system

Abstract

The system according to this embodiment aims to enable easy creation and implementation of projection mapping without requiring specialized knowledge. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and an implementation unit. The reception unit inputs event information. The generation unit analyzes the information input by the reception unit and generates a projection mapping plan. The implementation unit performs projection mapping based on the plan generated by the 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of generating and implementing a projection mapping plan is complicated and difficult to execute without expertise.

[0005] The system according to the embodiment aims to easily create and implement projection mapping even without expertise.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and an implementation unit. The reception unit inputs event information. The generation unit analyzes the information input by the reception unit and generates a projection mapping plan. The implementation unit performs projection mapping based on the plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows for easy creation and implementation of projection mapping without requiring specialized knowledge. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The projection mapping system according to an embodiment of the present invention is a system that uses a generative AI to easily create projection mappings and enhance the presentation of events and special personal occasions. The projection mapping system works by having the user input information about the event they wish to project projection mapping onto, which the generative AI then analyzes to generate an optimal projection mapping plan. Based on the generated plan, the projection mapping is then implemented. This system allows for the addition of memorable effects to events such as local fireworks displays, music events, and weddings and other ceremonial occasions. For example, the user inputs information about the event they wish to project projection mapping onto. For instance, they input information such as the date, time, location, and theme of a fireworks display. This information is then input into the generative AI. Next, the generative AI analyzes the input information and generates an optimal projection mapping plan. The generative AI creates a plan that considers the combination of images and music to match the event's theme and location. For example, if the theme of the fireworks display is "summer festival," the AI ​​generates a plan combining images and music related to summer festivals. Based on the generated plan, the projection mapping is then implemented. For example, at the fireworks display venue, images are projected and music plays based on the plan created by the generative AI. In this way, the presentation of events is enhanced. This system allows anyone to easily create projection mapping as long as they have the necessary equipment. Users can implement projection mapping simply by inputting information into the generating AI, without having to perform complex operations. This allows for the addition of memorable effects to local fireworks displays, music events, weddings, and other ceremonial occasions. For example, at a wedding, photos and videos of the bride and groom can be projected using projection mapping. This makes the wedding presentation even more spectacular and moves the guests. Also, at local fireworks displays, projecting images synchronized with the fireworks makes the presentation even more exciting. In this way, projection mapping using generating AI can enhance the presentation of various events and provide experiences that will last a lifetime.This allows the projection mapping system to handle everything from inputting event information to executing the projection mapping itself.

[0029] The projection mapping system according to this embodiment comprises a reception unit, a generation unit, and an implementation unit. The reception unit receives information about an event that the user wants to project using projection mapping. For example, the reception unit can receive information such as the date, time, location, and theme of a fireworks display. The generation unit analyzes the information entered by the reception unit and generates a projection mapping plan. The generation unit uses, for example, a generation AI to generate a plan that considers the combination of images and music according to the theme and location of the event. For example, if the theme of the fireworks display is "summer festival," the generation unit will generate a plan that combines images and music related to summer festivals. The implementation unit performs projection mapping based on the plan generated by the generation unit. The implementation unit projects images and plays music based on the generated plan. This allows the projection mapping system to consistently perform everything from inputting event information to implementing projection mapping. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the event information entered by the user into the generation AI, and the generation AI can analyze the information. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit uses the generation AI to generate a plan that takes into account the theme and location of the event, considering the combination of video and music. Some or all of the above-described processes in the implementation unit may be performed using AI, or not. For example, when the implementation unit projects video and plays music based on the generated plan, it can use AI to control the timing of the projection and the playback of the music.

[0030] The reception desk is where users input information about events they want to project using projection mapping. For example, the reception desk can input information such as the date, time, location, and theme of a fireworks display. Specifically, users input detailed event information through a dedicated interface. The interface is designed for intuitive operation and allows users to input details such as the number of participants and special requests, in addition to the date, time, location, and theme. Furthermore, the reception desk has a function to automatically save the information entered by the user, allowing for later editing and verification. For example, if a user wants to change the date and time of an event, they can easily make corrections. The reception desk also has a function to check the integrity and completeness of the information before sending it to the generation department. This prevents input errors and incomplete information from being sent to the generation department, improving the reliability of the entire system. In addition, the reception desk can provide advice and suggestions for the success of the event based on the information entered by the user. For example, by suggesting videos and music suitable for a specific theme based on past data, it can reduce the burden on the user and create a more attractive projection mapping experience.

[0031] The generation unit analyzes the information entered by the reception unit and generates a projection mapping plan. For example, using a generation AI, the generation unit generates a plan that considers the combination of video and music to match the event's theme and location. Specifically, the generation AI analyzes past data and trends based on the event information entered by the user and proposes the optimal combination of video and music. For example, if the theme of the fireworks display is "summer festival," the generation AI will automatically select video and music related to summer festivals and generate a plan. The generation AI understands the user's input using natural language processing technology and selects appropriate video materials using image recognition technology. In addition, for music selection, a music generation AI can be used to automatically generate music that matches the atmosphere of the event. Furthermore, the generation unit also has a function to present the generated plan to the user and allow the user to review and modify the plan. For example, if the user wants to change specific video or music in the generated plan, they can easily make modifications. This allows the generation unit to flexibly respond to user requests and provide the optimal projection mapping plan.

[0032] The implementation team carries out projection mapping based on the plan generated by the generation team. For example, the implementation team projects images and plays music based on the generated plan. Specifically, the implementation team uses high-performance projectors and sound systems to synchronize and project images and music onto the event venue. The projectors are optimally positioned according to the scale and location of the event to maximize the quality of the images. The sound system is designed to deliver music uniformly throughout the venue, providing an immersive sound effect. Furthermore, the implementation team can use AI to control the timing of projections and music playback. For example, the AI ​​detects environmental changes in real time and automatically adjusts the projection content and music playback. This allows for flexible responses to unexpected problems and environmental changes, ensuring optimal performance at all times. The implementation team also has the ability to monitor the progress of the event and adjust the projection content and music playback as needed. This enables the implementation team to realize high-quality projection mapping based on the generated plan and support the success of the event.

[0033] The generation unit can generate plans that take into account the combination of video and music to match the theme and location of the event. For example, the generation unit uses generation AI to generate plans that take into account the combination of video and music to match the theme and location of the event. For example, if the theme of a wedding is "romantic," the generation unit will generate a plan that combines romantic video and music. Also, if the theme of a concert is "classical," the generation unit can generate a plan that combines video to match classical music. Furthermore, if the theme of an outdoor event is "nature," the generation unit can generate a plan that combines natural scenery and sounds. In this way, the generation unit can provide projection mapping that is optimal for the theme and location of the event. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit uses generation AI to generate plans that take into account the combination of video and music to match the theme and location of the event.

