system

Abstract

The system according to this embodiment aims to increase awareness of local specialty products and increase the number of tourists. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a distribution unit, an analysis unit, an optimization unit, and a support unit. The collection unit collects information on local specialties and tourist spots. The generation unit generates a promotional video based on the information collected by the collection unit. The distribution unit distributes the video generated by the generation unit. The analysis unit analyzes the behavior patterns of target customers. The optimization unit optimizes the campaign content and distribution timing based on the analysis results obtained by the analysis unit. The support unit assists the campaign optimized by the optimization 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional technologies have problems such as insufficient awareness of local specialties, a decrease in the number of tourists, and a delay in the digitalization of local enterprises.

[0005] The system according to the embodiment aims to improve the awareness of local specialties and increase the number of tourists.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, a distribution unit, an analysis unit, an optimization unit, and a support unit. The collection unit collects information on local specialties and tourist attractions. The generation unit generates a promotional video based on the information collected by the collection unit. The distribution unit distributes the video generated by the generation unit. The analysis unit analyzes the behavioral patterns of target customers. The optimization unit optimizes the campaign content and distribution timing based on the analysis results obtained by the analysis unit. The support unit assists the campaign optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve awareness of local specialty products and increase the number of tourists. [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 applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

[0016] [First Embodiment] 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 system according to an embodiment of the present invention is a system that utilizes generative AI to promote increased awareness of local specialty products, increased tourist numbers, and the digitalization of local businesses. This system automatically generates promotional videos using generative AI and automates tasks by combining them with digital tools. For example, the system's generative AI automatically creates videos introducing the attractions of local specialty products and tourist spots. These videos effectively convey the characteristics of the specialty products and the attractions of the tourist spots, attracting the interest of viewers. This is expected to increase awareness of the specialty products and boost sales. Next, the system automates tasks by combining them with digital tools. For example, it uses AI-powered market analysis to understand the behavior patterns of target customers and optimizes campaign content and distribution timing based on that information. It also uses AI analysis functions to support event planning and management. This allows for the suggestion of optimal dates, locations, programs, and advertisements for events. This is expected to increase the number of visitors to tourist destinations and revitalize the local economy. Furthermore, the system automatically creates and digitally distributes tourist guidebooks using AI. This automates tasks such as information gathering, translation, editing, and design, significantly reducing the time required to create guidebooks. Furthermore, the accuracy of information will improve, enabling the provision of high-quality information to tourists. This will increase tourist satisfaction and is expected to lead to an increase in repeat visitors. By combining these functions, it will be possible to increase sales and awareness of local products, increase the number of visitors to tourist destinations, and improve the digital literacy of local businesses, thereby revitalizing the entire region's economy. For example, local producers can use AI to create promotional videos and effectively appeal to the market with their local products, leading to increased sales. Also, by spreading awareness of the attractions of tourist destinations, the influx of tourists will increase, revitalizing the local economy. In addition, local businesses can improve operational efficiency and enhance their competitiveness by utilizing digital tools. In summary, the system can promote increased awareness of local products, an increase in the number of tourists, and the digitalization of local businesses.

[0029] The system according to this embodiment comprises a collection unit, a generation unit, a distribution unit, an analysis unit, an optimization unit, and a support unit. The collection unit collects information on local specialties and tourist attractions. For example, the collection unit can collect information on the types of local specialties and detailed information on tourist attractions. The collection unit can also use AI to collect information from the internet and local databases. The generation unit uses generation AI to generate a promotional video based on the information collected by the collection unit. For example, the generation unit can generate a video that introduces the appeal of local specialties and tourist attractions. The generation unit uses generation AI to automatically determine the content and format of the video and generate a video that will interest viewers. The distribution unit distributes the video generated by the generation unit. For example, the distribution unit can distribute the video through online video distribution platforms and social media. The distribution unit uses AI to determine the optimal distribution timing and destinations and distribute the video effectively. The analysis unit analyzes the behavior patterns of target customers. For example, the analysis unit can analyze data such as website browsing history and purchase history to understand the behavior patterns of target customers. The analysis unit can use AI to quickly and accurately analyze large amounts of data. The optimization unit optimizes the campaign content and delivery timing based on the analysis results obtained by the analysis unit. The optimization unit can determine the optimal campaign content and delivery timing based, for example, on the behavior patterns of target customers. The optimization unit uses AI to optimize campaigns to maximize their effectiveness. The support unit supports campaigns optimized by the optimization unit. The support unit can, for example, support event planning and management. The support unit can use AI to suggest the optimal date, location, program, and advertising for events. As a result, the system according to this embodiment can collect information on local specialties and tourist spots, generate, distribute, optimize, and support promotional videos, which is expected to increase sales and brand awareness of local specialties, increase the number of visitors to tourist destinations, and improve the digital literacy of local businesses.

[0030] The data collection department gathers information on local specialties and tourist attractions. For example, it can collect detailed information on the types of local specialties and tourist attractions. Specifically, for local specialties, it collects detailed information such as their types, manufacturing methods, history, characteristics, and sales locations. For tourist attractions, it collects information such as their location, access methods, points of interest, historical background, and event information. The data collection department can also use AI to collect information from the internet and local databases. The AI ​​uses natural language processing technology to extract and organize highly relevant information from the vast amount of information on the internet. It can also obtain official information and the latest data from local databases. This allows the data collection department to efficiently collect accurate and detailed information on local specialties and tourist attractions. Furthermore, the data collection department can also collect feedback from local residents and tourists. For example, it can collect evaluations and opinions on local specialties and tourist attractions through surveys and social media comments, and use this information as a reference for further improvements and promotions. This allows the data collection department to gain a deeper understanding of the appeal of local specialties and tourist attractions and support effective promotional activities.

[0031] The generation unit uses a generation AI to generate promotional videos based on information collected by the collection unit. For example, the generation unit can generate videos showcasing local specialties or tourist attractions. Based on the collected information, the generation AI automatically creates video scenarios and combines video, audio, and text to produce engaging videos. Specifically, the generation AI can generate videos showcasing the manufacturing process and characteristics of local specialties, or videos highlighting the beautiful scenery and historical background of tourist attractions. Furthermore, the generation AI can customize the video content and format according to the viewer's interests. For example, it can generate videos with upbeat music and colorful visuals for younger audiences, and videos with calming music and a focus on historical context for senior audiences. This allows the generation unit to produce effective promotional videos that capture the viewer's attention. In addition, the generation unit can evaluate the quality of the generated videos and make corrections or improvements as needed. For example, the generation AI analyzes video viewing data and feedback, optimizing the video content and format based on viewer reactions. This ensures that the generation unit consistently delivers high-quality promotional videos, effectively conveying the appeal of local specialties and tourist attractions.

[0032] The distribution department distributes videos generated by the production department. For example, the distribution department can distribute videos through online video streaming platforms and social media. Specifically, the distribution department uploads videos to video streaming platforms and makes them widely available to viewers. It can also utilize social media to share videos and reach target customers directly. The distribution department uses AI to determine the optimal distribution timing and destinations, ensuring effective video distribution. The AI ​​analyzes viewer behavior patterns and interests to identify the most effective distribution timing and destinations. For example, it can select the time of day when viewers are most online, or select effective distribution destinations for specific regions or age groups. This allows the distribution department to maximize video views and engagement, enhancing promotional effectiveness. Furthermore, the distribution department can collect video viewing data and feedback after distribution and provide it to the analytics and optimization departments. This allows the distribution department to continuously evaluate the effectiveness of video distribution and identify areas for improvement for future distributions. This enables the distribution department to consistently implement optimal distribution strategies and effectively support the promotion of local products and tourist attractions.

[0033] The analytics department analyzes the behavioral patterns of target customers. For example, it can analyze data such as website browsing history and purchase history to understand target customer behavior patterns. Specifically, the analytics department collects website access logs, click data, purchase history, and social media engagement data, and uses AI to quickly and accurately analyze this data. The AI ​​uses machine learning algorithms to identify target customers' interests and purchasing behavior patterns, and classify them into customer segments. For example, it can identify customers with a high interest in specific local products or customers who frequently visit specific tourist spots. This allows the analytics department to gain a detailed understanding of target customer behavior patterns and provide data to maximize the effectiveness of promotional activities. Furthermore, the analytics department can also predict future customer behavior based on past data and trends. For example, it can predict seasonal sales trends for local products or fluctuations in visitor numbers to tourist spots, providing information to optimize the timing and content of promotional activities. This allows the analytics department to gain a deep understanding of target customer behavior patterns and support strategies to maximize the effectiveness of promotional activities.

