System

Abstract

An object of the system according to the embodiment is to provide optimal route guidance and customized recommendation to a user in consideration of real-time people flow and traffic information.SOLUTION: A system includes a destination input unit, a relay point input unit, a real-time information acquisition unit, a route calculation unit, a customization recommendation unit, an information summarization unit, and a recommendation unit. The destination input unit inputs a destination of the user. The relay point input unit inputs a relay point. The real-time information acquisition unit acquires real-time people flow and traffic information. The route calculation unit calculates an optimum route based on the information acquired by the real-time information acquisition unit. The customized recommender performs people flow prediction analysis and provides customized recommendations tailored to the user. The information summarizing unit summarizes the real-time information. The recommendation unit predicts user needs and recommends stores and products on the way.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to adequately provide optimal route guidance that takes into account real-time pedestrian flow and traffic information, or customized recommendations tailored to the user.

[0005] The system according to the embodiment aims to provide users with optimal route guidance and customized recommendations, taking into account real-time pedestrian flow and traffic information. [Means for solving the problem]

[0006] The system according to the embodiment includes a destination input unit, a waypoint input unit, a real-time information acquisition unit, a route calculation unit, a customized recommendation unit, an information summarization unit, and a recommendation unit. The destination input unit inputs the user's destination. The waypoint input unit inputs waypoints. The real-time information acquisition unit acquires real-time people flow and traffic information. The route calculation unit calculates the optimal route based on the information acquired by the real-time information acquisition unit. The customized recommendation unit performs people flow prediction analysis and provides customized recommendations tailored to the user. The information summarization unit summarizes the real-time information. The recommendation unit predicts user needs and recommends stores and products along the way. [Effects of the Invention]

[0007] The system according to the embodiment can provide users with optimal route guidance and customized recommendations by taking into account real-time people flow and traffic information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The optimum route guidance system according to an embodiment of the present invention is a system that inputs a user's destination and intermediate points and provides optimum route guidance that takes into account real-time pedestrian flow and traffic information. As a result, the optimum route guidance system inputs a user's destination and intermediate points and provides optimum route guidance that takes into account real-time pedestrian flow and traffic information, and makes recommendations customized to the user, making travel and movement more convenient.

[0029] An optimal route guidance system according to an embodiment includes a destination input unit, a relay point input unit, a real-time information acquisition unit, a route calculation unit, a customization recommendation unit, an information summarization unit, and a recommendation unit. The destination input unit inputs a user's destination. For example, if a user inputs, "Tell me the route from the station to the museum," the destination input unit inputs the destination based on this information. The destination input unit can also allow the user to input the destination by voice. The relay point input unit inputs the user's relay points. For example, if a user inputs, "I'd like to stop by a cafe on the way," the relay point input unit inputs the relay points based on this information. The relay point input unit can also allow the user to input the relay points by voice. The real-time information acquisition unit acquires real-time pedestrian flow and traffic information. For example, the real-time information acquisition unit acquires data from a traffic information service. The real-time information acquisition unit can also use a drone to take aerial photographs of traffic conditions and acquire the data. The real-time information acquisition unit can also collect public transportation operation data in real time. The route calculation unit calculates an optimal route based on the information acquired by the real-time information acquisition unit. For example, the route calculation unit calculates a route that avoids traffic jams and crowds. The route calculation unit can also calculate an optimal route taking weather data into account. The route calculation unit can also calculate an optimal route based on the user's emotional state. The customized recommendation unit performs people flow prediction analysis and provides customized recommendations for the user. For example, the customized recommendation unit can learn the user's past travel history and automatically suggest frequently visited places. The customized recommendation unit can also analyze social media posting data to predict events and gatherings. The customized recommendation unit can also use an emotion estimation function to predict people flow based on the user's emotional state and suggest a route that reduces stress. The information summarization unit summarizes real-time information. For example, the information summarization unit concisely displays traffic conditions and weather information. The information summarization unit can also automatically prioritize information based on importance. The information summarization unit can also add a voice notification function to enable hands-free information acquisition.The recommendation unit predicts user needs and recommends stores and products along the way. For example, the recommendation unit analyzes the user's purchase history and makes recommendations based on past purchasing patterns. The recommendation unit can also analyze the user's social media activity and make recommendations based on the user's interests. The recommendation unit can also use an emotion estimation function to recommend products and services that match a user's specific emotional state. As a result, the optimal route guidance system according to the embodiment inputs the user's destination and intermediate points, provides optimal route guidance that takes into account real-time pedestrian flow and traffic information, and makes customized recommendations tailored to the user, making travel and movement more convenient.

[0030] The destination input unit can analyze the user's voice input and automatically extract the destination and intermediate points using natural language processing. For example, when the user says, "Tell me the route from the station to the museum," the destination input unit converts the content into text using speech recognition technology and extracts the destination and intermediate points using natural language processing. Similarly, when the user says, "I'd like to stop by a cafe on the way," the destination input unit can convert the content into text using speech recognition technology and extract the destination and intermediate points using natural language processing. This makes it possible to analyze the user's voice input and automatically extract the destination and intermediate points using natural language processing.

[0031] The destination input unit can learn the user's past movement history and automatically suggest frequently visited places. For example, the destination input unit analyzes the user's past movement history, lists frequently visited places, and automatically suggests them the next time the destination is input. The destination input unit can also suggest cafes and restaurants that the user frequently visits based on the user's past movement history. The destination input unit can also learn the user's past movement history and prioritize suggesting places that the user frequently visits. In this way, the user's past movement history can be learned and frequently visited places can be automatically suggested.

[0032] The destination input unit can enable tap input on a map using image recognition technology. For example, when a user taps a destination and a relay point on the map, the destination input unit uses image recognition technology to identify the location and calculate a route. The destination input unit can also input a specific location as a destination when the user taps the location on the map. The destination input unit can also input multiple relay points as relay points when the user taps the locations on the map. This enables tap input on a map using image recognition technology.

