system

Abstract

The system according to the embodiment aims to allow a user to specify the places they want to go to using photos and to efficiently propose a travel itinerary. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit accepts input of photos and destinations from a user. The analysis unit analyzes the photos accepted by the reception unit and analyzes the destinations. The proposal unit proposes a travel itinerary based on the destinations analyzed by the analysis unit.

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 technologies have had the problem that it is difficult for users to specifically specify the places they want to go, making it difficult to efficiently plan their travel itinerary.

[0005] The system according to the embodiment aims to allow a user to specify the places they want to go to using photos and to efficiently propose a travel itinerary. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit accepts input of photos and destinations from a user. The analysis unit analyzes the photos accepted by the reception unit and analyzes the destinations. The proposal unit proposes a travel itinerary based on the destinations analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to specify the places they want to go to using photos and efficiently proposes a travel itinerary. [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) A travel suggestion system according to an embodiment of the present invention allows a user to load photos of a desired area, analyze the photos, and propose a travel itinerary. The travel suggestion system allows a user to load photos of a desired area and input, "I want to go to XX via photos A and B." The generation AI then analyzes the command and identifies the locations of photos A and B. The system then proposes a travel itinerary based on the identified locations. For example, the system may suggest a specific itinerary, such as one day around photo A and two days around photo B. The user can also receive text suggestions, such as stopping by a local product fair with a famous souvenir on the way home. This allows users to easily create their ideal travel plans. For example, if a user wants to visit a location depicted in a beautiful image while browsing the web, the system can simply load the image into the system, which will identify the location and propose a travel itinerary. Furthermore, by specifying a specific itinerary, the system can create a more ideal travel plan. Furthermore, the system can also make suggestions tailored to the user's needs, such as stopping by a local product fair with a famous souvenir on the way home. This ensures that users can visit places they don't want to miss during their trip. The travel suggestion system can pinpoint the locations the user wants to visit and propose an ideal travel plan. For example, the system can efficiently plan trips by easily identifying the places users want to go and suggesting travel itineraries. In addition, by providing suggestions based on the user's requests, the system can ensure that users visit places they do not want to miss during their trip.

[0029] A travel suggestion system according to an embodiment includes a reception unit, an analysis unit, and a suggestion unit. The reception unit accepts input of photos and destinations from a user. Photos from a user include, but are not limited to, JPEG format, PNG format, landscape photos, and portrait photos. The reception unit accepts photos by, for example, uploading photos of a region the user wants to visit to the system. The reception unit can also allow the user to enter the location they want to visit in text. The analysis unit uses a generation AI to analyze the photos accepted by the reception unit and identify destinations. The generation AI analyzes the photos using techniques such as a generative adversarial network (GAN) or a transformer. For example, the analysis unit inputs photos into the generation AI, analyzes the content of the photos, and identifies destinations. The analysis unit can also obtain detailed destination information based on the photo analysis results. The suggestion unit proposes a travel itinerary based on the destinations identified by the analysis unit. The suggestion unit plans a travel itinerary based on, for example, a date specified by the user. For example, the suggestion unit proposes a specific itinerary, such as one day around photo A and two days around photo B. The suggestion unit can also suggest a travel itinerary based on route destinations and souvenir locations specified by the user. This allows the travel suggestion system according to the embodiment to suggest a travel itinerary based on the user's input of photos and destinations.

[0030] The suggestion unit includes a route suggestion unit that plans a travel itinerary based on route points specified by the user. The route suggestion unit suggests a travel itinerary based on route points specified by the user. For example, if a user inputs, "I want to go to XX via photos A and B," the route suggestion unit identifies the locations of photos A and B and plans a travel itinerary based on the route points. The route suggestion unit can also obtain detailed information about route points specified by the user and suggest a travel itinerary based on that information. For example, the route suggestion unit obtains information about tourist spots and accommodations in route points specified by the user and plans a travel itinerary based on that information. This makes it possible to suggest a travel itinerary based on route points specified by the user.

[0031] The suggestion unit includes a souvenir suggestion unit that plans a travel itinerary based on the souvenir locations specified by the user. The souvenir suggestion unit suggests a travel itinerary based on the souvenir locations specified by the user. For example, if the user inputs, "I'd like to stop by a local product fair with famous souvenirs on my way home," the souvenir suggestion unit identifies the location of the local product fair and plans a travel itinerary based on that location. The souvenir suggestion unit can also obtain detailed information about the souvenir locations specified by the user and suggest a travel itinerary based on that information. For example, the souvenir suggestion unit obtains business hours and access information for the souvenir locations specified by the user and plans a travel itinerary based on that information. This makes it possible to suggest a travel itinerary based on the souvenir locations specified by the user.

[0032] The analysis unit can analyze the photo using the generative AI and analyze the destination. The generative AI analyzes the photo using technologies such as GAN (Generative Adversarial Network) and Transformer. For example, the analysis unit inputs a photo into the generative AI and analyzes the content of the photo to identify the destination. The analysis unit can also obtain detailed information about the destination based on the results of the photo analysis. For example, the analysis unit analyzes the content of the photo and obtains information about tourist spots and accommodations in the location. In this way, the use of the generative AI improves the accuracy of photo analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generative AI, or may be performed without using the generative AI. For example, the analysis unit can input a photo into the generative AI and analyze the content of the photo to identify the destination.

[0033] The suggestion unit can plan a travel itinerary based on a date specified by the user. The suggestion unit proposes a travel itinerary based on the date specified by the user. For example, if the user specifies "one day around photo A and two days around photo B," the suggestion unit plans a travel itinerary based on that date. The suggestion unit can also obtain detailed information about the date specified by the user and propose a travel itinerary based on that information. For example, the suggestion unit obtains weather forecasts and traffic information for the date specified by the user and plans a travel itinerary based on that information. In this way, a travel itinerary can be proposed based on the date specified by the user.

[0034] The reception unit can analyze the user's past travel history and select the optimal reception method. For example, the reception unit can preferentially accept photos related to places the user has visited in the past. The reception unit can also automatically filter photos related to specific areas from the user's past travel history. The reception unit can also customize the photo reception method based on the user's preferred travel style in the past. This makes it possible to select the optimal reception method based on the user's past travel history. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past travel history data into the generation AI and have the generation AI select the optimal reception method.