[0034] The Optimization Department optimizes campaign content and delivery timing based on analysis results obtained by the Analysis Department. For example, the Optimization Department can determine the optimal campaign content and delivery timing based on the behavioral patterns of target customers. Specifically, the Optimization Department uses AI to analyze the interests and behavioral patterns of target customers and design the most effective campaign content. For example, for customers with a high interest in a particular local product, a campaign centered on that product can be implemented, and for customers who frequently visit a particular tourist spot, a campaign introducing that tourist spot can be implemented. In addition, the Optimization Department can determine the time of day when target customers are most likely to be online, or delivery timing tailored to specific events or seasons, in order to optimize delivery timing. This allows the Optimization Department to maximize the effectiveness of campaigns and implement effective promotions to target customers. Furthermore, the Optimization Department can evaluate the effectiveness of campaigns after their implementation and identify areas for improvement for the next campaign. For example, it can analyze campaign viewing data and engagement data to identify which elements were most effective and reflect them in the next campaign. This allows the Optimization Department to consistently execute the optimal campaign strategy and effectively support the promotion of local products and tourist spots.

[0035] The Support Department assists with campaigns optimized by the Optimization Department. For example, the Support Department can support event planning and management. Specifically, it can use AI to suggest optimal dates, locations, programs, and advertising for events. The AI ​​analyzes past event data and target customer behavior patterns to propose the most effective event plan. For example, when holding an event about a specific local product, it suggests dates aligned with the product's harvest season and peak sales period, and selects a location that will attract the most participants. Regarding the event program, it can suggest the most effective content based on the target customer's interests. For example, it can suggest programs that allow participants to enjoyably experience the charm of local products and tourist spots, such as product tastings, manufacturing experiences, and guided tours of tourist attractions. Furthermore, the Support Department can also support event advertising and promotional activities. For example, it can utilize social media and internet advertising to widely publicize the event and effectively reach target customers. This allows the Support Department to effectively implement campaigns optimized by the Optimization Department and support the promotion of local products and tourist attractions. Furthermore, the support department can evaluate the effectiveness of the event after it has been held and identify areas for improvement for future events. This allows the support department to consistently plan and manage events optimally and effectively support the promotion of local specialties and tourist attractions.

[0036] The generation unit can automatically create tourist guidebooks. For example, the generation unit collects information on local tourist spots and special products and uses that information to create a tourist guidebook. Using generation AI, the generation unit automatically determines the content and design of the tourist guidebook, creating a high-quality guidebook. The generation unit automates tasks such as information gathering, translation, editing, and design necessary for creating a tourist guidebook, significantly reducing the time required to create the guidebook. By automating the creation of tourist guidebooks, tasks such as information gathering, translation, editing, and design are automated, significantly reducing the time required to create the guidebook.

[0037] The generation unit can digitally distribute tourist guidebooks. For example, the generation unit distributes the created tourist guidebooks in digital format. Using generation AI, the generation unit automatically determines the digital distribution method and destination of the tourist guidebooks, providing high-quality information to tourists. By digitally distributing tourist guidebooks, the generation unit can improve the accuracy of the information and provide high-quality information to tourists.

[0038] The support department can assist with event planning and management. For example, it can propose optimal dates, locations, programs, and advertising for events. The support department uses AI to support event planning and management, ensuring effective events. The support department can automate tasks such as information gathering, analysis, and proposals necessary for event planning and management, thereby supporting event success. This allows it to propose optimal dates, locations, programs, and advertising for events by assisting with event planning and management.

[0039] The data collection unit can select the optimal data collection method by referring to past data collection when gathering information on local specialties and tourist attractions. For example, the data collection unit can prioritize collecting information on popular specialties and tourist attractions in specific seasons based on past data collection. The data collection unit can analyze past data collection to select the most effective information collection method (interviews, questionnaires, online surveys, etc.). Based on past data collection, the data collection unit can select the optimal information collection method for a specific target group. This allows for efficient information collection by selecting the optimal data collection method by referring to past data collection. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can adjust the types of information it collects according to the season and events. For example, it can collect information on seasonal specialties and tourist spots and create promotional videos appropriate for each season. It can also collect information on local events (festivals, etc.) and create promotional videos tailored to those events. Depending on the season and events, it can collect information aimed at specific target groups and create customized promotional videos. By adjusting the types of information collected according to the season and events, more effective promotional videos can be created. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input seasonal and event information into a generating AI and have the generating AI adjust the types of information to be collected.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of information on local specialties and tourist spots in the vicinity based on the user's current location. The data collection unit can prioritize the collection of information on local specialties and tourist spots in a region based on the region the user plans to visit. The data collection unit can prioritize the collection of information on a highly relevant region based on the user's past travel history. This enables more effective data collection by prioritizing the collection of highly relevant information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant information.

[0042] The data collection unit can analyze social media activity and collect relevant information during data collection. For example, the data collection unit can collect information on local products and tourist spots that are trending on social media. The data collection unit can analyze users' social media activity and collect information on local products and tourist spots that they are interested in. The data collection unit can analyze word-of-mouth and reviews on social media and collect information on highly-rated local products and tourist spots. This enables more effective data collection by analyzing social media activity and collecting relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant information.

[0043] The generation unit can adjust the level of detail in a promotional video based on the importance of local products and tourist attractions. For example, the generation unit can generate a promotional video with detailed explanations and footage for highly important local products and tourist attractions. For less important local products and tourist attractions, the generation unit can generate a promotional video with concise explanations and footage. The generation unit can adjust the length and content of the video according to the importance of the local products and tourist attractions. This allows for the generation of more effective promotional videos by adjusting the level of detail based on the importance of local products and tourist attractions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input importance data for local products and tourist attractions into a generation AI and have the generation AI perform the adjustment of the level of detail in the video.

[0044] The generation unit can apply different generation algorithms depending on the category of local products or tourist attractions when generating promotional videos. For example, for local products in the food category, the generation unit can generate promotional videos using images and music that emphasize deliciousness. For tourist attractions in the natural landscape category, the generation unit can generate promotional videos using images and music that emphasize beautiful scenery. For tourist attractions in the cultural heritage category, the generation unit can generate promotional videos using images and music that emphasize the historical background. By applying different generation algorithms depending on the category of local products or tourist attractions, more effective promotional videos can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input category data of local products or tourist attractions into a generation AI and have the generation AI execute the application of the generation algorithm.

[0045] The generation unit can adjust the order of videos based on the relevance of local products and tourist spots when generating a promotional video. For example, the generation unit can generate a promotional video that introduces highly relevant local products and tourist spots in sequence. The generation unit can also generate a promotional video that introduces less relevant local products and tourist spots separately. The generation unit can optimize the order of videos based on the relevance of local products and tourist spots. This allows for the generation of more effective promotional videos by adjusting the order of videos based on the relevance of local products and tourist spots. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input relevance data of local products and tourist spots into a generation AI and have the generation AI perform the adjustment of the video order.

[0046] The generation unit can generate promotional videos incorporating the historical background of local products and tourist attractions. For example, the generation unit can generate a promotional video that includes footage and narration introducing the historical background of local products. The generation unit can generate a promotional video that includes footage and narration introducing the historical background of tourist attractions. The generation unit can adjust the content of the video based on the historical background of local products and tourist attractions. This allows for the generation of more effective promotional videos by incorporating the historical background of local products and tourist attractions. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input historical background data of local products and tourist attractions into a generation AI and have the generation AI perform video generation.

[0047] The distribution unit can select the optimal distribution method by referring to past distribution data when distributing video. For example, the distribution unit can select the most effective distribution method for a specific time period from past distribution data. The distribution unit can analyze past distribution data and select the most effective distribution method (email, social media, website, etc.). Based on past distribution data, the distribution unit can select the optimal distribution method for a specific target audience. This makes it possible to distribute video more effectively by selecting the optimal distribution method by referring to past distribution data. Some or all of the above processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input past distribution data into a generating AI and have the generating AI select the optimal distribution method.

[0048] The distribution unit can customize the content of videos based on the attribute information of the target customers. For example, the distribution unit can customize the video content according to the age group of the target customers. The distribution unit can customize the video content according to the interests of the target customers. The distribution unit can customize the video content based on the past purchase history of the target customers. This makes it possible to deliver more effective videos by customizing the content based on the attribute information of the target customers. Some or all of the above processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the attribute information of the target customers into a generating AI and have the generating AI perform the customization of the content.

[0049] The distribution unit can prioritize video distribution to highly relevant customers by considering geographical location information. For example, the distribution unit can prioritize videos of local specialties and tourist attractions based on the user's current location. The distribution unit can prioritize videos of local specialties and tourist attractions based on the region the user plans to visit. The distribution unit can prioritize videos of highly relevant regions based on the user's past travel history. This enables more effective video distribution by prioritizing distribution to highly relevant customers by considering geographical location information. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input geographical location information into a generating AI and have the generating AI perform priority distribution to highly relevant customers.

