system

Abstract

The system according to this embodiment aims to propose fresh and attractive plan proposals based on the user's intentions and to generate promotional videos. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, and a generation unit. The reception unit selects the user's intentions. The proposal unit proposes a plan based on the intentions selected by the reception unit. The generation unit generates a preview video based on the plan proposed by the proposal 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 performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to collect information and make a plan when planning how to spend the weekend with the family, and it is difficult to make a fresh plan.

[0005] The system according to the embodiment aims to propose a fresh and attractive plan based on the user's intention and generate a preview video.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a proposal unit, and a generation unit. The reception unit selects the user's intention. The proposal unit proposes a plan based on the intention selected by the reception unit. The generation unit generates a preview video based on the plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose fresh and attractive plan proposals based on the user's intentions and generate a preview video. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 weekend planning support system according to an embodiment of the present invention is a system designed to reduce the burden on working parents when planning how to spend weekends with their children and to pique children's interest. The weekend planning support system allows the user to select their personal preferences, such as their mood or what they want to do at the time, and the generating AI proposes several plan options based on the selected preferences. These include information necessary for going out to play, such as itineraries, reservations, and directions. Furthermore, the system utilizes the generating AI's video generation technology to create a preview video based on the proposed plan. This video is designed to pique children's interest and facilitate preparation. For example, if the user selects the preference to "go to the zoo," the generating AI gathers information on nearby zoos and proposes the optimal visit plan. This plan includes information such as the zoo's opening hours, admission fees, and access methods. Furthermore, the system utilizes the generating AI's video generation technology to create a preview video based on the proposed plan. For example, if a zoo visit plan is proposed, the generating AI generates a video introducing the zoo's highlights and access methods. This video is designed to allow children to visually understand where they are being taken. This system reduces the burden on working parents when planning weekend activities with their children. Users only need to select a plan suggested by the AI ​​generator, without having to gather complex information or create a plan themselves. In addition, the generated preview video can pique children's interest and make preparations go smoothly. For example, if a child watches the video and gets excited and says, "I want to go to the zoo!", preparations will proceed smoothly. This system can automatically generate plans that are tailored to the child's mood and the circumstances of each individual family. This solves the problem of creating similar plans every time and provides fresh plans. Furthermore, a promotional video based on the generated plan is also created, which stimulates children's intellectual curiosity and makes preparing for outings with children more efficient. In this way, the weekend planning support system reduces the burden on working parents when planning weekend activities with their children and can pique children's interest.

[0029] The weekend planning support system according to this embodiment comprises a reception unit, a proposal unit, and a generation unit. The reception unit selects the user's intentions. User intentions include, but are not limited to, the purpose of travel, budget, and preferences. For example, the reception unit can select the user's intention to "go to the zoo." The reception unit can also select the user's intention to "have a picnic in the park." Furthermore, the reception unit can also select the user's intention to "visit a museum." The proposal unit proposes a plan based on the intentions selected by the reception unit. The proposal unit generates a plan that matches the user's intentions, for example, using a generation AI. The generation AI can generate a plan based on various information sources. For example, the proposal unit can collect information from the internet and propose a plan that matches the user's intentions. The proposal unit can also propose a plan based on information from books and guidebooks. Furthermore, the proposal unit can also propose a plan based on the user's past data. The generation unit generates a preview video based on the plan proposed by the proposal unit. The generation unit, for example, uses a generation AI to create a preview video based on the proposed plan. The generation AI can generate a preview video based on the plan using video generation technology. For example, if a zoo visit plan is proposed, the generation unit will generate a video introducing the zoo's highlights and how to get there. The generation unit can also generate a video introducing the park's scenery and the picnic if a park picnic plan is proposed. Furthermore, if a museum visit plan is proposed, the generation unit can generate a video introducing the museum's exhibits and how to get there. As a result, the weekend planning support system according to this embodiment can propose a plan based on the user's intentions and generate a preview video, thereby stimulating children's interest and allowing preparation work to proceed smoothly.

[0030] The reception desk selects the user's preferences. These preferences include, but are not limited to, the purpose of travel, budget, and preferences. For example, the reception desk can select the user's preference to "go to the zoo." It can also select the user's preference to "have a picnic in the park." Furthermore, it can select the user's preference to "visit a museum." The reception desk provides an intuitive interface to allow users to easily input their preferences. For example, users can use a smartphone or tablet to select their preferences using touch or voice input. In addition, the reception desk learns the user's past selection history and preferences and collects data to make personalized suggestions. For example, a user who has visited a zoo in the past can be suggested new zoos or special events. The reception desk can also ask detailed questions when users select their preferences to collect more specific information. For example, a user who selects "go to the zoo" can be asked questions such as "which region's zoo would you like to visit?" or "are there any specific animals you would like to see?" to understand their preferences more specifically. This allows the reception department to accurately understand the user's intentions and provide appropriate information to the proposal and generation departments.

[0031] The proposal department proposes a plan based on the user's preferences selected by the reception department. The proposal department generates a plan that matches the user's preferences, for example, using generative AI. Generative AI can generate plans based on various information sources. For example, the proposal department collects information from the internet and proposes a plan that matches the user's preferences. The proposal department can also propose plans based on information from books and guidebooks. Furthermore, the proposal department can propose plans based on the user's past data. Generative AI uses natural language processing technology to analyze the user's preferences and generate the optimal plan. For example, if the user selects the preference "I want to go to the zoo," the generative AI collects information such as the zoo's opening hours, admission fees, access methods, and highlights, and proposes the optimal visit plan. The generative AI can also suggest information on restaurants and cafes according to the user's budget and preferences. Furthermore, the proposal department can make personalized suggestions based on the user's past behavior history and evaluations. For example, if the user had a high evaluation of a zoo when they visited it in the past, the proposal department can suggest other zoos where a similar experience can be had. This allows the proposal department to suggest the optimal plan based on the user's intentions, thereby improving user satisfaction.