[0035] The reception unit can perform filtering based on the user's current interests when receiving photos. For example, the reception unit can preferentially receive photos related to themes in which the user is currently interested. The reception unit can also filter related photos based on the user's recent search history. The reception unit can also analyze the user's social media activity and receive photos that match the user's current interests. This allows filtering of photos based on the user's current interests. Some or all of the above-described processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can input the user's interest data into the generation AI and have the generation AI perform filtering.

[0036] When accepting a photo, the acceptance unit can determine the optimal acceptance means depending on the user's input method. For example, if the user uses voice input, the acceptance unit accepts the photo using voice recognition technology. Furthermore, if the user uses text input, the acceptance unit can also accept the photo using text analysis technology. Furthermore, if the user uses image input, the acceptance unit can also accept the photo using image recognition technology. This makes it possible to select the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input the user's input data into the generation AI and have the generation AI select the optimal acceptance means.

[0037] When receiving photos, the reception unit can prioritize receiving photos that are highly relevant based on the user's geographical location information. For example, the reception unit can prioritize receiving photos of places close to the user's current location. The reception unit can also prioritize receiving photos related to places the user has visited in the past. The reception unit can also prioritize receiving photos that are highly relevant based on the distance from the user's current location. This makes it possible to prioritize receiving photos that are highly relevant based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant photos.

[0038] When receiving a photo, the reception unit can analyze the user's social media activity and receive related photos. For example, the reception unit can preferentially receive photos shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related photos. The reception unit can also receive related photos by referring to the activity of the user's friends on social media. This makes it possible to receive related photos based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI and have the generation AI select related photos.

[0039] The reception unit can customize the reception method based on the user's past feedback when receiving a photo. For example, the reception unit preferentially uses a photo reception method that the user has previously preferred. The reception unit can also adjust the reception method based on the user's past feedback. The reception unit can also avoid reception methods that the user has previously dissatisfied with. This makes it possible to customize the reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

[0040] When analyzing a photo, the analysis unit can optimize the analysis algorithm based on past analysis data. For example, the analysis unit selects an optimal algorithm based on past analysis data. The analysis unit can also refer to past analysis results to improve analysis accuracy. The analysis unit can also automatically adjust the analysis algorithm using past analysis data. This makes it possible to optimize the analysis algorithm based on past analysis data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past analysis data into the generation AI and have the generation AI optimize the analysis algorithm.

[0041] When analyzing a photo, the analysis unit can use different analysis methods depending on the category of the photo. For example, in the case of a landscape photo, the analysis unit applies a landscape analysis algorithm. In addition, in the case of a building photo, the analysis unit can also apply a building analysis algorithm. In addition, in the case of a portrait photo, the analysis unit can also apply a face recognition algorithm. This makes it possible to apply the optimal analysis method depending on the category of the photo. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input photo category data into the generation AI and have the generation AI select the optimal analysis method.

[0042] When analyzing a photo, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the analysis accuracy by referring to the user's past analysis results. The analysis unit can also optimize the analysis method using the user's past analysis results. This can improve the analysis accuracy based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the analysis accuracy.

[0043] When analyzing a photo, the analysis unit can set an analysis priority based on when the photo was taken. For example, the analysis unit prioritizes analyzing recently taken photos. The analysis unit can also prioritize analyzing photos taken in a specific season. The analysis unit can also prioritize analyzing photos taken within a period specified by the user. This makes it possible to determine the analysis priority based on when the photo was taken. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on when the photo was taken into the generation AI and have the generation AI set the analysis priority.

[0044] When analyzing photos, the analysis unit can set the order of analysis based on the relevance of the photos. For example, the analysis unit prioritizes analysis of highly relevant photos. The analysis unit can also prioritize analysis of photos related to a theme specified by the user. The analysis unit can also prioritize analysis of highly relevant photos based on the user's past analysis results. This makes it possible to adjust the order of analysis based on the relevance of the photos. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input photo relevance data into the generation AI and have the generation AI set the order of analysis.

[0045] When analyzing a photo, the analysis unit can set the level of detail of the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide a detailed analysis result. Furthermore, if the user is a beginner, the analysis unit can also provide a simple analysis result. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. This makes it possible to adjust the level of detail of the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI set the level of detail of the analysis.

[0046] When making a proposal, the suggestion unit can set the level of detail of the proposal based on the importance of the trip. For example, the suggestion unit makes a detailed proposal for an important trip. The suggestion unit can also make a brief proposal for a short trip. The suggestion unit can also make a detailed proposal for a trip in which the user is particularly interested. This makes it possible to adjust the level of detail of the proposal based on the importance of the trip. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input trip importance data into the generation AI and cause the generation AI to set the level of detail of the proposal.

[0047] When making suggestions, the suggestion unit can use different suggestion algorithms depending on the travel category. For example, in the case of a sightseeing trip, the suggestion unit makes suggestions mainly focusing on tourist spots. In addition, the suggestion unit can also suggest efficient travel routes in the case of a business trip. In addition, the suggestion unit can also suggest family-friendly activities in the case of a family trip. This makes it possible to apply the optimal suggestion algorithm depending on the travel category. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input travel category data into the generation AI and have the generation AI select the optimal suggestion algorithm.

[0048] When making a proposal, the suggestion unit can improve the accuracy of the proposal based on the user's past proposal results. The suggestion unit, for example, adjusts the proposal algorithm based on the user's past proposal results. The suggestion unit can also improve the proposal accuracy by referring to the user's past proposal results. The suggestion unit can also optimize the proposal method using the user's past proposal results. This makes it possible to improve the accuracy of the proposal based on the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0049] The suggestion unit can set the priority of the suggestions based on the time of trip submission when making suggestions. For example, the suggestion unit prioritizes suggestions for upcoming trips. The suggestion unit can also make detailed suggestions for long-term travel plans. The suggestion unit can also make quick suggestions when the user is in a hurry. This makes it possible to determine the priority of suggestions based on the time of trip submission. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input trip submission time data into the generation AI and have the generation AI set the priority of the suggestions.