[0050] The distribution unit can analyze social media activity during video distribution and deliver videos to relevant customers. For example, the distribution unit can prioritize the distribution of videos of local products or tourist attractions that are trending on social media. The distribution unit can analyze users' social media activity and prioritize the distribution of videos of local products or tourist attractions that they are interested in. The distribution unit can analyze word-of-mouth and reviews on social media and prioritize the distribution of videos of highly-rated local products or tourist attractions. This enables more effective video distribution by analyzing social media activity and delivering videos to relevant customers. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input social media activity data into a generating AI and have the generating AI perform distribution to relevant customers.

[0051] The analysis unit can select the optimal analysis method by referring to past analysis data when analyzing behavioral patterns. For example, the analysis unit can select the most effective analysis method for a specific behavioral pattern from past analysis data. The analysis unit can analyze past analysis data and select the most effective analysis method (questionnaire, interview, observation, etc.). Based on past analysis data, the analysis unit can select the optimal analysis method for a specific target group. This makes it possible to perform more effective analysis by selecting the optimal analysis method by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the selection of the optimal analysis method.

[0052] The analysis unit can customize the analysis content based on the target customer's attribute information when analyzing behavioral patterns. For example, the analysis unit can customize the behavioral pattern analysis content according to the target customer's age group. The analysis unit can customize the behavioral pattern analysis content according to the target customer's interests. The analysis unit can customize the behavioral pattern analysis content based on the target customer's past purchase history. This makes it possible to perform more effective analysis by customizing the analysis content based on the target customer's attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the target customer's attribute information into a generating AI and have the generating AI perform the customization of the analysis content.

[0053] The analysis unit can perform behavioral pattern analysis while taking geographical location information into consideration. For example, the analysis unit can prioritize analyzing behavioral patterns in the vicinity based on the user's current location. The analysis unit can prioritize analyzing behavioral patterns in a region based on the region the user plans to visit. The analysis unit can prioritize analyzing behavioral patterns in highly relevant regions based on the user's past travel history. By considering geographical location information during analysis, more effective analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical location information into a generating AI and have the generating AI perform the analysis.

[0054] The analysis unit can improve the accuracy of its analysis by referring to relevant literature when analyzing behavioral patterns. For example, the analysis unit can refer to relevant literature and perform behavioral pattern analysis based on the latest research results. The analysis unit can refer to relevant literature and perform behavioral pattern analysis based on past research results. The analysis unit can refer to relevant literature and perform behavioral pattern analysis for a specific target group. By improving the accuracy of the analysis by referring to relevant literature, more effective analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0055] The optimization unit can select the optimal optimization method by referring to past campaign data when optimizing a campaign. For example, the optimization unit can select the most effective optimization method for a specific target group from past campaign data. The optimization unit can analyze past campaign data and select the most effective optimization method (email, social media, website, etc.). Based on past campaign data, the optimization unit can select the most effective optimization method for a specific time period. This makes it possible to conduct more effective campaigns by selecting the optimal optimization method by referring to past campaign data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past campaign data into a generating AI and have the generating AI select the optimal optimization method.

[0056] The optimization unit can customize the optimization content based on the target customer's attribute information when optimizing a campaign. For example, the optimization unit can customize the campaign content according to the target customer's age group. The optimization unit can customize the campaign content according to the target customer's interests. The optimization unit can customize the campaign content based on the target customer's past purchase history. This makes it possible to run more effective campaigns by customizing the optimization content based on the target customer's attribute information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the target customer's attribute information into a generating AI and have the generating AI perform the customization of the optimization content.

[0057] The optimization unit can perform campaign optimization while taking geographical location information into consideration. For example, the optimization unit can prioritize optimizing campaigns for local products and tourist attractions near the user's current location. The optimization unit can prioritize optimizing campaigns for local products and tourist attractions in areas the user plans to visit. The optimization unit can prioritize optimizing campaigns for highly relevant areas based on the user's past travel history. By optimizing while taking geographical location information into consideration, more effective campaigns become possible. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input geographical location information into a generating AI and have the generating AI perform the optimization.

[0058] The optimization unit can improve the accuracy of campaign optimization by referring to relevant literature. For example, the optimization unit can optimize campaigns based on the latest research findings by referring to relevant literature. The optimization unit can optimize campaigns based on past research findings by referring to relevant literature. The optimization unit can optimize campaigns for specific target groups by referring to relevant literature. By improving the accuracy of optimization by referring to relevant literature, more effective campaigns become possible. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input data from relevant literature into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0059] The support department can select the optimal support method by referring to past support data when providing campaign support. For example, the support department can select the most effective support method for a specific target group from past support data. The support department can analyze past support data and select the most effective support means (email, social media, website, etc.). Based on past support data, the support department can select the most effective support method for a specific time period. This makes it possible to provide more effective support by selecting the optimal support method by referring to past support data. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input past support data into a generating AI and have the generating AI select the optimal support method.

[0060] The support department can customize the support provided during campaign support based on the target customer's attribute information. For example, the support department can customize the support based on the target customer's age group. The support department can customize the support based on the target customer's interests. The support department can customize the support based on the target customer's past purchase history. This allows for more effective support by customizing the support based on the target customer's attribute information. Some or all of the above processes in the support department may be performed using AI, for example, or without AI. For example, the support department can input the target customer's attribute information into a generating AI and have the generating AI perform the customization of the support.

[0061] The support unit can select the optimal support method when providing campaign support, taking geographical location information into consideration. For example, the support unit can prioritize providing campaign support for nearby local products and tourist attractions based on the user's current location. The support unit can prioritize providing campaign support for local products and tourist attractions in areas the user plans to visit. The support unit can prioritize providing campaign support for highly relevant areas based on the user's past travel history. By selecting the optimal support method while considering geographical location information, more effective support becomes possible. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input geographical location information into a generating AI and have the generating AI select the optimal support method.

[0062] The support department can improve the accuracy of its support by referring to relevant literature during campaign support. For example, the support department can refer to relevant literature and provide campaign support based on the latest research findings. The support department can refer to relevant literature and provide campaign support based on past research findings. The support department can refer to relevant literature and provide campaign support for specific target groups. By improving the accuracy of support by referring to relevant literature, more effective support becomes possible. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input data from relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of support.

[0063] The support department can collect user feedback and improve its support methods during campaign support. For example, the support department can collect user feedback and identify areas for improvement in support methods. The support department can customize support methods based on user feedback. The support department can analyze user feedback and evaluate the effectiveness of support methods. This allows for more effective support by collecting user feedback and improving support methods. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input user feedback data into a generating AI and have the generating AI implement improvements to support methods.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The data collection unit can update information in real time when gathering information on local specialties and tourist attractions. For example, it collects the latest information in real time, such as the harvest season for local specialties and event information for tourist attractions, and reflects it in the system. The data collection unit can also receive feedback from local producers and tourist facilities and update the information as needed. This allows the data collection unit to always provide users with accurate and timely information by offering the most up-to-date data.

[0066] The generation unit can create customized tourist guidebooks based on the user's interests when automatically generating them. For example, if a user is interested in a specific theme (history, nature, food culture, etc.), the generation unit will create a guidebook focused on that theme. The generation unit can provide individually customized guidebooks based on the user's past browsing history and survey results. This allows the generation unit to create more satisfying guidebooks by providing information tailored to the user's interests.

[0067] The generation unit can deliver tourist guidebooks digitally in a format optimized for the user's device. For example, the generation unit automatically adjusts the layout and format of the guidebook according to the device the user is using, such as a smartphone, tablet, or PC. The generation unit can provide the guidebook in the optimal display format to match the screen size and resolution of the user's device. This ensures that users can comfortably view the guidebook regardless of the device they are using.

[0068] The support department can strengthen its collaboration with local communities when assisting with event planning and management. For example, the support department can work with local residents and organizations to gather opinions and ideas regarding event planning and management. Through collaboration with local communities, the support department can plan events that are tailored to the characteristics and needs of the region. In this way, the support department can realize more community-based events by strengthening its cooperation with local communities.

[0069] The data collection department can select the optimal data collection method by referring to past data when gathering information on local specialties and tourist attractions. For example, the department can prioritize collecting information on popular specialties and tourist attractions during specific seasons based on past data. The department can analyze past data to select the most effective information gathering method (interviews, questionnaires, online surveys, etc.). Based on past data, the department can select the optimal information gathering method for a specific target group. This allows for efficient information gathering by selecting the optimal method by referring to past data.