[0032] The generation unit generates a preview video based on the plan proposed by the proposal unit. The generation unit, for example, uses a generation AI to create a preview video based on the proposed plan. The generation AI can generate a preview video based on the plan using video generation technology. For example, if a zoo visit plan is proposed, the generation unit will generate a video introducing the zoo's highlights and how to get there. Also, if a park picnic plan is proposed, the generation unit can generate a video introducing the park scenery and the picnic. Furthermore, if a museum visit plan is proposed, the generation unit can generate a video introducing the museum's exhibits and how to get there. The generation AI uses image recognition technology and speech synthesis technology to generate realistic images and sounds, providing users with an immersive preview video. For example, in a zoo visit plan, it can realistically reproduce images and sounds of animals to convey the appeal of the zoo to the user. Also, in a park picnic plan, it can realistically reproduce the park scenery and the picnic to convey the enjoyment of the picnic to the user. Furthermore, the museum visit plan can realistically recreate videos and explanations of the exhibits, conveying the museum's appeal to the user. This allows the generation unit to provide users with immersive preview videos, increasing their anticipation for the plan.

[0033] The proposal unit can generate plan proposals based on various information sources. For example, the proposal unit can collect information from the internet and propose a plan proposal that suits the user's preferences. For example, the proposal unit can collect information from travel sites and tourism information sites and generate the optimal plan proposal based on the user's preferences. The proposal unit can also propose plan proposals based on information from books and guidebooks. For example, the proposal unit can collect information from travel guidebooks and tourism magazines and generate a plan proposal that suits the user's preferences. Furthermore, the proposal unit can also propose plan proposals based on the user's past data. For example, the proposal unit can analyze the user's past travel history and selected activity data and generate a plan proposal that suits the user's preferences. In this way, by generating plan proposals based on various information sources, a wider variety of options can be provided. Some or all of the above processing in the proposal unit may be performed using, for example, a generation AI, or without a generation AI. For example, the proposal unit can input information from the internet into a generation AI, and the generation AI can generate a plan proposal.

[0034] The generation unit can generate a preview video to pique children's interest based on the proposed plan. The generation unit can, for example, use a generation AI to create a preview video based on the proposed plan. The generation AI can generate a preview video based on the plan using video generation technology. For example, if a zoo visit plan is proposed, the generation unit can generate a video introducing the zoo's highlights and how to get there. The generation unit can also generate a video introducing the park's scenery and the picnic if a park picnic plan is proposed. Furthermore, if a museum visit plan is proposed, the generation unit can generate a video introducing the museum's exhibits and how to get there. By generating a preview video to pique children's interest, the preparation process for the children can proceed smoothly. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the proposed plan into the generation AI, and the generation AI can generate a preview video.

[0035] The proposal unit can propose a plan that includes information such as itinerary, reservations, and directions. For example, the proposal unit uses generative AI to generate a plan that includes information such as itinerary, reservations, and directions based on the user's preferences. Generative AI can generate a plan that matches the user's preferences based on various information sources. For example, the proposal unit collects information from travel sites and tourism information sites and generates the optimal plan based on the user's preferences. The proposal unit can also propose a plan based on information from books and guidebooks. For example, the proposal unit collects information from travel guidebooks and tourism magazines and generates a plan that matches the user's preferences. Furthermore, the proposal unit can also propose a plan based on the user's past data. For example, the proposal unit analyzes the user's past travel history and selected activity data and generates a plan that matches the user's preferences. This allows the user to grasp all the necessary information at once by proposing a plan that includes information such as itinerary, reservations, and directions. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or without using generative AI. For example, the proposal department can input information from the internet into a generation AI, which can then generate a plan.

[0036] The generation unit can make the generated video visually understandable to children. For example, the generation unit uses a generation AI to create a preview video based on the proposed plan. The generation AI can use video generation technology to generate a preview video based on the plan. For example, if a zoo visit plan is proposed, the generation unit can generate a video introducing the zoo's highlights and how to get there. Also, if a park picnic plan is proposed, the generation unit can generate a video introducing the park's scenery and how to have a picnic. Furthermore, if a museum visit plan is proposed, the generation unit can generate a video introducing the museum's exhibits and how to get there. This makes it easier to capture children's interest by generating videos that are visually understandable to children. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input a proposed plan into the generation AI, and the generation AI can generate a preview video.

[0037] The reception desk can analyze a user's past selection history and suggest the most suitable options. For example, the reception desk can use AI to analyze a user's past selection history. The AI ​​can, for instance, analyze trends in activities the user has previously selected and suggest similar activities. It can also evaluate the user's satisfaction with previously selected activities and prioritize suggesting highly-rated activities. Furthermore, the AI ​​can analyze the frequency of previously selected activities and suggest less frequent activities. For example, the reception desk can input data on activities the user has previously selected into the AI, which can then suggest the most suitable options. This allows the reception desk to suggest the most suitable options to the user by analyzing their past selection history.

[0038] The reception desk can filter the user's preferences based on their current lifestyle and areas of interest. For example, the reception desk can use AI to analyze the user's current lifestyle and areas of interest. The AI ​​can filter the preference options based on information such as the user's family structure, work situation, and health status. The AI ​​can also filter the preference options based on information such as the user's hobbies, topics of interest, and past search history. For example, the reception desk can suggest activities that fit the user's available time based on their current lifestyle. The reception desk can also suggest activities that will interest the user based on their areas of interest. Furthermore, the reception desk can suggest activities that the whole family can enjoy based on the user's family structure. By filtering based on the user's lifestyle and areas of interest, more appropriate options can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input data on the user's lifestyle and areas of interest into the AI, which can then filter the preference options.

[0039] The reception desk can prioritize presenting highly relevant options by considering the user's geographical location when the user selects their preference. For example, the reception desk can use AI to analyze the user's geographical location. The AI ​​can, for example, prioritize suggesting activities close to the user's current location. It can also suggest easily accessible activities based on the user's geographical location. Furthermore, the AI ​​can suggest local events and activities based on the user's geographical location. This allows for the provision of more relevant options by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI, which can then present highly relevant options.

[0040] The reception desk can analyze the user's social media activity and present relevant options when the user selects their preference. For example, the reception desk can use AI to analyze the user's social media activity. The AI ​​can, for example, analyze the user's social media posts and suggest activities that might interest them. The AI ​​can also suggest relevant activities based on the activity of the user's friends on social media. Furthermore, the AI ​​can analyze the user's social media hashtags and suggest relevant activities. This allows the reception desk to provide relevant options to the user by analyzing their social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's social media activity into the AI, which can then present relevant options.