[0034] The generation unit can generate plans that combine videos and music related to the theme of a fireworks display. For example, the generation unit uses a generation AI to generate plans that combine videos and music related to the theme of a fireworks display. For example, if the theme of the fireworks display is "summer festival," the generation unit will generate a plan that combines videos and music related to summer festivals. Also, if the theme of the fireworks display is "traditional event," the generation unit can generate a plan that combines videos and music related to traditional events. Furthermore, if the theme of the fireworks display is "specific color or shape," the generation unit can generate a plan that combines videos and music related to that color or shape. In this way, the generation unit can provide projection mapping specifically tailored to fireworks displays. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit uses a generation AI to generate plans that combine videos and music related to the theme of a fireworks display.

[0035] The implementation unit can project images and play music based on the generated plan. For example, the implementation unit can project images and play music based on the generated plan. For example, the implementation unit can project images using a projector and play music using sound equipment. The implementation unit can also adjust the projection method and music playback method. For example, the implementation unit can select the optimal projection method and playback method based on the type of projector and the specifications of the sound equipment. Furthermore, the implementation unit can adjust the projection timing and music playback timing. For example, the implementation unit can adjust the projection timing and playback timing to synchronize the images and music. This allows the implementation unit to perform projection mapping based on the generated plan. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, when the implementation unit projects images and plays music based on the generated plan, it can use AI to control the projection timing and music playback.

[0036] The reception desk can input information such as the date, time, location, and theme of the wedding. For example, the reception desk can input the date and time of the wedding in a calendar format, select the location on a map, and select the theme from a dropdown menu. The reception desk can also input detailed information about the wedding. For example, the reception desk can input information such as the number of wedding guests and any special requests. This allows the reception desk to input information specific to the wedding. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the wedding information entered by the user into a generating AI, which can then analyze the information.

[0037] The generation unit can generate plans that combine videos and music related to the wedding theme. For example, the generation unit uses a generation AI to generate plans that combine videos and music related to the wedding theme. For example, if the wedding theme is "romantic," the generation unit will generate a plan that combines romantic videos and music. Also, if the wedding theme is "classical," the generation unit can generate a plan that combines classic videos and music. Furthermore, if the wedding theme is "modern," the generation unit can generate a plan that combines modern videos and music. In this way, the generation unit can provide projection mapping specifically for weddings. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit uses a generation AI to generate plans that combine videos and music related to the wedding theme.

[0038] The reception desk can refer to past event information and provide guidance to improve the accuracy of the information entered by the user. For example, the reception desk can automatically display suggested input content based on event information previously entered by the user. The reception desk can also provide hints to improve the accuracy of the input content by referring to information about events the user has previously attended. Furthermore, the reception desk can analyze patterns in the information entered by the user in the past and suggest the optimal input method. In this way, the reception desk can improve input accuracy based on past event information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can retrieve past event information from a database and analyze the information using AI. Furthermore, the reception desk can display tooltips and help messages to provide guidance to improve the accuracy of the information entered by the user based on past event information. In this way, the reception desk can support the user in entering accurate information.

[0039] The reception system can automatically complete input content when an event is entered, taking into account the user's past event participation history. For example, the reception system can automatically complete input content based on information from events the user has previously attended. It can also automatically complete input content by referring to event information previously entered by the user. Furthermore, the reception system can analyze the user's past event participation history and automatically complete the most suitable input content. This allows the reception system to automatically complete input content based on past event participation history. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can retrieve the user's past event participation history from a database and analyze the information using AI. Furthermore, the reception system can use predictive algorithms to automatically complete input content based on the user's past event participation history. This allows the reception system to support users in entering event information quickly and accurately.

[0040] The reception system can prioritize the input of highly relevant event information by considering the user's geographical location when the user enters event information. For example, the reception system can prioritize event information that is close to the user's current location. It can also prioritize event information related to places the user has visited in the past. Furthermore, the reception system can prioritize the input of highly relevant event information by considering the distance from the user's current location. In this way, the reception system can prioritize the input of highly relevant information based on geographical location information. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI. For example, the reception system can acquire the user's geographical location information and analyze the information using AI. Furthermore, the reception system can customize the input interface to prioritize the input of highly relevant information based on the user's geographical location information. In this way, the reception system can support users in entering event information quickly and accurately.

[0041] The reception desk can analyze a user's social media activity when they input event information and automatically input relevant information. For example, the reception desk can analyze a user's social media posts and automatically input relevant event information. It can also analyze a user's social media friendships and automatically input relevant event information. Furthermore, it can analyze a user's social media activity history and automatically input relevant event information. This allows the reception desk to automatically input relevant information based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can retrieve a user's social media activity from a database and analyze the information using AI. Furthermore, the reception desk can use predictive algorithms to automatically input relevant information based on the user's social media activity. This allows the reception desk to support users in inputting event information quickly and accurately.

[0042] The generation unit can adjust the level of detail in a plan based on the importance of the event during plan generation. For example, for important events, the generation unit generates a detailed plan. It can also generate a standard plan for general events. Furthermore, for simple events, it can generate a concise plan. This allows the generation unit to provide a plan with a level of detail appropriate to the importance of the event. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit uses generation AI to adjust the level of detail in a plan based on the importance of the event. Furthermore, the generation unit can use criteria such as the number of participants, media attention, and the organizer's intentions to evaluate the importance of the event. This allows the generation unit to provide an optimal plan according to the importance of the event.

[0043] The generation unit can apply different generation algorithms depending on the event category when generating a plan. For example, in the case of a music event, the generation unit can apply an algorithm that generates videos synchronized with the music. Similarly, in the case of a fireworks display, it can apply an algorithm that generates videos synchronized with the timing of the fireworks. Furthermore, in the case of a wedding, it can apply an algorithm that generates emotionally moving videos. This allows the generation unit to provide the optimal plan for each event category. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit uses the generation AI to apply different generation algorithms depending on the event category. Furthermore, the generation unit can use criteria such as weddings, concerts, and corporate events to evaluate event categories. This allows the generation unit to provide the optimal plan for each event category.

