system

Abstract

The system according to this embodiment aims to analyze the setup status of an event and provide effective suggestions for improvement. [Solution] The system according to the embodiment comprises an image input unit, an analysis unit, a revised proposal generation unit, and a provision unit. The image input unit inputs the setup status of an event as an image. The analysis unit analyzes the image input by the image input unit. The revised proposal generation unit generates a revised proposal based on the results of the analysis by the analysis unit. The provision unit provides the revised proposal generated by the revised proposal generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, the establishment and modification of event events depend on the feelings and senses of the person in charge, there is no index for the amendment, and there is a risk that the response time will be prolonged.

[0005] The system according to the embodiment aims to analyze the establishment situation of event events and provide an effective amendment.

Means for Solving the Problems

[0006] The system according to the embodiment comprises an image input unit, an analysis unit, a revised proposal generation unit, and a provision unit. The image input unit inputs the setup status of an event as an image. The analysis unit analyzes the image input by the image input unit. The revised proposal generation unit generates a revised proposal based on the results of the analysis by the analysis unit. The provision unit provides the revised proposal generated by the revised proposal generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the setup status of an event and provide effective suggestions for improvement. [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 manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] [[ID=**10**]]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] Note: In the translation of line 10, "FIG." is used as an abbreviation for "Figure" in English patent texts.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 event setup support system according to an embodiment of the present invention is a system that incorporates VMD (Visual Merchandising) indicators and utilizes AI to propose effective revision plans for the setup and revision of events held in commercial facilities. The event setup support system takes images of the event setup as input, the AI ​​analyzes the images, and generates revision plans based on VMD indicators. The generated revision plans are provided to the person in charge as specific advice. This system standardizes the quality of setups and reduces the time required for revisions. For example, images of the event setup are input. At this time, images including the overall setup and details of each display are taken and input to the AI. For example, images of the overall event venue and the displays of each booth are taken. This information is input to the AI. Next, the AI ​​analyzes the input images. The AI ​​analyzes the images based on VMD indicators and identifies problems and areas for improvement in the setup. For example, it can identify cases where the display placement is inappropriate or where there is a lack of visually appealing elements. Based on the problems and areas for improvement identified by the AI, the system generates revised suggestions. These suggestions are provided to the person in charge as specific advice. For example, they may include suggestions to change the placement of displays or to add visually appealing elements. In this way, the person in charge can improve the setup based on these specific suggestions. This system ensures consistent setup quality. Because the AI ​​proposes revisions based on objective indicators, rather than relying on the intuition or sense of the person in charge, the quality of the setup is kept consistent. Furthermore, the time required for revisions is reduced. Because the AI ​​generates revision suggestions quickly, the person in charge can receive specific advice in a short amount of time. For example, images taken after setup can be input into the AI, and revision suggestions can be received within minutes. In addition, the AI ​​can continuously learn and improve the accuracy of its revision suggestions. For example, it can learn from past revision suggestions and their results to generate more effective suggestions. This further improves the quality of the setup and increases the success rate of events.As a result, the event setup support system can incorporate VMD (Visual Merchandising) metrics and use AI to propose effective modification plans for the setup and modification of events held within commercial facilities.

[0029] The event setup support system according to this embodiment comprises an image input unit, an analysis unit, a revised proposal generation unit, and a provision unit. The image input unit inputs the setup status of the event as an image. The image input unit can, for example, use a camera to capture the overall setup and details of each display. The image input unit can, for example, use a high-resolution camera to acquire detailed images. The image input unit can also, for example, use a drone to acquire images from above. Furthermore, the image input unit can also, for example, easily acquire images using a smartphone camera. The analysis unit analyzes the images input by the image input unit. The analysis unit can, for example, use AI to analyze the images and identify problems and areas for improvement in the setup. The analysis unit can, for example, identify cases where the display placement is inappropriate or where there is a lack of visually appealing elements. Furthermore, the analysis unit can, for example, use AI to automatically identify specific display elements within the image. Furthermore, the analysis unit can, for example, use AI to analyze the color balance and lighting conditions within the image. The revision proposal generation unit generates revision proposals based on the results analyzed by the analysis unit. The revision proposal generation unit can, for example, use AI to generate revision proposals and provide them as specific advice. The revision proposal generation unit can generate, for example, suggestions to change the display layout or suggestions to add visually appealing elements. The revision proposal generation unit can also, for example, learn from past revision proposals and their results to improve the accuracy of the revision proposals. Furthermore, the revision proposal generation unit can, for example, use AI to determine the priority of revision proposals. The provision unit provides the revision proposals generated by the revision proposal generation unit. The provision unit provides the revision proposals as, for example, specific advice. The provision unit can, for example, provide revision proposals quickly. Furthermore, the provision unit can, for example, estimate the user's emotions and adjust the method of providing revision proposals based on the estimated user emotions. Furthermore, the provision unit can, for example, select the optimal provision method considering the user's device information. As a result, the event setup support system according to the embodiment can efficiently analyze the setup status of an event and provide revision proposals.

[0030] The image input unit inputs images of the setup status of events and exhibitions. For example, the image input unit uses a camera to capture the overall setup and details of each display. Specifically, by using a high-resolution camera, even the smallest details of the setup can be captured clearly. This makes it possible to record not only the overall setup but also the placement of each display and the details of the decorations in detail. In addition, by using a drone, wide-area images can be obtained from above, allowing for an overview of the overall layout of the setup and the flow of people. Furthermore, by using a smartphone camera, the setup status can be easily photographed and immediately input into the system. This allows on-site personnel to quickly record the setup status and reflect it in the system in real time. The image input unit centrally manages images from these various devices and transmits them to the analysis unit. This provides a foundation for comprehensively understanding the setup from the overall picture to the smallest details and for efficient analysis.

[0031] The analysis unit analyzes images input by the image input unit. For example, the analysis unit uses AI to analyze images and identify problems and areas for improvement in the setup. Specifically, it utilizes image recognition technology using deep learning to automatically identify each element of the setup. For example, it can identify cases where the display placement is inappropriate or where there is a lack of visually appealing elements. Furthermore, the AI ​​can analyze the color balance and lighting conditions within the image and suggest improvements to enhance its visual appeal. Based on these analysis results, the analysis unit conducts an overall evaluation of the setup and lists specific areas for improvement. In addition, the analysis unit can utilize past data and statistical information to analyze setup trends and patterns, which can be used to optimize future setups. As a result, the analysis unit can accurately grasp the current state of the setup and build a foundation for providing efficient and effective improvement plans.

