System

Abstract

An object of a system according to an embodiment is to provide a custom-made travel plan based on a user's hobby and life.SOLUTION: A system according to an embodiment includes a data collection unit an analysis unit a travel plan generation unit and a moving image generation unit. The data collection unit collects photograph data of a user from a photograph management application. The analysis unit analyzes the photographic data collected by the data collection unit, and analyzes the life and hobbies of the user. The travel plan generation unit generates a custom-made travel plan based on the data analyzed by the analysis unit. The moving image generation part generates a travel experience moving image utilizing the user's face and scenery on the basis of the travel plan generated by the travel plan generation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to provide a custom-made travel plan based on a user's hobbies and lifestyle.

[0005] The system according to the embodiment aims to provide a custom-made travel plan based on the user's hobbies and lifestyle. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a travel plan generation unit, and a video generation unit. The data collection unit collects user photo data from a photo management app. The analysis unit analyzes the photo data collected by the data collection unit and analyzes the user's lifestyle and hobbies. The travel plan generation unit generates a custom-made travel plan based on the data analyzed by the analysis unit. The video generation unit generates a travel experience video that utilizes the user's face and scenery based on the travel plan generated by the travel plan generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a custom-made travel plan based on the user's hobbies and lifestyle. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

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

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The travel plan providing system according to an embodiment of the present invention collects user photo data, analyzes it with a generation AI, designs a custom-made travel plan, and generates a realistic travel experience video with a video generation AI. As a result, the travel plan providing system can provide a custom-made travel plan based on the user's preferences and generate a realistic travel experience video, thereby encouraging the user to make a travel decision.

[0029] The travel plan providing system according to the embodiment includes a data collection unit, an analysis unit, a travel plan generation unit, and a video generation unit. The data collection unit collects user photo data from a photo management app. For example, the data collection unit acquires photo data from a photo management app such as Google Photos. The data collection unit can also collect user photo data from cloud storage. The data collection unit can also collect photo data directly from the user's device. The analysis unit analyzes the photo data collected by the data collection unit to analyze the user's lifestyle and hobbies. For example, the generation AI can identify locations and activities captured in photos using image recognition technology. The generation AI can also analyze user preferences using machine learning algorithms. The generation AI can also analyze metadata in the photo data to identify user behavior patterns. The travel plan generation unit generates a customized travel plan based on the data analyzed by the analysis unit. For example, the generation AI can design a travel plan based on the user's favorite tourist spots and activities. The generation AI can also customize the travel plan to suit the user's preferences. The generation AI can also optimize the travel plan to suit the user's budget and schedule. The video generation unit generates a travel experience video that utilizes the user's face and scenery based on the travel plan generated by the travel plan generation unit. For example, the video generation AI combines a photo of the user's face with scenery at the travel destination to create a video that makes the user feel as if they are actually there. The video generation AI can also generate a realistic travel experience video based on the user's travel plan. Furthermore, the video generation AI can customize the content of the video to suit the user's preferences. As a result, the travel plan providing system according to the embodiment can provide a custom-made travel plan based on the user's preferences and generate a realistic travel experience video, thereby encouraging the user to make a travel decision. For example, by watching the generated travel experience video, the user can realistically experience the appeal of the travel destination. Furthermore, the user can find the perfect travel destination for themselves through the custom-made travel plan.Furthermore, the travel plan providing system can efficiently and effectively design and propose travel plans based on the user's preferences.

[0030] The data collection unit analyzes metadata included in the photo data and can perform a detailed analysis of the user's behavioral patterns and seasonal preferences. For example, the data collection unit analyzes the location information of the photos to identify locations frequently visited by the user. For example, if a particular city or tourist spot is frequently visited, the data collection unit determines that the location matches the user's preferences. The data collection unit also analyzes the time the photos were taken to identify activities the user prefers in a particular season or time of day. For example, if many photos were taken at the beach in the summer, the data collection unit determines that the user prefers summer beaches. The data collection unit also analyzes the user's behavioral patterns based on the photo metadata. For example, if the user tends to enjoy outdoor activities on weekends, the data collection unit designs a travel plan based on that information. This allows for a detailed analysis of the user's behavioral patterns and seasonal preferences.

[0031] The data collection unit analyzes the user's photo data, extracts preferences for specific colors and compositions, and can suggest travel destination scenery based on that. The data collection unit, for example, analyzes the photo data to identify the user's preferred colors. For example, if there are many photos of blue seas and green mountains, the data collection unit can suggest travel destination scenery based on those colors. The data collection unit also analyzes the composition of the photos to identify the user's preferred shooting style. For example, if there are many landscape photos taken with a wide-angle lens, the data collection unit can suggest tourist spots that suit that style. The data collection unit also analyzes the color and composition of the photos in combination to suggest scenery that suits the user's preferences. For example, it can suggest specific situations such as a beach at sunset or a snow-capped mountain. This makes it possible to suggest travel destination scenery based on the user's preferences.