[0044] The generation unit can determine the priority of plans based on the timing of events when generating plans. For example, the generation unit will prioritize generating plans for upcoming events. It can also postpone the generation of plans for long-term events. Furthermore, for seasonal events, the generation unit can prioritize generating plans appropriate for the season. This allows the generation unit to provide plan priorities according to the timing of events. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit uses generation AI to determine the priority of plans based on the timing of events. Furthermore, the generation unit can use criteria such as season, specific dates, and holidays to evaluate the timing of events. This allows the generation unit to provide the optimal plan according to the timing of events.

[0045] The generation unit can adjust the order of plans based on the relevance of events during plan generation. For example, the generation unit will prioritize generating plans for highly relevant events. It can also postpone the generation of plans for less relevant events. Furthermore, if multiple events are related, the generation unit can generate plans in order of relevance. This allows the generation unit to provide a plan order that is appropriate to the relevance of events. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit adjusts the order of plans based on the relevance of events using the generation AI. Furthermore, the generation unit can use criteria such as the degree of theme matching, commonalities among participants, and past event history to evaluate the relevance of events. This allows the generation unit to provide an optimal plan that is appropriate to the relevance of events.

[0046] The implementation unit can select the optimal implementation method by analyzing the user's past event participation history during implementation. For example, the implementation unit can select the optimal implementation method based on information about events the user has previously participated in. Alternatively, the implementation unit can select the optimal implementation method by referring to the user's past event participation history. Furthermore, the implementation unit can analyze the user's past event participation history to select the optimal implementation method. This allows the implementation unit to provide the optimal implementation method based on past event participation history. Some or all of the above processing in the implementation unit may be performed using AI, or without AI. For example, the implementation unit can obtain the user's past event participation history from a database and analyze the information using AI. Furthermore, the implementation unit can use a predictive algorithm to select the optimal implementation method based on the user's past event participation history. This allows the implementation unit to support the user in experiencing the optimal projection mapping.

[0047] The implementation unit can customize the projection mapping methods based on the user's current lifestyle during implementation. For example, if the user is busy, the implementation unit can implement a simple and efficient projection mapping. Alternatively, if the user is relaxed, the implementation unit can implement a detailed and visually rich projection mapping. Furthermore, if the user is interested in a particular theme, the implementation unit can implement projection mapping tailored to that theme. This allows the implementation unit to provide projection mapping methods that are appropriate to the user's current lifestyle. Some or all of the above processing in the implementation unit may be performed using AI, for example, or not. For example, the implementation unit can obtain the user's current lifestyle from a database and analyze the information using AI. Furthermore, the implementation unit can use predictive algorithms to customize the projection mapping methods based on the user's current lifestyle. This allows the implementation unit to support the user in experiencing the optimal projection mapping.

[0048] The implementation unit can select the optimal implementation method during implementation, taking into account the user's geographical location information. For example, the implementation unit may prioritize projection mapping in locations close to the user's current location. It may also prioritize projection mapping related to locations the user has visited in the past. Furthermore, the implementation unit may select the optimal implementation method by considering the distance from the user's current location. This allows the implementation unit to provide the optimal implementation method based on geographical location information. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can acquire the user's geographical location information and analyze the information using AI. Furthermore, the implementation unit can use a predictive algorithm to select the optimal implementation method based on the user's geographical location information. This allows the implementation unit to support the user in experiencing the optimal projection mapping.

[0049] The implementation unit can analyze the user's social media activity during implementation and propose projection mapping methods. For example, the implementation unit can analyze the content of the user's social media posts and propose relevant projection mapping methods. It can also analyze the user's social media friendships and propose relevant projection mapping methods. Furthermore, the implementation unit can analyze the user's social media activity history and propose relevant projection mapping methods. This allows the implementation unit to provide the optimal projection mapping method based on social media activity. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can obtain the user's social media activity from a database and analyze the information using AI. Furthermore, the implementation unit can use a predictive algorithm to propose projection mapping methods based on the user's social media activity. This allows the implementation unit to support the user in experiencing the optimal projection mapping.

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

[0051] The reception desk can refer to past event information and provide guidance to improve the accuracy of the information entered by the user. For example, it can automatically display suggested inputs based on event information the user has entered in the past. It can also provide hints to improve the accuracy of the input by referring to information about events the user has attended in the past. Furthermore, it can analyze patterns in the information the user has entered in the past and suggest the optimal input method. In this way, the reception desk can improve input accuracy based on past event information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can retrieve past event information from a database and analyze the information using AI. Furthermore, the reception desk can display tooltips and help messages to provide guidance to improve the accuracy of the information entered by the user based on past event information. In this way, the reception desk can support the user in entering accurate information.

[0052] The implementation unit can select the optimal implementation method by analyzing the user's past event participation history during implementation. For example, it can select the optimal implementation method based on information about events the user has previously attended. It can also select the optimal implementation method by referring to the user's past event participation history. Furthermore, it can select the optimal implementation method by analyzing the user's past event participation history. This allows the implementation unit to provide the optimal implementation method based on past event participation history. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can obtain the user's past event participation history from a database and analyze the information using AI. Furthermore, the implementation unit can use a predictive algorithm to select the optimal implementation method based on the user's past event participation history. This allows the implementation unit to support the user in experiencing the optimal projection mapping.

[0053] The generation unit can adjust the level of detail in a plan based on the importance of the event during plan generation. For example, it can generate a detailed plan for important events, a standard plan for general events, and a concise plan for simple events. This allows the generation unit to provide a plan with a level of detail appropriate to the importance of the event. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit uses generation AI to adjust the level of detail in the plan based on the importance of the event. Furthermore, the generation unit can use criteria such as the number of participants, media attention, and the organizer's intentions to evaluate the importance of the event. This allows the generation unit to provide an optimal plan according to the importance of the event.

[0054] The reception desk can prioritize the input of highly relevant event information by considering the user's geographical location when the user enters event information. For example, it can prioritize event information that is close to the user's current location. It can also prioritize event information related to places the user has visited in the past. Furthermore, it can prioritize highly relevant event information by considering the distance from the user's current location. In this way, the reception desk can prioritize the input of highly relevant information based on geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can acquire the user's geographical location information and analyze the information using AI. Furthermore, the reception desk can customize the input interface to prioritize the input of highly relevant information based on the user's geographical location information. In this way, the reception desk can support users in entering event information quickly and accurately.

