System

Abstract

An object of a system according to an embodiment is to automatically propose and navigate a travel plan that matches preferences and conditions of a user.SOLUTION: A system includes a reception part, an analysis part, a provision part, and a navigation part. The reception unit receives an input of a desired town, the number of people, a budget, a time, and a favorite genre from a user. The analysis unit analyzes the data of the past town wrestling program on the basis of the information received by the reception unit, and proposes a course, a shop name, and a walking order that match the conditions of the user. The provision unit provides the course proposed by the analysis unit as a bookmark and a route map of the trip. The navigation unit performs navigation based on the information provided by the providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to automatically propose and navigate travel plans that match a user's preferences and conditions.

[0005] The system according to the embodiment aims to automatically propose and navigate a travel plan that matches the user's preferences and conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a navigation unit. The reception unit receives input from the user about the city they want to visit, the number of people, their budget, time, and preferred genres. The analysis unit analyzes data from past city strolling programs based on the information received by the reception unit, and suggests a course, shop names, and walking order that meets the user's requirements. The provision unit provides the course suggested by the analysis unit as a travel guide and route map. The navigation unit navigates based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically propose and navigate a travel plan that matches the user's preferences and conditions. [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 coordination system according to an embodiment of the present invention allows users to input their desired city, number of people, budget, time, and preferred genres, and then suggests routes, store names, and walking orders actually visited by celebrities on past city-walking programs, coordinating their holiday plans. When a user inputs their desired city, number of people, budget, time, and preferred genres, the system uses a generation AI to analyze data from past city-walking programs and suggest routes, store names, and walking orders that fit the user's criteria. These suggestions are provided as a travel guide and route map. The coordination system also uses a personalized AI character to learn city, facility, and store information and even navigate the user. Furthermore, the coordination system learns store website information and past store reviews, and by linking location information, users can enjoy realistic conversations. For example, the coordination system allows users to input their desired city, number of people, budget, time, and preferred genres. For example, if a user inputs "Shinjuku, two people, budget 5,000 yen, three hours, cafe hopping," the generation AI analyzes past program data and suggests a route to visit cafes visited by celebrities in Shinjuku. The proposed route is provided as a travel guide and route map. Users can actually walk around the city following the proposed route. The coordination system also uses a personalized AI character to learn about the city, facilities, and stores and navigate the route. For example, if a user selects "cafe hopping," the AI ​​character navigates while providing information about the cafes. Furthermore, the coordination system learns store website information and past store reviews, and by linking location information, users can enjoy realistic conversations. For example, when a user arrives at a cafe, the AI ​​character can provide information such as, "This cafe received high praise from celebrities who visited in the past." This allows the coordination system to efficiently coordinate holiday plans by proposing and navigating the optimal route based on the user's requirements.For example, when a user inputs the city they want to visit, the number of people, their budget, time, and preferred genre, the generation AI analyzes data from past city-walking programs and suggests a course, shop names, and walking order that suits the user's criteria. The suggested course is provided as a travel guide and route map. Users can then actually walk around the city by following the suggested course. The coordination system also uses a personalized AI character to learn about the city, facilities, and shops, and even navigate the route. Furthermore, the coordination system learns store website information and past store reviews, and by linking location information, users can enjoy realistic conversations. This allows users to enjoy a course that suits their preferences and efficiently coordinate how they spend their holidays.

[0029] A coordination system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a navigation unit. The reception unit accepts input from a user of a desired city, number of people, budget, time, and preferred genre. For example, the reception unit provides an interface for the user to input the desired city, number of people, budget, time, and preferred genre. The reception unit can also analyze the user's input and convert it into an appropriate format. The analysis unit analyzes data from past city-walking programs based on the information accepted by the reception unit and proposes a course, store names, and walking order that meets the user's requirements. For example, the analysis unit uses a generation AI to analyze data from past city-walking programs and propose a course that meets the user's requirements. The analysis unit can also learn store website information and past store reviews and link location information. The provision unit provides the course proposed by the analysis unit as a travel guide and a route map. For example, the provision unit provides the proposed course as a travel guide, allowing the user to confirm the course. The provision unit also provides a route map and map information for the user to follow the course. The navigation unit navigates based on the information provided by the providing unit. For example, the navigation unit navigates in real time as the user follows a course. The navigation unit can also use an AI character based on the user's preferences to learn town, facility, and store information and provide navigation. As a result, the coordination system according to the embodiment can efficiently coordinate how to spend a holiday by proposing and navigating the optimal course based on the user's requirements.

[0030] The coordination system further includes a navigation unit in which an AI character based on the user's preferences learns information about towns, facilities, and stores and provides navigation. The navigation unit uses an AI character based on the user's preferences to learn information about towns, facilities, and stores and provide navigation. For example, if the user selects "cafe hopping," the navigation unit navigates while providing information about cafes. Furthermore, if the user selects "shopping," the navigation unit can also navigate while providing shopping information. Furthermore, if the user selects "historical tour," the navigation unit can also navigate while providing information about historical places. This enables navigation based on the user's preferences. Some or all of the above-described processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input information about an AI character based on the user's preferences into the generation AI and have the generation AI execute the navigation content.

[0031] Furthermore, in the coordination system, the analysis unit learns store website information or past store reviews and links location information. The analysis unit learns store website information or past store reviews and links location information. For example, the analysis unit collects store website information and learns information such as menus, business hours, and reviews. The analysis unit can also collect past store reviews and learn information such as star ratings, comments, and rankings. Furthermore, by linking location information, the analysis unit can determine the location of stores visited by users and suggest optimal routes. This enables analysis that takes store reviews and location information into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input store website information and past store reviews into the generation AI and have the generation AI link the location information.

[0032] The providing unit can provide the proposed course as a travel itinerary and a route map. The providing unit provides the proposed course as a travel itinerary and a route map. For example, the providing unit provides the proposed course as a travel itinerary, allowing the user to check the course. The providing unit also provides a route map, providing map information for the user to follow the course. This allows the user to easily check the proposed course. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit may input information about the proposed course into the generation AI and cause the generation AI to generate a travel itinerary and a route map.

[0033] The navigation unit can provide past rating information when the user arrives. The navigation unit provides past rating information when the user arrives. For example, the navigation unit can provide past rating information of celebrities when the user arrives at a cafe. The navigation unit can also provide past rating information of celebrities when the user arrives at a restaurant. Furthermore, the navigation unit can provide past rating information of celebrities when the user arrives at a tourist destination. This allows the user to check rating information of places visited in real time. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the navigation unit can input information about the place where the user arrives into the generation AI and cause the generation AI to provide past rating information.