[0032] In addition to photo data, the data collection unit also collects users' social media posts or comments on review sites, enabling more multifaceted preference analysis. The data collection unit, for example, analyzes users' social media posts and combines them with photo data to analyze preferences. For example, it analyzes the content of Instagram posts and hashtags to identify the user's favorite activities and places. The data collection unit also collects comments on review sites and analyzes users' ratings and impressions. For example, it identifies places and activities that users have given high ratings to based on reviews of travel destinations. The data collection unit also integrates data from social media posts and review sites to analyze users' preferences from multiple angles. For example, it identifies places where positive posts on social media coincide with high ratings on review sites. This enables more multifaceted preference analysis.

[0033] The data collection unit collects data from a photo management app, including photo data of family and friends, and can propose group travel plans. For example, the data collection unit collects photo data of family and friends and analyzes the preferences of the entire group. For example, it can propose activities and tourist spots that the whole family can enjoy. The data collection unit also analyzes the photo data of group members to identify common preferences. For example, if everyone likes beach resorts, it can design a travel plan based on that information. The data collection unit also integrates the photo data of family and friends and analyzes the behavioral patterns of the entire group. For example, if everyone tends to enjoy outdoor activities, it can propose a travel plan based on that information. This makes it possible to propose group travel plans.

[0034] The travel plan generation unit can analyze the user's past travel history and suggest similar tourist destinations that have not been visited. The travel plan generation unit, for example, analyzes the user's past travel history and extracts characteristics of visited tourist destinations. For example, to a user who likes places rich in nature or historical tourist destinations, it suggests similar tourist destinations that have not been visited. The travel plan generation unit also identifies patterns of tourist destinations that the user prefers based on the past travel history. For example, to a user who likes beach resorts, it suggests beach resorts that have not been visited. The travel plan generation unit also analyzes the user's travel history and builds a system that suggests similar tourist destinations that have not been visited. For example, it automatically suggests new tourist destinations that are similar to tourist destinations that have been visited in the past. This makes it possible to suggest similar tourist destinations that have not been visited based on the user's past travel history.

[0035] The travel plan generation unit can collect health data of the user and design a travel plan that suits the user's physical strength. The travel plan generation unit, for example, analyzes the user's step count data and designs a travel plan that suits the user's physical strength. For example, it suggests hiking or walking tours to a user who records a large number of steps on a daily basis. The travel plan generation unit also suggests activities that suit the user's physical strength based on the user's heart rate data. For example, it suggests active sports or adventure tours to a user with a stable heart rate. The travel plan generation unit also builds a system that designs a travel plan that suits the user's physical strength based on the health data. For example, it suggests a reasonable travel plan based on step count and heart rate data. This makes it possible to design a travel plan that suits the user's physical strength.

[0036] The travel plan generation unit can incorporate information about local cultural events and festivals into travel plan suggestions to provide a special experience. The travel plan generation unit, for example, collects information about local cultural events and festivals and incorporates it into the travel plan. For example, it suggests events that will be held during the time the user will be visiting. The travel plan generation unit also provides a special experience by incorporating local cultural events into the travel plan. For example, it includes local festivals and traditional events in the travel plan. The travel plan generation unit also designs a travel plan that provides a special experience to the user based on local festival information. For example, it incorporates music festivals and art events into the travel plan. In this way, by incorporating local cultural events and festivals, a special experience can be provided.

[0037] The travel plan generation unit can also use the generation AI to analyze photo data of the user's pet and propose travel plans that allow pets to be brought along. The travel plan generation unit, for example, analyzes photo data of the user's pet and proposes travel plans that allow pets to be brought along. For example, it designs a plan that includes pet-friendly accommodations and restaurants. The travel plan generation unit also proposes activities that pets can enjoy based on the photo data of the pet. For example, it designs a travel plan that includes hiking trails and beaches that can be enjoyed with pets. The travel plan generation unit also uses the generation AI to analyze photo data of the user's pet and build a system that proposes travel plans that allow pets to be brought along. For example, it automatically suggests pet-friendly tourist spots and activities. This makes it possible to propose travel plans that allow pets to be brought along.

[0038] The video generation unit can analyze a user's past travel photos or videos and generate a remix video that combines them. The video generation unit, for example, analyzes a user's past travel photos and videos and generates a remix video that combines them. For example, it edits photos and videos of multiple travel destinations into a single story. The video generation unit also generates a remix video that looks back on the user's memories based on past travel photos and videos. For example, it edits photos and videos based on a specific theme or event. The video generation unit also builds a system that analyzes a user's past travel data and generates a remix video that combines them. For example, it automatically generates a video that collects highlight scenes from the trip. This makes it possible to generate a remix video that combines past travel photos and videos.