[0032] The revision proposal generation unit generates revision proposals based on the results analyzed by the analysis unit. For example, the revision proposal generation unit can use AI to generate revision proposals and provide them as specific advice. Specifically, it can generate suggestions such as changing the display layout or adding visually appealing elements. The AI ​​can learn from past revision proposals and their results to improve the accuracy of the revision proposals. For example, by learning successful layout and decoration patterns from past events and applying them to the current setup, it can generate more effective revision proposals. Furthermore, the AI ​​can prioritize revision proposals and propose improvements in order of effectiveness. This allows the revision proposal generation unit to quickly provide efficient and effective revision proposals, supporting the optimization of the setup. In addition, the revision proposal generation unit can collect user feedback and continuously improve the accuracy and effectiveness of the revision proposals. This allows the revision proposal generation unit to always provide highly accurate revision proposals based on the latest information, supporting the optimization of the setup.

[0033] The service provider provides the proposed revisions generated by the proposed revision generation unit. The service provider provides the proposed revisions as specific advice, for example. Specifically, it can provide proposed revisions quickly, allowing users to implement them immediately. The service provider can also estimate the user's emotions and adjust the method of providing the proposed revisions based on those emotions. For example, if the user is feeling stressed, the service provider can provide the proposed revisions with gentle language and encouraging messages. Furthermore, the service provider can select the optimal method of delivery considering the user's device information. For example, it can provide proposed revisions to smartphone users via push notifications or in-app messages, and to PC users via email or dashboards. This allows the service provider to provide proposed revisions to users in the most optimal way and support the optimization of the setup. In addition, the service provider can collect user feedback and continuously improve the method and content of providing proposed revisions. This allows the service provider to always provide highly accurate proposed revisions based on the latest information and support the optimization of the setup.

[0034] The revision proposal generation unit can generate revision proposals based on VMD (Visual Merchandising) indicators. For example, it generates revision proposals considering visual arrangement, color balance, and lighting conditions. For example, it can generate proposals to change the display arrangement or to add visually appealing elements. Furthermore, it can generate proposals to optimize the display arrangement and color balance based on VMD indicators. Additionally, it can generate proposals to adjust lighting conditions based on VMD indicators. This allows for improved setup quality by generating revision proposals based on VMD indicators.

[0035] The service provider can offer specific advice, such as suggestions for revising the display layout or adding visually appealing elements. They can also provide detailed explanations of the revisions and show how the person in charge should implement them. Furthermore, they can provide diagrams or illustrations to visually represent the revisions. Additionally, they can provide specific steps for implementing the revisions. By providing specific advice, the accuracy of the revisions can be improved.

[0036] The analysis unit can identify problems and areas for improvement in the setup. For example, it can identify cases where the display placement is inappropriate or where there is a lack of visually appealing elements. The analysis unit can also automatically identify specific display elements within an image using AI. For example, it can analyze the color balance and lighting conditions within an image using AI. Furthermore, the analysis unit can analyze the overall setup using AI to identify problems and areas for improvement. In addition, the analysis unit can analyze the detailed aspects of the setup using AI to identify problems and areas for improvement. By identifying problems and areas for improvement in the setup, the accuracy of proposed revisions can be improved.

[0037] The revision proposal generation unit can improve the accuracy of revision proposals by learning from past revision proposals and their results. For example, the revision proposal generation unit can store past revision proposals and their results in a database and learn from them using AI. For example, the revision proposal generation unit can generate more effective revision proposals based on past revision proposals and their results. Furthermore, the revision proposal generation unit can analyze past revision proposals and their results and build a feedback loop to improve the accuracy of revision proposals. In addition, the revision proposal generation unit can continuously learn from past revision proposals and their results using AI to improve the accuracy of revision proposals. In this way, the accuracy of revision proposals can be improved by learning from past revision proposals and their results.

[0038] The service provider can quickly provide revised proposals. For example, the service provider can use AI to quickly generate revised proposals and provide them to the person in charge. For example, the service provider can generate revised proposals in real time and provide them to the person in charge immediately. In addition, the service provider can, for example, automatically generate revised proposals and notify the person in charge. Furthermore, the service provider has, for example, an interface for quickly providing revised proposals. This reduces the time required for revisions by providing revised proposals quickly.

[0039] The image input unit can automatically acquire multiple images from different angles and under different lighting conditions when an image is input. For example, the AI ​​in the image input unit can automatically acquire images from different angles to grasp the overall picture. For example, the AI ​​in the image input unit can acquire images under different lighting conditions to emphasize visual elements. Furthermore, the AI ​​in the image input unit can integrate multiple images to enable detailed analysis. This makes detailed analysis possible by acquiring multiple images from different angles and under different lighting conditions. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input image data acquired from different angles and under different lighting conditions into a generating AI and have the generating AI perform a detailed analysis.

[0040] The image input unit can prioritize acquiring images that focus on specific display elements when an image is input. For example, the image input unit can use AI to automatically detect specific display elements and acquire images that focus on those elements. The image input unit can also use AI to acquire images based on display elements specified by the user. Furthermore, the image input unit can use AI to prioritize the analysis of important display elements and acquire detailed images. This makes it possible to analyze important elements by prioritizing the acquisition of images that focus on specific display elements. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input image data that focuses on specific display elements to a generating AI and have the generating AI perform a detailed analysis.

[0041] The image input unit can prioritize the acquisition of highly relevant images when an image is input, taking into account the user's geographical location information. For example, if the user is in a specific region, the image input unit will prioritize the acquisition of images related to that region. The image input unit can also, for example, use AI to select the most suitable image based on the user's location information. Furthermore, if the user is on the move, the image input unit can prioritize the acquisition of images related to the user's current location. In this way, by considering the user's geographical location information, highly relevant images can be prioritized. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of highly relevant images.

[0042] The image input unit can analyze the user's social media activity and retrieve relevant images when an image is input. For example, the image input unit can analyze the user's social media posts and retrieve relevant images. The image input unit can also retrieve relevant images by referring to posts from the user's followers and friends, for example. Furthermore, the image input unit can select the most suitable image based on the user's past social media activity, for example. In this way, relevant images can be retrieved by analyzing the user's social media activity. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input the user's social media data into a generating AI and have the generating AI select relevant images.

[0043] The analysis unit can automatically identify specific display elements within an image during analysis, thereby improving the accuracy of the analysis. For example, the analysis unit can use AI to automatically detect specific display elements and perform analysis based on those elements. The analysis unit can also use AI to prioritize the analysis of important display elements, thereby improving accuracy. Furthermore, the analysis unit can use AI to integrate multiple display elements and perform a more detailed analysis. This improves the accuracy of the analysis by automatically identifying specific display elements. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data with identified specific display elements into a generating AI and have the generating AI perform a detailed analysis.

