System

Abstract

A system is provided.SOLUTION: A system includes means for acquiring a destination and a relay point input by a user, means for collecting people flow data in real time from a Wi-Fi access point, a Bluetooth beacon, camera video analysis, and the like, means for acquiring traffic information from a traffic information service, means for analyzing people flow and a traffic situation on the basis of the collected data, means for calculating an optimal route related to the destination and the relay point of the user, and means for presenting the calculated optimal route to the user.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] Current navigation systems do not fully reflect real-time pedestrian and traffic information, resulting in frequent congestion and traffic jams. To solve this problem, it is necessary to develop a system that provides optimal route guidance based on real-time collected data and predictive analysis, simply by inputting the user's destination and intermediate points. It is also necessary to predict the user's needs and recommend stores and products along the way, making travel and transportation more convenient and satisfying for users. [Means for solving the problem]

[0005] To solve this problem, the present invention provides a system that includes the following means: First, a means for acquiring a destination and intermediate points entered by a user is provided. Next, a means for collecting people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera image analysis, etc., and a means for acquiring traffic information from a traffic information service is provided. Next, a means for analyzing people flow and traffic conditions based on this data is provided, and a means for calculating an optimal route for the user's destination and intermediate points is provided. Next, a means for presenting the calculated optimal route to the user is provided. Furthermore, by adding a means for recommending stores and products along the way to the user based on the analysis results, the user's purchasing motivation can be encouraged and the travel satisfaction can be increased. Furthermore, by including a means for collecting user feedback and reflecting it in the next service, the accuracy and reliability of the system are improved.

[0006] "User" refers to an individual who uses the system to input destinations and intermediate points.

[0007] "Destination" refers to a location that the user sets as the destination.

[0008] A "way point" refers to a point that a user passes through before reaching a destination.

[0009] "Wi-Fi access point" refers to a device that provides wireless internet connectivity and is used to collect people flow data.

[0010] "Bluetooth beacon" refers to a device that uses Bluetooth technology to provide device location and people flow information.

[0011] "Camera image analysis" refers to the technology of analyzing video data acquired by a camera to understand people flow and traffic conditions.

[0012] "Traffic information services" refers to external services or systems that provide real-time traffic conditions and congestion information.

[0013] "People flow data" refers to data that shows the movements and stay patterns of people within a specific area.

[0014] "Predictive analytics" refers to the technology of predicting future situations based on past data.

[0015] An "optimal route" refers to a travel route that maximizes travel time and convenience based on the destination and intermediate points.

[0016] "Route guidance" refers to the means of providing information to a user about a calculated optimal route.

[0017] "Recommendation" refers to a function that recommends stores and products along the way to users.

[0018] "Feedback" refers to the thoughts and opinions that users provide to the system, which are used to improve the system. [Brief explanation of the drawings]

[0019] [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. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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, a 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), and an APU (Accelerated Processing Unit).

[0023] 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.

[0024] 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.

[0025] 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), Bluetooth (registered trademark), etc.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0031] 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.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The system of the present invention generates an optimal route based on the destination and intermediate points entered by the user, taking into account real-time people flow data and traffic information, and guides the user along the route.The system also aims to make travel more convenient and stimulate purchasing by predicting the user's needs and recommending stores and products along the way.

[0041] An embodiment of this system will be described below with specific examples.

[0042] First, the user launches the dedicated application on a device such as a smartphone or tablet, and inputs "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination. This input data is sent from the device to the server.

[0043] When the server receives the transmitted data, it first collects real-time people flow data from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc. In addition, it obtains real-time data such as traffic conditions, congestion information, and public transport delay information from traffic information services.

[0044] The server then analyzes the collected data to determine the current congestion situation around Tokyo Station, Shimbashi, and Ginza. This analysis combines the number of connected devices obtained from Wi-Fi access points, the signal strength of Bluetooth beacons, and people flow analysis data from camera footage to determine the latest situation at each location where users move in real time. Machine learning models are also applied based on past data to predict congestion and traffic conditions over the next few hours.

[0045] The server then calculates the optimal route from the departure point specified by the user to the destination via intermediate points. This optimal route is calculated using route search algorithms such as Dijkstra's algorithm or A algorithm. The calculated optimal route is sent from the server to the device, and the device displays specific route guidance to the user. For example, guidance such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza" may be displayed.

[0046] Furthermore, the server will recommend stores and products that will be useful during the journey based on the user's past travel history and interest data. For example, it will suggest recommended cafes to stop by in Shimbashi or shopping spots in Ginza. This information will also be sent to the device and notified to the user.

[0047] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0048] By implementing the above flow, the system of the present invention can provide the user with efficient and convenient travel guidance and suggest useful information along the way.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] The user launches the application on their smartphone or tablet and enters "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination.

[0052] Step 2:

[0053] The terminal sends the user's input data (starting point, intermediate points, and destination) to the server.

[0054] Step 3:

[0055] The server analyzes the received user input data and starts obtaining the real-time data that will be required from now on.

[0056] Step 4:

[0057] The server collects information about connected devices from Wi-Fi access points around Tokyo Station, Shinbashi, and Ginza.

[0058] Step 5:

[0059] The server collects information on the flow of people at each location from Bluetooth beacons, which allows the degree of congestion at each location to be determined.

[0060] Step 6:

[0061] The server accesses the camera video analysis system, acquires video data from each location, and analyzes people flow data.

[0062] Step 7:

[0063] The server uses the traffic information service API to obtain real-time traffic congestion information and public transport delay information.

[0064] Step 8:

[0065] The server integrates the collected data and analyzes the current congestion and traffic conditions at Tokyo Station, Shimbashi, and Ginza, and also applies machine learning models based on past data to predict congestion and traffic conditions over the next few hours.

[0066] Step 9:

[0067] The server calculates the optimal route from the starting point to the intermediate points and the destination based on the user's input data and real-time and predicted data. Here, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[0068] Step 10:

[0069] The server sends the calculated optimal route guidance to the terminal.

[0070] Step 11:

[0071] The device displays the optimal route guidance it has received to the user. For example, specific route instructions such as "Take the Yamanote Line from Tokyo Station to Shinbashi, then walk from Shinbashi to Ginza" are displayed.

[0072] Step 12:

[0073] The server recommends stores and products along the way based on the user's past travel history and interest data.

[0074] Step 13:

[0075] The server sends recommendation information to the device, which then notifies the user. For example, users can receive push notifications about cafes to stop by in Shimbashi or shopping spots in Ginza.

[0076] Step 14:

[0077] After arriving in Ginza, users can enter feedback within the app about the accuracy and convenience of the route guidance.

[0078] Step 15:

[0079] The terminal sends the user's feedback to the server.

[0080] Step 16:

[0081] The server stores the collected feedback data and analyzes it to improve our services in the future.

[0082] Example 1

[0083] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0084] In today's urban areas, traffic congestion and congestion are constantly changing, making it difficult for users to reach their destinations efficiently. While providing useful information and store suggestions while traveling would improve user convenience and encourage purchasing, such information provision is lacking. Existing systems have limited real-time data collection and analysis capabilities, and do not adequately provide optimal route guidance and recommendations tailored to user needs. Furthermore, they lack a mechanism for collecting user feedback and incorporating it into future service improvements.

[0085] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0086] In this invention, the server includes: a means for acquiring a destination and intermediate points entered by a user; a means for transmitting data from a terminal to the server using a communication network; a means for collecting people flow data in real time from Wi-Fi access points, short-range wireless communication beacons, video analysis devices, etc.; a means for acquiring traffic information from a traffic information service; a means for analyzing people flow and traffic conditions based on the collected data; a means for predicting congestion and traffic conditions using the analyzed data and a prediction algorithm; a means for calculating an optimal route for the user's destination and intermediate points; and a means for presenting the calculated optimal route to the user. This allows users to reach their destination efficiently and also receive useful store and product information along the way. Furthermore, the quality of service can be continuously improved based on feedback, thereby achieving higher user satisfaction.

[0087] "User" refers to an individual or corporation that uses the system to input a destination and intermediate points and receives suggestions for the optimal route, stores, and products.

[0088] "Destination" refers to the final destination that the user enters into the system.

[0089] A "waypoint" refers to a point that a user passes through on the way to their destination.

[0090] The term "means for acquiring" refers to a device or program for recording the destination and intermediate points entered by the user and transmitting them to the server.

[0091] A "communications network" is an infrastructure for transmitting data, including the Internet, wireless communications, cellular networks, etc.

[0092] "Terminal" refers to a portable information and communication device used by a user, such as a smartphone or tablet.

[0093] A "server" refers to a remote computer system that processes large amounts of data, handles user queries, and analyzes data.

[0094] "Wi-Fi access point" refers to a physical device or endpoint for connecting to the Internet via a wireless network.

[0095] A "near field communication beacon" is a device that uses short-range wireless communication technology such as Bluetooth to communicate with devices within a certain range.

[0096] "Video analysis device" refers to a device or software that analyzes camera footage to grasp the flow of people and congestion conditions in real time.

[0097] "Traffic information service" refers to an external information service that provides real-time information on traffic conditions and public transportation.

[0098] "Means for analysis" refers to a device or program that analyzes people flow and traffic conditions based on collected data and grasps current and future congestion conditions.

[0099] "Predictive algorithms" refer to mathematical methods that use machine learning models and statistical methods to predict future congestion and traffic conditions.

[0100] The "optimal route" refers to the most efficient route when a user travels from a starting point to a destination via intermediate points.

[0101] "Calculation means" refers to a device or program for deriving the optimal route based on the user's input data and the analysis results.

[0102] "Presenting means" refers to a device or program for visually or audibly notifying the user of the calculated optimal route or recommendation information.

[0103] The "recommending means" refers to a device or program for recommending stores and products that are useful to the user during their travels.

[0104] "Means for collection" refers to a device or program used to collect user feedback and improve the service based on that data.

[0105] The system of this invention collects real-time people flow data and traffic information based on the starting point, intermediate points, and destination entered by the user, and calculates and provides the optimal route. It also aims to make travel more convenient and stimulate purchasing by predicting the user's needs and recommending stores and products along the way.

[0106] First, the user launches the dedicated application on a device such as a smartphone or tablet, and inputs "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination. This input data is sent from the device to the server.

[0107] When the server receives the transmitted data, it first collects real-time people flow data from Wi-Fi access points, short-range wireless communication beacons, video analysis devices, etc. In addition, it obtains real-time data such as traffic conditions, congestion information, and public transport delay information from traffic information services.

[0108] The server then analyzes the collected data to determine the current congestion situation around Tokyo Station, Shimbashi, and Ginza. This analysis combines the number of connected devices obtained from Wi-Fi access points, the signal strength of short-range communication beacons, and people flow analysis data from video analytics equipment to determine the latest situation at each location where users are moving in real time. Machine learning models are also applied based on past data to predict congestion and traffic conditions over the next few hours.

[0109] The server then calculates the optimal route from the departure point specified by the user to the destination via intermediate points. This optimal route is calculated using route search algorithms such as Dijkstra's algorithm or A algorithm. The calculated optimal route is sent from the server to the device, and the device displays specific route guidance to the user. For example, guidance such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza" may be displayed.

[0110] Furthermore, the server will recommend stores and products that will be useful during the journey based on the user's past travel history and interest data. For example, it will suggest recommended cafes to stop by in Shimbashi or shopping spots in Ginza. This information will also be sent to the device and notified to the user.

[0111] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0112] This enables the system of the present invention to provide efficient and convenient travel guidance to users and suggest useful information along the way.

[0113] Examples and prompts:

[0114] For example, the prompt below shows the situation when a user wants to know the best route from Tokyo Station to Ginza via Shimbashi and recommendations for stores to stop at along the way.

[0115] Example prompt sentence:

[0116] User: What is the best route from Tokyo Station to Ginza via Shimbashi, and what stores should I stop at along the way?

[0117] server:

[0118] 1. Take the Yamanote Line from Tokyo Station and get off at Shimbashi.

[0119] 2. Walk from Shimbashi Station to Ginza.

[0120] 3. Recommended cafe near Shimbashi Station: Cafe ABC

[0121] 4. Recommended Shopping Spots in Ginza: Shop XYZ

[0122] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0123] Step 1: User Input

[0124] Users launch a dedicated application on their smartphone or tablet and input their starting point, intermediate points, and destination. For example, they can input "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination.

[0125] Input: Start point, stopover point, and destination information.

[0126] Output: This information is saved in the device.

[0127] Step 2: Send data to the server

[0128] The terminal sends the data entered by the user (starting point, intermediate points, and destination) to the server via a communication network.

[0129] Input: User-entered start, stop, and destination data.

[0130] Output: The data sent to the server.

[0131] Step 3: Data collection by the server

[0132] The server collects real-time people flow data from Wi-Fi access points, short-range wireless communication beacons, video analysis devices, etc. It also obtains congestion and delay information from traffic information services.

[0133] Input: User's destination and intermediate destination data sent to the server.

[0134] Output: Real-time people flow data, traffic information data.

[0135] Step 4: Data analysis

[0136] The server analyzes pedestrian flow and traffic conditions based on the collected data. It integrates data from the number of devices connected to Wi-Fi access points, beacon signal strength, and video analysis equipment to grasp pedestrian flow conditions. It also uses machine learning models to predict future congestion and traffic conditions.

[0137] Input: Real-time people flow data, traffic information data.

[0138] Output: Congestion status, traffic conditions and forecast data for each location.

[0139] Step 5: Optimal route calculation

[0140] The server uses Dijkstra's algorithm or the A algorithm to calculate the optimal route based on user input, taking into account analytical results and forecast data.

[0141] Input: Data on starting point, intermediate points, destination, congestion status, traffic conditions and forecast data.

[0142] Output: Information about the best route.

[0143] Step 6: Send directions

[0144] The server then sends the calculated optimal route to the device, which uses this information to provide real-time route guidance to the user.

[0145] Input: The optimal route calculated by the server.

[0146] Output: The optimal route information sent to the device.

[0147] Step 7: Recommendations

[0148] The server then recommends stores and products that will be useful during the journey based on the user's past travel history and interest data, and this information is also later sent to the device.

[0149] Input: User's past travel history, interest data.

[0150] Output: Information about recommended stores and products.

[0151] Step 8: Submit your recommendation

[0152] The server transmits the recommendation information to the terminal, and the terminal notifies the user of this information.

[0153] Input: Information recommended by the server.

[0154] Output: Recommendation information sent to the device.

[0155] Step 9: Users provide feedback

[0156] After arriving at their destination, users provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0157] Input: Feedback information from the user.

[0158] Output: Feedback information sent to the server, service improvement data.

[0159] (Application example 1)

[0160] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0161] An efficient and safe navigation system is important for modern autonomous vehicles, but conventional systems are unable to fully reflect real-time people flow data and traffic information, making it difficult to provide optimal route guidance or store and product recommendations that meet user needs. Overcoming these shortcomings and providing users with a higher quality travel experience is essential.

[0162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0163] In this invention, the server includes means for acquiring destinations and waypoints entered by a user, means for collecting people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera image analysis, etc., means for acquiring traffic information from traffic information services, means for analyzing people flow and traffic conditions, means for calculating an optimal route for the user's destination and waypoints, means for presenting the calculated optimal route to the user, and means for being incorporated into the navigation system of the autonomous vehicle and providing route guidance. This enables optimal route guidance based on real-time data and can also recommend stores and products along the way to the user.

[0164] "Destination" is the location that the user sets as the destination.

[0165] A "waypoint" is a place where a user stops on the way to their destination.

[0166] A "Wi-Fi access point" is a device that provides Internet connectivity in a wireless LAN environment.

[0167] A "Bluetooth beacon" is a device that uses Bluetooth technology to detect devices in close proximity.

[0168] "Camera video analysis" is a technology that analyzes video data captured by a camera and extracts specific information.

[0169] "People flow data" is data that represents the movement patterns and density of people within a specific area.

[0170] "Traffic information service" is a service that provides real-time traffic conditions and congestion information.

[0171] The "optimal route" is the most efficient travel route calculated based on the input destination and intermediate points, taking into account real-time data.

[0172] "Navigation system" is a general term for devices and systems that provide route guidance to a destination.

[0173] "Recommendation" refers to recommending specific services or products based on a user's interests and behavioral history.

[0174] "Feedback" means opinions and evaluations provided by users regarding the results and experiences of using the service.

[0175] The system of the present invention is implemented as a navigation system for an autonomous vehicle. First, the user inputs the destination and intermediate points. This input data is sent to a server via a smartphone app or a terminal in the vehicle. The server performs the following processes based on the input data.

[0176] Hardware and software configuration

[0177] Hardware

[0178] Smartphone: A device for inputting a user's destination and intermediate points.

[0179] Navigation system for autonomous vehicles: A system for guiding users to their destination.

[0180] Server: A central control unit for data analysis and route calculation.

[0181] software

[0182] Route search algorithm: Calculates the optimal route using Dijkstra's algorithm or A algorithm.

[0183] Data analysis software: Software for analyzing real-time people flow data and traffic information.

[0184] Recommendation engine: Recommends products and services based on the user's interests.

[0185] Data collection and analysis

[0186] The server collects real-time pedestrian flow data from Wi-Fi access points, Bluetooth beacons, and camera image analysis. It also obtains traffic conditions and congestion information from traffic information services. Based on this data, it analyzes the current situation at each point the user moves and calculates the optimal route.

[0187] Calculating the best route

[0188] Based on the collected data, the server applies Dijkstra's algorithm and A algorithm to calculate the optimal route for the user's destination and intermediate points. The calculated route is immediately sent to the user's smartphone or the navigation system of the autonomous vehicle.

[0189] Route guidance and service recommendations

[0190] The navigation system displays the calculated optimal route for the user and provides guidance. At the same time, the server recommends stores and products that the user can stop by along the way based on the user's past travel history and interest data. This information is notified to the user in real time via their smartphone or navigation system.

[0191] Specific examples

[0192] For example, if a user travels from Tokyo Station to Ginza and sets Shinbashi as a stopover, the server receives this information and collects real-time people flow data and traffic information. As a result, it can recommend the optimal route using the Yamanote Line and cafes that can be visited in Shinbashi.

[0193] Prompt Sentence Examples

[0194] User: Please tell me the route from "Tokyo Station" to "Ginza". I would like to stop at "Shinbashi" on the way.

[0195] System: Checking current congestion and traffic information...

[0196] System: Take the Yamanote Line from Tokyo Station, get off at Shimbashi Station, stop at the "Cafe Break" cafe, then walk to Ginza. The system calculates the optimal route, taking into account traffic information and congestion.

[0197] System: The address of the cafe "Cafe Break" is 5-4-1 Shinbashi, and its business hours are 9:00-21:00.

[0198] In this way, the system of the present invention can provide efficient and convenient travel guidance to users and suggest useful information along the way.

[0199] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0200] Step 1:

[0201] The user inputs the destination and intermediate points using a smartphone or a terminal in the vehicle. This user input data is sent from the terminal to the server. The input data includes the destination, starting point, and intermediate points.

[0202] Step 2:

[0203] The server receives input data sent by the user. It then collects people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc. It also obtains the latest traffic information using traffic information services. The input here is data from Wi-Fi access points and traffic information services, and people flow and traffic data is output based on this data.

[0204] Step 3:

[0205] The server analyzes the congestion and traffic conditions related to the user's destination and intermediate points based on the collected real-time people flow data and traffic information. Using data analysis software, this data is processed and calculated to grasp the latest conditions at each point. The output of this step is congestion forecast data for each point.

[0206] Step 4:

[0207] The server uses the analyzed data to calculate the optimal route for the destination and intermediate points specified by the user using Dijkstra's algorithm or A algorithm. The input is the congestion forecast data obtained in step 3, and the output is the route information for the optimal route.