[0034] The analysis unit can analyze data on stations, stores, and map information visited during a specific program. The analysis unit analyzes data on stations, stores, and map information visited during a specific program. For example, the analysis unit collects and analyzes data on stations and stores visited during a specific program. The analysis unit can also collect and analyze map information used during a specific program. Furthermore, the analysis unit can collect and analyze data such as the names of celebrities who visited during a specific program and the broadcast date and time. This enables analysis based on specific program data. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on stations, stores, and map information visited during a specific program into a generation AI and have the generation AI perform the analysis.

[0035] The reception unit can analyze the user's past input history and suggest an input method. The reception unit analyzes the user's past input history and suggests the optimal input method. For example, the reception unit automatically displays cities and stations that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest cities and stations to be used during a specific time period based on the user's past input history. This provides the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI suggest the optimal input method.

[0036] The reception unit can automatically complete related towns and stations based on the user's current location information when the user inputs the destination. The reception unit automatically completes related towns and stations based on the user's current location information when the user inputs the destination. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the starting point. The reception unit can also suggest optimal candidate locations by considering the distance from the current location when the user inputs a destination. Furthermore, when the user uses the app while traveling, the reception unit can update the user's current location in real time and reflect it as the starting point. This provides optimal candidate locations based on the user's current location. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's current location information to the generation AI and cause the generation AI to automatically complete related towns and stations.

[0037] The reception unit can analyze the user's voice input at the time of input and automatically classify the input content using natural language processing. The reception unit can analyze the user's voice input at the time of input and automatically classify the input content using natural language processing. For example, the reception unit can automatically set the departure point and destination when the user simply voice-inputs "cafe hopping in Shinjuku." The reception unit can also automatically classify the conditions and set the input content when the user voice-inputs "two people, budget 5,000 yen, three hours." The reception unit can also automatically classify information related to cafes and set the input content when the user voice-inputs "cafe hopping." This enables automatic classification based on voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's voice input data to a generation AI and have the generation AI automatically classify the input content using natural language processing.

[0038] The reception unit can analyze the user's social media activity at the time of input and suggest related towns and stations. The reception unit can analyze the user's social media activity at the time of input and suggest related towns and stations. For example, the reception unit can suggest locations where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related towns and stations. Furthermore, the reception unit can suggest related towns and stations based on the activity of the user's friends on social media. This provides optimal candidate locations based on social media activity. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest related towns and stations.

[0039] The reception unit can customize the input content by reflecting the user's past travel history when the user inputs the information. The reception unit customizes the input content by reflecting the user's past travel history when the user inputs the information. For example, the reception unit automatically displays cities and stations that the user has visited in the past as candidates. The reception unit can also predict and suggest places visited during a specific time period based on the user's past travel history. Furthermore, the reception unit can analyze the user's past travel history and suggest optimal candidate locations. This provides optimal input content based on the user's past travel history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past travel history data into the generation AI and have the generation AI customize the input content.

[0040] The reception unit can select an input method based on the user's device information at the time of input. The reception unit selects the optimal input method by taking the user's device information into consideration at the time of input. For example, if the user is using a smartphone, the reception unit can provide an input method that matches the screen size. Furthermore, if the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can also provide an input method that is simple and highly visible. This provides the optimal input method based on the device information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's device information to the generation AI and have the generation AI select the optimal input method.

[0041] The analysis unit can improve the analysis accuracy by referring to past analysis data during analysis. The analysis unit improves the analysis accuracy by referring to past analysis data during analysis. For example, the analysis unit suggests a route that suits the user's preferences based on past analysis data. The analysis unit can also suggest a route that avoids congestion based on past analysis data. Furthermore, the analysis unit can analyze past analysis data and suggest the most efficient route. This makes it possible to improve the analysis accuracy based on past analysis data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past analysis data into the generation AI and have the generation AI improve the analysis accuracy.

[0042] During analysis, the analysis unit can customize the analysis results based on the user's past travel history. During analysis, the analysis unit customizes the analysis results by taking the user's past travel history into consideration. For example, the analysis unit suggests an optimal route based on places the user has visited in the past. The analysis unit can also predict and suggest places visited during a specific time period from the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and suggest the most efficient route. This makes it possible to customize the analysis results based on the past travel history. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's past travel history data into the generation AI and have the generation AI customize the analysis results.

[0043] The analysis unit can optimize the analysis results based on the user's current location information during analysis. The analysis unit optimizes the analysis results by taking the user's current location information into account during analysis. For example, the analysis unit suggests an optimal route based on the user's current location. The analysis unit can also suggest optimal candidate locations by taking into account the distance from the user's current location. Furthermore, when the user uses the app while traveling, the analysis unit can update the user's current location in real time and suggest an optimal route. This provides optimal analysis results based on the current location information. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's current location information into the generation AI and cause the generation AI to optimize the analysis results.

[0044] The analysis unit can analyze the user's social media activity during the analysis and reflect related information in the analysis. The analysis unit can analyze the user's social media activity during the analysis and reflect related information in the analysis. For example, the analysis unit can reflect the location where the user checked in on social media in the analysis. The analysis unit can also analyze the content of the user's social media posts and reflect related information in the analysis. Furthermore, the analysis unit can also reflect related information in the analysis with reference to the activities of the user's friends on social media. This provides an analysis result based on social media activity. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's social media activity data into the generation AI and have the generation AI analyze the related information.

[0045] The analysis unit can optimize the analysis algorithm by reflecting the user's past feedback during analysis. The analysis unit can optimize the analysis algorithm by reflecting the user's past feedback during analysis. For example, the analysis unit optimizes the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also extract specific patterns from the user's past feedback and optimize the analysis algorithm. Furthermore, the analysis unit can analyze the user's past feedback and propose the most efficient analysis algorithm. This makes it possible to optimize the analysis algorithm based on past feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past feedback data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0046] The analysis unit can customize the analysis results based on the user's device information during analysis. The analysis unit customizes the analysis results taking into account the user's device information during analysis. For example, if the user is using a smartphone, the analysis unit can provide analysis results tailored to the screen size. Furthermore, if the user is using a tablet, the analysis unit can provide analysis results optimized for a large screen. Furthermore, if the user is using a smartwatch, the analysis unit can provide analysis results that are concise and highly visible. This provides optimal analysis results based on the device information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's device information into the generation AI and have the generation AI customize the analysis results.