[0044] The analysis unit can optimize its analysis algorithm by referring to past analysis results during the analysis process. For example, the analysis unit can use AI to learn from past analysis results and optimize its analysis algorithm. The analysis unit can also use AI to perform highly accurate analyses based on past analysis results. Furthermore, the analysis unit can use AI to continuously learn and improve its analysis algorithm. This allows for the optimization of the analysis algorithm by referring to past analysis results, enabling highly accurate analyses. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis results into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0045] The analysis unit can perform analysis while considering the geographical distribution of the images. For example, the analysis unit can use AI to select the optimal analysis method based on the geographical distribution of the images. The analysis unit can also use AI to adjust the analysis results, for example, by considering geographical factors. Furthermore, the analysis unit can use AI to perform a detailed analysis based on the geographical distribution of the images. This makes it possible to perform an optimal analysis by considering the geographical distribution of the images. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of the images into a generating AI and have the generating AI select the optimal analysis method.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis process. For example, the analysis unit can use AI to refer to relevant literature and improve the accuracy of the analysis. The analysis unit can also use AI to perform a detailed analysis based on relevant data. Furthermore, the analysis unit can use AI to integrate literature and data and perform a highly accurate analysis. This improves the accuracy of the analysis by referring to relevant literature and data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature and data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0047] The revision proposal generation unit can prioritize generating revision proposals that focus on specific display elements. For example, the revision proposal generation unit can use AI to automatically detect specific display elements and generate revision proposals based on those elements. The revision proposal generation unit can also use AI to generate revision proposals based on display elements specified by the user. Furthermore, the revision proposal generation unit can use AI to prioritize the analysis of important display elements and generate revision proposals based on those elements. This allows for the prioritization of revision proposals that focus on specific display elements, thereby enabling the modification of important elements. Some or all of the above-described processes in the revision proposal generation unit may be performed using AI, for example, or without AI. For example, the revision proposal generation unit can cause the generation AI to perform the generation of revision proposals that focus on specific display elements.

[0048] The revision proposal generation unit can improve the accuracy of revision proposals by learning from past revision proposals and their results during the generation process. For example, the revision proposal generation unit can use AI to learn from past revision proposals and generate highly accurate revision proposals. For example, the revision proposal generation unit can use AI to generate effective revision proposals based on past revision results. Furthermore, the revision proposal generation unit can use AI to continuously learn and improve the accuracy of revision proposals. This improves the accuracy of revision proposals by learning from past revision proposals and their results. Some or all of the above-described processes in the revision proposal generation unit may be performed using AI, for example, or without AI. For example, the revision proposal generation unit can input past revision proposals and their results into a generation AI and have the generation AI perform the task of improving the accuracy of revision proposals.

[0049] The revision proposal generation unit can generate revision proposals while considering geographical factors. For example, the revision proposal generation unit can use AI to generate the optimal revision proposal by considering geographical factors. For example, the revision proposal generation unit can also use AI to provide detailed revision proposals based on geographical factors. Furthermore, the revision proposal generation unit can use AI to generate effective revision proposals by considering geographical factors. In this way, the optimal revision proposal can be generated by considering geographical factors. Some or all of the above-described processes in the revision proposal generation unit may be performed using AI, for example, or without AI. For example, the revision proposal generation unit can input geographical factor data into a generation AI and cause the generation AI to perform the generation of the optimal revision proposal.

[0050] The revision proposal generation unit can improve the accuracy of the revision proposals by referring to relevant literature and data during the generation process. For example, the revision proposal generation unit can use AI to refer to relevant literature and generate highly accurate revision proposals. For example, the revision proposal generation unit can also use AI to provide detailed revision proposals based on relevant data. Furthermore, the revision proposal generation unit can use AI to integrate literature and data and generate effective revision proposals. This improves the accuracy of the revision proposals by referring to relevant literature and data. Some or all of the above-described processes in the revision proposal generation unit may be performed using AI, for example, or without AI. For example, the revision proposal generation unit can input relevant literature and data into the generation AI and have the generation AI perform the task of improving the accuracy of the revision proposals.

[0051] The delivery unit can select the optimal delivery method by referring to the user's past feedback at the time of delivery. For example, the delivery unit can select the optimal delivery method for revised versions based on the user's past feedback. For example, the delivery unit can also prioritize delivery methods that the user has preferred in the past. Furthermore, the delivery unit can analyze user feedback and select an effective delivery method. This allows the optimal delivery method to be selected by referring to the user's past feedback. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past feedback data into a generating AI and have the generating AI select the optimal delivery method.

[0052] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can select a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can also select a delivery method optimized for a larger screen. Furthermore, if the user is using a desktop computer, the delivery unit can select a delivery method that includes detailed information. In this way, the optimal delivery method can be selected by taking into account the user's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into a generating AI and have the generating AI select the optimal delivery method.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The event setup support system can also be equipped with a real-time feedback unit. The real-time feedback unit analyzes images taken during setup in real time and immediately provides suggested corrections. For example, AI can analyze images taken by cameras during setup in real time and immediately provide suggested corrections regarding display placement and color balance. Furthermore, the real-time feedback unit can provide real-time feedback on the effectiveness of the corrections implemented by the setup staff and provide further suggested corrections. This improves setup efficiency and enables rapid, high-quality setup.

[0055] The event setup support system can also include a progress monitoring unit that monitors the progress of the setup and provides revision suggestions as needed. For example, the progress monitoring unit can provide revision suggestions regarding the overall layout in the initial stages of setup, and then provide revision suggestions regarding detailed display placement and decoration as the setup progresses. The progress monitoring unit can also monitor the progress of the setup in real time and update the revision suggestions as necessary. This allows for the provision of optimal revision suggestions at each stage of setup, supporting efficient setup.

[0056] The event setup support system can also include a historical reference unit that references past setup data and generates revised plans based on past successes. For example, the historical reference unit stores past event setup data in a database, and AI analyzes this data to generate revised plans. By generating revised plans based on past successes, effective setup can be achieved. Furthermore, the historical reference unit can learn from past revised plans and their results to improve the accuracy of its own revisions. This allows for the provision of more effective revised plans by utilizing past data.

[0057] The event setup support system can also include a progress monitoring unit that monitors the progress of the setup in real time and provides revised suggestions as the setup progresses. For example, the progress monitoring unit can provide revised suggestions regarding the overall layout in the initial stages of setup, and then provide revised suggestions regarding the placement of detailed displays and decorations as the setup progresses. The progress monitoring unit can also monitor the progress of the setup in real time and update the revised suggestions as needed. This allows for the provision of optimal revised suggestions at each stage of setup, supporting efficient setup.