[0208] Step 5:

[0209] The calculated optimal route is sent to the user's smartphone or the autonomous vehicle's navigation system. The device displays the route information of the received optimal route to the user and provides detailed route guidance. The output of this step is the route guidance information displayed on the navigation screen.

[0210] Step 6:

[0211] The server recommends stores and products that are useful during travel based on the user's past travel history and interest data. A recommendation engine is used to analyze this interest data and generate information on the most suitable stores and products. The input is the user's past data, and the output is the suggested store and product information.

[0212] Step 7:

[0213] After arriving at the destination, the user provides feedback through the app about the accuracy and convenience of the route guidance. The device sends this feedback data to the server, which analyzes it and reflects it in the next service improvement. The output of this step is the analyzed feedback data.

[0214] Through the above process, users can receive useful information along with optimal route guidance in real time.

[0215] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0216] The system of the present invention generates an optimal route based on the destination and intermediate points entered by the user, taking into account real-time people flow data and traffic information, and provides guidance to the user.In addition, by combining it with an emotion engine that recognizes the user's emotional state, it provides even more personalized route guidance and recommendations.

[0217] First, the user launches the dedicated application on a device such as a smartphone or tablet, and inputs "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination. This input data is sent from the device to the server.

[0218] When the server receives the transmitted data, it first collects real-time people flow data from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc. In addition, it obtains real-time data such as traffic conditions, congestion information, and public transport delay information from traffic information services.

[0219] The server then analyzes the collected data to determine the current congestion situation around Tokyo Station, Shimbashi, and Ginza. This analysis combines the number of Wi-Fi connected devices, Bluetooth beacon signal strength, and people flow analysis data from camera footage to determine the latest situation at each location where users move in real time. Machine learning models are also applied based on past data to predict congestion and traffic conditions over the next few hours.

[0220] The server then calculates the optimal route from the departure point specified by the user to the destination via intermediate points. This optimal route is calculated using route search algorithms such as Dijkstra's algorithm or A algorithm. The calculated optimal route is sent from the server to the device, and the device displays specific route guidance to the user. For example, guidance such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza" may be displayed.

[0221] Furthermore, the server will recommend stores and products that will be useful during the journey based on the user's past travel history and interest data. For example, it will suggest recommended cafes to stop by in Shimbashi or shopping spots in Ginza. This information will also be sent to the device and notified to the user.

[0222] The present invention further provides a form that incorporates an emotion engine. The emotion engine recognizes emotions from the user's facial expressions and voice, and grasps the user's real-time emotional state. Depending on this emotional state, the server recalculates the optimal route and optimizes the recommendation information. For example, if the user is tired, the server can provide a more comfortable and stress-free travel route or recommend a cafe or rest area where the user can refresh themselves.

[0223] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0224] By implementing the above process, the system of the present invention can provide users with efficient and convenient travel guidance, and by using an emotion engine, it can provide more personalized travel guidance and recommendations.

[0225] The processing flow will be explained below.

[0226] Step 1:

[0227] The user launches the application on their smartphone or tablet and enters "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination.

[0228] Step 2:

[0229] The terminal sends the user's input data (starting point, intermediate points, and destination) to the server.

[0230] Step 3:

[0231] The server analyzes the received user input data and starts obtaining the required real-time data.

[0232] Step 4:

[0233] The server collects information about connected devices from Wi-Fi access points around Tokyo Station, Shinbashi, and Ginza.

[0234] Step 5:

[0235] The server collects information on the flow of people at each location from Bluetooth beacons, which allows the degree of congestion at each location to be determined.

[0236] Step 6:

[0237] The server accesses the camera video analysis system, acquires video data from each location, and analyzes people flow data.

[0238] Step 7:

[0239] The server uses the traffic information service API to obtain real-time traffic congestion information and public transport delay information.

[0240] Step 8:

[0241] The server integrates the collected data and analyzes the current congestion and traffic conditions at Tokyo Station, Shimbashi, and Ginza, and also applies machine learning models based on past data to predict congestion and traffic conditions over the next few hours.

[0242] Step 9:

[0243] The server calculates the optimal route from the starting point to the intermediate points and the destination based on the user's input data and real-time and predicted data. Here, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[0244] Step 10:

[0245] The server sends the calculated optimal route guidance to the terminal.

[0246] Step 11:

[0247] The device displays the optimal route guidance it has received to the user. For example, specific route instructions such as "Take the Yamanote Line from Tokyo Station to Shinbashi, then walk from Shinbashi to Ginza" are displayed.

[0248] Step 12:

[0249] The device uses the user's facial recognition and voice input functions to detect their current emotional state. For example, it can obtain emotional information such as whether the user is tired or stressed from their facial expression or tone of voice.

[0250] Step 13:

[0251] The emotion data acquired by the device is transmitted to the server.

[0252] Step 14:

[0253] The server analyzes the emotional data and understands the user's current emotional state.

[0254] Step 15:

[0255] The server recalculates the optimal route based on the user's emotional state: for example, if the user is tired, a more comfortable and less congested route will be suggested.

[0256] Step 16:

[0257] The server generates store and product recommendations based on the user's emotional state, suggesting, for example, relaxing cafes and comfortable rest areas.

[0258] Step 17:

[0259] The server sends the recalculated optimal route and recommendation information to the terminal.

[0260] Step 18:

[0261] The device displays the recalculated optimal route guidance and recommendation information to the user.

[0262] Step 19:

[0263] After arriving at their destination, users can enter feedback within the app about the accuracy and convenience of the route guidance.

[0264] Step 20:

[0265] The terminal sends the user's feedback to the server.

[0266] Step 21:

[0267] The server stores the collected feedback data and analyzes it to improve our services in the future.

[0268] Example 2

[0269] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0270] Conventional navigation systems have difficulty providing optimal routes that take into account real-time people flow data and traffic information. Furthermore, they lack a means to provide personalized route guidance and recommendations that take into account the user's emotional state. As a result, users are prone to feeling stressed, and there are problems with reduced convenience during travel.

[0271] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0272] In this invention, the server includes means for acquiring destinations and intermediate points entered by a user, means for collecting people flow data in real time from wireless communication devices, location information beacons, image analysis devices, etc., means for acquiring traffic information from external information provision services, means for analyzing people flow and traffic conditions based on the collected data, means for calculating an optimal route for the user's destination and intermediate points, means for presenting the calculated optimal route to the user, and means for recognizing emotions from the user's facial expressions and voice and optimizing route and recommendation information based on the recognized emotions. This makes it possible to provide efficient and convenient travel guidance taking real-time data into consideration, and furthermore, by using an emotion engine, it becomes possible to provide more personalized travel guidance and recommendations to the user.

[0273] "User" means an individual or corporation that uses the system.

[0274] "Destination" is the final destination to which the user wishes to travel.

[0275] A "waypoint" is a point that a user passes through on the way to the destination.

[0276] A "wireless communication device" is a device such as a Wi-Fi access point or Bluetooth beacon that collects data within its communication range in real time.

[0277] A "location beacon" is a device that is placed in a specific location and communicates with devices within a certain range to provide location information.

[0278] An "image analysis device" is a device that analyzes camera footage to detect and analyze the movements of objects and people.

[0279] "External information provision services" is a general term for services that provide information such as the operation status of public transportation and road traffic conditions.

[0280] "People flow data" is data that shows the movements and stay patterns of people in a specific area.

[0281] "Traffic information" refers to information about traffic, such as road congestion and delays in public transportation.

[0282] "Analysis" is the process of integrating collected data to identify patterns and trends.

[0283] The "optimal route" is the most efficient and convenient route for the user, taking into consideration travel time and convenience.

[0284] "On the way" means the part of the route from the starting point to the destination.

[0285] A "shop" is a place that provides commercial services.

[0286] "Goods" means any goods or services sold or provided.

[0287] "Recommendation" is the act of suggesting appropriate options based on the user's interests and travel situation.

[0288] "Feedback" refers to users' evaluations and opinions of services and systems.

[0289] An "emotion engine" is a device or system that analyzes a user's facial expressions and voice data to recognize their emotional state.

[0290] "Personalization" refers to providing optimal information and services tailored to the characteristics and circumstances of each individual user.

[0291] "System" is a collective term for a series of hardware and software that integrates the above functions and provides services to users.

[0292] The system of the present invention generates an optimal route based on the destination and intermediate points entered by the user, taking into account real-time people flow data and traffic information, and provides guidance to the user.In addition, by combining it with an emotion engine that recognizes the user's emotional state, it provides even more personalized route guidance and recommendations.

[0293] Hardware and Data Acquisition

[0294] Users use a device such as a smartphone or tablet to launch a dedicated application and input their starting point, intermediate points, and destination. This input data is sent from the device to a server. The server collects real-time people flow data through sensor devices such as Wi-Fi access points, Bluetooth beacons, and image analysis devices (cameras). It also obtains current traffic conditions and delay information from external information services (traffic information services).

[0295] Data analysis and route calculation

[0296] The server integrates and analyzes the collected people flow data and traffic information. Specifically, it integrates the number of connected devices from Wi-Fi access points, the signal strength of Bluetooth beacons, and people flow analysis data from camera footage to understand the current congestion situation at each location. It also applies machine learning models to predict congestion and traffic conditions over the next few hours by comparing this data with past data.

[0297] The server uses a route search algorithm (e.g., Dijkstra's algorithm, A algorithm) to calculate the optimal route from the starting point specified by the user to the destination via intermediate points. This optimal route is sent from the server to the terminal, and specific route guidance is displayed to the user.

[0298] Personalization and Recommendations

[0299] The server recommends stores and products that are useful during travel based on the user's past travel history and interest data. For example, it suggests recommended cafes to stop by at intermediate points or shopping spots near the destination. This information is also sent to the device and notified to the user.

[0300] The emotion engine also recognizes the user's emotions from their facial expressions and voice, and recalculates the optimal route and optimizes recommendation information based on that emotional state. For example, if the user is tired, the server will provide a more comfortable and stress-free route or recommend a cafe or rest area where they can refresh themselves.

[0301] Collecting feedback and improving our services

[0302] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0303] Specific examples

[0304] If a user inputs "From Tokyo Station to Ginza, via Shimbashi, and want to stop by a cafe," the system will provide the optimal route based on real-time data and suggest recommended cafes in Shimbashi.

[0305] "Please provide the best route from Tokyo Station to Ginza via Shimbashi, taking into account real-time traffic and people flow data."

[0306] As explained above, the system of the present invention provides efficient and convenient travel guidance to users, and by using an emotion engine, it is possible to provide more personalized travel guidance and recommendations.

[0307] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0308] Step 1:

[0309] The user launches a dedicated application using a smartphone or tablet and inputs the starting point, intermediate points, and destination.

[0310] Specifically, the user enters "Tokyo Station," "Shinbashi," and "Ginza" into the input fields on the app screen and presses the send button. The input data is sent from the device to the server. The input is the user's destination information, and the output is the data sent to the server.

[0311] Step 2:

[0312] The server receives the transmitted data and collects real-time people flow data from wireless communication devices, location information beacons, image analysis devices, etc.

[0313] Specifically, the server sends a data acquisition request to each sensor device, and the sensor devices return real-time people flow data. The input is the data collected from the sensor devices, and the output is the integrated real-time data.

[0314] Step 3:

[0315] The server acquires traffic information from an external information providing service.

[0316] Specifically, the server sends a request to the traffic information API, and traffic information is returned to the server as an API response. The input is the API request data, and the output is the acquired traffic information.

[0317] Step 4:

[0318] The server integrates the people flow data and traffic information collected and performs analysis.

[0319] Specifically, the server aggregates Wi-Fi, Bluetooth, and camera data and applies analytical algorithms. The input is the aggregated data set, and the output is the latest situational understanding.

[0320] Step 5:

[0321] The server compares the data with past data and uses machine learning models to predict congestion and traffic conditions several hours in the future.

[0322] Specifically, the server inputs current and past data into the machine learning model and outputs future prediction data. The input is past and current data, and the output is the prediction model result.

[0323] Step 6:

[0324] The server calculates the optimal route based on the user's specifications using a route search algorithm.

[0325] Specifically, the server executes Dijkstra's algorithm or A algorithm to generate the optimal route. The input is the destination information specified by the user and the server's analysis results, and the output is the optimal route data.

[0326] Step 7:

[0327] The server transmits the calculated optimum route to the terminal, which then displays specific route guidance to the user.

[0328] Specifically, the terminal screen displays instructions such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza." The input is optimal route data, and the output is the content displayed to the user.

[0329] Step 8:

[0330] The server recommends stores and products that will be useful while traveling based on the user's past travel history and interest data.

[0331] Specifically, the server runs a recommendation algorithm to generate a list of optimal stores and products, and sends it to the terminal. The input is the user's history data, and the output is recommendation information.

[0332] Step 9:

[0333] The terminal receives the recommendation information from the server and notifies the user.

[0334] Specifically, the device displays "recommended cafes in Shimbashi" or "shopping spots in Ginza." The input is recommendation information, and the output is the notification content to the user.

[0335] Step 10:

[0336] The server uses an emotion engine to analyze the user's facial expressions and voice and recognize their emotional state.

[0337] Specifically, it analyzes data captured by the device's camera and microphone and sends the results to a server. The input is the captured facial expression and voice data, and the output is the recognized emotional state.

[0338] Step 11:

[0339] The server recalculates and optimizes the optimal route and recommendation information based on the recognized emotion information.

[0340] Specifically, the server uses the data obtained by the emotion engine to adjust the route and recommendation information and send it to the device. The input is the recognized emotion information, and the output is the optimized route and recommendation information.

[0341] Step 12:

[0342] Once the user arrives at their destination, they will be provided with feedback on the accuracy and convenience of the route guidance and service.

[0343] Specifically, a feedback input form is displayed on the terminal, and the user enters and submits their evaluation or comment. The input is the user's feedback data, and the output is the data to be sent to the server.

[0344] Step 13:

[0345] The server analyzes the feedback data and reflects it in the next service improvement.

[0346] Specifically, the server analyzes the feedback data, identifies problems and areas for improvement, and updates the system's algorithms and data. The input is the feedback data, and the output is updated system information.

[0347] (Application example 2)

[0348] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0349] Conventional route guidance systems provide optimal routes based on traffic information and people flow data, but they are unable to provide personalized guidance or recommendations that take into account the user's emotional state. As a result, even if the user is tired or stressed, they can only present a standard route, making it difficult to provide a comfortable travel experience that suits the user's situation. In addition, there are insufficient means to collect user feedback and use it to improve services.

[0350] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring a destination and intermediate points entered by a user, means for collecting people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera image analysis, etc., means for acquiring traffic information from a traffic information service, means for recognizing the user's emotional state, means for calculating an optimal route and personalized recommended information based on the emotional state, and means for presenting the calculated optimal route and recommended information to the user. This makes it possible to provide more personalized route guidance and recommendations that take the user's emotional state into consideration, significantly improving the user's travel experience.

[0351] The "means for acquiring the destination and intermediate points entered by the user" is a function that receives the departure point, intermediate points, and destination specified by the user via an electronic device and transmits them to the server.

[0352] "Means of collecting people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc." refers to a method of understanding people's movements and congestion levels in specific locations in real time using wireless communication technology and video analysis technology.

[0353] "Means for obtaining traffic information from traffic information services" refers to a method for collecting real-time traffic data from external services that provide information on traffic conditions, congestion, delays in public transportation, and the like.

[0354] "Means for analyzing people flow and traffic conditions based on collected data" refers to the process of using acquired people flow data and traffic information to analyze congestion and traffic conditions at each location and generate information optimal for the user's travel.

[0355] "Means for recognizing the user's emotional state" refers to technology that analyzes the user's facial expressions and voice to identify their emotions at that time.

[0356] "Means for calculating optimal routes and personalized recommended information based on emotional state" refers to a function that calculates and presents information such as the most comfortable and least stressful route, stores and rest areas suitable for the user, etc., based on the recognized emotions.

[0357] "Means for presenting the calculated optimal route and recommended information to the user" refers to a method for displaying the calculated route guidance and recommended information on the user's device.

[0358] "Means of recommending stores and rest areas to users along the way based on analysis results and emotional state" is a function that recommends the most suitable stores and rest areas that users can stop at along the way based on real-time data and emotional state.

[0359] "Means of collecting feedback from users and reflecting it in the next service" refers to the process of collecting opinions and impressions provided by users and using them to improve the service.

[0360] The system for realizing this application example operates by combining multiple pieces of hardware and software.

[0361] First, the user starts a dedicated application on a device such as a smartphone or tablet and inputs the starting point, intermediate points, and destination. This data is then sent from the device to the server.

[0362] The server receives the transmitted data and collects people flow data in real time using Wi-Fi access points, Bluetooth beacons, camera image analysis, etc. It also obtains the latest traffic conditions, congestion information, and public transport delay information from traffic information services.

[0363] Furthermore, the server runs an emotion engine to analyze the user's facial expressions and voice and recognize their emotional state. This emotion engine uses a software library called "face_recognition" and an audio emotion recognition module called "AudioEmotionRecognition."

[0364] Based on the collected real-time data, traffic information, and the user's emotional state, the server calculates the optimal route and personalized recommendations. The algorithms used for this calculation include Dijkstra's algorithm and A algorithm. The calculated optimal route and recommendations are sent from the server to the device and presented to the user.

[0365] As a concrete example, consider the case where a user travels from "Tokyo Station" to "Ginza" and takes a break in "Shinbashi." If the emotion engine recognizes the user's state as "tired," the server will select a less crowded route and recommend cafes and rest areas that the user can stop at in Shinbashi. Conversely, if the user is recognized as "happy," the normal optimal route will be selected.

[0366] After arriving at their destination, users can provide feedback on the accuracy and usefulness of the route guidance and recommendations. This feedback data is also sent to the server, which analyzes it and reflects it in future service improvements.

[0367] This system can provide more comfortable and personalized travel guidance that takes into account the user's emotional state.

[0368] Examples of prompts:

[0369] A user specifies that they want to travel from "Tokyo Station" to "Ginza" and enters "Shinbashi" as a stopover point. This user may be tired. Obtain real-time people flow data and traffic information, and suggest the best route and rest stops based on their emotional state.

[0370] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0371] Step 1:

[0372] The user launches a dedicated application on their smartphone or tablet and inputs the starting point, intermediate points, and destination. The input data is sent from the device to the server. The input data includes the starting point "Tokyo Station," the intermediate point "Shinbashi," and the destination "Ginza," and this is the output data sent to the server.

[0373] Step 2:

[0374] The server receives the input data and collects real-time people flow data from Wi-Fi access points, Bluetooth beacons, and camera video analysis. This allows for a grasp of the congestion situation in a specific area. Data processing involves analyzing the number of Wi-Fi connected devices, Bluetooth beacon signal strength, and camera video data, and the resulting real-time people flow data is generated.

[0375] Step 3:

[0376] The server obtains real-time traffic data from the traffic information service. This traffic data includes information on road congestion, delays in public transportation, etc. The collected traffic data becomes input data for understanding the real-time situation.

[0377] Step 4:

[0378] Based on the collected people flow and traffic data, the server analyzes the specific congestion and traffic conditions at each location. Data processing here includes congestion prediction using machine learning models learned from past data. The output is the analyzed current people flow and traffic condition data.

[0379] Step 5:

[0380] The server recognizes the user's emotional state from their facial expressions and voice. The "face_recognition" library and "AudioEmotionRecognition" module are used for emotion recognition. The input requires the user's image data and voice data, and the output is the user's emotional state (e.g., "tired" or "happy").

[0381] Step 6:

[0382] The server calculates optimal routes and personalized recommendations based on the emotional state. Using route-finding algorithms such as Dijkstra's algorithm and A-algorithm, and taking the emotional state into account, it suggests routes that avoid crowds and rest spots where people can relax. Input data includes analyzed pedestrian flow and traffic data and the emotional state, and the output includes optimal routes and recommendations.

