system

Abstract

The system according to the embodiment aims to provide an appropriate travel plan that takes into consideration the congestion situation at tourist spots and weather information. [Solution] A system according to an embodiment includes a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives tourist spot designations from a user. The collection unit collects congestion and weather information based on the tourist spot information received by the reception unit. The analysis unit analyzes the information collected by the collection unit and creates an appropriate travel plan. The provision unit provides the travel plan created by the analysis unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making travel plans that take into account the congestion situation at tourist spots and weather information.

[0005] The system according to the embodiment aims to provide an appropriate travel plan that takes into consideration the congestion situation at tourist spots and weather information. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives tourist spot designations from a user. The collection unit collects congestion and weather information based on the tourist spot information received by the reception unit. The analysis unit analyzes the information collected by the collection unit and creates an appropriate travel plan. The provision unit provides the travel plan created by the analysis unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide an appropriate travel plan that takes into consideration the congestion status of tourist spots and weather information. [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 plan creation system according to an embodiment of the present invention generates an optimal travel plan by taking into account the congestion and weather information of tourist spots specified by the user. This system allows the user to input the tourist spots they wish to visit, and a generation AI collects the current congestion and weather information for those tourist spots and creates an optimal travel plan based on the collected information. For example, if a tourist spot is crowded, the system can suggest alternative time slots or other tourist spots, and if the weather is bad, it can suggest indoor tourist spots. This allows the user to enjoy a comfortable trip. First, the user inputs the tourist spots they wish to visit. To do this, the user simply inputs their departure point and destination. For example, they might input "I want to go from my home to the tourist spot." This information is input into the generation AI. The generation AI then analyzes the input information and creates a video showing how to get from their current location to the destination. The generation AI calculates the optimal route based on map data and generates a video that follows that route. For example, if a user inputs a route from their home to a tourist spot, a video that follows that route is generated. The generated video begins navigation based on the orientation of the user's smartphone. For example, if the user points their smartphone north, the video also begins navigation based on the north. This allows the user to receive navigation based on their facing direction. Furthermore, the video moves on the screen in sync with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows the user to be navigated at their own pace. This system has a simple structure that can be easily used by both children and the elderly, making it popular with everyone. The user can be navigated intuitively without performing complex operations. Furthermore, since the viewpoint of the smartphone is the axis for all purposes, the user will not get lost, and since the smartphone is held horizontally, walking safety is ensured. For example, if the user is walking with the smartphone held horizontally, the video will also be displayed horizontally, allowing the user to walk safely. This allows the travel plan creation system to provide the optimal travel plan based on the user's specifications, taking into account congestion and weather information.

[0029] An itinerary creation system according to an embodiment includes a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives designations of tourist spots from a user. The user can input, for example, the name and location information of a tourist spot they would like to visit. The reception unit analyzes the input information and acquires detailed information about the tourist spot. For example, the reception unit acquires detailed information from an associated database based on the name and location information of the tourist spot. The collection unit collects congestion and weather information based on the tourist spot information received by the reception unit. The collection unit can collect current congestion and weather information about the tourist spot using, for example, public data on the Internet or sensor data. For example, the collection unit collects and analyzes sensor data to grasp the congestion status of the tourist spot in real time. The collection unit also collects and analyzes weather data to grasp weather information about the tourist spot in real time. The analysis unit analyzes the information collected by the collection unit and creates an appropriate itinerary. The analysis unit applies an algorithm to generate an optimal itinerary based on, for example, the collected congestion and weather information. For example, the analysis unit can suggest a different time of day or another tourist spot if the tourist spot is crowded, and suggest indoor tourist spots if the weather is bad. The providing unit provides the travel plan created by the analysis unit to the user. The providing unit can, for example, display the created travel plan on the user's device. For example, the providing unit can display the travel plan on the user's smartphone or tablet and provide navigation. The providing unit can also estimate the user's emotions and adjust the display method of the travel plan based on the estimated emotions. As a result, the travel plan creation system according to the embodiment can provide an optimal travel plan that takes into account congestion and weather information based on the user's specifications.

[0030] The collection unit can collect the current congestion status of tourist attractions. For example, the collection unit collects and analyzes sensor data to grasp the congestion status of tourist attractions in real time. For example, the collection unit collects data from cameras and sensors installed at tourist attractions and calculates the congestion level. The collection unit can also collect the congestion status of tourist attractions using public data on the Internet. For example, the collection unit collects information from the official websites and social media of tourist attractions and analyzes the congestion level. This makes it possible to grasp the congestion status of tourist attractions in real time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze data using an AI model to collect the congestion status of tourist attractions and calculate the congestion level.

[0031] The collection unit can collect current weather information for tourist attractions. The collection unit, for example, collects and analyzes weather data to grasp weather information for tourist attractions in real time. For example, the collection unit collects data from the Japan Meteorological Agency or a weather service provider and analyzes weather information for the tourist attractions. The collection unit can also collect data from weather sensors installed at the tourist attractions and analyze the weather information. For example, the collection unit collects data such as temperature, precipitation, and wind speed at the tourist attractions and analyzes the weather information. This allows weather information for the tourist attractions to be grasped in real time. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze data using an AI model to collect weather information for the tourist attractions and generate weather information.

[0032] The analysis unit can create an appropriate travel plan based on the collected congestion and weather information. The analysis unit, for example, applies an algorithm to generate an optimal travel plan based on the collected congestion and weather information. For example, the analysis unit can suggest a different time period or different tourist spots when the area is crowded, and can suggest indoor tourist spots when the weather is bad. The analysis unit can suggest a different time period to the user when the area is highly crowded. For example, the analysis unit can suggest early morning or evening time periods to avoid tourist spots when they are highly crowded. The analysis unit can also suggest indoor tourist spots to the user when the weather is bad. For example, the analysis unit can suggest indoor facilities such as museums and art galleries when it rains. This allows an optimal travel plan to be created based on the collected information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected data into an AI model to generate an optimal travel plan.