[0055] The implementation unit can customize the projection mapping methods based on the user's current lifestyle during implementation. For example, if the user is busy, a simple and efficient projection mapping can be implemented. If the user is relaxed, a detailed and visually rich projection mapping can be implemented. Furthermore, if the user is interested in a particular theme, projection mapping tailored to that theme can be implemented. In this way, the implementation unit can provide projection mapping methods that are appropriate to the user's current lifestyle. Some or all of the above processing in the implementation unit may be performed using AI, for example, or not. For example, the implementation unit can obtain the user's current lifestyle from a database and analyze the information using AI. Furthermore, the implementation unit can use predictive algorithms to customize the projection mapping methods based on the user's current lifestyle. In this way, the implementation unit can support the user in experiencing the optimal projection mapping.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives information about the event the user wants to project using projection mapping. For example, they can enter information such as the date, time, location, and theme of a fireworks display. Step 2: The generation unit analyzes the information entered by the reception unit and generates a projection mapping plan. Using generation AI, the generation unit generates a plan that takes into account the combination of video and music according to the event's theme and location. For example, if the theme of the fireworks display is "summer festival," it will generate a plan that combines video and music related to summer festivals. Step 3: The implementation unit performs projection mapping based on the plan generated by the generation unit. The implementation unit projects images and plays music based on the generated plan. This allows the projection mapping system to handle everything from inputting event information to performing projection mapping in a consistent manner.

[0058] (Example of form 2) The projection mapping system according to an embodiment of the present invention is a system that uses a generative AI to easily create projection mappings and enhance the presentation of events and special personal occasions. The projection mapping system works by having the user input information about the event they wish to project projection mapping onto, which the generative AI then analyzes to generate an optimal projection mapping plan. Based on the generated plan, the projection mapping is then implemented. This system allows for the addition of memorable effects to events such as local fireworks displays, music events, and weddings and other ceremonial occasions. For example, the user inputs information about the event they wish to project projection mapping onto. For instance, they input information such as the date, time, location, and theme of a fireworks display. This information is then input into the generative AI. Next, the generative AI analyzes the input information and generates an optimal projection mapping plan. The generative AI creates a plan that considers the combination of images and music to match the event's theme and location. For example, if the theme of the fireworks display is "summer festival," the AI ​​generates a plan combining images and music related to summer festivals. Based on the generated plan, the projection mapping is then implemented. For example, at the fireworks display venue, images are projected and music plays based on the plan created by the generative AI. In this way, the presentation of events is enhanced. This system allows anyone to easily create projection mapping as long as they have the necessary equipment. Users can implement projection mapping simply by inputting information into the generating AI, without having to perform complex operations. This allows for the addition of memorable effects to local fireworks displays, music events, weddings, and other ceremonial occasions. For example, at a wedding, photos and videos of the bride and groom can be projected using projection mapping. This makes the wedding presentation even more spectacular and moves the guests. Also, at local fireworks displays, projecting images synchronized with the fireworks makes the presentation even more exciting. In this way, projection mapping using generating AI can enhance the presentation of various events and provide experiences that will last a lifetime.This allows the projection mapping system to handle everything from inputting event information to executing the projection mapping itself.

[0059] The projection mapping system according to this embodiment comprises a reception unit, a generation unit, and an implementation unit. The reception unit receives information about an event that the user wants to project using projection mapping. For example, the reception unit can receive information such as the date, time, location, and theme of a fireworks display. The generation unit analyzes the information entered by the reception unit and generates a projection mapping plan. The generation unit uses, for example, a generation AI to generate a plan that considers the combination of images and music according to the theme and location of the event. For example, if the theme of the fireworks display is "summer festival," the generation unit will generate a plan that combines images and music related to summer festivals. The implementation unit performs projection mapping based on the plan generated by the generation unit. The implementation unit projects images and plays music based on the generated plan. This allows the projection mapping system to consistently perform everything from inputting event information to implementing projection mapping. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the event information entered by the user into the generation AI, and the generation AI can analyze the information. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit uses the generation AI to generate a plan that takes into account the theme and location of the event, considering the combination of video and music. Some or all of the above-described processes in the implementation unit may be performed using AI, or not. For example, when the implementation unit projects video and plays music based on the generated plan, it can use AI to control the timing of the projection and the playback of the music.

[0060] The reception desk is where users input information about events they want to project using projection mapping. For example, the reception desk can input information such as the date, time, location, and theme of a fireworks display. Specifically, users input detailed event information through a dedicated interface. The interface is designed for intuitive operation and allows users to input details such as the number of participants and special requests, in addition to the date, time, location, and theme. Furthermore, the reception desk has a function to automatically save the information entered by the user, allowing for later editing and verification. For example, if a user wants to change the date and time of an event, they can easily make corrections. The reception desk also has a function to check the integrity and completeness of the information before sending it to the generation department. This prevents input errors and incomplete information from being sent to the generation department, improving the reliability of the entire system. In addition, the reception desk can provide advice and suggestions for the success of the event based on the information entered by the user. For example, by suggesting videos and music suitable for a specific theme based on past data, it can reduce the burden on the user and create a more attractive projection mapping experience.

[0061] The generation unit analyzes the information entered by the reception unit and generates a projection mapping plan. For example, using a generation AI, the generation unit generates a plan that considers the combination of video and music to match the event's theme and location. Specifically, the generation AI analyzes past data and trends based on the event information entered by the user and proposes the optimal combination of video and music. For example, if the theme of the fireworks display is "summer festival," the generation AI will automatically select video and music related to summer festivals and generate a plan. The generation AI understands the user's input using natural language processing technology and selects appropriate video materials using image recognition technology. In addition, for music selection, a music generation AI can be used to automatically generate music that matches the atmosphere of the event. Furthermore, the generation unit also has a function to present the generated plan to the user and allow the user to review and modify the plan. For example, if the user wants to change specific video or music in the generated plan, they can easily make modifications. This allows the generation unit to flexibly respond to user requests and provide the optimal projection mapping plan.

[0062] The implementation team carries out projection mapping based on the plan generated by the generation team. For example, the implementation team projects images and plays music based on the generated plan. Specifically, the implementation team uses high-performance projectors and sound systems to synchronize and project images and music onto the event venue. The projectors are optimally positioned according to the scale and location of the event to maximize the quality of the images. The sound system is designed to deliver music uniformly throughout the venue, providing an immersive sound effect. Furthermore, the implementation team can use AI to control the timing of projections and music playback. For example, the AI ​​detects environmental changes in real time and automatically adjusts the projection content and music playback. This allows for flexible responses to unexpected problems and environmental changes, ensuring optimal performance at all times. The implementation team also has the ability to monitor the progress of the event and adjust the projection content and music playback as needed. This enables the implementation team to realize high-quality projection mapping based on the generated plan and support the success of the event.