[0383] Step 7:

[0384] The calculated optimal route and recommended information are sent from the server to the device and presented to the user. The device receives this information and displays specific route guidance and recommended information to the user. For example, the optimal route guidance may be something like "take the Yamanote Line from Tokyo Station to Shinbashi, then walk from Shinbashi to Ginza."

[0385] Step 8:

[0386] After arriving at their destination, users provide feedback on route guidance and recommendations through the application. This feedback is sent from the device to the server, which analyzes the data and uses it to improve the service for the next time. The analyzed data is used to improve the next route calculation and recommendation algorithm.

[0387] 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.

[0388] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0389] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0390] [Second embodiment]

[0391] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0392] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0393] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0394] 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.

[0395] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0396] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0397] 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.

[0398] 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.

[0399] 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 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.

[0400] 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.

[0401] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0402] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0403] The system of the present invention generates an optimal route based on the destination and intermediate points entered by the user, taking into account real-time people flow data and traffic information, and guides the user along the route.The system also aims to make travel more convenient and stimulate purchasing by predicting the user's needs and recommending stores and products along the way.

[0404] An embodiment of this system will be described below with specific examples.

[0405] First, the user launches the dedicated application on a device such as a smartphone or tablet, and inputs "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination. This input data is sent from the device to the server.

[0406] When the server receives the transmitted data, it first collects real-time people flow data from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc. In addition, it obtains real-time data such as traffic conditions, congestion information, and public transport delay information from traffic information services.

[0407] The server then analyzes the collected data to determine the current congestion situation around Tokyo Station, Shimbashi, and Ginza. This analysis combines the number of connected devices obtained from Wi-Fi access points, the signal strength of Bluetooth beacons, and people flow analysis data from camera footage to determine the latest situation at each location where users move in real time. Machine learning models are also applied based on past data to predict congestion and traffic conditions over the next few hours.

[0408] The server then calculates the optimal route from the departure point specified by the user to the destination via intermediate points. This optimal route is calculated using route search algorithms such as Dijkstra's algorithm or A algorithm. The calculated optimal route is sent from the server to the device, and the device displays specific route guidance to the user. For example, guidance such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza" may be displayed.

[0409] Furthermore, the server will recommend stores and products that will be useful during the journey based on the user's past travel history and interest data. For example, it will suggest recommended cafes to stop by in Shimbashi or shopping spots in Ginza. This information will also be sent to the device and notified to the user.

[0410] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0411] By implementing the above flow, the system of the present invention can provide the user with efficient and convenient travel guidance and suggest useful information along the way.

[0412] The processing flow will be explained below.

[0413] Step 1:

[0414] The user launches the application on their smartphone or tablet and enters "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination.

[0415] Step 2:

[0416] The terminal sends the user's input data (starting point, intermediate points, and destination) to the server.

[0417] Step 3:

[0418] The server analyzes the received user input data and starts obtaining the real-time data that will be required from now on.

[0419] Step 4:

[0420] The server collects information about connected devices from Wi-Fi access points around Tokyo Station, Shinbashi, and Ginza.

[0421] Step 5:

[0422] The server collects information on the flow of people at each location from Bluetooth beacons, which allows the degree of congestion at each location to be determined.

[0423] Step 6:

[0424] The server accesses the camera video analysis system, acquires video data from each location, and analyzes people flow data.

[0425] Step 7:

[0426] The server uses the traffic information service API to obtain real-time traffic congestion information and public transport delay information.

[0427] Step 8:

[0428] The server integrates the collected data and analyzes the current congestion and traffic conditions at Tokyo Station, Shimbashi, and Ginza, and also applies machine learning models based on past data to predict congestion and traffic conditions over the next few hours.

[0429] Step 9:

[0430] The server calculates the optimal route from the starting point to the intermediate points and the destination based on the user's input data and real-time and predicted data. Here, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[0431] Step 10:

[0432] The server sends the calculated optimal route guidance to the terminal.

[0433] Step 11:

[0434] The device displays the optimal route guidance it has received to the user. For example, specific route instructions such as "Take the Yamanote Line from Tokyo Station to Shinbashi, then walk from Shinbashi to Ginza" are displayed.

[0435] Step 12:

[0436] The server recommends stores and products along the way based on the user's past travel history and interest data.

[0437] Step 13:

[0438] The server sends recommendation information to the device, which then notifies the user. For example, users can receive push notifications about cafes to stop by in Shimbashi or shopping spots in Ginza.

[0439] Step 14:

[0440] After arriving in Ginza, users can enter feedback within the app about the accuracy and convenience of the route guidance.

[0441] Step 15:

[0442] The terminal sends the user's feedback to the server.

[0443] Step 16:

[0444] The server stores the collected feedback data and analyzes it to improve our services in the future.

[0445] Example 1

[0446] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0447] In today's urban areas, traffic congestion and congestion are constantly changing, making it difficult for users to reach their destinations efficiently. While providing useful information and store suggestions while traveling would improve user convenience and encourage purchasing, such information provision is lacking. Existing systems have limited real-time data collection and analysis capabilities, and do not adequately provide optimal route guidance and recommendations tailored to user needs. Furthermore, they lack a mechanism for collecting user feedback and incorporating it into future service improvements.

[0448] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0449] In this invention, the server includes: a means for acquiring a destination and intermediate points entered by a user; a means for transmitting data from a terminal to the server using a communication network; a means for collecting people flow data in real time from Wi-Fi access points, short-range wireless communication beacons, video analysis devices, etc.; a means for acquiring traffic information from a traffic information service; a means for analyzing people flow and traffic conditions based on the collected data; a means for predicting congestion and traffic conditions using the analyzed data and a prediction algorithm; a means for calculating an optimal route for the user's destination and intermediate points; and a means for presenting the calculated optimal route to the user. This allows users to reach their destination efficiently and also receive useful store and product information along the way. Furthermore, the quality of service can be continuously improved based on feedback, thereby achieving higher user satisfaction.

[0450] "User" refers to an individual or corporation that uses the system to input a destination and intermediate points and receives suggestions for the optimal route, stores, and products.

[0451] "Destination" refers to the final destination that the user enters into the system.

[0452] A "waypoint" refers to a point that a user passes through on the way to their destination.

[0453] The term "means for acquiring" refers to a device or program for recording the destination and intermediate points entered by the user and transmitting them to the server.

[0454] A "communications network" is an infrastructure for transmitting data, including the Internet, wireless communications, cellular networks, etc.

[0455] "Terminal" refers to a portable information and communication device used by a user, such as a smartphone or tablet.

[0456] A "server" refers to a remote computer system that processes large amounts of data, handles user queries, and analyzes data.

[0457] "Wi-Fi access point" refers to a physical device or endpoint for connecting to the Internet via a wireless network.

[0458] A "near field communication beacon" is a device that uses short-range wireless communication technology such as Bluetooth to communicate with devices within a certain range.

[0459] "Video analysis device" refers to a device or software that analyzes camera footage to grasp the flow of people and congestion conditions in real time.

[0460] "Traffic information service" refers to an external information service that provides real-time information on traffic conditions and public transportation.

[0461] "Means for analysis" refers to a device or program that analyzes people flow and traffic conditions based on collected data and grasps current and future congestion conditions.

[0462] "Predictive algorithms" refer to mathematical methods that use machine learning models and statistical methods to predict future congestion and traffic conditions.

[0463] The "optimal route" refers to the most efficient route when a user travels from a starting point to a destination via intermediate points.

[0464] "Calculation means" refers to a device or program for deriving the optimal route based on the user's input data and the analysis results.

[0465] "Presenting means" refers to a device or program for visually or audibly notifying the user of the calculated optimal route or recommendation information.

[0466] The "recommending means" refers to a device or program for recommending stores and products that are useful to the user during their travels.

[0467] "Means for collection" refers to a device or program used to collect user feedback and improve the service based on that data.

[0468] The system of this invention collects real-time people flow data and traffic information based on the starting point, intermediate points, and destination entered by the user, and calculates and provides the optimal route. It also aims to make travel more convenient and stimulate purchasing by predicting the user's needs and recommending stores and products along the way.

[0469] First, the user launches the dedicated application on a device such as a smartphone or tablet, and inputs "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination. This input data is sent from the device to the server.

[0470] When the server receives the transmitted data, it first collects real-time people flow data from Wi-Fi access points, short-range wireless communication beacons, video analysis devices, etc. In addition, it obtains real-time data such as traffic conditions, congestion information, and public transport delay information from traffic information services.

[0471] The server then analyzes the collected data to determine the current congestion situation around Tokyo Station, Shimbashi, and Ginza. This analysis combines the number of connected devices obtained from Wi-Fi access points, the signal strength of short-range communication beacons, and people flow analysis data from video analytics equipment to determine the latest situation at each location where users are moving in real time. Machine learning models are also applied based on past data to predict congestion and traffic conditions over the next few hours.

[0472] The server then calculates the optimal route from the departure point specified by the user to the destination via intermediate points. This optimal route is calculated using route search algorithms such as Dijkstra's algorithm or A algorithm. The calculated optimal route is sent from the server to the device, and the device displays specific route guidance to the user. For example, guidance such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza" may be displayed.

[0473] Furthermore, the server will recommend stores and products that will be useful during the journey based on the user's past travel history and interest data. For example, it will suggest recommended cafes to stop by in Shimbashi or shopping spots in Ginza. This information will also be sent to the device and notified to the user.

[0474] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0475] This enables the system of the present invention to provide efficient and convenient travel guidance to users and suggest useful information along the way.

[0476] Examples and prompts:

[0477] For example, the prompt below shows the situation when a user wants to know the best route from Tokyo Station to Ginza via Shimbashi and recommendations for stores to stop at along the way.

[0478] Example prompt sentence:

[0479] User: What is the best route from Tokyo Station to Ginza via Shimbashi, and what stores should I stop at along the way?

[0480] server:

[0481] 1. Take the Yamanote Line from Tokyo Station and get off at Shimbashi.

[0482] 2. Walk from Shimbashi Station to Ginza.

[0483] 3. Recommended cafe near Shimbashi Station: Cafe ABC

[0484] 4. Recommended Shopping Spots in Ginza: Shop XYZ

[0485] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0486] Step 1: User Input

[0487] Users launch a dedicated application on their smartphone or tablet and input their starting point, intermediate points, and destination. For example, they can input "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination.

[0488] Input: Start point, stopover point, and destination information.

[0489] Output: This information is saved in the device.

[0490] Step 2: Send data to the server

[0491] The terminal sends the data entered by the user (starting point, intermediate points, and destination) to the server via a communication network.

[0492] Input: User-entered start, stop, and destination data.

[0493] Output: The data sent to the server.

[0494] Step 3: Data collection by the server

[0495] The server collects real-time people flow data from Wi-Fi access points, short-range wireless communication beacons, video analysis devices, etc. It also obtains congestion and delay information from traffic information services.

[0496] Input: User's destination and intermediate destination data sent to the server.

[0497] Output: Real-time people flow data, traffic information data.

[0498] Step 4: Data analysis

[0499] The server analyzes pedestrian flow and traffic conditions based on the collected data. It integrates data from the number of devices connected to Wi-Fi access points, beacon signal strength, and video analysis equipment to grasp pedestrian flow conditions. It also uses machine learning models to predict future congestion and traffic conditions.

[0500] Input: Real-time people flow data, traffic information data.

[0501] Output: Congestion status, traffic conditions and forecast data for each location.

[0502] Step 5: Optimal route calculation

[0503] The server uses Dijkstra's algorithm or the A algorithm to calculate the optimal route based on user input, taking into account analytical results and forecast data.

[0504] Input: Data on starting point, intermediate points, destination, congestion status, traffic conditions and forecast data.

[0505] Output: Information about the best route.

[0506] Step 6: Send directions

[0507] The server then sends the calculated optimal route to the device, which uses this information to provide real-time route guidance to the user.

[0508] Input: The optimal route calculated by the server.

[0509] Output: The optimal route information sent to the device.

[0510] Step 7: Recommendations

[0511] The server then recommends stores and products that will be useful during the journey based on the user's past travel history and interest data, and this information is also later sent to the device.

[0512] Input: User's past travel history, interest data.

[0513] Output: Information about recommended stores and products.

[0514] Step 8: Submit your recommendation

[0515] The server transmits the recommendation information to the terminal, and the terminal notifies the user of this information.

[0516] Input: Information recommended by the server.

[0517] Output: Recommendation information sent to the device.

[0518] Step 9: Users provide feedback

[0519] After arriving at their destination, users provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0520] Input: Feedback information from the user.

[0521] Output: Feedback information sent to the server, service improvement data.

[0522] (Application example 1)

[0523] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0524] An efficient and safe navigation system is important for modern autonomous vehicles, but conventional systems are unable to fully reflect real-time people flow data and traffic information, making it difficult to provide optimal route guidance or store and product recommendations that meet user needs. Overcoming these shortcomings and providing users with a higher quality travel experience is essential.

[0525] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0526] In this invention, the server includes means for acquiring destinations and waypoints entered by a user, means for collecting people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera image analysis, etc., means for acquiring traffic information from traffic information services, means for analyzing people flow and traffic conditions, means for calculating an optimal route for the user's destination and waypoints, means for presenting the calculated optimal route to the user, and means for being incorporated into the navigation system of the autonomous vehicle and providing route guidance. This enables optimal route guidance based on real-time data and can also recommend stores and products along the way to the user.

[0527] "Destination" is the location that the user sets as the destination.

[0528] A "waypoint" is a place where a user stops on the way to their destination.

[0529] A "Wi-Fi access point" is a device that provides Internet connectivity in a wireless LAN environment.

[0530] A "Bluetooth beacon" is a device that uses Bluetooth technology to detect devices in close proximity.

[0531] "Camera video analysis" is a technology that analyzes video data captured by a camera and extracts specific information.

[0532] "People flow data" is data that represents the movement patterns and density of people within a specific area.

[0533] "Traffic information service" is a service that provides real-time traffic conditions and congestion information.

[0534] The "optimal route" is the most efficient travel route calculated based on the input destination and intermediate points, taking into account real-time data.

[0535] "Navigation system" is a general term for devices and systems that provide route guidance to a destination.

[0536] "Recommendation" refers to recommending specific services or products based on a user's interests and behavioral history.

[0537] "Feedback" means opinions and evaluations provided by users regarding the results and experiences of using the service.

[0538] The system of the present invention is implemented as a navigation system for an autonomous vehicle. First, the user inputs the destination and intermediate points. This input data is sent to a server via a smartphone app or a terminal in the vehicle. The server performs the following processes based on the input data.

[0539] Hardware and software configuration

[0540] Hardware

[0541] Smartphone: A device for inputting a user's destination and intermediate points.

[0542] Navigation system for autonomous vehicles: A system for guiding users to their destination.

[0543] Server: A central control unit for data analysis and route calculation.

[0544] software

[0545] Route search algorithm: Calculates the optimal route using Dijkstra's algorithm or A algorithm.

[0546] Data analysis software: Software for analyzing real-time people flow data and traffic information.

[0547] Recommendation engine: Recommends products and services based on the user's interests.

[0548] Data collection and analysis

[0549] The server collects real-time pedestrian flow data from Wi-Fi access points, Bluetooth beacons, and camera image analysis. It also obtains traffic conditions and congestion information from traffic information services. Based on this data, it analyzes the current situation at each point the user moves and calculates the optimal route.

[0550] Calculating the best route

[0551] Based on the collected data, the server applies Dijkstra's algorithm and A algorithm to calculate the optimal route for the user's destination and intermediate points. The calculated route is immediately sent to the user's smartphone or the navigation system of the autonomous vehicle.

[0552] Route guidance and service recommendations

[0553] The navigation system displays the calculated optimal route for the user and provides guidance. At the same time, the server recommends stores and products that the user can stop by along the way based on the user's past travel history and interest data. This information is notified to the user in real time via their smartphone or navigation system.

[0554] Specific examples

[0555] For example, if a user travels from Tokyo Station to Ginza and sets Shinbashi as a stopover, the server receives this information and collects real-time people flow data and traffic information. As a result, it can recommend the optimal route using the Yamanote Line and cafes that can be visited in Shinbashi.

[0556] Prompt Sentence Examples

[0557] User: Please tell me the route from "Tokyo Station" to "Ginza". I would like to stop at "Shinbashi" on the way.

[0558] System: Checking current congestion and traffic information...

[0559] System: Take the Yamanote Line from Tokyo Station, get off at Shimbashi Station, stop at the "Cafe Break" cafe, then walk to Ginza. The system calculates the optimal route, taking into account traffic information and congestion.

[0560] System: The address of the cafe "Cafe Break" is 5-4-1 Shinbashi, and its business hours are 9:00-21:00.

[0561] In this way, the system of the present invention can provide efficient and convenient travel guidance to users and suggest useful information along the way.

[0562] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0563] Step 1:

[0564] The user inputs the destination and intermediate points using a smartphone or a terminal in the vehicle. This user input data is sent from the terminal to the server. The input data includes the destination, starting point, and intermediate points.

[0565] Step 2:

[0566] The server receives input data sent by the user. It then collects people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc. It also obtains the latest traffic information using traffic information services. The input here is data from Wi-Fi access points and traffic information services, and people flow and traffic data is output based on this data.

[0567] Step 3:

[0568] The server analyzes the congestion and traffic conditions related to the user's destination and intermediate points based on the collected real-time people flow data and traffic information. Using data analysis software, this data is processed and calculated to grasp the latest conditions at each point. The output of this step is congestion forecast data for each point.

[0569] Step 4:

[0570] The server uses the analyzed data to calculate the optimal route for the destination and intermediate points specified by the user using Dijkstra's algorithm or A algorithm. The input is the congestion forecast data obtained in step 3, and the output is the route information for the optimal route.

[0571] Step 5:

[0572] The calculated optimal route is sent to the user's smartphone or the autonomous vehicle's navigation system. The device displays the route information of the received optimal route to the user and provides detailed route guidance. The output of this step is the route guidance information displayed on the navigation screen.

[0573] Step 6:

[0574] The server recommends stores and products that are useful during travel based on the user's past travel history and interest data. A recommendation engine is used to analyze this interest data and generate information on the most suitable stores and products. The input is the user's past data, and the output is the suggested store and product information.

[0575] Step 7:

[0576] After arriving at the destination, the user provides feedback through the app about the accuracy and convenience of the route guidance. The device sends this feedback data to the server, which analyzes it and reflects it in the next service improvement. The output of this step is the analyzed feedback data.

[0577] Through the above process, users can receive useful information along with optimal route guidance in real time.

[0578] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0579] The system of the present invention generates an optimal route based on the destination and intermediate points entered by the user, taking into account real-time people flow data and traffic information, and provides guidance to the user.In addition, by combining it with an emotion engine that recognizes the user's emotional state, it provides even more personalized route guidance and recommendations.

[0580] First, the user launches the dedicated application on a device such as a smartphone or tablet, and inputs "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination. This input data is sent from the device to the server.

[0581] When the server receives the transmitted data, it first collects real-time people flow data from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc. In addition, it obtains real-time data such as traffic conditions, congestion information, and public transport delay information from traffic information services.

[0582] The server then analyzes the collected data to determine the current congestion situation around Tokyo Station, Shimbashi, and Ginza. This analysis combines the number of Wi-Fi connected devices, Bluetooth beacon signal strength, and people flow analysis data from camera footage to determine the latest situation at each location where users move in real time. Machine learning models are also applied based on past data to predict congestion and traffic conditions over the next few hours.

[0583] The server then calculates the optimal route from the departure point specified by the user to the destination via intermediate points. This optimal route is calculated using route search algorithms such as Dijkstra's algorithm or A algorithm. The calculated optimal route is sent from the server to the device, and the device displays specific route guidance to the user. For example, guidance such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza" may be displayed.