[0033] The destination input unit can integrate destinations and relay points input by multiple users simultaneously and propose the optimal route for the group. For example, when multiple users input destinations and relay points simultaneously, the destination input unit integrates the information and calculates the optimal route for the entire group. Furthermore, when multiple users input different destinations and relay points, the destination input unit can integrate them and propose the optimal route. Furthermore, the destination input unit can propose the optimal route taking into account the wishes of the entire group. In this way, the destinations and relay points input by multiple users simultaneously can be integrated and the optimal route can be proposed for the group.

[0034] The real-time information acquisition unit can use a drone to take aerial photographs of real-time traffic conditions and analyze the data using the generation AI. For example, the real-time information acquisition unit can use a drone to take aerial photographs of traffic conditions at major intersections and roads, analyze the data using the generation AI, and provide real-time traffic information. The real-time information acquisition unit can also use a drone to take aerial photographs of traffic accident sites and analyze the data using the generation AI. The real-time information acquisition unit can also use a drone to take aerial photographs of public transportation operation conditions and analyze the data using the generation AI. This makes it possible to use a drone to take aerial photographs of real-time traffic conditions and analyze the data using the generation AI.

[0035] The real-time information acquisition unit collects operation data of public transportation in real time and can instantly reflect information about delays and cancellations. The real-time information acquisition unit, for example, collects operation data of public transportation in real time and instantly reflects information about delays and cancellations to propose the optimal route. The real-time information acquisition unit can also collect operation data of public transportation in real time and instantly reflect changes to operation schedules. The real-time information acquisition unit can also collect operation data of public transportation in real time and propose alternative means. This makes it possible to collect operation data of public transportation in real time and instantly reflect information about delays and cancellations.

[0036] The real-time information acquisition unit can take into account weather data in addition to real-time traffic information and propose an optimal route depending on the weather. For example, the real-time information acquisition unit can take into account weather data in addition to real-time traffic information and propose a route with a roof on a rainy day. The real-time information acquisition unit can also take into account weather data and propose a route that is less slippery on a snowy day. The real-time information acquisition unit can also take into account weather data and propose a route that avoids the wind on a windy day. In this way, it is possible to propose an optimal route depending on the weather by taking into account weather data in addition to real-time traffic information.

[0037] The real-time information acquisition unit can provide real-time pedestrian flow information corresponding to different modes of transportation, such as bicycles and walking. The real-time information acquisition unit can provide real-time pedestrian flow information corresponding to different modes of transportation, such as bicycles and walking, and suggest optimal routes. The real-time information acquisition unit can also provide information on bicycle-only roads and pedestrian-only roads. The real-time information acquisition unit can also suggest routes suitable for bicycles and walking. This makes it possible to provide real-time pedestrian flow information corresponding to different modes of transportation, such as bicycles and walking.

[0038] The customization recommendation unit can analyze social media posting data and predict events and gatherings. For example, the customization recommendation unit can analyze social media posting data and predict events and gatherings in a specific area. The customization recommendation unit can also analyze social media posting data and predict locations that are expected to be crowded during specific time periods. The customization recommendation unit can also analyze social media posting data and predict locations where specific events and gatherings will be held. In this way, it is possible to analyze social media posting data and predict events and gatherings.

[0039] The customized recommendation unit can use past people flow data to learn people flow patterns by season and time of day, thereby improving prediction accuracy. The customized recommendation unit, for example, analyzes past people flow data to learn people flow patterns by season and time of day, thereby improving prediction accuracy. The customized recommendation unit can also use past people flow data to predict locations that are expected to be crowded during specific seasons or time of day. The customized recommendation unit can also use past people flow data to predict when specific events or gatherings will be held. In this way, past people flow data can be used to learn people flow patterns by season and time of day, thereby improving prediction accuracy.

[0040] The customization recommendation unit can notify the user in advance of places that the user is likely to visit based on the people flow prediction. The customization recommendation unit builds a system that notifies the user in advance of places that the user is likely to visit based on, for example, people flow prediction data. For example, the customization recommendation unit predicts and notifies the user of cafes and restaurants that the user frequently visits. The customization recommendation unit can also notify the user in advance of tourist spots that the user is likely to visit based on the people flow prediction data. The customization recommendation unit can also notify the user in advance of events and gatherings that the user is likely to visit based on the people flow prediction data. In this way, it is possible to notify the user in advance of places that the user is likely to visit based on the people flow prediction.

[0041] The customization recommendation unit can use the people flow prediction data to display the congestion status of commercial facilities and tourist destinations in real time. The customization recommendation unit, for example, uses the people flow prediction data to build a system that displays the congestion status of commercial facilities and tourist destinations in real time. For example, it displays the congestion status of shopping malls and theme parks. The customization recommendation unit can also use the people flow prediction data to display, in real time, locations that are expected to be crowded during specific time periods. The customization recommendation unit can also use the people flow prediction data to display, in real time, the congestion status of specific events or gatherings. In this way, the congestion status of commercial facilities and tourist destinations can be displayed in real time using the people flow prediction data.

[0042] The information summarizing unit can automatically set the priority of information according to its importance when summarizing real-time information. For example, when summarizing real-time information, the information summarizing unit automatically sets the priority of information according to its importance and provides it to the user. Furthermore, when summarizing real-time information, the information summarizing unit can also preferentially display information with a high level of urgency. Furthermore, when summarizing real-time information, the information summarizing unit can also set the priority of information based on the user's level of interest. This makes it possible to automatically set the priority of information according to its importance when summarizing real-time information.

[0043] The information summarizing unit can learn the user's past behavioral patterns and provide individually customized summary information. The information summarizing unit, for example, builds a system that learns the user's past behavioral patterns and provides individually customized summary information. For example, it summarizes information based on the user's frequently used means of transportation and routes. The information summarizing unit can also learn the user's past behavioral patterns and summarize information based on the user's interests. The information summarizing unit can also learn the user's past behavioral patterns and summarize information according to the user's needs. In this way, it is possible to learn the user's past behavioral patterns and provide individually customized summary information.

[0044] The information summarizing unit can add a voice notification function when summarizing real-time information, allowing the user to obtain the information hands-free. For example, the information summarizing unit can add a voice notification function when summarizing real-time information, allowing the user to obtain the information hands-free. The information summarizing unit can also notify the user of important information in real time using the voice notification function. The information summarizing unit can also notify the user of information based on the user's level of interest using the voice notification function. In this way, the information summarizing unit can add a voice notification function when summarizing real-time information, allowing the user to obtain the information hands-free.