[0063] The generation unit can generate plans that take into account the combination of video and music to match the theme and location of the event. For example, the generation unit uses generation AI to generate plans that take into account the combination of video and music to match the theme and location of the event. For example, if the theme of a wedding is "romantic," the generation unit will generate a plan that combines romantic video and music. Also, if the theme of a concert is "classical," the generation unit can generate a plan that combines video to match classical music. Furthermore, if the theme of an outdoor event is "nature," the generation unit can generate a plan that combines natural scenery and sounds. In this way, the generation unit can provide projection mapping that is optimal for the theme and location of the event. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit uses generation AI to generate plans that take into account the combination of video and music to match the theme and location of the event.

[0064] The generation unit can generate plans that combine videos and music related to the theme of a fireworks display. For example, the generation unit uses a generation AI to generate plans that combine videos and music related to the theme of a fireworks display. For example, if the theme of the fireworks display is "summer festival," the generation unit will generate a plan that combines videos and music related to summer festivals. Also, if the theme of the fireworks display is "traditional event," the generation unit can generate a plan that combines videos and music related to traditional events. Furthermore, if the theme of the fireworks display is "specific color or shape," the generation unit can generate a plan that combines videos and music related to that color or shape. In this way, the generation unit can provide projection mapping specifically tailored to fireworks displays. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit uses a generation AI to generate plans that combine videos and music related to the theme of a fireworks display.

[0065] The implementation unit can project images and play music based on the generated plan. For example, the implementation unit can project images and play music based on the generated plan. For example, the implementation unit can project images using a projector and play music using sound equipment. The implementation unit can also adjust the projection method and music playback method. For example, the implementation unit can select the optimal projection method and playback method based on the type of projector and the specifications of the sound equipment. Furthermore, the implementation unit can adjust the projection timing and music playback timing. For example, the implementation unit can adjust the projection timing and playback timing to synchronize the images and music. This allows the implementation unit to perform projection mapping based on the generated plan. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, when the implementation unit projects images and plays music based on the generated plan, it can use AI to control the projection timing and music playback.

[0066] The reception desk can input information such as the date, time, location, and theme of the wedding. For example, the reception desk can input the date and time of the wedding in a calendar format, select the location on a map, and select the theme from a dropdown menu. The reception desk can also input detailed information about the wedding. For example, the reception desk can input information such as the number of wedding guests and any special requests. This allows the reception desk to input information specific to the wedding. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the wedding information entered by the user into a generating AI, which can then analyze the information.

[0067] The generation unit can generate plans that combine videos and music related to the wedding theme. For example, the generation unit uses a generation AI to generate plans that combine videos and music related to the wedding theme. For example, if the wedding theme is "romantic," the generation unit will generate a plan that combines romantic videos and music. Also, if the wedding theme is "classical," the generation unit can generate a plan that combines classic videos and music. Furthermore, if the wedding theme is "modern," the generation unit can generate a plan that combines modern videos and music. In this way, the generation unit can provide projection mapping specifically for weddings. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit uses a generation AI to generate plans that combine videos and music related to the wedding theme.

[0068] The reception desk can estimate the user's emotions and customize the event information input interface based on the estimated emotions. For example, if the user is nervous, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of event information. In this way, the reception desk can provide an input interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use facial recognition technology or voice analysis technology to estimate the user's emotions. Furthermore, the reception desk can accumulate user emotion data and analyze long-term emotional trends. For example, the reception desk analyzes user emotional data over time to identify patterns in emotional changes. This allows the reception desk to gain a detailed understanding of the user's emotions and customize the input interface accordingly.

[0069] The reception desk can refer to past event information and provide guidance to improve the accuracy of the information entered by the user. For example, the reception desk can automatically display suggested input content based on event information previously entered by the user. The reception desk can also provide hints to improve the accuracy of the input content by referring to information about events the user has previously attended. Furthermore, the reception desk can analyze patterns in the information entered by the user in the past and suggest the optimal input method. In this way, the reception desk can improve input accuracy based on past event information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can retrieve past event information from a database and analyze the information using AI. Furthermore, the reception desk can display tooltips and help messages to provide guidance to improve the accuracy of the information entered by the user based on past event information. In this way, the reception desk can support the user in entering accurate information.

[0070] The reception system can automatically complete input content when an event is entered, taking into account the user's past event participation history. For example, the reception system can automatically complete input content based on information from events the user has previously attended. It can also automatically complete input content by referring to event information previously entered by the user. Furthermore, the reception system can analyze the user's past event participation history and automatically complete the most suitable input content. This allows the reception system to automatically complete input content based on past event participation history. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can retrieve the user's past event participation history from a database and analyze the information using AI. Furthermore, the reception system can use predictive algorithms to automatically complete input content based on the user's past event participation history. This allows the reception system to support users in entering event information quickly and accurately.

[0071] The reception desk can estimate the user's emotions and prioritize input information based on the estimated emotions. For example, if the user is nervous, the reception desk may prioritize inputting important information. If the user is relaxed, the reception desk may also prioritize inputting detailed information. Furthermore, if the user is in a hurry, the reception desk may prioritize inputting minimal information. This allows the reception desk to prioritize input information according to 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 reception desk may be performed using AI, or not. For example, the reception desk may use facial recognition technology or voice analysis technology to estimate the user's emotions. Furthermore, the reception desk can accumulate user emotion data and analyze long-term emotional trends. For example, the reception desk may analyze user emotion data over time to identify patterns of emotional change. This allows the reception desk to gain a detailed understanding of the user's emotions and prioritize the input information accordingly.

[0072] The reception system can prioritize the input of highly relevant event information by considering the user's geographical location when the user enters event information. For example, the reception system can prioritize event information that is close to the user's current location. It can also prioritize event information related to places the user has visited in the past. Furthermore, the reception system can prioritize the input of highly relevant event information by considering the distance from the user's current location. In this way, the reception system can prioritize the input of highly relevant information based on geographical location information. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI. For example, the reception system can acquire the user's geographical location information and analyze the information using AI. Furthermore, the reception system can customize the input interface to prioritize the input of highly relevant information based on the user's geographical location information. In this way, the reception system can support users in entering event information quickly and accurately.