[0070] The information gathering unit can adjust the types of information it collects according to the season and events. For example, it can collect information on seasonal local products and tourist spots and create promotional videos tailored to the season. It can also collect information on local events (festivals, etc.) and create promotional videos that match the events. Furthermore, it can collect information targeted at specific demographics according to the season and events and create customized promotional videos. By adjusting the types of information collected according to the season and events, it is possible to create more effective promotional videos.

[0071] The data collection unit can prioritize the collection of highly relevant information by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of information on local specialties and tourist spots in the vicinity based on the user's current location. The data collection unit can prioritize the collection of information on local specialties and tourist spots in areas the user plans to visit. The data collection unit can prioritize the collection of information on highly relevant areas based on the user's past travel history. This enables more effective data collection by prioritizing the collection of highly relevant information while considering geographical location information.

[0072] The data collection unit can analyze social media activity and collect relevant information during the information gathering process. For example, the data collection unit can collect information on local products and tourist spots that are trending on social media. The data collection unit can analyze users' social media activity and collect information on local products and tourist spots that they are interested in. The data collection unit can analyze word-of-mouth and reviews on social media and collect information on highly-rated local products and tourist spots. This allows for more effective information gathering by analyzing social media activity and collecting relevant information.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The data collection unit gathers information on local specialties and tourist attractions. For example, the data collection unit can collect information on the types of local specialties and detailed information on tourist attractions. The data collection unit can also use AI to collect information from the internet and local databases. Step 2: The generation unit uses generation AI to generate a promotional video based on the information collected by the collection unit. For example, the generation unit can generate a video showcasing local specialties or tourist attractions. Using generation AI, the generation unit automatically determines the content and format of the video and generates a video that will capture the viewer's interest. Step 3: The distribution unit distributes the video generated by the generation unit. The distribution unit can distribute the video, for example, through online video distribution platforms or social media. The distribution unit uses AI to determine the optimal distribution timing and destinations, and distributes the video effectively. Step 4: The analytics department analyzes the behavioral patterns of target customers. The analytics department can understand the behavioral patterns of target customers by analyzing data such as website browsing history and purchase history. The analytics department can use AI to quickly and accurately analyze large amounts of data. Step 5: The optimization unit optimizes the campaign content and delivery timing based on the analysis results obtained by the analysis unit. For example, the optimization unit can determine the optimal campaign content and delivery timing based on the behavior patterns of target customers. The optimization unit uses AI to optimize the campaign to maximize its effectiveness. Step 6: The support department assists with campaigns optimized by the optimization department. For example, the support department can assist with event planning and management. The support department can use AI to suggest the optimal dates, locations, programs, and advertising for events.

[0075] (Example of form 2) The system according to an embodiment of the present invention is a system that utilizes generative AI to promote increased awareness of local specialty products, increased tourist numbers, and the digitalization of local businesses. This system automatically generates promotional videos using generative AI and automates tasks by combining them with digital tools. For example, the system's generative AI automatically creates videos introducing the attractions of local specialty products and tourist spots. These videos effectively convey the characteristics of the specialty products and the attractions of the tourist spots, attracting the interest of viewers. This is expected to increase awareness of the specialty products and boost sales. Next, the system automates tasks by combining them with digital tools. For example, it uses AI-powered market analysis to understand the behavior patterns of target customers and optimizes campaign content and distribution timing based on that information. It also uses AI analysis functions to support event planning and management. This allows for the suggestion of optimal dates, locations, programs, and advertisements for events. This is expected to increase the number of visitors to tourist destinations and revitalize the local economy. Furthermore, the system automatically creates and digitally distributes tourist guidebooks using AI. This automates tasks such as information gathering, translation, editing, and design, significantly reducing the time required to create guidebooks. Furthermore, the accuracy of information will improve, enabling the provision of high-quality information to tourists. This will increase tourist satisfaction and is expected to lead to an increase in repeat visitors. By combining these functions, it will be possible to increase sales and awareness of local products, increase the number of visitors to tourist destinations, and improve the digital literacy of local businesses, thereby revitalizing the entire region's economy. For example, local producers can use AI to create promotional videos and effectively appeal to the market with their local products, leading to increased sales. Also, by spreading awareness of the attractions of tourist destinations, the influx of tourists will increase, revitalizing the local economy. In addition, local businesses can improve operational efficiency and enhance their competitiveness by utilizing digital tools. In summary, the system can promote increased awareness of local products, an increase in the number of tourists, and the digitalization of local businesses.

[0076] The system according to this embodiment comprises a collection unit, a generation unit, a distribution unit, an analysis unit, an optimization unit, and a support unit. The collection unit collects information on local specialties and tourist attractions. For example, the collection unit can collect information on the types of local specialties and detailed information on tourist attractions. The collection unit can also use AI to collect information from the internet and local databases. The generation unit uses generation AI to generate a promotional video based on the information collected by the collection unit. For example, the generation unit can generate a video that introduces the appeal of local specialties and tourist attractions. The generation unit uses generation AI to automatically determine the content and format of the video and generate a video that will interest viewers. The distribution unit distributes the video generated by the generation unit. For example, the distribution unit can distribute the video through online video distribution platforms and social media. The distribution unit uses AI to determine the optimal distribution timing and destinations and distribute the video effectively. The analysis unit analyzes the behavior patterns of target customers. For example, the analysis unit can analyze data such as website browsing history and purchase history to understand the behavior patterns of target customers. The analysis unit can use AI to quickly and accurately analyze large amounts of data. The optimization unit optimizes the campaign content and delivery timing based on the analysis results obtained by the analysis unit. The optimization unit can determine the optimal campaign content and delivery timing based, for example, on the behavior patterns of target customers. The optimization unit uses AI to optimize campaigns to maximize their effectiveness. The support unit supports campaigns optimized by the optimization unit. The support unit can, for example, support event planning and management. The support unit can use AI to suggest the optimal date, location, program, and advertising for events. As a result, the system according to this embodiment can collect information on local specialties and tourist spots, generate, distribute, optimize, and support promotional videos, which is expected to increase sales and brand awareness of local specialties, increase the number of visitors to tourist destinations, and improve the digital literacy of local businesses.

[0077] The data collection department gathers information on local specialties and tourist attractions. For example, it can collect detailed information on the types of local specialties and tourist attractions. Specifically, for local specialties, it collects detailed information such as their types, manufacturing methods, history, characteristics, and sales locations. For tourist attractions, it collects information such as their location, access methods, points of interest, historical background, and event information. The data collection department can also use AI to collect information from the internet and local databases. The AI ​​uses natural language processing technology to extract and organize highly relevant information from the vast amount of information on the internet. It can also obtain official information and the latest data from local databases. This allows the data collection department to efficiently collect accurate and detailed information on local specialties and tourist attractions. Furthermore, the data collection department can also collect feedback from local residents and tourists. For example, it can collect evaluations and opinions on local specialties and tourist attractions through surveys and social media comments, and use this information as a reference for further improvements and promotions. This allows the data collection department to gain a deeper understanding of the appeal of local specialties and tourist attractions and support effective promotional activities.

[0078] The generation unit uses a generation AI to generate promotional videos based on information collected by the collection unit. For example, the generation unit can generate videos showcasing local specialties or tourist attractions. Based on the collected information, the generation AI automatically creates video scenarios and combines video, audio, and text to produce engaging videos. Specifically, the generation AI can generate videos showcasing the manufacturing process and characteristics of local specialties, or videos highlighting the beautiful scenery and historical background of tourist attractions. Furthermore, the generation AI can customize the video content and format according to the viewer's interests. For example, it can generate videos with upbeat music and colorful visuals for younger audiences, and videos with calming music and a focus on historical context for senior audiences. This allows the generation unit to produce effective promotional videos that capture the viewer's attention. In addition, the generation unit can evaluate the quality of the generated videos and make corrections or improvements as needed. For example, the generation AI analyzes video viewing data and feedback, optimizing the video content and format based on viewer reactions. This ensures that the generation unit consistently delivers high-quality promotional videos, effectively conveying the appeal of local specialties and tourist attractions.

[0079] The distribution department distributes videos generated by the production department. For example, the distribution department can distribute videos through online video streaming platforms and social media. Specifically, the distribution department uploads videos to video streaming platforms and makes them widely available to viewers. It can also utilize social media to share videos and reach target customers directly. The distribution department uses AI to determine the optimal distribution timing and destinations, ensuring effective video distribution. The AI ​​analyzes viewer behavior patterns and interests to identify the most effective distribution timing and destinations. For example, it can select the time of day when viewers are most online, or select effective distribution destinations for specific regions or age groups. This allows the distribution department to maximize video views and engagement, enhancing promotional effectiveness. Furthermore, the distribution department can collect video viewing data and feedback after distribution and provide it to the analytics and optimization departments. This allows the distribution department to continuously evaluate the effectiveness of video distribution and identify areas for improvement for future distributions. This enables the distribution department to consistently implement optimal distribution strategies and effectively support the promotion of local products and tourist attractions.