[0045] The information summarizing unit can visualize the summarized information and make it intuitively understandable with graphs and charts. The information summarizing unit, for example, builds a system that summarizes real-time information and visualizes it with graphs and charts so that it can be intuitively understood. For example, the information summarizing unit can display traffic conditions with a bar graph. The information summarizing unit can also display the summarized information with a pie chart or a line graph. The information summarizing unit can also display the summarized information with infographics. In this way, the summarized information can be visualized and made intuitively understandable with graphs and charts.

[0046] The recommendation unit can analyze a user's purchase history and make recommendations based on past purchase patterns. The recommendation unit, for example, analyzes a user's purchase history and builds a system that recommends optimal products and services based on past purchase patterns. For example, the recommendation unit recommends related products based on products that the user frequently purchases. The recommendation unit can also analyze a user's purchase history and recommend products that are purchased in a specific season. The recommendation unit can also analyze a user's purchase history and recommend products that match the user's preferences. This makes it possible to analyze a user's purchase history and make recommendations based on past purchase patterns.

[0047] The recommendation unit can analyze the user's social media activity and make recommendations based on the user's interests and concerns. The recommendation unit can analyze the user's social media activity, such as posts and "likes," and recommend products and services based on the user's interests and concerns. The recommendation unit can also analyze the accounts the user follows on social media and recommend related products and services. The recommendation unit can also analyze the user's social media activity and recommend events and activities that match the user's interests. This makes it possible to analyze the user's social media activity and make recommendations based on the user's interests and concerns.

[0048] The recommendation unit can recommend nearby stores and services in real time based on the user's location information. The recommendation unit, for example, builds a system that recommends nearby stores and services in real time based on the user's location information. For example, the recommendation unit suggests the restaurant closest to the user's current location. The recommendation unit can also recommend nearby tourist spots based on the user's location information. The recommendation unit can also recommend nearby shopping malls and events based on the user's location information. This makes it possible to recommend nearby stores and services in real time based on the user's location information.

[0049] The recommendation unit can display reviews and ratings by other users for the recommended product or service to support decision-making. The recommendation unit, for example, displays reviews and ratings by other users for the recommended product or service, thereby building a system to support user decision-making. For example, it displays star ratings and comments for the product. The recommendation unit can also display feedback and ratings for the service. The recommendation unit can also enable the user to compare options based on reviews and ratings by other users. This makes it possible to display reviews and ratings by other users for the recommended product or service to support decision-making.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The destination input unit can analyze the user's voice input and automatically extract the destination and intermediate points using natural language processing. For example, if the user says, "Tell me the route from the station to the museum," the destination input unit converts the content into text using speech recognition technology and extracts the destination and intermediate points using natural language processing. Similarly, if the user says, "I'd like to stop by a cafe on the way," the destination input unit can convert the content into text using speech recognition technology and extract the destination and intermediate points using natural language processing. This makes it possible to analyze the user's voice input and automatically extract the destination and intermediate points using natural language processing.

[0052] The destination input unit can learn the user's past travel history and automatically suggest frequently visited places. For example, it can analyze the user's past travel history, make a list of frequently visited places, and automatically suggest them the next time the destination is entered. The destination input unit can also suggest cafes and restaurants that the user frequently visits based on the user's past travel history. The destination input unit can also learn the user's past travel history and prioritize suggesting places that the user frequently visits. This makes it possible to learn the user's past travel history and automatically suggest frequently visited places.

[0053] The destination input unit can enable tap input on a map using image recognition technology. For example, when a user taps a destination and a relay point on the map, the image recognition technology is used to identify the location and calculate a route. The destination input unit can also input a specific location as a destination when the user taps the location on the map. The destination input unit can also input multiple relay points as relay points when the user taps the multiple relay points on the map. This enables tap input on a map using image recognition technology.

[0054] The destination input unit can integrate destinations and intermediate points input by multiple users simultaneously and propose the optimal route for the group. For example, if multiple users input destinations and intermediate points simultaneously, the information is integrated and the optimal route for the entire group is calculated. Furthermore, if multiple users input different destinations and intermediate points, the destination input unit can integrate them and propose the optimal route. Furthermore, the destination input unit can propose the optimal route taking into account the wishes of the entire group. In this way, the destinations and intermediate points input by multiple users simultaneously can be integrated and the optimal route for the group can be proposed.

[0055] The real-time information acquisition unit can use a drone to take aerial photographs of real-time traffic conditions and analyze the data using the generation AI. For example, a drone can be used to take aerial photographs of traffic conditions at major intersections and roads, and the data can be analyzed using the generation AI to provide real-time traffic information. The real-time information acquisition unit can also use a drone to take aerial photographs of traffic accident sites and analyze the data using the generation AI. The real-time information acquisition unit can also use a drone to take aerial photographs of public transportation operation status and analyze the data using the generation AI. This makes it possible to use a drone to take aerial photographs of real-time traffic conditions and analyze the data using the generation AI.