[0047] The providing unit can select an information provision method based on the user's past usage history at the time of providing information. The providing unit selects the optimal information provision method by referring to the user's past usage history at the time of providing information. For example, the providing unit selects the optimal method based on information provision methods used by the user in the past. The providing unit can also predict and suggest an information provision method to be used during a specific time period based on the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and select the most efficient information provision method. This provides the optimal information provision method based on the past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past usage history data into the generation AI and have the generation AI select an information provision method.

[0048] The providing unit can customize the information based on the user's current location information when providing the information. The providing unit customizes the information taking into account the user's current location information when providing the information. For example, the providing unit provides optimal information based on the user's current location. The providing unit can also provide optimal information taking into account the distance from the user's current location. Furthermore, when the user uses the app while on the move, the providing unit can update the user's current location in real time and provide optimal information. This provides optimal information based on the current location information. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's current location information to the generation AI and cause the generation AI to customize the information.

[0049] The providing unit can analyze the user's voice input and provide information using natural language processing when providing the information. The providing unit can analyze the user's voice input and provide information using natural language processing when providing the information. For example, when the user simply voice-inputs "cafe hopping in Shinjuku," the providing unit automatically provides related information. Furthermore, when the user voice-inputs "two people, budget 5,000 yen, three hours," the system can automatically classify the conditions and provide optimal information. Furthermore, when the user voice-inputs "cafe hopping," the system can automatically provide information related to cafes. This provides optimal information based on the voice input. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's voice input data into a generation AI and have the generation AI provide information using natural language processing.

[0050] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit can provide information about locations where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. This provides optimal information based on social media activity. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related information.

[0051] The providing unit can customize the information provision method by reflecting the user's past feedback when providing information. The providing unit customizes the information provision method by reflecting the user's past feedback when providing information. For example, the providing unit customizes the information provision method based on feedback provided by the user in the past. The providing unit can also extract specific patterns from the user's past feedback and customize the information provision method. Furthermore, the providing unit can analyze the user's past feedback and suggest the most efficient information provision method. This provides the optimal information provision method based on the past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the information provision method.

[0052] The providing unit can select an information provision method based on the user's device information at the time of provision. The providing unit selects the optimal information provision method taking the user's device information into consideration at the time of provision. For example, if the user is using a smartphone, the providing unit can provide an information provision method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide an information provision method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide an information provision method that is concise and highly visible. This provides the optimal information provision method based on the device information. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select an information provision method.

[0053] During navigation, the navigation unit can suggest a route based on the user's past movement history. During navigation, the navigation unit suggests an optimal route by referring to the user's past movement history. For example, the navigation unit suggests an optimal route based on routes the user has used in the past. The navigation unit can also suggest a route that avoids congestion based on the user's past movement history. Furthermore, the navigation unit can analyze the user's past movement history and suggest the most efficient route. This provides an optimal route based on the past movement history. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the navigation unit can input the user's past movement history data into the generation AI and have the generation AI suggest an optimal route.

[0054] During navigation, the navigation unit can update the route in real time based on the user's current location information. During navigation, the navigation unit updates the route in real time, taking into account the user's current location information. For example, the navigation unit updates the user's current location in real time while the user is moving and performs navigation. The navigation unit can also update the user's current location in real time and suggest an optimal route as the user approaches the destination. Furthermore, if the user gets lost, the navigation unit can update the user's current location in real time and perform navigation again. This allows the optimal route based on the current location information to be provided in real time. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the navigation unit can input the user's current location information to the generation AI and cause the generation AI to perform route updates in real time.

[0055] During navigation, the navigation unit can analyze the user's voice input and perform navigation using natural language processing. During navigation, the navigation unit can analyze the user's voice input and perform navigation using natural language processing. For example, the navigation unit can automatically set the next destination when the user simply voice-inputs, "Where is the next cafe?". The navigation unit can also automatically set a route and perform navigation when the user voice-inputs, "Tell me the route to my next destination." Furthermore, when the user voice-inputs, "I'm going to go cafe hopping," the navigation unit can automatically provide information related to cafes while performing navigation. This provides optimal navigation based on the voice input. Some or all of the above-described processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the navigation unit can input the user's voice input data into a generation AI and have the generation AI perform navigation using natural language processing.

[0056] The navigation unit can analyze the user's social media activity during navigation and reflect related information in the navigation. The navigation unit can analyze the user's social media activity during navigation and reflect related information in the navigation. For example, the navigation unit can reflect locations where the user has checked in on social media in the navigation. The navigation unit can also analyze the content of the user's social media posts and reflect related information in the navigation. Furthermore, the navigation unit can also reflect related information in the navigation based on the activity of the user's friends on social media. This provides optimal navigation based on social media activity. Some or all of the above-described processing in the navigation unit can be performed using, or without, a generation AI. For example, the navigation unit can input the user's social media activity data into the generation AI and have the generation AI perform navigation of related information.

[0057] The navigation unit can customize the navigation method by reflecting the user's past feedback during navigation. The navigation unit customizes the navigation method by reflecting the user's past feedback during navigation. For example, the navigation unit customizes the navigation method based on feedback provided by the user in the past. The navigation unit can also extract specific patterns from the user's past feedback and customize the navigation method. Furthermore, the navigation unit can analyze the user's past feedback and suggest the most efficient navigation method. This provides an optimal navigation method based on the past feedback. Some or all of the above-described processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the navigation unit can input the user's past feedback data into the generation AI and have the generation AI customize the navigation method.

[0058] The navigation unit can select the optimal navigation method during navigation by taking into account the user's device information. The navigation unit selects the optimal navigation method during navigation by taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit can provide a navigation method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can also provide a navigation method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the navigation unit can also provide a navigation method that is simple and highly visible. This provides the optimal navigation method based on the device information. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the navigation unit can input the user's device information into the generation AI and have the generation AI select a navigation method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit not only accepts the user's input, but can also predict and suggest towns and stores that the user is likely to like based on the user's past behavioral history. For example, the reception unit may analyze places the user has visited in the past and services the user has used, and suggest similar places and services. The reception unit may also learn the user's past behavioral patterns, and predict and suggest places the user is likely to visit during specific times or seasons. Furthermore, the reception unit may preferentially suggest places and services that the user has given a high rating to, based on the user's past ratings and feedback. This enables personalized suggestions based on the user's past behavioral history.

[0061] The analysis unit not only learns store website information and past store reviews, but also analyzes users' social media activities and incorporates related information into the analysis. For example, the analysis unit collects the places where users check in on social media and the content of their posts to understand the users' preferences and interests. The analysis unit can also analyze the activities of the users' friends and followers to suggest places and events that the users might be interested in. Furthermore, the analysis unit can analyze trends and popular places on social media to provide users with the latest information. This enables more personalized analysis based on social media activities.