[0039] The video generation unit can synthesize the user's voice using video generation AI to create a travel experience video with narration. The video generation unit, for example, synthesizes the user's voice to create a travel experience video with narration. For example, a narration in which the user talks about their experiences at a travel destination is added to the video. The video generation unit also uses video generation AI to build a system that synthesizes the user's voice and adds narration to the travel experience video. For example, a video that tells a travel story based on the user's voice is generated. The video generation unit also provides a more realistic experience by synthesizing the user's voice and creating a travel experience video with narration. For example, a narration in which the user talks about their impressions at a travel destination is incorporated into the video. In this way, the user's voice can be synthesized to create a travel experience video with narration.

[0040] The video generation unit can use video generation AI to synthesize the faces of the user's friends and family to generate a group travel experience video. For example, the video generation unit uses video generation AI to synthesize the faces of the user's friends and family to generate a group travel experience video. For example, scenes of the whole family enjoying themselves at a travel destination are added to the video. The video generation unit also analyzes photo data of the user's friends and family and builds a system that generates a group travel experience video based on that data. For example, scenes of touring tourist spots with friends are incorporated into the video. The video generation unit also uses video generation AI to synthesize the faces of the user's friends and family to generate a group travel experience video, providing a more realistic experience. For example, scenes of the whole family enjoying themselves at a travel destination are added to the video. In this way, the faces of the user's friends and family can be synthesized to generate a group travel experience video.

[0041] The video generation unit can add local music and sound effects to the generated travel experience video to provide a more realistic video. The video generation unit, for example, adds local music to the generated travel experience video to enhance the sense of realism. For example, traditional music or background music of the travel destination is incorporated into the video. The video generation unit also adds sound effects to the video to provide a more realistic experience. For example, local natural sounds such as the sound of waves and birds chirping are incorporated into the video. The video generation unit also builds a system for generating realistic travel experience videos based on local music and sound effects. For example, audio data that reproduces the soundscape of the travel destination is added to the video. This allows the addition of local music and sound effects to provide a more realistic video.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data collection unit collects the user's purchase history data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the user's purchase history and optimize the travel plan. For example, if the user frequently purchases outdoor equipment, the unit can suggest a travel plan that includes outdoor activities. Also, if the user likes to eat at high-end restaurants, the unit can design a meal plan based on that information. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the user's purchase history. This makes it possible to analyze preferences based on the user's purchase history and optimize the travel plan.

[0044] The data collection unit collects the user's health data in addition to the user's photo data, and the travel plan generation unit can optimize the travel plan based on the user's health condition. For example, the unit can analyze the user's step count data and design a travel plan based on their physical strength. For example, hiking or walking tours can be suggested to a user who records a lot of steps on a daily basis. Activities based on the user's heart rate data can also be suggested based on their physical strength. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the user's health data. This makes it possible to optimize the travel plan based on the user's health condition.

[0045] The data collection unit collects the user's social media posting data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the social media posting data and optimize the travel plan. For example, the unit can analyze the photos and comments the user posted on Instagram to identify their favorite tourist spots and activities. It can also analyze the content of the user's posts on Twitter and design a travel plan based on their impressions and reviews of travel destinations. Furthermore, it can adjust each element of the travel plan (accommodation, meals, activities) based on the social media posting data. This makes it possible to analyze preferences based on the user's social media posting data and optimize the travel plan.

[0046] The data collection unit collects the user's music playback history data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the music playback history and optimize the travel plan. For example, if the user frequently plays music of a particular genre, music festivals and events related to that genre can be suggested. Also, if the user prefers relaxing music, a relaxing travel plan can be designed based on that information. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the music playback history. This allows preferences to be analyzed based on the user's music playback history and the travel plan to be optimized.

[0047] The data collection unit collects the user's reading history data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the reading history and optimize the travel plan. For example, if the user frequently reads books of a particular genre, tourist spots and events related to that genre can be suggested. Also, if the user prefers a relaxing reading environment, a relaxing travel plan can be designed based on that information. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the reading history. This makes it possible to analyze preferences based on the user's reading history and optimize the travel plan.

[0048] The data collection unit collects the user's exercise history data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the exercise history and optimize the travel plan. For example, if the user frequently participates in a particular sport or fitness activity, the travel plan generation unit can suggest a travel plan related to that activity. Also, if the user prefers a relaxing exercise environment, the unit can design a relaxing travel plan based on that information. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the exercise history. This allows the travel plan to be optimized based on the user's exercise history and preferences to be analyzed.

[0049] The data collection unit collects the user's movie viewing history data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the movie viewing history and optimize the travel plan. For example, if the user frequently watches movies of a particular genre, tourist spots and events related to that genre can be suggested. Also, if the user prefers a relaxing movie environment, a relaxing travel plan can be designed based on that information. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the movie viewing history. This makes it possible to analyze preferences based on the user's movie viewing history and optimize the travel plan.

