System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a user's destination and waypoints; means for obtaining real-time traffic and people flow information; means for generating an optimal route based on the input destination and waypoints; means for performing predictive analysis based on the user's preferences; means for recommending relevant stores or products along the optimal route based on the predictive analysis; and means for summarizing and providing the obtained real-time information 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] In today's modern transportation environment, users often find it difficult to determine the optimal route to their destination, lacking accurate route guidance that reflects real-time traffic conditions and pedestrian flow information. Furthermore, there is a lack of means to provide users with information about stores and products they might want to stop at during their trip, based on their preferences, resulting in missed opportunities to stimulate potential purchases. In these situations, users are overwhelmed with information and find it difficult to make appropriate decisions. Therefore, there is a need for a system that can solve these issues and make travel and transportation more convenient. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for inputting a user's destination and intermediate points, a means for acquiring real-time traffic and people flow information, a means for generating an optimal route based on the input destination and intermediate points, a means for performing predictive analysis based on the user's preferences, a means for recommending related stores or products along the optimal route based on the predictive analysis, and a means for summarizing the acquired real-time information and providing it to the user. This allows users to travel based on accurate, real-time traffic information and receive store and product information that matches their preferences, thereby improving the comfort and efficiency of travel. It also helps users avoid information overload and make appropriate decisions more easily.

[0006] "User" refers to someone who uses this system to input their destination and intermediate points and receive optimal route guidance and store recommendations.

[0007] "Destination" refers to the location the user ultimately wants to reach.

[0008] An "intermediate point" refers to any point at which a user stops on the way to a destination.

[0009] "Real-time traffic information" refers to current traffic conditions, road congestion, accident information, etc., and means data obtained in real time.

[0010] "People flow information" refers to data that shows the flow of people and the degree of congestion in a specific area or on a route.

[0011] An "optimal route" refers to the most efficient route from the departure point to the destination, calculated taking into account traffic conditions and pedestrian flow information.

[0012] "Predictive analytics" refers to the process of predicting future behavior and needs based on users' past data, preferences, and behavioral patterns.

[0013] "Recommendation" refers to recommending related stores and products based on the user's preferences.

[0014] "Store" refers to a commercial facility or service provider that a user may visit.

[0015] "Products" refers to products and services that you may purchase.

[0016] "Summarization" refers to the process of summarizing detailed information or complex data in a concise form that is easy for users to understand. [Brief explanation of the drawings]

[0017] [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 showing 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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention is a system that allows users to set their destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. Furthermore, this system has the ability to predict and analyze users' preferences and recommend stores and products that they might be interested in while traveling. It also provides users with a summary of real-time information, preventing information overload and supporting appropriate decision-making.

[0039] System implementation

[0040] 1. User's destination and stopover input (terminal):

[0041] User: Enter the destination and intermediate points. For example, consider traveling from Tokyo Station (35.681236,139.767125) to Shinjuku Station (35.689487,139.691706).

[0042] 2. System initialization (server):

[0043] Server: Set the API key and database connection information required during the initialization phase. This setting is required later to obtain real-time traffic and people flow information.

[0044] 3. Acquisition of real-time traffic and people flow information (server):

[0045] Server: Sends requests to a dedicated API to retrieve up-to-date traffic and people flow data for origins, destinations, and intermediate points.

[0046] Example: Obtain traffic information for Akihabara on the way from Tokyo Station to Shinjuku Station to understand the congestion situation.

[0047] 4. Generate optimal route (server):

[0048] Server: Generates the optimal route based on real-time traffic and pedestrian flow information. This route is calculated by taking into account various factors (e.g., traffic conditions, delays, congestion, etc.) from the departure point to the destination.

[0049] Example: Providing the most efficient route from Tokyo Station to Shinjuku Station, passing through Akihabara, avoiding congestion and delays.

[0050] 5. Predictive analysis based on user preferences (server):

[0051] Server: Utilizes machine learning algorithms based on the user's past behavioral data and input data to predict and analyze user preferences.

[0052] Example: Analyzing past data to see if users are interested in electronic devices or cafes.

[0053] 6. Store and product recommendations (server):

[0054] Server: Based on the results of predictive analysis, it lists and recommends stores and products that are close to the user's current location and that match their interests.

[0055] Example: Recommending electronics stores and cafes to users near their current location.

[0056] 7. Real-time information summary (server):

[0057] Server: Concisely summarizes the acquired traffic data and people flow information and converts it into a format that is easy for users to understand.

[0058] Example: Present summary information to the user, such as "Traffic conditions are moderate, delays expected approximately 10 minutes."

[0059] 8. Integration of optimal routes and recommendation information (server):

[0060] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[0061] Example: Providing users with the optimal route from Tokyo Station to Shinjuku Station, along with information on stores that may be of interest along the way.