[0041] The proposal department can analyze the user's past behavior history to generate the optimal plan when proposing a plan. For example, the proposal department uses a generative AI to analyze the user's past behavior history. The generative AI can, for example, propose relevant plans based on the user's history of places they have visited in the past. The generative AI can also propose plans that are highly satisfying based on the user's past behavior history. Furthermore, the generative AI can analyze the user's past behavior history and propose plans that offer new experiences. For example, the proposal department can input data on places the user has visited in the past into the generative AI, which can then generate the optimal plan. In this way, by analyzing past behavior history, the proposal department can provide the user with the most suitable plan.

[0042] The proposal unit can customize the proposed plan based on the user's current living situation. For example, the proposal unit uses a generative AI to analyze the user's current living situation. The generative AI can customize the plan based on information such as the user's family structure, work situation, and health status. For example, the proposal unit can propose a plan with ample time based on the user's current living situation. It can also propose a plan that the whole family can enjoy based on the user's family structure. Furthermore, the proposal unit can propose a plan that fits the user's budget based on their current living situation. By customizing the plan based on the user's current living situation, a more appropriate plan can be provided. Some or all of the above processing in the proposal unit may be performed using a generative AI, or not. For example, the proposal unit can input data on the user's living situation into a generative AI, which can then customize the plan.

[0043] The proposal unit can generate an optimal plan by considering the user's geographical location when proposing a plan. For example, the proposal unit analyzes the user's geographical location using a generative AI. The generative AI can, for example, propose a plan that visits places close to the user's current location. It can also propose a plan that visits easily accessible places based on the user's geographical location. Furthermore, the generative AI can propose a plan that includes local events and activities based on the user's geographical location. This allows the proposal unit to provide the user with the most suitable plan by considering geographical location. Some or all of the above processing in the proposal unit may be performed using a generative AI, or without one. For example, the proposal unit can input the user's geographical location into the generative AI, which can then generate the optimal plan.

[0044] The proposal unit can generate a plan by analyzing the user's social media activity when proposing a plan. For example, the proposal unit can use a generative AI to analyze the user's social media activity. The generative AI can, for example, analyze the user's social media posts and propose an interesting plan. Furthermore, the generative AI can also propose a plan that is relevant to the user by referencing the activities of the user's friends on social media. In addition, the generative AI can analyze the user's social media hashtags and propose a plan that is relevant to the user. This allows the proposal unit to provide relevant plans to the user by analyzing their social media activity. Some or all of the above processing in the proposal unit may be performed using a generative AI, or without one. For example, the proposal unit can input data on the user's social media activity into a generative AI, which can then generate a plan.

[0045] The generation unit can generate optimal video content by analyzing the user's past viewing history when creating a trailer video. For example, the generation unit uses a generation AI to analyze the user's past viewing history. The generation AI can, for example, analyze the trends of videos the user has watched in the past and generate trailer videos with similar content. The generation AI can also generate trailer videos with interesting content based on the user's past viewing history. Furthermore, the generation AI can analyze the user's past viewing history and generate trailer videos that offer a new experience. For example, the generation unit can input data on videos the user has watched in the past into the generation AI, which can then generate optimal video content. In this way, by analyzing past viewing history, the system can provide the user with the most suitable video content.

[0046] The generation unit can customize the video content based on the user's current living situation when generating a preview video. For example, the generation unit uses a generation AI to analyze the user's current living situation. The generation AI can customize the video content based on information such as the user's family structure, work situation, and health status. For example, the generation unit can generate video content that allows the user ample time based on their current living situation. The generation unit can also generate video content that the whole family can enjoy based on the user's family structure. Furthermore, the generation unit can generate video content that fits the user's budget based on their current living situation. By customizing the video content based on the user's current living situation, more appropriate videos can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input data on the user's living situation into the generation AI, which can then customize the video content.

[0047] The generation unit can generate optimal video content by considering the user's geographical location information when generating a preview video. For example, the generation unit analyzes the user's geographical location information using a generation AI. The generation AI can, for example, generate a video of a planned itinerary that visits places close to the user's current location. Furthermore, the generation AI can generate a video of a planned itinerary that visits easily accessible places based on the user's geographical location information. In addition, the generation AI can generate a video of a planned itinerary that includes local events and activities based on the user's geographical location information. This allows the generation unit to provide the user with optimal video content by considering geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate optimal video content.

[0048] The generation unit can generate video content by analyzing the user's social media activity when generating a preview video. For example, the generation unit can use a generation AI to analyze the user's social media activity. The generation AI can analyze the user's social media posts and generate videos with interesting content. Furthermore, the generation AI can also generate videos with relevant content by referencing the activities of the user's friends on social media. In addition, the generation AI can analyze the user's social media hashtags and generate videos with relevant content. For example, the generation unit can analyze the user's social media posts and generate videos with interesting content. Furthermore, the generation unit can generate videos with relevant content by referencing the activities of the user's friends on social media. Furthermore, the generation unit can analyze the user's social media hashtags and generate videos with relevant content. This allows the generation unit to provide users with relevant video content by analyzing their social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or without one. For example, the generation unit can input data on the user's social media activity into the generation AI, which can then generate video content.

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

[0050] The generation unit can analyze a user's past viewing history and generate the most suitable trailer video. For example, it can analyze the trends of videos a user has watched in the past and generate a trailer video with similar content. It can also generate a trailer video with interesting content based on the user's past viewing history. Furthermore, it can analyze a user's past viewing history and generate a trailer video that offers a new experience. In this way, by analyzing past viewing history, the generation unit can provide the user with the most suitable trailer video. 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 the user's viewing history data into a generation AI, which can then generate the most suitable trailer video.

[0051] The proposal unit can customize the plan based on the user's current living situation. For example, it can customize the plan based on information such as the user's family structure, work situation, and health status. For instance, it can propose a plan with ample time based on the user's current living situation. It can also propose a plan that the whole family can enjoy based on the user's family structure. Furthermore, it can propose a plan that fits the user's budget based on their current living situation. By customizing the plan based on the user's current living situation, it is possible to provide a more appropriate plan. Some or all of the above processing in the proposal unit may be performed using, for example, a generation AI, or not. For example, the proposal unit can input data on the user's living situation into a generation AI, which can then customize the plan.

[0052] The reception desk can analyze the user's past selection history and suggest the most suitable options. For example, it can analyze the trends of activities the user has previously selected and suggest similar activities. It can also evaluate the user's satisfaction with activities they have previously selected and prioritize suggesting highly-rated activities. Furthermore, it can analyze the frequency of activities the user has previously selected and suggest less frequent activities. In this way, by analyzing past selection history, the reception desk can suggest the most suitable options for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's selection history data into an AI, which can then suggest the most suitable options.