[0033] The providing unit can suggest a different time period or a different tourist spot if the tourist spot is crowded. The providing unit suggests a different time period or a different tourist spot to the user, for example, depending on the congestion level of the tourist spot. For example, if the tourist spot is highly crowded, the providing unit can suggest an early morning or evening time period to the user. Furthermore, if the tourist spot is highly crowded, the providing unit can also suggest a different tourist spot to the user. For example, the providing unit suggests a nearby tourist spot instead of the crowded tourist spot. This makes it possible to suggest a different time period or a different tourist spot depending on the congestion level. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the congestion level of the tourist spot into an AI model and suggest a different time period or a different tourist spot.

[0034] The providing unit can suggest indoor tourist spots when the weather is bad. The providing unit suggests indoor tourist spots to the user, for example, according to weather information for the tourist spot. For example, the providing unit can suggest indoor facilities such as museums and art galleries to the user when the weather is bad at the tourist spot. The providing unit can also suggest indoor facilities such as shopping malls and movie theaters to the user when the weather is bad at the tourist spot. This makes it possible to suggest tourist spots suitable for bad weather. Some or all of the above-mentioned processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input weather information for the tourist spot into an AI model and suggest indoor tourist spots.

[0035] The reception unit can analyze the user's past travel history and automatically suggest optimal tourist spots. The reception unit, for example, uses a database or log data to collect and analyze the user's past travel history. For example, the reception unit retrieves and analyzes the history of tourist spots the user has visited from the database. The reception unit also suggests optimal tourist spots based on the user's past travel history. For example, the reception unit can suggest similar tourist spots based on the user's ratings of tourist spots visited in the past and the length of stay at those tourist spots. The reception unit can also predict and suggest favorite tourist spots from the user's past travel history. For example, the reception unit can suggest optimal tourist spots based on the ratings of tourist spots visited in the past. This makes it possible to suggest optimal tourist spots based on the user's past travel history. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past travel history data into a generation AI and have the generation AI suggest optimal tourist spots.

[0036] The reception unit can customize tourist spot suggestions based on the user's current interests. The reception unit, for example, uses search history and social media activity to collect and analyze the user's current interests. For example, the reception unit identifies the user's current interests based on keywords recently searched and social media activity. The reception unit also customizes tourist spot suggestions based on the user's current interests. For example, the reception unit can suggest tourist spots based on themes that the user is currently interested in. Furthermore, the reception unit can also suggest tourist spots based on keywords recently searched by the user. For example, the reception unit can suggest tourist spots based on events and activities in which the user is participating. This makes it possible to suggest tourist spots based on the user's current interests. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current interest data into a generation AI and have the generation AI execute tourist spot suggestions.

[0037] The reception unit can prioritize suggesting nearby tourist spots by taking into account the user's geographical location information. The reception unit, for example, uses GPS data or a location information service to collect and analyze the user's geographical location information. For example, the reception unit obtains the user's current location from GPS data and suggests nearby tourist spots. The reception unit also suggests optimal tourist spots based on the user's geographical location information. For example, the reception unit can suggest tourist spots within walking distance from the user's current location. Furthermore, the reception unit can suggest tourist spots that are easily accessible from the user's current location by public transportation. For example, the reception unit can suggest tourist spots that are a short drive from the user's current location. This allows nearby tourist spots to be prioritized based on the user's current location. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to suggest optimal tourist spots.

[0038] The reception unit can analyze the user's social media activity and suggest related tourist spots. The reception unit, for example, uses the API and data analysis tools of a social media platform to collect and analyze the user's social media activity. For example, the reception unit collects and analyzes information about tourist spots that the user has "liked" on social media. The reception unit can also collect and analyze information about tourist spots visited by influencers the user follows. For example, the reception unit can suggest tourist spots based on photos the user has shared on social media. This makes it possible to suggest related tourist spots based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into a generation AI and have the generation AI suggest related tourist spots.

[0039] The collection unit can collect future forecast information by referring to past congestion and weather data. The collection unit, for example, uses a database or statistical data to collect and analyze past congestion and weather data. For example, the collection unit obtains past congestion data of tourist spots from a database and collects future congestion forecasts. The collection unit can also collect future weather forecasts based on past weather data of tourist spots. For example, the collection unit collects future congestion forecasts based on past event data. This makes it possible to collect future forecast information based on past data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past congestion and weather data into a generation AI and have the generation AI execute future forecast information.

[0040] The collection unit can apply different information collection methods depending on the category of the tourist attraction. The collection unit, for example, uses a database or a classification algorithm to apply different information collection methods depending on the category of the tourist attraction. For example, if the tourist attraction is outdoors, the collection unit can prioritize collecting weather information. Also, if the tourist attraction is indoors, the collection unit can prioritize collecting congestion information. For example, for natural tourist attractions, the collection unit collects both weather and congestion information in a balanced manner. This makes it possible to collect information according to the category of the tourist attraction. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input tourist attraction category data into the generation AI and have the generation AI execute the optimal information collection method.

[0041] The collection unit can adjust the scope of information collection taking into account the geographic distribution of tourist attractions. The collection unit, for example, uses map data and location information services to collect and analyze the geographic distribution of tourist attractions. For example, the collection unit collects detailed information in areas where tourist attractions are concentrated. Furthermore, the collection unit can collect a wide range of information in areas where tourist attractions are dispersed. For example, the collection unit also collects information on surrounding tourist attractions in areas with few tourist attractions. This makes it possible to collect information according to the geographic distribution of tourist attractions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographic distribution data of tourist attractions into the generation AI and adjust the scope of information collection.

[0042] The collection unit can collect related news and event information and add it to the tourist attraction information. The collection unit, for example, uses a news feed or an event calendar to collect related news and event information. For example, the collection unit collects event information held at tourist attractions. The collection unit can also collect news related to tourist attractions. For example, the collection unit collects event information held in the vicinity of tourist attractions. This makes it possible to collect news and event information related to tourist attractions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input related news and event information into a generation AI to add it to the tourist attraction information.