[0050] The data collection unit collects the user's game play history data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the game play history and optimize the travel plan. For example, if the user frequently plays games of a particular genre, tourist spots and events related to that genre can be suggested. Also, if the user prefers a relaxing gaming environment, a relaxing travel plan can be designed based on that information. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the game play history. This makes it possible to analyze preferences based on the user's game play history and optimize the travel plan.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The data collection unit collects the user's photo data from a photo management app. For example, the data collection unit acquires photo data from a photo management app such as Google Photo. The data collection unit can also collect the user's photo data from cloud storage. Furthermore, the data collection unit can also collect photo data directly from the user's device. Step 2: The analysis unit analyzes the photo data collected by the data collection unit and analyzes the user's lifestyle and hobbies. For example, the generation AI uses image recognition technology to identify the locations and activities shown in the photos. The generation AI can also analyze the user's preferences using machine learning algorithms. Furthermore, the generation AI can analyze the metadata of the photo data to identify the user's behavioral patterns. Step 3: The travel plan generation unit generates a custom travel plan based on the data analyzed by the analysis unit. For example, the generation AI designs a travel plan based on the user's favorite tourist spots and activities. The generation AI can also customize the travel plan to suit the user's preferences. Furthermore, the generation AI can optimize the travel plan to suit the user's budget and schedule. Step 4: The video generation unit generates a travel experience video that utilizes the user's face and scenery based on the travel plan generated by the travel plan generation unit. For example, the video generation AI can combine a photo of the user's face with the scenery of the travel destination to create a video that makes the user feel as if they are actually in that place. The video generation AI can also generate a realistic travel experience video based on the user's travel plan. Furthermore, the video generation AI can customize the content of the video to suit the user's preferences.

[0053] (Example 2) The travel plan providing system according to an embodiment of the present invention collects user photo data, analyzes it with a generation AI, designs a custom-made travel plan, and generates a realistic travel experience video with a video generation AI. As a result, the travel plan providing system can provide a custom-made travel plan based on the user's preferences and generate a realistic travel experience video, thereby encouraging the user to make a travel decision.

[0054] The travel plan providing system according to the embodiment includes a data collection unit, an analysis unit, a travel plan generation unit, and a video generation unit. The data collection unit collects user photo data from a photo management app. For example, the data collection unit acquires photo data from a photo management app such as Google Photos. The data collection unit can also collect user photo data from cloud storage. The data collection unit can also collect photo data directly from the user's device. The analysis unit analyzes the photo data collected by the data collection unit to analyze the user's lifestyle and hobbies. For example, the generation AI can identify locations and activities captured in photos using image recognition technology. The generation AI can also analyze user preferences using machine learning algorithms. The generation AI can also analyze metadata in the photo data to identify user behavior patterns. The travel plan generation unit generates a customized travel plan based on the data analyzed by the analysis unit. For example, the generation AI can design a travel plan based on the user's favorite tourist spots and activities. The generation AI can also customize the travel plan to suit the user's preferences. The generation AI can also optimize the travel plan to suit the user's budget and schedule. The video generation unit generates a travel experience video that utilizes the user's face and scenery based on the travel plan generated by the travel plan generation unit. For example, the video generation AI combines a photo of the user's face with scenery at the travel destination to create a video that makes the user feel as if they are actually there. The video generation AI can also generate a realistic travel experience video based on the user's travel plan. Furthermore, the video generation AI can customize the content of the video to suit the user's preferences. As a result, the travel plan providing system according to the embodiment can provide a custom-made travel plan based on the user's preferences and generate a realistic travel experience video, thereby encouraging the user to make a travel decision. For example, by watching the generated travel experience video, the user can realistically experience the appeal of the travel destination. Furthermore, the user can find the perfect travel destination for themselves through the custom-made travel plan.Furthermore, the travel plan providing system can efficiently and effectively design and propose travel plans based on the user's preferences.

[0055] The data collection unit can infer emotions from a user's photo data and identify places and activities that evoke positive emotions. For example, the data collection unit analyzes the user's photo data and uses facial recognition technology to analyze the facial expressions of people in the photos. For example, it identifies places and activities with a high number of smiling photos and extracts them as elements that evoke positive emotions. The data collection unit also analyzes metadata (location information, time information) included in the photos to infer the user's emotions at specific locations and time periods. For example, if a large number of photos were taken at a specific tourist spot, it determines that that location evokes positive emotions for the user. The data collection unit also uses natural language processing technology on the photo data to analyze captions and comments related to the photos and infer emotions. For example, it identifies photos with a high number of positive expressions such as "fun" and "beautiful." This allows the data collection unit to optimize travel plans based on the user's emotions.