[0058] The event setup support system can also include a historical reference unit that references past setup data and generates revised plans based on past successes. For example, the historical reference unit stores past event setup data in a database, and AI analyzes this data to generate revised plans. By generating revised plans based on past successes, effective setup can be achieved. Furthermore, the historical reference unit can learn from past revised plans and their results to improve the accuracy of its own revisions. This allows for the provision of more effective revised plans by utilizing past data.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The image input section inputs images of the event setup. For example, a camera can be used to capture the overall setup and details of each display. High-resolution cameras, drones, smartphone cameras, etc., can be used to obtain detailed images. Step 2: The analysis unit analyzes the image input by the image input unit. For example, it uses AI to analyze the image and identify problems and areas for improvement in the setup. It can identify cases where the display placement is inappropriate or where there is a lack of visually appealing elements. It can also automatically identify specific display elements within the image and analyze the color balance and lighting conditions. Step 3: The revision proposal generation unit generates revision proposals based on the results analyzed by the analysis unit. For example, it can use AI to generate revision proposals and provide them as specific advice. It can generate suggestions such as changing the display layout or adding visually appealing elements. It can also learn from past revision proposals and their results to improve the accuracy of the revision proposals. Furthermore, it can determine the priority of the revision proposals. Step 4: The delivery unit provides the proposed revisions generated by the proposed revision generation unit. For example, it can quickly provide proposed revisions as specific advice. It can also estimate the user's emotions and adjust the method of providing the proposed revisions based on the estimated user emotions. It can also select the optimal delivery method by considering the user's device information.

[0061] (Example of form 2) The event setup support system according to an embodiment of the present invention is a system that incorporates VMD (Visual Merchandising) indicators and utilizes AI to propose effective revision plans for the setup and revision of events held in commercial facilities. The event setup support system takes images of the event setup as input, the AI ​​analyzes the images, and generates revision plans based on VMD indicators. The generated revision plans are provided to the person in charge as specific advice. This system standardizes the quality of setups and reduces the time required for revisions. For example, images of the event setup are input. At this time, images including the overall setup and details of each display are taken and input to the AI. For example, images of the overall event venue and the displays of each booth are taken. This information is input to the AI. Next, the AI ​​analyzes the input images. The AI ​​analyzes the images based on VMD indicators and identifies problems and areas for improvement in the setup. For example, it can identify cases where the display placement is inappropriate or where there is a lack of visually appealing elements. Based on the problems and areas for improvement identified by the AI, the system generates revised suggestions. These suggestions are provided to the person in charge as specific advice. For example, they may include suggestions to change the placement of displays or to add visually appealing elements. In this way, the person in charge can improve the setup based on these specific suggestions. This system ensures consistent setup quality. Because the AI ​​proposes revisions based on objective indicators, rather than relying on the intuition or sense of the person in charge, the quality of the setup is kept consistent. Furthermore, the time required for revisions is reduced. Because the AI ​​generates revision suggestions quickly, the person in charge can receive specific advice in a short amount of time. For example, images taken after setup can be input into the AI, and revision suggestions can be received within minutes. In addition, the AI ​​can continuously learn and improve the accuracy of its revision suggestions. For example, it can learn from past revision suggestions and their results to generate more effective suggestions. This further improves the quality of the setup and increases the success rate of events.As a result, the event setup support system can incorporate VMD (Visual Merchandising) metrics and use AI to propose effective modification plans for the setup and modification of events held within commercial facilities.

[0062] The event setup support system according to this embodiment comprises an image input unit, an analysis unit, a revised proposal generation unit, and a provision unit. The image input unit inputs the setup status of the event as an image. The image input unit can, for example, use a camera to capture the overall setup and details of each display. The image input unit can, for example, use a high-resolution camera to acquire detailed images. The image input unit can also, for example, use a drone to acquire images from above. Furthermore, the image input unit can also, for example, easily acquire images using a smartphone camera. The analysis unit analyzes the images input by the image input unit. The analysis unit can, for example, use AI to analyze the images and identify problems and areas for improvement in the setup. The analysis unit can, for example, identify cases where the display placement is inappropriate or where there is a lack of visually appealing elements. Furthermore, the analysis unit can, for example, use AI to automatically identify specific display elements within the image. Furthermore, the analysis unit can, for example, use AI to analyze the color balance and lighting conditions within the image. The revision proposal generation unit generates revision proposals based on the results analyzed by the analysis unit. The revision proposal generation unit can, for example, use AI to generate revision proposals and provide them as specific advice. The revision proposal generation unit can generate, for example, suggestions to change the display layout or suggestions to add visually appealing elements. The revision proposal generation unit can also, for example, learn from past revision proposals and their results to improve the accuracy of the revision proposals. Furthermore, the revision proposal generation unit can, for example, use AI to determine the priority of revision proposals. The provision unit provides the revision proposals generated by the revision proposal generation unit. The provision unit provides the revision proposals as, for example, specific advice. The provision unit can, for example, provide revision proposals quickly. Furthermore, the provision unit can, for example, estimate the user's emotions and adjust the method of providing revision proposals based on the estimated user emotions. Furthermore, the provision unit can, for example, select the optimal provision method considering the user's device information. As a result, the event setup support system according to the embodiment can efficiently analyze the setup status of an event and provide revision proposals.

[0063] The image input unit inputs images of the setup status of events and exhibitions. For example, the image input unit uses a camera to capture the overall setup and details of each display. Specifically, by using a high-resolution camera, even the smallest details of the setup can be captured clearly. This makes it possible to record not only the overall setup but also the placement of each display and the details of the decorations in detail. In addition, by using a drone, wide-area images can be obtained from above, allowing for an overview of the overall layout of the setup and the flow of people. Furthermore, by using a smartphone camera, the setup status can be easily photographed and immediately input into the system. This allows on-site personnel to quickly record the setup status and reflect it in the system in real time. The image input unit centrally manages images from these various devices and transmits them to the analysis unit. This provides a foundation for comprehensively understanding the setup from the overall picture to the smallest details and for efficient analysis.