[0053] The generation unit can customize the video content based on the user's current lifestyle when generating a preview video. For example, it can customize the video content based on information such as the user's family structure, work situation, and health status. For instance, it can generate video content that allows the user to have ample time based on their current lifestyle. It can also generate video content that the whole family can enjoy based on the user's family structure. Furthermore, it can generate video content that fits the user's budget based on their current lifestyle. By customizing the video content based on the user's current lifestyle, it is possible to provide more appropriate videos. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input data on the user's lifestyle into the generation AI, which can then customize the video content.

[0054] The proposal unit can generate an optimal plan by considering the user's geographical location when proposing a plan. For example, it can propose a plan that visits places close to the user's current location. It can also propose a plan that visits easily accessible places based on the user's geographical location. Furthermore, it can propose a plan that includes local events and activities based on the user's geographical location. In this way, by considering geographical location, the system can provide the user with the most suitable plan. Some or all of the above processing in the proposal unit may be performed using, for example, a generation AI, or without a generation AI. For example, the proposal unit can input the user's geographical location information into a generation AI, which can then generate an optimal plan.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk selects the user's preferences. These preferences include, for example, the purpose of the trip, budget, and preferences. Specifically, the user can select preferences such as "I want to go to the zoo," "I want to have a picnic in the park," or "I want to visit a museum." Step 2: The proposal department proposes a plan based on the preferences selected by the reception department. The proposal department uses generative AI to generate a plan that matches the user's preferences. For example, it proposes a plan based on information from the internet, information from books and guidebooks, and the user's past data. Step 3: The generation unit generates a preview video based on the plan proposed by the proposal unit. The generation unit uses generation AI to create a preview video based on the plan. For example, if a zoo visit plan is proposed, it will generate a video introducing the zoo's highlights and how to get there. If a park picnic plan is proposed, it will generate a video introducing the park's scenery and the picnic. Furthermore, if a museum visit plan is proposed, it will generate a video introducing the museum's exhibits and how to get there.

[0057] (Example of form 2) The weekend planning support system according to an embodiment of the present invention is a system designed to reduce the burden on working parents when planning how to spend weekends with their children and to pique children's interest. The weekend planning support system allows the user to select their personal preferences, such as their mood or what they want to do at the time, and the generating AI proposes several plan options based on the selected preferences. These include information necessary for going out to play, such as itineraries, reservations, and directions. Furthermore, the system utilizes the generating AI's video generation technology to create a preview video based on the proposed plan. This video is designed to pique children's interest and facilitate preparation. For example, if the user selects the preference to "go to the zoo," the generating AI gathers information on nearby zoos and proposes the optimal visit plan. This plan includes information such as the zoo's opening hours, admission fees, and access methods. Furthermore, the system utilizes the generating AI's video generation technology to create a preview video based on the proposed plan. For example, if a zoo visit plan is proposed, the generating AI generates a video introducing the zoo's highlights and access methods. This video is designed to allow children to visually understand where they are being taken. This system reduces the burden on working parents when planning weekend activities with their children. Users only need to select a plan suggested by the AI ​​generator, without having to gather complex information or create a plan themselves. In addition, the generated preview video can pique children's interest and make preparations go smoothly. For example, if a child watches the video and gets excited and says, "I want to go to the zoo!", preparations will proceed smoothly. This system can automatically generate plans that are tailored to the child's mood and the circumstances of each individual family. This solves the problem of creating similar plans every time and provides fresh plans. Furthermore, a promotional video based on the generated plan is also created, which stimulates children's intellectual curiosity and makes preparing for outings with children more efficient. In this way, the weekend planning support system reduces the burden on working parents when planning weekend activities with their children and can pique children's interest.

[0058] The weekend planning support system according to this embodiment comprises a reception unit, a proposal unit, and a generation unit. The reception unit selects the user's intentions. User intentions include, but are not limited to, the purpose of travel, budget, and preferences. For example, the reception unit can select the user's intention to "go to the zoo." The reception unit can also select the user's intention to "have a picnic in the park." Furthermore, the reception unit can also select the user's intention to "visit a museum." The proposal unit proposes a plan based on the intentions selected by the reception unit. The proposal unit generates a plan that matches the user's intentions, for example, using a generation AI. The generation AI can generate a plan based on various information sources. For example, the proposal unit can collect information from the internet and propose a plan that matches the user's intentions. The proposal unit can also propose a plan based on information from books and guidebooks. Furthermore, the proposal unit can also propose a plan based on the user's past data. The generation unit generates a preview video based on the plan proposed by the proposal unit. The generation unit, for example, uses a generation AI to create a preview video based on the proposed plan. The generation AI can generate a preview video based on the plan using video generation technology. For example, if a zoo visit plan is proposed, the generation unit will generate a video introducing the zoo's highlights and how to get there. The generation unit can also generate a video introducing the park's scenery and the picnic if a park picnic plan is proposed. Furthermore, if a museum visit plan is proposed, the generation unit can generate a video introducing the museum's exhibits and how to get there. As a result, the weekend planning support system according to this embodiment can propose a plan based on the user's intentions and generate a preview video, thereby stimulating children's interest and allowing preparation work to proceed smoothly.

[0059] The reception desk selects the user's preferences. These preferences include, but are not limited to, the purpose of travel, budget, and preferences. For example, the reception desk can select the user's preference to "go to the zoo." It can also select the user's preference to "have a picnic in the park." Furthermore, it can select the user's preference to "visit a museum." The reception desk provides an intuitive interface to allow users to easily input their preferences. For example, users can use a smartphone or tablet to select their preferences using touch or voice input. In addition, the reception desk learns the user's past selection history and preferences and collects data to make personalized suggestions. For example, a user who has visited a zoo in the past can be suggested new zoos or special events. The reception desk can also ask detailed questions when users select their preferences to collect more specific information. For example, a user who selects "go to the zoo" can be asked questions such as "which region's zoo would you like to visit?" or "are there any specific animals you would like to see?" to understand their preferences more specifically. This allows the reception department to accurately understand the user's intentions and provide appropriate information to the proposal and generation departments.