[0056] The data collection unit analyzes metadata included in the photo data and can perform a detailed analysis of the user's behavioral patterns and seasonal preferences. For example, the data collection unit analyzes the location information of the photos to identify locations frequently visited by the user. For example, if a particular city or tourist spot is frequently visited, the data collection unit determines that the location matches the user's preferences. The data collection unit also analyzes the time the photos were taken to identify activities the user prefers in a particular season or time of day. For example, if many photos were taken at the beach in the summer, the data collection unit determines that the user prefers summer beaches. The data collection unit also analyzes the user's behavioral patterns based on the photo metadata. For example, if the user tends to enjoy outdoor activities on weekends, the data collection unit designs a travel plan based on that information. This allows for a detailed analysis of the user's behavioral patterns and seasonal preferences.

[0057] The data collection unit analyzes the user's photo data, extracts preferences for specific colors and compositions, and can suggest travel destination scenery based on that. The data collection unit, for example, analyzes the photo data to identify the user's preferred colors. For example, if there are many photos of blue seas and green mountains, the data collection unit can suggest travel destination scenery based on those colors. The data collection unit also analyzes the composition of the photos to identify the user's preferred shooting style. For example, if there are many landscape photos taken with a wide-angle lens, the data collection unit can suggest tourist spots that suit that style. The data collection unit also analyzes the color and composition of the photos in combination to suggest scenery that suits the user's preferences. For example, it can suggest specific situations such as a beach at sunset or a snow-capped mountain. This makes it possible to suggest travel destination scenery based on the user's preferences.

[0058] In addition to photo data, the data collection unit also collects users' social media posts or comments on review sites, enabling more multifaceted preference analysis. The data collection unit, for example, analyzes users' social media posts and combines them with photo data to analyze preferences. For example, it analyzes the content of Instagram posts and hashtags to identify the user's favorite activities and places. The data collection unit also collects comments on review sites and analyzes users' ratings and impressions. For example, it identifies places and activities that users have given high ratings to based on reviews of travel destinations. The data collection unit also integrates data from social media posts and review sites to analyze users' preferences from multiple angles. For example, it identifies places where positive posts on social media coincide with high ratings on review sites. This enables more multifaceted preference analysis.

[0059] The data collection unit collects data from a photo management app, including photo data of family and friends, and can propose group travel plans. For example, the data collection unit collects photo data of family and friends and analyzes the preferences of the entire group. For example, it can propose activities and tourist spots that the whole family can enjoy. The data collection unit also analyzes the photo data of group members to identify common preferences. For example, if everyone likes beach resorts, it can design a travel plan based on that information. The data collection unit also integrates the photo data of family and friends and analyzes the behavioral patterns of the entire group. For example, if everyone tends to enjoy outdoor activities, it can propose a travel plan based on that information. This makes it possible to propose group travel plans.

[0060] The data collection unit can use the emotion estimation function to analyze the emotions of places the user has visited in the past and propose a plan to revisit places that caused positive emotions. The data collection unit, for example, analyzes the user's past travel photos and uses the emotion estimation function to identify places that caused positive emotions. For example, the data collection unit proposes a plan to revisit places with many smiling photos. The data collection unit also analyzes metadata included in the past travel photos to estimate emotions at specific places and activities. For example, the data collection unit designs a revisit plan based on the positive emotions felt at specific tourist spots. The data collection unit also uses the emotion estimation function to perform a detailed analysis of the emotions of places the user has visited in the past and proposes a plan to revisit places that caused positive emotions. For example, the data collection unit designs a revisit plan based on emotions felt at specific seasons or events. This makes it possible to propose a plan to revisit places where the user felt positive emotions in the past.

[0061] The travel plan generation unit can optimize each element of the travel plan (accommodation, meals, activities) based on the user's emotional data. The travel plan generation unit, for example, analyzes the user's emotional data and suggests accommodations that evoke positive emotions. For example, it designs an accommodation plan based on hotels and resorts that have received high ratings from past trips. The travel plan generation unit also suggests meals and restaurants that the user prefers based on the emotional data. For example, it designs a meal plan based on positive emotions felt at specific dishes or restaurants. The travel plan generation unit also analyzes the user's emotional data and suggests activities that evoke positive emotions. For example, it designs a travel plan based on activities that were enjoyed on past trips. In this way, the travel plan can be optimized based on the user's emotional data.

[0062] The travel plan generation unit can analyze the user's past travel history and suggest similar tourist destinations that have not been visited. The travel plan generation unit, for example, analyzes the user's past travel history and extracts characteristics of visited tourist destinations. For example, to a user who likes places rich in nature or historical tourist destinations, it suggests similar tourist destinations that have not been visited. The travel plan generation unit also identifies patterns of tourist destinations that the user prefers based on the past travel history. For example, to a user who likes beach resorts, it suggests beach resorts that have not been visited. The travel plan generation unit also analyzes the user's travel history and builds a system that suggests similar tourist destinations that have not been visited. For example, it automatically suggests new tourist destinations that are similar to tourist destinations that have been visited in the past. This makes it possible to suggest similar tourist destinations that have not been visited based on the user's past travel history.