[0073] The reception desk can analyze a user's social media activity when they input event information and automatically input relevant information. For example, the reception desk can analyze a user's social media posts and automatically input relevant event information. It can also analyze a user's social media friendships and automatically input relevant event information. Furthermore, it can analyze a user's social media activity history and automatically input relevant event information. This allows the reception desk to automatically input relevant information based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can retrieve a user's social media activity from a database and analyze the information using AI. Furthermore, the reception desk can use predictive algorithms to automatically input relevant information based on the user's social media activity. This allows the reception desk to support users in inputting event information quickly and accurately.

[0074] The generation unit can estimate the user's emotions and adjust the way the generated plan is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a plan with visually stimulating effects. In this way, the generation unit can provide a way of presenting the plan that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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 generation unit is performed using the generation AI. For example, the generation unit adjusts the way the plan is presented based on the user's emotions using the generation AI. Furthermore, the generation unit can accumulate user emotion data and analyze long-term emotional trends. For example, the generation unit can analyze user emotion data over time to identify patterns of emotional change. This allows the generation unit to understand the user's emotions in detail and adjust how the plan is presented.

[0075] The generation unit can adjust the level of detail in a plan based on the importance of the event during plan generation. For example, for important events, the generation unit generates a detailed plan. It can also generate a standard plan for general events. Furthermore, for simple events, it can generate a concise plan. This allows the generation unit to provide a plan with a level of detail appropriate to the importance of the event. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit uses generation AI to adjust the level of detail in a plan based on the importance of the event. Furthermore, the generation unit can use criteria such as the number of participants, media attention, and the organizer's intentions to evaluate the importance of the event. This allows the generation unit to provide an optimal plan according to the importance of the event.

[0076] The generation unit can apply different generation algorithms depending on the event category when generating a plan. For example, in the case of a music event, the generation unit can apply an algorithm that generates videos synchronized with the music. Similarly, in the case of a fireworks display, it can apply an algorithm that generates videos synchronized with the timing of the fireworks. Furthermore, in the case of a wedding, it can apply an algorithm that generates emotionally moving videos. This allows the generation unit to provide the optimal plan for each event category. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit uses the generation AI to apply different generation algorithms depending on the event category. Furthermore, the generation unit can use criteria such as weddings, concerts, and corporate events to evaluate event categories. This allows the generation unit to provide the optimal plan for each event category.

[0077] The generation unit can estimate the user's emotions and adjust the length of the plan it generates based on those emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise plan. If the user is relaxed, it can generate a longer plan with more detailed explanations. Furthermore, if the user is excited, it can generate a plan with visually stimulating effects. This allows the generation unit to provide plan lengths that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit uses the generation AI to adjust the length of the plan based on the user's emotions. Furthermore, the generation unit can accumulate user emotion data and analyze long-term emotional trends. For example, the generation unit can analyze user emotion data over time to identify patterns of emotional change. This allows the generation unit to understand the user's emotions in detail and adjust the length of the plan accordingly.

[0078] The generation unit can determine the priority of plans based on the timing of events when generating plans. For example, the generation unit will prioritize generating plans for upcoming events. It can also postpone the generation of plans for long-term events. Furthermore, for seasonal events, the generation unit can prioritize generating plans appropriate for the season. This allows the generation unit to provide plan priorities according to the timing of events. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit uses generation AI to determine the priority of plans based on the timing of events. Furthermore, the generation unit can use criteria such as season, specific dates, and holidays to evaluate the timing of events. This allows the generation unit to provide the optimal plan according to the timing of events.

[0079] The generation unit can adjust the order of plans based on the relevance of events during plan generation. For example, the generation unit will prioritize generating plans for highly relevant events. It can also postpone the generation of plans for less relevant events. Furthermore, if multiple events are related, the generation unit can generate plans in order of relevance. This allows the generation unit to provide a plan order that is appropriate to the relevance of events. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit adjusts the order of plans based on the relevance of events using the generation AI. Furthermore, the generation unit can use criteria such as the degree of theme matching, commonalities among participants, and past event history to evaluate the relevance of events. This allows the generation unit to provide an optimal plan that is appropriate to the relevance of events.

[0080] The implementation unit can estimate the user's emotions and adjust the projection mapping method based on the estimated emotions. For example, if the user is relaxed, the implementation unit can implement projection mapping that proceeds at a leisurely pace. If the user is in a hurry, the implementation unit can also implement projection mapping that emphasizes the shortest route. Furthermore, if the user is excited, the implementation unit can implement projection mapping with visually stimulating effects. In this way, the implementation unit can provide a projection mapping method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the implementation unit may be performed using AI, for example, or not using AI. For example, the implementation unit can use facial recognition technology or voice analysis technology to estimate the user's emotions. Furthermore, the implementation unit can accumulate user emotion data and analyze long-term emotional trends. For example, the implementation team analyzes user emotional data over time to identify patterns of emotional change. This allows the implementation team to gain a detailed understanding of user emotions and adjust the projection mapping implementation method accordingly.

[0081] The implementation unit can select the optimal implementation method by analyzing the user's past event participation history during implementation. For example, the implementation unit can select the optimal implementation method based on information about events the user has previously participated in. Alternatively, the implementation unit can select the optimal implementation method by referring to the user's past event participation history. Furthermore, the implementation unit can analyze the user's past event participation history to select the optimal implementation method. This allows the implementation unit to provide the optimal implementation method based on past event participation history. Some or all of the above processing in the implementation unit may be performed using AI, or without AI. For example, the implementation unit can obtain the user's past event participation history from a database and analyze the information using AI. Furthermore, the implementation unit can use a predictive algorithm to select the optimal implementation method based on the user's past event participation history. This allows the implementation unit to support the user in experiencing the optimal projection mapping.

[0082] The implementation unit can customize the projection mapping methods based on the user's current lifestyle during implementation. For example, if the user is busy, the implementation unit can implement a simple and efficient projection mapping. Alternatively, if the user is relaxed, the implementation unit can implement a detailed and visually rich projection mapping. Furthermore, if the user is interested in a particular theme, the implementation unit can implement projection mapping tailored to that theme. This allows the implementation unit to provide projection mapping methods that are appropriate to the user's current lifestyle. Some or all of the above processing in the implementation unit may be performed using AI, for example, or not. For example, the implementation unit can obtain the user's current lifestyle from a database and analyze the information using AI. Furthermore, the implementation unit can use predictive algorithms to customize the projection mapping methods based on the user's current lifestyle. This allows the implementation unit to support the user in experiencing the optimal projection mapping.