[0050] When making a suggestion, the suggestion unit can set the order of suggestions based on the relevance of the trips. For example, the suggestion unit can prioritize suggesting trips in which the user is particularly interested. The suggestion unit can also make highly relevant suggestions based on the user's past travel history. The suggestion unit can also adjust the order of suggestions based on the user's current interests. This makes it possible to adjust the order of suggestions based on the relevance of the trips. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input trip relevance data into the generation AI and have the generation AI set the order of suggestions.

[0051] When making a proposal, the suggestion unit can set the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is an expert, the suggestion unit can make a proposal that uses a lot of technical terminology. Also, if the user is a beginner, the suggestion unit can make a proposal using simple language. Also, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to execute the use of technical terminology.

[0052] When suggesting a waypoint, the waypoint suggestion unit can suggest an optimal waypoint based on the user's past travel history. The waypoint suggestion unit can, for example, suggest related waypoints based on places the user has visited in the past. The waypoint suggestion unit can also suggest waypoints related to a specific region from the user's past travel history. The waypoint suggestion unit can also suggest waypoints based on the user's preferred travel style in the past. This makes it possible to suggest optimal waypoints based on the user's past travel history. Some or all of the above-mentioned processing in the waypoint suggestion unit may be performed using or without the generation AI. For example, the waypoint suggestion unit can input the user's past travel history data into the generation AI and cause the generation AI to suggest optimal waypoints.

[0053] When proposing a route, the route suggestion unit can set the route based on the user's current interests. For example, the route suggestion unit can suggest route related to a theme that the user is currently interested in. The route suggestion unit can also suggest related route based on the user's recent search history. The route suggestion unit can also analyze the user's social media activity and suggest route that matches the user's current interests. This allows route suggestions to be customized based on the user's current interests. Some or all of the above-described processing in the route suggestion unit may be performed using or without the generation AI. For example, the route suggestion unit can input the user's interest data into the generation AI and have the generation AI set the route.

[0054] When proposing a route, the route suggestion unit can suggest an optimal route based on the user's geographical location information. For example, the route suggestion unit prioritizes proposing route points close to the user's current location. The route suggestion unit can also suggest route points related to places the user has visited in the past. The route suggestion unit can also suggest highly relevant route points based on the distance from the user's current location. This makes it possible to suggest optimal route points based on the user's geographical location information. Some or all of the above-described processing in the route suggestion unit may be performed using or without the generation AI. For example, the route suggestion unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest optimal route points.

[0055] When suggesting a route, the route suggestion unit can analyze the user's social media activity and suggest related route. The route suggestion unit can, for example, suggest route related to places the user has shared on social media. The route suggestion unit can also analyze the content of the user's social media posts and suggest related route. The route suggestion unit can also suggest related route based on the activity of the user's friends on social media. This makes it possible to suggest related route based on the user's social media activity. Some or all of the above-described processing in the route suggestion unit may be performed using or without the generation AI. For example, the route suggestion unit can input the user's social media data into the generation AI and cause the generation AI to suggest related route.

[0056] When suggesting souvenirs, the souvenir suggestion unit can suggest optimal souvenirs based on the user's past souvenir purchase history. For example, the souvenir suggestion unit can suggest related souvenirs based on souvenirs the user has purchased in the past. The souvenir suggestion unit can also suggest souvenirs related to a specific region based on the user's past souvenir purchase history. The souvenir suggestion unit can also suggest souvenirs based on the style of souvenirs the user has previously preferred. This makes it possible to suggest optimal souvenirs based on the user's past souvenir purchase history. Some or all of the above-described processing in the souvenir suggestion unit may be performed using or without the generation AI. For example, the souvenir suggestion unit can input the user's past souvenir purchase history data into the generation AI and have the generation AI suggest optimal souvenirs.

[0057] When suggesting souvenirs, the souvenir suggestion unit can set souvenirs based on the user's current interests. For example, the souvenir suggestion unit can suggest souvenirs related to a theme that the user is currently interested in. The souvenir suggestion unit can also suggest related souvenirs based on the user's recent search history. The souvenir suggestion unit can also analyze the user's social media activity and suggest souvenirs that match the user's current interests. This allows souvenirs to be customized based on the user's current interests. Some or all of the above-mentioned processing in the souvenir suggestion unit may be performed using or without the generation AI. For example, the souvenir suggestion unit can input the user's interest data into the generation AI and have the generation AI execute the souvenir settings.

[0058] When suggesting souvenirs, the souvenir suggestion unit can suggest optimal souvenirs based on the user's geographical location information. For example, the souvenir suggestion unit prioritizes suggesting souvenirs that can be purchased in locations close to the user's current location. The souvenir suggestion unit can also suggest souvenirs related to places the user has previously visited. The souvenir suggestion unit can also suggest highly relevant souvenirs based on the distance from the user's current location. This makes it possible to suggest optimal souvenirs based on the user's geographical location information. Some or all of the above-described processing in the souvenir suggestion unit may be performed using or without the generation AI. For example, the souvenir suggestion unit can input the user's geographical location information into the generation AI and have the generation AI suggest optimal souvenirs.

[0059] When suggesting souvenirs, the souvenir suggestion unit can analyze the user's social media activity and suggest related souvenirs. For example, the souvenir suggestion unit makes suggestions related to souvenirs shared by the user on social media. The souvenir suggestion unit can also analyze the content of the user's social media posts and suggest related souvenirs. The souvenir suggestion unit can also suggest related souvenirs by referring to the activities of the user's friends on social media. This makes it possible to suggest related souvenirs based on the user's social media activity. Some or all of the above-mentioned processing in the souvenir suggestion unit may be performed using or without the generation AI. For example, the souvenir suggestion unit can input the user's social media data into the generation AI and have the generation AI suggest related souvenirs.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The suggestion unit can monitor the user's health condition and adjust the travel itinerary based on the health condition. For example, if the user feels tired, the suggestion unit can incorporate time for rest into the itinerary. Also, if the user has specific health conditions, the suggestion unit can suggest travel plans that take those conditions into consideration. Furthermore, the suggestion unit can suggest optimal travel destinations and activities based on the user's health data. This makes it possible to provide flexible travel plans that suit the user's health condition.