[0063] The travel plan generation unit can collect health data of the user and design a travel plan that suits the user's physical strength. The travel plan generation unit, for example, analyzes the user's step count data and designs a travel plan that suits the user's physical strength. For example, it suggests hiking or walking tours to a user who records a large number of steps on a daily basis. The travel plan generation unit also suggests activities that suit the user's physical strength based on the user's heart rate data. For example, it suggests active sports or adventure tours to a user with a stable heart rate. The travel plan generation unit also builds a system that designs a travel plan that suits the user's physical strength based on the health data. For example, it suggests a reasonable travel plan based on step count and heart rate data. This makes it possible to design a travel plan that suits the user's physical strength.

[0064] The travel plan generation unit can incorporate information about local cultural events and festivals into travel plan suggestions to provide a special experience. The travel plan generation unit, for example, collects information about local cultural events and festivals and incorporates it into the travel plan. For example, it suggests events that will be held during the time the user will be visiting. The travel plan generation unit also provides a special experience by incorporating local cultural events into the travel plan. For example, it includes local festivals and traditional events in the travel plan. The travel plan generation unit also designs a travel plan that provides a special experience to the user based on local festival information. For example, it incorporates music festivals and art events into the travel plan. In this way, by incorporating local cultural events and festivals, a special experience can be provided.

[0065] The travel plan generation unit can also use the generation AI to analyze photo data of the user's pet and propose travel plans that allow pets to be brought along. The travel plan generation unit, for example, analyzes photo data of the user's pet and proposes travel plans that allow pets to be brought along. For example, it designs a plan that includes pet-friendly accommodations and restaurants. The travel plan generation unit also proposes activities that pets can enjoy based on the photo data of the pet. For example, it designs a travel plan that includes hiking trails and beaches that can be enjoyed with pets. The travel plan generation unit also uses the generation AI to analyze photo data of the user's pet and build a system that proposes travel plans that allow pets to be brought along. For example, it automatically suggests pet-friendly tourist spots and activities. This makes it possible to propose travel plans that allow pets to be brought along.

[0066] The travel plan generation unit uses the emotion estimation function to monitor the user's emotions in real time when browsing travel plans, and can dynamically suggest plans that pique the user's interest. The travel plan generation unit, for example, monitors the user's emotions in real time when browsing travel plans, and suggests plans that pique the user's interest. For example, plans that evoke stronger positive emotions are preferentially displayed. The travel plan generation unit also uses the emotion estimation function to dynamically adjust the travel plan based on the user's emotional response. For example, plans that include elements that pique the user's interest are suggested in real time. The travel plan generation unit also builds a system that dynamically suggests travel plans that pique the user's interest based on the user's emotion data. For example, plans with high emotion scores are preferentially displayed. This makes it possible to dynamically suggest plans that pique the user's interest according to the user's emotions.

[0067] The video generation unit can estimate the user's emotions and generate a travel experience video that emphasizes scenes that evoke positive emotions. The video generation unit, for example, analyzes the user's emotion data and identifies scenes that evoke positive emotions. For example, based on photos and videos of smiling faces, it emphasizes and inserts positive scenes into the travel experience video. The video generation unit also uses the emotion estimation function to identify places and activities that the user previously felt positive about and generates a travel experience video that emphasizes those. For example, it creates a video centered around specific tourist spots and activities. The video generation unit also builds a system that generates a travel experience video that emphasizes scenes that evoke positive emotions based on the user's emotion data. For example, it prioritizes incorporating scenes with high emotion scores into the video. This makes it possible to generate a travel experience video that emphasizes scenes that evoke positive emotions.

[0068] The video generation unit can analyze a user's past travel photos or videos and generate a remix video that combines them. The video generation unit, for example, analyzes a user's past travel photos and videos and generates a remix video that combines them. For example, it edits photos and videos of multiple travel destinations into a single story. The video generation unit also generates a remix video that looks back on the user's memories based on past travel photos and videos. For example, it edits photos and videos based on a specific theme or event. The video generation unit also builds a system that analyzes a user's past travel data and generates a remix video that combines them. For example, it automatically generates a video that collects highlight scenes from the trip. This makes it possible to generate a remix video that combines past travel photos and videos.