[0080] The analytics department analyzes the behavioral patterns of target customers. For example, it can analyze data such as website browsing history and purchase history to understand target customer behavior patterns. Specifically, the analytics department collects website access logs, click data, purchase history, and social media engagement data, and uses AI to quickly and accurately analyze this data. The AI ​​uses machine learning algorithms to identify target customers' interests and purchasing behavior patterns, and classify them into customer segments. For example, it can identify customers with a high interest in specific local products or customers who frequently visit specific tourist spots. This allows the analytics department to gain a detailed understanding of target customer behavior patterns and provide data to maximize the effectiveness of promotional activities. Furthermore, the analytics department can also predict future customer behavior based on past data and trends. For example, it can predict seasonal sales trends for local products or fluctuations in visitor numbers to tourist spots, providing information to optimize the timing and content of promotional activities. This allows the analytics department to gain a deep understanding of target customer behavior patterns and support strategies to maximize the effectiveness of promotional activities.

[0081] The Optimization Department optimizes campaign content and delivery timing based on analysis results obtained by the Analysis Department. For example, the Optimization Department can determine the optimal campaign content and delivery timing based on the behavioral patterns of target customers. Specifically, the Optimization Department uses AI to analyze the interests and behavioral patterns of target customers and design the most effective campaign content. For example, for customers with a high interest in a particular local product, a campaign centered on that product can be implemented, and for customers who frequently visit a particular tourist spot, a campaign introducing that tourist spot can be implemented. In addition, the Optimization Department can determine the time of day when target customers are most likely to be online, or delivery timing tailored to specific events or seasons, in order to optimize delivery timing. This allows the Optimization Department to maximize the effectiveness of campaigns and implement effective promotions to target customers. Furthermore, the Optimization Department can evaluate the effectiveness of campaigns after their implementation and identify areas for improvement for the next campaign. For example, it can analyze campaign viewing data and engagement data to identify which elements were most effective and reflect them in the next campaign. This allows the Optimization Department to consistently execute the optimal campaign strategy and effectively support the promotion of local products and tourist spots.

[0082] The Support Department assists with campaigns optimized by the Optimization Department. For example, the Support Department can support event planning and management. Specifically, it can use AI to suggest optimal dates, locations, programs, and advertising for events. The AI ​​analyzes past event data and target customer behavior patterns to propose the most effective event plan. For example, when holding an event about a specific local product, it suggests dates aligned with the product's harvest season and peak sales period, and selects a location that will attract the most participants. Regarding the event program, it can suggest the most effective content based on the target customer's interests. For example, it can suggest programs that allow participants to enjoyably experience the charm of local products and tourist spots, such as product tastings, manufacturing experiences, and guided tours of tourist attractions. Furthermore, the Support Department can also support event advertising and promotional activities. For example, it can utilize social media and internet advertising to widely publicize the event and effectively reach target customers. This allows the Support Department to effectively implement campaigns optimized by the Optimization Department and support the promotion of local products and tourist attractions. Furthermore, the support department can evaluate the effectiveness of the event after it has been held and identify areas for improvement for future events. This allows the support department to consistently plan and manage events optimally and effectively support the promotion of local specialties and tourist attractions.

[0083] The generation unit can automatically create tourist guidebooks. For example, the generation unit collects information on local tourist spots and special products and uses that information to create a tourist guidebook. Using generation AI, the generation unit automatically determines the content and design of the tourist guidebook, creating a high-quality guidebook. The generation unit automates tasks such as information gathering, translation, editing, and design necessary for creating a tourist guidebook, significantly reducing the time required to create the guidebook. By automating the creation of tourist guidebooks, tasks such as information gathering, translation, editing, and design are automated, significantly reducing the time required to create the guidebook.

[0084] The generation unit can digitally distribute tourist guidebooks. For example, the generation unit distributes the created tourist guidebooks in digital format. Using generation AI, the generation unit automatically determines the digital distribution method and destination of the tourist guidebooks, providing high-quality information to tourists. By digitally distributing tourist guidebooks, the generation unit can improve the accuracy of the information and provide high-quality information to tourists.

[0085] The support department can assist with event planning and management. For example, it can propose optimal dates, locations, programs, and advertising for events. The support department uses AI to support event planning and management, ensuring effective events. The support department can automate tasks such as information gathering, analysis, and proposals necessary for event planning and management, thereby supporting event success. This allows it to propose optimal dates, locations, programs, and advertising for events by assisting with event planning and management.

[0086] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is excited, the data collection unit can immediately begin collecting information and provide the latest information in real time. If the user is relaxed, the data collection unit can reduce the frequency of information collection and provide only the necessary information. If the user is stressed, the data collection unit can temporarily stop collecting information and resume it after the user has calmed down. This allows information to be collected at a more appropriate time by adjusting the timing of information collection 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information collection.

[0087] The data collection unit can select the optimal data collection method by referring to past data collection when gathering information on local specialties and tourist attractions. For example, the data collection unit can prioritize collecting information on popular specialties and tourist attractions in specific seasons based on past data collection. The data collection unit can analyze past data collection to select the most effective information collection method (interviews, questionnaires, online surveys, etc.). Based on past data collection, the data collection unit can select the optimal information collection method for a specific target group. This allows for efficient information collection by selecting the optimal data collection method by referring to past data collection. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection into a generating AI and have the generating AI select the optimal data collection method.

[0088] The data collection unit can adjust the types of information it collects according to the season and events. For example, it can collect information on seasonal specialties and tourist spots and create promotional videos appropriate for each season. It can also collect information on local events (festivals, etc.) and create promotional videos tailored to those events. Depending on the season and events, it can collect information aimed at specific target groups and create customized promotional videos. By adjusting the types of information collected according to the season and events, more effective promotional videos can be created. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input seasonal and event information into a generating AI and have the generating AI adjust the types of information to be collected.

[0089] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting information on the latest local products and tourist attractions. If the user is relaxed, the data collection unit may prioritize collecting information on past popular attractions. If the user is stressed, the data collection unit may prioritize collecting information on relaxing tourist attractions and local products. This allows for the collection of more appropriate information by prioritizing the information to collect 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 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information to collect.

[0090] The data collection unit can prioritize the collection of highly relevant information by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of information on local specialties and tourist spots in the vicinity based on the user's current location. The data collection unit can prioritize the collection of information on local specialties and tourist spots in a region based on the region the user plans to visit. The data collection unit can prioritize the collection of information on a highly relevant region based on the user's past travel history. This enables more effective data collection by prioritizing the collection of highly relevant information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant information.

[0091] The data collection unit can analyze social media activity and collect relevant information during data collection. For example, the data collection unit can collect information on local products and tourist spots that are trending on social media. The data collection unit can analyze users' social media activity and collect information on local products and tourist spots that they are interested in. The data collection unit can analyze word-of-mouth and reviews on social media and collect information on highly-rated local products and tourist spots. This enables more effective data collection by analyzing social media activity and collecting relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant information.

[0092] The generation unit can estimate the user's emotions and adjust the presentation of the promotional video based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a promotional video using calm music and relaxed visuals. If the user is excited, the generation unit can generate a promotional video using upbeat music and dynamic visuals. If the user is stressed, the generation unit can generate a promotional video using relaxing music and calm visuals. This allows for the creation of more effective videos by adjusting the presentation of the promotional video according 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the presentation of the promotional video.

[0093] The generation unit can adjust the level of detail in a promotional video based on the importance of local products and tourist attractions. For example, the generation unit can generate a promotional video with detailed explanations and footage for highly important local products and tourist attractions. For less important local products and tourist attractions, the generation unit can generate a promotional video with concise explanations and footage. The generation unit can adjust the length and content of the video according to the importance of the local products and tourist attractions. This allows for the generation of more effective promotional videos by adjusting the level of detail based on the importance of local products and tourist attractions. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input importance data for local products and tourist attractions into a generation AI and have the generation AI perform the adjustment of the level of detail in the video.