[0062] The suggestion unit can take into account the user's budget information and suggest a travel plan that suits the budget. For example, if the user is planning a trip on a low budget, it can suggest cost-effective accommodations and transportation options. On the other hand, if the user is planning a trip on a high budget, it can suggest luxurious accommodations and special activities. Furthermore, the suggestion unit can also suggest dining and shopping options at the travel destination according to the user's budget. This makes it possible to provide the user with an optimal travel plan that suits their budget.

[0063] The suggestion unit can analyze the user's past travel history and suggest new travel plans based on the user's past travel experiences. For example, it can suggest places similar to places the user has visited in the past. It can also suggest activities at new travel destinations based on activities the user has previously preferred. It can also consider places and activities the user has avoided in the past and suggest travel plans that avoid them. This makes it possible to provide the user with an optimal travel plan based on their past travel experiences.

[0064] The suggestion unit can suggest nearby tourist spots and activities based on the user's current geographical location information. For example, it can suggest tourist spots within walking distance of the user's current location. It can also suggest activities within a driving distance of the user. Furthermore, if the user uses public transportation, it can also make suggestions that include access information. This makes it possible to provide the most suitable tourist spots and activities based on the user's current location.

[0065] The suggestion unit analyzes the user's social media activity and can suggest travel plans based on the places and activities visited by the user's friends. For example, it can suggest tourist spots that the user's friends have highly rated. It can also suggest new activities based on the activities the user's friends have participated in. It can also suggest travel destinations and activities based on photos and comments shared by the user's friends. This makes it possible to provide optimal travel plans that utilize the user's social network.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit accepts the user's input of a photo and destination. Photos from the user include, for example, JPEG format, PNG format, landscape photos, portrait photos, etc. The reception unit accepts photos by uploading photos of the area the user wants to visit to the system. Users can also enter the location they want to visit in text. Step 2: The analysis unit uses the generation AI to analyze the photo received by the reception unit and identify the destination. The generation AI analyzes the photo using technologies such as GAN (Generative Adversarial Network) and Transformer. The analysis unit analyzes the content of the photo to identify the destination, and can also obtain detailed information about the destination based on the results of the photo analysis. Step 3: The suggestion unit proposes a travel itinerary based on the destinations identified by the analysis unit. The suggestion unit plans the travel itinerary based on the itinerary specified by the user, and proposes a specific itinerary, for example, one day around photo A and two days around photo B. The suggestion unit can also propose a travel itinerary based on stopovers and souvenir locations specified by the user.

[0068] (Example 2) A travel suggestion system according to an embodiment of the present invention allows a user to load photos of a desired area, analyze the photos, and propose a travel itinerary. The travel suggestion system allows a user to load photos of a desired area and input, "I want to go to XX via photos A and B." The generation AI then analyzes the command and identifies the locations of photos A and B. The system then proposes a travel itinerary based on the identified locations. For example, the system may suggest a specific itinerary, such as one day around photo A and two days around photo B. The user can also receive text suggestions, such as stopping by a local product fair with a famous souvenir on the way home. This allows users to easily create their ideal travel plans. For example, if a user wants to visit a location depicted in a beautiful image while browsing the web, the system can simply load the image into the system, which will identify the location and propose a travel itinerary. Furthermore, by specifying a specific itinerary, the system can create a more ideal travel plan. Furthermore, the system can also make suggestions tailored to the user's needs, such as stopping by a local product fair with a famous souvenir on the way home. This ensures that users can visit places they don't want to miss during their trip. The travel suggestion system can pinpoint the locations the user wants to visit and propose an ideal travel plan. For example, the system can efficiently plan trips by easily identifying the places users want to go and suggesting travel itineraries. In addition, by providing suggestions based on the user's requests, the system can ensure that users visit places they do not want to miss during their trip.

[0069] A travel suggestion system according to an embodiment includes a reception unit, an analysis unit, and a suggestion unit. The reception unit accepts input of photos and destinations from a user. Photos from a user include, but are not limited to, JPEG format, PNG format, landscape photos, and portrait photos. The reception unit accepts photos by, for example, uploading photos of a region the user wants to visit to the system. The reception unit can also allow the user to enter the location they want to visit in text. The analysis unit uses a generation AI to analyze the photos accepted by the reception unit and identify destinations. The generation AI analyzes the photos using techniques such as a generative adversarial network (GAN) or a transformer. For example, the analysis unit inputs photos into the generation AI, analyzes the content of the photos, and identifies destinations. The analysis unit can also obtain detailed destination information based on the photo analysis results. The suggestion unit proposes a travel itinerary based on the destinations identified by the analysis unit. The suggestion unit plans a travel itinerary based on, for example, a date specified by the user. For example, the suggestion unit proposes a specific itinerary, such as one day around photo A and two days around photo B. The suggestion unit can also suggest a travel itinerary based on route destinations and souvenir locations specified by the user. This allows the travel suggestion system according to the embodiment to suggest a travel itinerary based on the user's input of photos and destinations.

[0070] The suggestion unit includes a route suggestion unit that plans a travel itinerary based on route points specified by the user. The route suggestion unit suggests a travel itinerary based on route points specified by the user. For example, if a user inputs, "I want to go to XX via photos A and B," the route suggestion unit identifies the locations of photos A and B and plans a travel itinerary based on the route points. The route suggestion unit can also obtain detailed information about route points specified by the user and suggest a travel itinerary based on that information. For example, the route suggestion unit obtains information about tourist spots and accommodations in route points specified by the user and plans a travel itinerary based on that information. This makes it possible to suggest a travel itinerary based on route points specified by the user.

[0071] The suggestion unit includes a souvenir suggestion unit that plans a travel itinerary based on the souvenir locations specified by the user. The souvenir suggestion unit suggests a travel itinerary based on the souvenir locations specified by the user. For example, if the user inputs, "I'd like to stop by a local product fair with famous souvenirs on my way home," the souvenir suggestion unit identifies the location of the local product fair and plans a travel itinerary based on that location. The souvenir suggestion unit can also obtain detailed information about the souvenir locations specified by the user and suggest a travel itinerary based on that information. For example, the souvenir suggestion unit obtains business hours and access information for the souvenir locations specified by the user and plans a travel itinerary based on that information. This makes it possible to suggest a travel itinerary based on the souvenir locations specified by the user.