[0060] The proposal department proposes a plan based on the user's preferences selected by the reception department. The proposal department generates a plan that matches the user's preferences, for example, using generative AI. Generative AI can generate plans based on various information sources. For example, the proposal department collects information from the internet and proposes a plan that matches the user's preferences. The proposal department can also propose plans based on information from books and guidebooks. Furthermore, the proposal department can propose plans based on the user's past data. Generative AI uses natural language processing technology to analyze the user's preferences and generate the optimal plan. For example, if the user selects the preference "I want to go to the zoo," the generative AI collects information such as the zoo's opening hours, admission fees, access methods, and highlights, and proposes the optimal visit plan. The generative AI can also suggest information on restaurants and cafes according to the user's budget and preferences. Furthermore, the proposal department can make personalized suggestions based on the user's past behavior history and evaluations. For example, if the user had a high evaluation of a zoo when they visited it in the past, the proposal department can suggest other zoos where a similar experience can be had. This allows the proposal department to suggest the optimal plan based on the user's intentions, thereby improving user satisfaction.

[0061] The generation unit generates a preview video based on the plan proposed by the proposal unit. The generation unit, for example, uses a generation AI to create a preview video based on the proposed plan. The generation AI can generate a preview video based on the plan using video generation technology. For example, if a zoo visit plan is proposed, the generation unit will generate a video introducing the zoo's highlights and how to get there. Also, if a park picnic plan is proposed, the generation unit can generate a video introducing the park scenery and the picnic. Furthermore, if a museum visit plan is proposed, the generation unit can generate a video introducing the museum's exhibits and how to get there. The generation AI uses image recognition technology and speech synthesis technology to generate realistic images and sounds, providing users with an immersive preview video. For example, in a zoo visit plan, it can realistically reproduce images and sounds of animals to convey the appeal of the zoo to the user. Also, in a park picnic plan, it can realistically reproduce the park scenery and the picnic to convey the enjoyment of the picnic to the user. Furthermore, the museum visit plan can realistically recreate videos and explanations of the exhibits, conveying the museum's appeal to the user. This allows the generation unit to provide users with immersive preview videos, increasing their anticipation for the plan.

[0062] The proposal unit can generate plan proposals based on various information sources. For example, the proposal unit can collect information from the internet and propose a plan proposal that suits the user's preferences. For example, the proposal unit can collect information from travel sites and tourism information sites and generate the optimal plan proposal based on the user's preferences. The proposal unit can also propose plan proposals based on information from books and guidebooks. For example, the proposal unit can collect information from travel guidebooks and tourism magazines and generate a plan proposal that suits the user's preferences. Furthermore, the proposal unit can also propose plan proposals based on the user's past data. For example, the proposal unit can analyze the user's past travel history and selected activity data and generate a plan proposal that suits the user's preferences. In this way, by generating plan proposals based on various information sources, a wider variety of options can be provided. Some or all of the above processing in the proposal unit may be performed using, for example, a generation AI, or without a generation AI. For example, the proposal unit can input information from the internet into a generation AI, and the generation AI can generate a plan proposal.

[0063] The generation unit can generate a preview video to pique children's interest based on the proposed plan. The generation unit can, for example, use a generation AI to create a preview video based on the proposed plan. The generation AI can generate a preview video based on the plan using video generation technology. For example, if a zoo visit plan is proposed, the generation unit can generate a video introducing the zoo's highlights and how to get there. The generation unit can also generate a video introducing the park's scenery and the picnic if a park picnic plan is proposed. Furthermore, if a museum visit plan is proposed, the generation unit can generate a video introducing the museum's exhibits and how to get there. By generating a preview video to pique children's interest, the preparation process for the children can proceed smoothly. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the proposed plan into the generation AI, and the generation AI can generate a preview video.

[0064] The proposal unit can propose a plan that includes information such as itinerary, reservations, and directions. For example, the proposal unit uses generative AI to generate a plan that includes information such as itinerary, reservations, and directions based on the user's preferences. Generative AI can generate a plan that matches the user's preferences based on various information sources. For example, the proposal unit collects information from travel sites and tourism information sites and generates the optimal plan based on the user's preferences. The proposal unit can also propose a plan based on information from books and guidebooks. For example, the proposal unit collects information from travel guidebooks and tourism magazines and generates a plan that matches the user's preferences. Furthermore, the proposal unit can also propose a plan based on the user's past data. For example, the proposal unit analyzes the user's past travel history and selected activity data and generates a plan that matches the user's preferences. This allows the user to grasp all the necessary information at once by proposing a plan that includes information such as itinerary, reservations, and directions. Some or all of the above processing in the proposal unit may be performed using, for example, generative AI, or without using generative AI. For example, the proposal department can input information from the internet into a generation AI, which can then generate a plan.

[0065] The generation unit can make the generated video visually understandable to children. For example, the generation unit uses a generation AI to create a preview video based on the proposed plan. The generation AI can use video generation technology to generate a preview video based on the plan. For example, if a zoo visit plan is proposed, the generation unit can generate a video introducing the zoo's highlights and how to get there. Also, if a park picnic plan is proposed, the generation unit can generate a video introducing the park's scenery and how to have a picnic. Furthermore, if a museum visit plan is proposed, the generation unit can generate a video introducing the museum's exhibits and how to get there. This makes it easier to capture children's interest by generating videos that are visually understandable to children. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input a proposed plan into the generation AI, and the generation AI can generate a preview video.

[0066] The reception desk can estimate the user's emotions and present options based on those emotions. The reception desk uses an emotion estimation function, such as an emotion engine or generative AI, to estimate the user's emotions. The emotion engine or generative AI can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, the emotion engine or generative AI can estimate the user's emotions based on survey results. For example, if the reception desk is feeling stressed, it will prioritize presenting relaxing activities. If the user is excited, it can also prioritize presenting active activities. Furthermore, if the user is tired, it can prioritize presenting restful activities. This allows for the provision of more appropriate choices by presenting options based on the user's emotions. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input the user's emotion data into a generative AI, which can then present options.

[0067] The reception desk can analyze a user's past selection history and suggest the most suitable options. For example, the reception desk can use AI to analyze a user's past selection history. The AI ​​can, for instance, analyze trends in activities the user has previously selected and suggest similar activities. It can also evaluate the user's satisfaction with previously selected activities and prioritize suggesting highly-rated activities. Furthermore, the AI ​​can analyze the frequency of previously selected activities and suggest less frequent activities. For example, the reception desk can input data on activities the user has previously selected into the AI, which can then suggest the most suitable options. This allows the reception desk to suggest the most suitable options to the user by analyzing their past selection history.