[0094] The generation unit can apply different generation algorithms depending on the category of local products or tourist attractions when generating promotional videos. For example, for local products in the food category, the generation unit can generate promotional videos using images and music that emphasize deliciousness. For tourist attractions in the natural landscape category, the generation unit can generate promotional videos using images and music that emphasize beautiful scenery. For tourist attractions in the cultural heritage category, the generation unit can generate promotional videos using images and music that emphasize the historical background. By applying different generation algorithms depending on the category of local products or tourist attractions, more effective promotional videos can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input category data of local products or tourist attractions into a generation AI and have the generation AI execute the application of the generation algorithm.

[0095] The generation unit can estimate the user's emotions and adjust the length of the promotional video based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise promotional video. If the user is relaxed, the generation unit can generate a longer promotional video with detailed explanations. If the user is excited, the generation unit can generate a promotional video with visually stimulating effects. This allows for the creation of more effective videos by adjusting the length of the promotional video according 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the promotional video.

[0096] The generation unit can adjust the order of videos based on the relevance of local products and tourist spots when generating a promotional video. For example, the generation unit can generate a promotional video that introduces highly relevant local products and tourist spots in sequence. The generation unit can also generate a promotional video that introduces less relevant local products and tourist spots separately. The generation unit can optimize the order of videos based on the relevance of local products and tourist spots. This allows for the generation of more effective promotional videos by adjusting the order of videos based on the relevance of local products and tourist spots. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input relevance data of local products and tourist spots into a generation AI and have the generation AI perform the adjustment of the video order.

[0097] The generation unit can generate promotional videos incorporating the historical background of local products and tourist attractions. For example, the generation unit can generate a promotional video that includes footage and narration introducing the historical background of local products. The generation unit can generate a promotional video that includes footage and narration introducing the historical background of tourist attractions. The generation unit can adjust the content of the video based on the historical background of local products and tourist attractions. This allows for the generation of more effective promotional videos by incorporating the historical background of local products and tourist attractions. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input historical background data of local products and tourist attractions into a generation AI and have the generation AI perform video generation.

[0098] The distribution unit can estimate the user's emotions and adjust the timing of video distribution based on the estimated emotions. For example, if the user is excited, the distribution unit can immediately distribute video and provide the latest information in real time. If the user is relaxed, the distribution unit can reduce the frequency of video distribution and provide only the necessary information. If the user is stressed, the distribution unit can temporarily stop video distribution and resume it after the user has calmed down. This allows for more effective video distribution by adjusting the timing of video distribution 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 distribution unit may be performed using AI or not using AI. For example, the distribution unit can input user emotion data into a generative AI and have the generative AI adjust the timing of video distribution.

[0099] The distribution unit can select the optimal distribution method by referring to past distribution data when distributing video. For example, the distribution unit can select the most effective distribution method for a specific time period from past distribution data. The distribution unit can analyze past distribution data and select the most effective distribution method (email, social media, website, etc.). Based on past distribution data, the distribution unit can select the optimal distribution method for a specific target audience. This makes it possible to distribute video more effectively by selecting the optimal distribution method by referring to past distribution data. Some or all of the above processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input past distribution data into a generating AI and have the generating AI select the optimal distribution method.

[0100] The distribution unit can customize the content of videos based on the attribute information of the target customers. For example, the distribution unit can customize the video content according to the age group of the target customers. The distribution unit can customize the video content according to the interests of the target customers. The distribution unit can customize the video content based on the past purchase history of the target customers. This makes it possible to deliver more effective videos by customizing the content based on the attribute information of the target customers. Some or all of the above processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the attribute information of the target customers into a generating AI and have the generating AI perform the customization of the content.

[0101] The distribution unit can estimate the user's emotions and determine the priority of video distribution based on the estimated emotions. For example, if the user is excited, the distribution unit can prioritize the distribution of the latest videos. If the user is relaxed, the distribution unit can prioritize the distribution of past popular videos. If the user is stressed, the distribution unit can prioritize the distribution of relaxing videos. This allows for more effective video distribution by prioritizing video distribution 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 distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input user emotion data into a generative AI and have the generative AI determine the priority of video distribution.

[0102] The distribution unit can prioritize video distribution to highly relevant customers by considering geographical location information. For example, the distribution unit can prioritize videos of local specialties and tourist attractions based on the user's current location. The distribution unit can prioritize videos of local specialties and tourist attractions based on the region the user plans to visit. The distribution unit can prioritize videos of highly relevant regions based on the user's past travel history. This enables more effective video distribution by prioritizing distribution to highly relevant customers by considering geographical location information. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input geographical location information into a generating AI and have the generating AI perform priority distribution to highly relevant customers.

[0103] The distribution unit can analyze social media activity during video distribution and deliver videos to relevant customers. For example, the distribution unit can prioritize the distribution of videos of local products or tourist attractions that are trending on social media. The distribution unit can analyze users' social media activity and prioritize the distribution of videos of local products or tourist attractions that they are interested in. The distribution unit can analyze word-of-mouth and reviews on social media and prioritize the distribution of videos of highly-rated local products or tourist attractions. This enables more effective video distribution by analyzing social media activity and delivering videos to relevant customers. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input social media activity data into a generating AI and have the generating AI perform distribution to relevant customers.

[0104] The analysis unit can estimate the user's emotions and adjust the behavioral pattern analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed behavioral pattern analysis. If the user is excited, the analysis unit can perform a rapid behavioral pattern analysis. If the user is stressed, the analysis unit can temporarily stop the behavioral pattern analysis and resume it after the user has calmed down. This allows for more effective analysis by adjusting the behavioral pattern analysis method 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the behavioral pattern analysis method.

[0105] The analysis unit can select the optimal analysis method by referring to past analysis data when analyzing behavioral patterns. For example, the analysis unit can select the most effective analysis method for a specific behavioral pattern from past analysis data. The analysis unit can analyze past analysis data and select the most effective analysis method (questionnaire, interview, observation, etc.). Based on past analysis data, the analysis unit can select the optimal analysis method for a specific target group. This makes it possible to perform more effective analysis by selecting the optimal analysis method by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the selection of the optimal analysis method.

[0106] The analysis unit can customize the analysis content based on the target customer's attribute information when analyzing behavioral patterns. For example, the analysis unit can customize the behavioral pattern analysis content according to the target customer's age group. The analysis unit can customize the behavioral pattern analysis content according to the target customer's interests. The analysis unit can customize the behavioral pattern analysis content based on the target customer's past purchase history. This makes it possible to perform more effective analysis by customizing the analysis content based on the target customer's attribute information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the target customer's attribute information into a generating AI and have the generating AI perform the customization of the analysis content.

[0107] The analysis unit can estimate the user's emotions and adjust the order in which it displays the analysis results of behavioral patterns based on the estimated emotions. For example, if the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. If the user is excited, the analysis unit can prioritize displaying concise analysis results. If the user is stressed, the analysis unit can prioritize displaying brief analysis results. By adjusting the order in which the analysis results of behavioral patterns are displayed according to the user's emotions, it becomes possible to display analysis results more effectively. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the analysis results.

[0108] The analysis unit can perform behavioral pattern analysis while taking geographical location information into consideration. For example, the analysis unit can prioritize analyzing behavioral patterns in the vicinity based on the user's current location. The analysis unit can prioritize analyzing behavioral patterns in a region based on the region the user plans to visit. The analysis unit can prioritize analyzing behavioral patterns in highly relevant regions based on the user's past travel history. By considering geographical location information during analysis, more effective analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical location information into a generating AI and have the generating AI perform the analysis.

[0109] The analysis unit can improve the accuracy of its analysis by referring to relevant literature when analyzing behavioral patterns. For example, the analysis unit can refer to relevant literature and perform behavioral pattern analysis based on the latest research results. The analysis unit can refer to relevant literature and perform behavioral pattern analysis based on past research results. The analysis unit can refer to relevant literature and perform behavioral pattern analysis for a specific target group. By improving the accuracy of the analysis by referring to relevant literature, more effective analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0110] The optimization unit can estimate the user's emotions and adjust the campaign content and delivery timing based on the estimated emotions. For example, if the user is relaxed, the optimization unit can set detailed campaign content and a relaxed delivery timing. If the user is excited, the optimization unit can set concise campaign content and a rapid delivery timing. If the user is stressed, the optimization unit can set relaxing campaign content and delivery timing. By adjusting the campaign content and delivery timing according to the user's emotions, a more effective campaign becomes possible. 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 optimization unit may be performed using AI, for example, or not using AI. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI adjust the campaign content and delivery timing.

[0111] The optimization unit can select the optimal optimization method by referring to past campaign data when optimizing a campaign. For example, the optimization unit can select the most effective optimization method for a specific target group from past campaign data. The optimization unit can analyze past campaign data and select the most effective optimization method (email, social media, website, etc.). Based on past campaign data, the optimization unit can select the most effective optimization method for a specific time period. This makes it possible to conduct more effective campaigns by selecting the optimal optimization method by referring to past campaign data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input past campaign data into a generating AI and have the generating AI select the optimal optimization method.