[0072] The analysis unit can analyze the photo using the generative AI and analyze the destination. The generative AI analyzes the photo using technologies such as GAN (Generative Adversarial Network) and Transformer. For example, the analysis unit inputs a photo into the generative AI and analyzes the content of the photo to identify the destination. The analysis unit can also obtain detailed information about the destination based on the results of the photo analysis. For example, the analysis unit analyzes the content of the photo and obtains information about tourist spots and accommodations in the location. In this way, the use of the generative AI improves the accuracy of photo analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using the generative AI, or may be performed without using the generative AI. For example, the analysis unit can input a photo into the generative AI and analyze the content of the photo to identify the destination.

[0073] The suggestion unit can plan a travel itinerary based on a date specified by the user. The suggestion unit proposes a travel itinerary based on the date specified by the user. For example, if the user specifies "one day around photo A and two days around photo B," the suggestion unit plans a travel itinerary based on that date. The suggestion unit can also obtain detailed information about the date specified by the user and propose a travel itinerary based on that information. For example, the suggestion unit obtains weather forecasts and traffic information for the date specified by the user and plans a travel itinerary based on that information. In this way, a travel itinerary can be proposed based on the date specified by the user.

[0074] The reception unit can estimate the user's emotions and set the timing for accepting photos based on the estimated user emotions. For example, if the user is excited, the reception unit can immediately accept photos and start processing before the user's excitement cools down. Furthermore, if the user is relaxed, the reception unit can slowly accept photos to allow the user to enter them calmly. Furthermore, if the user is stressed, the reception unit can provide a simple interface and quickly accept photos. This allows the timing for accepting photos to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0075] The reception unit can analyze the user's past travel history and select the optimal reception method. For example, the reception unit can preferentially accept photos related to places the user has visited in the past. The reception unit can also automatically filter photos related to specific areas from the user's past travel history. The reception unit can also customize the photo reception method based on the user's preferred travel style in the past. This makes it possible to select the optimal reception method based on the user's past travel history. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can input the user's past travel history data into the generation AI and have the generation AI select the optimal reception method.

[0076] The reception unit can perform filtering based on the user's current interests when receiving photos. For example, the reception unit can preferentially receive photos related to themes in which the user is currently interested. The reception unit can also filter related photos based on the user's recent search history. The reception unit can also analyze the user's social media activity and receive photos that match the user's current interests. This allows filtering of photos based on the user's current interests. Some or all of the above-described processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can input the user's interest data into the generation AI and have the generation AI perform filtering.

[0077] When accepting a photo, the acceptance unit can determine the optimal acceptance means depending on the user's input method. For example, if the user uses voice input, the acceptance unit accepts the photo using voice recognition technology. Furthermore, if the user uses text input, the acceptance unit can also accept the photo using text analysis technology. Furthermore, if the user uses image input, the acceptance unit can also accept the photo using image recognition technology. This makes it possible to select the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the acceptance unit can input the user's input data into the generation AI and have the generation AI select the optimal acceptance means.

[0078] The reception unit can estimate the user's emotions and set a priority order for the photos to be received based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize receiving related photos. Furthermore, if the user is relaxed, the reception unit can adjust the reception order to receive photos. Furthermore, if the user is stressed, the reception unit can prioritize receiving important photos. This allows the priority order of photos to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0079] When receiving photos, the reception unit can prioritize receiving photos that are highly relevant based on the user's geographical location information. For example, the reception unit can prioritize receiving photos of places close to the user's current location. The reception unit can also prioritize receiving photos related to places the user has visited in the past. The reception unit can also prioritize receiving photos that are highly relevant based on the distance from the user's current location. This makes it possible to prioritize receiving photos that are highly relevant based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant photos.

[0080] When receiving a photo, the reception unit can analyze the user's social media activity and receive related photos. For example, the reception unit can preferentially receive photos shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related photos. The reception unit can also receive related photos by referring to the activity of the user's friends on social media. This makes it possible to receive related photos based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI and have the generation AI select related photos.

[0081] The reception unit can customize the reception method based on the user's past feedback when receiving a photo. For example, the reception unit preferentially uses a photo reception method that the user has previously preferred. The reception unit can also adjust the reception method based on the user's past feedback. The reception unit can also avoid reception methods that the user has previously dissatisfied with. This makes it possible to customize the reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI customize the reception method.

[0082] The analysis unit can estimate the user's emotions and set a photo analysis method based on the estimated user emotions. For example, if the user is excited, the analysis unit can quickly analyze the photo and provide the results. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. Furthermore, if the user is stressed, the analysis unit can perform a simple analysis and provide quick results. This allows the photo analysis method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0083] When analyzing a photo, the analysis unit can optimize the analysis algorithm based on past analysis data. For example, the analysis unit selects an optimal algorithm based on past analysis data. The analysis unit can also refer to past analysis results to improve analysis accuracy. The analysis unit can also automatically adjust the analysis algorithm using past analysis data. This makes it possible to optimize the analysis algorithm based on past analysis data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past analysis data into the generation AI and have the generation AI optimize the analysis algorithm.

[0084] When analyzing a photo, the analysis unit can use different analysis methods depending on the category of the photo. For example, in the case of a landscape photo, the analysis unit applies a landscape analysis algorithm. In addition, in the case of a building photo, the analysis unit can also apply a building analysis algorithm. In addition, in the case of a portrait photo, the analysis unit can also apply a face recognition algorithm. This makes it possible to apply the optimal analysis method depending on the category of the photo. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input photo category data into the generation AI and have the generation AI select the optimal analysis method.

[0085] When analyzing a photo, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the analysis accuracy by referring to the user's past analysis results. The analysis unit can also optimize the analysis method using the user's past analysis results. This can improve the analysis accuracy based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the analysis accuracy.