[0043] The analysis unit can analyze the success rate of past travel plans and apply an optimal plan creation algorithm. The analysis unit, for example, uses a database and feedback data to collect and analyze the success rate of past travel plans. For example, the analysis unit creates a plan with a high success rate based on evaluations of past travel plans. The analysis unit can also create a plan that reflects improvements based on feedback on past travel plans. For example, the analysis unit analyzes data on past travel plans and applies an optimal plan creation algorithm. This makes it possible to create an optimal travel plan based on past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input success rate data of past travel plans into the generation AI and have the generation AI execute the optimal plan creation algorithm.

[0044] The analysis unit can apply different analysis methods depending on the characteristics of the tourist spot. The analysis unit, for example, uses a database or classification algorithm to collect and analyze the characteristics of the tourist spot. For example, in the case of a natural tourist spot, the analysis unit can apply an analysis method that emphasizes weather information. In addition, in the case of an urban tourist spot, the analysis unit can apply an analysis method that emphasizes congestion status. For example, in the case of a cultural tourist spot, the analysis unit applies an analysis method that emphasizes event information. This makes it possible to perform analysis according to the characteristics of the tourist spot. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input characteristic data of the tourist spot into the generation AI and have the generation AI execute the optimal analysis method.

[0045] The analysis unit can propose an optimal travel route taking into account the user's geographical location information. The analysis unit, for example, uses GPS data or location information services to collect and analyze the user's geographical location information. For example, the analysis unit obtains the user's current location from the GPS data and proposes an optimal travel route. The analysis unit also proposes optimal tourist spots based on the user's geographical location information. For example, the analysis unit can propose tourist spots closest to the user's current location. Furthermore, the analysis unit can propose tourist spots that are easily accessible from the user's current location by public transportation. For example, the analysis unit can propose tourist spots that are a short drive from the user's current location. This makes it possible to propose an optimal travel route based on the user's current location. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to the generation AI and cause the generation AI to propose an optimal travel route.

[0046] The analysis unit can analyze the user's social media activity and suggest related travel plans. The analysis unit, for example, uses the API and data analysis tools of a social media platform to collect and analyze the user's social media activity. For example, the analysis unit collects and analyzes information about tourist spots that the user has "liked" on social media. The analysis unit can also collect and analyze information about tourist spots visited by influencers the user follows. For example, the analysis unit can suggest travel plans based on photos the user has shared on social media. This makes it possible to suggest related travel plans based on the user's social media activity. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's social media activity data into a generation AI and have the generation AI suggest related travel plans.

[0047] The provision unit can select the optimal plan provision method by referring to the user's past feedback. The provision unit, for example, uses a database or survey results to collect and analyze the user's past feedback. For example, the provision unit prioritizes plan provision methods that the user has previously rated highly. The provision unit can also select a plan provision method that reflects improvements based on the user's past feedback. For example, the provision unit analyzes the user's past feedback and selects the optimal plan provision method. This makes it possible to select the optimal plan provision method based on the user's past feedback. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's past feedback data into a generation AI and cause the generation AI to execute the optimal plan provision method.

[0048] The providing unit can adjust the level of detail of the travel plan it provides according to the user's needs. For example, the providing unit uses surveys and feedback data to collect and analyze the user's needs. For example, if the user requests detailed information, the providing unit can provide a plan that includes detailed descriptions and photos of tourist spots. Furthermore, if the user requests concise information, the providing unit can provide a concise plan that covers the main points. For example, if the user requests specific information, the providing unit can provide a plan that focuses on that information. This makes it possible to provide a detailed travel plan that meets the user's needs. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input user needs data into a generating AI and cause the generating AI to execute an optimal plan providing method.

[0049] The provision unit can select the optimal plan provision method taking into account the user's device information. The provision unit, for example, uses information on the device type, OS, and browser to collect and analyze the user's device information. For example, if the user is using a smartphone, the provision unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the provision unit can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the provision unit can provide a display method that is simple and highly visible. This makes it possible to select a plan provision method optimized for the user's device. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's device information into a generation AI and cause the generation AI to execute the optimal plan provision method.

[0050] The providing unit can analyze the user's social media activity and provide related travel plans. The providing unit, for example, uses the API and data analysis tools of a social media platform to collect and analyze the user's social media activity. For example, the providing unit can collect and analyze information on tourist spots that the user has "liked" on social media. The providing unit can also collect and analyze information on tourist spots visited by influencers the user follows. For example, the providing unit can suggest travel plans based on photos the user has shared on social media. This makes it possible to provide related travel plans based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related travel plans.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The analysis unit can also monitor the user's health status and create an optimal travel plan. For example, the analysis unit collects the user's heart rate and step count data and evaluates the user's health status. If the user is tired, the analysis unit can suggest tourist spots to rest. If the user is active, the analysis unit can suggest plans that include hiking or sports activities. Furthermore, the analysis unit can make meal suggestions based on the user's health data. For example, if the user needs a certain nutrient, the analysis unit can suggest restaurants that contain that nutrient.

[0053] The providing unit can collect real-time feedback from the user during the trip and dynamically adjust the travel plan. For example, the providing unit can collect impressions and ratings from the user when they arrive at a tourist spot and reflect these in suggesting the next tourist spot. Also, if the user finishes sightseeing earlier than planned, the providing unit can add a new tourist spot. Furthermore, if the user likes a particular tourist spot, the providing unit can preferentially suggest tourist spots in the vicinity of that spot. This allows the travel plan to be flexibly adjusted based on the user's real-time feedback.

[0054] The analysis unit can analyze the user's past travel history and automatically suggest optimal tourist spots. For example, the analysis unit retrieves and analyzes the history of tourist spots that the user has visited in the past from a database. The analysis unit can also suggest optimal tourist spots based on the user's past travel history. Furthermore, the analysis unit can predict and suggest favorite tourist spots from the user's past travel history. This makes it possible to suggest optimal tourist spots based on the user's past travel history.