[0068] The reception desk can filter the user's preferences based on their current lifestyle and areas of interest. For example, the reception desk can use AI to analyze the user's current lifestyle and areas of interest. The AI ​​can filter the preference options based on information such as the user's family structure, work situation, and health status. The AI ​​can also filter the preference options based on information such as the user's hobbies, topics of interest, and past search history. For example, the reception desk can suggest activities that fit the user's available time based on their current lifestyle. The reception desk can also suggest activities that will interest the user based on their areas of interest. Furthermore, the reception desk can suggest activities that the whole family can enjoy based on the user's family structure. By filtering based on the user's lifestyle and areas of interest, more appropriate options can be provided. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input data on the user's lifestyle and areas of interest into the AI, which can then filter the preference options.

[0069] The reception desk can estimate the user's emotions and determine the priority of their preferences based on those emotions. The reception desk uses an emotion estimation function, such as an emotion engine or generative AI, to estimate the user's emotions. The emotion engine or generative AI can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, the emotion engine or generative AI can estimate the user's emotions based on survey results. For example, if the user is relaxed, the reception desk will prioritize offering relaxing activities. If the user is excited, the reception desk can also prioritize offering active activities. Furthermore, if the user is tired, the reception desk can prioritize offering activities that allow them to rest. This allows the reception desk to provide more appropriate choices by determining the priority of preferences based on the user's emotions. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input the user's emotion data into a generative AI, which can then determine the priority of preferences.

[0070] The reception desk can prioritize presenting highly relevant options by considering the user's geographical location when the user selects their preference. For example, the reception desk can use AI to analyze the user's geographical location. The AI ​​can, for example, prioritize suggesting activities close to the user's current location. It can also suggest easily accessible activities based on the user's geographical location. Furthermore, the AI ​​can suggest local events and activities based on the user's geographical location. This allows for the provision of more relevant options by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location into the AI, which can then present highly relevant options.

[0071] The reception desk can analyze the user's social media activity and present relevant options when the user selects their preference. For example, the reception desk can use AI to analyze the user's social media activity. The AI ​​can, for example, analyze the user's social media posts and suggest activities that might interest them. The AI ​​can also suggest relevant activities based on the activity of the user's friends on social media. Furthermore, the AI ​​can analyze the user's social media hashtags and suggest relevant activities. This allows the reception desk to provide relevant options to the user by analyzing their social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's social media activity into the AI, which can then present relevant options.

[0072] The proposal unit can estimate the user's emotions and adjust the presentation of the plan based on the estimated emotions. The proposal unit estimates the user's emotions using an emotion estimation function, for example, an emotion engine or a generative AI. The emotion engine or generative AI can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions based on survey results. For example, if the user is relaxed, the proposal unit will present the plan in a calm manner. If the user is excited, the proposal unit can present the plan in a lively manner. Furthermore, if the user is tired, the proposal unit can present the plan in a simple and highly visible manner. By adjusting the presentation of the plan based on the user's emotions, a more appropriate plan can be provided. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input user emotion data into a generative AI, which can then adjust the presentation of the plan.

[0073] The proposal department can analyze the user's past behavior history to generate the optimal plan when proposing a plan. For example, the proposal department uses a generative AI to analyze the user's past behavior history. The generative AI can, for example, propose relevant plans based on the user's history of places they have visited in the past. The generative AI can also propose plans that are highly satisfying based on the user's past behavior history. Furthermore, the generative AI can analyze the user's past behavior history and propose plans that offer new experiences. For example, the proposal department can input data on places the user has visited in the past into the generative AI, which can then generate the optimal plan. In this way, by analyzing past behavior history, the proposal department can provide the user with the most suitable plan.

[0074] The proposal unit can customize the proposed plan based on the user's current living situation. For example, the proposal unit uses a generative AI to analyze the user's current living situation. The generative AI can customize the plan based on information such as the user's family structure, work situation, and health status. For example, the proposal unit can propose a plan with ample time based on the user's current living situation. It can also propose a plan that the whole family can enjoy based on the user's family structure. Furthermore, the proposal unit can propose a plan that fits the user's budget based on their current living situation. By customizing the plan based on the user's current living situation, a more appropriate plan can be provided. Some or all of the above processing in the proposal unit may be performed using a generative AI, or not. For example, the proposal unit can input data on the user's living situation into a generative AI, which can then customize the plan.

[0075] The suggestion unit can estimate the user's emotions and determine the priority of plan proposals based on the estimated user emotions. The suggestion unit estimates the user's emotions using an emotion estimation function, for example, an emotion engine or generative AI. The emotion engine or generative AI can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, it can estimate the user's emotions based on survey results. For example, if the user is relaxed, the suggestion unit will prioritize presenting plan proposals that promote relaxation. If the user is excited, the suggestion unit can also prioritize presenting plan proposals that promote activity. Furthermore, if the user is tired, the suggestion unit can also prioritize presenting plan proposals that promote rest. By prioritizing plan proposals based on the user's emotions, more appropriate plan proposals can be provided. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, which can then determine the priority of plan proposals.

[0076] The proposal unit can generate an optimal plan by considering the user's geographical location when proposing a plan. For example, the proposal unit analyzes the user's geographical location using a generative AI. The generative AI can, for example, propose a plan that visits places close to the user's current location. It can also propose a plan that visits easily accessible places based on the user's geographical location. Furthermore, the generative AI can propose a plan that includes local events and activities based on the user's geographical location. This allows the proposal unit to provide the user with the most suitable plan by considering geographical location. Some or all of the above processing in the proposal unit may be performed using a generative AI, or without one. For example, the proposal unit can input the user's geographical location into the generative AI, which can then generate the optimal plan.

[0077] The proposal unit can generate a plan by analyzing the user's social media activity when proposing a plan. For example, the proposal unit can use a generative AI to analyze the user's social media activity. The generative AI can, for example, analyze the user's social media posts and propose an interesting plan. Furthermore, the generative AI can also propose a plan that is relevant to the user by referencing the activities of the user's friends on social media. In addition, the generative AI can analyze the user's social media hashtags and propose a plan that is relevant to the user. This allows the proposal unit to provide relevant plans to the user by analyzing their social media activity. Some or all of the above processing in the proposal unit may be performed using a generative AI, or without one. For example, the proposal unit can input data on the user's social media activity into a generative AI, which can then generate a plan.