[0056] The real-time information acquisition unit can take into account weather data in addition to real-time traffic information and propose an optimal route depending on the weather. For example, the real-time information acquisition unit can take into account weather data in addition to real-time traffic information and propose a route with a roof on a rainy day. The real-time information acquisition unit can also take into account weather data and propose a route that is less slippery on a snowy day. The real-time information acquisition unit can also take into account weather data and propose a route that avoids the wind on a windy day. In this way, the optimal route depending on the weather can be proposed taking into account weather data in addition to real-time traffic information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The destination input unit inputs the user's destination. For example, if the user inputs "Tell me the route from the station to the museum," the destination input unit inputs the destination based on this information. The destination input unit can also allow the user to input the destination by voice. Step 2: The relay point input unit inputs the relay points of the user. For example, if the user inputs "I'd like to stop by a cafe on the way," the relay point input unit inputs relay points based on this information. The relay point input unit can also allow the user to input relay points by voice. Step 3: The real-time information acquisition unit acquires real-time pedestrian flow and traffic information. For example, the real-time information acquisition unit acquires data from a traffic information service. It can also acquire data by using a drone to take aerial photographs of traffic conditions. It can also collect operation data of public transportation in real time. Step 4: The route calculation unit calculates the optimal route based on the information acquired by the real-time information acquisition unit. For example, it calculates a route that avoids traffic jams and congestion. It can also calculate the optimal route taking weather data into account. It can also calculate the optimal route based on the user's emotional state. Step 5: The customized recommendation unit performs people flow prediction analysis and provides customized recommendations for the user. For example, it can learn the user's past travel history and automatically suggest frequently visited places. It can also analyze social media posting data to predict events and gatherings. Furthermore, it can use emotion estimation functions to predict people flow based on the user's emotional state and suggest routes that reduce stress. Step 6: The information summarization unit summarizes real-time information. For example, it can display traffic and weather information in a concise format. It can also automatically prioritize information based on its importance. It can also add a voice notification function, allowing users to obtain information hands-free. Step 7: The recommendation unit predicts the user's needs and recommends stores and products along the way. For example, it analyzes the user's purchase history and makes recommendations based on past purchasing patterns. It can also analyze social media activity and make recommendations based on interests and concerns. Furthermore, it can use emotion estimation functions to recommend products and services that match the user's specific emotional state.

[0059] (Example 2) The optimum route guidance system according to an embodiment of the present invention is a system that inputs a user's destination and intermediate points and provides optimum route guidance that takes into account real-time pedestrian flow and traffic information. As a result, the optimum route guidance system inputs a user's destination and intermediate points and provides optimum route guidance that takes into account real-time pedestrian flow and traffic information, and makes recommendations customized to the user, making travel and movement more convenient.

[0060] An optimal route guidance system according to an embodiment includes a destination input unit, a relay point input unit, a real-time information acquisition unit, a route calculation unit, a customization recommendation unit, an information summarization unit, and a recommendation unit. The destination input unit inputs a user's destination. For example, if a user inputs, "Tell me the route from the station to the museum," the destination input unit inputs the destination based on this information. The destination input unit can also allow the user to input the destination by voice. The relay point input unit inputs the user's relay points. For example, if a user inputs, "I'd like to stop by a cafe on the way," the relay point input unit inputs the relay points based on this information. The relay point input unit can also allow the user to input the relay points by voice. The real-time information acquisition unit acquires real-time pedestrian flow and traffic information. For example, the real-time information acquisition unit acquires data from a traffic information service. The real-time information acquisition unit can also use a drone to take aerial photographs of traffic conditions and acquire the data. The real-time information acquisition unit can also collect public transportation operation data in real time. The route calculation unit calculates an optimal route based on the information acquired by the real-time information acquisition unit. For example, the route calculation unit calculates a route that avoids traffic jams and crowds. The route calculation unit can also calculate an optimal route taking weather data into account. The route calculation unit can also calculate an optimal route based on the user's emotional state. The customized recommendation unit performs people flow prediction analysis and provides customized recommendations for the user. For example, the customized recommendation unit can learn the user's past travel history and automatically suggest frequently visited places. The customized recommendation unit can also analyze social media posting data to predict events and gatherings. The customized recommendation unit can also use an emotion estimation function to predict people flow based on the user's emotional state and suggest a route that reduces stress. The information summarization unit summarizes real-time information. For example, the information summarization unit concisely displays traffic conditions and weather information. The information summarization unit can also automatically prioritize information based on importance. The information summarization unit can also add a voice notification function to enable hands-free information acquisition.The recommendation unit predicts user needs and recommends stores and products along the way. For example, the recommendation unit analyzes the user's purchase history and makes recommendations based on past purchasing patterns. The recommendation unit can also analyze the user's social media activity and make recommendations based on the user's interests. The recommendation unit can also use an emotion estimation function to recommend products and services that match a user's specific emotional state. As a result, the optimal route guidance system according to the embodiment inputs the user's destination and intermediate points, provides optimal route guidance that takes into account real-time pedestrian flow and traffic information, and makes customized recommendations tailored to the user, making travel and movement more convenient.

[0061] The destination input unit can analyze the user's voice input and automatically extract the destination and intermediate points using natural language processing. For example, when the user says, "Tell me the route from the station to the museum," the destination input unit converts the content into text using speech recognition technology and extracts the destination and intermediate points using natural language processing. Similarly, when the user says, "I'd like to stop by a cafe on the way," the destination input unit can convert the content into text using speech recognition technology and extract the destination and intermediate points using natural language processing. This makes it possible to analyze the user's voice input and automatically extract the destination and intermediate points using natural language processing.

[0062] The destination input unit can learn the user's past movement history and automatically suggest frequently visited places. For example, the destination input unit analyzes the user's past movement history, lists frequently visited places, and automatically suggests them the next time the destination is input. The destination input unit can also suggest cafes and restaurants that the user frequently visits based on the user's past movement history. The destination input unit can also learn the user's past movement history and prioritize suggesting places that the user frequently visits. In this way, the user's past movement history can be learned and frequently visited places can be automatically suggested.

[0063] The destination input unit can use the emotion estimation function to suggest destinations and intermediate points according to the user's emotional state. For example, if the user is feeling stressed, the destination input unit can use the emotion estimation function to suggest places where the user can relax. For example, if the user says "I'm tired," the destination input unit can suggest nearby parks or cafes. Furthermore, if the user is feeling like they want to have fun, the destination input unit can also use the emotion estimation function to provide information about events and festivals. Furthermore, if the user is looking for a new experience, the destination input unit can also use the emotion estimation function to suggest new activities or events. In this way, the emotion estimation function can be used to suggest destinations and intermediate points according to the user's emotional state.

[0064] The destination input unit can enable tap input on a map using image recognition technology. For example, when a user taps a destination and a relay point on the map, the destination input unit uses image recognition technology to identify the location and calculate a route. The destination input unit can also input a specific location as a destination when the user taps the location on the map. The destination input unit can also input multiple relay points as relay points when the user taps the locations on the map. This enables tap input on a map using image recognition technology.

