system

Abstract

The system according to this embodiment aims to allow users to easily generate a walking itinerary and perform navigation. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives input from the user regarding area, period, and category. The generation unit generates a walking tour schedule based on the information received by the reception unit. The navigation unit performs navigation based on the schedule generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for a user to easily generate a walking itinerary and perform navigation.

[0005] The system according to an embodiment aims to enable a user to easily generate a walking itinerary and perform navigation.

Means for Solving the Problems

[0006] The system according to an embodiment includes a reception unit, a generation unit, and a navigation unit. The reception unit receives an input of an area, a period, and a category from a user. The generation unit generates a walking itinerary based on the information received by the reception unit. The navigation unit performs navigation based on the itinerary generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to easily generate a walking itinerary and perform navigation. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The street stroll generation system according to an embodiment of the present invention is a system that allows users to easily plan a street stroll. This street stroll generation system allows users to select the area, duration, and category they wish to stroll through, and the generating AI generates an optimal street stroll itinerary and displays the navigation route results. For example, when a user selects an area they wish to stroll through, candidates such as Ebisu or Shimokitazawa are recommended based on their current location. Alternatively, they can specify an area using free text. Next, they select a duration, choosing from 1 hour, 2 hours, 3 hours, or 5 hours. Furthermore, they select a category, selecting from multiple categories such as cafes, lunch, dinner, art, and parks. They can also specify a category using free text. Finally, pressing the confirm button displays a street stroll itinerary and navigation route results. This function allows users to easily plan a street stroll. The generating AI analyzes the user's input information and generates an optimal street stroll itinerary. For example, if a user inputs that they want to enjoy cafes and art in Ebisu, the generating AI calculates the optimal route based on that information and generates an itinerary. Based on the generated itinerary, the navigation route results are displayed. This allows users to enjoy exploring the city without getting lost. It can also provide suggestions based on the user's past activity history and preferences. For example, it can offer optimal suggestions based on places the user has visited in the past and highly-rated spots. Furthermore, a feature has been added to suggest highly-rated spots. This allows users to enjoy exploring the city based on reliable information. In short, the city exploration generation system makes it easy for users to plan their city explorations.

[0029] The street stroll generation system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives input from the user regarding area, period, and category. For example, when a user selects an area they want to stroll around, the reception unit recommends candidates such as Ebisu or Shimokitazawa based on the user's current location. The reception unit also allows the user to specify an area using free text. Next, the reception unit selects a period. Users can choose from 1 hour, 2 hours, 3 hours, or 5 hours. Furthermore, the reception unit selects a category. Users can select from multiple categories such as cafes, lunch, dinner, art, and parks. The reception unit also allows the user to specify a category using free text. The generation unit uses a generation AI to generate a street stroll schedule based on the information received by the reception unit. For example, if a user inputs that they want to enjoy cafes and art in Ebisu, the generation AI calculates the optimal route based on that information and generates an itinerary. The generation unit displays the navigation route results based on the generated itinerary. The navigation unit provides navigation based on the schedule generated by the generation unit. The navigation unit displays, for example, a navigation route result based on the generated itinerary, allowing the user to enjoy strolling around town without getting lost. This enables the town stroll generation system according to the embodiment to easily plan a town stroll. Some or all of the above-described processes in the reception unit, generation unit, and navigation unit may be performed using AI, for example, or without AI. For example, the reception unit can input user input information into the generation AI and have the generation AI perform analysis. The generation unit can analyze the user input information using the generation AI and generate an optimal town stroll itinerary. The navigation unit can perform navigation based on the itinerary generated by the generation AI.

[0030] The reception desk accepts input from users regarding area, duration, and category. For example, when a user selects an area they want to explore, the reception desk recommends options such as Ebisu or Shimokitazawa based on their current location. This allows users to easily select the most suitable area based on their current location. The reception desk also allows users to specify an area using free text, so users can freely input specific areas. Next, the reception desk allows users to select a duration. Users can choose from 1 hour, 2 hours, 3 hours, or 5 hours. This allows users to set an appropriate amount of time for their exploration according to their schedule. Furthermore, the reception desk allows users to select a category. Multiple categories such as cafes, lunch, dinner, art, and parks can be selected. This allows users to choose a category that matches their interests and preferences, enabling them to plan a more fulfilling exploration experience. The reception desk also allows users to specify categories using free text, so users can freely input categories that match their specific interests and needs. For example, a user can specify a particular category such as "vintage shops" or "live music venues." This allows the reception desk to respond to diverse user needs and support flexible exploration planning. Furthermore, the reception desk can learn from the user's past input history and preferences to provide more personalized recommendations. For example, a user who has frequently selected cafes in the past can be given priority in displaying new cafe options. This allows the reception desk to support the user in selecting the optimal area, duration, and category according to their preferences, providing a more satisfying city exploration experience.

[0031] The generation unit uses a generation AI to generate a city stroll itinerary based on information received by the reception unit. For example, if a user inputs that they want to enjoy cafes and art in Ebisu, the generation AI calculates the optimal route based on that information and generates an itinerary. First, the generation AI searches a database of cafes and art spots within the area selected by the user, and selects the best spots considering information such as ratings, reviews, and distance. Next, it calculates the travel time and distance between the selected spots and generates a route that can be efficiently visited within the period specified by the user. For example, it sets the stay time at the cafe to 1 hour and the stay time at the art spot to 2 hours, and adjusts the overall itinerary considering travel time. Based on the generated itinerary, the generation unit displays the navigation route results. The generation AI can also learn the user's past city stroll history and preferences to generate a more personalized itinerary. For example, it can prioritize selecting spots that have been visited in the past or spots with high ratings to provide an itinerary that suits the user's preferences. In addition, the generation unit can dynamically adjust the itinerary based on information that is updated in real time. For example, the system recalculates the optimal route, taking into account changes in weather and congestion levels at various locations, and provides users with the latest information. This allows the generation unit to create flexible and optimal city exploration itineraries tailored to user needs, supporting users in enjoying their city exploration comfortably.