[0078] The generation unit can estimate the user's emotions and adjust the content of the trailer video based on the estimated emotions. The generation unit estimates the user's emotions using an emotion estimation function, for example, using an emotion engine or a generation AI. The emotion engine or generation AI can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, the emotion engine or generation AI can estimate the user's emotions based on survey results. For example, if the user is relaxed, the generation unit can generate a trailer video using calm music and visuals. If the user is excited, the generation unit can also generate a trailer video using lively music and visuals. Furthermore, if the user is tired, the generation unit can generate a simple and highly visual trailer video. In this way, by adjusting the content of the trailer video based on the user's emotions, a more appropriate video can be provided. 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 user emotion data into a generation AI, and the generation AI can adjust the content of the trailer video.

[0079] The generation unit can generate optimal video content by analyzing the user's past viewing history when creating a trailer video. For example, the generation unit uses a generation AI to analyze the user's past viewing history. The generation AI can, for example, analyze the trends of videos the user has watched in the past and generate trailer videos with similar content. The generation AI can also generate trailer videos with interesting content based on the user's past viewing history. Furthermore, the generation AI can analyze the user's past viewing history and generate trailer videos that offer a new experience. For example, the generation unit can input data on videos the user has watched in the past into the generation AI, which can then generate optimal video content. In this way, by analyzing past viewing history, the system can provide the user with the most suitable video content.

[0080] The generation unit can customize the video content based on the user's current living situation when generating a preview video. For example, the generation unit uses a generation AI to analyze the user's current living situation. The generation AI can customize the video content based on information such as the user's family structure, work situation, and health status. For example, the generation unit can generate video content that allows the user ample time based on their current living situation. The generation unit can also generate video content that the whole family can enjoy based on the user's family structure. Furthermore, the generation unit can generate video content that fits the user's budget based on their current living situation. By customizing the video content based on the user's current living situation, more appropriate videos can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input data on the user's living situation into the generation AI, which can then customize the video content.

[0081] The generation unit can estimate the user's emotions and adjust the display order of the trailer videos based on the estimated emotions. The generation unit estimates the user's emotions using an emotion estimation function, for example, an emotion engine or a generation AI. The emotion engine or generation AI can estimate the user's emotions using, for example, facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, the emotion engine or generation AI can estimate the user's emotions based on survey results. For example, if the user is relaxed, the generation unit can display calm videos first. If the user is excited, the generation unit can also display lively videos first. Furthermore, if the user is tired, the generation unit can display simple and easy-to-understand videos first. This allows for the provision of more appropriate videos by adjusting the display order of trailer videos based on the user's emotions. 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 user emotion data into a generation AI, which can then adjust the display order of the trailer videos.

[0082] The generation unit can generate optimal video content by considering the user's geographical location information when generating a preview video. For example, the generation unit analyzes the user's geographical location information using a generation AI. The generation AI can, for example, generate a video of a planned itinerary that visits places close to the user's current location. Furthermore, the generation AI can generate a video of a planned itinerary that visits easily accessible places based on the user's geographical location information. In addition, the generation AI can generate a video of a planned itinerary that includes local events and activities based on the user's geographical location information. This allows the generation unit to provide the user with optimal video content by considering geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate optimal video content.

[0083] The generation unit can generate video content by analyzing the user's social media activity when generating a preview video. For example, the generation unit can use a generation AI to analyze the user's social media activity. The generation AI can analyze the user's social media posts and generate videos with interesting content. Furthermore, the generation AI can also generate videos with relevant content by referencing the activities of the user's friends on social media. In addition, the generation AI can analyze the user's social media hashtags and generate videos with relevant content. For example, the generation unit can analyze the user's social media posts and generate videos with interesting content. Furthermore, the generation unit can generate videos with relevant content by referencing the activities of the user's friends on social media. Furthermore, the generation unit can analyze the user's social media hashtags and generate videos with relevant content. This allows the generation unit to provide users with relevant video content by analyzing their social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, or without one. For example, the generation unit can input data on the user's social media activity into the generation AI, which can then generate video content.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The suggestion unit can estimate the user's emotions and adjust the content of the plan based on the estimated emotions. For example, if the user is relaxed, it can suggest a plan that includes calming activities. If the user is excited, it can suggest a plan that includes active activities. Furthermore, if the user is tired, it can suggest a plan that includes activities that allow for rest. In this way, by adjusting the content of the plan based on the user's emotions, a more appropriate plan can be provided. Some or all of the above processing in the suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the suggestion unit can input user emotion data into a generative AI, and the generative AI can adjust the content of the plan.

[0086] The generation unit can analyze a user's past viewing history and generate the most suitable trailer video. For example, it can analyze the trends of videos a user has watched in the past and generate a trailer video with similar content. It can also generate a trailer video with interesting content based on the user's past viewing history. Furthermore, it can analyze a user's past viewing history and generate a trailer video that offers a new experience. In this way, by analyzing past viewing history, the generation unit can provide the user with the most suitable trailer video. 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 the user's viewing history data into a generation AI, which can then generate the most suitable trailer video.

[0087] The reception desk can estimate the user's emotions and present options based on those emotions. For example, if the user is stressed, it can prioritize relaxing activities. If the user is excited, it can prioritize active activities. Furthermore, if the user is tired, it can prioritize resting activities. By presenting options based on the user's emotions, it can provide more appropriate choices. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input user emotion data into a generative AI, which can then present options.

[0088] The proposal unit can customize the plan based on the user's current living situation. For example, it can customize the plan based on information such as the user's family structure, work situation, and health status. For instance, it can propose a plan with ample time based on the user's current living situation. It can also propose a plan that the whole family can enjoy based on the user's family structure. Furthermore, it can propose a plan that fits the user's budget based on their current living situation. By customizing the plan based on the user's current living situation, it is possible to provide a more appropriate plan. Some or all of the above processing in the proposal unit may be performed using, for example, a generation AI, or not. For example, the proposal unit can input data on the user's living situation into a generation AI, which can then customize the plan.

[0089] The generation unit can estimate the user's emotions and adjust the content of the trailer video based on those emotions. For example, if the user is relaxed, it can generate a trailer video using calming music and visuals. If the user is excited, it can generate a trailer video using lively music and visuals. Furthermore, if the user is tired, it can generate a simple and highly visual trailer video. By adjusting the content of the trailer video based on the user's emotions, it is possible to provide a more appropriate video. 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 user emotion data into a generation AI, which can then adjust the content of the trailer video.