[0055] The providing unit can customize the tourist attraction suggestions based on the user's current interests. For example, the providing unit can use search history and social media activity to collect and analyze the user's current interests. The providing unit can also customize the tourist attraction suggestions based on the user's current interests. Furthermore, the providing unit can also suggest tourist attractions based on keywords recently searched by the user. This makes it possible to suggest tourist attractions according to the user's current interests.

[0056] The collection unit can adjust the scope of information collection taking into account the geographical distribution of tourist attractions. For example, the collection unit can collect detailed information in areas where tourist attractions are concentrated. Also, in areas where tourist attractions are dispersed, the collection unit can collect a wide range of information. Furthermore, in areas with few tourist attractions, the collection unit can also collect information on surrounding tourist attractions. This makes it possible to collect information according to the geographical distribution of tourist attractions.

[0057] The provision unit can select the optimal plan provision method by taking into consideration the user's device information. For example, the provision unit uses information on the device type, OS, and browser to collect and analyze the user's device information. Furthermore, if the user is using a smartphone, it can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, it can provide a display method that is optimized for a large screen. This makes it possible to select a plan provision method that is optimized for the user's device.

[0058] The analysis unit can analyze the user's social media activity and suggest related travel plans. For example, the analysis unit uses the APIs and data analysis tools of social media platforms to collect and analyze the user's social media activity. The analysis unit can also collect and analyze information about tourist spots that the user has "liked" on social media. Furthermore, the analysis unit can also collect and analyze information about tourist spots visited by influencers that the user follows. This makes it possible to suggest related travel plans based on the user's social media activity.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit accepts the user's designation of a tourist spot. The user can input the name and location information of the tourist spot they want to visit. The reception unit can analyze the input information and obtain detailed information about the tourist spot. For example, the reception unit can obtain detailed information from a related database based on the name and location information of the tourist spot. Step 2: The collection unit collects congestion status and weather information based on the tourist spot information received by the reception unit. The collection unit can collect current congestion status and weather information at tourist spots using public data on the Internet and sensor data. For example, the collection unit collects and analyzes sensor data to grasp the tourist spot's congestion status in real time. The collection unit also collects and analyzes weather data to grasp the tourist spot's weather information in real time. Step 3: The analysis unit analyzes the information collected by the collection unit and creates an appropriate itinerary. The analysis unit applies an algorithm to generate an optimal itinerary based on the collected congestion and weather information. For example, if it is crowded, the analysis unit can suggest a different time or different tourist spot, and if the weather is bad, it can suggest indoor tourist spots. Step 4: The providing unit provides the travel plan created by the analysis unit to the user. The providing unit can display the created travel plan on the user's device. For example, the providing unit can display the travel plan on the user's smartphone or tablet and provide navigation. The providing unit can also estimate the user's emotions and adjust the way the travel plan is displayed based on the estimated emotions.

[0061] (Example 2) A travel plan creation system according to an embodiment of the present invention generates an optimal travel plan by taking into account the congestion and weather information of tourist spots specified by the user. This system allows the user to input the tourist spots they wish to visit, and a generation AI collects the current congestion and weather information for those tourist spots and creates an optimal travel plan based on the collected information. For example, if a tourist spot is crowded, the system can suggest alternative time slots or other tourist spots, and if the weather is bad, it can suggest indoor tourist spots. This allows the user to enjoy a comfortable trip. First, the user inputs the tourist spots they wish to visit. To do this, the user simply inputs their departure point and destination. For example, they might input "I want to go from my home to the tourist spot." This information is input into the generation AI. The generation AI then analyzes the input information and creates a video showing how to get from their current location to the destination. The generation AI calculates the optimal route based on map data and generates a video that follows that route. For example, if a user inputs a route from their home to a tourist spot, a video that follows that route is generated. The generated video begins navigation based on the orientation of the user's smartphone. For example, if the user points their smartphone north, the video also begins navigation based on the north. This allows the user to receive navigation based on their facing direction. Furthermore, the video moves on the screen in sync with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows the user to be navigated at their own pace. This system has a simple structure that can be easily used by both children and the elderly, making it popular with everyone. The user can be navigated intuitively without performing complex operations. Furthermore, since the viewpoint of the smartphone is the axis for all purposes, the user will not get lost, and since the smartphone is held horizontally, walking safety is ensured. For example, if the user is walking with the smartphone held horizontally, the video will also be displayed horizontally, allowing the user to walk safely. This allows the travel plan creation system to provide the optimal travel plan based on the user's specifications, taking into account congestion and weather information.

[0062] An itinerary creation system according to an embodiment includes a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives designations of tourist spots from a user. The user can input, for example, the name and location information of a tourist spot they would like to visit. The reception unit analyzes the input information and acquires detailed information about the tourist spot. For example, the reception unit acquires detailed information from an associated database based on the name and location information of the tourist spot. The collection unit collects congestion and weather information based on the tourist spot information received by the reception unit. The collection unit can collect current congestion and weather information about the tourist spot using, for example, public data on the Internet or sensor data. For example, the collection unit collects and analyzes sensor data to grasp the congestion status of the tourist spot in real time. The collection unit also collects and analyzes weather data to grasp weather information about the tourist spot in real time. The analysis unit analyzes the information collected by the collection unit and creates an appropriate itinerary. The analysis unit applies an algorithm to generate an optimal itinerary based on, for example, the collected congestion and weather information. For example, the analysis unit can suggest a different time of day or another tourist spot if the tourist spot is crowded, and suggest indoor tourist spots if the weather is bad. The providing unit provides the travel plan created by the analysis unit to the user. The providing unit can, for example, display the created travel plan on the user's device. For example, the providing unit can display the travel plan on the user's smartphone or tablet and provide navigation. The providing unit can also estimate the user's emotions and adjust the display method of the travel plan based on the estimated emotions. As a result, the travel plan creation system according to the embodiment can provide an optimal travel plan that takes into account congestion and weather information based on the user's specifications.

