can input presentation content using voice input, with speech recognition technology converting the speech to text. Step 2: The analysis department uses generative AI to analyze the presentation content and slides entered by the reception department. Specifically, it analyzes the presentation structure, slide design, visual elements, and text content. For example, it uses text generation AI (LLM) to analyze the presentation content and multimodal generation AI to analyze the slide design and visual elements. Step 3: The specific team identifies areas for improvement based on the analysis conducted by the analysis team. For example, they might identify areas for improvement in slide layout or text simplification, and make specific suggestions for shortening redundant text. They might also suggest adding visual elements or changing the slide design. Step 4: The providing team provides advice to the user based on the areas for improvement identified by the specific team. Specifically, they suggest concrete layout changes, additions of visual elements, and text revisions to improve the slide design. For example, they might provide specific steps for changing the slide layout or specific methods for adding visual elements.

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0116] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and inputs the content and slides of the user's presentation. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content and slides of the presentation using a generation AI. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies areas for improvement based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides advice to the user based on the identified areas for improvement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 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.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and inputs the content and slides of the user's presentation. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content and slides of the presentation using generating AI. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies areas for improvement based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides advice to the user based on the identified areas for improvement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and inputs the content and slides of the user's presentation. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the content and slides of the presentation using a generation AI. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies areas for improvement based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides advice to the user based on the identified areas for improvement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, identification unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and inputs the content and slides of the user's presentation. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the content and slides of the presentation using a generation AI. The identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and identifies areas for improvement based on the analysis results. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides advice to the user based on the identified areas for improvement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A reception area where users input the content and slides of their presentations, An analysis unit analyzes the content and slides of the presentation entered by the reception unit, An identification unit identifies areas for improvement based on the results of the analysis performed by the aforementioned analysis unit, The system includes a provisioning unit that provides advice to the user based on the improvement points identified by the aforementioned specific unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Analyze the presentation's structure, slide design, visual elements, and text content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The specified part is, Identify areas for improvement in the slide layout. The system described in Appendix 1, characterized by the features described herein. (Note 4) The specified part is, Identify text simplification The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, I propose specific layout changes to improve the slide design. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, I suggest adding visual elements. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Suggesting text revisions The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of input in the presentation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past presentation history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering presentation data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of presentations to input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When inputting presentations, the system prioritizes inputting presentations that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When entering a presentation, the system analyzes the user's social media activity and inputs relevant presentations. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the presentation category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, prioritize the analysis based on the presentation submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of the presentations. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, We estimate user sentiment and adjust the method for identifying areas for improvement based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, When identifying areas for improvement, consider the interrelationships within the presentation to enhance specific accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The specified part is, When identifying areas for improvement, consider the attribute information of the presenter. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, It estimates the user's emotions and adjusts how improvements are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The specified part is, When identifying areas for improvement, consider the geographical distribution of the presentations. The system described in Appendix 1, characterized by the features described herein. (Note 25) The specified part is, When identifying areas for improvement, refer to relevant literature in the presentation to enhance specific accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the presentation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the presentation category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing advice, prioritize the advice based on the presentation submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing advice, adjust the order of advice based on the relevance of the presentation. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input the content and slides of their presentations, An analysis unit analyzes the content and slides of the presentation entered by the reception unit, An identification unit identifies areas for improvement based on the results of the analysis performed by the aforementioned analysis unit, The system includes a provisioning unit that provides advice to the user based on the improvement points identified by the aforementioned specific unit. A system characterized by the following features.

2. The aforementioned analysis unit is Analyze the presentation's structure, slide design, visual elements, and text content. The system according to feature 1.

3. The specified part is, Identify areas for improvement in the slide layout. The system according to feature 1.

4. The specified part is, Identify text simplification The system according to feature 1.

5. The aforementioned supply unit is, I propose specific layout changes to improve the slide design. The system according to feature 1.

6. The aforementioned supply unit is, I suggest adding visual elements. The system according to feature 1.

7. The aforementioned supply unit is, Suggesting text revisions The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of input in the presentation based on the estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is Analyze the user's past presentation history and select the optimal input method. The system according to feature 1.

10. The aforementioned reception unit is When entering presentation data, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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