[0064] The analysis unit analyzes images input by the image input unit. For example, the analysis unit uses AI to analyze images and identify problems and areas for improvement in the setup. Specifically, it utilizes image recognition technology using deep learning to automatically identify each element of the setup. For example, it can identify cases where the display placement is inappropriate or where there is a lack of visually appealing elements. Furthermore, the AI ​​can analyze the color balance and lighting conditions within the image and suggest improvements to enhance its visual appeal. Based on these analysis results, the analysis unit conducts an overall evaluation of the setup and lists specific areas for improvement. In addition, the analysis unit can utilize past data and statistical information to analyze setup trends and patterns, which can be used to optimize future setups. As a result, the analysis unit can accurately grasp the current state of the setup and build a foundation for providing efficient and effective improvement plans.

[0065] The revision proposal generation unit generates revision proposals based on the results analyzed by the analysis unit. For example, the revision proposal generation unit can use AI to generate revision proposals and provide them as specific advice. Specifically, it can generate suggestions such as changing the display layout or adding visually appealing elements. The AI ​​can learn from past revision proposals and their results to improve the accuracy of the revision proposals. For example, by learning successful layout and decoration patterns from past events and applying them to the current setup, it can generate more effective revision proposals. Furthermore, the AI ​​can prioritize revision proposals and propose improvements in order of effectiveness. This allows the revision proposal generation unit to quickly provide efficient and effective revision proposals, supporting the optimization of the setup. In addition, the revision proposal generation unit can collect user feedback and continuously improve the accuracy and effectiveness of the revision proposals. This allows the revision proposal generation unit to always provide highly accurate revision proposals based on the latest information, supporting the optimization of the setup.

[0066] The service provider provides the proposed revisions generated by the proposed revision generation unit. The service provider provides the proposed revisions as specific advice, for example. Specifically, it can provide proposed revisions quickly, allowing users to implement them immediately. The service provider can also estimate the user's emotions and adjust the method of providing the proposed revisions based on those emotions. For example, if the user is feeling stressed, the service provider can provide the proposed revisions with gentle language and encouraging messages. Furthermore, the service provider can select the optimal method of delivery considering the user's device information. For example, it can provide proposed revisions to smartphone users via push notifications or in-app messages, and to PC users via email or dashboards. This allows the service provider to provide proposed revisions to users in the most optimal way and support the optimization of the setup. In addition, the service provider can collect user feedback and continuously improve the method and content of providing proposed revisions. This allows the service provider to always provide highly accurate proposed revisions based on the latest information and support the optimization of the setup.

[0067] The revision proposal generation unit can generate revision proposals based on VMD (Visual Merchandising) indicators. For example, it generates revision proposals considering visual arrangement, color balance, and lighting conditions. For example, it can generate proposals to change the display arrangement or to add visually appealing elements. Furthermore, it can generate proposals to optimize the display arrangement and color balance based on VMD indicators. Additionally, it can generate proposals to adjust lighting conditions based on VMD indicators. This allows for improved setup quality by generating revision proposals based on VMD indicators.

[0068] The service provider can offer specific advice, such as suggestions for revising the display layout or adding visually appealing elements. They can also provide detailed explanations of the revisions and show how the person in charge should implement them. Furthermore, they can provide diagrams or illustrations to visually represent the revisions. Additionally, they can provide specific steps for implementing the revisions. By providing specific advice, the accuracy of the revisions can be improved.

[0069] The analysis unit can identify problems and areas for improvement in the setup. For example, it can identify cases where the display placement is inappropriate or where there is a lack of visually appealing elements. The analysis unit can also automatically identify specific display elements within an image using AI. For example, it can analyze the color balance and lighting conditions within an image using AI. Furthermore, the analysis unit can analyze the overall setup using AI to identify problems and areas for improvement. In addition, the analysis unit can analyze the detailed aspects of the setup using AI to identify problems and areas for improvement. By identifying problems and areas for improvement in the setup, the accuracy of proposed revisions can be improved.

[0070] The revision proposal generation unit can improve the accuracy of revision proposals by learning from past revision proposals and their results. For example, the revision proposal generation unit can store past revision proposals and their results in a database and learn from them using AI. For example, the revision proposal generation unit can generate more effective revision proposals based on past revision proposals and their results. Furthermore, the revision proposal generation unit can analyze past revision proposals and their results and build a feedback loop to improve the accuracy of revision proposals. In addition, the revision proposal generation unit can continuously learn from past revision proposals and their results using AI to improve the accuracy of revision proposals. In this way, the accuracy of revision proposals can be improved by learning from past revision proposals and their results.

[0071] The service provider can quickly provide revised proposals. For example, the service provider can use AI to quickly generate revised proposals and provide them to the person in charge. For example, the service provider can generate revised proposals in real time and provide them to the person in charge immediately. In addition, the service provider can, for example, automatically generate revised proposals and notify the person in charge. Furthermore, the service provider has, for example, an interface for quickly providing revised proposals. This reduces the time required for revisions by providing revised proposals quickly.

[0072] The image input unit can estimate the user's emotions and adjust the timing of image input based on the estimated emotions. For example, if the user is stressed, the AI ​​can automatically acquire an image to reduce the user's burden. The image input unit can also allow the user to manually select the timing of image input if the user is relaxed. Furthermore, if the user is in a hurry, the AI ​​can quickly acquire an image and immediately begin analysis. This reduces the user's burden by adjusting the timing of image input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 image input unit may be performed using AI or not using AI. For example, the image input unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0073] The image input unit can automatically acquire multiple images from different angles and under different lighting conditions when an image is input. For example, the AI ​​in the image input unit can automatically acquire images from different angles to grasp the overall picture. For example, the AI ​​in the image input unit can acquire images under different lighting conditions to emphasize visual elements. Furthermore, the AI ​​in the image input unit can integrate multiple images to enable detailed analysis. This makes detailed analysis possible by acquiring multiple images from different angles and under different lighting conditions. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input image data acquired from different angles and under different lighting conditions into a generating AI and have the generating AI perform a detailed analysis.

[0074] The image input unit can prioritize acquiring images that focus on specific display elements when an image is input. For example, the image input unit can use AI to automatically detect specific display elements and acquire images that focus on those elements. The image input unit can also use AI to acquire images based on display elements specified by the user. Furthermore, the image input unit can use AI to prioritize the analysis of important display elements and acquire detailed images. This makes it possible to analyze important elements by prioritizing the acquisition of images that focus on specific display elements. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input image data that focuses on specific display elements to a generating AI and have the generating AI perform a detailed analysis.

[0075] The image input unit can estimate the user's emotions and determine the priority of images to input based on the estimated emotions. For example, if the user is nervous, the AI ​​will prioritize acquiring important images. If the user is relaxed, the AI ​​can also prioritize acquiring images that help grasp the overall picture. Furthermore, if the user is in a hurry, the AI ​​can prioritize acquiring images that require quick analysis. This ensures that important images are prioritized by determining image priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 image input unit may be performed using AI or not. For example, the image input unit can input user emotion data into a generative AI and have the generative AI determine the priority of images.