[0063] The collection unit can collect the current congestion status of tourist attractions. For example, the collection unit collects and analyzes sensor data to grasp the congestion status of tourist attractions in real time. For example, the collection unit collects data from cameras and sensors installed at tourist attractions and calculates the congestion level. The collection unit can also collect the congestion status of tourist attractions using public data on the Internet. For example, the collection unit collects information from the official websites and social media of tourist attractions and analyzes the congestion level. This makes it possible to grasp the congestion status of tourist attractions in real time. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze data using an AI model to collect the congestion status of tourist attractions and calculate the congestion level.

[0064] The collection unit can collect current weather information for tourist attractions. The collection unit, for example, collects and analyzes weather data to grasp weather information for tourist attractions in real time. For example, the collection unit collects data from the Japan Meteorological Agency or a weather service provider and analyzes weather information for the tourist attractions. The collection unit can also collect data from weather sensors installed at the tourist attractions and analyze the weather information. For example, the collection unit collects data such as temperature, precipitation, and wind speed at the tourist attractions and analyzes the weather information. This allows weather information for the tourist attractions to be grasped in real time. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can analyze data using an AI model to collect weather information for the tourist attractions and generate weather information.

[0065] The analysis unit can create an appropriate travel plan based on the collected congestion and weather information. The analysis unit, for example, applies an algorithm to generate an optimal travel plan based on the collected congestion and weather information. For example, the analysis unit can suggest a different time period or different tourist spots when the area is crowded, and can suggest indoor tourist spots when the weather is bad. The analysis unit can suggest a different time period to the user when the area is highly crowded. For example, the analysis unit can suggest early morning or evening time periods to avoid tourist spots when they are highly crowded. The analysis unit can also suggest indoor tourist spots to the user when the weather is bad. For example, the analysis unit can suggest indoor facilities such as museums and art galleries when it rains. This allows an optimal travel plan to be created based on the collected information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected data into an AI model to generate an optimal travel plan.

[0066] The providing unit can suggest a different time period or a different tourist spot if the tourist spot is crowded. The providing unit suggests a different time period or a different tourist spot to the user, for example, depending on the congestion level of the tourist spot. For example, if the tourist spot is highly crowded, the providing unit can suggest an early morning or evening time period to the user. Furthermore, if the tourist spot is highly crowded, the providing unit can also suggest a different tourist spot to the user. For example, the providing unit suggests a nearby tourist spot instead of the crowded tourist spot. This makes it possible to suggest a different time period or a different tourist spot depending on the congestion level. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the congestion level of the tourist spot into an AI model and suggest a different time period or a different tourist spot.

[0067] The providing unit can suggest indoor tourist spots when the weather is bad. The providing unit suggests indoor tourist spots to the user, for example, according to weather information for the tourist spot. For example, the providing unit can suggest indoor facilities such as museums and art galleries to the user when the weather is bad at the tourist spot. The providing unit can also suggest indoor facilities such as shopping malls and movie theaters to the user when the weather is bad at the tourist spot. This makes it possible to suggest tourist spots suitable for bad weather. Some or all of the above-mentioned processing by the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input weather information for the tourist spot into an AI model and suggest indoor tourist spots.

[0068] The reception unit can estimate the user's emotions and suggest tourist spots based on the estimated user emotions. The reception unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The reception unit can also record the user's voice and estimate the emotions using a voice analysis algorithm. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotions. The reception unit also suggests tourist spots based on the user's emotions. For example, if the user is feeling stressed, the reception unit can suggest tourist spots where the user can relax. If the user is excited, the reception unit can suggest tourist spots where the user can be active. For example, if the user is tired, the reception unit can suggest tourist spots where the user can rest. This makes it possible to suggest tourist spots based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0069] The reception unit can analyze the user's past travel history and automatically suggest optimal tourist spots. The reception unit, for example, uses a database or log data to collect and analyze the user's past travel history. For example, the reception unit retrieves and analyzes the history of tourist spots the user has visited from the database. The reception unit also suggests optimal tourist spots based on the user's past travel history. For example, the reception unit can suggest similar tourist spots based on the user's ratings of tourist spots visited in the past and the length of stay at those tourist spots. The reception unit can also predict and suggest favorite tourist spots from the user's past travel history. For example, the reception unit can suggest optimal tourist spots based on the ratings of tourist spots visited in the past. This makes it possible to suggest optimal tourist spots based on the user's past travel history. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past travel history data into a generation AI and have the generation AI suggest optimal tourist spots.

[0070] The reception unit can customize tourist spot suggestions based on the user's current interests. The reception unit, for example, uses search history and social media activity to collect and analyze the user's current interests. For example, the reception unit identifies the user's current interests based on keywords recently searched and social media activity. The reception unit also customizes tourist spot suggestions based on the user's current interests. For example, the reception unit can suggest tourist spots based on themes that the user is currently interested in. Furthermore, the reception unit can also suggest tourist spots based on keywords recently searched by the user. For example, the reception unit can suggest tourist spots based on events and activities in which the user is participating. This makes it possible to suggest tourist spots based on the user's current interests. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current interest data into a generation AI and have the generation AI execute tourist spot suggestions.

[0071] The reception unit can estimate the user's emotions and prioritize tourist spots based on the estimated user emotions. The reception unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The reception unit can also record the user's voice and estimate the emotions using a voice analysis algorithm. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotions. The reception unit can also prioritize tourist spots based on the user's emotions. For example, if the user wants to relax, the reception unit can prioritize tourist spots that offer relaxation. Also, if the user wants to be active, the reception unit can prioritize tourist spots with plenty of activities. For example, if the user wants a cultural experience, the reception unit can prioritize historical tourist spots. This makes it possible to prioritize tourist spots according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0072] The reception unit can prioritize suggesting nearby tourist spots by taking into account the user's geographical location information. The reception unit, for example, uses GPS data or a location information service to collect and analyze the user's geographical location information. For example, the reception unit obtains the user's current location from GPS data and suggests nearby tourist spots. The reception unit also suggests optimal tourist spots based on the user's geographical location information. For example, the reception unit can suggest tourist spots within walking distance from the user's current location. Furthermore, the reception unit can suggest tourist spots that are easily accessible from the user's current location by public transportation. For example, the reception unit can suggest tourist spots that are a short drive from the user's current location. This allows nearby tourist spots to be prioritized based on the user's current location. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to suggest optimal tourist spots.