[0032] The navigation unit provides navigation based on the itinerary generated by the generation unit. For example, based on the generated itinerary, the navigation unit displays navigation route results that allow the user to enjoy exploring the city without getting lost. Specifically, the navigation unit tracks the user's current location in real time and displays the optimal route to the next spot to visit. The navigation route is not only displayed on a map, but can also provide intuitive guidance to the user using functions such as voice guidance and vibration notifications. For example, it provides voice guidance when approaching the next corner or destination, supporting the user in moving without getting lost. Furthermore, the navigation unit can dynamically adjust the route considering real-time updated traffic information and congestion. For example, if a particular road is congested or a spot is crowded, it will suggest an alternative route to allow the user to move smoothly. In addition, the navigation unit can collect user feedback and continuously improve the accuracy and ease of use of the navigation. For example, by providing feedback on a particular route or guidance, the system can learn from that information and reflect it in future navigation. In this way, the navigation unit can provide users with intuitive and easy-to-use navigation, making the city exploration experience more comfortable and enjoyable.

[0033] The suggestion unit can make suggestions based on the user's past behavior history and preferences. For example, the suggestion unit can suggest new relevant spots based on the user's past visit history. It can also predict and suggest spots that the user might be interested in based on their past search history. Furthermore, the suggestion unit can make the most suitable suggestions based on the user's past purchase history. This makes it possible to make suggestions based on the user's past behavior history and preferences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past behavior history into a generating AI and have the generating AI perform the analysis.

[0034] The evaluation unit can suggest spots with high review ratings. For example, the evaluation unit can suggest new relevant spots based on the user's past review ratings. It can also predict and suggest spots that the user might be interested in based on their past comments. Furthermore, the evaluation unit can make the most suitable suggestions based on the reliability of the evaluators. In this way, by suggesting spots with high review ratings, it can provide highly reliable information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the user's past review ratings into a generating AI and have the generating AI perform the analysis.

[0035] The reception desk can analyze the user's past input history and automatically suggest the most suitable input options. For example, the reception desk can automatically display areas, periods, and categories that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest areas, periods, and categories to be used during specific time periods based on the user's past input history. This improves input efficiency by suggesting the most suitable input options based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI perform the analysis.

[0036] The reception desk can complete inputs such as area, period, and category by considering the user's current activities and schedule. For example, the reception desk can refer to the user's calendar information and suggest areas, periods, and categories based on their schedule. The reception desk can also prioritize displaying nearby areas based on the user's current location. Furthermore, the reception desk can analyze the user's past behavior patterns and suggest the most suitable areas, periods, and categories. This allows for more appropriate suggestions by completing inputs while considering the user's current activities and schedule. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's calendar information into a generating AI and have the generating AI perform the analysis.

[0037] The reception desk can prioritize displaying highly relevant candidates when users input area, period, and category information, taking into account their geographical location. For example, the reception desk can prioritize displaying areas close to the user's current location. It can also suggest highly relevant areas based on the user's past travel history. Furthermore, it can suggest the optimal period and category based on the user's current location information. This improves user convenience by displaying highly relevant candidates that take the user's geographical location information into consideration. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI perform the analysis.

[0038] The reception desk can analyze the user's social media activity and suggest relevant options when the user inputs area, period, and category. For example, the reception desk can suggest relevant areas based on the user's social media check-in information. It can also analyze the user's social media posts and suggest categories of interest. Furthermore, the reception desk can suggest relevant periods and categories based on the activity of the user's friends on social media. This improves user convenience by analyzing the user's social media activity and suggesting relevant options. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into a generating AI and have the generating AI perform the analysis.

[0039] The generation unit can generate an optimal itinerary by referring to the user's past city exploration history during the generation process. For example, the generation unit can generate an optimal itinerary based on spots the user has visited in the past. The generation unit can also suggest routes that avoid crowds based on the user's past city exploration history. Furthermore, the generation unit can analyze the user's past city exploration history and generate the most efficient itinerary. This improves user convenience by generating an optimal itinerary based on the user's past city exploration history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past city exploration history into a generation AI and have the generation AI perform the analysis.

[0040] The generation unit can customize the schedule based on the user's current interests during the generation process. For example, the generation unit can prioritize generating schedules based on categories the user is currently interested in. It can also generate schedules that include relevant locations based on the user's current interests. Furthermore, the generation unit can generate customized schedules that reflect the user's current interests. This improves user convenience by customizing the schedule based on the user's current interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current interests into a generation AI and have the generation AI perform analysis.

[0041] The generation unit can generate an optimal itinerary while considering the user's geographical location information. For example, the generation unit can prioritize spots close to the user's current location when generating the itinerary. The generation unit can also suggest highly relevant spots based on the user's past travel history. Furthermore, the generation unit can suggest the optimal route based on the user's current location information. This improves user convenience by generating an optimal itinerary that considers the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the analysis.

[0042] The generation unit can analyze the user's social media activity and customize the schedule during the generation process. For example, the generation unit can suggest relevant spots based on the user's social media check-in information. It can also analyze the user's social media posts and suggest spots of interest. Furthermore, the generation unit can suggest relevant spots based on the activity of the user's friends on social media. By analyzing the user's social media activity and customizing the schedule, user convenience is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity into a generation AI and have the generation AI perform the analysis.

[0043] The navigation unit can suggest the optimal route during navigation by referring to the user's past travel history. For example, the navigation unit can suggest the optimal route based on routes the user has used in the past. The navigation unit can also suggest routes that avoid congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and suggest the most efficient route. This improves user convenience by suggesting the optimal route by referring to the user's past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's past travel history into a generating AI and have the generating AI perform the analysis.

[0044] The navigation unit can dynamically change the route during navigation, taking into account the user's current traffic conditions. For example, the navigation unit can suggest the optimal route based on real-time traffic congestion information. It can also suggest the optimal route considering the real-time operation status of public transportation. Furthermore, the navigation unit can suggest detour routes based on real-time road construction information. By dynamically changing the route based on the user's current traffic conditions, user convenience is improved. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input real-time traffic information into a generating AI and have the generating AI perform analysis.