[0062] The providing unit not only provides the proposed course as a travel itinerary and route map, but can also dynamically update the course based on the user's real-time location information. For example, if the user arrives at a destination earlier than planned, the providing unit can suggest the next destination earlier. Also, if the user is running behind schedule, the providing unit can suggest a shortened course. Furthermore, if the user wants to stop at a new location along the way, the providing unit can add that location to the course and recalculate and provide the optimal route. This makes it possible to provide flexible courses that correspond to the user's real-time situation.

[0063] The analysis unit not only analyzes data on stations, stores, and map information visited in a particular program, but can also suggest new places that the user may be interested in based on the user's past travel history. For example, the analysis unit may analyze places the user has visited in the past and services the user has used, and suggest similar places and services. The analysis unit may also predict and suggest places that the user is likely to visit during certain times of the day or season based on the user's past travel history. Furthermore, the analysis unit may prioritize suggesting places and services that the user has given a high rating to, based on the user's past ratings and feedback. This enables personalized suggestions based on the user's past travel history.

[0064] The reception unit not only analyzes the user's past input history and suggests input methods, but also automatically displays options that the user is likely to prefer based on the user's past input content. For example, the reception unit automatically displays cities and stations that the user has frequently input in the past as candidates. It can also suggest optimal options based on the genres and budgets the user has selected in the past. Furthermore, it can predict and suggest places that the user is likely to visit during specific times or seasons based on the user's past input history. This makes it possible to make personalized suggestions based on the user's past input history.

[0065] The reception unit not only automatically completes relevant towns and stations based on the user's current location information when the user inputs the route, but also suggests optimal routes and destinations by taking into account the user's real-time travel situation. For example, when the user uses the app while traveling, the reception unit updates the user's current location in real time and suggests optimal routes. It can also suggest the next destination as the user approaches the destination. Furthermore, if the user gets lost, the reception unit can update the user's current location in real time and suggest the optimal route again. This enables flexible suggestions based on the user's real-time travel situation.

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

[0067] Step 1: The reception unit receives input from the user regarding the city they want to go to, the number of people, their budget, time, and preferred genres. For example, the reception unit provides an interface for the user to input the city they want to go to, the number of people, their budget, time, and preferred genres. The reception unit can also analyze the user's input and convert it into an appropriate format. Step 2: Based on the information received by the reception unit, the analysis unit analyzes data from past city strolling programs and suggests a route, store names, and walking order that meets the user's criteria. For example, the analysis unit uses a generation AI to analyze data from past city strolling programs and suggest a route that meets the user's criteria. The analysis unit can also learn store website information and past store reviews, and link location information. Step 3: The providing unit provides the course suggested by the analyzing unit as a travel itinerary and a route map. For example, the providing unit provides the suggested course as a travel itinerary so that the user can check the course. The providing unit also provides a route map and map information for the user to follow the course. Step 4: The navigation unit navigates based on the information provided by the information provider. For example, the navigation unit navigates in real time as the user follows a course. The navigation unit can also use an AI character that learns city, facility, and store information based on the user's preferences and navigates accordingly.

[0068] (Example 2) A coordination system according to an embodiment of the present invention allows users to input their desired city, number of people, budget, time, and preferred genres, and then suggests routes, store names, and walking orders actually visited by celebrities on past city-walking programs, coordinating their holiday plans. When a user inputs their desired city, number of people, budget, time, and preferred genres, the system uses a generation AI to analyze data from past city-walking programs and suggest routes, store names, and walking orders that fit the user's criteria. These suggestions are provided as a travel guide and route map. The coordination system also uses a personalized AI character to learn city, facility, and store information and even navigate the user. Furthermore, the coordination system learns store website information and past store reviews, and by linking location information, users can enjoy realistic conversations. For example, the coordination system allows users to input their desired city, number of people, budget, time, and preferred genres. For example, if a user inputs "Shinjuku, two people, budget 5,000 yen, three hours, cafe hopping," the generation AI analyzes past program data and suggests a route to visit cafes visited by celebrities in Shinjuku. The proposed route is provided as a travel guide and route map. Users can actually walk around the city following the proposed route. The coordination system also uses a personalized AI character to learn about the city, facilities, and stores and navigate the route. For example, if a user selects "cafe hopping," the AI ​​character navigates while providing information about the cafes. Furthermore, the coordination system learns store website information and past store reviews, and by linking location information, users can enjoy realistic conversations. For example, when a user arrives at a cafe, the AI ​​character can provide information such as, "This cafe received high praise from celebrities who visited in the past." This allows the coordination system to efficiently coordinate holiday plans by proposing and navigating the optimal route based on the user's requirements.For example, when a user inputs the city they want to visit, the number of people, their budget, time, and preferred genre, the generation AI analyzes data from past city-walking programs and suggests a course, shop names, and walking order that suits the user's criteria. The suggested course is provided as a travel guide and route map. Users can then actually walk around the city by following the suggested course. The coordination system also uses a personalized AI character to learn about the city, facilities, and shops, and even navigate the route. Furthermore, the coordination system learns store website information and past store reviews, and by linking location information, users can enjoy realistic conversations. This allows users to enjoy a course that suits their preferences and efficiently coordinate how they spend their holidays.

[0069] A coordination system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a navigation unit. The reception unit accepts input from a user of a desired city, number of people, budget, time, and preferred genre. For example, the reception unit provides an interface for the user to input the desired city, number of people, budget, time, and preferred genre. The reception unit can also analyze the user's input and convert it into an appropriate format. The analysis unit analyzes data from past city-walking programs based on the information accepted by the reception unit and proposes a course, store names, and walking order that meets the user's requirements. For example, the analysis unit uses a generation AI to analyze data from past city-walking programs and propose a course that meets the user's requirements. The analysis unit can also learn store website information and past store reviews and link location information. The provision unit provides the course proposed by the analysis unit as a travel guide and a route map. For example, the provision unit provides the proposed course as a travel guide, allowing the user to confirm the course. The provision unit also provides a route map and map information for the user to follow the course. The navigation unit navigates based on the information provided by the providing unit. For example, the navigation unit navigates in real time as the user follows a course. The navigation unit can also use an AI character based on the user's preferences to learn town, facility, and store information and provide navigation. As a result, the coordination system according to the embodiment can efficiently coordinate how to spend a holiday by proposing and navigating the optimal course based on the user's requirements.