[0086] The analysis unit can estimate the user's emotions and set analysis priorities based on the estimated user emotions. For example, if the user is excited, the analysis unit can prioritize analyzing related photos. Furthermore, if the user is relaxed, the analysis unit can adjust the analysis order to analyze photos. Furthermore, if the user is stressed, the analysis unit can prioritize analyzing important photos. This allows the analysis priorities to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0087] When analyzing a photo, the analysis unit can set an analysis priority based on when the photo was taken. For example, the analysis unit prioritizes analyzing recently taken photos. The analysis unit can also prioritize analyzing photos taken in a specific season. The analysis unit can also prioritize analyzing photos taken within a period specified by the user. This makes it possible to determine the analysis priority based on when the photo was taken. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on when the photo was taken into the generation AI and have the generation AI set the analysis priority.

[0088] When analyzing photos, the analysis unit can set the order of analysis based on the relevance of the photos. For example, the analysis unit prioritizes analysis of highly relevant photos. The analysis unit can also prioritize analysis of photos related to a theme specified by the user. The analysis unit can also prioritize analysis of highly relevant photos based on the user's past analysis results. This makes it possible to adjust the order of analysis based on the relevance of the photos. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input photo relevance data into the generation AI and have the generation AI set the order of analysis.

[0089] When analyzing a photo, the analysis unit can set the level of detail of the analysis according to the user's level of expertise. For example, if the user is an expert, the analysis unit can provide a detailed analysis result. Furthermore, if the user is a beginner, the analysis unit can also provide a simple analysis result. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the user's level of expertise. This makes it possible to adjust the level of detail of the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI set the level of detail of the analysis.

[0090] The suggestion unit can estimate the user's emotion and set a method for expressing suggestions based on the estimated user's emotion. For example, if the user is excited, the suggestion unit can make visually stimulating suggestions. If the user is relaxed, the suggestion unit can make suggestions using calm expressions. If the user is stressed, the suggestion unit can make simple and easy-to-understand suggestions. This allows the method for expressing suggestions to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0091] When making a proposal, the suggestion unit can set the level of detail of the proposal based on the importance of the trip. For example, the suggestion unit makes a detailed proposal for an important trip. The suggestion unit can also make a brief proposal for a short trip. The suggestion unit can also make a detailed proposal for a trip in which the user is particularly interested. This makes it possible to adjust the level of detail of the proposal based on the importance of the trip. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input trip importance data into the generation AI and cause the generation AI to set the level of detail of the proposal.

[0092] When making suggestions, the suggestion unit can use different suggestion algorithms depending on the travel category. For example, in the case of a sightseeing trip, the suggestion unit makes suggestions mainly focusing on tourist spots. In addition, the suggestion unit can also suggest efficient travel routes in the case of a business trip. In addition, the suggestion unit can also suggest family-friendly activities in the case of a family trip. This makes it possible to apply the optimal suggestion algorithm depending on the travel category. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input travel category data into the generation AI and have the generation AI select the optimal suggestion algorithm.

[0093] When making a proposal, the suggestion unit can improve the accuracy of the proposal based on the user's past proposal results. The suggestion unit, for example, adjusts the proposal algorithm based on the user's past proposal results. The suggestion unit can also improve the proposal accuracy by referring to the user's past proposal results. The suggestion unit can also optimize the proposal method using the user's past proposal results. This makes it possible to improve the accuracy of the proposal based on the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0094] The suggestion unit can estimate the user's emotion and set the length of the suggestion based on the estimated user's emotion. For example, if the user is excited, the suggestion unit can make a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can make a longer suggestion with detailed explanations. If the user is stressed, the suggestion unit can make a concise, easy-to-understand suggestion. This allows the length of the suggestion to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0095] The suggestion unit can set the priority of the suggestions based on the time of trip submission when making suggestions. For example, the suggestion unit prioritizes suggestions for upcoming trips. The suggestion unit can also make detailed suggestions for long-term travel plans. The suggestion unit can also make quick suggestions when the user is in a hurry. This makes it possible to determine the priority of suggestions based on the time of trip submission. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input trip submission time data into the generation AI and have the generation AI set the priority of the suggestions.

[0096] When making a suggestion, the suggestion unit can set the order of suggestions based on the relevance of the trips. For example, the suggestion unit can prioritize suggesting trips in which the user is particularly interested. The suggestion unit can also make highly relevant suggestions based on the user's past travel history. The suggestion unit can also adjust the order of suggestions based on the user's current interests. This makes it possible to adjust the order of suggestions based on the relevance of the trips. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input trip relevance data into the generation AI and have the generation AI set the order of suggestions.

[0097] When making a proposal, the suggestion unit can set the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is an expert, the suggestion unit can make a proposal that uses a lot of technical terminology. Also, if the user is a beginner, the suggestion unit can make a proposal using simple language. Also, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to execute the use of technical terminology.

[0098] The route suggestion unit can estimate the user's emotions and set a route suggestion method based on the estimated user's emotions. For example, if the user is excited, the route suggestion unit can suggest visually stimulating route points. Furthermore, if the user is relaxed, the route suggestion unit can also suggest route points with a calming atmosphere. Furthermore, if the user is stressed, the route suggestion unit can also suggest route points that are relaxing. This allows the route suggestion method to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the route suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the route suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0099] When suggesting a waypoint, the waypoint suggestion unit can suggest an optimal waypoint based on the user's past travel history. The waypoint suggestion unit can, for example, suggest related waypoints based on places the user has visited in the past. The waypoint suggestion unit can also suggest waypoints related to a specific region from the user's past travel history. The waypoint suggestion unit can also suggest waypoints based on the user's preferred travel style in the past. This makes it possible to suggest optimal waypoints based on the user's past travel history. Some or all of the above-mentioned processing in the waypoint suggestion unit may be performed using or without the generation AI. For example, the waypoint suggestion unit can input the user's past travel history data into the generation AI and cause the generation AI to suggest optimal waypoints.

[0100] When proposing a route, the route suggestion unit can set the route based on the user's current interests. For example, the route suggestion unit can suggest route related to a theme that the user is currently interested in. The route suggestion unit can also suggest related route based on the user's recent search history. The route suggestion unit can also analyze the user's social media activity and suggest route that matches the user's current interests. This allows route suggestions to be customized based on the user's current interests. Some or all of the above-described processing in the route suggestion unit may be performed using or without the generation AI. For example, the route suggestion unit can input the user's interest data into the generation AI and have the generation AI set the route.