[0069] The video generation unit can synthesize the user's voice using video generation AI to create a travel experience video with narration. The video generation unit, for example, synthesizes the user's voice to create a travel experience video with narration. For example, a narration in which the user talks about their experiences at a travel destination is added to the video. The video generation unit also uses video generation AI to build a system that synthesizes the user's voice and adds narration to the travel experience video. For example, a video that tells a travel story based on the user's voice is generated. The video generation unit also provides a more realistic experience by synthesizing the user's voice and creating a travel experience video with narration. For example, a narration in which the user talks about their impressions at a travel destination is incorporated into the video. In this way, the user's voice can be synthesized to create a travel experience video with narration.

[0070] The video generation unit can use video generation AI to synthesize the faces of the user's friends and family to generate a group travel experience video. For example, the video generation unit uses video generation AI to synthesize the faces of the user's friends and family to generate a group travel experience video. For example, scenes of the whole family enjoying themselves at a travel destination are added to the video. The video generation unit also analyzes photo data of the user's friends and family and builds a system that generates a group travel experience video based on that data. For example, scenes of touring tourist spots with friends are incorporated into the video. The video generation unit also uses video generation AI to synthesize the faces of the user's friends and family to generate a group travel experience video, providing a more realistic experience. For example, scenes of the whole family enjoying themselves at a travel destination are added to the video. In this way, the faces of the user's friends and family can be synthesized to generate a group travel experience video.

[0071] The video generation unit can add local music and sound effects to the generated travel experience video to provide a more realistic video. The video generation unit, for example, adds local music to the generated travel experience video to enhance the sense of realism. For example, traditional music or background music of the travel destination is incorporated into the video. The video generation unit also adds sound effects to the video to provide a more realistic experience. For example, local natural sounds such as the sound of waves and birds chirping are incorporated into the video. The video generation unit also builds a system for generating realistic travel experience videos based on local music and sound effects. For example, audio data that reproduces the soundscape of the travel destination is added to the video. This allows the addition of local music and sound effects to provide a more realistic video.

[0072] The video generation unit can use the emotion estimation function to monitor the emotions of a user when watching a video in real time and automatically play the most appropriate scenes. The video generation unit, for example, builds a system that monitors the emotions of a user when watching a video in real time and automatically plays the most appropriate scenes. For example, it prioritizes playing scenes that intensify positive emotions. The video generation unit also uses the emotion estimation function to dynamically adjust the scenes played in the video based on the user's emotional response. For example, it plays scenes that arouse interest in real time. The video generation unit also generates a travel experience video that automatically plays the most appropriate scenes based on the user's emotion data. For example, it prioritizes playing scenes with a high emotion score. This makes it possible to automatically play the most appropriate scenes according to the user's emotions.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The data collection unit collects the user's voice data in addition to the user's photo data, and the travel plan generation unit can infer emotions from the user's tone of voice and speaking style and optimize the travel plan. For example, the unit can analyze the excited tone of voice when the user talks about their travel destination and include those locations in the travel plan. It can also identify locations where the user speaks in a relaxed voice and suggest relaxing activities. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the user's tone of voice. This makes it possible to infer emotions from the user's tone of voice and speaking style and optimize the travel plan.

[0075] The data collection unit collects the user's purchase history data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the user's purchase history and optimize the travel plan. For example, if the user frequently purchases outdoor equipment, the unit can suggest a travel plan that includes outdoor activities. Also, if the user likes to eat at high-end restaurants, the unit can design a meal plan based on that information. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the user's purchase history. This makes it possible to analyze preferences based on the user's purchase history and optimize the travel plan.

[0076] The data collection unit collects the user's health data in addition to the user's photo data, and the travel plan generation unit can optimize the travel plan based on the user's health condition. For example, the unit can analyze the user's step count data and design a travel plan based on their physical strength. For example, hiking or walking tours can be suggested to a user who records a lot of steps on a daily basis. Activities based on the user's heart rate data can also be suggested based on their physical strength. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the user's health data. This makes it possible to optimize the travel plan based on the user's health condition.

[0077] The data collection unit collects the user's social media posting data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the social media posting data and optimize the travel plan. For example, the unit can analyze the photos and comments the user posted on Instagram to identify their favorite tourist spots and activities. It can also analyze the content of the user's posts on Twitter and design a travel plan based on their impressions and reviews of travel destinations. Furthermore, it can adjust each element of the travel plan (accommodation, meals, activities) based on the social media posting data. This makes it possible to analyze preferences based on the user's social media posting data and optimize the travel plan.

[0078] The data collection unit collects the user's music playback history data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the music playback history and optimize the travel plan. For example, if the user frequently plays music of a particular genre, music festivals and events related to that genre can be suggested. Also, if the user prefers relaxing music, a relaxing travel plan can be designed based on that information. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the music playback history. This allows preferences to be analyzed based on the user's music playback history and the travel plan to be optimized.

[0079] The data collection unit collects the user's reading history data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the reading history and optimize the travel plan. For example, if the user frequently reads books of a particular genre, tourist spots and events related to that genre can be suggested. Also, if the user prefers a relaxing reading environment, a relaxing travel plan can be designed based on that information. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the reading history. This makes it possible to analyze preferences based on the user's reading history and optimize the travel plan.