[0076] The image input unit can prioritize the acquisition of highly relevant images when an image is input, taking into account the user's geographical location information. For example, if the user is in a specific region, the image input unit will prioritize the acquisition of images related to that region. The image input unit can also, for example, use AI to select the most suitable image based on the user's location information. Furthermore, if the user is on the move, the image input unit can prioritize the acquisition of images related to the user's current location. In this way, by considering the user's geographical location information, highly relevant images can be prioritized. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of highly relevant images.

[0077] The image input unit can analyze the user's social media activity and retrieve relevant images when an image is input. For example, the image input unit can analyze the user's social media posts and retrieve relevant images. The image input unit can also retrieve relevant images by referring to posts from the user's followers and friends, for example. Furthermore, the image input unit can select the most suitable image based on the user's past social media activity, for example. In this way, relevant images can be retrieved by analyzing the user's social media activity. Some or all of the above processing in the image input unit may be performed using AI, for example, or without AI. For example, the image input unit can input the user's social media data into a generating AI and have the generating AI select relevant images.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is stressed, the AI ​​can relax the analysis criteria and provide results quickly. If the user is relaxed, the AI ​​can perform a detailed analysis and provide highly accurate results. If the user is in a hurry, the AI ​​can perform an analysis that focuses on key elements. This allows for quick and highly accurate analysis by adjusting the analysis criteria based on the user's 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 analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis criteria.

[0079] The analysis unit can automatically identify specific display elements within an image during analysis, thereby improving the accuracy of the analysis. For example, the analysis unit can use AI to automatically detect specific display elements and perform analysis based on those elements. The analysis unit can also use AI to prioritize the analysis of important display elements, thereby improving accuracy. Furthermore, the analysis unit can use AI to integrate multiple display elements and perform a more detailed analysis. This improves the accuracy of the analysis by automatically identifying specific display elements. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data with identified specific display elements into a generating AI and have the generating AI perform a detailed analysis.

[0080] The analysis unit can optimize its analysis algorithm by referring to past analysis results during the analysis process. For example, the analysis unit can use AI to learn from past analysis results and optimize its analysis algorithm. The analysis unit can also use AI to perform highly accurate analyses based on past analysis results. Furthermore, the analysis unit can use AI to continuously learn and improve its analysis algorithm. This allows for the optimization of the analysis algorithm by referring to past analysis results, enabling highly accurate analyses. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis results into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit can also sequentially display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying concise analysis results. In this way, by adjusting the display order of analysis results based on the user's emotions, important information can be provided preferentially. 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the analysis results.

[0082] The analysis unit can perform analysis while considering the geographical distribution of the images. For example, the analysis unit can use AI to select the optimal analysis method based on the geographical distribution of the images. The analysis unit can also use AI to adjust the analysis results, for example, by considering geographical factors. Furthermore, the analysis unit can use AI to perform a detailed analysis based on the geographical distribution of the images. This makes it possible to perform an optimal analysis by considering the geographical distribution of the images. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of the images into a generating AI and have the generating AI select the optimal analysis method.

[0083] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis process. For example, the analysis unit can use AI to refer to relevant literature and improve the accuracy of the analysis. The analysis unit can also use AI to perform a detailed analysis based on relevant data. Furthermore, the analysis unit can use AI to integrate literature and data and perform a highly accurate analysis. This improves the accuracy of the analysis by referring to relevant literature and data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature and data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0084] The revision proposal generation unit can estimate the user's emotions and adjust the way the revision proposal is presented based on the estimated emotions. For example, if the user is stressed, the revision proposal generation unit can provide a simple and easy-to-understand revision proposal. For example, if the user is relaxed, the revision proposal generation unit can also provide a detailed revision proposal. Furthermore, if the user is in a hurry, the revision proposal generation unit can provide a revision proposal that can be quickly implemented. In this way, by adjusting the way the revision proposal is presented based on the user's emotions, it is possible to provide a revision proposal that is easy for the user to understand. 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 revision proposal generation unit may be performed using AI, for example, or without using AI. For example, the revision proposal generation unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the way the revision proposal is presented.

[0085] The revision proposal generation unit can prioritize generating revision proposals that focus on specific display elements. For example, the revision proposal generation unit can use AI to automatically detect specific display elements and generate revision proposals based on those elements. The revision proposal generation unit can also use AI to generate revision proposals based on display elements specified by the user. Furthermore, the revision proposal generation unit can use AI to prioritize the analysis of important display elements and generate revision proposals based on those elements. This allows for the prioritization of revision proposals that focus on specific display elements, thereby enabling the modification of important elements. Some or all of the above-described processes in the revision proposal generation unit may be performed using AI, for example, or without AI. For example, the revision proposal generation unit can cause the generation AI to perform the generation of revision proposals that focus on specific display elements.

[0086] The revision proposal generation unit can improve the accuracy of revision proposals by learning from past revision proposals and their results during the generation process. For example, the revision proposal generation unit can use AI to learn from past revision proposals and generate highly accurate revision proposals. For example, the revision proposal generation unit can use AI to generate effective revision proposals based on past revision results. Furthermore, the revision proposal generation unit can use AI to continuously learn and improve the accuracy of revision proposals. This improves the accuracy of revision proposals by learning from past revision proposals and their results. Some or all of the above-described processes in the revision proposal generation unit may be performed using AI, for example, or without AI. For example, the revision proposal generation unit can input past revision proposals and their results into a generation AI and have the generation AI perform the task of improving the accuracy of revision proposals.

[0087] The revision proposal generation unit can estimate the user's emotions and determine the priority of revision proposals based on the estimated emotions. For example, if the user is stressed, the revision proposal generation unit will prioritize providing important revision proposals. For example, if the user is relaxed, the revision proposal generation unit can also sequentially provide detailed revision proposals. Furthermore, if the user is in a hurry, the revision proposal generation unit can prioritize providing revision proposals that can be quickly implemented. In this way, by determining the priority of revision proposals based on the user's emotions, important revision proposals can be provided preferentially. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the revision proposal generation unit may be performed using AI, for example, or without AI. For example, the revision proposal generation unit can input user emotion data into a generative AI and have the generative AI determine the priority of revision proposals.