[0112] The optimization unit can customize the optimization content based on the target customer's attribute information when optimizing a campaign. For example, the optimization unit can customize the campaign content according to the target customer's age group. The optimization unit can customize the campaign content according to the target customer's interests. The optimization unit can customize the campaign content based on the target customer's past purchase history. This makes it possible to run more effective campaigns by customizing the optimization content based on the target customer's attribute information. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the target customer's attribute information into a generating AI and have the generating AI perform the customization of the optimization content.

[0113] The optimization unit can estimate the user's emotions and determine campaign priorities based on those emotions. For example, if the user is excited, the optimization unit can prioritize delivering the latest campaigns. If the user is relaxed, the optimization unit can prioritize delivering popular past campaigns. If the user is stressed, the optimization unit can prioritize delivering relaxing campaigns. This allows for more effective campaigns by prioritizing campaigns 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 optimization unit may be performed using AI or not. For example, the optimization unit can input user emotion data into a generative AI and have the generative AI determine campaign priorities.

[0114] The optimization unit can perform campaign optimization while taking geographical location information into consideration. For example, the optimization unit can prioritize optimizing campaigns for local products and tourist attractions near the user's current location. The optimization unit can prioritize optimizing campaigns for local products and tourist attractions in areas the user plans to visit. The optimization unit can prioritize optimizing campaigns for highly relevant areas based on the user's past travel history. By optimizing while taking geographical location information into consideration, more effective campaigns become possible. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input geographical location information into a generating AI and have the generating AI perform the optimization.

[0115] The optimization unit can improve the accuracy of campaign optimization by referring to relevant literature. For example, the optimization unit can optimize campaigns based on the latest research findings by referring to relevant literature. The optimization unit can optimize campaigns based on past research findings by referring to relevant literature. The optimization unit can optimize campaigns for specific target groups by referring to relevant literature. By improving the accuracy of optimization by referring to relevant literature, more effective campaigns become possible. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input data from relevant literature into a generating AI and have the generating AI perform the optimization accuracy improvement.

[0116] The support unit can estimate the user's emotions and adjust the campaign support method based on the estimated user emotions. For example, if the user is relaxed, the support unit can provide detailed campaign support methods. If the user is excited, the support unit can provide rapid campaign support methods. If the user is stressed, the support unit can provide relaxing campaign support methods. This allows for more effective support by adjusting the campaign support method 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 support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the campaign support method.

[0117] The support department can select the optimal support method by referring to past support data when providing campaign support. For example, the support department can select the most effective support method for a specific target group from past support data. The support department can analyze past support data and select the most effective support means (email, social media, website, etc.). Based on past support data, the support department can select the most effective support method for a specific time period. This makes it possible to provide more effective support by selecting the optimal support method by referring to past support data. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input past support data into a generating AI and have the generating AI select the optimal support method.

[0118] The support department can customize the support provided during campaign support based on the target customer's attribute information. For example, the support department can customize the support based on the target customer's age group. The support department can customize the support based on the target customer's interests. The support department can customize the support based on the target customer's past purchase history. This allows for more effective support by customizing the support based on the target customer's attribute information. Some or all of the above processes in the support department may be performed using AI, for example, or without AI. For example, the support department can input the target customer's attribute information into a generating AI and have the generating AI perform the customization of the support.

[0119] The support unit can estimate the user's emotions and prioritize campaign support based on those emotions. For example, if the user is excited, the support unit can prioritize providing the latest campaign support. If the user is relaxed, the support unit can prioritize providing past popular campaign support. If the user is stressed, the support unit can prioritize providing relaxing campaign support. This allows for more effective support by prioritizing campaign support 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 support unit may be performed using AI or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI determine the priority of campaign support.

[0120] The support unit can select the optimal support method when providing campaign support, taking geographical location information into consideration. For example, the support unit can prioritize providing campaign support for nearby local products and tourist attractions based on the user's current location. The support unit can prioritize providing campaign support for local products and tourist attractions in areas the user plans to visit. The support unit can prioritize providing campaign support for highly relevant areas based on the user's past travel history. By selecting the optimal support method while considering geographical location information, more effective support becomes possible. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input geographical location information into a generating AI and have the generating AI select the optimal support method.

[0121] The support department can improve the accuracy of its support by referring to relevant literature during campaign support. For example, the support department can refer to relevant literature and provide campaign support based on the latest research findings. The support department can refer to relevant literature and provide campaign support based on past research findings. The support department can refer to relevant literature and provide campaign support for specific target groups. By improving the accuracy of support by referring to relevant literature, more effective support becomes possible. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input data from relevant literature into a generating AI and have the generating AI perform the task of improving the accuracy of support.

[0122] The support department can collect user feedback and improve its support methods during campaign support. For example, the support department can collect user feedback and identify areas for improvement in support methods. The support department can customize support methods based on user feedback. The support department can analyze user feedback and evaluate the effectiveness of support methods. This allows for more effective support by collecting user feedback and improving support methods. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input user feedback data into a generating AI and have the generating AI implement improvements to support methods.

[0123] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0124] The data collection unit can update information in real time when gathering information on local specialties and tourist attractions. For example, it collects the latest information in real time, such as the harvest season for local specialties and event information for tourist attractions, and reflects it in the system. The data collection unit can also receive feedback from local producers and tourist facilities and update the information as needed. This allows the data collection unit to always provide users with accurate and timely information by offering the most up-to-date data.

[0125] The generation unit can create customized tourist guidebooks based on the user's interests when automatically generating them. For example, if a user is interested in a specific theme (history, nature, food culture, etc.), the generation unit will create a guidebook focused on that theme. The generation unit can provide individually customized guidebooks based on the user's past browsing history and survey results. This allows the generation unit to create more satisfying guidebooks by providing information tailored to the user's interests.

[0126] The generation unit can deliver tourist guidebooks digitally in a format optimized for the user's device. For example, the generation unit automatically adjusts the layout and format of the guidebook according to the device the user is using, such as a smartphone, tablet, or PC. The generation unit can provide the guidebook in the optimal display format to match the screen size and resolution of the user's device. This ensures that users can comfortably view the guidebook regardless of the device they are using.

[0127] The support department can strengthen its collaboration with local communities when assisting with event planning and management. For example, the support department can work with local residents and organizations to gather opinions and ideas regarding event planning and management. Through collaboration with local communities, the support department can plan events that are tailored to the characteristics and needs of the region. In this way, the support department can realize more community-based events by strengthening its cooperation with local communities.

[0128] The data collection unit can estimate the user's emotions and customize the content of information collection based on those emotions. For example, if the user is excited, the data collection unit will prioritize collecting highly entertaining information. If the user is relaxed, the data collection unit can prioritize collecting information about relaxing tourist spots and local products. If the user is stressed, the data collection unit can prioritize collecting information that helps relieve stress. In this way, the data collection unit can provide more appropriate information by customizing the content of information collection according to the user's emotions.

[0129] The data collection department can select the optimal data collection method by referring to past data when gathering information on local specialties and tourist attractions. For example, the department can prioritize collecting information on popular specialties and tourist attractions during specific seasons based on past data. The department can analyze past data to select the most effective information gathering method (interviews, questionnaires, online surveys, etc.). Based on past data, the department can select the optimal information gathering method for a specific target group. This allows for efficient information gathering by selecting the optimal method by referring to past data.

[0130] The information gathering unit can adjust the types of information it collects according to the season and events. For example, it can collect information on seasonal local products and tourist spots and create promotional videos tailored to the season. It can also collect information on local events (festivals, etc.) and create promotional videos that match the events. Furthermore, it can collect information targeted at specific demographics according to the season and events and create customized promotional videos. By adjusting the types of information collected according to the season and events, it is possible to create more effective promotional videos.

[0131] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on those emotions. For example, if the user is excited, the unit will prioritize collecting information on the latest local products and tourist attractions. If the user is relaxed, the unit can prioritize collecting information on past popular attractions. If the user is stressed, the unit can prioritize collecting information on relaxing tourist attractions and local products. By prioritizing the information to collect according to the user's emotions, more relevant information can be collected.

[0132] The data collection unit can prioritize the collection of highly relevant information by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of information on local specialties and tourist spots in the vicinity based on the user's current location. The data collection unit can prioritize the collection of information on local specialties and tourist spots in areas the user plans to visit. The data collection unit can prioritize the collection of information on highly relevant areas based on the user's past travel history. This enables more effective data collection by prioritizing the collection of highly relevant information while considering geographical location information.