[0045] The navigation unit can present the optimal route during navigation, taking into account the user's geographical location. For example, the navigation unit prioritizes navigation to spots close to the user's current location. The navigation unit can also suggest highly relevant spots based on the user's past travel history. Furthermore, the navigation unit can suggest the optimal route based on the user's current location. This improves user convenience by presenting the optimal route while considering the user's geographical location. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's geographical location information into a generating AI and have the generating AI perform analysis.

[0046] The navigation unit can analyze the user's social media activity and customize the route during navigation. For example, the navigation unit can suggest relevant spots based on the user's social media check-in information. It can also analyze the user's social media posts and suggest spots of interest. Furthermore, the navigation unit can suggest relevant spots based on the activity of the user's friends on social media. By analyzing the user's social media activity and customizing the route, user convenience is improved. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's social media activity into a generating AI and have the generating AI perform the analysis.

[0047] The suggestion unit can make optimal suggestions by referring to the user's past behavior history. For example, the suggestion unit can suggest new related spots based on spots the user has visited in the past. It can also predict and suggest spots that the user might be interested in based on their past behavior history. Furthermore, the suggestion unit can analyze the user's past behavior patterns and make the most appropriate suggestions. This improves user convenience by making optimal suggestions based on the user's past behavior history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past behavior history into a generating AI and have the generating AI perform the analysis.

[0048] The suggestion unit can customize suggestions based on the user's current interests. For example, it can prioritize suggestions based on categories the user is currently interested in. It can also suggest relevant locations based on the user's current interests. Furthermore, it can provide customized suggestions that reflect the user's current interests. This improves user convenience by customizing suggestions based on the user's current interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's current interests into a generating AI and have the generating AI perform analysis.

[0049] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, the suggestion unit can prioritize suggesting spots close to the user's current location. It can also suggest highly relevant spots based on the user's past travel history. Furthermore, the suggestion unit can suggest optimal spots based on the user's current location information. By making optimal suggestions that consider the user's geographical location information, user convenience is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis.

[0050] The suggestion unit can analyze the user's social media activity and customize the suggestions when making them. For example, the suggestion unit can suggest relevant spots based on the user's social media check-in information. It can also analyze the user's social media posts and suggest spots of interest. Furthermore, the suggestion unit can suggest relevant spots based on the activity of the user's friends on social media. By analyzing the user's social media activity and customizing the suggestions, the user's convenience is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity into a generating AI and have the generating AI perform the analysis.

[0051] The evaluation unit can refer to the user's past evaluation history to present the most suitable evaluation. For example, the evaluation unit can present evaluations of new spots related to the user based on spots the user has previously evaluated. The evaluation unit can also predict and present evaluations of spots that the user might be interested in based on the user's past evaluation history. Furthermore, the evaluation unit can analyze the user's past evaluation patterns and present the most suitable evaluation. This improves user convenience by providing the most suitable evaluation by referring to the user's past evaluation history. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's past evaluation history into a generating AI and have the generating AI perform the analysis.

[0052] The evaluation unit can customize evaluations based on the user's current interests. For example, the evaluation unit can prioritize evaluations for categories the user is currently interested in. It can also present evaluations of relevant spots based on the user's current interests. Furthermore, the evaluation unit can present customized evaluations that reflect the user's current interests. By customizing evaluations based on the user's current interests, user convenience is improved. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's current interests into a generating AI and have the generating AI perform the analysis.

[0053] The evaluation unit can present the optimal evaluation while considering the user's geographical location information. For example, the evaluation unit can prioritize presenting evaluations of spots close to the user's current location. The evaluation unit can also present evaluations of highly relevant spots based on the user's past travel history. Furthermore, the evaluation unit can present the optimal spot evaluation based on the user's current location information. This improves user convenience by presenting the optimal evaluation while considering the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis.

[0054] The evaluation unit can analyze the user's social media activity and customize the evaluation during the evaluation process. For example, the evaluation unit can present evaluations of relevant spots based on the user's social media check-in information. It can also analyze the user's social media posts and present evaluations of spots of interest. Furthermore, the evaluation unit can present evaluations of relevant spots based on the activity of the user's friends on social media. By analyzing the user's social media activity and customizing the evaluation, user convenience is improved. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's social media activity into a generating AI and have the generating AI perform the analysis.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can monitor the user's health status and dynamically change the input interface for area, duration, and category based on that status. For example, if the user is tired, it can prioritize displaying areas and categories that can be enjoyed in a short amount of time. If the user is healthy and active, it can also suggest longer walks around town. Furthermore, if the user has a specific health problem, it can suggest areas and categories that take that problem into consideration. This improves user convenience by providing an input interface that is tailored to the user's health status. Health status monitoring is performed, for example, using sensors in wearable devices or smartphones. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's health data into a generating AI and have the generating AI perform analysis.

[0057] The suggestion section can analyze the user's hobbies and interests and make suggestions based on them. For example, if the user is interested in music, it can suggest music-related spots. If the user is interested in sports, it can suggest sports-related events and facilities. Furthermore, if the user is interested in art, it can suggest art galleries and museums. This improves user convenience by providing suggestions based on the user's hobbies and interests. The analysis of hobbies and interests is performed, for example, based on the user's past behavior history and social media posts. Some or all of the above processing in the suggestion section may be performed using AI, or not. For example, the suggestion section can input the user's hobbies and interests data into a generating AI and have the generating AI perform the analysis.

[0058] The reception desk can analyze the user's past input history and automatically suggest the most suitable input options. For example, it can automatically display areas, periods, and categories that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest areas, periods, and categories that the user will use at specific times based on their past input history. This improves input efficiency by suggesting the most suitable input options based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI perform the analysis.

[0059] The navigation unit can dynamically change the route considering the user's current traffic conditions. For example, it can suggest the optimal route based on real-time traffic congestion information. It can also suggest the optimal route considering the real-time operation status of public transportation. Furthermore, it can suggest detour routes based on real-time road construction information. By dynamically changing the route considering the user's current traffic conditions, user convenience is improved. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input real-time traffic information into a generating AI and have the generating AI perform analysis.