[0070] The coordination system further includes a navigation unit in which an AI character based on the user's preferences learns information about towns, facilities, and stores and provides navigation. The navigation unit uses an AI character based on the user's preferences to learn information about towns, facilities, and stores and provide navigation. For example, if the user selects "cafe hopping," the navigation unit navigates while providing information about cafes. Furthermore, if the user selects "shopping," the navigation unit can also navigate while providing shopping information. Furthermore, if the user selects "historical tour," the navigation unit can also navigate while providing information about historical places. This enables navigation based on the user's preferences. Some or all of the above-described processing in the navigation unit may be performed using, or without, a generation AI. For example, the navigation unit can input information about an AI character based on the user's preferences into the generation AI and have the generation AI execute the navigation content.

[0071] Furthermore, in the coordination system, the analysis unit learns store website information or past store reviews and links location information. The analysis unit learns store website information or past store reviews and links location information. For example, the analysis unit collects store website information and learns information such as menus, business hours, and reviews. The analysis unit can also collect past store reviews and learn information such as star ratings, comments, and rankings. Furthermore, by linking location information, the analysis unit can determine the location of stores visited by users and suggest optimal routes. This enables analysis that takes store reviews and location information into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input store website information and past store reviews into the generation AI and have the generation AI link the location information.

[0072] The providing unit can provide the proposed course as a travel itinerary and a route map. The providing unit provides the proposed course as a travel itinerary and a route map. For example, the providing unit provides the proposed course as a travel itinerary, allowing the user to check the course. The providing unit also provides a route map, providing map information for the user to follow the course. This allows the user to easily check the proposed course. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit may input information about the proposed course into the generation AI and cause the generation AI to generate a travel itinerary and a route map.

[0073] The navigation unit can provide past rating information when the user arrives. The navigation unit provides past rating information when the user arrives. For example, the navigation unit can provide past rating information of celebrities when the user arrives at a cafe. The navigation unit can also provide past rating information of celebrities when the user arrives at a restaurant. Furthermore, the navigation unit can provide past rating information of celebrities when the user arrives at a tourist destination. This allows the user to check rating information of places visited in real time. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the navigation unit can input information about the place where the user arrives into the generation AI and cause the generation AI to provide past rating information.

[0074] The analysis unit can analyze data on stations, stores, and map information visited during a specific program. The analysis unit analyzes data on stations, stores, and map information visited during a specific program. For example, the analysis unit collects and analyzes data on stations and stores visited during a specific program. The analysis unit can also collect and analyze map information used during a specific program. Furthermore, the analysis unit can collect and analyze data such as the names of celebrities who visited during a specific program and the broadcast date and time. This enables analysis based on specific program data. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on stations, stores, and map information visited during a specific program into a generation AI and have the generation AI perform the analysis.

[0075] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This provides an interface design that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the design of the input interface.

[0076] The reception unit can analyze the user's past input history and suggest an input method. The reception unit analyzes the user's past input history and suggests the optimal input method. For example, the reception unit automatically displays cities and stations that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest cities and stations to be used during a specific time period based on the user's past input history. This provides the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past input history data into the generation AI and have the generation AI suggest the optimal input method.

[0077] The reception unit can automatically complete related towns and stations based on the user's current location information when the user inputs the destination. The reception unit automatically completes related towns and stations based on the user's current location information when the user inputs the destination. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the starting point. The reception unit can also suggest optimal candidate locations by considering the distance from the current location when the user inputs a destination. Furthermore, when the user uses the app while traveling, the reception unit can update the user's current location in real time and reflect it as the starting point. This provides optimal candidate locations based on the user's current location. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's current location information to the generation AI and cause the generation AI to automatically complete related towns and stations.

[0078] The reception unit can analyze the user's voice input at the time of input and automatically classify the input content using natural language processing. The reception unit can analyze the user's voice input at the time of input and automatically classify the input content using natural language processing. For example, the reception unit can automatically set the departure point and destination when the user simply voice-inputs "cafe hopping in Shinjuku." The reception unit can also automatically classify the conditions and set the input content when the user voice-inputs "two people, budget 5,000 yen, three hours." The reception unit can also automatically classify information related to cafes and set the input content when the user voice-inputs "cafe hopping." This enables automatic classification based on voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's voice input data to a generation AI and have the generation AI automatically classify the input content using natural language processing.

[0079] The reception unit can estimate the user's emotions and adjust the priority of input items based on the estimated user emotions. The reception unit can estimate the user's emotions and adjust the priority of input items based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize displaying the most important input items to simplify the input procedure. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input to enable quick input. This provides the priority of input items according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the priority of the input items.

[0080] The reception unit can analyze the user's social media activity at the time of input and suggest related towns and stations. The reception unit can analyze the user's social media activity at the time of input and suggest related towns and stations. For example, the reception unit can suggest locations where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related towns and stations. Furthermore, the reception unit can suggest related towns and stations based on the activity of the user's friends on social media. This provides optimal candidate locations based on social media activity. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest related towns and stations.

[0081] The reception unit can customize the input content by reflecting the user's past travel history when the user inputs the information. The reception unit customizes the input content by reflecting the user's past travel history when the user inputs the information. For example, the reception unit automatically displays cities and stations that the user has visited in the past as candidates. The reception unit can also predict and suggest places visited during a specific time period based on the user's past travel history. Furthermore, the reception unit can analyze the user's past travel history and suggest optimal candidate locations. This provides optimal input content based on the user's past travel history. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past travel history data into the generation AI and have the generation AI customize the input content.

[0082] The reception unit can select an input method based on the user's device information at the time of input. The reception unit selects the optimal input method by taking the user's device information into consideration at the time of input. For example, if the user is using a smartphone, the reception unit can provide an input method that matches the screen size. Furthermore, if the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can also provide an input method that is simple and highly visible. This provides the optimal input method based on the device information. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's device information to the generation AI and have the generation AI select the optimal input method.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide more options. If the user is in a hurry, the analysis unit can perform a quick analysis and prioritize the most important information. Furthermore, if the user is excited, the analysis unit can provide analysis results with visually stimulating effects. This enables the analysis algorithm to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0084] The analysis unit can improve the analysis accuracy by referring to past analysis data during analysis. The analysis unit improves the analysis accuracy by referring to past analysis data during analysis. For example, the analysis unit suggests a route that suits the user's preferences based on past analysis data. The analysis unit can also suggest a route that avoids congestion based on past analysis data. Furthermore, the analysis unit can analyze past analysis data and suggest the most efficient route. This makes it possible to improve the analysis accuracy based on past analysis data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past analysis data into the generation AI and have the generation AI improve the analysis accuracy.