[0101] The route suggestion unit can estimate the user's emotions and prioritize route points based on the estimated user emotions. For example, when the user is excited, the route suggestion unit prioritizes related route points. Furthermore, when the user is relaxed, the route suggestion unit can adjust the order of route points and suggest them. Furthermore, when the user is stressed, the route suggestion unit can prioritize important route points. This allows the priority of route points to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the route suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the route suggestion unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0102] When proposing a route, the route suggestion unit can suggest an optimal route based on the user's geographical location information. For example, the route suggestion unit prioritizes proposing route points close to the user's current location. The route suggestion unit can also suggest route points related to places the user has visited in the past. The route suggestion unit can also suggest highly relevant route points based on the distance from the user's current location. This makes it possible to suggest optimal route points based on the user's geographical location information. Some or all of the above-described processing in the route suggestion unit may be performed using or without the generation AI. For example, the route suggestion unit can input the user's geographical location information to the generation AI and cause the generation AI to suggest optimal route points.

[0103] When suggesting a route, the route suggestion unit can analyze the user's social media activity and suggest related route. The route suggestion unit can, for example, suggest route related to places the user has shared on social media. The route suggestion unit can also analyze the content of the user's social media posts and suggest related route. The route suggestion unit can also suggest related route based on the activity of the user's friends on social media. This makes it possible to suggest related route based on the user's social media activity. Some or all of the above-described processing in the route suggestion unit may be performed using or without the generation AI. For example, the route suggestion unit can input the user's social media data into the generation AI and cause the generation AI to suggest related route.

[0104] The souvenir suggestion unit can estimate the user's emotions and set a souvenir suggestion method based on the estimated user's emotions. For example, if the user is excited, the souvenir suggestion unit can suggest visually stimulating souvenirs. Furthermore, if the user is relaxed, the souvenir suggestion unit can suggest souvenirs with a calming atmosphere. Furthermore, if the user is stressed, the souvenir suggestion unit can suggest souvenirs that will help the user relax. This allows the souvenir suggestion method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the souvenir suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the souvenir suggestion unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0105] When suggesting souvenirs, the souvenir suggestion unit can suggest optimal souvenirs based on the user's past souvenir purchase history. For example, the souvenir suggestion unit can suggest related souvenirs based on souvenirs the user has purchased in the past. The souvenir suggestion unit can also suggest souvenirs related to a specific region based on the user's past souvenir purchase history. The souvenir suggestion unit can also suggest souvenirs based on the style of souvenirs the user has previously preferred. This makes it possible to suggest optimal souvenirs based on the user's past souvenir purchase history. Some or all of the above-described processing in the souvenir suggestion unit may be performed using or without the generation AI. For example, the souvenir suggestion unit can input the user's past souvenir purchase history data into the generation AI and have the generation AI suggest optimal souvenirs.

[0106] When suggesting souvenirs, the souvenir suggestion unit can set souvenirs based on the user's current interests. For example, the souvenir suggestion unit can suggest souvenirs related to a theme that the user is currently interested in. The souvenir suggestion unit can also suggest related souvenirs based on the user's recent search history. The souvenir suggestion unit can also analyze the user's social media activity and suggest souvenirs that match the user's current interests. This allows souvenirs to be customized based on the user's current interests. Some or all of the above-mentioned processing in the souvenir suggestion unit may be performed using or without the generation AI. For example, the souvenir suggestion unit can input the user's interest data into the generation AI and have the generation AI execute the souvenir settings.

[0107] The souvenir suggestion unit can estimate the user's emotions and prioritize souvenirs based on the estimated user emotions. For example, if the user is excited, the souvenir suggestion unit can prioritize relevant souvenirs. Furthermore, if the user is relaxed, the souvenir suggestion unit can adjust the order of souvenirs to be suggested. Furthermore, if the user is stressed, the souvenir suggestion unit can prioritize important souvenirs. This allows the souvenir priority to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the souvenir suggestion unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the souvenir suggestion unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0108] When suggesting souvenirs, the souvenir suggestion unit can suggest optimal souvenirs based on the user's geographical location information. For example, the souvenir suggestion unit prioritizes suggesting souvenirs that can be purchased in locations close to the user's current location. The souvenir suggestion unit can also suggest souvenirs related to places the user has previously visited. The souvenir suggestion unit can also suggest highly relevant souvenirs based on the distance from the user's current location. This makes it possible to suggest optimal souvenirs based on the user's geographical location information. Some or all of the above-described processing in the souvenir suggestion unit may be performed using or without the generation AI. For example, the souvenir suggestion unit can input the user's geographical location information into the generation AI and have the generation AI suggest optimal souvenirs.

[0109] When suggesting souvenirs, the souvenir suggestion unit can analyze the user's social media activity and suggest related souvenirs. For example, the souvenir suggestion unit makes suggestions related to souvenirs shared by the user on social media. The souvenir suggestion unit can also analyze the content of the user's social media posts and suggest related souvenirs. The souvenir suggestion unit can also suggest related souvenirs by referring to the activities of the user's friends on social media. This makes it possible to suggest related souvenirs based on the user's social media activity. Some or all of the above-mentioned processing in the souvenir suggestion unit may be performed using or without the generation AI. For example, the souvenir suggestion unit can input the user's social media data into the generation AI and have the generation AI suggest related souvenirs. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and route suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and allows the user to upload photos of areas they want to visit. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the photos using a generation AI to identify destinations. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes a travel itinerary based on the identified destinations. The route suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and plans a travel itinerary based on route points specified by the user. The souvenir suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes a travel itinerary based on souvenir locations specified by the user. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and route suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and allows the user to upload photos of areas they want to visit. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes photos using a generation AI to identify destinations. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes a travel itinerary based on the identified destinations. The route suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and plans a travel itinerary based on route points specified by the user. The souvenir suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes a travel itinerary based on souvenir locations specified by the user. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, and route suggestion unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314, and allows the user to upload photos of areas they want to visit. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes photos using a generation AI to identify destinations. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes a travel itinerary based on the identified destinations. The route suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and plans a travel itinerary based on route points specified by the user. The souvenir suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes a travel itinerary based on souvenir locations specified by the user. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, suggestion unit, and route suggestion unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows the user to upload photos of areas they want to visit. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and analyzes the photos using a generation AI to identify destinations. The suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes a travel itinerary based on the identified destinations. The route suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and plans a travel itinerary based on route points specified by the user. The souvenir suggestion unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and proposes a travel itinerary based on souvenir locations specified by the user.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The suggestion unit can monitor the user's health condition and adjust the travel itinerary based on the health condition. For example, if the user feels tired, the suggestion unit can incorporate time for rest into the itinerary. Also, if the user has specific health conditions, the suggestion unit can suggest travel plans that take those conditions into consideration. Furthermore, the suggestion unit can suggest optimal travel destinations and activities based on the user's health data. This makes it possible to provide flexible travel plans that suit the user's health condition.