[0065] The destination input unit can integrate destinations and relay points input by multiple users simultaneously and propose the optimal route for the group. For example, when multiple users input destinations and relay points simultaneously, the destination input unit integrates the information and calculates the optimal route for the entire group. Furthermore, when multiple users input different destinations and relay points, the destination input unit can integrate them and propose the optimal route. Furthermore, the destination input unit can propose the optimal route taking into account the wishes of the entire group. In this way, the destinations and relay points input by multiple users simultaneously can be integrated and the optimal route can be proposed for the group.

[0066] The destination input unit can use the emotion estimation function to analyze the emotion of the user when entering data in real time, thereby providing an interface for reducing stress. For example, when the user enters a destination and intermediate points, the destination input unit can use the emotion estimation function to provide a relaxing interface if the user is feeling stressed. The destination input unit can also use the emotion estimation function to analyze the emotion of the user when entering data in real time, thereby providing a user-friendly design. The destination input unit can also use the emotion estimation function to analyze the emotion of the user when entering data in real time, thereby providing an intuitive operation method. In this way, the emotion estimation function can be used to analyze the emotion of the user when entering data in real time, thereby providing an interface for reducing stress.

[0067] The real-time information acquisition unit can use a drone to take aerial photographs of real-time traffic conditions and analyze the data using the generation AI. For example, the real-time information acquisition unit can use a drone to take aerial photographs of traffic conditions at major intersections and roads, analyze the data using the generation AI, and provide real-time traffic information. The real-time information acquisition unit can also use a drone to take aerial photographs of traffic accident sites and analyze the data using the generation AI. The real-time information acquisition unit can also use a drone to take aerial photographs of public transportation operation conditions and analyze the data using the generation AI. This makes it possible to use a drone to take aerial photographs of real-time traffic conditions and analyze the data using the generation AI.

[0068] The real-time information acquisition unit collects operation data of public transportation in real time and can instantly reflect information about delays and cancellations. The real-time information acquisition unit, for example, collects operation data of public transportation in real time and instantly reflects information about delays and cancellations to propose the optimal route. The real-time information acquisition unit can also collect operation data of public transportation in real time and instantly reflect changes to operation schedules. The real-time information acquisition unit can also collect operation data of public transportation in real time and propose alternative means. This makes it possible to collect operation data of public transportation in real time and instantly reflect information about delays and cancellations.

[0069] The real-time information acquisition unit can use the emotion estimation function to preferentially suggest a route that avoids congestion when the user has the emotion of wanting to avoid congestion. For example, when the user has the emotion of wanting to avoid congestion, the real-time information acquisition unit can use the emotion estimation function to preferentially suggest a route that avoids congestion. Furthermore, when the user has the emotion of wanting to avoid congestion, the real-time information acquisition unit can also use the emotion estimation function to suggest a route with a lower degree of congestion. Furthermore, when the user has the emotion of wanting to avoid congestion, the real-time information acquisition unit can also use the emotion estimation function to suggest an alternative route to avoid congestion. In this way, when the user has the emotion of wanting to avoid congestion, the emotion estimation function can preferentially suggest a route that avoids congestion.

[0070] The real-time information acquisition unit can take into account weather data in addition to real-time traffic information and propose an optimal route depending on the weather. For example, the real-time information acquisition unit can take into account weather data in addition to real-time traffic information and propose a route with a roof on a rainy day. The real-time information acquisition unit can also take into account weather data and propose a route that is less slippery on a snowy day. The real-time information acquisition unit can also take into account weather data and propose a route that avoids the wind on a windy day. In this way, it is possible to propose an optimal route depending on the weather by taking into account weather data in addition to real-time traffic information.

[0071] The real-time information acquisition unit can provide real-time pedestrian flow information corresponding to different modes of transportation, such as bicycles and walking. The real-time information acquisition unit can provide real-time pedestrian flow information corresponding to different modes of transportation, such as bicycles and walking, and suggest optimal routes. The real-time information acquisition unit can also provide information on bicycle-only roads and pedestrian-only roads. The real-time information acquisition unit can also suggest routes suitable for bicycles and walking. This makes it possible to provide real-time pedestrian flow information corresponding to different modes of transportation, such as bicycles and walking.

[0072] The real-time information acquisition unit can use the emotion estimation function to suggest a scenic route when the user feels like relaxing. For example, the real-time information acquisition unit can use the emotion estimation function to suggest a scenic route when the user feels like relaxing. Furthermore, the real-time information acquisition unit can also use the emotion estimation function to suggest a route with many natural landscapes when the user feels like relaxing. Furthermore, the real-time information acquisition unit can also use the emotion estimation function to suggest a quiet route when the user feels like relaxing. In this way, the emotion estimation function can be used to suggest a scenic route when the user feels like relaxing.

[0073] The customization recommendation unit can analyze social media posting data and predict events and gatherings. For example, the customization recommendation unit can analyze social media posting data and predict events and gatherings in a specific area. The customization recommendation unit can also analyze social media posting data and predict locations that are expected to be crowded during specific time periods. The customization recommendation unit can also analyze social media posting data and predict locations where specific events and gatherings will be held. In this way, it is possible to analyze social media posting data and predict events and gatherings.

[0074] The customized recommendation unit can use past people flow data to learn people flow patterns by season and time of day, thereby improving prediction accuracy. The customized recommendation unit, for example, analyzes past people flow data to learn people flow patterns by season and time of day, thereby improving prediction accuracy. The customized recommendation unit can also use past people flow data to predict locations that are expected to be crowded during specific seasons or time of day. The customized recommendation unit can also use past people flow data to predict when specific events or gatherings will be held. In this way, past people flow data can be used to learn people flow patterns by season and time of day, thereby improving prediction accuracy.

[0075] The customized recommendation unit can use the emotion estimation function to predict people flow according to the emotional state of the user and suggest a route that reduces stress. The customized recommendation unit, for example, analyzes the emotional state of the user using the emotion estimation function and predicts people flow to reduce stress. The customized recommendation unit can also analyze the emotional state of the user using the emotion estimation function and predict people flow to avoid congestion. The customized recommendation unit can also analyze the emotional state of the user using the emotion estimation function and suggest a route that allows relaxation. In this way, the emotion estimation function can be used to predict people flow according to the emotional state of the user and suggest a route that reduces stress.