[0083] The implementation unit can estimate the user's emotions and determine the priority of projection mapping based on the estimated emotions. For example, if the user is tense, the implementation unit may prioritize important parts. If the user is relaxed, the implementation unit may also prioritize detailed parts. Furthermore, if the user is in a hurry, the implementation unit may prioritize minimal parts. In this way, the implementation unit can provide projection mapping priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the implementation unit may be performed using AI, for example, or not using AI. For example, the implementation unit may use facial recognition technology or voice analysis technology to estimate the user's emotions. Furthermore, the implementation unit can accumulate user emotion data and analyze long-term emotional trends. For example, the implementation unit may analyze user emotion data over time to identify patterns of emotional change. This allows the implementation team to gain a detailed understanding of user emotions and determine the priorities for projection mapping.

[0084] The implementation unit can select the optimal implementation method during implementation, taking into account the user's geographical location information. For example, the implementation unit may prioritize projection mapping in locations close to the user's current location. It may also prioritize projection mapping related to locations the user has visited in the past. Furthermore, the implementation unit may select the optimal implementation method by considering the distance from the user's current location. This allows the implementation unit to provide the optimal implementation method based on geographical location information. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can acquire the user's geographical location information and analyze the information using AI. Furthermore, the implementation unit can use a predictive algorithm to select the optimal implementation method based on the user's geographical location information. This allows the implementation unit to support the user in experiencing the optimal projection mapping.

[0085] The implementation unit can analyze the user's social media activity during implementation and propose projection mapping methods. For example, the implementation unit can analyze the content of the user's social media posts and propose relevant projection mapping methods. It can also analyze the user's social media friendships and propose relevant projection mapping methods. Furthermore, the implementation unit can analyze the user's social media activity history and propose relevant projection mapping methods. This allows the implementation unit to provide the optimal projection mapping method based on social media activity. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can obtain the user's social media activity from a database and analyze the information using AI. Furthermore, the implementation unit can use a predictive algorithm to propose projection mapping methods based on the user's social media activity. This allows the implementation unit to support the user in experiencing the optimal projection mapping.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception desk can estimate the user's emotions and customize the event information input interface based on the estimated emotions. For example, if the user is nervous, it can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of event information. In this way, the reception desk can provide an input interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can use facial recognition technology or voice analysis technology to estimate the user's emotions. Furthermore, the reception desk can accumulate user emotion data and analyze long-term emotional trends. For example, the reception desk can analyze user emotion data over time to identify patterns of emotional change. This allows the reception desk to gain a detailed understanding of the user's emotions and customize the input interface accordingly.

[0088] The generation unit can estimate the user's emotions and adjust the way the generated plan is presented based on the estimated emotions. For example, if the user is relaxed, it can generate a plan that proceeds at a leisurely pace. If the user is in a hurry, it can generate a plan that emphasizes the shortest route. Furthermore, if the user is excited, it can generate a plan with visually stimulating effects. In this way, the generation unit can provide a way of presenting the plan that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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 generation unit is performed using the generation AI. For example, the generation unit adjusts the way the plan is presented based on the user's emotions using the generation AI. Furthermore, the generation unit can accumulate user emotion data and analyze long-term emotional trends. For example, the generation unit can analyze user emotion data over time and identify patterns of emotional change. In this way, the generation unit can gain a detailed understanding of the user's emotions and adjust the way the plan is presented.

[0089] The implementation unit can estimate the user's emotions and adjust the projection mapping method based on the estimated emotions. For example, if the user is relaxed, projection mapping can be performed at a leisurely pace. If the user is in a hurry, projection mapping can be performed that emphasizes the shortest route. Furthermore, if the user is excited, projection mapping can be performed with visually stimulating effects. In this way, the implementation unit can provide a projection mapping method that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the implementation unit may be performed using AI, for example, or not using AI. For example, the implementation unit can use facial recognition technology or voice analysis technology to estimate the user's emotions. Furthermore, the implementation unit can accumulate user emotion data and analyze long-term emotional trends. For example, the implementation unit can analyze user emotion data over time to identify patterns of emotional change. This allows the implementation team to gain a detailed understanding of the users' emotions and adjust the projection mapping implementation method accordingly.

[0090] The generation unit can estimate the user's emotions and adjust the length of the plan it generates based on those emotions. For example, if the user is in a hurry, it can generate a short, concise plan. If the user is relaxed, it can generate a longer plan with detailed explanations. Furthermore, if the user is excited, it can generate a plan with visually stimulating effects. This allows the generation unit to provide plan lengths that match the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using generative AI. For example, the generation unit uses generative AI to adjust the length of the plan based on the user's emotions. Furthermore, the generation unit can accumulate user emotion data and analyze long-term emotional trends. For example, the generation unit can analyze user emotion data over time to identify patterns of emotion change. This allows the generation unit to gain a detailed understanding of the user's emotions and adjust the plan length accordingly.

[0091] The implementation unit can estimate the user's emotions and determine the priority of projection mapping based on the estimated emotions. For example, if the user is nervous, important parts can be prioritized. If the user is relaxed, detailed parts can be prioritized. Furthermore, if the user is in a hurry, minimal parts can be prioritized. In this way, the implementation unit can provide projection mapping priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the implementation unit may be performed using AI, for example, or not using AI. For example, the implementation unit can use facial recognition technology or voice analysis technology to estimate the user's emotions. Furthermore, the implementation unit can accumulate user emotion data and analyze long-term emotional trends. For example, the implementation unit can analyze user emotion data over time to identify patterns of emotional change. This allows the implementation team to gain a detailed understanding of user emotions and determine the priorities for projection mapping.

[0092] The reception desk can refer to past event information and provide guidance to improve the accuracy of the information entered by the user. For example, it can automatically display suggested inputs based on event information the user has entered in the past. It can also provide hints to improve the accuracy of the input by referring to information about events the user has attended in the past. Furthermore, it can analyze patterns in the information the user has entered in the past and suggest the optimal input method. In this way, the reception desk can improve input accuracy based on past event information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can retrieve past event information from a database and analyze the information using AI. Furthermore, the reception desk can display tooltips and help messages to provide guidance to improve the accuracy of the information entered by the user based on past event information. In this way, the reception desk can support the user in entering accurate information.