[0073] The reception unit can analyze the user's social media activity and suggest related tourist spots. The reception unit, for example, uses the API and data analysis tools of a social media platform to collect and analyze the user's social media activity. For example, the reception unit collects and analyzes information about tourist spots that the user has "liked" on social media. The reception unit can also collect and analyze information about tourist spots visited by influencers the user follows. For example, the reception unit can suggest tourist spots based on photos the user has shared on social media. This makes it possible to suggest related tourist spots based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into a generation AI and have the generation AI suggest related tourist spots.

[0074] The collection unit can estimate the user's emotions and adjust the frequency of collecting congestion and weather information based on the estimated user emotions. The collection unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The collection unit can also record the user's voice and estimate the emotions using a voice analysis algorithm. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotions. The collection unit can also adjust the frequency of collecting congestion and weather information based on the user's emotions. For example, if the user is feeling stressed, the collection unit can frequently collect congestion information and update it in real time. The collection unit can also reduce the frequency of collecting weather information if the user is relaxed. For example, if the user is in a hurry, the collection unit can increase the frequency of collecting congestion information. This makes it possible to adjust the frequency of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0075] The collection unit can collect future forecast information by referring to past congestion and weather data. The collection unit, for example, uses a database or statistical data to collect and analyze past congestion and weather data. For example, the collection unit obtains past congestion data of tourist spots from a database and collects future congestion forecasts. The collection unit can also collect future weather forecasts based on past weather data of tourist spots. For example, the collection unit collects future congestion forecasts based on past event data. This makes it possible to collect future forecast information based on past data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past congestion and weather data into a generation AI and have the generation AI execute future forecast information.

[0076] The collection unit can apply different information collection methods depending on the category of the tourist attraction. The collection unit, for example, uses a database or a classification algorithm to apply different information collection methods depending on the category of the tourist attraction. For example, if the tourist attraction is outdoors, the collection unit can prioritize collecting weather information. Also, if the tourist attraction is indoors, the collection unit can prioritize collecting congestion information. For example, for natural tourist attractions, the collection unit collects both weather and congestion information in a balanced manner. This makes it possible to collect information according to the category of the tourist attraction. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input tourist attraction category data into the generation AI and have the generation AI execute the optimal information collection method.

[0077] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The collection unit can also record the user's voice and estimate the emotions using a voice analysis algorithm. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the collection unit determines the priority of information to be collected based on the user's emotions. For example, if the user wants to relax, the collection unit can prioritize collecting weather information. Also, if the user wants to be active, the collection unit can prioritize collecting congestion information. For example, if the user wants to have a cultural experience, the collection unit can prioritize collecting event information. This makes it possible to determine the priority of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0078] The collection unit can adjust the scope of information collection taking into account the geographic distribution of tourist attractions. The collection unit, for example, uses map data and location information services to collect and analyze the geographic distribution of tourist attractions. For example, the collection unit collects detailed information in areas where tourist attractions are concentrated. Furthermore, the collection unit can collect a wide range of information in areas where tourist attractions are dispersed. For example, the collection unit also collects information on surrounding tourist attractions in areas with few tourist attractions. This makes it possible to collect information according to the geographic distribution of tourist attractions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographic distribution data of tourist attractions into the generation AI and adjust the scope of information collection.

[0079] The collection unit can collect related news and event information and add it to the tourist attraction information. The collection unit, for example, uses a news feed or an event calendar to collect related news and event information. For example, the collection unit collects event information held at tourist attractions. The collection unit can also collect news related to tourist attractions. For example, the collection unit collects event information held in the vicinity of tourist attractions. This makes it possible to collect news and event information related to tourist attractions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input related news and event information into a generation AI to add it to the tourist attraction information.

[0080] The analysis unit can estimate the user's emotions and adjust the travel plan creation method based on the estimated user emotions. The analysis unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The analysis unit can also record the user's voice and estimate the emotions using a voice analysis algorithm. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the analysis unit can adjust the travel plan creation method based on the user's emotions. For example, the analysis unit can create a leisurely travel plan if the user wants to relax. Alternatively, the analysis unit can create a travel plan with plenty of activities if the user wants to be active. For example, the analysis unit can create a travel plan that includes historical tourist spots if the user wants a cultural experience. This makes it possible to create a travel plan that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 analysis unit may be performed using, or without, an AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0081] The analysis unit can analyze the success rate of past travel plans and apply an optimal plan creation algorithm. The analysis unit, for example, uses a database and feedback data to collect and analyze the success rate of past travel plans. For example, the analysis unit creates a plan with a high success rate based on evaluations of past travel plans. The analysis unit can also create a plan that reflects improvements based on feedback on past travel plans. For example, the analysis unit analyzes data on past travel plans and applies an optimal plan creation algorithm. This makes it possible to create an optimal travel plan based on past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input success rate data of past travel plans into the generation AI and have the generation AI execute the optimal plan creation algorithm.

[0082] The analysis unit can apply different analysis methods depending on the characteristics of the tourist spot. The analysis unit, for example, uses a database or classification algorithm to collect and analyze the characteristics of the tourist spot. For example, in the case of a natural tourist spot, the analysis unit can apply an analysis method that emphasizes weather information. In addition, in the case of an urban tourist spot, the analysis unit can apply an analysis method that emphasizes congestion status. For example, in the case of a cultural tourist spot, the analysis unit applies an analysis method that emphasizes event information. This makes it possible to perform analysis according to the characteristics of the tourist spot. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input characteristic data of the tourist spot into the generation AI and have the generation AI execute the optimal analysis method.