[0060] The navigation unit can suggest the optimal route by referring to the user's past travel history. For example, it can suggest the optimal route based on routes the user has used in the past. It can also suggest routes that avoid congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and suggest the most efficient route. This improves user convenience by suggesting the optimal route by referring to the user's past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's past travel history into a generating AI and have the generating AI perform the analysis.

[0061] The suggestion unit can analyze the user's social media activity and customize suggestions. For example, it can suggest relevant spots based on the user's social media check-in information. It can also analyze the user's social media posts and suggest spots of interest. Furthermore, it can suggest relevant spots based on the activity of the user's friends on social media. By analyzing the user's social media activity and customizing suggestions, user convenience is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity into a generating AI and have the generating AI perform the analysis.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk accepts input from the user regarding area, duration, and category. For example, when a user selects an area they want to explore, options such as Ebisu or Shimokitazawa are recommended based on their current location. Users can also specify an area using free text. Next, they select a duration, choosing from 1 hour, 2 hours, 3 hours, or 5 hours. Finally, they select a category, with options including cafes, lunch, dinner, art, and parks. They can also specify a category using free text. Step 2: The generation unit uses the generation AI to generate a city stroll itinerary based on the information received by the reception unit. For example, if a user inputs that they want to enjoy cafes and art in Ebisu, the generation AI calculates the optimal route based on that information and generates an itinerary. The generation unit then displays the navigation route results based on the generated itinerary. Step 3: The navigation unit performs navigation based on the itinerary generated by the generation unit. For example, based on the generated itinerary, it displays navigation route results that allow the user to enjoy exploring the city without getting lost.

[0064] (Example of form 2) The street stroll generation system according to an embodiment of the present invention is a system that allows users to easily plan a street stroll. This street stroll generation system allows users to select the area, duration, and category they wish to stroll through, and the generating AI generates an optimal street stroll itinerary and displays the navigation route results. For example, when a user selects an area they wish to stroll through, candidates such as Ebisu or Shimokitazawa are recommended based on their current location. Alternatively, they can specify an area using free text. Next, they select a duration, choosing from 1 hour, 2 hours, 3 hours, or 5 hours. Furthermore, they select a category, selecting from multiple categories such as cafes, lunch, dinner, art, and parks. They can also specify a category using free text. Finally, pressing the confirm button displays a street stroll itinerary and navigation route results. This function allows users to easily plan a street stroll. The generating AI analyzes the user's input information and generates an optimal street stroll itinerary. For example, if a user inputs that they want to enjoy cafes and art in Ebisu, the generating AI calculates the optimal route based on that information and generates an itinerary. Based on the generated itinerary, the navigation route results are displayed. This allows users to enjoy exploring the city without getting lost. It can also provide suggestions based on the user's past activity history and preferences. For example, it can offer optimal suggestions based on places the user has visited in the past and highly-rated spots. Furthermore, a feature has been added to suggest highly-rated spots. This allows users to enjoy exploring the city based on reliable information. In short, the city exploration generation system makes it easy for users to plan their city explorations.

[0065] The street stroll generation system according to this embodiment comprises a reception unit, a generation unit, and a navigation unit. The reception unit receives input from the user regarding area, period, and category. For example, when a user selects an area they want to stroll around, the reception unit recommends candidates such as Ebisu or Shimokitazawa based on the user's current location. The reception unit also allows the user to specify an area using free text. Next, the reception unit selects a period. Users can choose from 1 hour, 2 hours, 3 hours, or 5 hours. Furthermore, the reception unit selects a category. Users can select from multiple categories such as cafes, lunch, dinner, art, and parks. The reception unit also allows the user to specify a category using free text. The generation unit uses a generation AI to generate a street stroll schedule based on the information received by the reception unit. For example, if a user inputs that they want to enjoy cafes and art in Ebisu, the generation AI calculates the optimal route based on that information and generates an itinerary. The generation unit displays the navigation route results based on the generated itinerary. The navigation unit provides navigation based on the schedule generated by the generation unit. The navigation unit displays, for example, a navigation route result based on the generated itinerary, allowing the user to enjoy strolling around town without getting lost. This enables the town stroll generation system according to the embodiment to easily plan a town stroll. Some or all of the above-described processes in the reception unit, generation unit, and navigation unit may be performed using AI, for example, or without AI. For example, the reception unit can input user input information into the generation AI and have the generation AI perform analysis. The generation unit can analyze the user input information using the generation AI and generate an optimal town stroll itinerary. The navigation unit can perform navigation based on the itinerary generated by the generation AI.

[0066] The reception desk accepts input from users regarding area, duration, and category. For example, when a user selects an area they want to explore, the reception desk recommends options such as Ebisu or Shimokitazawa based on their current location. This allows users to easily select the most suitable area based on their current location. The reception desk also allows users to specify an area using free text, so users can freely input specific areas. Next, the reception desk allows users to select a duration. Users can choose from 1 hour, 2 hours, 3 hours, or 5 hours. This allows users to set an appropriate amount of time for their exploration according to their schedule. Furthermore, the reception desk allows users to select a category. Multiple categories such as cafes, lunch, dinner, art, and parks can be selected. This allows users to choose a category that matches their interests and preferences, enabling them to plan a more fulfilling exploration experience. The reception desk also allows users to specify categories using free text, so users can freely input categories that match their specific interests and needs. For example, a user can specify a particular category such as "vintage shops" or "live music venues." This allows the reception desk to respond to diverse user needs and support flexible exploration planning. Furthermore, the reception desk can learn from the user's past input history and preferences to provide more personalized recommendations. For example, a user who has frequently selected cafes in the past can be given priority in displaying new cafe options. This allows the reception desk to support the user in selecting the optimal area, duration, and category according to their preferences, providing a more satisfying city exploration experience.