[0584] Furthermore, the server will recommend stores and products that will be useful during the journey based on the user's past travel history and interest data. For example, it will suggest recommended cafes to stop by in Shimbashi or shopping spots in Ginza. This information will also be sent to the device and notified to the user.

[0585] The present invention further provides a form that incorporates an emotion engine. The emotion engine recognizes emotions from the user's facial expressions and voice, and grasps the user's real-time emotional state. Depending on this emotional state, the server recalculates the optimal route and optimizes the recommendation information. For example, if the user is tired, the server can provide a more comfortable and stress-free travel route or recommend a cafe or rest area where the user can refresh themselves.

[0586] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0587] By implementing the above process, the system of the present invention can provide users with efficient and convenient travel guidance, and by using an emotion engine, it can provide more personalized travel guidance and recommendations.

[0588] The processing flow will be explained below.

[0589] Step 1:

[0590] The user launches the application on their smartphone or tablet and enters "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination.

[0591] Step 2:

[0592] The terminal sends the user's input data (starting point, intermediate points, and destination) to the server.

[0593] Step 3:

[0594] The server analyzes the received user input data and starts obtaining the required real-time data.

[0595] Step 4:

[0596] The server collects information about connected devices from Wi-Fi access points around Tokyo Station, Shinbashi, and Ginza.

[0597] Step 5:

[0598] The server collects information on the flow of people at each location from Bluetooth beacons, which allows the degree of congestion at each location to be determined.

[0599] Step 6:

[0600] The server accesses the camera video analysis system, acquires video data from each location, and analyzes people flow data.

[0601] Step 7:

[0602] The server uses the traffic information service API to obtain real-time traffic congestion information and public transport delay information.

[0603] Step 8:

[0604] The server integrates the collected data and analyzes the current congestion and traffic conditions at Tokyo Station, Shimbashi, and Ginza, and also applies machine learning models based on past data to predict congestion and traffic conditions over the next few hours.

[0605] Step 9:

[0606] The server calculates the optimal route from the starting point to the intermediate points and the destination based on the user's input data and real-time and predicted data. Here, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[0607] Step 10:

[0608] The server sends the calculated optimal route guidance to the terminal.

[0609] Step 11:

[0610] The device displays the optimal route guidance it has received to the user. For example, specific route instructions such as "Take the Yamanote Line from Tokyo Station to Shinbashi, then walk from Shinbashi to Ginza" are displayed.

[0611] Step 12:

[0612] The device uses the user's facial recognition and voice input functions to detect their current emotional state. For example, it can obtain emotional information such as whether the user is tired or stressed from their facial expression or tone of voice.

[0613] Step 13:

[0614] The emotion data acquired by the device is transmitted to the server.

[0615] Step 14:

[0616] The server analyzes the emotional data and understands the user's current emotional state.

[0617] Step 15:

[0618] The server recalculates the optimal route based on the user's emotional state: for example, if the user is tired, a more comfortable and less congested route will be suggested.

[0619] Step 16:

[0620] The server generates store and product recommendations based on the user's emotional state, suggesting, for example, relaxing cafes and comfortable rest areas.

[0621] Step 17:

[0622] The server sends the recalculated optimal route and recommendation information to the terminal.

[0623] Step 18:

[0624] The device displays the recalculated optimal route guidance and recommendation information to the user.

[0625] Step 19:

[0626] After arriving at their destination, users can enter feedback within the app about the accuracy and convenience of the route guidance.

[0627] Step 20:

[0628] The terminal sends the user's feedback to the server.

[0629] Step 21:

[0630] The server stores the collected feedback data and analyzes it to improve our services in the future.

[0631] Example 2

[0632] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0633] Conventional navigation systems have difficulty providing optimal routes that take into account real-time people flow data and traffic information. Furthermore, they lack a means to provide personalized route guidance and recommendations that take into account the user's emotional state. As a result, users are prone to feeling stressed, and there are problems with reduced convenience during travel.

[0634] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0635] In this invention, the server includes means for acquiring destinations and intermediate points entered by a user, means for collecting people flow data in real time from wireless communication devices, location information beacons, image analysis devices, etc., means for acquiring traffic information from external information provision services, means for analyzing people flow and traffic conditions based on the collected data, means for calculating an optimal route for the user's destination and intermediate points, means for presenting the calculated optimal route to the user, and means for recognizing emotions from the user's facial expressions and voice and optimizing route and recommendation information based on the recognized emotions. This makes it possible to provide efficient and convenient travel guidance taking real-time data into consideration, and furthermore, by using an emotion engine, it becomes possible to provide more personalized travel guidance and recommendations to the user.

[0636] "User" means an individual or corporation that uses the system.

[0637] "Destination" is the final destination to which the user wishes to travel.

[0638] A "waypoint" is a point that a user passes through on the way to the destination.

[0639] A "wireless communication device" is a device such as a Wi-Fi access point or Bluetooth beacon that collects data within its communication range in real time.

[0640] A "location beacon" is a device that is placed in a specific location and communicates with devices within a certain range to provide location information.

[0641] An "image analysis device" is a device that analyzes camera footage to detect and analyze the movements of objects and people.

[0642] "External information provision services" is a general term for services that provide information such as the operation status of public transportation and road traffic conditions.

[0643] "People flow data" is data that shows the movements and stay patterns of people in a specific area.

[0644] "Traffic information" refers to information about traffic, such as road congestion and delays in public transportation.

[0645] "Analysis" is the process of integrating collected data to identify patterns and trends.

[0646] The "optimal route" is the most efficient and convenient route for the user, taking into consideration travel time and convenience.

[0647] "On the way" means the part of the route from the starting point to the destination.

[0648] A "shop" is a place that provides commercial services.

[0649] "Goods" means any goods or services sold or provided.

[0650] "Recommendation" is the act of suggesting appropriate options based on the user's interests and travel situation.

[0651] "Feedback" refers to users' evaluations and opinions of services and systems.

[0652] An "emotion engine" is a device or system that analyzes a user's facial expressions and voice data to recognize their emotional state.

[0653] "Personalization" refers to providing optimal information and services tailored to the characteristics and circumstances of each individual user.

[0654] "System" is a collective term for a series of hardware and software that integrates the above functions and provides services to users.

[0655] The system of the present invention generates an optimal route based on the destination and intermediate points entered by the user, taking into account real-time people flow data and traffic information, and provides guidance to the user.In addition, by combining it with an emotion engine that recognizes the user's emotional state, it provides even more personalized route guidance and recommendations.

[0656] Hardware and Data Acquisition

[0657] Users use a device such as a smartphone or tablet to launch a dedicated application and input their starting point, intermediate points, and destination. This input data is sent from the device to a server. The server collects real-time people flow data through sensor devices such as Wi-Fi access points, Bluetooth beacons, and image analysis devices (cameras). It also obtains current traffic conditions and delay information from external information services (traffic information services).

[0658] Data analysis and route calculation

[0659] The server integrates and analyzes the collected people flow data and traffic information. Specifically, it integrates the number of connected devices from Wi-Fi access points, the signal strength of Bluetooth beacons, and people flow analysis data from camera footage to understand the current congestion situation at each location. It also applies machine learning models to predict congestion and traffic conditions over the next few hours by comparing this data with past data.

[0660] The server uses a route search algorithm (e.g., Dijkstra's algorithm, A algorithm) to calculate the optimal route from the starting point specified by the user to the destination via intermediate points. This optimal route is sent from the server to the terminal, and specific route guidance is displayed to the user.

[0661] Personalization and Recommendations

[0662] The server recommends stores and products that are useful during travel based on the user's past travel history and interest data. For example, it suggests recommended cafes to stop by at intermediate points or shopping spots near the destination. This information is also sent to the device and notified to the user.

[0663] The emotion engine also recognizes the user's emotions from their facial expressions and voice, and recalculates the optimal route and optimizes recommendation information based on that emotional state. For example, if the user is tired, the server will provide a more comfortable and stress-free route or recommend a cafe or rest area where they can refresh themselves.

[0664] Collecting feedback and improving our services

[0665] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0666] Specific examples

[0667] If a user inputs "From Tokyo Station to Ginza, via Shimbashi, and want to stop by a cafe," the system will provide the optimal route based on real-time data and suggest recommended cafes in Shimbashi.

[0668] "Please provide the best route from Tokyo Station to Ginza via Shimbashi, taking into account real-time traffic and people flow data."

[0669] As explained above, the system of the present invention provides efficient and convenient travel guidance to users, and by using an emotion engine, it is possible to provide more personalized travel guidance and recommendations.

[0670] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0671] Step 1:

[0672] The user launches a dedicated application using a smartphone or tablet and inputs the starting point, intermediate points, and destination.

[0673] Specifically, the user enters "Tokyo Station," "Shinbashi," and "Ginza" into the input fields on the app screen and presses the send button. The input data is sent from the device to the server. The input is the user's destination information, and the output is the data sent to the server.

[0674] Step 2:

[0675] The server receives the transmitted data and collects real-time people flow data from wireless communication devices, location information beacons, image analysis devices, etc.

[0676] Specifically, the server sends a data acquisition request to each sensor device, and the sensor devices return real-time people flow data. The input is the data collected from the sensor devices, and the output is the integrated real-time data.

[0677] Step 3:

[0678] The server acquires traffic information from an external information providing service.

[0679] Specifically, the server sends a request to the traffic information API, and traffic information is returned to the server as an API response. The input is the API request data, and the output is the acquired traffic information.

[0680] Step 4:

[0681] The server integrates the people flow data and traffic information collected and performs analysis.

[0682] Specifically, the server aggregates Wi-Fi, Bluetooth, and camera data and applies analytical algorithms. The input is the aggregated data set, and the output is the latest situational understanding.

[0683] Step 5:

[0684] The server compares the data with past data and uses machine learning models to predict congestion and traffic conditions several hours in the future.

[0685] Specifically, the server inputs current and past data into the machine learning model and outputs future prediction data. The input is past and current data, and the output is the prediction model result.

[0686] Step 6:

[0687] The server calculates the optimal route based on the user's specifications using a route search algorithm.

[0688] Specifically, the server executes Dijkstra's algorithm or A algorithm to generate the optimal route. The input is the destination information specified by the user and the server's analysis results, and the output is the optimal route data.

[0689] Step 7:

[0690] The server transmits the calculated optimum route to the terminal, which then displays specific route guidance to the user.

[0691] Specifically, the terminal screen displays instructions such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza." The input is optimal route data, and the output is the content displayed to the user.

[0692] Step 8:

[0693] The server recommends stores and products that will be useful while traveling based on the user's past travel history and interest data.

[0694] Specifically, the server runs a recommendation algorithm to generate a list of optimal stores and products, and sends it to the terminal. The input is the user's history data, and the output is recommendation information.

[0695] Step 9:

[0696] The terminal receives the recommendation information from the server and notifies the user.

[0697] Specifically, the device displays "recommended cafes in Shimbashi" or "shopping spots in Ginza." The input is recommendation information, and the output is the notification content to the user.

[0698] Step 10:

[0699] The server uses an emotion engine to analyze the user's facial expressions and voice and recognize their emotional state.

[0700] Specifically, it analyzes data captured by the device's camera and microphone and sends the results to a server. The input is the captured facial expression and voice data, and the output is the recognized emotional state.

[0701] Step 11:

[0702] The server recalculates and optimizes the optimal route and recommendation information based on the recognized emotion information.

[0703] Specifically, the server uses the data obtained by the emotion engine to adjust the route and recommendation information and send it to the device. The input is the recognized emotion information, and the output is the optimized route and recommendation information.

[0704] Step 12:

[0705] Once the user arrives at their destination, they will be provided with feedback on the accuracy and convenience of the route guidance and service.

[0706] Specifically, a feedback input form is displayed on the terminal, and the user enters and submits their evaluation or comment. The input is the user's feedback data, and the output is the data to be sent to the server.

[0707] Step 13:

[0708] The server analyzes the feedback data and reflects it in the next service improvement.

[0709] Specifically, the server analyzes the feedback data, identifies problems and areas for improvement, and updates the system's algorithms and data. The input is the feedback data, and the output is updated system information.

[0710] (Application example 2)

[0711] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0712] Conventional route guidance systems provide optimal routes based on traffic information and people flow data, but they are unable to provide personalized guidance or recommendations that take into account the user's emotional state. As a result, even if the user is tired or stressed, they can only present a standard route, making it difficult to provide a comfortable travel experience that suits the user's situation. In addition, there are insufficient means to collect user feedback and use it to improve services.

[0713] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring a destination and intermediate points entered by a user, means for collecting people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera image analysis, etc., means for acquiring traffic information from a traffic information service, means for recognizing the user's emotional state, means for calculating an optimal route and personalized recommended information based on the emotional state, and means for presenting the calculated optimal route and recommended information to the user. This makes it possible to provide more personalized route guidance and recommendations that take the user's emotional state into consideration, significantly improving the user's travel experience.

[0714] The "means for acquiring the destination and intermediate points entered by the user" is a function that receives the departure point, intermediate points, and destination specified by the user via an electronic device and transmits them to the server.

[0715] "Means of collecting people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc." refers to a method of understanding people's movements and congestion levels in specific locations in real time using wireless communication technology and video analysis technology.

[0716] "Means for obtaining traffic information from traffic information services" refers to a method for collecting real-time traffic data from external services that provide information on traffic conditions, congestion, delays in public transportation, and the like.

[0717] "Means for analyzing people flow and traffic conditions based on collected data" refers to the process of using acquired people flow data and traffic information to analyze congestion and traffic conditions at each location and generate information optimal for the user's travel.

[0718] "Means for recognizing the user's emotional state" refers to technology that analyzes the user's facial expressions and voice to identify their emotions at that time.

[0719] "Means for calculating optimal routes and personalized recommended information based on emotional state" refers to a function that calculates and presents information such as the most comfortable and least stressful route, stores and rest areas suitable for the user, etc., based on the recognized emotions.

[0720] "Means for presenting the calculated optimal route and recommended information to the user" refers to a method for displaying the calculated route guidance and recommended information on the user's device.

[0721] "Means of recommending stores and rest areas to users along the way based on analysis results and emotional state" is a function that recommends the most suitable stores and rest areas that users can stop at along the way based on real-time data and emotional state.

[0722] "Means of collecting feedback from users and reflecting it in the next service" refers to the process of collecting opinions and impressions provided by users and using them to improve the service.

[0723] The system for realizing this application example operates by combining multiple pieces of hardware and software.

[0724] First, the user starts a dedicated application on a device such as a smartphone or tablet and inputs the starting point, intermediate points, and destination. This data is then sent from the device to the server.

[0725] The server receives the transmitted data and collects people flow data in real time using Wi-Fi access points, Bluetooth beacons, camera image analysis, etc. It also obtains the latest traffic conditions, congestion information, and public transport delay information from traffic information services.

[0726] Furthermore, the server runs an emotion engine to analyze the user's facial expressions and voice and recognize their emotional state. This emotion engine uses a software library called "face_recognition" and an audio emotion recognition module called "AudioEmotionRecognition."

[0727] Based on the collected real-time data, traffic information, and the user's emotional state, the server calculates the optimal route and personalized recommendations. The algorithms used for this calculation include Dijkstra's algorithm and A algorithm. The calculated optimal route and recommendations are sent from the server to the device and presented to the user.

[0728] As a concrete example, consider the case where a user travels from "Tokyo Station" to "Ginza" and takes a break in "Shinbashi." If the emotion engine recognizes the user's state as "tired," the server will select a less crowded route and recommend cafes and rest areas that the user can stop at in Shinbashi. Conversely, if the user is recognized as "happy," the normal optimal route will be selected.

[0729] After arriving at their destination, users can provide feedback on the accuracy and usefulness of the route guidance and recommendations. This feedback data is also sent to the server, which analyzes it and reflects it in future service improvements.

[0730] This system can provide more comfortable and personalized travel guidance that takes into account the user's emotional state.

[0731] Examples of prompts:

[0732] A user specifies that they want to travel from "Tokyo Station" to "Ginza" and enters "Shinbashi" as a stopover point. This user may be tired. Obtain real-time people flow data and traffic information, and suggest the best route and rest stops based on their emotional state.

[0733] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0734] Step 1:

[0735] The user launches a dedicated application on their smartphone or tablet and inputs the starting point, intermediate points, and destination. The input data is sent from the device to the server. The input data includes the starting point "Tokyo Station," the intermediate point "Shinbashi," and the destination "Ginza," and this is the output data sent to the server.

[0736] Step 2:

[0737] The server receives the input data and collects real-time people flow data from Wi-Fi access points, Bluetooth beacons, and camera video analysis. This allows for a grasp of the congestion situation in a specific area. Data processing involves analyzing the number of Wi-Fi connected devices, Bluetooth beacon signal strength, and camera video data, and the resulting real-time people flow data is generated.

[0738] Step 3:

[0739] The server obtains real-time traffic data from the traffic information service. This traffic data includes information on road congestion, delays in public transportation, etc. The collected traffic data becomes input data for understanding the real-time situation.

[0740] Step 4:

[0741] Based on the collected people flow and traffic data, the server analyzes the specific congestion and traffic conditions at each location. Data processing here includes congestion prediction using machine learning models learned from past data. The output is the analyzed current people flow and traffic condition data.

[0742] Step 5:

[0743] The server recognizes the user's emotional state from their facial expressions and voice. The "face_recognition" library and "AudioEmotionRecognition" module are used for emotion recognition. The input requires the user's image data and voice data, and the output is the user's emotional state (e.g., "tired" or "happy").

[0744] Step 6:

[0745] The server calculates optimal routes and personalized recommendations based on the emotional state. Using route-finding algorithms such as Dijkstra's algorithm and A-algorithm, and taking the emotional state into account, it suggests routes that avoid crowds and rest spots where people can relax. Input data includes analyzed pedestrian flow and traffic data and the emotional state, and the output includes optimal routes and recommendations.

[0746] Step 7:

[0747] The calculated optimal route and recommended information are sent from the server to the device and presented to the user. The device receives this information and displays specific route guidance and recommended information to the user. For example, the optimal route guidance may be something like "take the Yamanote Line from Tokyo Station to Shinbashi, then walk from Shinbashi to Ginza."

[0748] Step 8:

[0749] After arriving at their destination, users provide feedback on route guidance and recommendations through the application. This feedback is sent from the device to the server, which analyzes the data and uses it to improve the service for the next time. The analyzed data is used to improve the next route calculation and recommendation algorithm.

[0750] 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.

[0751] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0752] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0753] [Third embodiment]

[0754] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0755] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0756] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0757] 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.

[0758] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0759] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0760] 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.

[0761] 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.

[0762] 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 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.

[0763] 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.

[0764] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0765] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0766] The system of the present invention generates an optimal route based on the destination and intermediate points entered by the user, taking into account real-time people flow data and traffic information, and guides the user along the route.The system also aims to make travel more convenient and stimulate purchasing by predicting the user's needs and recommending stores and products along the way.

[0767] An embodiment of this system will be described below with specific examples.

[0768] First, the user launches the dedicated application on a device such as a smartphone or tablet, and inputs "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination. This input data is sent from the device to the server.

[0769] When the server receives the transmitted data, it first collects real-time people flow data from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc. In addition, it obtains real-time data such as traffic conditions, congestion information, and public transport delay information from traffic information services.

[0770] The server then analyzes the collected data to determine the current congestion situation around Tokyo Station, Shimbashi, and Ginza. This analysis combines the number of connected devices obtained from Wi-Fi access points, the signal strength of Bluetooth beacons, and people flow analysis data from camera footage to determine the latest situation at each location where users move in real time. Machine learning models are also applied based on past data to predict congestion and traffic conditions over the next few hours.

[0771] The server then calculates the optimal route from the departure point specified by the user to the destination via intermediate points. This optimal route is calculated using route search algorithms such as Dijkstra's algorithm or A algorithm. The calculated optimal route is sent from the server to the device, and the device displays specific route guidance to the user. For example, guidance such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza" may be displayed.