[0112] The suggestion unit can estimate the user's emotions and select a travel destination based on the estimated emotions. For example, if the user is feeling stressed, it can suggest a travel destination where the user can relax. If the user is excited, it can suggest a travel destination where the user can enjoy active activities. Furthermore, if the user is sad, it can suggest a place that has a healing effect. In this way, it is possible to select the optimal travel destination according to the user's emotions.

[0113] The suggestion unit can take into account the user's budget information and suggest a travel plan that suits the budget. For example, if the user is planning a trip on a low budget, it can suggest cost-effective accommodations and transportation options. On the other hand, if the user is planning a trip on a high budget, it can suggest luxurious accommodations and special activities. Furthermore, the suggestion unit can also suggest dining and shopping options at the travel destination according to the user's budget. This makes it possible to provide the user with an optimal travel plan that suits their budget.

[0114] The suggestion unit can estimate the user's emotions and suggest activities for the trip based on the estimated emotions. For example, if the user feels like relaxing, it can suggest relaxation activities such as spas and hot springs. If the user feels like staying active, it can suggest activities such as hiking or sports. Furthermore, if the user is looking for a cultural experience, it can suggest museums and historical tourist spots. This makes it possible to provide the optimal activities according to the user's emotions.

[0115] The suggestion unit can analyze the user's past travel history and suggest new travel plans based on the user's past travel experiences. For example, it can suggest places similar to places the user has visited in the past. It can also suggest activities at new travel destinations based on activities the user has previously preferred. It can also consider places and activities the user has avoided in the past and suggest travel plans that avoid them. This makes it possible to provide the user with an optimal travel plan based on their past travel experiences.

[0116] The suggestion unit can estimate the user's emotions and suggest a meal plan for the trip based on the estimated emotions. For example, if the user feels like relaxing, it can suggest a restaurant with a calm atmosphere. If the user is excited, it can suggest a lively restaurant or bar. Furthermore, if the user is concerned about their health, it can suggest a restaurant that offers healthy menus. In this way, it is possible to provide an optimal meal plan according to the user's emotions.

[0117] The suggestion unit can suggest nearby tourist spots and activities based on the user's current geographical location information. For example, it can suggest tourist spots within walking distance of the user's current location. It can also suggest activities within a driving distance of the user. Furthermore, if the user uses public transportation, it can also make suggestions that include access information. This makes it possible to provide the most suitable tourist spots and activities based on the user's current location.

[0118] The suggestion unit can estimate the user's emotions and suggest a shopping plan for the trip based on the estimated emotions. For example, if the user feels like relaxing, a quiet shopping area can be suggested. If the user is excited, a bustling shopping mall or market can be suggested. Furthermore, if the user is looking for a specific product, a store where that product can be purchased can be suggested. This makes it possible to provide an optimal shopping plan according to the user's emotions.

[0119] The suggestion unit analyzes the user's social media activity and can suggest travel plans based on the places and activities visited by the user's friends. For example, it can suggest tourist spots that the user's friends have highly rated. It can also suggest new activities based on the activities the user's friends have participated in. It can also suggest travel destinations and activities based on photos and comments shared by the user's friends. This makes it possible to provide optimal travel plans that utilize the user's social network.

[0120] The suggestion unit can estimate the user's emotions and suggest entertainment options for the trip based on the estimated emotions. For example, if the user feels like relaxing, a quiet movie theater or theater can be suggested. If the user is excited, a live concert or sporting event can be suggested. Furthermore, if the user is looking for a cultural experience, a museum or art gallery can be suggested. This allows the optimal entertainment to be provided according to the user's emotions.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The reception unit accepts the user's input of a photo and destination. Photos from the user include, for example, JPEG format, PNG format, landscape photos, portrait photos, etc. The reception unit accepts photos by uploading photos of the area the user wants to visit to the system. Users can also enter the location they want to visit in text. Step 2: The analysis unit uses the generation AI to analyze the photo received by the reception unit and identify the destination. The generation AI analyzes the photo using technologies such as GAN (Generative Adversarial Network) and Transformer. The analysis unit analyzes the content of the photo to identify the destination, and can also obtain detailed information about the destination based on the results of the photo analysis. Step 3: The suggestion unit proposes a travel itinerary based on the destinations identified by the analysis unit. The suggestion unit plans the travel itinerary based on the itinerary specified by the user, and proposes a specific itinerary, for example, one day around photo A and two days around photo B. The suggestion unit can also propose a travel itinerary based on stopovers and souvenir locations specified by the user.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 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 reception unit that receives input of a photo and a destination from a user; an analysis unit that analyzes the photo received by the reception unit and analyzes the destination; a proposal unit that proposes a travel itinerary based on the destination analyzed by the analysis unit. A system characterized by:

2. The proposal unit A route suggestion unit is provided that plans a travel itinerary based on route destinations specified by the user.

2. The system of claim 1.

3. The proposal unit A souvenir suggestion unit is provided that plans a travel itinerary based on souvenir locations specified by the user.

2. The system of claim 1.

4. The analysis unit Analyze photos and destinations using generative AI 2. The system of claim 1.

5. The proposal unit Plan a trip based on user-specified dates 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and set the timing for accepting photos based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past travel history and select the optimal reception method 2. The system of claim 1.

8. The reception unit When accepting photos, filter them based on the user's current interests 2. The system of claim 1.

9. The reception unit When accepting photos, determine the optimal acceptance method depending on the user's input method 2. The system of claim 1.

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