[0085] During analysis, the analysis unit can customize the analysis results based on the user's past travel history. During analysis, the analysis unit customizes the analysis results by taking the user's past travel history into consideration. For example, the analysis unit suggests an optimal route based on places the user has visited in the past. The analysis unit can also predict and suggest places visited during a specific time period from the user's past travel history. Furthermore, the analysis unit can analyze the user's past travel history and suggest the most efficient route. This makes it possible to customize the analysis results based on the past travel history. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's past travel history data into the generation AI and have the generation AI customize the analysis results.

[0086] The analysis unit can optimize the analysis results based on the user's current location information during analysis. The analysis unit optimizes the analysis results by taking the user's current location information into account during analysis. For example, the analysis unit suggests an optimal route based on the user's current location. The analysis unit can also suggest optimal candidate locations by taking into account the distance from the user's current location. Furthermore, when the user uses the app while traveling, the analysis unit can update the user's current location in real time and suggest an optimal route. This provides optimal analysis results based on the current location information. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's current location information into the generation AI and cause the generation AI to optimize the analysis results.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This provides a display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0088] The analysis unit can analyze the user's social media activity during the analysis and reflect related information in the analysis. The analysis unit can analyze the user's social media activity during the analysis and reflect related information in the analysis. For example, the analysis unit can reflect the location where the user checked in on social media in the analysis. The analysis unit can also analyze the content of the user's social media posts and reflect related information in the analysis. Furthermore, the analysis unit can also reflect related information in the analysis with reference to the activities of the user's friends on social media. This provides an analysis result based on social media activity. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's social media activity data into the generation AI and have the generation AI analyze the related information.

[0089] The analysis unit can optimize the analysis algorithm by reflecting the user's past feedback during analysis. The analysis unit can optimize the analysis algorithm by reflecting the user's past feedback during analysis. For example, the analysis unit optimizes the analysis algorithm based on feedback provided by the user in the past. The analysis unit can also extract specific patterns from the user's past feedback and optimize the analysis algorithm. Furthermore, the analysis unit can analyze the user's past feedback and propose the most efficient analysis algorithm. This makes it possible to optimize the analysis algorithm based on past feedback. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past feedback data into the generation AI and cause the generation AI to optimize the analysis algorithm.

[0090] The analysis unit can customize the analysis results based on the user's device information during analysis. The analysis unit customizes the analysis results taking into account the user's device information during analysis. For example, if the user is using a smartphone, the analysis unit can provide analysis results tailored to the screen size. Furthermore, if the user is using a tablet, the analysis unit can provide analysis results optimized for a large screen. Furthermore, if the user is using a smartwatch, the analysis unit can provide analysis results that are concise and highly visible. This provides optimal analysis results based on the device information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's device information into the generation AI and have the generation AI customize the analysis results.

[0091] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible information format. If the user is relaxed, the providing unit can also provide a format including detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide an information format that focuses on the main points. This allows the information format to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the information format.

[0092] The providing unit can select an information provision method based on the user's past usage history at the time of providing information. The providing unit selects the optimal information provision method by referring to the user's past usage history at the time of providing information. For example, the providing unit selects the optimal method based on information provision methods used by the user in the past. The providing unit can also predict and suggest an information provision method to be used during a specific time period based on the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and select the most efficient information provision method. This provides the optimal information provision method based on the past usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past usage history data into the generation AI and have the generation AI select an information provision method.

[0093] The providing unit can customize the information based on the user's current location information when providing the information. The providing unit customizes the information taking into account the user's current location information when providing the information. For example, the providing unit provides optimal information based on the user's current location. The providing unit can also provide optimal information taking into account the distance from the user's current location. Furthermore, when the user uses the app while on the move, the providing unit can update the user's current location in real time and provide optimal information. This provides optimal information based on the current location information. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's current location information to the generation AI and cause the generation AI to customize the information.

[0094] The providing unit can analyze the user's voice input and provide information using natural language processing when providing the information. The providing unit can analyze the user's voice input and provide information using natural language processing when providing the information. For example, when the user simply voice-inputs "cafe hopping in Shinjuku," the providing unit automatically provides related information. Furthermore, when the user voice-inputs "two people, budget 5,000 yen, three hours," the system can automatically classify the conditions and provide optimal information. Furthermore, when the user voice-inputs "cafe hopping," the system can automatically provide information related to cafes. This provides optimal information based on the voice input. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's voice input data into a generation AI and have the generation AI provide information using natural language processing.

[0095] The providing unit can estimate the user's emotions and adjust the priority of information to be provided based on the estimated user emotions. The providing unit can estimate the user's emotions and adjust the priority of information to be provided based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can prioritize displaying the most important information and simplify the information provision procedure. The providing unit can also provide detailed information and suggest a customizable information provision method when the user is relaxed. Furthermore, when the user is in a hurry, the providing unit can prioritize voice input to quickly provide information. This allows information to be prioritized according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input user emotion data into the generation AI and cause the generation AI to adjust the priority of information.

[0096] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit can analyze the user's social media activity and provide related information at the time of providing. For example, the providing unit can provide information about locations where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. This provides optimal information based on social media activity. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related information.

[0097] The providing unit can customize the information provision method by reflecting the user's past feedback when providing information. The providing unit customizes the information provision method by reflecting the user's past feedback when providing information. For example, the providing unit customizes the information provision method based on feedback provided by the user in the past. The providing unit can also extract specific patterns from the user's past feedback and customize the information provision method. Furthermore, the providing unit can analyze the user's past feedback and suggest the most efficient information provision method. This provides the optimal information provision method based on the past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the information provision method.

[0098] The providing unit can select an information provision method based on the user's device information at the time of provision. The providing unit selects the optimal information provision method taking the user's device information into consideration at the time of provision. For example, if the user is using a smartphone, the providing unit can provide an information provision method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide an information provision method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide an information provision method that is concise and highly visible. This provides the optimal information provision method based on the device information. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select an information provision method.

[0099] The navigation unit can estimate the user's emotions and adjust the navigation method based on the estimated user's emotions. The navigation unit can estimate the user's emotions and adjust the navigation method based on the estimated user's emotions. For example, if the user is nervous, the navigation unit can provide a simple, highly visible navigation method. If the user is relaxed, the navigation unit can also provide a navigation method that includes detailed information. Furthermore, if the user is in a hurry, the navigation unit can also provide a navigation method that focuses on the main points. This provides a navigation method that corresponds to 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the navigation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the navigation unit can input the user's emotion data into the generation AI and have the generation AI adjust the navigation method.