[0772] Furthermore, the server will recommend stores and products that will be useful during the journey based on the user's past travel history and interest data. For example, it will suggest recommended cafes to stop by in Shimbashi or shopping spots in Ginza. This information will also be sent to the device and notified to the user.

[0773] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0774] By implementing the above flow, the system of the present invention can provide the user with efficient and convenient travel guidance and suggest useful information along the way.

[0775] The processing flow will be explained below.

[0776] Step 1:

[0777] The user launches the application on their smartphone or tablet and enters "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination.

[0778] Step 2:

[0779] The terminal sends the user's input data (starting point, intermediate points, and destination) to the server.

[0780] Step 3:

[0781] The server analyzes the received user input data and starts obtaining the real-time data that will be required from now on.

[0782] Step 4:

[0783] The server collects information about connected devices from Wi-Fi access points around Tokyo Station, Shinbashi, and Ginza.

[0784] Step 5:

[0785] The server collects information on the flow of people at each location from Bluetooth beacons, which allows the degree of congestion at each location to be determined.

[0786] Step 6:

[0787] The server accesses the camera video analysis system, acquires video data from each location, and analyzes people flow data.

[0788] Step 7:

[0789] The server uses the traffic information service API to obtain real-time traffic congestion information and public transport delay information.

[0790] Step 8:

[0791] The server integrates the collected data and analyzes the current congestion and traffic conditions at Tokyo Station, Shimbashi, and Ginza, and also applies machine learning models based on past data to predict congestion and traffic conditions over the next few hours.

[0792] Step 9:

[0793] The server calculates the optimal route from the starting point to the intermediate points and the destination based on the user's input data and real-time and predicted data. Here, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[0794] Step 10:

[0795] The server sends the calculated optimal route guidance to the terminal.

[0796] Step 11:

[0797] The device displays the optimal route guidance it has received to the user. For example, specific route instructions such as "Take the Yamanote Line from Tokyo Station to Shinbashi, then walk from Shinbashi to Ginza" are displayed.

[0798] Step 12:

[0799] The server recommends stores and products along the way based on the user's past travel history and interest data.

[0800] Step 13:

[0801] The server sends recommendation information to the device, which then notifies the user. For example, users can receive push notifications about cafes to stop by in Shimbashi or shopping spots in Ginza.

[0802] Step 14:

[0803] After arriving in Ginza, users can enter feedback within the app about the accuracy and convenience of the route guidance.

[0804] Step 15:

[0805] The terminal sends the user's feedback to the server.

[0806] Step 16:

[0807] The server stores the collected feedback data and analyzes it to improve our services in the future.

[0808] Example 1

[0809] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0810] In today's urban areas, traffic congestion and congestion are constantly changing, making it difficult for users to reach their destinations efficiently. While providing useful information and store suggestions while traveling would improve user convenience and encourage purchasing, such information provision is lacking. Existing systems have limited real-time data collection and analysis capabilities, and do not adequately provide optimal route guidance and recommendations tailored to user needs. Furthermore, they lack a mechanism for collecting user feedback and incorporating it into future service improvements.

[0811] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0812] In this invention, the server includes: a means for acquiring a destination and intermediate points entered by a user; a means for transmitting data from a terminal to the server using a communication network; a means for collecting people flow data in real time from Wi-Fi access points, short-range wireless communication beacons, video analysis devices, etc.; a means for acquiring traffic information from a traffic information service; a means for analyzing people flow and traffic conditions based on the collected data; a means for predicting congestion and traffic conditions using the analyzed data and a prediction algorithm; a means for calculating an optimal route for the user's destination and intermediate points; and a means for presenting the calculated optimal route to the user. This allows users to reach their destination efficiently and also receive useful store and product information along the way. Furthermore, the quality of service can be continuously improved based on feedback, thereby achieving higher user satisfaction.

[0813] "User" refers to an individual or corporation that uses the system to input a destination and intermediate points and receives suggestions for the optimal route, stores, and products.

[0814] "Destination" refers to the final destination that the user enters into the system.

[0815] A "waypoint" refers to a point that a user passes through on the way to their destination.

[0816] The term "means for acquiring" refers to a device or program for recording the destination and intermediate points entered by the user and transmitting them to the server.

[0817] A "communications network" is an infrastructure for transmitting data, including the Internet, wireless communications, cellular networks, etc.

[0818] "Terminal" refers to a portable information and communication device used by a user, such as a smartphone or tablet.

[0819] A "server" refers to a remote computer system that processes large amounts of data, handles user queries, and analyzes data.

[0820] "Wi-Fi access point" refers to a physical device or endpoint for connecting to the Internet via a wireless network.

[0821] A "near field communication beacon" is a device that uses short-range wireless communication technology such as Bluetooth to communicate with devices within a certain range.

[0822] "Video analysis device" refers to a device or software that analyzes camera footage to grasp the flow of people and congestion conditions in real time.

[0823] "Traffic information service" refers to an external information service that provides real-time information on traffic conditions and public transportation.

[0824] "Means for analysis" refers to a device or program that analyzes people flow and traffic conditions based on collected data and grasps current and future congestion conditions.

[0825] "Predictive algorithms" refer to mathematical methods that use machine learning models and statistical methods to predict future congestion and traffic conditions.

[0826] The "optimal route" refers to the most efficient route when a user travels from a starting point to a destination via intermediate points.

[0827] "Calculation means" refers to a device or program for deriving the optimal route based on the user's input data and the analysis results.

[0828] "Presenting means" refers to a device or program for visually or audibly notifying the user of the calculated optimal route or recommendation information.

[0829] The "recommending means" refers to a device or program for recommending stores and products that are useful to the user during their travels.

[0830] "Means for collection" refers to a device or program used to collect user feedback and improve the service based on that data.

[0831] The system of this invention collects real-time people flow data and traffic information based on the starting point, intermediate points, and destination entered by the user, and calculates and provides the optimal route. It also aims to make travel more convenient and stimulate purchasing by predicting the user's needs and recommending stores and products along the way.

[0832] First, the user launches the dedicated application on a device such as a smartphone or tablet, and inputs "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination. This input data is sent from the device to the server.

[0833] When the server receives the transmitted data, it first collects real-time people flow data from Wi-Fi access points, short-range wireless communication beacons, video analysis devices, etc. In addition, it obtains real-time data such as traffic conditions, congestion information, and public transport delay information from traffic information services.

[0834] The server then analyzes the collected data to determine the current congestion situation around Tokyo Station, Shimbashi, and Ginza. This analysis combines the number of connected devices obtained from Wi-Fi access points, the signal strength of short-range communication beacons, and people flow analysis data from video analytics equipment to determine the latest situation at each location where users are moving in real time. Machine learning models are also applied based on past data to predict congestion and traffic conditions over the next few hours.

[0835] The server then calculates the optimal route from the departure point specified by the user to the destination via intermediate points. This optimal route is calculated using route search algorithms such as Dijkstra's algorithm or A algorithm. The calculated optimal route is sent from the server to the device, and the device displays specific route guidance to the user. For example, guidance such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza" may be displayed.

[0836] Furthermore, the server will recommend stores and products that will be useful during the journey based on the user's past travel history and interest data. For example, it will suggest recommended cafes to stop by in Shimbashi or shopping spots in Ginza. This information will also be sent to the device and notified to the user.

[0837] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0838] This enables the system of the present invention to provide efficient and convenient travel guidance to users and suggest useful information along the way.

[0839] Examples and prompts:

[0840] For example, the prompt below shows the situation when a user wants to know the best route from Tokyo Station to Ginza via Shimbashi and recommendations for stores to stop at along the way.

[0841] Example prompt sentence:

[0842] User: What is the best route from Tokyo Station to Ginza via Shimbashi, and what stores should I stop at along the way?

[0843] server:

[0844] 1. Take the Yamanote Line from Tokyo Station and get off at Shimbashi.

[0845] 2. Walk from Shimbashi Station to Ginza.

[0846] 3. Recommended cafe near Shimbashi Station: Cafe ABC

[0847] 4. Recommended Shopping Spots in Ginza: Shop XYZ

[0848] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0849] Step 1: User Input

[0850] Users launch a dedicated application on their smartphone or tablet and input their starting point, intermediate points, and destination. For example, they can input "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination.

[0851] Input: Start point, stopover point, and destination information.

[0852] Output: This information is saved in the device.

[0853] Step 2: Send data to the server

[0854] The terminal sends the data entered by the user (starting point, intermediate points, and destination) to the server via a communication network.

[0855] Input: User-entered start, stop, and destination data.

[0856] Output: The data sent to the server.

[0857] Step 3: Data collection by the server

[0858] The server collects real-time people flow data from Wi-Fi access points, short-range wireless communication beacons, video analysis devices, etc. It also obtains congestion and delay information from traffic information services.

[0859] Input: User's destination and intermediate destination data sent to the server.

[0860] Output: Real-time people flow data, traffic information data.

[0861] Step 4: Data analysis

[0862] The server analyzes pedestrian flow and traffic conditions based on the collected data. It integrates data from the number of devices connected to Wi-Fi access points, beacon signal strength, and video analysis equipment to grasp pedestrian flow conditions. It also uses machine learning models to predict future congestion and traffic conditions.

[0863] Input: Real-time people flow data, traffic information data.

[0864] Output: Congestion status, traffic conditions and forecast data for each location.

[0865] Step 5: Optimal route calculation

[0866] The server uses Dijkstra's algorithm or the A algorithm to calculate the optimal route based on user input, taking into account analytical results and forecast data.

[0867] Input: Data on starting point, intermediate points, destination, congestion status, traffic conditions and forecast data.

[0868] Output: Information about the best route.

[0869] Step 6: Send directions

[0870] The server then sends the calculated optimal route to the device, which uses this information to provide real-time route guidance to the user.

[0871] Input: The optimal route calculated by the server.

[0872] Output: The optimal route information sent to the device.

[0873] Step 7: Recommendations

[0874] The server then recommends stores and products that will be useful during the journey based on the user's past travel history and interest data, and this information is also later sent to the device.

[0875] Input: User's past travel history, interest data.

[0876] Output: Information about recommended stores and products.

[0877] Step 8: Submit your recommendation

[0878] The server transmits the recommendation information to the terminal, and the terminal notifies the user of this information.

[0879] Input: Information recommended by the server.

[0880] Output: Recommendation information sent to the device.

[0881] Step 9: Users provide feedback

[0882] After arriving at their destination, users provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0883] Input: Feedback information from the user.

[0884] Output: Feedback information sent to the server, service improvement data.

[0885] (Application example 1)

[0886] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0887] An efficient and safe navigation system is important for modern autonomous vehicles, but conventional systems are unable to fully reflect real-time people flow data and traffic information, making it difficult to provide optimal route guidance or store and product recommendations that meet user needs. Overcoming these shortcomings and providing users with a higher quality travel experience is essential.

[0888] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0889] In this invention, the server includes means for acquiring destinations and waypoints entered by a user, means for collecting people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera image analysis, etc., means for acquiring traffic information from traffic information services, means for analyzing people flow and traffic conditions, means for calculating an optimal route for the user's destination and waypoints, means for presenting the calculated optimal route to the user, and means for being incorporated into the navigation system of the autonomous vehicle and providing route guidance. This enables optimal route guidance based on real-time data and can also recommend stores and products along the way to the user.

[0890] "Destination" is the location that the user sets as the destination.

[0891] A "waypoint" is a place where a user stops on the way to their destination.

[0892] A "Wi-Fi access point" is a device that provides Internet connectivity in a wireless LAN environment.

[0893] A "Bluetooth beacon" is a device that uses Bluetooth technology to detect devices in close proximity.

[0894] "Camera video analysis" is a technology that analyzes video data captured by a camera and extracts specific information.

[0895] "People flow data" is data that represents the movement patterns and density of people within a specific area.

[0896] "Traffic information service" is a service that provides real-time traffic conditions and congestion information.

[0897] The "optimal route" is the most efficient travel route calculated based on the input destination and intermediate points, taking into account real-time data.

[0898] "Navigation system" is a general term for devices and systems that provide route guidance to a destination.

[0899] "Recommendation" refers to recommending specific services or products based on a user's interests and behavioral history.

[0900] "Feedback" means opinions and evaluations provided by users regarding the results and experiences of using the service.

[0901] The system of the present invention is implemented as a navigation system for an autonomous vehicle. First, the user inputs the destination and intermediate points. This input data is sent to a server via a smartphone app or a terminal in the vehicle. The server performs the following processes based on the input data.

[0902] Hardware and software configuration

[0903] Hardware

[0904] Smartphone: A device for inputting a user's destination and intermediate points.

[0905] Navigation system for autonomous vehicles: A system for guiding users to their destination.

[0906] Server: A central control unit for data analysis and route calculation.

[0907] software

[0908] Route search algorithm: Calculates the optimal route using Dijkstra's algorithm or A algorithm.

[0909] Data analysis software: Software for analyzing real-time people flow data and traffic information.

[0910] Recommendation engine: Recommends products and services based on the user's interests.

[0911] Data collection and analysis

[0912] The server collects real-time pedestrian flow data from Wi-Fi access points, Bluetooth beacons, and camera image analysis. It also obtains traffic conditions and congestion information from traffic information services. Based on this data, it analyzes the current situation at each point the user moves and calculates the optimal route.

[0913] Calculating the best route

[0914] Based on the collected data, the server applies Dijkstra's algorithm and A algorithm to calculate the optimal route for the user's destination and intermediate points. The calculated route is immediately sent to the user's smartphone or the navigation system of the autonomous vehicle.

[0915] Route guidance and service recommendations

[0916] The navigation system displays the calculated optimal route for the user and provides guidance. At the same time, the server recommends stores and products that the user can stop by along the way based on the user's past travel history and interest data. This information is notified to the user in real time via their smartphone or navigation system.

[0917] Specific examples

[0918] For example, if a user travels from Tokyo Station to Ginza and sets Shinbashi as a stopover, the server receives this information and collects real-time people flow data and traffic information. As a result, it can recommend the optimal route using the Yamanote Line and cafes that can be visited in Shinbashi.

[0919] Prompt Sentence Examples

[0920] User: Please tell me the route from "Tokyo Station" to "Ginza". I would like to stop at "Shinbashi" on the way.

[0921] System: Checking current congestion and traffic information...

[0922] System: Take the Yamanote Line from Tokyo Station, get off at Shimbashi Station, stop at the "Cafe Break" cafe, then walk to Ginza. The system calculates the optimal route, taking into account traffic information and congestion.

[0923] System: The address of the cafe "Cafe Break" is 5-4-1 Shinbashi, and its business hours are 9:00-21:00.

[0924] In this way, the system of the present invention can provide efficient and convenient travel guidance to users and suggest useful information along the way.

[0925] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0926] Step 1:

[0927] The user inputs the destination and intermediate points using a smartphone or a terminal in the vehicle. This user input data is sent from the terminal to the server. The input data includes the destination, starting point, and intermediate points.

[0928] Step 2:

[0929] The server receives input data sent by the user. It then collects people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc. It also obtains the latest traffic information using traffic information services. The input here is data from Wi-Fi access points and traffic information services, and people flow and traffic data is output based on this data.

[0930] Step 3:

[0931] The server analyzes the congestion and traffic conditions related to the user's destination and intermediate points based on the collected real-time people flow data and traffic information. Using data analysis software, this data is processed and calculated to grasp the latest conditions at each point. The output of this step is congestion forecast data for each point.

[0932] Step 4:

[0933] The server uses the analyzed data to calculate the optimal route for the destination and intermediate points specified by the user using Dijkstra's algorithm or A algorithm. The input is the congestion forecast data obtained in step 3, and the output is the route information for the optimal route.

[0934] Step 5:

[0935] The calculated optimal route is sent to the user's smartphone or the autonomous vehicle's navigation system. The device displays the route information of the received optimal route to the user and provides detailed route guidance. The output of this step is the route guidance information displayed on the navigation screen.

[0936] Step 6:

[0937] The server recommends stores and products that are useful during travel based on the user's past travel history and interest data. A recommendation engine is used to analyze this interest data and generate information on the most suitable stores and products. The input is the user's past data, and the output is the suggested store and product information.

[0938] Step 7:

[0939] After arriving at the destination, the user provides feedback through the app about the accuracy and convenience of the route guidance. The device sends this feedback data to the server, which analyzes it and reflects it in the next service improvement. The output of this step is the analyzed feedback data.

[0940] Through the above process, users can receive useful information along with optimal route guidance in real time.

[0941] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0942] The system of the present invention generates an optimal route based on the destination and intermediate points entered by the user, taking into account real-time people flow data and traffic information, and provides guidance to the user.In addition, by combining it with an emotion engine that recognizes the user's emotional state, it provides even more personalized route guidance and recommendations.

[0943] First, the user launches the dedicated application on a device such as a smartphone or tablet, and inputs "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination. This input data is sent from the device to the server.

[0944] When the server receives the transmitted data, it first collects real-time people flow data from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc. In addition, it obtains real-time data such as traffic conditions, congestion information, and public transport delay information from traffic information services.

[0945] The server then analyzes the collected data to determine the current congestion situation around Tokyo Station, Shimbashi, and Ginza. This analysis combines the number of Wi-Fi connected devices, Bluetooth beacon signal strength, and people flow analysis data from camera footage to determine the latest situation at each location where users move in real time. Machine learning models are also applied based on past data to predict congestion and traffic conditions over the next few hours.

[0946] The server then calculates the optimal route from the departure point specified by the user to the destination via intermediate points. This optimal route is calculated using route search algorithms such as Dijkstra's algorithm or A algorithm. The calculated optimal route is sent from the server to the device, and the device displays specific route guidance to the user. For example, guidance such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza" may be displayed.

[0947] Furthermore, the server will recommend stores and products that will be useful during the journey based on the user's past travel history and interest data. For example, it will suggest recommended cafes to stop by in Shimbashi or shopping spots in Ginza. This information will also be sent to the device and notified to the user.

[0948] The present invention further provides a form that incorporates an emotion engine. The emotion engine recognizes emotions from the user's facial expressions and voice, and grasps the user's real-time emotional state. Depending on this emotional state, the server recalculates the optimal route and optimizes the recommendation information. For example, if the user is tired, the server can provide a more comfortable and stress-free travel route or recommend a cafe or rest area where the user can refresh themselves.

[0949] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[0950] By implementing the above process, the system of the present invention can provide users with efficient and convenient travel guidance, and by using an emotion engine, it can provide more personalized travel guidance and recommendations.

[0951] The processing flow will be explained below.

[0952] Step 1:

[0953] The user launches the application on their smartphone or tablet and enters "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination.

[0954] Step 2:

[0955] The terminal sends the user's input data (starting point, intermediate points, and destination) to the server.

[0956] Step 3:

[0957] The server analyzes the received user input data and starts obtaining the required real-time data.

[0958] Step 4:

[0959] The server collects information about connected devices from Wi-Fi access points around Tokyo Station, Shinbashi, and Ginza.

[0960] Step 5:

[0961] The server collects information on the flow of people at each location from Bluetooth beacons, which allows the degree of congestion at each location to be determined.

[0962] Step 6:

[0963] The server accesses the camera video analysis system, acquires video data from each location, and analyzes people flow data.

[0964] Step 7:

[0965] The server uses the traffic information service API to obtain real-time traffic congestion information and public transport delay information.

[0966] Step 8:

[0967] The server integrates the collected data and analyzes the current congestion and traffic conditions at Tokyo Station, Shimbashi, and Ginza, and also applies machine learning models based on past data to predict congestion and traffic conditions over the next few hours.

[0968] Step 9:

[0969] The server calculates the optimal route from the starting point to the intermediate points and the destination based on the user's input data and real-time and predicted data. Here, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[0970] Step 10:

[0971] The server sends the calculated optimal route guidance to the terminal.