[0076] The customization recommendation unit can notify the user in advance of places that the user is likely to visit based on the people flow prediction. The customization recommendation unit builds a system that notifies the user in advance of places that the user is likely to visit based on, for example, people flow prediction data. For example, the customization recommendation unit predicts and notifies the user of cafes and restaurants that the user frequently visits. The customization recommendation unit can also notify the user in advance of tourist spots that the user is likely to visit based on the people flow prediction data. The customization recommendation unit can also notify the user in advance of events and gatherings that the user is likely to visit based on the people flow prediction data. In this way, it is possible to notify the user in advance of places that the user is likely to visit based on the people flow prediction.

[0077] The customization recommendation unit can use the people flow prediction data to display the congestion status of commercial facilities and tourist destinations in real time. The customization recommendation unit, for example, uses the people flow prediction data to build a system that displays the congestion status of commercial facilities and tourist destinations in real time. For example, it displays the congestion status of shopping malls and theme parks. The customization recommendation unit can also use the people flow prediction data to display, in real time, locations that are expected to be crowded during specific time periods. The customization recommendation unit can also use the people flow prediction data to display, in real time, the congestion status of specific events or gatherings. In this way, the congestion status of commercial facilities and tourist destinations can be displayed in real time using the people flow prediction data.

[0078] The customized recommendation unit can use the emotion estimation function to provide information about events and festivals when the user is feeling like having fun. For example, when the user is feeling like having fun, the customized recommendation unit can use the emotion estimation function to provide information about events and festivals. Furthermore, when the user is feeling like having fun, the customized recommendation unit can also use the emotion estimation function to provide information about specific events and festivals. Furthermore, when the user is feeling like having fun, the customized recommendation unit can also use the emotion estimation function to suggest events and festivals that match the user's interests. In this way, when the user is feeling like having fun, the emotion estimation function can provide information about events and festivals.

[0079] The information summarizing unit can automatically set the priority of information according to its importance when summarizing real-time information. For example, when summarizing real-time information, the information summarizing unit automatically sets the priority of information according to its importance and provides it to the user. Furthermore, when summarizing real-time information, the information summarizing unit can also preferentially display information with a high level of urgency. Furthermore, when summarizing real-time information, the information summarizing unit can also set the priority of information based on the user's level of interest. This makes it possible to automatically set the priority of information according to its importance when summarizing real-time information.

[0080] The information summarizing unit can learn the user's past behavioral patterns and provide individually customized summary information. The information summarizing unit, for example, builds a system that learns the user's past behavioral patterns and provides individually customized summary information. For example, it summarizes information based on the user's frequently used means of transportation and routes. The information summarizing unit can also learn the user's past behavioral patterns and summarize information based on the user's interests. The information summarizing unit can also learn the user's past behavioral patterns and summarize information according to the user's needs. In this way, it is possible to learn the user's past behavioral patterns and provide individually customized summary information.

[0081] The information summarizing unit can use the emotion estimation function to prioritize display of the most important information when the user is in a hurry. The information summarizing unit, for example, uses the emotion estimation function to build a system that prioritizes display of the most important information when the user is in a hurry. For example, traffic delay information and emergency news are displayed with priority. The information summarizing unit can also use the emotion estimation function to prioritize display of information with high urgency when the user is in a hurry. The information summarizing unit can also use the emotion estimation function to prioritize display of information based on the user's interest level when the user is in a hurry. In this way, the emotion estimation function can be used to prioritize display of the most important information when the user is in a hurry.

[0082] The information summarizing unit can add a voice notification function when summarizing real-time information, allowing the user to obtain the information hands-free. For example, the information summarizing unit can add a voice notification function when summarizing real-time information, allowing the user to obtain the information hands-free. The information summarizing unit can also notify the user of important information in real time using the voice notification function. The information summarizing unit can also notify the user of information based on the user's level of interest using the voice notification function. In this way, the information summarizing unit can add a voice notification function when summarizing real-time information, allowing the user to obtain the information hands-free.

[0083] The information summarizing unit can visualize the summarized information and make it intuitively understandable with graphs and charts. The information summarizing unit, for example, builds a system that summarizes real-time information and visualizes it with graphs and charts so that it can be intuitively understood. For example, the information summarizing unit can display traffic conditions with a bar graph. The information summarizing unit can also display the summarized information with a pie chart or a line graph. The information summarizing unit can also display the summarized information with infographics. In this way, the summarized information can be visualized and made intuitively understandable with graphs and charts.

[0084] The information summarizing unit can use the emotion estimation function to provide relaxing music and scenery images when a user wants to relax. For example, the information summarizing unit builds a system that uses the emotion estimation function to provide relaxing music and scenery images when a user wants to relax. For example, when a user is feeling stressed, relaxing music is played. The information summarizing unit can also use the emotion estimation function to display scenery images of natural landscapes when a user wants to relax. The information summarizing unit can also use the emotion estimation function to play quiet music when a user wants to relax. In this way, the emotion estimation function can be used to provide relaxing music and scenery images when a user wants to relax.

[0085] The recommendation unit can analyze a user's purchase history and make recommendations based on past purchase patterns. The recommendation unit, for example, analyzes a user's purchase history and builds a system that recommends optimal products and services based on past purchase patterns. For example, the recommendation unit recommends related products based on products that the user frequently purchases. The recommendation unit can also analyze a user's purchase history and recommend products that are purchased in a specific season. The recommendation unit can also analyze a user's purchase history and recommend products that match the user's preferences. This makes it possible to analyze a user's purchase history and make recommendations based on past purchase patterns.

[0086] The recommendation unit can analyze the user's social media activity and make recommendations based on the user's interests and concerns. The recommendation unit can analyze the user's social media activity, such as posts and "likes," and recommend products and services based on the user's interests and concerns. The recommendation unit can also analyze the accounts the user follows on social media and recommend related products and services. The recommendation unit can also analyze the user's social media activity and recommend events and activities that match the user's interests. This makes it possible to analyze the user's social media activity and make recommendations based on the user's interests and concerns.