[0067] The generation unit uses a generation AI to generate a city stroll itinerary based on information received by the reception unit. For example, if a user inputs that they want to enjoy cafes and art in Ebisu, the generation AI calculates the optimal route based on that information and generates an itinerary. First, the generation AI searches a database of cafes and art spots within the area selected by the user, and selects the best spots considering information such as ratings, reviews, and distance. Next, it calculates the travel time and distance between the selected spots and generates a route that can be efficiently visited within the period specified by the user. For example, it sets the stay time at the cafe to 1 hour and the stay time at the art spot to 2 hours, and adjusts the overall itinerary considering travel time. Based on the generated itinerary, the generation unit displays the navigation route results. The generation AI can also learn the user's past city stroll history and preferences to generate a more personalized itinerary. For example, it can prioritize selecting spots that have been visited in the past or spots with high ratings to provide an itinerary that suits the user's preferences. In addition, the generation unit can dynamically adjust the itinerary based on information that is updated in real time. For example, the system recalculates the optimal route, taking into account changes in weather and congestion levels at various locations, and provides users with the latest information. This allows the generation unit to create flexible and optimal city exploration itineraries tailored to user needs, supporting users in enjoying their city exploration comfortably.

[0068] The navigation unit provides navigation based on the itinerary generated by the generation unit. For example, based on the generated itinerary, the navigation unit displays navigation route results that allow the user to enjoy exploring the city without getting lost. Specifically, the navigation unit tracks the user's current location in real time and displays the optimal route to the next spot to visit. The navigation route is not only displayed on a map, but can also provide intuitive guidance to the user using functions such as voice guidance and vibration notifications. For example, it provides voice guidance when approaching the next corner or destination, supporting the user in moving without getting lost. Furthermore, the navigation unit can dynamically adjust the route considering real-time updated traffic information and congestion. For example, if a particular road is congested or a spot is crowded, it will suggest an alternative route to allow the user to move smoothly. In addition, the navigation unit can collect user feedback and continuously improve the accuracy and ease of use of the navigation. For example, by providing feedback on a particular route or guidance, the system can learn from that information and reflect it in future navigation. In this way, the navigation unit can provide users with intuitive and easy-to-use navigation, making the city exploration experience more comfortable and enjoyable.

[0069] The suggestion unit can make suggestions based on the user's past behavior history and preferences. For example, the suggestion unit can suggest new relevant spots based on the user's past visit history. It can also predict and suggest spots that the user might be interested in based on their past search history. Furthermore, the suggestion unit can make the most suitable suggestions based on the user's past purchase history. This makes it possible to make suggestions based on the user's past behavior history and preferences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past behavior history into a generating AI and have the generating AI perform the analysis.

[0070] The evaluation unit can suggest spots with high review ratings. For example, the evaluation unit can suggest new relevant spots based on the user's past review ratings. It can also predict and suggest spots that the user might be interested in based on their past comments. Furthermore, the evaluation unit can make the most suitable suggestions based on the reliability of the evaluators. In this way, by suggesting spots with high review ratings, it can provide highly reliable information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the user's past review ratings into a generating AI and have the generating AI perform the analysis.

[0071] The reception desk can estimate the user's emotions and dynamically change the input interface for area, duration, and category based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of area, duration, and category. This improves user convenience by providing an input interface that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI perform analysis.

[0072] The reception desk can analyze the user's past input history and automatically suggest the most suitable input options. For example, the reception desk can automatically display areas, periods, and categories that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest areas, periods, and categories to be used during specific time periods based on the user's past input history. This improves input efficiency by suggesting the most suitable input options based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI perform the analysis.

[0073] The reception desk can complete inputs such as area, period, and category by considering the user's current activities and schedule. For example, the reception desk can refer to the user's calendar information and suggest areas, periods, and categories based on their schedule. The reception desk can also prioritize displaying nearby areas based on the user's current location. Furthermore, the reception desk can analyze the user's past behavior patterns and suggest the most suitable areas, periods, and categories. This allows for more appropriate suggestions by completing inputs while considering the user's current activities and schedule. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's calendar information into a generating AI and have the generating AI perform the analysis.

[0074] The reception desk can estimate the user's emotions and determine input priorities based on those priorities. For example, if the user is in a hurry, the reception desk might prioritize inputting the area, leaving the time period and category for later. If the user is relaxed, the reception desk might offer detailed input options and suggest customizable input methods. Furthermore, if the user is stressed, the reception desk might provide a simple interface and minimize the input steps. This improves user convenience by prioritizing inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform analysis.

[0075] The reception desk can prioritize displaying highly relevant candidates when users input area, period, and category information, taking into account their geographical location. For example, the reception desk can prioritize displaying areas close to the user's current location. It can also suggest highly relevant areas based on the user's past travel history. Furthermore, it can suggest the optimal period and category based on the user's current location information. This improves user convenience by displaying highly relevant candidates that take the user's geographical location information into consideration. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI perform the analysis.

[0076] The reception desk can analyze the user's social media activity and suggest relevant options when the user inputs area, period, and category. For example, the reception desk can suggest relevant areas based on the user's social media check-in information. It can also analyze the user's social media posts and suggest categories of interest. Furthermore, the reception desk can suggest relevant periods and categories based on the activity of the user's friends on social media. This improves user convenience by analyzing the user's social media activity and suggesting relevant options. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity into a generating AI and have the generating AI perform the analysis.

[0077] The generation unit can estimate the user's emotions and adjust the method of generating the city stroll itinerary based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate an itinerary that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate an itinerary that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate an itinerary that includes visually stimulating spots. This improves user convenience by adjusting the method of generating the city stroll itinerary 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI perform analysis.

[0078] The generation unit can generate an optimal itinerary by referring to the user's past city exploration history during the generation process. For example, the generation unit can generate an optimal itinerary based on spots the user has visited in the past. The generation unit can also suggest routes that avoid crowds based on the user's past city exploration history. Furthermore, the generation unit can analyze the user's past city exploration history and generate the most efficient itinerary. This improves user convenience by generating an optimal itinerary based on the user's past city exploration history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past city exploration history into a generation AI and have the generation AI perform the analysis.