[0083] The analysis unit can estimate the user's emotions and prioritize travel plans based on the estimated user emotions. The analysis unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The analysis unit can also record the user's voice and estimate the emotions using a voice analysis algorithm. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit can also prioritize travel plans based on the user's emotions. For example, if the user wants to relax, the analysis unit can prioritize relaxing tourist spots. If the user wants to be active, the analysis unit can prioritize tourist spots with plenty of activities. For example, if the user wants a cultural experience, the analysis unit can prioritize historical tourist spots. This allows the prioritization of travel plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 analysis unit may be performed using, or without, an AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0084] The analysis unit can propose an optimal travel route taking into account the user's geographical location information. The analysis unit, for example, uses GPS data or location information services to collect and analyze the user's geographical location information. For example, the analysis unit obtains the user's current location from the GPS data and proposes an optimal travel route. The analysis unit also proposes optimal tourist spots based on the user's geographical location information. For example, the analysis unit can propose tourist spots closest to the user's current location. Furthermore, the analysis unit can propose tourist spots that are easily accessible from the user's current location by public transportation. For example, the analysis unit can propose tourist spots that are a short drive from the user's current location. This makes it possible to propose an optimal travel route based on the user's current location. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information to the generation AI and cause the generation AI to propose an optimal travel route.

[0085] The analysis unit can analyze the user's social media activity and suggest related travel plans. The analysis unit, for example, uses the API and data analysis tools of a social media platform to collect and analyze the user's social media activity. For example, the analysis unit collects and analyzes information about tourist spots that the user has "liked" on social media. The analysis unit can also collect and analyze information about tourist spots visited by influencers the user follows. For example, the analysis unit can suggest travel plans based on photos the user has shared on social media. This makes it possible to suggest related travel plans based on the user's social media activity. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's social media activity data into a generation AI and have the generation AI suggest related travel plans.

[0086] The providing unit can estimate the user's emotions and adjust the display method of the travel plan based on the estimated user emotions. The providing unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The providing unit can also record the user's voice and estimate the emotions using a voice analysis algorithm. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the providing unit can adjust the display method of the travel plan based on the user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This makes it possible to provide a display method of the travel plan that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative 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 providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0087] The provision unit can select the optimal plan provision method by referring to the user's past feedback. The provision unit, for example, uses a database or survey results to collect and analyze the user's past feedback. For example, the provision unit prioritizes plan provision methods that the user has previously rated highly. The provision unit can also select a plan provision method that reflects improvements based on the user's past feedback. For example, the provision unit analyzes the user's past feedback and selects the optimal plan provision method. This makes it possible to select the optimal plan provision method based on the user's past feedback. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's past feedback data into a generation AI and cause the generation AI to execute the optimal plan provision method.

[0088] The providing unit can adjust the level of detail of the travel plan it provides according to the user's needs. For example, the providing unit uses surveys and feedback data to collect and analyze the user's needs. For example, if the user requests detailed information, the providing unit can provide a plan that includes detailed descriptions and photos of tourist spots. Furthermore, if the user requests concise information, the providing unit can provide a concise plan that covers the main points. For example, if the user requests specific information, the providing unit can provide a plan that focuses on that information. This makes it possible to provide a detailed travel plan that meets the user's needs. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input user needs data into a generating AI and cause the generating AI to execute an optimal plan providing method.

[0089] The providing unit can estimate the user's emotions and prioritize travel plans based on the estimated user emotions. The providing unit, for example, uses facial expression recognition technology or voice analysis technology to estimate the user's emotions. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The providing unit can also record the user's voice and estimate the emotions using a voice analysis algorithm. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the providing unit prioritizes travel plans based on the user's emotions. For example, if the user wants to relax, the providing unit can prioritize relaxing tourist spots. Also, if the user wants to be active, the providing unit can prioritize tourist spots with plenty of activities. For example, if the user wants a cultural experience, the providing unit can prioritize historical tourist spots. This makes it possible to prioritize travel plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative 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 providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0090] The provision unit can select the optimal plan provision method taking into account the user's device information. The provision unit, for example, uses information on the device type, OS, and browser to collect and analyze the user's device information. For example, if the user is using a smartphone, the provision unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the provision unit can provide a display method optimized for a large screen. For example, if the user is using a smartwatch, the provision unit can provide a display method that is simple and highly visible. This makes it possible to select a plan provision method optimized for the user's device. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's device information into a generation AI and cause the generation AI to execute the optimal plan provision method.

[0091] The providing unit can analyze the user's social media activity and provide related travel plans. The providing unit, for example, uses the API and data analysis tools of a social media platform to collect and analyze the user's social media activity. For example, the providing unit can collect and analyze information on tourist spots that the user has "liked" on social media. The providing unit can also collect and analyze information on tourist spots visited by influencers the user follows. For example, the providing unit can suggest travel plans based on photos the user has shared on social media. This makes it possible to provide related travel plans based on the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related travel plans. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, analysis unit, and provision unit, 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 receives tourist spot designations from the user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects tourist spot congestion and weather information. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to create an optimal travel plan. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and displays the generated travel plan on the user's device. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and provision unit 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 receives designations of tourist spots from the user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on the congestion status and weather of tourist spots. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to create an optimal travel plan. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and displays the generated travel plan on the user's device. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset terminal 314 and receives designations of tourist spots from the user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on the congestion status and weather of tourist spots. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to create an optimal travel plan. The provision unit is realized, for example, by the control unit 46A of the headset terminal 314 and displays the generated travel plan on the user's device. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, analysis unit, and provision 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 receives designations of tourist spots from the user. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information on the congestion status and weather of tourist spots. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to create an optimal travel plan. The provision unit is realized, for example, by the control unit 46A of the robot 414 and displays the generated travel plan on the user's device.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can also monitor the user's health status and create an optimal travel plan. For example, the analysis unit collects the user's heart rate and step count data and evaluates the user's health status. If the user is tired, the analysis unit can suggest tourist spots to rest. If the user is active, the analysis unit can suggest plans that include hiking or sports activities. Furthermore, the analysis unit can make meal suggestions based on the user's health data. For example, if the user needs a certain nutrient, the analysis unit can suggest restaurants that contain that nutrient.