[0972] Step 11:

[0973] The device displays the optimal route guidance it has received to the user. For example, specific route instructions such as "Take the Yamanote Line from Tokyo Station to Shinbashi, then walk from Shinbashi to Ginza" are displayed.

[0974] Step 12:

[0975] The device uses the user's facial recognition and voice input functions to detect their current emotional state. For example, it can obtain emotional information such as whether the user is tired or stressed from their facial expression or tone of voice.

[0976] Step 13:

[0977] The emotion data acquired by the device is transmitted to the server.

[0978] Step 14:

[0979] The server analyzes the emotional data and understands the user's current emotional state.

[0980] Step 15:

[0981] The server recalculates the optimal route based on the user's emotional state: for example, if the user is tired, a more comfortable and less congested route will be suggested.

[0982] Step 16:

[0983] The server generates store and product recommendations based on the user's emotional state, suggesting, for example, relaxing cafes and comfortable rest areas.

[0984] Step 17:

[0985] The server sends the recalculated optimal route and recommendation information to the terminal.

[0986] Step 18:

[0987] The device displays the recalculated optimal route guidance and recommendation information to the user.

[0988] Step 19:

[0989] After arriving at their destination, users can enter feedback within the app about the accuracy and convenience of the route guidance.

[0990] Step 20:

[0991] The terminal sends the user's feedback to the server.

[0992] Step 21:

[0993] The server stores the collected feedback data and analyzes it to improve our services in the future.

[0994] Example 2

[0995] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0996] Conventional navigation systems have difficulty providing optimal routes that take into account real-time people flow data and traffic information. Furthermore, they lack a means to provide personalized route guidance and recommendations that take into account the user's emotional state. As a result, users are prone to feeling stressed, and there are problems with reduced convenience during travel.

[0997] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0998] In this invention, the server includes means for acquiring destinations and intermediate points entered by a user, means for collecting people flow data in real time from wireless communication devices, location information beacons, image analysis devices, etc., means for acquiring traffic information from external information provision services, means for analyzing people flow and traffic conditions based on the collected data, means for calculating an optimal route for the user's destination and intermediate points, means for presenting the calculated optimal route to the user, and means for recognizing emotions from the user's facial expressions and voice and optimizing route and recommendation information based on the recognized emotions. This makes it possible to provide efficient and convenient travel guidance taking real-time data into consideration, and furthermore, by using an emotion engine, it becomes possible to provide more personalized travel guidance and recommendations to the user.

[0999] "User" means an individual or corporation that uses the system.

[1000] "Destination" is the final destination to which the user wishes to travel.

[1001] A "waypoint" is a point that a user passes through on the way to the destination.

[1002] A "wireless communication device" is a device such as a Wi-Fi access point or Bluetooth beacon that collects data within its communication range in real time.

[1003] A "location beacon" is a device that is placed in a specific location and communicates with devices within a certain range to provide location information.

[1004] An "image analysis device" is a device that analyzes camera footage to detect and analyze the movements of objects and people.

[1005] "External information provision services" is a general term for services that provide information such as the operation status of public transportation and road traffic conditions.

[1006] "People flow data" is data that shows the movements and stay patterns of people in a specific area.

[1007] "Traffic information" refers to information about traffic, such as road congestion and delays in public transportation.

[1008] "Analysis" is the process of integrating collected data to identify patterns and trends.

[1009] The "optimal route" is the most efficient and convenient route for the user, taking into consideration travel time and convenience.

[1010] "On the way" means the part of the route from the starting point to the destination.

[1011] A "shop" is a place that provides commercial services.

[1012] "Goods" means any goods or services sold or provided.

[1013] "Recommendation" is the act of suggesting appropriate options based on the user's interests and travel situation.

[1014] "Feedback" refers to users' evaluations and opinions of services and systems.

[1015] An "emotion engine" is a device or system that analyzes a user's facial expressions and voice data to recognize their emotional state.

[1016] "Personalization" refers to providing optimal information and services tailored to the characteristics and circumstances of each individual user.

[1017] "System" is a collective term for a series of hardware and software that integrates the above functions and provides services to users.

[1018] The system of the present invention generates an optimal route based on the destination and intermediate points entered by the user, taking into account real-time people flow data and traffic information, and provides guidance to the user.In addition, by combining it with an emotion engine that recognizes the user's emotional state, it provides even more personalized route guidance and recommendations.

[1019] Hardware and Data Acquisition

[1020] Users use a device such as a smartphone or tablet to launch a dedicated application and input their starting point, intermediate points, and destination. This input data is sent from the device to a server. The server collects real-time people flow data through sensor devices such as Wi-Fi access points, Bluetooth beacons, and image analysis devices (cameras). It also obtains current traffic conditions and delay information from external information services (traffic information services).

[1021] Data analysis and route calculation

[1022] The server integrates and analyzes the collected people flow data and traffic information. Specifically, it integrates the number of connected devices from Wi-Fi access points, the signal strength of Bluetooth beacons, and people flow analysis data from camera footage to understand the current congestion situation at each location. It also applies machine learning models to predict congestion and traffic conditions over the next few hours by comparing this data with past data.

[1023] The server uses a route search algorithm (e.g., Dijkstra's algorithm, A algorithm) to calculate the optimal route from the starting point specified by the user to the destination via intermediate points. This optimal route is sent from the server to the terminal, and specific route guidance is displayed to the user.

[1024] Personalization and Recommendations

[1025] The server recommends stores and products that are useful during travel based on the user's past travel history and interest data. For example, it suggests recommended cafes to stop by at intermediate points or shopping spots near the destination. This information is also sent to the device and notified to the user.

[1026] The emotion engine also recognizes the user's emotions from their facial expressions and voice, and recalculates the optimal route and optimizes recommendation information based on that emotional state. For example, if the user is tired, the server will provide a more comfortable and stress-free route or recommend a cafe or rest area where they can refresh themselves.

[1027] Collecting feedback and improving our services

[1028] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[1029] Specific examples

[1030] If a user inputs "From Tokyo Station to Ginza, via Shimbashi, and want to stop by a cafe," the system will provide the optimal route based on real-time data and suggest recommended cafes in Shimbashi.

[1031] "Please provide the best route from Tokyo Station to Ginza via Shimbashi, taking into account real-time traffic and people flow data."

[1032] As explained above, the system of the present invention provides efficient and convenient travel guidance to users, and by using an emotion engine, it is possible to provide more personalized travel guidance and recommendations.

[1033] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1034] Step 1:

[1035] The user launches a dedicated application using a smartphone or tablet and inputs the starting point, intermediate points, and destination.

[1036] Specifically, the user enters "Tokyo Station," "Shinbashi," and "Ginza" into the input fields on the app screen and presses the send button. The input data is sent from the device to the server. The input is the user's destination information, and the output is the data sent to the server.

[1037] Step 2:

[1038] The server receives the transmitted data and collects real-time people flow data from wireless communication devices, location information beacons, image analysis devices, etc.

[1039] Specifically, the server sends a data acquisition request to each sensor device, and the sensor devices return real-time people flow data. The input is the data collected from the sensor devices, and the output is the integrated real-time data.

[1040] Step 3:

[1041] The server acquires traffic information from an external information providing service.

[1042] Specifically, the server sends a request to the traffic information API, and traffic information is returned to the server as an API response. The input is the API request data, and the output is the acquired traffic information.

[1043] Step 4:

[1044] The server integrates the people flow data and traffic information collected and performs analysis.

[1045] Specifically, the server aggregates Wi-Fi, Bluetooth, and camera data and applies analytical algorithms. The input is the aggregated data set, and the output is the latest situational understanding.

[1046] Step 5:

[1047] The server compares the data with past data and uses machine learning models to predict congestion and traffic conditions several hours in the future.

[1048] Specifically, the server inputs current and past data into the machine learning model and outputs future prediction data. The input is past and current data, and the output is the prediction model result.

[1049] Step 6:

[1050] The server calculates the optimal route based on the user's specifications using a route search algorithm.

[1051] Specifically, the server executes Dijkstra's algorithm or A algorithm to generate the optimal route. The input is the destination information specified by the user and the server's analysis results, and the output is the optimal route data.

[1052] Step 7:

[1053] The server transmits the calculated optimum route to the terminal, which then displays specific route guidance to the user.

[1054] Specifically, the terminal screen displays instructions such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza." The input is optimal route data, and the output is the content displayed to the user.

[1055] Step 8:

[1056] The server recommends stores and products that will be useful while traveling based on the user's past travel history and interest data.

[1057] Specifically, the server runs a recommendation algorithm to generate a list of optimal stores and products, and sends it to the terminal. The input is the user's history data, and the output is recommendation information.

[1058] Step 9:

[1059] The terminal receives the recommendation information from the server and notifies the user.

[1060] Specifically, the device displays "recommended cafes in Shimbashi" or "shopping spots in Ginza." The input is recommendation information, and the output is the notification content to the user.

[1061] Step 10:

[1062] The server uses an emotion engine to analyze the user's facial expressions and voice and recognize their emotional state.

[1063] Specifically, it analyzes data captured by the device's camera and microphone and sends the results to a server. The input is the captured facial expression and voice data, and the output is the recognized emotional state.

[1064] Step 11:

[1065] The server recalculates and optimizes the optimal route and recommendation information based on the recognized emotion information.

[1066] Specifically, the server uses the data obtained by the emotion engine to adjust the route and recommendation information and send it to the device. The input is the recognized emotion information, and the output is the optimized route and recommendation information.

[1067] Step 12:

[1068] Once the user arrives at their destination, they will be provided with feedback on the accuracy and convenience of the route guidance and service.

[1069] Specifically, a feedback input form is displayed on the terminal, and the user enters and submits their evaluation or comment. The input is the user's feedback data, and the output is the data to be sent to the server.

[1070] Step 13:

[1071] The server analyzes the feedback data and reflects it in the next service improvement.

[1072] Specifically, the server analyzes the feedback data, identifies problems and areas for improvement, and updates the system's algorithms and data. The input is the feedback data, and the output is updated system information.

[1073] (Application example 2)

[1074] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1075] Conventional route guidance systems provide optimal routes based on traffic information and people flow data, but they are unable to provide personalized guidance or recommendations that take into account the user's emotional state. As a result, even if the user is tired or stressed, they can only present a standard route, making it difficult to provide a comfortable travel experience that suits the user's situation. In addition, there are insufficient means to collect user feedback and use it to improve services.

[1076] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring a destination and intermediate points entered by a user, means for collecting people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera image analysis, etc., means for acquiring traffic information from a traffic information service, means for recognizing the user's emotional state, means for calculating an optimal route and personalized recommended information based on the emotional state, and means for presenting the calculated optimal route and recommended information to the user. This makes it possible to provide more personalized route guidance and recommendations that take the user's emotional state into consideration, significantly improving the user's travel experience.

[1077] The "means for acquiring the destination and intermediate points entered by the user" is a function that receives the departure point, intermediate points, and destination specified by the user via an electronic device and transmits them to the server.

[1078] "Means of collecting people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc." refers to a method of understanding people's movements and congestion levels in specific locations in real time using wireless communication technology and video analysis technology.

[1079] "Means for obtaining traffic information from traffic information services" refers to a method for collecting real-time traffic data from external services that provide information on traffic conditions, congestion, delays in public transportation, and the like.

[1080] "Means for analyzing people flow and traffic conditions based on collected data" refers to the process of using acquired people flow data and traffic information to analyze congestion and traffic conditions at each location and generate information optimal for the user's travel.

[1081] "Means for recognizing the user's emotional state" refers to technology that analyzes the user's facial expressions and voice to identify their emotions at that time.

[1082] "Means for calculating optimal routes and personalized recommended information based on emotional state" refers to a function that calculates and presents information such as the most comfortable and least stressful route, stores and rest areas suitable for the user, etc., based on the recognized emotions.

[1083] "Means for presenting the calculated optimal route and recommended information to the user" refers to a method for displaying the calculated route guidance and recommended information on the user's device.

[1084] "Means of recommending stores and rest areas to users along the way based on analysis results and emotional state" is a function that recommends the most suitable stores and rest areas that users can stop at along the way based on real-time data and emotional state.

[1085] "Means of collecting feedback from users and reflecting it in the next service" refers to the process of collecting opinions and impressions provided by users and using them to improve the service.

[1086] The system for realizing this application example operates by combining multiple pieces of hardware and software.

[1087] First, the user starts a dedicated application on a device such as a smartphone or tablet and inputs the starting point, intermediate points, and destination. This data is then sent from the device to the server.

[1088] The server receives the transmitted data and collects people flow data in real time using Wi-Fi access points, Bluetooth beacons, camera image analysis, etc. It also obtains the latest traffic conditions, congestion information, and public transport delay information from traffic information services.

[1089] Furthermore, the server runs an emotion engine to analyze the user's facial expressions and voice and recognize their emotional state. This emotion engine uses a software library called "face_recognition" and an audio emotion recognition module called "AudioEmotionRecognition."

[1090] Based on the collected real-time data, traffic information, and the user's emotional state, the server calculates the optimal route and personalized recommendations. The algorithms used for this calculation include Dijkstra's algorithm and A algorithm. The calculated optimal route and recommendations are sent from the server to the device and presented to the user.

[1091] As a concrete example, consider the case where a user travels from "Tokyo Station" to "Ginza" and takes a break in "Shinbashi." If the emotion engine recognizes the user's state as "tired," the server will select a less crowded route and recommend cafes and rest areas that the user can stop at in Shinbashi. Conversely, if the user is recognized as "happy," the normal optimal route will be selected.

[1092] After arriving at their destination, users can provide feedback on the accuracy and usefulness of the route guidance and recommendations. This feedback data is also sent to the server, which analyzes it and reflects it in future service improvements.

[1093] This system can provide more comfortable and personalized travel guidance that takes into account the user's emotional state.

[1094] Examples of prompts:

[1095] A user specifies that they want to travel from "Tokyo Station" to "Ginza" and enters "Shinbashi" as a stopover point. This user may be tired. Obtain real-time people flow data and traffic information, and suggest the best route and rest stops based on their emotional state.

[1096] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1097] Step 1:

[1098] The user launches a dedicated application on their smartphone or tablet and inputs the starting point, intermediate points, and destination. The input data is sent from the device to the server. The input data includes the starting point "Tokyo Station," the intermediate point "Shinbashi," and the destination "Ginza," and this is the output data sent to the server.

[1099] Step 2:

[1100] The server receives the input data and collects real-time people flow data from Wi-Fi access points, Bluetooth beacons, and camera video analysis. This allows for a grasp of the congestion situation in a specific area. Data processing involves analyzing the number of Wi-Fi connected devices, Bluetooth beacon signal strength, and camera video data, and the resulting real-time people flow data is generated.

[1101] Step 3:

[1102] The server obtains real-time traffic data from the traffic information service. This traffic data includes information on road congestion, delays in public transportation, etc. The collected traffic data becomes input data for understanding the real-time situation.

[1103] Step 4:

[1104] Based on the collected people flow and traffic data, the server analyzes the specific congestion and traffic conditions at each location. Data processing here includes congestion prediction using machine learning models learned from past data. The output is the analyzed current people flow and traffic condition data.

[1105] Step 5:

[1106] The server recognizes the user's emotional state from their facial expressions and voice. The "face_recognition" library and "AudioEmotionRecognition" module are used for emotion recognition. The input requires the user's image data and voice data, and the output is the user's emotional state (e.g., "tired" or "happy").

[1107] Step 6:

[1108] The server calculates optimal routes and personalized recommendations based on the emotional state. Using route-finding algorithms such as Dijkstra's algorithm and A-algorithm, and taking the emotional state into account, it suggests routes that avoid crowds and rest spots where people can relax. Input data includes analyzed pedestrian flow and traffic data and the emotional state, and the output includes optimal routes and recommendations.

[1109] Step 7:

[1110] The calculated optimal route and recommended information are sent from the server to the device and presented to the user. The device receives this information and displays specific route guidance and recommended information to the user. For example, the optimal route guidance may be something like "take the Yamanote Line from Tokyo Station to Shinbashi, then walk from Shinbashi to Ginza."

[1111] Step 8:

[1112] After arriving at their destination, users provide feedback on route guidance and recommendations through the application. This feedback is sent from the device to the server, which analyzes the data and uses it to improve the service for the next time. The analyzed data is used to improve the next route calculation and recommendation algorithm.

[1113] 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.

[1114] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1115] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1116] [Fourth embodiment]

[1117] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1120] 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.

[1121] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1122] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1123] 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.

[1124] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1125] 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.

[1126] 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 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.

[1127] 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.

[1128] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1129] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1130] The system of the present invention generates an optimal route based on the destination and intermediate points entered by the user, taking into account real-time people flow data and traffic information, and guides the user along the route.The system also aims to make travel more convenient and stimulate purchasing by predicting the user's needs and recommending stores and products along the way.

[1131] An embodiment of this system will be described below with specific examples.

[1132] First, the user launches the dedicated application on a device such as a smartphone or tablet, and inputs "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination. This input data is sent from the device to the server.

[1133] When the server receives the transmitted data, it first collects real-time people flow data from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc. In addition, it obtains real-time data such as traffic conditions, congestion information, and public transport delay information from traffic information services.

[1134] The server then analyzes the collected data to determine the current congestion situation around Tokyo Station, Shimbashi, and Ginza. This analysis combines the number of connected devices obtained from Wi-Fi access points, the signal strength of Bluetooth beacons, and people flow analysis data from camera footage to determine the latest situation at each location where users move in real time. Machine learning models are also applied based on past data to predict congestion and traffic conditions over the next few hours.

[1135] The server then calculates the optimal route from the departure point specified by the user to the destination via intermediate points. This optimal route is calculated using route search algorithms such as Dijkstra's algorithm or A algorithm. The calculated optimal route is sent from the server to the device, and the device displays specific route guidance to the user. For example, guidance such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza" may be displayed.

[1136] Furthermore, the server will recommend stores and products that will be useful during the journey based on the user's past travel history and interest data. For example, it will suggest recommended cafes to stop by in Shimbashi or shopping spots in Ginza. This information will also be sent to the device and notified to the user.

[1137] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[1138] By implementing the above flow, the system of the present invention can provide the user with efficient and convenient travel guidance and suggest useful information along the way.

[1139] The processing flow will be explained below.

[1140] Step 1:

[1141] The user launches the application on their smartphone or tablet and enters "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination.

[1142] Step 2:

[1143] The terminal sends the user's input data (starting point, intermediate points, and destination) to the server.

[1144] Step 3:

[1145] The server analyzes the received user input data and starts obtaining the real-time data that will be required from now on.

[1146] Step 4:

[1147] The server collects information about connected devices from Wi-Fi access points around Tokyo Station, Shinbashi, and Ginza.

[1148] Step 5:

[1149] The server collects information on the flow of people at each location from Bluetooth beacons, which allows the degree of congestion at each location to be determined.

[1150] Step 6:

[1151] The server accesses the camera video analysis system, acquires video data from each location, and analyzes people flow data.

[1152] Step 7:

[1153] The server uses the traffic information service API to obtain real-time traffic congestion information and public transport delay information.

[1154] Step 8:

[1155] The server integrates the collected data and analyzes the current congestion and traffic conditions at Tokyo Station, Shimbashi, and Ginza, and also applies machine learning models based on past data to predict congestion and traffic conditions over the next few hours.

[1156] Step 9:

[1157] The server calculates the optimal route from the starting point to the intermediate points and the destination based on the user's input data and real-time and predicted data. Here, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[1158] Step 10:

[1159] The server sends the calculated optimal route guidance to the terminal.

[1160] Step 11:

[1161] The device displays the optimal route guidance it has received to the user. For example, specific route instructions such as "Take the Yamanote Line from Tokyo Station to Shinbashi, then walk from Shinbashi to Ginza" are displayed.

[1162] Step 12:

[1163] The server recommends stores and products along the way based on the user's past travel history and interest data.