[0079] The generation unit can customize the schedule based on the user's current interests during the generation process. For example, the generation unit can prioritize generating schedules based on categories the user is currently interested in. It can also generate schedules that include relevant locations based on the user's current interests. Furthermore, the generation unit can generate customized schedules that reflect the user's current interests. This improves user convenience by customizing the schedule based on the user's current interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current interests into a generation AI and have the generation AI perform analysis.

[0080] The generation unit can estimate the user's emotions and determine the priority of the itinerary to generate based on the estimated emotions. For example, if the user is in a hurry, the generation unit will prioritize important spots in the itinerary. If the user is relaxed, the generation unit can also generate an itinerary that proceeds at a leisurely pace. Furthermore, if the user is excited, the generation unit can generate an itinerary that includes visually stimulating spots. This improves user convenience by prioritizing the itinerary 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform analysis.

[0081] The generation unit can generate an optimal itinerary while considering the user's geographical location information. For example, the generation unit can prioritize spots close to the user's current location when generating the itinerary. The generation unit can also suggest highly relevant spots based on the user's past travel history. Furthermore, the generation unit can suggest the optimal route based on the user's current location information. This improves user convenience by generating an optimal itinerary that considers the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI perform the analysis.

[0082] The generation unit can analyze the user's social media activity and customize the schedule during the generation process. For example, the generation unit can suggest relevant spots based on the user's social media check-in information. It can also analyze the user's social media posts and suggest spots of interest. Furthermore, the generation unit can suggest relevant spots based on the activity of the user's friends on social media. By analyzing the user's social media activity and customizing the schedule, user convenience is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity into a generation AI and have the generation AI perform the analysis.

[0083] The navigation unit can estimate the user's emotions and adjust the navigation display method based on the estimated emotions. For example, if the user is tense, the navigation unit can provide a simple and highly visible display method. If the user is relaxed, the navigation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the navigation unit can provide a concise display method. By adjusting the navigation display method according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input user emotion data into a generative AI and have the generative AI perform analysis.

[0084] The navigation unit can suggest the optimal route during navigation by referring to the user's past travel history. For example, the navigation unit can suggest the optimal route based on routes the user has used in the past. The navigation unit can also suggest routes that avoid congestion based on the user's past travel history. Furthermore, the navigation unit can analyze the user's past travel history and suggest the most efficient route. This improves user convenience by suggesting the optimal route by referring to the user's past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's past travel history into a generating AI and have the generating AI perform the analysis.

[0085] The navigation unit can dynamically change the route during navigation, taking into account the user's current traffic conditions. For example, the navigation unit can suggest the optimal route based on real-time traffic congestion information. It can also suggest the optimal route considering the real-time operation status of public transportation. Furthermore, the navigation unit can suggest detour routes based on real-time road construction information. By dynamically changing the route based on the user's current traffic conditions, user convenience is improved. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input real-time traffic information into a generating AI and have the generating AI perform analysis.

[0086] The navigation unit can estimate the user's emotions and determine navigation priorities based on those emotions. For example, if the user is in a hurry, the navigation unit will prioritize navigation to important spots. If the user is relaxed, the navigation unit can also provide navigation at a leisurely pace. Furthermore, if the user is excited, the navigation unit can provide navigation that includes visually stimulating spots. This improves user convenience by determining navigation priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the navigation unit may be performed using AI, or not using AI. For example, the navigation unit can input user emotion data into a generative AI and have the generative AI perform analysis.

[0087] The navigation unit can present the optimal route during navigation, taking into account the user's geographical location. For example, the navigation unit prioritizes navigation to spots close to the user's current location. The navigation unit can also suggest highly relevant spots based on the user's past travel history. Furthermore, the navigation unit can suggest the optimal route based on the user's current location. This improves user convenience by presenting the optimal route while considering the user's geographical location. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's geographical location information into a generating AI and have the generating AI perform analysis.

[0088] The navigation unit can analyze the user's social media activity and customize the route during navigation. For example, the navigation unit can suggest relevant spots based on the user's social media check-in information. It can also analyze the user's social media posts and suggest spots of interest. Furthermore, the navigation unit can suggest relevant spots based on the activity of the user's friends on social media. By analyzing the user's social media activity and customizing the route, user convenience is improved. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's social media activity into a generating AI and have the generating AI perform the analysis.

[0089] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can present suggestions in a calm tone. If the user is relaxed, it can present suggestions in a bright tone. Furthermore, if the user is in a hurry, the suggestion unit can present suggestions quickly and concisely. By adjusting the way suggestions are presented according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform analysis.

[0090] The suggestion unit can make optimal suggestions by referring to the user's past behavior history. For example, the suggestion unit can suggest new related spots based on spots the user has visited in the past. It can also predict and suggest spots that the user might be interested in based on their past behavior history. Furthermore, the suggestion unit can analyze the user's past behavior patterns and make the most appropriate suggestions. This improves user convenience by making optimal suggestions based on the user's past behavior history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past behavior history into a generating AI and have the generating AI perform the analysis.

[0091] The suggestion unit can customize suggestions based on the user's current interests. For example, it can prioritize suggestions based on categories the user is currently interested in. It can also suggest relevant locations based on the user's current interests. Furthermore, it can provide customized suggestions that reflect the user's current interests. This improves user convenience by customizing suggestions based on the user's current interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's current interests into a generating AI and have the generating AI perform analysis.

[0092] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit will prioritize suggesting important spots. If the user is relaxed, the suggestion unit can also suggest spots that can be enjoyed at a leisurely pace. Furthermore, if the user is excited, the suggestion unit can prioritize suggesting visually stimulating spots. This improves user convenience by determining the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform analysis.

[0093] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, the suggestion unit can prioritize suggesting spots close to the user's current location. It can also suggest highly relevant spots based on the user's past travel history. Furthermore, the suggestion unit can suggest optimal spots based on the user's current location information. By making optimal suggestions that consider the user's geographical location information, user convenience is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis.