[0133] The data collection unit can analyze social media activity and collect relevant information during the information gathering process. For example, the data collection unit can collect information on local products and tourist spots that are trending on social media. The data collection unit can analyze users' social media activity and collect information on local products and tourist spots that they are interested in. The data collection unit can analyze word-of-mouth and reviews on social media and collect information on highly-rated local products and tourist spots. This allows for more effective information gathering by analyzing social media activity and collecting relevant information.

[0134] The following briefly describes the processing flow for example form 2.

[0135] Step 1: The data collection unit gathers information on local specialties and tourist attractions. For example, the data collection unit can collect information on the types of local specialties and detailed information on tourist attractions. The data collection unit can also use AI to collect information from the internet and local databases. Step 2: The generation unit uses generation AI to generate a promotional video based on the information collected by the collection unit. For example, the generation unit can generate a video showcasing local specialties or tourist attractions. Using generation AI, the generation unit automatically determines the content and format of the video and generates a video that will capture the viewer's interest. Step 3: The distribution unit distributes the video generated by the generation unit. The distribution unit can distribute the video, for example, through online video distribution platforms or social media. The distribution unit uses AI to determine the optimal distribution timing and destinations, and distributes the video effectively. Step 4: The analytics department analyzes the behavioral patterns of target customers. The analytics department can understand the behavioral patterns of target customers by analyzing data such as website browsing history and purchase history. The analytics department can use AI to quickly and accurately analyze large amounts of data. Step 5: The optimization unit optimizes the campaign content and delivery timing based on the analysis results obtained by the analysis unit. For example, the optimization unit can determine the optimal campaign content and delivery timing based on the behavior patterns of target customers. The optimization unit uses AI to optimize the campaign to maximize its effectiveness. Step 6: The support department assists with campaigns optimized by the optimization department. For example, the support department can assist with event planning and management. The support department can use AI to suggest the optimal dates, locations, programs, and advertising for events.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the collection unit, generation unit, distribution unit, analysis unit, optimization unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information on local specialties and tourist spots using the camera 42 and communication I / F 44 of the smart device 14. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a promotional video based on the collected information. The distribution unit is implemented in the control unit 46A of the smart device 14 and distributes the generated video through video distribution platforms and social media on the internet. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the behavioral patterns of target customers. The optimization unit is implemented in the specific processing unit 290 of the data processing unit 12 and optimizes the content and distribution timing of the campaign based on the analysis results. The support unit is implemented in the control unit 46A of the smart device 14 and supports the optimized campaign. 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.

[0140] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the collection unit, generation unit, distribution unit, analysis unit, optimization unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information on local specialties and tourist spots using the camera 42 and communication I / F 44 of the smart glasses 214. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a promotional video based on the collected information. The distribution unit is implemented, for example, by the control unit 46A of the smart glasses 214, and distributes the generated video through video distribution platforms and social media on the internet. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the behavioral patterns of target customers. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and optimizes the content and distribution timing of the campaign based on the analysis results. The support unit is implemented, for example, by the control unit 46A of the smart glasses 214, and supports the optimized campaign. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the collection unit, generation unit, distribution unit, analysis unit, optimization unit, and support unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information on local specialties and tourist spots using the camera 42 and communication I / F 44 of the headset terminal 314. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a promotional video based on the collected information. The distribution unit is implemented, for example, by the control unit 46A of the headset terminal 314, and distributes the generated video through video distribution platforms and social media on the internet. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the behavioral patterns of target customers. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and optimizes the content and distribution timing of the campaign based on the analysis results. The support unit is implemented, for example, by the control unit 46A of the headset terminal 314, and supports the optimized campaign. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0172] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Each of the multiple elements described above, including the collection unit, generation unit, distribution unit, analysis unit, optimization unit, and support unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects information on local specialties and tourist spots using the camera 42 and communication I / F 44 of the robot 414. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates a promotional video based on the collected information. The distribution unit is implemented, for example, by the control unit 46A of the robot 414, and distributes the generated video through video distribution platforms and social media on the internet. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the behavior patterns of target customers. The optimization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and optimizes the content and distribution timing of the campaign based on the analysis results. The support unit is implemented, for example, by the control unit 46A of the robot 414, and supports the optimized campaign. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] (Note 1) A system characterized by comprising: a collection unit that collects information on local specialties and tourist spots; a generation unit that generates a promotional video based on the information collected by the collection unit; a distribution unit that distributes the video generated by the generation unit; an analysis unit that analyzes the behavioral patterns of target customers; an optimization unit that optimizes the content and distribution timing of a campaign based on the analysis results obtained by the analysis unit; and a support unit that assists the campaign optimized by the optimization unit. (Note 2) The generating unit is Automatically create tourist guidebooks. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Digital distribution of tourist guidebooks The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit, Support for event planning and management The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The system described in Appendix 1 is characterized in that the collection unit selects an efficient collection method based on past collection data when collecting information on local specialties and tourist spots. (Note 7) The aforementioned collection unit is When gathering information, adjust the type of information collected according to the season and events. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information by considering geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When gathering information, analyze social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is The system estimates user emotions and adjusts the presentation of the promotional video based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating promotional videos, adjust the level of detail in the video based on the importance of local products and tourist attractions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating promotional videos, different generation algorithms are applied depending on the category of local products or tourist attractions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the length of the promotional video based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating promotional videos, the order of the videos is adjusted based on the relevance of local products and tourist attractions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When creating promotional videos, we incorporate the historical background of local products and tourist attractions into the video. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned distribution unit, It estimates the user's emotions and adjusts the timing of video delivery based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The system described in Appendix 1, wherein the distribution unit selects an effective distribution method based on past distribution data when distributing video. (Note 19) The aforementioned distribution unit, When delivering videos, customize the content based on the attribute information of the target customers. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned distribution unit, It estimates user sentiment and prioritizes video delivery based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned distribution unit, When distributing videos, prioritize delivery to highly relevant customers by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned distribution unit, When distributing videos, analyze social media activity and deliver them to relevant customers. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method of behavioral patterns based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is When analyzing behavioral patterns, the optimal analysis method is selected by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is When analyzing behavioral patterns, customize the analysis based on the attribute information of the target customer. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is It estimates the user's emotions and adjusts the order in which it displays the analysis results of behavioral patterns based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is When analyzing behavioral patterns, geographical location information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is When analyzing behavioral patterns, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 29) The optimization unit, The system estimates user sentiment and adjusts campaign content and delivery timing based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The system according to Appendix 1, characterized in that the optimization unit selects an effective optimization method based on past campaign data when optimizing a campaign. (Note 31) The optimization unit, When optimizing a campaign, customize the optimization based on the attribute information of your target customers. The system described in Appendix 1, characterized by the features described herein. (Note 32) The optimization unit, The system estimates user sentiment and prioritizes campaigns based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The optimization unit, When optimizing a campaign, take geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The optimization unit, When optimizing campaigns, refer to relevant literature to improve the accuracy of optimization. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned support unit, We estimate user sentiment and adjust campaign support methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 36) The system described in Appendix 1 is characterized in that the support unit selects an effective support method based on past support data when providing support for a campaign. (Note 37) The aforementioned support unit, When providing campaign support, customize the support content based on the attribute information of the target customers. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned support unit, It estimates user sentiment and prioritizes campaign support based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned support unit, When providing campaign support, we select the most suitable support method by considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned support unit, When supporting a campaign, refer to relevant literature to improve the accuracy of the support. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned support unit, When supporting a campaign, we collect user feedback and improve our support methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0208] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A system characterized by comprising: a collection unit that collects information on local specialties and tourist spots; a generation unit that generates a promotional video based on the information collected by the collection unit; a distribution unit that distributes the video generated by the generation unit; an analysis unit that analyzes the behavioral patterns of target customers; an optimization unit that optimizes the content and distribution timing of a campaign based on the analysis results obtained by the analysis unit; and a support unit that assists the campaign optimized by the optimization unit.

2. The generating unit is Automatically create tourist guidebooks. The system according to feature 1.

3. The generating unit is Digital distribution of tourist guidebooks The system according to feature 1.

4. The aforementioned support unit, Support for event planning and management The system according to feature 1.

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

6. The system according to claim 1, characterized in that the collection unit selects an efficient collection method based on past collection data when collecting information on local specialties and tourist spots.

7. The aforementioned collection unit is When gathering information, adjust the type of information collected according to the season and events. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information by considering geographical location. The system according to feature 1.

10. The aforementioned collection unit is When gathering information, analyze social media activity and collect relevant information. The system according to feature 1.

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