[1164] Step 13:

[1165] The server sends recommendation information to the device, which then notifies the user. For example, users can receive push notifications about cafes to stop by in Shimbashi or shopping spots in Ginza.

[1166] Step 14:

[1167] After arriving in Ginza, users can enter feedback within the app about the accuracy and convenience of the route guidance.

[1168] Step 15:

[1169] The terminal sends the user's feedback to the server.

[1170] Step 16:

[1171] The server stores the collected feedback data and analyzes it to improve our services in the future.

[1172] Example 1

[1173] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1174] In today's urban areas, traffic congestion and congestion are constantly changing, making it difficult for users to reach their destinations efficiently. While providing useful information and store suggestions while traveling would improve user convenience and encourage purchasing, such information provision is lacking. Existing systems have limited real-time data collection and analysis capabilities, and do not adequately provide optimal route guidance and recommendations tailored to user needs. Furthermore, they lack a mechanism for collecting user feedback and incorporating it into future service improvements.

[1175] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1176] In this invention, the server includes: a means for acquiring a destination and intermediate points entered by a user; a means for transmitting data from a terminal to the server using a communication network; a means for collecting people flow data in real time from Wi-Fi access points, short-range wireless communication beacons, video analysis devices, etc.; a means for acquiring traffic information from a traffic information service; a means for analyzing people flow and traffic conditions based on the collected data; a means for predicting congestion and traffic conditions using the analyzed data and a prediction algorithm; a means for calculating an optimal route for the user's destination and intermediate points; and a means for presenting the calculated optimal route to the user. This allows users to reach their destination efficiently and also receive useful store and product information along the way. Furthermore, the quality of service can be continuously improved based on feedback, thereby achieving higher user satisfaction.

[1177] "User" refers to an individual or corporation that uses the system to input a destination and intermediate points and receives suggestions for the optimal route, stores, and products.

[1178] "Destination" refers to the final destination that the user enters into the system.

[1179] A "waypoint" refers to a point that a user passes through on the way to their destination.

[1180] The term "means for acquiring" refers to a device or program for recording the destination and intermediate points entered by the user and transmitting them to the server.

[1181] A "communications network" is an infrastructure for transmitting data, including the Internet, wireless communications, cellular networks, etc.

[1182] "Terminal" refers to a portable information and communication device used by a user, such as a smartphone or tablet.

[1183] A "server" refers to a remote computer system that processes large amounts of data, handles user queries, and analyzes data.

[1184] "Wi-Fi access point" refers to a physical device or endpoint for connecting to the Internet via a wireless network.

[1185] A "near field communication beacon" is a device that uses short-range wireless communication technology such as Bluetooth to communicate with devices within a certain range.

[1186] "Video analysis device" refers to a device or software that analyzes camera footage to grasp the flow of people and congestion conditions in real time.

[1187] "Traffic information service" refers to an external information service that provides real-time information on traffic conditions and public transportation.

[1188] "Means for analysis" refers to a device or program that analyzes people flow and traffic conditions based on collected data and grasps current and future congestion conditions.

[1189] "Predictive algorithms" refer to mathematical methods that use machine learning models and statistical methods to predict future congestion and traffic conditions.

[1190] The "optimal route" refers to the most efficient route when a user travels from a starting point to a destination via intermediate points.

[1191] "Calculation means" refers to a device or program for deriving the optimal route based on the user's input data and the analysis results.

[1192] "Presenting means" refers to a device or program for visually or audibly notifying the user of the calculated optimal route or recommendation information.

[1193] The "recommending means" refers to a device or program for recommending stores and products that are useful to the user during their travels.

[1194] "Means for collection" refers to a device or program used to collect user feedback and improve the service based on that data.

[1195] The system of this invention collects real-time people flow data and traffic information based on the starting point, intermediate points, and destination entered by the user, and calculates and provides the optimal route. It also aims to make travel more convenient and stimulate purchasing by predicting the user's needs and recommending stores and products along the way.

[1196] First, the user launches the dedicated application on a device such as a smartphone or tablet, and inputs "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination. This input data is sent from the device to the server.

[1197] When the server receives the transmitted data, it first collects real-time people flow data from Wi-Fi access points, short-range wireless communication beacons, video analysis devices, etc. In addition, it obtains real-time data such as traffic conditions, congestion information, and public transport delay information from traffic information services.

[1198] The server then analyzes the collected data to determine the current congestion situation around Tokyo Station, Shimbashi, and Ginza. This analysis combines the number of connected devices obtained from Wi-Fi access points, the signal strength of short-range communication beacons, and people flow analysis data from video analytics equipment to determine the latest situation at each location where users are moving in real time. Machine learning models are also applied based on past data to predict congestion and traffic conditions over the next few hours.

[1199] The server then calculates the optimal route from the departure point specified by the user to the destination via intermediate points. This optimal route is calculated using route search algorithms such as Dijkstra's algorithm or A algorithm. The calculated optimal route is sent from the server to the device, and the device displays specific route guidance to the user. For example, guidance such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza" may be displayed.

[1200] Furthermore, the server will recommend stores and products that will be useful during the journey based on the user's past travel history and interest data. For example, it will suggest recommended cafes to stop by in Shimbashi or shopping spots in Ginza. This information will also be sent to the device and notified to the user.

[1201] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[1202] This enables the system of the present invention to provide efficient and convenient travel guidance to users and suggest useful information along the way.

[1203] Examples and prompts:

[1204] For example, the prompt below shows the situation when a user wants to know the best route from Tokyo Station to Ginza via Shimbashi and recommendations for stores to stop at along the way.

[1205] Example prompt sentence:

[1206] User: What is the best route from Tokyo Station to Ginza via Shimbashi, and what stores should I stop at along the way?

[1207] server:

[1208] 1. Take the Yamanote Line from Tokyo Station and get off at Shimbashi.

[1209] 2. Walk from Shimbashi Station to Ginza.

[1210] 3. Recommended cafe near Shimbashi Station: Cafe ABC

[1211] 4. Recommended Shopping Spots in Ginza: Shop XYZ

[1212] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1213] Step 1: User Input

[1214] Users launch a dedicated application on their smartphone or tablet and input their starting point, intermediate points, and destination. For example, they can input "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination.

[1215] Input: Start point, stopover point, and destination information.

[1216] Output: This information is saved in the device.

[1217] Step 2: Send data to the server

[1218] The terminal sends the data entered by the user (starting point, intermediate points, and destination) to the server via a communication network.

[1219] Input: User-entered start, stop, and destination data.

[1220] Output: The data sent to the server.

[1221] Step 3: Data collection by the server

[1222] The server collects real-time people flow data from Wi-Fi access points, short-range wireless communication beacons, video analysis devices, etc. It also obtains congestion and delay information from traffic information services.

[1223] Input: User's destination and intermediate destination data sent to the server.

[1224] Output: Real-time people flow data, traffic information data.

[1225] Step 4: Data analysis

[1226] The server analyzes pedestrian flow and traffic conditions based on the collected data. It integrates data from the number of devices connected to Wi-Fi access points, beacon signal strength, and video analysis equipment to grasp pedestrian flow conditions. It also uses machine learning models to predict future congestion and traffic conditions.

[1227] Input: Real-time people flow data, traffic information data.

[1228] Output: Congestion status, traffic conditions and forecast data for each location.

[1229] Step 5: Optimal route calculation

[1230] The server uses Dijkstra's algorithm or the A algorithm to calculate the optimal route based on user input, taking into account analytical results and forecast data.

[1231] Input: Data on starting point, intermediate points, destination, congestion status, traffic conditions and forecast data.

[1232] Output: Information about the best route.

[1233] Step 6: Send directions

[1234] The server then sends the calculated optimal route to the device, which uses this information to provide real-time route guidance to the user.

[1235] Input: The optimal route calculated by the server.

[1236] Output: The optimal route information sent to the device.

[1237] Step 7: Recommendations

[1238] The server then recommends stores and products that will be useful during the journey based on the user's past travel history and interest data, and this information is also later sent to the device.

[1239] Input: User's past travel history, interest data.

[1240] Output: Information about recommended stores and products.

[1241] Step 8: Submit your recommendation

[1242] The server transmits the recommendation information to the terminal, and the terminal notifies the user of this information.

[1243] Input: Information recommended by the server.

[1244] Output: Recommendation information sent to the device.

[1245] Step 9: Users provide feedback

[1246] After arriving at their destination, users provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[1247] Input: Feedback information from the user.

[1248] Output: Feedback information sent to the server, service improvement data.

[1249] (Application example 1)

[1250] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1251] An efficient and safe navigation system is important for modern autonomous vehicles, but conventional systems are unable to fully reflect real-time people flow data and traffic information, making it difficult to provide optimal route guidance or store and product recommendations that meet user needs. Overcoming these shortcomings and providing users with a higher quality travel experience is essential.

[1252] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1253] In this invention, the server includes means for acquiring destinations and waypoints entered by a user, means for collecting people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera image analysis, etc., means for acquiring traffic information from traffic information services, means for analyzing people flow and traffic conditions, means for calculating an optimal route for the user's destination and waypoints, means for presenting the calculated optimal route to the user, and means for being incorporated into the navigation system of the autonomous vehicle and providing route guidance. This enables optimal route guidance based on real-time data and can also recommend stores and products along the way to the user.

[1254] "Destination" is the location that the user sets as the destination.

[1255] A "waypoint" is a place where a user stops on the way to their destination.

[1256] A "Wi-Fi access point" is a device that provides Internet connectivity in a wireless LAN environment.

[1257] A "Bluetooth beacon" is a device that uses Bluetooth technology to detect devices in close proximity.

[1258] "Camera video analysis" is a technology that analyzes video data captured by a camera and extracts specific information.

[1259] "People flow data" is data that represents the movement patterns and density of people within a specific area.

[1260] "Traffic information service" is a service that provides real-time traffic conditions and congestion information.

[1261] The "optimal route" is the most efficient travel route calculated based on the input destination and intermediate points, taking into account real-time data.

[1262] "Navigation system" is a general term for devices and systems that provide route guidance to a destination.

[1263] "Recommendation" refers to recommending specific services or products based on a user's interests and behavioral history.

[1264] "Feedback" means opinions and evaluations provided by users regarding the results and experiences of using the service.

[1265] The system of the present invention is implemented as a navigation system for an autonomous vehicle. First, the user inputs the destination and intermediate points. This input data is sent to a server via a smartphone app or a terminal in the vehicle. The server performs the following processes based on the input data.

[1266] Hardware and software configuration

[1267] Hardware

[1268] Smartphone: A device for inputting a user's destination and intermediate points.

[1269] Navigation system for autonomous vehicles: A system for guiding users to their destination.

[1270] Server: A central control unit for data analysis and route calculation.

[1271] software

[1272] Route search algorithm: Calculates the optimal route using Dijkstra's algorithm or A algorithm.

[1273] Data analysis software: Software for analyzing real-time people flow data and traffic information.

[1274] Recommendation engine: Recommends products and services based on the user's interests.

[1275] Data collection and analysis

[1276] The server collects real-time pedestrian flow data from Wi-Fi access points, Bluetooth beacons, and camera image analysis. It also obtains traffic conditions and congestion information from traffic information services. Based on this data, it analyzes the current situation at each point the user moves and calculates the optimal route.

[1277] Calculating the best route

[1278] Based on the collected data, the server applies Dijkstra's algorithm and A algorithm to calculate the optimal route for the user's destination and intermediate points. The calculated route is immediately sent to the user's smartphone or the navigation system of the autonomous vehicle.

[1279] Route guidance and service recommendations

[1280] The navigation system displays the calculated optimal route for the user and provides guidance. At the same time, the server recommends stores and products that the user can stop by along the way based on the user's past travel history and interest data. This information is notified to the user in real time via their smartphone or navigation system.

[1281] Specific examples

[1282] For example, if a user travels from Tokyo Station to Ginza and sets Shinbashi as a stopover, the server receives this information and collects real-time people flow data and traffic information. As a result, it can recommend the optimal route using the Yamanote Line and cafes that can be visited in Shinbashi.

[1283] Prompt Sentence Examples

[1284] User: Please tell me the route from "Tokyo Station" to "Ginza". I would like to stop at "Shinbashi" on the way.

[1285] System: Checking current congestion and traffic information...

[1286] System: Take the Yamanote Line from Tokyo Station, get off at Shimbashi Station, stop at the "Cafe Break" cafe, then walk to Ginza. The system calculates the optimal route, taking into account traffic information and congestion.

[1287] System: The address of the cafe "Cafe Break" is 5-4-1 Shinbashi, and its business hours are 9:00-21:00.

[1288] In this way, the system of the present invention can provide efficient and convenient travel guidance to users and suggest useful information along the way.

[1289] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1290] Step 1:

[1291] The user inputs the destination and intermediate points using a smartphone or a terminal in the vehicle. This user input data is sent from the terminal to the server. The input data includes the destination, starting point, and intermediate points.

[1292] Step 2:

[1293] The server receives input data sent by the user. It then collects people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc. It also obtains the latest traffic information using traffic information services. The input here is data from Wi-Fi access points and traffic information services, and people flow and traffic data is output based on this data.

[1294] Step 3:

[1295] The server analyzes the congestion and traffic conditions related to the user's destination and intermediate points based on the collected real-time people flow data and traffic information. Using data analysis software, this data is processed and calculated to grasp the latest conditions at each point. The output of this step is congestion forecast data for each point.

[1296] Step 4:

[1297] The server uses the analyzed data to calculate the optimal route for the destination and intermediate points specified by the user using Dijkstra's algorithm or A algorithm. The input is the congestion forecast data obtained in step 3, and the output is the route information for the optimal route.

[1298] Step 5:

[1299] The calculated optimal route is sent to the user's smartphone or the autonomous vehicle's navigation system. The device displays the route information of the received optimal route to the user and provides detailed route guidance. The output of this step is the route guidance information displayed on the navigation screen.

[1300] Step 6:

[1301] The server recommends stores and products that are useful during travel based on the user's past travel history and interest data. A recommendation engine is used to analyze this interest data and generate information on the most suitable stores and products. The input is the user's past data, and the output is the suggested store and product information.

[1302] Step 7:

[1303] After arriving at the destination, the user provides feedback through the app about the accuracy and convenience of the route guidance. The device sends this feedback data to the server, which analyzes it and reflects it in the next service improvement. The output of this step is the analyzed feedback data.

[1304] Through the above process, users can receive useful information along with optimal route guidance in real time.

[1305] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1306] The system of the present invention generates an optimal route based on the destination and intermediate points entered by the user, taking into account real-time people flow data and traffic information, and provides guidance to the user.In addition, by combining it with an emotion engine that recognizes the user's emotional state, it provides even more personalized route guidance and recommendations.

[1307] First, the user launches the dedicated application on a device such as a smartphone or tablet, and inputs "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination. This input data is sent from the device to the server.

[1308] When the server receives the transmitted data, it first collects real-time people flow data from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc. In addition, it obtains real-time data such as traffic conditions, congestion information, and public transport delay information from traffic information services.

[1309] The server then analyzes the collected data to determine the current congestion situation around Tokyo Station, Shimbashi, and Ginza. This analysis combines the number of Wi-Fi connected devices, Bluetooth beacon signal strength, and people flow analysis data from camera footage to determine the latest situation at each location where users move in real time. Machine learning models are also applied based on past data to predict congestion and traffic conditions over the next few hours.

[1310] The server then calculates the optimal route from the departure point specified by the user to the destination via intermediate points. This optimal route is calculated using route search algorithms such as Dijkstra's algorithm or A algorithm. The calculated optimal route is sent from the server to the device, and the device displays specific route guidance to the user. For example, guidance such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza" may be displayed.

[1311] Furthermore, the server will recommend stores and products that will be useful during the journey based on the user's past travel history and interest data. For example, it will suggest recommended cafes to stop by in Shimbashi or shopping spots in Ginza. This information will also be sent to the device and notified to the user.

[1312] The present invention further provides a form that incorporates an emotion engine. The emotion engine recognizes emotions from the user's facial expressions and voice, and grasps the user's real-time emotional state. Depending on this emotional state, the server recalculates the optimal route and optimizes the recommendation information. For example, if the user is tired, the server can provide a more comfortable and stress-free travel route or recommend a cafe or rest area where the user can refresh themselves.

[1313] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[1314] By implementing the above process, the system of the present invention can provide users with efficient and convenient travel guidance, and by using an emotion engine, it can provide more personalized travel guidance and recommendations.

[1315] The processing flow will be explained below.

[1316] Step 1:

[1317] The user launches the application on their smartphone or tablet and enters "Tokyo Station" as the starting point, "Shinbashi" as the intermediate point, and "Ginza" as the destination.

[1318] Step 2:

[1319] The terminal sends the user's input data (starting point, intermediate points, and destination) to the server.

[1320] Step 3:

[1321] The server analyzes the received user input data and starts obtaining the required real-time data.

[1322] Step 4:

[1323] The server collects information about connected devices from Wi-Fi access points around Tokyo Station, Shinbashi, and Ginza.

[1324] Step 5:

[1325] The server collects information on the flow of people at each location from Bluetooth beacons, which allows the degree of congestion at each location to be determined.

[1326] Step 6:

[1327] The server accesses the camera video analysis system, acquires video data from each location, and analyzes people flow data.

[1328] Step 7:

[1329] The server uses the traffic information service API to obtain real-time traffic congestion information and public transport delay information.

[1330] Step 8:

[1331] The server integrates the collected data and analyzes the current congestion and traffic conditions at Tokyo Station, Shimbashi, and Ginza, and also applies machine learning models based on past data to predict congestion and traffic conditions over the next few hours.

[1332] Step 9:

[1333] The server calculates the optimal route from the starting point to the intermediate points and the destination based on the user's input data and real-time and predicted data. Here, it uses route search algorithms such as Dijkstra's algorithm and A algorithm.

[1334] Step 10:

[1335] The server sends the calculated optimal route guidance to the terminal.

[1336] Step 11:

[1337] The device displays the optimal route guidance it has received to the user. For example, specific route instructions such as "Take the Yamanote Line from Tokyo Station to Shinbashi, then walk from Shinbashi to Ginza" are displayed.

[1338] Step 12:

[1339] The device uses the user's facial recognition and voice input functions to detect their current emotional state. For example, it can obtain emotional information such as whether the user is tired or stressed from their facial expression or tone of voice.

[1340] Step 13:

[1341] The emotion data acquired by the device is transmitted to the server.

[1342] Step 14:

[1343] The server analyzes the emotional data and understands the user's current emotional state.

[1344] Step 15:

[1345] The server recalculates the optimal route based on the user's emotional state: for example, if the user is tired, a more comfortable and less congested route will be suggested.

[1346] Step 16:

[1347] The server generates store and product recommendations based on the user's emotional state, suggesting, for example, relaxing cafes and comfortable rest areas.

[1348] Step 17:

[1349] The server sends the recalculated optimal route and recommendation information to the terminal.

[1350] Step 18:

[1351] The device displays the recalculated optimal route guidance and recommendation information to the user.

[1352] Step 19:

[1353] After arriving at their destination, users can enter feedback within the app about the accuracy and convenience of the route guidance.

[1354] Step 20:

[1355] The terminal sends the user's feedback to the server.

[1356] Step 21:

[1357] The server stores the collected feedback data and analyzes it to improve our services in the future.

[1358] Example 2

[1359] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1360] Conventional navigation systems have difficulty providing optimal routes that take into account real-time people flow data and traffic information. Furthermore, they lack a means to provide personalized route guidance and recommendations that take into account the user's emotional state. As a result, users are prone to feeling stressed, and there are problems with reduced convenience during travel.