[0094] The suggestion unit can analyze the user's social media activity and customize the suggestions when making them. For example, the suggestion unit can suggest relevant spots based on the user's social media check-in information. It can also analyze the user's social media posts and suggest spots of interest. Furthermore, the suggestion unit can suggest relevant spots based on the activity of the user's friends on social media. By analyzing the user's social media activity and customizing the suggestions, the user's convenience is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity into a generating AI and have the generating AI perform the analysis.

[0095] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide a simple and highly visible display method. If the user is relaxed, the evaluation unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can provide a concise display method. By adjusting the display method of the evaluation according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform analysis.

[0096] The evaluation unit can refer to the user's past evaluation history to present the most suitable evaluation. For example, the evaluation unit can present evaluations of new spots related to the user based on spots the user has previously evaluated. The evaluation unit can also predict and present evaluations of spots that the user might be interested in based on the user's past evaluation history. Furthermore, the evaluation unit can analyze the user's past evaluation patterns and present the most suitable evaluation. This improves user convenience by providing the most suitable evaluation by referring to the user's past evaluation history. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's past evaluation history into a generating AI and have the generating AI perform the analysis.

[0097] The evaluation unit can customize evaluations based on the user's current interests. For example, the evaluation unit can prioritize evaluations for categories the user is currently interested in. It can also present evaluations of relevant spots based on the user's current interests. Furthermore, the evaluation unit can present customized evaluations that reflect the user's current interests. By customizing evaluations based on the user's current interests, user convenience is improved. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's current interests into a generating AI and have the generating AI perform the analysis.

[0098] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated emotions. For example, if the user is in a hurry, the evaluation unit may prioritize presenting evaluations of important spots. If the user is relaxed, the evaluation unit may also provide detailed evaluations. Furthermore, if the user is excited, the evaluation unit may prioritize presenting evaluations of visually stimulating spots. This improves user convenience by determining the priority of evaluations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit may input user emotion data into a generative AI and have the generative AI perform analysis.

[0099] The evaluation unit can present the optimal evaluation while considering the user's geographical location information. For example, the evaluation unit can prioritize presenting evaluations of spots close to the user's current location. The evaluation unit can also present evaluations of highly relevant spots based on the user's past travel history. Furthermore, the evaluation unit can present the optimal spot evaluation based on the user's current location information. This improves user convenience by presenting the optimal evaluation while considering the user's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis.

[0100] The evaluation unit can analyze the user's social media activity and customize the evaluation during the evaluation process. For example, the evaluation unit can present evaluations of relevant spots based on the user's social media check-in information. It can also analyze the user's social media posts and present evaluations of spots of interest. Furthermore, the evaluation unit can present evaluations of relevant spots based on the activity of the user's friends on social media. By analyzing the user's social media activity and customizing the evaluation, user convenience is improved. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's social media activity into a generating AI and have the generating AI perform the analysis.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The reception desk can monitor the user's health status and dynamically change the input interface for area, duration, and category based on that status. For example, if the user is tired, it can prioritize displaying areas and categories that can be enjoyed in a short amount of time. If the user is healthy and active, it can also suggest longer walks around town. Furthermore, if the user has a specific health problem, it can suggest areas and categories that take that problem into consideration. This improves user convenience by providing an input interface that is tailored to the user's health status. Health status monitoring is performed, for example, using sensors in wearable devices or smartphones. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's health data into a generating AI and have the generating AI perform analysis.

[0103] The suggestion section can analyze the user's hobbies and interests and make suggestions based on them. For example, if the user is interested in music, it can suggest music-related spots. If the user is interested in sports, it can suggest sports-related events and facilities. Furthermore, if the user is interested in art, it can suggest art galleries and museums. This improves user convenience by providing suggestions based on the user's hobbies and interests. The analysis of hobbies and interests is performed, for example, based on the user's past behavior history and social media posts. Some or all of the above processing in the suggestion section may be performed using AI, or not. For example, the suggestion section can input the user's hobbies and interests data into a generating AI and have the generating AI perform the analysis.

[0104] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. By adjusting the display method of the evaluation according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform analysis.

[0105] The reception desk can analyze the user's past input history and automatically suggest the most suitable input options. For example, it can automatically display areas, periods, and categories that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest areas, periods, and categories that the user will use at specific times based on their past input history. This improves input efficiency by suggesting the most suitable input options based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI perform the analysis.

[0106] The generation unit can estimate the user's emotions and adjust the method of generating the city stroll itinerary based on the estimated user emotions. For example, if the user is relaxed, it can generate an itinerary that proceeds at a leisurely pace. If the user is in a hurry, it can also generate an itinerary that emphasizes the shortest route. Furthermore, if the user is excited, it can generate an itinerary that includes visually stimulating spots. This improves user convenience by adjusting the method of generating the city stroll itinerary 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform analysis.

[0107] The navigation unit can dynamically change the route considering the user's current traffic conditions. For example, it can suggest the optimal route based on real-time traffic congestion information. It can also suggest the optimal route considering the real-time operation status of public transportation. Furthermore, it can suggest detour routes based on real-time road construction information. By dynamically changing the route considering the user's current traffic conditions, user convenience is improved. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input real-time traffic information into a generating AI and have the generating AI perform analysis.

[0108] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, suggestions can be presented in a calm tone. If the user is relaxed, suggestions can be presented in a cheerful tone. Furthermore, if the user is in a hurry, suggestions can be presented quickly and concisely. By adjusting the way suggestions are presented according to the user's emotions, user convenience is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform analysis.

[0109] The navigation unit can suggest the optimal route by referring to the user's past travel history. For example, it can suggest the optimal route based on routes the user has used in the past. It can also suggest routes that avoid congestion based on the user's past travel history. Furthermore, it can analyze the user's past travel history and suggest the most efficient route. This improves user convenience by suggesting the optimal route by referring to the user's past travel history. Some or all of the above processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's past travel history into a generating AI and have the generating AI perform the analysis.