[0088] The revision proposal generation unit can generate revision proposals while considering geographical factors. For example, the revision proposal generation unit can use AI to generate the optimal revision proposal by considering geographical factors. For example, the revision proposal generation unit can also use AI to provide detailed revision proposals based on geographical factors. Furthermore, the revision proposal generation unit can use AI to generate effective revision proposals by considering geographical factors. In this way, the optimal revision proposal can be generated by considering geographical factors. Some or all of the above-described processes in the revision proposal generation unit may be performed using AI, for example, or without AI. For example, the revision proposal generation unit can input geographical factor data into a generation AI and cause the generation AI to perform the generation of the optimal revision proposal.

[0089] The revision proposal generation unit can improve the accuracy of the revision proposals by referring to relevant literature and data during the generation process. For example, the revision proposal generation unit can use AI to refer to relevant literature and generate highly accurate revision proposals. For example, the revision proposal generation unit can also use AI to provide detailed revision proposals based on relevant data. Furthermore, the revision proposal generation unit can use AI to integrate literature and data and generate effective revision proposals. This improves the accuracy of the revision proposals by referring to relevant literature and data. Some or all of the above-described processes in the revision proposal generation unit may be performed using AI, for example, or without AI. For example, the revision proposal generation unit can input relevant literature and data into the generation AI and have the generation AI perform the task of improving the accuracy of the revision proposals.

[0090] The service provider can estimate the user's emotions and adjust the method of providing suggested solutions based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple and easy-to-understand suggested solutions. If the user is relaxed, the service provider can also provide detailed suggested solutions. Furthermore, if the user is in a hurry, the service provider can provide quickly actionable suggested solutions. By adjusting the method of providing suggested solutions based on the user's emotions, the service provider can provide solutions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the method of providing suggested solutions.

[0091] The delivery unit can select the optimal delivery method by referring to the user's past feedback at the time of delivery. For example, the delivery unit can select the optimal delivery method for revised versions based on the user's past feedback. For example, the delivery unit can also prioritize delivery methods that the user has preferred in the past. Furthermore, the delivery unit can analyze user feedback and select an effective delivery method. This allows the optimal delivery method to be selected by referring to the user's past feedback. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past feedback data into a generating AI and have the generating AI select the optimal delivery method.

[0092] The service provider can estimate the user's emotions and adjust the order in which revision suggestions are provided based on the estimated emotions. For example, if the user is stressed, the service provider may prioritize providing important revision suggestions. If the user is relaxed, the service provider may also sequentially provide detailed revision suggestions. Furthermore, if the user is in a hurry, the service provider may prioritize providing revision suggestions that can be quickly implemented. This allows for the prioritization of important revision suggestions by adjusting the order in which they are provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the order in which revision suggestions are provided.

[0093] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can select a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can also select a delivery method optimized for a larger screen. Furthermore, if the user is using a desktop computer, the delivery unit can select a delivery method that includes detailed information. In this way, the optimal delivery method can be selected by taking into account the user's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into a generating AI and have the generating AI select the optimal delivery method.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The event setup support system can also be equipped with a real-time feedback unit. The real-time feedback unit analyzes images taken during setup in real time and immediately provides suggested corrections. For example, AI can analyze images taken by cameras during setup in real time and immediately provide suggested corrections regarding display placement and color balance. Furthermore, the real-time feedback unit can provide real-time feedback on the effectiveness of the corrections implemented by the setup staff and provide further suggested corrections. This improves setup efficiency and enables rapid, high-quality setup.

[0096] The event setup support system can further include an emotion-prioritizing unit that estimates the user's emotions and determines the priority of proposed revisions based on those emotions. For example, if the user is feeling stressed, the emotion-prioritizing unit will prioritize providing important revisions. If the user is relaxed, it can also sequentially provide detailed revisions. Furthermore, if the user is in a hurry, it can prioritize providing revisions that can be quickly implemented. In this way, by determining the priority of revisions based on the user's emotions, the system can provide the most suitable revisions for the user.

[0097] The event setup support system can also include a progress monitoring unit that monitors the progress of the setup and provides revision suggestions as needed. For example, the progress monitoring unit can provide revision suggestions regarding the overall layout in the initial stages of setup, and then provide revision suggestions regarding detailed display placement and decoration as the setup progresses. The progress monitoring unit can also monitor the progress of the setup in real time and update the revision suggestions as necessary. This allows for the provision of optimal revision suggestions at each stage of setup, supporting efficient setup.

[0098] The event setup support system can further include an emotion expression unit that estimates the user's emotions and adjusts the way the revised proposals are presented based on those estimated emotions. For example, if the user is feeling stressed, the emotion expression unit can provide a simple and easy-to-understand revised proposal. If the user is relaxed, it can also provide a detailed revised proposal. Furthermore, if the user is in a hurry, it can provide a quickly actionable revised proposal. In this way, by adjusting the way the revised proposals are presented based on the user's emotions, the system can provide revised proposals that are easy for the user to understand.

[0099] The event setup support system can also include a historical reference unit that references past setup data and generates revised plans based on past successes. For example, the historical reference unit stores past event setup data in a database, and AI analyzes this data to generate revised plans. By generating revised plans based on past successes, effective setup can be achieved. Furthermore, the historical reference unit can learn from past revised plans and their results to improve the accuracy of its own revisions. This allows for the provision of more effective revised plans by utilizing past data.

[0100] The event setup support system may further include an emotion-sequencing unit that estimates the user's emotions and adjusts the order in which revision suggestions are provided based on those emotions. For example, if the user is nervous, the emotion-sequencing unit may prioritize providing important revision suggestions. If the user is relaxed, it may also provide detailed revision suggestions sequentially. Furthermore, if the user is in a hurry, it may prioritize providing revision suggestions that can be quickly implemented. In this way, by adjusting the order in which revision suggestions are provided based on the user's emotions, important revision suggestions can be prioritized.

[0101] The event setup support system can also include a progress monitoring unit that monitors the progress of the setup in real time and provides revised suggestions as the setup progresses. For example, the progress monitoring unit can provide revised suggestions regarding the overall layout in the initial stages of setup, and then provide revised suggestions regarding the placement of detailed displays and decorations as the setup progresses. The progress monitoring unit can also monitor the progress of the setup in real time and update the revised suggestions as needed. This allows for the provision of optimal revised suggestions at each stage of setup, supporting efficient setup.

[0102] The event setup support system can further include an emotion-prioritizing unit that estimates the user's emotions and determines the priority of revision proposals based on those emotions. For example, if the user is stressed, the emotion-prioritizing unit will prioritize providing important revision proposals. If the user is relaxed, it can also sequentially provide detailed revision proposals. Furthermore, if the user is in a hurry, it can prioritize providing revision proposals that can be quickly implemented. In this way, by determining the priority of revision proposals based on the user's emotions, the system can provide the most suitable revision proposals for the user.