[1361] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1362] In this invention, the server includes means for acquiring destinations and intermediate points entered by a user, means for collecting people flow data in real time from wireless communication devices, location information beacons, image analysis devices, etc., means for acquiring traffic information from external information provision services, means for analyzing people flow and traffic conditions based on the collected data, means for calculating an optimal route for the user's destination and intermediate points, means for presenting the calculated optimal route to the user, and means for recognizing emotions from the user's facial expressions and voice and optimizing route and recommendation information based on the recognized emotions. This makes it possible to provide efficient and convenient travel guidance taking real-time data into consideration, and furthermore, by using an emotion engine, it becomes possible to provide more personalized travel guidance and recommendations to the user.

[1363] "User" means an individual or corporation that uses the system.

[1364] "Destination" is the final destination to which the user wishes to travel.

[1365] A "waypoint" is a point that a user passes through on the way to the destination.

[1366] A "wireless communication device" is a device such as a Wi-Fi access point or Bluetooth beacon that collects data within its communication range in real time.

[1367] A "location beacon" is a device that is placed in a specific location and communicates with devices within a certain range to provide location information.

[1368] An "image analysis device" is a device that analyzes camera footage to detect and analyze the movements of objects and people.

[1369] "External information provision services" is a general term for services that provide information such as the operation status of public transportation and road traffic conditions.

[1370] "People flow data" is data that shows the movements and stay patterns of people in a specific area.

[1371] "Traffic information" refers to information about traffic, such as road congestion and delays in public transportation.

[1372] "Analysis" is the process of integrating collected data to identify patterns and trends.

[1373] The "optimal route" is the most efficient and convenient route for the user, taking into consideration travel time and convenience.

[1374] "On the way" means the part of the route from the starting point to the destination.

[1375] A "shop" is a place that provides commercial services.

[1376] "Goods" means any goods or services sold or provided.

[1377] "Recommendation" is the act of suggesting appropriate options based on the user's interests and travel situation.

[1378] "Feedback" refers to users' evaluations and opinions of services and systems.

[1379] An "emotion engine" is a device or system that analyzes a user's facial expressions and voice data to recognize their emotional state.

[1380] "Personalization" refers to providing optimal information and services tailored to the characteristics and circumstances of each individual user.

[1381] "System" is a collective term for a series of hardware and software that integrates the above functions and provides services to users.

[1382] The system of the present invention generates an optimal route based on the destination and intermediate points entered by the user, taking into account real-time people flow data and traffic information, and provides guidance to the user.In addition, by combining it with an emotion engine that recognizes the user's emotional state, it provides even more personalized route guidance and recommendations.

[1383] Hardware and Data Acquisition

[1384] Users use a device such as a smartphone or tablet to launch a dedicated application and input their starting point, intermediate points, and destination. This input data is sent from the device to a server. The server collects real-time people flow data through sensor devices such as Wi-Fi access points, Bluetooth beacons, and image analysis devices (cameras). It also obtains current traffic conditions and delay information from external information services (traffic information services).

[1385] Data analysis and route calculation

[1386] The server integrates and analyzes the collected people flow data and traffic information. Specifically, it integrates the number of connected devices from Wi-Fi access points, the signal strength of Bluetooth beacons, and people flow analysis data from camera footage to understand the current congestion situation at each location. It also applies machine learning models to predict congestion and traffic conditions over the next few hours by comparing this data with past data.

[1387] The server uses a route search algorithm (e.g., Dijkstra's algorithm, A algorithm) to calculate the optimal route from the starting point specified by the user to the destination via intermediate points. This optimal route is sent from the server to the terminal, and specific route guidance is displayed to the user.

[1388] Personalization and Recommendations

[1389] The server recommends stores and products that are useful during travel based on the user's past travel history and interest data. For example, it suggests recommended cafes to stop by at intermediate points or shopping spots near the destination. This information is also sent to the device and notified to the user.

[1390] The emotion engine also recognizes the user's emotions from their facial expressions and voice, and recalculates the optimal route and optimizes recommendation information based on that emotional state. For example, if the user is tired, the server will provide a more comfortable and stress-free route or recommend a cafe or rest area where they can refresh themselves.

[1391] Collecting feedback and improving our services

[1392] After arriving at their destination, users can provide feedback through the app about the accuracy and convenience of the route guidance. This feedback is sent from the device to the server, which analyzes the data and reflects it in future service improvements.

[1393] Specific examples

[1394] If a user inputs "From Tokyo Station to Ginza, via Shimbashi, and want to stop by a cafe," the system will provide the optimal route based on real-time data and suggest recommended cafes in Shimbashi.

[1395] "Please provide the best route from Tokyo Station to Ginza via Shimbashi, taking into account real-time traffic and people flow data."

[1396] As explained above, the system of the present invention provides efficient and convenient travel guidance to users, and by using an emotion engine, it is possible to provide more personalized travel guidance and recommendations.

[1397] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1398] Step 1:

[1399] The user launches a dedicated application using a smartphone or tablet and inputs the starting point, intermediate points, and destination.

[1400] Specifically, the user enters "Tokyo Station," "Shinbashi," and "Ginza" into the input fields on the app screen and presses the send button. The input data is sent from the device to the server. The input is the user's destination information, and the output is the data sent to the server.

[1401] Step 2:

[1402] The server receives the transmitted data and collects real-time people flow data from wireless communication devices, location information beacons, image analysis devices, etc.

[1403] Specifically, the server sends a data acquisition request to each sensor device, and the sensor devices return real-time people flow data. The input is the data collected from the sensor devices, and the output is the integrated real-time data.

[1404] Step 3:

[1405] The server acquires traffic information from an external information providing service.

[1406] Specifically, the server sends a request to the traffic information API, and traffic information is returned to the server as an API response. The input is the API request data, and the output is the acquired traffic information.

[1407] Step 4:

[1408] The server integrates the people flow data and traffic information collected and performs analysis.

[1409] Specifically, the server aggregates Wi-Fi, Bluetooth, and camera data and applies analytical algorithms. The input is the aggregated data set, and the output is the latest situational understanding.

[1410] Step 5:

[1411] The server compares the data with past data and uses machine learning models to predict congestion and traffic conditions several hours in the future.

[1412] Specifically, the server inputs current and past data into the machine learning model and outputs future prediction data. The input is past and current data, and the output is the prediction model result.

[1413] Step 6:

[1414] The server calculates the optimal route based on the user's specifications using a route search algorithm.

[1415] Specifically, the server executes Dijkstra's algorithm or A algorithm to generate the optimal route. The input is the destination information specified by the user and the server's analysis results, and the output is the optimal route data.

[1416] Step 7:

[1417] The server transmits the calculated optimum route to the terminal, which then displays specific route guidance to the user.

[1418] Specifically, the terminal screen displays instructions such as "Take the Yamanote Line from Tokyo Station, get off at Shimbashi, and walk from Shimbashi Station to Ginza." The input is optimal route data, and the output is the content displayed to the user.

[1419] Step 8:

[1420] The server recommends stores and products that will be useful while traveling based on the user's past travel history and interest data.

[1421] Specifically, the server runs a recommendation algorithm to generate a list of optimal stores and products, and sends it to the terminal. The input is the user's history data, and the output is recommendation information.

[1422] Step 9:

[1423] The terminal receives the recommendation information from the server and notifies the user.

[1424] Specifically, the device displays "recommended cafes in Shimbashi" or "shopping spots in Ginza." The input is recommendation information, and the output is the notification content to the user.

[1425] Step 10:

[1426] The server uses an emotion engine to analyze the user's facial expressions and voice and recognize their emotional state.

[1427] Specifically, it analyzes data captured by the device's camera and microphone and sends the results to a server. The input is the captured facial expression and voice data, and the output is the recognized emotional state.

[1428] Step 11:

[1429] The server recalculates and optimizes the optimal route and recommendation information based on the recognized emotion information.

[1430] Specifically, the server uses the data obtained by the emotion engine to adjust the route and recommendation information and send it to the device. The input is the recognized emotion information, and the output is the optimized route and recommendation information.

[1431] Step 12:

[1432] Once the user arrives at their destination, they will be provided with feedback on the accuracy and convenience of the route guidance and service.

[1433] Specifically, a feedback input form is displayed on the terminal, and the user enters and submits their evaluation or comment. The input is the user's feedback data, and the output is the data to be sent to the server.

[1434] Step 13:

[1435] The server analyzes the feedback data and reflects it in the next service improvement.

[1436] Specifically, the server analyzes the feedback data, identifies problems and areas for improvement, and updates the system's algorithms and data. The input is the feedback data, and the output is updated system information.

[1437] (Application example 2)

[1438] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1439] Conventional route guidance systems provide optimal routes based on traffic information and people flow data, but they are unable to provide personalized guidance or recommendations that take into account the user's emotional state. As a result, even if the user is tired or stressed, they can only present a standard route, making it difficult to provide a comfortable travel experience that suits the user's situation. In addition, there are insufficient means to collect user feedback and use it to improve services.

[1440] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring a destination and intermediate points entered by a user, means for collecting people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera image analysis, etc., means for acquiring traffic information from a traffic information service, means for recognizing the user's emotional state, means for calculating an optimal route and personalized recommended information based on the emotional state, and means for presenting the calculated optimal route and recommended information to the user. This makes it possible to provide more personalized route guidance and recommendations that take the user's emotional state into consideration, significantly improving the user's travel experience.

[1441] The "means for acquiring the destination and intermediate points entered by the user" is a function that receives the departure point, intermediate points, and destination specified by the user via an electronic device and transmits them to the server.

[1442] "Means of collecting people flow data in real time from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc." refers to a method of understanding people's movements and congestion levels in specific locations in real time using wireless communication technology and video analysis technology.

[1443] "Means for obtaining traffic information from traffic information services" refers to a method for collecting real-time traffic data from external services that provide information on traffic conditions, congestion, delays in public transportation, and the like.

[1444] "Means for analyzing people flow and traffic conditions based on collected data" refers to the process of using acquired people flow data and traffic information to analyze congestion and traffic conditions at each location and generate information optimal for the user's travel.

[1445] "Means for recognizing the user's emotional state" refers to technology that analyzes the user's facial expressions and voice to identify their emotions at that time.

[1446] "Means for calculating optimal routes and personalized recommended information based on emotional state" refers to a function that calculates and presents information such as the most comfortable and least stressful route, stores and rest areas suitable for the user, etc., based on the recognized emotions.

[1447] "Means for presenting the calculated optimal route and recommended information to the user" refers to a method for displaying the calculated route guidance and recommended information on the user's device.

[1448] "Means of recommending stores and rest areas to users along the way based on analysis results and emotional state" is a function that recommends the most suitable stores and rest areas that users can stop at along the way based on real-time data and emotional state.

[1449] "Means of collecting feedback from users and reflecting it in the next service" refers to the process of collecting opinions and impressions provided by users and using them to improve the service.

[1450] The system for realizing this application example operates by combining multiple pieces of hardware and software.

[1451] First, the user starts a dedicated application on a device such as a smartphone or tablet and inputs the starting point, intermediate points, and destination. This data is then sent from the device to the server.

[1452] The server receives the transmitted data and collects people flow data in real time using Wi-Fi access points, Bluetooth beacons, camera image analysis, etc. It also obtains the latest traffic conditions, congestion information, and public transport delay information from traffic information services.

[1453] Furthermore, the server runs an emotion engine to analyze the user's facial expressions and voice and recognize their emotional state. This emotion engine uses a software library called "face_recognition" and an audio emotion recognition module called "AudioEmotionRecognition."

[1454] Based on the collected real-time data, traffic information, and the user's emotional state, the server calculates the optimal route and personalized recommendations. The algorithms used for this calculation include Dijkstra's algorithm and A algorithm. The calculated optimal route and recommendations are sent from the server to the device and presented to the user.

[1455] As a concrete example, consider the case where a user travels from "Tokyo Station" to "Ginza" and takes a break in "Shinbashi." If the emotion engine recognizes the user's state as "tired," the server will select a less crowded route and recommend cafes and rest areas that the user can stop at in Shinbashi. Conversely, if the user is recognized as "happy," the normal optimal route will be selected.

[1456] After arriving at their destination, users can provide feedback on the accuracy and usefulness of the route guidance and recommendations. This feedback data is also sent to the server, which analyzes it and reflects it in future service improvements.

[1457] This system can provide more comfortable and personalized travel guidance that takes into account the user's emotional state.

[1458] Examples of prompts:

[1459] A user specifies that they want to travel from "Tokyo Station" to "Ginza" and enters "Shinbashi" as a stopover point. This user may be tired. Obtain real-time people flow data and traffic information, and suggest the best route and rest stops based on their emotional state.

[1460] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1461] Step 1:

[1462] The user launches a dedicated application on their smartphone or tablet and inputs the starting point, intermediate points, and destination. The input data is sent from the device to the server. The input data includes the starting point "Tokyo Station," the intermediate point "Shinbashi," and the destination "Ginza," and this is the output data sent to the server.

[1463] Step 2:

[1464] The server receives the input data and collects real-time people flow data from Wi-Fi access points, Bluetooth beacons, and camera video analysis. This allows for a grasp of the congestion situation in a specific area. Data processing involves analyzing the number of Wi-Fi connected devices, Bluetooth beacon signal strength, and camera video data, and the resulting real-time people flow data is generated.

[1465] Step 3:

[1466] The server obtains real-time traffic data from the traffic information service. This traffic data includes information on road congestion, delays in public transportation, etc. The collected traffic data becomes input data for understanding the real-time situation.

[1467] Step 4:

[1468] Based on the collected people flow and traffic data, the server analyzes the specific congestion and traffic conditions at each location. Data processing here includes congestion prediction using machine learning models learned from past data. The output is the analyzed current people flow and traffic condition data.

[1469] Step 5:

[1470] The server recognizes the user's emotional state from their facial expressions and voice. The "face_recognition" library and "AudioEmotionRecognition" module are used for emotion recognition. The input requires the user's image data and voice data, and the output is the user's emotional state (e.g., "tired" or "happy").

[1471] Step 6:

[1472] The server calculates optimal routes and personalized recommendations based on the emotional state. Using route-finding algorithms such as Dijkstra's algorithm and A-algorithm, and taking the emotional state into account, it suggests routes that avoid crowds and rest spots where people can relax. Input data includes analyzed pedestrian flow and traffic data and the emotional state, and the output includes optimal routes and recommendations.

[1473] Step 7:

[1474] The calculated optimal route and recommended information are sent from the server to the device and presented to the user. The device receives this information and displays specific route guidance and recommended information to the user. For example, the optimal route guidance may be something like "take the Yamanote Line from Tokyo Station to Shinbashi, then walk from Shinbashi to Ginza."

[1475] Step 8:

[1476] After arriving at their destination, users provide feedback on route guidance and recommendations through the application. This feedback is sent from the device to the server, which analyzes the data and uses it to improve the service for the next time. The analyzed data is used to improve the next route calculation and recommendation algorithm.

[1477] 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.

[1478] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1479] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1480] 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.

[1481] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1482] 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.

[1483] 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).

[1484] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1485] 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."

[1486] 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.

[1487] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1488] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1489] 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.

[1490] 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.

[1491] 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.

[1492] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1493] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1494] 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.

[1495] 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.

[1496] 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.

[1497] 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.

[1498] The following is further disclosed regarding the above embodiment.

[1499] (Claim 1)

[1500] means for acquiring destinations and intermediate points input by a user;

[1501] A means of collecting real-time people flow data from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc.

[1502] means for obtaining traffic information from a traffic information service;

[1503] A means of analyzing people flow and traffic conditions based on the collected data;

[1504] means for calculating an optimal route for a user's destination and intermediate points;

[1505] means for presenting the calculated optimum route to a user;

[1506] A system including:

[1507] (Claim 2)

[1508] The system according to claim 1, further comprising means for recommending stores and products along the way to the user based on the analysis results.

[1509] (Claim 3)

[1510] 2. The system according to claim 1, further comprising means for collecting feedback from users and reflecting the feedback in future services.

[1511] "Example 1"

[1512] (Claim 1)

[1513] means for acquiring destinations and intermediate points input by a user;

[1514] means for transmitting data from the terminal to a server using a communications network;

[1515] A means of collecting real-time people flow data from Wi-Fi access points, short-range wireless communication beacons, video analysis devices, etc.

[1516] means for obtaining traffic information from a traffic information service;

[1517] A means of analyzing people flow and traffic conditions based on the collected data;

[1518] a means for predicting congestion and traffic conditions using analytical data and predictive algorithms;

[1519] means for calculating an optimal route for a user's destination and intermediate points;

[1520] means for presenting the calculated optimum route to a user;

[1521] A system including:

[1522] (Claim 2)

[1523] The system according to claim 1, further comprising means for recommending stores and products along the way to the user based on the analysis results.

[1524] (Claim 3)

[1525] 10. The system according to claim 1, further comprising means for collecting feedback from users and reflecting the feedback in future service improvements.

[1526] "Application Example 1"

[1527] (Claim 1)

[1528] means for acquiring destinations and intermediate points input by a user;

[1529] A means of collecting real-time people flow data from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc.

[1530] means for obtaining traffic information from a traffic information service;

[1531] A means of analyzing people flow and traffic conditions based on the collected data;

[1532] means for calculating an optimal route for a user's destination and intermediate points;

[1533] means for presenting the calculated optimum route to a user;

[1534] a means for being integrated into the navigation system of an autonomous vehicle to provide route guidance;

[1535] A system including:

[1536] (Claim 2)

[1537] The system according to claim 1, further comprising means for recommending stores and products along the way to the user based on the analysis results.

[1538] (Claim 3)

[1539] 2. The system according to claim 1, further comprising means for collecting feedback from users and reflecting the feedback in future services.

[1540] "Example 2: Combining Emotion Engines"

[1541] (Claim 1)

[1542] means for acquiring destinations and intermediate points input by a user;

[1543] A means for collecting people flow data in real time from wireless communication devices, location information beacons, image analysis devices, etc.;

[1544] A means for acquiring traffic information from an external information providing service;

[1545] A means of analyzing people flow and traffic conditions based on the collected data;

[1546] means for calculating an optimal route for a user's destination and intermediate points;

[1547] means for presenting the calculated optimum route to a user;

[1548] A means for recognizing emotions from a user's facial expressions and voice, and optimizing routes and recommendation information based on the recognized emotions;

[1549] A system including:

[1550] (Claim 2)

[1551] The system according to claim 1, further comprising means for recommending stores and products along the way to the user based on the analysis results.

[1552] (Claim 3)

[1553] 2. The system according to claim 1, further comprising means for collecting feedback from users and reflecting the feedback in future services.

[1554] "Application example 2 when combining emotion engines"

[1555] (Claim 1)

[1556] means for acquiring destinations and intermediate points input by a user;

[1557] A means of collecting real-time people flow data from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc.

[1558] means for obtaining traffic information from a traffic information service;

[1559] A means of analyzing people flow and traffic conditions based on the collected data;

[1560] means for recognizing the emotional state of a user;

[1561] means for calculating an optimal route and personalized recommendations based on the emotional state;

[1562] means for presenting the calculated optimum route and recommended information to the user;

[1563] A system including:

[1564] (Claim 2)

[1565] 10. The system of claim 1, further comprising means for recommending shops and rest areas along the way to the user based on the analysis results and the user's emotional state.

[1566] (Claim 3)

[1567] 2. The system according to claim 1, further comprising means for collecting feedback from users and reflecting the feedback in future services. [Explanation of symbols]

[1568] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for acquiring destinations and intermediate points input by a user; A means of collecting real-time people flow data from Wi-Fi access points, Bluetooth beacons, camera video analysis, etc. means for obtaining traffic information from a traffic information service; A means of analyzing people flow and traffic conditions based on the collected data; means for calculating an optimal route for a user's destination and intermediate points; means for presenting the calculated optimum route to a user; A system including:

2. The system according to claim 1 , further comprising means for recommending stores and products along the way to the user based on the analysis results.

3. 2. The system according to claim 1, further comprising means for collecting feedback from users and reflecting the feedback in future services.

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