[0110] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated user emotions. For example, if the user is in a hurry, it can prioritize evaluations of important spots. If the user is relaxed, it can provide detailed evaluations. Furthermore, if the user is excited, it can prioritize evaluations of visually stimulating spots. This improves user convenience by determining the priority of evaluations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform analysis.

[0111] The suggestion unit can analyze the user's social media activity and customize suggestions. For example, it can suggest relevant spots based on the user's social media check-in information. It can also analyze the user's social media posts and suggest spots of interest. Furthermore, it can suggest relevant spots based on the activity of the user's friends on social media. By analyzing the user's social media activity and customizing suggestions, user convenience is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's social media activity into a generating AI and have the generating AI perform the analysis.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk accepts input from the user regarding area, duration, and category. For example, when a user selects an area they want to explore, options such as Ebisu or Shimokitazawa are recommended based on their current location. Users can also specify an area using free text. Next, they select a duration, choosing from 1 hour, 2 hours, 3 hours, or 5 hours. Finally, they select a category, with options including cafes, lunch, dinner, art, and parks. They can also specify a category using free text. Step 2: The generation unit uses the generation AI to generate a city stroll itinerary based on the information received by the reception unit. For example, if a user inputs that they want to enjoy cafes and art in Ebisu, the generation AI calculates the optimal route based on that information and generates an itinerary. The generation unit then displays the navigation route results based on the generated itinerary. Step 3: The navigation unit performs navigation based on the itinerary generated by the generation unit. For example, based on the generated itinerary, it displays navigation route results that allow the user to enjoy exploring the city without getting lost.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, proposal unit, and evaluation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user input information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input information to generate an optimal city stroll schedule. The navigation unit is implemented by the control unit 46A of the smart device 14 and performs navigation based on the generated schedule. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes suggestions based on the user's past behavior history and preferences. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes spots with high review ratings. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, suggestion unit, and evaluation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user input information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input information to generate an optimal city stroll schedule. The navigation unit is implemented by the control unit 46A of the smart glasses 214 and performs navigation based on the generated schedule. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes suggestions based on the user's past behavior history and preferences. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests spots with high review ratings. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, suggestion unit, and evaluation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user input information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input information to generate an optimal city stroll schedule. The navigation unit is implemented by the control unit 46A of the headset terminal 314 and provides navigation based on the generated schedule. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes suggestions based on the user's past behavior history and preferences. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests spots with high review ratings. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, generation unit, navigation unit, proposal unit, and evaluation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user input information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user input information to generate an optimal city stroll schedule. The navigation unit is implemented by the control unit 46A of the robot 414 and performs navigation based on the generated schedule. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes suggestions based on the user's past behavior history and preferences. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes spots with high review ratings. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) A reception desk that accepts input from users regarding area, period, and category, A generation unit generates a walking tour itinerary based on the information received by the reception unit, The system includes a navigation unit that performs navigation based on the schedule generated by the generation unit. A system characterized by the following features. (Note 2) It includes a suggestion section that makes recommendations based on the user's past behavior history and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has an evaluation department that suggests highly-rated spots based on customer reviews. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the input interface for area, time period, and category based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It analyzes the user's past input history and automatically suggests the most suitable input options. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When entering area, period, and category, the system will complete the input based on the user's current activity status and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering area, period, and category, the system prioritizes displaying highly relevant suggestions based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter area, time period, and category, the system analyzes their social media activity and suggests relevant options. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is The system estimates the user's emotions and adjusts how the walking tour itinerary is generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During the generation process, the system references the user's past city walking history to generate the optimal itinerary. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During creation, the schedule is customized based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and determines the priority of the schedule to be generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During the generation process, the optimal schedule is generated based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the schedule is customized by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned navigation unit is It estimates the user's emotions and adjusts how navigation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned navigation unit is During navigation, the system refers to the user's past travel history to suggest the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned navigation unit is During navigation, the route is dynamically changed based on the user's current traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned navigation unit is It estimates the user's emotions and determines navigation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned navigation unit is During navigation, the system will suggest the optimal route based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned navigation unit is During navigation, analyze the user's social media activity to customize the route. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, we refer to the user's past behavior history to provide the most suitable recommendations. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, customize them based on the user's current interests and preferences. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, we will provide the most suitable suggestions based on the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making suggestions, analyze the user's social media activity to customize the suggestions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The evaluation unit, It estimates the user's sentiment and adjusts how ratings are displayed based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 29) The evaluation unit, During the evaluation process, the system will refer to the user's past evaluation history to suggest the most appropriate rating. The system described in Appendix 3, characterized by the features described herein. (Note 30) The evaluation unit, During the evaluation process, the evaluation is customized based on the user's current interests and preferences. The system described in Appendix 3, characterized by the features described herein. (Note 31) The evaluation unit, It estimates the user's emotions and determines the priority of evaluations based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The evaluation unit, During the evaluation process, the system will present the most appropriate evaluation based on the user's geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 33) The evaluation unit, During the evaluation process, the system analyzes the user's social media activity to customize the evaluation. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts input from users regarding area, period, and category, A generation unit generates a walking tour itinerary based on the information received by the reception unit, The system includes a navigation unit that performs navigation based on the schedule generated by the generation unit. A system characterized by the following features.

2. It includes a suggestion section that makes recommendations based on the user's past behavior history and preferences. The system according to feature 1.

3. It has an evaluation department that suggests highly-rated spots based on customer reviews. The system according to feature 1.

4. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the input interface for area, time period, and category based on the estimated user emotions. The system according to feature 1.

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

6. The aforementioned reception unit is When entering area, period, and category, the system will complete the input based on the user's current activity status and schedule. The system according to feature 1.

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

8. The aforementioned reception unit is When entering area, period, and category, the system prioritizes displaying highly relevant suggestions based on the user's geographical location. The system according to feature 1.

9. The aforementioned reception unit is When users enter area, time period, and category, the system analyzes their social media activity and suggests relevant options. The system according to feature 1.

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