[0103] The event setup support system can also include a historical reference unit that references past setup data and generates revised plans based on past successes. For example, the historical reference unit stores past event setup data in a database, and AI analyzes this data to generate revised plans. By generating revised plans based on past successes, effective setup can be achieved. Furthermore, the historical reference unit can learn from past revised plans and their results to improve the accuracy of its own revisions. This allows for the provision of more effective revised plans by utilizing past data.

[0104] The event setup support system can further include an emotion expression unit that estimates the user's emotions and adjusts the way the revised proposals are presented based on those estimated emotions. For example, if the user is feeling stressed, the emotion expression unit can provide a simple and easy-to-understand revised proposal. If the user is relaxed, it can also provide a detailed revised proposal. Furthermore, if the user is in a hurry, it can provide a quickly actionable revised proposal. In this way, by adjusting the way the revised proposals are presented based on the user's emotions, the system can provide revised proposals that are easy for the user to understand.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The image input section inputs images of the event setup. For example, a camera can be used to capture the overall setup and details of each display. High-resolution cameras, drones, smartphone cameras, etc., can be used to obtain detailed images. Step 2: The analysis unit analyzes the image input by the image input unit. For example, it uses AI to analyze the image and identify problems and areas for improvement in the setup. It can identify cases where the display placement is inappropriate or where there is a lack of visually appealing elements. It can also automatically identify specific display elements within the image and analyze the color balance and lighting conditions. Step 3: The revision proposal generation unit generates revision proposals based on the results analyzed by the analysis unit. For example, it can use AI to generate revision proposals and provide them as specific advice. It can generate suggestions such as changing the display layout or adding visually appealing elements. It can also learn from past revision proposals and their results to improve the accuracy of the revision proposals. Furthermore, it can determine the priority of the revision proposals. Step 4: The delivery unit provides the proposed revisions generated by the proposed revision generation unit. For example, it can quickly provide proposed revisions as specific advice. It can also estimate the user's emotions and adjust the method of providing the proposed revisions based on the estimated user emotions. It can also select the optimal delivery method by considering the user's device information.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the image input unit, analysis unit, revision proposal generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the image input unit can capture the overall picture of the setup and details of each display using the camera 42 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and uses AI to analyze the image and identify problems and areas for improvement in the setup. The revision proposal generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates revision proposals based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14, for example, and provides the generated revision proposals to the person in charge. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the image input unit, analysis unit, revision proposal generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the image input unit can capture the overall picture of the setup and details of each display using the camera 42 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and uses AI to analyze the image and identify problems and areas for improvement in the setup. The revision proposal generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates revision proposals based on the analysis results. The provision unit is implemented in the control unit 46A of the smart glasses 214, for example, and provides the generated revision proposals to the person in charge. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the image input unit, analysis unit, revision proposal generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the image input unit can capture the overall setup and details of each display using the camera 42 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which uses AI to analyze images and identify problems and areas for improvement in the setup. The revision proposal generation unit is implemented in the specific processing unit 290 of the data processing unit 12, which generates revision proposals based on the analysis results. The provision unit is implemented in the control unit 46A of the headset terminal 314, which provides the generated revision proposals to the person in charge. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the image input unit, analysis unit, revision proposal generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the image input unit can use the camera 42 of the robot 414 to capture the overall picture of the setup and details of each display. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and uses AI to analyze the images and identify problems and areas for improvement in the setup. The revision proposal generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates revision proposals based on the analysis results. The provision unit is implemented in the control unit 46A of the robot 414, for example, and provides the generated revision proposals to the person in charge. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) An image input section for inputting images of the setup status of events and exhibitions, An analysis unit that analyzes the image input by the aforementioned image input unit, A revision proposal generation unit generates revision proposals based on the results of the analysis performed by the aforementioned analysis unit, The system includes a provisioning unit that provides the revised proposals generated by the revised proposal generation unit. A system characterized by the following features. (Note 2) The aforementioned revision proposal generation unit, Generate revised proposals based on VMD metrics. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provide revised suggestions as specific advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Identify problems and areas for improvement in the setup. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned revision proposal generation unit, Learn from past revision proposals and their results to improve the accuracy of revision proposals. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide revised proposals promptly. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned image input unit is It estimates the user's emotions and adjusts the timing of image input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned image input unit is When an image is input, multiple images are automatically acquired from different angles and under different lighting conditions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned image input unit is When an image is input, prioritize retrieving images that focus on a specific display element. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned image input unit is It estimates the user's emotions and determines the priority of input images based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned image input unit is When an image is input, the system prioritizes retrieving images 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 12) The aforementioned image input unit is When an image is entered, the system analyzes the user's social media activity and retrieves relevant images. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, it automatically identifies specific display elements within the image to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the geographical distribution of the images will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature and data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned revision proposal generation unit, The system estimates the user's emotions and adjusts the way the proposed revisions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned revision proposal generation unit, When generating revision proposals, prioritize generating proposals that focus on specific display elements. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned revision proposal generation unit, When generating revised proposals, the system learns from past proposals and their results to improve the accuracy of the proposed revisions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned revision proposal generation unit, The system estimates user sentiment and prioritizes proposed revisions based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned revision proposal generation unit, When generating revised proposals, geographical factors are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned revision proposal generation unit, When generating revised proposals, we improve the accuracy of the proposals by referring to relevant literature and data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate the user's emotions and adjust how we provide suggested solutions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, we will refer to past user feedback to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the order in which suggested revisions are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 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. An image input section for inputting images of the setup status of events and exhibitions, An analysis unit that analyzes the image input by the aforementioned image input unit, A revision proposal generation unit generates revision proposals based on the results of the analysis performed by the aforementioned analysis unit, The system includes a provisioning unit that provides the revised proposals generated by the revised proposal generation unit. A system characterized by the following features.

2. The aforementioned revision proposal generation unit, Generate revised proposals based on VMD metrics. The system according to feature 1.

3. The aforementioned supply unit is, Provide revised suggestions as specific advice. The system according to feature 1.

4. The aforementioned analysis unit, Identify problems and areas for improvement in the setup. The system according to feature 1.

5. The aforementioned revision proposal generation unit, Learn from past revision proposals and their results to improve the accuracy of revision proposals. The system according to feature 1.

6. The aforementioned supply unit is, Provide revised proposals promptly. The system according to feature 1.

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

8. The aforementioned image input unit is When an image is input, multiple images are automatically acquired from different angles and under different lighting conditions. The system according to feature 1.

Images