[0080] The data collection unit collects the user's exercise history data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the exercise history and optimize the travel plan. For example, if the user frequently participates in a particular sport or fitness activity, the travel plan generation unit can suggest a travel plan related to that activity. Also, if the user prefers a relaxing exercise environment, the unit can design a relaxing travel plan based on that information. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the exercise history. This allows the travel plan to be optimized based on the user's exercise history and preferences to be analyzed.

[0081] The data collection unit collects the user's movie viewing history data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the movie viewing history and optimize the travel plan. For example, if the user frequently watches movies of a particular genre, tourist spots and events related to that genre can be suggested. Also, if the user prefers a relaxing movie environment, a relaxing travel plan can be designed based on that information. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the movie viewing history. This makes it possible to analyze preferences based on the user's movie viewing history and optimize the travel plan.

[0082] The data collection unit collects the user's game play history data in addition to the user's photo data, and the travel plan generation unit can analyze the user's preferences based on the game play history and optimize the travel plan. For example, if the user frequently plays games of a particular genre, tourist spots and events related to that genre can be suggested. Also, if the user prefers a relaxing gaming environment, a relaxing travel plan can be designed based on that information. Furthermore, each element of the travel plan (accommodation, meals, activities) can be adjusted based on the game play history. This makes it possible to analyze preferences based on the user's game play history and optimize the travel plan.

[0083] In addition to the user's photo data, the data collection unit can use the user's emotion estimation function to analyze the user's emotions at places they have visited in the past and propose a plan to revisit places that caused positive emotions. For example, the data collection unit can analyze the user's past travel photos and use the emotion estimation function to identify places that caused positive emotions. For example, the data collection unit can propose a plan to revisit places with many smiling photos. The data collection unit can also analyze metadata included in past travel photos to estimate emotions at specific places or activities. Furthermore, the emotion estimation function can be used to perform a detailed analysis of the emotions at places the user has visited in the past and propose a plan to revisit places that caused positive emotions. This makes it possible to propose a plan to revisit places where the user felt positive emotions in the past.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The data collection unit collects the user's photo data from a photo management app. For example, the data collection unit acquires photo data from a photo management app such as Google Photo. The data collection unit can also collect the user's photo data from cloud storage. Furthermore, the data collection unit can also collect photo data directly from the user's device. Step 2: The analysis unit analyzes the photo data collected by the data collection unit and analyzes the user's lifestyle and hobbies. For example, the generation AI uses image recognition technology to identify the locations and activities shown in the photos. The generation AI can also analyze the user's preferences using machine learning algorithms. Furthermore, the generation AI can analyze the metadata of the photo data to identify the user's behavioral patterns. Step 3: The travel plan generation unit generates a custom travel plan based on the data analyzed by the analysis unit. For example, the generation AI designs a travel plan based on the user's favorite tourist spots and activities. The generation AI can also customize the travel plan to suit the user's preferences. Furthermore, the generation AI can optimize the travel plan to suit the user's budget and schedule. Step 4: The video generation unit generates a travel experience video that utilizes the user's face and scenery based on the travel plan generated by the travel plan generation unit. For example, the video generation AI can combine a photo of the user's face with the scenery of the travel destination to create a video that makes the user feel as if they are actually in that place. The video generation AI can also generate a realistic travel experience video based on the user's travel plan. Furthermore, the video generation AI can customize the content of the video to suit the user's preferences.

[0086] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0091] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0092] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0093] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0094] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0096] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0097] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0098] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0100] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0101] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0106] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0127] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0133] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0136] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0137] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0138] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0139] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0140] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0141] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0142] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0143] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0144] 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.

[0145] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0146] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0147] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0148] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0149] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0150] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0151] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0152] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0153] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects user photo data from a photo management app; an analysis unit that analyzes the photo data collected by the data collection unit and analyzes the user's lifestyle and hobbies; a travel plan generation unit that generates a custom-made travel plan based on the data analyzed by the analysis unit; a video generation unit that generates a travel experience video using the user's face and scenery based on the travel plan generated by the travel plan generation unit; A system characterized by:

2. The data collection unit Estimate emotions from the user's photo data and identify places and activities that evoke positive emotions 2. The system of claim 1.

3. The data collection unit The metadata included in the photo data is analyzed to analyze the user's behavioral patterns and seasonal preferences in detail.

2. The system of claim 1.

4. The data collection unit Analyze the user's photo data, extract specific color and composition preferences, and suggest scenery of the travel destination based on that.

2. The system of claim 1.

5. The data collection unit In addition to the photo data, the user's social media posts or comments on review sites are also collected to conduct a more multifaceted preference analysis.

2. The system of claim 1.

Images