[0087] The recommendation unit can use the emotion estimation function to recommend products and services that match the user's emotion when the user is in a specific emotional state. For example, when the user is in a specific emotional state, the recommendation unit uses the emotion estimation function to build a system that recommends products and services that match the emotion. For example, when the user is feeling stressed, the recommendation unit can suggest products that will help the user relax. Furthermore, when the user is feeling happy, the recommendation unit can also use the emotion estimation function to recommend products that are suitable for celebrations. Furthermore, when the user is feeling sad, the recommendation unit can also use the emotion estimation function to recommend products that will soothe the user's mood. In this way, when the user is in a specific emotional state, the emotion estimation function can be used to recommend products and services that match the user's emotion.

[0088] The recommendation unit can recommend nearby stores and services in real time based on the user's location information. The recommendation unit, for example, builds a system that recommends nearby stores and services in real time based on the user's location information. For example, the recommendation unit suggests the restaurant closest to the user's current location. The recommendation unit can also recommend nearby tourist spots based on the user's location information. The recommendation unit can also recommend nearby shopping malls and events based on the user's location information. This makes it possible to recommend nearby stores and services in real time based on the user's location information.

[0089] The recommendation unit can display reviews and ratings by other users for the recommended product or service to support decision-making. The recommendation unit, for example, displays reviews and ratings by other users for the recommended product or service, thereby building a system to support user decision-making. For example, it displays star ratings and comments for the product. The recommendation unit can also display feedback and ratings for the service. The recommendation unit can also enable the user to compare options based on reviews and ratings by other users. This makes it possible to display reviews and ratings by other users for the recommended product or service to support decision-making.

[0090] The recommendation unit can use the emotion estimation function to recommend new activities and events when the user is looking for a new experience. For example, the recommendation unit builds a system that uses the emotion estimation function to recommend new activities and events when the user is looking for a new experience. For example, a new sporting event can be suggested when the user is excited. The recommendation unit can also use the emotion estimation function to suggest new tourist spots and activities when the user is looking for a new experience. The recommendation unit can also use the emotion estimation function to suggest new cultural events and workshops when the user is looking for a new experience. In this way, the emotion estimation function can be used to recommend new activities and events when the user is looking for a new experience.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The destination input unit inputs the user's destination. For example, if the user inputs, "Tell me the route from the station to the museum," the destination input unit inputs the destination based on this information. The destination input unit can also allow the user to input the destination by voice. The intermediate point input unit inputs the user's intermediate points. For example, if the user inputs, "I'd like to stop by a cafe on the way," the intermediate point input unit inputs the intermediate points based on this information. The intermediate point input unit can also allow the user to input the intermediate points by voice. The real-time information acquisition unit acquires real-time pedestrian flow and traffic information. For example, the real-time information acquisition unit acquires data from a traffic information service. The real-time information acquisition unit can also use a drone to take aerial photographs of traffic conditions and acquire the data. The real-time information acquisition unit can also collect operation data of public transportation in real time. The route calculation unit calculates an optimal route based on the information acquired by the real-time information acquisition unit. For example, the route calculation unit calculates a route that avoids traffic jams and congestion. The route calculation unit can also calculate an optimal route taking weather data into account. The route calculation unit can also calculate the optimal route based on the user's emotional state. The customized recommendation unit performs people flow prediction analysis and provides customized recommendations for the user. For example, the customized recommendation unit learns the user's past travel history and automatically suggests frequently visited places. The customized recommendation unit can also analyze social media posting data and predict events and gatherings. The customized recommendation unit can also use an emotion estimation function to predict people flow based on the user's emotional state and suggest routes that reduce stress. The information summarization unit summarizes real-time information. For example, the information summarization unit concisely displays traffic and weather information. The information summarization unit can also automatically prioritize information based on its importance. The information summarization unit can also add a voice notification function to enable hands-free information acquisition. The recommendation unit predicts user needs and recommends stores and products along the way. For example, the recommendation unit analyzes the user's purchasing history and makes recommendations based on past purchasing patterns.The recommendation unit can also analyze the user's social media activity and make recommendations based on the user's interests. Furthermore, the recommendation unit can use an emotion estimation function to recommend products and services that match a user's specific emotional state. As a result, the optimal route guidance system according to the embodiment inputs the user's destination and intermediate points, provides optimal route guidance that takes into account real-time pedestrian flow and traffic information, and makes recommendations customized to the user, making travel and transportation more convenient.

[0093] The destination input unit can analyze the user's voice input and automatically extract the destination and intermediate points using natural language processing. For example, if the user says, "Tell me the route from the station to the museum," the destination input unit converts the content into text using speech recognition technology and extracts the destination and intermediate points using natural language processing. Similarly, if the user says, "I'd like to stop by a cafe on the way," the destination input unit can convert the content into text using speech recognition technology and extract the destination and intermediate points using natural language processing. This makes it possible to analyze the user's voice input and automatically extract the destination and intermediate points using natural language processing.

[0094] The destination input unit can learn the user's past travel history and automatically suggest frequently visited places. For example, it can analyze the user's past travel history, make a list of frequently visited places, and automatically suggest them the next time the destination is entered. The destination input unit can also suggest cafes and restaurants that the user frequently visits based on the user's past travel history. The destination input unit can also learn the user's past travel history and prioritize suggesting places that the user frequently visits. This makes it possible to learn the user's past travel history and automatically suggest frequently visited places.

[0095] The destination input unit can use the emotion estimation function to suggest destinations and stopover points according to the user's emotional state. For example, if the user is feeling stressed, the emotion estimation function can be used to suggest places where the user can relax. For example, if the user says "I'm tired," the destination input unit can suggest nearby parks or cafes. Furthermore, if the user is feeling like they want to have fun, the destination input unit can also use the emotion estimation function to provide information about events and festivals. Furthermore, if the user is looking for a new experience, the destination input unit can also use the emotion estimation function to suggest new activities or events. In this way, the emotion estimation function can be used to suggest destinations and stopover points according to the user's emotional state.