[0090] The reception desk can analyze the user's past selection history and suggest the most suitable options. For example, it can analyze the trends of activities the user has previously selected and suggest similar activities. It can also evaluate the user's satisfaction with activities they have previously selected and prioritize suggesting highly-rated activities. Furthermore, it can analyze the frequency of activities the user has previously selected and suggest less frequent activities. In this way, by analyzing past selection history, the reception desk can suggest the most suitable options for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's selection history data into an AI, which can then suggest the most suitable options.

[0091] The proposal unit can estimate the user's emotions and adjust the presentation of the plan based on those emotions. For example, if the user is relaxed, the plan can be presented in a calm manner. If the user is excited, the plan can be presented in a lively manner. Furthermore, if the user is tired, the plan can be presented in a simple and easily recognizable manner. By adjusting the presentation of the plan based on the user's emotions, a more appropriate plan can be provided. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI. For example, the proposal unit can input user emotion data into a generative AI, which can then adjust the presentation of the plan.

[0092] The generation unit can customize the video content based on the user's current lifestyle when generating a preview video. For example, it can customize the video content based on information such as the user's family structure, work situation, and health status. For instance, it can generate video content that allows the user to have ample time based on their current lifestyle. It can also generate video content that the whole family can enjoy based on the user's family structure. Furthermore, it can generate video content that fits the user's budget based on their current lifestyle. By customizing the video content based on the user's current lifestyle, it is possible to provide more appropriate videos. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input data on the user's lifestyle into the generation AI, which can then customize the video content.

[0093] The reception desk can estimate the user's emotions and prioritize their preferences based on those emotions. For example, if the user is relaxed, it can prioritize relaxing activities. If the user is excited, it can prioritize active activities. Furthermore, if the user is tired, it can prioritize activities that allow them to rest. By prioritizing preferences based on the user's emotions, it is possible to provide more appropriate choices. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input user emotion data into a generative AI, which can then determine the priority of preferences.

[0094] The proposal unit can generate an optimal plan by considering the user's geographical location when proposing a plan. For example, it can propose a plan that visits places close to the user's current location. It can also propose a plan that visits easily accessible places based on the user's geographical location. Furthermore, it can propose a plan that includes local events and activities based on the user's geographical location. In this way, by considering geographical location, the system can provide the user with the most suitable plan. Some or all of the above processing in the proposal unit may be performed using, for example, a generation AI, or without a generation AI. For example, the proposal unit can input the user's geographical location information into a generation AI, which can then generate an optimal plan.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The reception desk selects the user's preferences. These preferences include, for example, the purpose of the trip, budget, and preferences. Specifically, the user can select preferences such as "I want to go to the zoo," "I want to have a picnic in the park," or "I want to visit a museum." Step 2: The proposal department proposes a plan based on the preferences selected by the reception department. The proposal department uses generative AI to generate a plan that matches the user's preferences. For example, it proposes a plan based on information from the internet, information from books and guidebooks, and the user's past data. Step 3: The generation unit generates a preview video based on the plan proposed by the proposal unit. The generation unit uses generation AI to create a preview video based on the plan. For example, if a zoo visit plan is proposed, it will generate a video introducing the zoo's highlights and how to get there. If a park picnic plan is proposed, it will generate a video introducing the park's scenery and the picnic. Furthermore, if a museum visit plan is proposed, it will generate a video introducing the museum's exhibits and how to get there.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the reception unit, proposal unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and selects the user's intentions. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a plan using a generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a preview video based on the proposed plan. The generation unit may also be implemented by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the reception unit, proposal unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and selects the user's intentions. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a plan using generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a preview video based on the proposed plan. The generation unit may also be implemented by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

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

[0120] The 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.

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

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

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

[0124] Figure 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.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the 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.

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

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0131] The data processing system 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.

[0132] Each of the multiple elements described above, including the reception unit, proposal unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and selects the user's intentions. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a plan using a generation AI. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a preview video based on the proposed plan. The generation unit may also be implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

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

[0136] The 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.

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

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the reception unit, proposal unit, and generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and selects the user's intentions. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a plan using a generation AI. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a preview video based on the proposed plan. The generation unit may also be implemented by, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A reception desk where users select their preferences, A proposal department that proposes a plan based on the intentions selected by the aforementioned reception department, The system includes a generation unit that generates a preview video based on the proposed plan submitted by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Generate a plan based on various information sources. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the proposed plan, generate a teaser video to capture children's interest. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose a plan that includes information such as itinerary, reservations, and directions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The generated videos are designed to be visually understandable for children. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and presents options based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past selection history and suggests the most suitable options. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When selecting preferences, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of their intentions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users select their preferences, the system prioritizes presenting highly relevant options by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users select their preferences, the system analyzes their social media activity and presents relevant options. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, The system estimates the user's emotions and adjusts the presentation of the plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When proposing a plan, the system analyzes the user's past behavior history to generate the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When proposing a plan, customize the plan based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, The system estimates user emotions and prioritizes plan proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When proposing a plan, the system generates the optimal plan by taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When proposing a plan, the system analyzes users' social media activity to generate the plan. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates user emotions and adjusts the content of the trailer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a preview video, the system analyzes the user's past viewing history to create the most suitable video content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a trailer video, the video content is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is The system estimates the user's emotions and adjusts the display order of the trailer videos based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating a preview video, the system takes the user's geographical location into consideration to generate the most suitable video content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating a trailer video, the system analyzes the user's social media activity to generate the video content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where users select their preferences, A proposal department that proposes a plan based on the intentions selected by the aforementioned reception department, The system includes a generation unit that generates a preview video based on the proposed plan submitted by the proposal unit. A system characterized by the following features.

2. The aforementioned proposal section is, Generate a plan based on various information sources. The system according to feature 1.

3. The generating unit is Based on the proposed plan, generate a teaser video to capture children's interest. The system according to feature 1.

4. The aforementioned proposal section is, We propose a plan that includes information such as itinerary, reservations, and directions. The system according to feature 1.

5. The generating unit is The generated videos are designed to be visually understandable for children. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and presents options based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past selection history and suggests the most suitable options. The system according to feature 1.

8. The aforementioned reception unit is When selecting preferences, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of their intentions based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When users select their preferences, the system prioritizes presenting highly relevant options by considering their geographical location. The system according to feature 1.

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