[0100] During navigation, the navigation unit can suggest a route based on the user's past movement history. During navigation, the navigation unit suggests an optimal route by referring to the user's past movement history. For example, the navigation unit suggests an optimal route based on routes the user has used in the past. The navigation unit can also suggest a route that avoids congestion based on the user's past movement history. Furthermore, the navigation unit can analyze the user's past movement history and suggest the most efficient route. This provides an optimal route based on the past movement history. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the navigation unit can input the user's past movement history data into the generation AI and have the generation AI suggest an optimal route.

[0101] During navigation, the navigation unit can update the route in real time based on the user's current location information. During navigation, the navigation unit updates the route in real time, taking into account the user's current location information. For example, the navigation unit updates the user's current location in real time while the user is moving and performs navigation. The navigation unit can also update the user's current location in real time and suggest an optimal route as the user approaches the destination. Furthermore, if the user gets lost, the navigation unit can update the user's current location in real time and perform navigation again. This allows the optimal route based on the current location information to be provided in real time. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the navigation unit can input the user's current location information to the generation AI and cause the generation AI to perform route updates in real time.

[0102] During navigation, the navigation unit can analyze the user's voice input and perform navigation using natural language processing. During navigation, the navigation unit can analyze the user's voice input and perform navigation using natural language processing. For example, the navigation unit can automatically set the next destination when the user simply voice-inputs, "Where is the next cafe?". The navigation unit can also automatically set a route and perform navigation when the user voice-inputs, "Tell me the route to my next destination." Furthermore, when the user voice-inputs, "I'm going to go cafe hopping," the navigation unit can automatically provide information related to cafes while performing navigation. This provides optimal navigation based on the voice input. Some or all of the above-described processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the navigation unit can input the user's voice input data into a generation AI and have the generation AI perform navigation using natural language processing.

[0103] The navigation unit can estimate a user's emotions and adjust navigation priorities based on the estimated user emotions. The navigation unit can estimate a user's emotions and adjust navigation priorities based on the estimated user emotions. For example, when a user is feeling stressed, the navigation unit can prioritize displaying the most important navigation information and simplifying the navigation procedure. The navigation unit can also provide detailed navigation information and suggest customizable navigation methods when the user is relaxed. Furthermore, when a user is in a hurry, the navigation unit can prioritize voice input and quickly provide navigation information. This provides navigation priorities according to 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 navigation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the navigation unit can input user emotion data into the generation AI and have the generation AI adjust the navigation priorities.

[0104] The navigation unit can analyze the user's social media activity during navigation and reflect related information in the navigation. The navigation unit can analyze the user's social media activity during navigation and reflect related information in the navigation. For example, the navigation unit can reflect locations where the user has checked in on social media in the navigation. The navigation unit can also analyze the content of the user's social media posts and reflect related information in the navigation. Furthermore, the navigation unit can also reflect related information in the navigation based on the activity of the user's friends on social media. This provides optimal navigation based on social media activity. Some or all of the above-described processing in the navigation unit can be performed using, or without, a generation AI. For example, the navigation unit can input the user's social media activity data into the generation AI and have the generation AI perform navigation of related information.

[0105] The navigation unit can customize the navigation method by reflecting the user's past feedback during navigation. The navigation unit customizes the navigation method by reflecting the user's past feedback during navigation. For example, the navigation unit customizes the navigation method based on feedback provided by the user in the past. The navigation unit can also extract specific patterns from the user's past feedback and customize the navigation method. Furthermore, the navigation unit can analyze the user's past feedback and suggest the most efficient navigation method. This provides an optimal navigation method based on the past feedback. Some or all of the above-described processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the navigation unit can input the user's past feedback data into the generation AI and have the generation AI customize the navigation method.

[0106] The navigation unit can select the optimal navigation method during navigation by taking into account the user's device information. The navigation unit selects the optimal navigation method during navigation by taking into account the user's device information. For example, if the user is using a smartphone, the navigation unit can provide a navigation method that matches the screen size. Furthermore, if the user is using a tablet, the navigation unit can also provide a navigation method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the navigation unit can also provide a navigation method that is simple and highly visible. This provides the optimal navigation method based on the device information. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the navigation unit can input the user's device information into the generation AI and have the generation AI select a navigation method. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, provision unit, and navigation 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 can accept input from the user of the desired city, number of people, budget, time, and preferred genre using the reception device 38 of the smart device 14. The analysis unit analyzes data from past city walking programs using the specific processing unit 290 of the data processing device 12 and proposes a course, shop names, and walking order that meets the user's requirements. The provision unit uses the output device 40 of the smart device 14 to present the proposed course as a travel itinerary and route map. The navigation unit uses the control unit 46A of the smart device 14 to navigate the user in real time as they follow the course. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, and navigation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive input from the user regarding the desired city, number of people, budget, time, and preferred genre using the microphone 238 of the smart glasses 214. The analysis unit analyzes data from past city walking programs using the specific processing unit 290 of the data processing device 12 and proposes a course, shop names, and walking order that meets the user's requirements. The provision unit uses the speaker 240 of the smart glasses 214 to present the proposed course as a travel itinerary and route map. The navigation unit navigates the user in real time as they follow the course using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, and navigation unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can receive input from the user regarding the desired city, number of people, budget, time, and preferred genre using the microphone 238 of the headset terminal 314. The analysis unit uses the specific processing unit 290 of the data processing device 12 to analyze data from past city strolling programs and propose a course, shop names, and walking order that meets the user's requirements. The provision unit uses the display 343 of the headset terminal 314 to present the proposed course as a travel itinerary and route map. The navigation unit uses the control unit 46A of the headset terminal 314 to navigate in real time as the user follows the course. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, provision unit, and navigation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can use the microphone 238 of the robot 414 to receive input from the user regarding the city they want to visit, the number of people, their budget, time, and preferred genres. The analysis unit uses the specific processing unit 290 of the data processing device 12 to analyze data from past city strolling programs and propose a course, shop names, and walking order that meets the user's requirements. The provision unit uses the speaker 240 of the robot 414 to present the proposed course as a travel guide and route map. The navigation unit uses the control unit 46A of the robot 414 to navigate in real time as the user follows the course.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit not only accepts the user's input, but can also predict and suggest towns and stores that the user is likely to like based on the user's past behavioral history. For example, the reception unit may analyze places the user has visited in the past and services the user has used, and suggest similar places and services. The reception unit may also learn the user's past behavioral patterns, and predict and suggest places the user is likely to visit during specific times or seasons. Furthermore, the reception unit may preferentially suggest places and services that the user has given a high rating to, based on the user's past ratings and feedback. This enables personalized suggestions based on the user's past behavioral history.