[0096] The destination input unit can enable tap input on a map using image recognition technology. For example, when a user taps a destination and a relay point on the map, the image recognition technology is used to identify the location and calculate a route. The destination input unit can also input a specific location as a destination when the user taps the location on the map. The destination input unit can also input multiple relay points as relay points when the user taps the multiple relay points on the map. This enables tap input on a map using image recognition technology.

[0097] The destination input unit can integrate destinations and intermediate points input by multiple users simultaneously and propose the optimal route for the group. For example, if multiple users input destinations and intermediate points simultaneously, the information is integrated and the optimal route for the entire group is calculated. Furthermore, if multiple users input different destinations and intermediate points, the destination input unit can integrate them and propose the optimal route. Furthermore, the destination input unit can propose the optimal route taking into account the wishes of the entire group. In this way, the destinations and intermediate points input by multiple users simultaneously can be integrated and the optimal route for the group can be proposed.

[0098] The destination input unit can use the emotion estimation function to analyze the emotion of the user when entering data in real time, thereby providing an interface for reducing stress. For example, if the user is feeling stressed when entering a destination and intermediate points, the emotion estimation function can be used to provide a relaxing interface. The destination input unit can also use the emotion estimation function to analyze the emotion of the user when entering data in real time, thereby providing a user-friendly design. The destination input unit can also use the emotion estimation function to analyze the emotion of the user when entering data in real time, thereby providing an intuitive operation method. In this way, the emotion estimation function can be used to analyze the emotion of the user when entering data in real time, thereby providing an interface for reducing stress.

[0099] The real-time information acquisition unit can use a drone to take aerial photographs of real-time traffic conditions and analyze the data using the generation AI. For example, a drone can be used to take aerial photographs of traffic conditions at major intersections and roads, and the data can be analyzed using the generation AI to provide real-time traffic information. The real-time information acquisition unit can also use a drone to take aerial photographs of traffic accident sites and analyze the data using the generation AI. The real-time information acquisition unit can also use a drone to take aerial photographs of public transportation operation status and analyze the data using the generation AI. This makes it possible to use a drone to take aerial photographs of real-time traffic conditions and analyze the data using the generation AI.

[0100] The real-time information acquisition unit can use the emotion estimation function to preferentially suggest a route that avoids congestion when the user has the emotion of wanting to avoid congestion. For example, when the user has the emotion of wanting to avoid congestion, the emotion estimation function is used to preferentially suggest a route that avoids congestion. Furthermore, when the user has the emotion of wanting to avoid congestion, the real-time information acquisition unit can also use the emotion estimation function to suggest a route with a lower degree of congestion. Furthermore, when the user has the emotion of wanting to avoid congestion, the real-time information acquisition unit can also use the emotion estimation function to suggest an alternative route to avoid congestion. In this way, when the user has the emotion of wanting to avoid congestion, the emotion estimation function can preferentially suggest a route that avoids congestion.

[0101] The real-time information acquisition unit can take into account weather data in addition to real-time traffic information and propose an optimal route depending on the weather. For example, the real-time information acquisition unit can take into account weather data in addition to real-time traffic information and propose a route with a roof on a rainy day. The real-time information acquisition unit can also take into account weather data and propose a route that is less slippery on a snowy day. The real-time information acquisition unit can also take into account weather data and propose a route that avoids the wind on a windy day. In this way, the optimal route depending on the weather can be proposed taking into account weather data in addition to real-time traffic information.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The destination input unit inputs the user's destination. For example, if the user inputs "Tell me the route from the station to the museum," the destination input unit inputs the destination based on this information. The destination input unit can also allow the user to input the destination by voice. Step 2: The relay point input unit inputs the relay points of the user. For example, if the user inputs "I'd like to stop by a cafe on the way," the relay point input unit inputs relay points based on this information. The relay point input unit can also allow the user to input relay points by voice. Step 3: The real-time information acquisition unit acquires real-time pedestrian flow and traffic information. For example, the real-time information acquisition unit acquires data from a traffic information service. It can also acquire data by using a drone to take aerial photographs of traffic conditions. It can also collect operation data of public transportation in real time. Step 4: The route calculation unit calculates the optimal route based on the information acquired by the real-time information acquisition unit. For example, it calculates a route that avoids traffic jams and congestion. It can also calculate the optimal route taking weather data into account. It can also calculate the optimal route based on the user's emotional state. Step 5: The customized recommendation unit performs people flow prediction analysis and provides customized recommendations for the user. For example, it can learn the user's past travel history and automatically suggest frequently visited places. It can also analyze social media posting data to predict events and gatherings. Furthermore, it can use emotion estimation functions to predict people flow based on the user's emotional state and suggest routes that reduce stress. Step 6: The information summarization unit summarizes real-time information. For example, it can display traffic and weather information in a concise format. It can also automatically prioritize information based on its importance. It can also add a voice notification function, allowing users to obtain information hands-free. Step 7: The recommendation unit predicts the user's needs and recommends stores and products along the way. For example, it analyzes the user's purchase history and makes recommendations based on past purchasing patterns. It can also analyze social media activity and make recommendations based on interests and concerns. Furthermore, it can use emotion estimation functions to recommend products and services that match the user's specific emotional state.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a destination input unit for inputting a user's destination; a relay point input section for inputting relay points; a real-time information acquisition unit that acquires real-time people flow and traffic information; a route calculation unit that calculates an optimal route based on the information acquired by the real-time information acquisition unit; a customized recommendation unit that performs people flow prediction analysis and provides customized recommendations to users; an information summarizing unit that summarizes real-time information; A recommendation unit that predicts user needs and recommends stores and products along the way. A system characterized by:

2. The real-time information acquisition unit Using drones to capture real-time aerial images of traffic conditions, and analyzing the data using AI The system of claim 1 .

3. The customized recommendation unit: Analyzing social media posting data to predict events and gatherings The system of claim 1 .

4. The information summarizing unit When summarizing the real-time information, the priority of the information is automatically set according to importance. The system of claim 1 .

5. The recommendation unit Using an emotion estimation function, when the user is in a specific emotional state, the product or service that matches the emotion is recommended. The system of claim 1 .

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