[0093] The implementation unit can select the optimal implementation method by analyzing the user's past event participation history during implementation. For example, it can select the optimal implementation method based on information about events the user has previously attended. It can also select the optimal implementation method by referring to the user's past event participation history. Furthermore, it can select the optimal implementation method by analyzing the user's past event participation history. This allows the implementation unit to provide the optimal implementation method based on past event participation history. Some or all of the above processing in the implementation unit may be performed using AI, for example, or without AI. For example, the implementation unit can obtain the user's past event participation history from a database and analyze the information using AI. Furthermore, the implementation unit can use a predictive algorithm to select the optimal implementation method based on the user's past event participation history. This allows the implementation unit to support the user in experiencing the optimal projection mapping.

[0094] The generation unit can adjust the level of detail in a plan based on the importance of the event during plan generation. For example, it can generate a detailed plan for important events, a standard plan for general events, and a concise plan for simple events. This allows the generation unit to provide a plan with a level of detail appropriate to the importance of the event. Some or all of the above processing in the generation unit is performed using generation AI. For example, the generation unit uses generation AI to adjust the level of detail in the plan based on the importance of the event. Furthermore, the generation unit can use criteria such as the number of participants, media attention, and the organizer's intentions to evaluate the importance of the event. This allows the generation unit to provide an optimal plan according to the importance of the event.

[0095] The reception desk can prioritize the input of highly relevant event information by considering the user's geographical location when the user enters event information. For example, it can prioritize event information that is close to the user's current location. It can also prioritize event information related to places the user has visited in the past. Furthermore, it can prioritize highly relevant event information by considering the distance from the user's current location. In this way, the reception desk can prioritize the input of highly relevant information based on geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can acquire the user's geographical location information and analyze the information using AI. Furthermore, the reception desk can customize the input interface to prioritize the input of highly relevant information based on the user's geographical location information. In this way, the reception desk can support users in entering event information quickly and accurately.

[0096] The implementation unit can customize the projection mapping methods based on the user's current lifestyle during implementation. For example, if the user is busy, a simple and efficient projection mapping can be implemented. If the user is relaxed, a detailed and visually rich projection mapping can be implemented. Furthermore, if the user is interested in a particular theme, projection mapping tailored to that theme can be implemented. In this way, the implementation unit can provide projection mapping methods that are appropriate to the user's current lifestyle. Some or all of the above processing in the implementation unit may be performed using AI, for example, or not. For example, the implementation unit can obtain the user's current lifestyle from a database and analyze the information using AI. Furthermore, the implementation unit can use predictive algorithms to customize the projection mapping methods based on the user's current lifestyle. In this way, the implementation unit can support the user in experiencing the optimal projection mapping.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception desk receives information about the event the user wants to project using projection mapping. For example, they can enter information such as the date, time, location, and theme of a fireworks display. Step 2: The generation unit analyzes the information entered by the reception unit and generates a projection mapping plan. Using generation AI, the generation unit generates a plan that takes into account the combination of video and music according to the event's theme and location. For example, if the theme of the fireworks display is "summer festival," it will generate a plan that combines video and music related to summer festivals. Step 3: The implementation unit performs projection mapping based on the plan generated by the generation unit. The implementation unit projects images and plays music based on the generated plan. This allows the projection mapping system to handle everything from inputting event information to performing projection mapping in a consistent manner.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the reception unit, generation unit, and implementation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and transmits the event information entered by the user to the generation AI. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses the generation AI to generate a plan that matches the theme and location of the event. The implementation unit is implemented by, for example, the control unit 46A of the smart device 14 and projects images and plays music based on the generated plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the reception unit, generation unit, and implementation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and transmits the event information entered by the user to the generation AI. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses the generation AI to generate a plan that matches the theme and location of the event. The implementation unit is implemented by, for example, the control unit 46A of the smart glasses 214 and projects images and plays music based on the generated plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reception unit, generation unit, and implementation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and transmits the event information entered by the user to the generation AI. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses the generation AI to generate a plan that matches the theme and location of the event. The implementation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and projects video and plays music based on the generated plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements, including the reception unit, generation unit, and implementation unit described above, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and transmits the event information entered by the user to the generation AI. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses the generation AI to generate a plan that matches the theme and location of the event. The implementation unit is implemented by, for example, the control unit 46A of the robot 414 and projects images and plays music based on the generated plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

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

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) The reception desk where event information is entered, A generation unit analyzes the information input by the reception unit and generates a projection mapping plan, The system includes an implementation unit that performs projection mapping based on the plan generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is We generate a plan that considers the combination of video and music to match the event's theme and location. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate a plan that combines videos and music related to the theme of the fireworks display. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned implementation unit is Project images and play music based on the generated plan. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Enter information such as the date, time, location, and theme of your wedding. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate a plan that combines videos and music related to the wedding theme. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and customizes the event information input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is This guide provides information from past events to help improve the accuracy of user input. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering event information, the system automatically completes the input content by considering the user's past event participation history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering event information, the system prioritizes input of highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering event information, the system analyzes the user's social media activity and automatically fills in relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts how the generated plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a plan, adjust the level of detail in the plan based on the importance of the events. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a plan, different generation algorithms are applied depending on the event category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the plan generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a plan, prioritize the plan based on the timing of the event. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a plan, adjust the order of the plan based on the relevance of the events. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned implementation unit is The system estimates the user's emotions and adjusts the projection mapping implementation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned implementation unit is During implementation, the optimal implementation method will be selected by analyzing the user's past event participation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned implementation unit is During implementation, the projection mapping method will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned implementation unit is It estimates the user's emotions and determines the priority of projection mapping based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned implementation unit is During implementation, the optimal implementation method will be selected, taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned implementation unit is During implementation, we will analyze users' social media activity and propose projection mapping methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 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. The reception desk where event information is entered, A generation unit analyzes the information input by the reception unit and generates a projection mapping plan, The system includes an implementation unit that performs projection mapping based on the plan generated by the generation unit. A system characterized by the following features.

2. The generating unit is We generate a plan that considers the combination of video and music to match the event's theme and location. The system according to feature 1.

3. The generating unit is Generate a plan that combines videos and music related to the theme of the fireworks display. The system according to feature 1.

4. The aforementioned implementation unit is Project images and play music based on the generated plan. The system according to feature 1.

5. The aforementioned reception unit is Enter information such as the date, time, location, and theme of your wedding. The system according to feature 1.

6. The generating unit is Generate a plan that combines videos and music related to the wedding theme. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and customizes the event information input interface based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is This guide provides information from past events to help improve the accuracy of user input. The system according to feature 1.

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