[0094] The providing unit can collect real-time feedback from the user during the trip and dynamically adjust the travel plan. For example, the providing unit can collect impressions and ratings from the user when they arrive at a tourist spot and reflect these in suggesting the next tourist spot. Also, if the user finishes sightseeing earlier than planned, the providing unit can add a new tourist spot. Furthermore, if the user likes a particular tourist spot, the providing unit can preferentially suggest tourist spots in the vicinity of that spot. This allows the travel plan to be flexibly adjusted based on the user's real-time feedback.

[0095] The collection unit can estimate the user's emotions and predict the congestion status of tourist spots based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can make a prediction to avoid crowded tourist spots. Also, if the user is relaxed, the collection unit can determine that crowded tourist spots are not a problem. Furthermore, the collection unit can adjust the congestion prediction algorithm based on the user's emotion data. This makes it possible to predict congestion based on the user's emotions.

[0096] The analysis unit can estimate the user's emotions and adjust the way the travel plan is created based on the estimated user's emotions. For example, if the user wants to relax, the analysis unit can create a leisurely travel plan. If the user wants to be active, the analysis unit can create a travel plan with plenty of activities. Furthermore, if the user wants a cultural experience, the analysis unit can create a travel plan that includes historical tourist spots. This makes it possible to create a travel plan that suits the user's emotions.

[0097] The providing unit can estimate the user's emotions and adjust the display method of the travel plan based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. In this way, it is possible to provide a display method of the travel plan that corresponds to the user's emotions.

[0098] The analysis unit can analyze the user's past travel history and automatically suggest optimal tourist spots. For example, the analysis unit retrieves and analyzes the history of tourist spots that the user has visited in the past from a database. The analysis unit can also suggest optimal tourist spots based on the user's past travel history. Furthermore, the analysis unit can predict and suggest favorite tourist spots from the user's past travel history. This makes it possible to suggest optimal tourist spots based on the user's past travel history.

[0099] The providing unit can customize the tourist attraction suggestions based on the user's current interests. For example, the providing unit can use search history and social media activity to collect and analyze the user's current interests. The providing unit can also customize the tourist attraction suggestions based on the user's current interests. Furthermore, the providing unit can also suggest tourist attractions based on keywords recently searched by the user. This makes it possible to suggest tourist attractions according to the user's current interests.

[0100] The collection unit can adjust the scope of information collection taking into account the geographical distribution of tourist attractions. For example, the collection unit can collect detailed information in areas where tourist attractions are concentrated. Also, in areas where tourist attractions are dispersed, the collection unit can collect a wide range of information. Furthermore, in areas with few tourist attractions, the collection unit can also collect information on surrounding tourist attractions. This makes it possible to collect information according to the geographical distribution of tourist attractions.

[0101] The provision unit can select the optimal plan provision method by taking into consideration the user's device information. For example, the provision unit uses information on the device type, OS, and browser to collect and analyze the user's device information. Furthermore, if the user is using a smartphone, it can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, it can provide a display method that is optimized for a large screen. This makes it possible to select a plan provision method that is optimized for the user's device.

[0102] The analysis unit can analyze the user's social media activity and suggest related travel plans. For example, the analysis unit uses the APIs and data analysis tools of social media platforms to collect and analyze the user's social media activity. The analysis unit can also collect and analyze information about tourist spots that the user has "liked" on social media. Furthermore, the analysis unit can also collect and analyze information about tourist spots visited by influencers that the user follows. This makes it possible to suggest related travel plans based on the user's social media activity.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit accepts the user's designation of a tourist spot. The user can input the name and location information of the tourist spot they want to visit. The reception unit can analyze the input information and obtain detailed information about the tourist spot. For example, the reception unit can obtain detailed information from a related database based on the name and location information of the tourist spot. Step 2: The collection unit collects congestion status and weather information based on the tourist spot information received by the reception unit. The collection unit can collect current congestion status and weather information at tourist spots using public data on the Internet and sensor data. For example, the collection unit collects and analyzes sensor data to grasp the tourist spot's congestion status in real time. The collection unit also collects and analyzes weather data to grasp the tourist spot's weather information in real time. Step 3: The analysis unit analyzes the information collected by the collection unit and creates an appropriate itinerary. The analysis unit applies an algorithm to generate an optimal itinerary based on the collected congestion and weather information. For example, if it is crowded, the analysis unit can suggest a different time or different tourist spot, and if the weather is bad, it can suggest indoor tourist spots. Step 4: The providing unit provides the travel plan created by the analysis unit to the user. The providing unit can display the created travel plan on the user's device. For example, the providing unit can display the travel plan on the user's smartphone or tablet and provide navigation. The providing unit can also estimate the user's emotions and adjust the way the travel plan is displayed based on the estimated emotions.

[0105] 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.

[0106] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 designations of tourist spots from users; a collection unit that collects congestion and weather information based on the information about tourist spots received by the reception unit; an analysis unit that analyzes the information collected by the collection unit and creates an appropriate travel plan; a providing unit that provides the travel plan created by the analysis unit to the user. A system characterized by:

2. The collecting unit Collecting current congestion status of tourist attractions 2. The system of claim 1.

3. The collecting unit Collect current weather information for tourist spots 2. The system of claim 1.

4. The analysis unit Create an appropriate travel plan based on the collected traffic and weather information 2. The system of claim 1.

5. The providing unit If it's crowded, suggest a different time or another tourist spot.

2. The system of claim 1.

6. The providing unit Suggest indoor attractions if the weather is bad 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and suggest tourist spots based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's past travel history and automatically suggests the best tourist spots 2. The system of claim 1.

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