[0109] The navigation unit not only navigates with an AI character tailored to the user's preferences, but can also estimate the user's real-time emotional state and provide a navigation method tailored to that emotion. For example, if the user is tired, the navigation unit can suggest the shortest route, reducing the user's burden. If the user is excited, the navigation unit can provide additional information about tourist spots and events, further increasing the user's excitement. Furthermore, if the user is lost, the navigation unit can provide detailed map information and photos to help the user reach their destination with confidence. This enables flexible navigation tailored to the user's emotions.

[0110] The analysis unit not only learns store website information and past store reviews, but also analyzes users' social media activities and incorporates related information into the analysis. For example, the analysis unit collects the places where users check in on social media and the content of their posts to understand the users' preferences and interests. The analysis unit can also analyze the activities of the users' friends and followers to suggest places and events that the users might be interested in. Furthermore, the analysis unit can analyze trends and popular places on social media to provide users with the latest information. This enables more personalized analysis based on social media activities.

[0111] The providing unit not only provides the proposed course as a travel itinerary and route map, but can also dynamically update the course based on the user's real-time location information. For example, if the user arrives at a destination earlier than planned, the providing unit can suggest the next destination earlier. Also, if the user is running behind schedule, the providing unit can suggest a shortened course. Furthermore, if the user wants to stop at a new location along the way, the providing unit can add that location to the course and recalculate and provide the optimal route. This makes it possible to provide flexible courses that correspond to the user's real-time situation.

[0112] The navigation unit can not only provide past evaluation information when the user arrives, but also estimate the user's real-time emotional state and provide information according to that emotion. For example, if the user is excited, the navigation unit can provide the history of the place or special episodes to further increase the user's excitement. Also, if the user is tired, the navigation unit can suggest places to relax or rest spots. Furthermore, if the user is lost, the navigation unit can provide detailed map information and photos to help the user reach their destination with peace of mind. This makes it possible to provide information flexibly according to the user's emotions.

[0113] The analysis unit not only analyzes data on stations, stores, and map information visited in a particular program, but can also suggest new places that the user may be interested in based on the user's past travel history. For example, the analysis unit may analyze places the user has visited in the past and services the user has used, and suggest similar places and services. The analysis unit may also predict and suggest places that the user is likely to visit during certain times of the day or season based on the user's past travel history. Furthermore, the analysis unit may prioritize suggesting places and services that the user has given a high rating to, based on the user's past ratings and feedback. This enables personalized suggestions based on the user's past travel history.

[0114] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user's emotions, as well as suggest an input method according to the user's emotions. For example, if the user is nervous, the reception unit can preferentially suggest voice input, allowing the user to input in a relaxed manner. Also, if the user is having fun, the reception unit can suggest an interactive input method, making the input task more enjoyable. Furthermore, if the user is tired, the reception unit can suggest a simple, highly visible input method, making the input task easier. In this way, the optimal input method according to the user's emotions is provided.

[0115] The reception unit not only analyzes the user's past input history and suggests input methods, but also automatically displays options that the user is likely to prefer based on the user's past input content. For example, the reception unit automatically displays cities and stations that the user has frequently input in the past as candidates. It can also suggest optimal options based on the genres and budgets the user has selected in the past. Furthermore, it can predict and suggest places that the user is likely to visit during specific times or seasons based on the user's past input history. This makes it possible to make personalized suggestions based on the user's past input history.

[0116] The reception unit not only automatically completes relevant towns and stations based on the user's current location information when the user inputs the route, but also suggests optimal routes and destinations by taking into account the user's real-time travel situation. For example, when the user uses the app while traveling, the reception unit updates the user's current location in real time and suggests optimal routes. It can also suggest the next destination as the user approaches the destination. Furthermore, if the user gets lost, the reception unit can update the user's current location in real time and suggest the optimal route again. This enables flexible suggestions based on the user's real-time travel situation.

[0117] The reception unit not only analyzes the user's voice input during input and automatically classifies the input content using natural language processing, but also estimates the user's emotions and suggests an input method according to the emotions. For example, if the user is nervous, the reception unit may preferentially suggest voice input, allowing the user to input in a relaxed manner. Also, if the user is having fun, the reception unit may suggest an interactive input method, making the input task more enjoyable. Furthermore, if the user is tired, the reception unit may suggest a simple, highly visible input method, making the input task easier. In this way, the optimal input method according to the user's emotions is provided.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives input from the user regarding the city they want to go to, the number of people, their budget, time, and preferred genres. For example, the reception unit provides an interface for the user to input the city they want to go to, the number of people, their budget, time, and preferred genres. The reception unit can also analyze the user's input and convert it into an appropriate format. Step 2: Based on the information received by the reception unit, the analysis unit analyzes data from past city strolling programs and suggests a route, store names, and walking order that meets the user's criteria. For example, the analysis unit uses a generation AI to analyze data from past city strolling programs and suggest a route that meets the user's criteria. The analysis unit can also learn store website information and past store reviews, and link location information. Step 3: The providing unit provides the course suggested by the analyzing unit as a travel itinerary and a route map. For example, the providing unit provides the suggested course as a travel itinerary so that the user can check the course. The providing unit also provides a route map and map information for the user to follow the course. Step 4: The navigation unit navigates based on the information provided by the information provider. For example, the navigation unit navigates in real time as the user follows a course. The navigation unit can also use an AI character that learns city, facility, and store information based on the user's preferences and navigates accordingly.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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, in order to avoid confusion and to 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives input from users about the city they want to go to, the number of people, their budget, time, and preferred genres; an analysis unit that analyzes data from past strolling programs based on the information received by the reception unit and suggests a course, shop names, and walking order that meet the user's requirements; a providing unit that provides the course proposed by the analysis unit as a travel guide and a route map; a navigation unit that performs navigation based on the information provided by the providing unit. A system characterized by:

2. The navigation unit An AI character based on the user's preferences learns information about the city, facilities, and stores and navigates the area.

2. The system of claim 1.

3. The analysis unit Learn store website information or past store reviews and link location information 2. The system of claim 1.

4. The providing unit Providing suggested routes as itineraries and route maps 2. The system of claim 1.

5. The navigation unit Provide users with historical ratings information when they arrive 2. The system of claim 1.

6. The analysis unit Analyzing data on stations, stores, and map information visited during a specific program 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests input methods 2. The system of claim 1.

9. The reception unit As you type, it auto-completes relevant cities and stations based on your current location.

2. The system of claim 1.

10. The reception unit As you type, it analyzes your voice input and automatically categorizes it using natural language processing.

2. The system of claim 1.

Images