[0062] 9. Displaying results to the user (terminal):

[0063] Terminal: Displays the final results to the user, including details of the best route, real-time traffic information, and recommended stores and products.

[0064] Example: Providing a route from Tokyo Station to Shinjuku Station, a summary of the traffic conditions in Akihabara along the way, and information on cafes and electronics stores in the Akihabara area.

[0065] In this way, users can find the optimal route based on real-time information while traveling, and receive store information tailored to their individual preferences. This system not only allows users to travel efficiently and comfortably, but also opens up new shopping opportunities.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] User: Enter the destination and intermediate point. This sets the origin (Tokyo Station), destination (Shinjuku Station), and intermediate point (Akihabara).

[0069] Step 2:

[0070] Server: Initializes the system, including setting up any necessary API keys and database connection information.

[0071] Step 3:

[0072] Server: Sends an API request to obtain targeted traffic and people flow information based on the input destination and intermediate points. Specifically, it obtains this information from an external service that provides traffic and people flow data for Akihabara for the route from Tokyo Station to Shinjuku Station.

[0073] Step 4:

[0074] Server: Analyzes real-time traffic and people flow information and generates the optimal route from the departure point to the destination. For example, it calculates a route from Tokyo Station to Shinjuku Station that avoids congestion and delays when passing through Akihabara.

[0075] Step 5:

[0076] Server: Performs predictive analysis based on the user's past data and input preferences, using machine learning algorithms to predict the store category (e.g., electronics store or cafe) that the user might be interested in.

[0077] Step 6:

[0078] Server: Based on the results of predictive analysis, the server recommends stores and products related to the user's current location and the optimal route. For example, it creates a list of electronics stores and cafes near Tokyo Station or Akihabara and prepares the information to provide to the user.

[0079] Step 7:

[0080] Server: Summarizes the acquired real-time information succinctly, organizing it in a way that is easy for users to understand, such as "Traffic conditions are moderate, with delays expected to be approximately 10 minutes."

[0081] Step 8:

[0082] Server: Integrates optimal route information and recommendation information to create a detailed travel plan that allows users to travel efficiently and conveniently.

[0083] Step 9:

[0084] Terminal: Presents users with integrated information, including a map of the optimal route, real-time traffic information, delay predictions, and recommendations for stores and products that may interest them.

[0085] Step 10:

[0086] User: Based on the information provided, the user makes the most efficient and comfortable journey, stopping at recommended stores as needed. Users can understand traffic conditions and options along the way in real time, allowing them to travel efficiently and comfortably.

[0087] Through these steps, users can determine the optimal route based on real-time information and receive recommendations for stores and products they are interested in. This system prevents information overload and helps users make better decisions, greatly improving the user's travel experience.

[0088] Example 1

[0089] 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."

[0090] Current navigation systems are specialized in providing destination guidance, but they do not fully consider real-time traffic conditions and pedestrian flow, and lack the functionality to provide store and product information based on user preferences. As a result, users not only have difficulty traveling efficiently and comfortably, but also have problems obtaining appropriate information while traveling.

[0091] 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.

[0092] In this invention, the server includes means for inputting a user's destination and intermediate points, means for acquiring real-time traffic information and people flow information, means for generating an optimal route based on the input destination and intermediate points, means for performing predictive analysis based on the user's preferences, means for recommending related stores or products along the optimal route based on the predictive analysis, means for summarizing the acquired real-time information and providing it to the user, means for integrating the optimal route and recommendation information based on the user's destination and intermediate point information, and means for displaying the integrated result on the user's terminal. This allows the user to obtain an optimal route based on real-time traffic conditions and simultaneously obtain information on stores and products that interest them.

[0093] "User" means a person who uses the System to set destinations and waypoints.

[0094] "Destinations and intermediate points" refers to the starting point, intermediate points, and destination when a user travels.

[0095] "Real-time traffic information" means data and information that indicates current traffic conditions, including traffic congestion, road closures, traffic accidents, etc.

[0096] "People flow information" refers to data and information about the movement and gathering of people in a particular area.

[0097] An "optimal route" refers to the most efficient route between the departure point and the destination, taking into maximum consideration conditions such as travel time and congestion.

[0098] "Predictive analytics" is an analytical method for predicting future behavior and interests based on a user's past behavior and input data.

[0099] "Related stores or products" refers to stores or products located along the optimal route based on the user's preferences and interests.

[0100] "Recommending" means recommending appropriate stores and products based on the user's interests and needs.

[0101] "Summarization" refers to the process of concisely summarizing large amounts of acquired data and converting it into a format that is easy for users to understand.

[0102] "Integrating" means bringing together different pieces of information or data to generate a complete whole.

[0103] "Terminal" means a device that allows a user to operate the system and input or view information.

[0104] This invention is a system that allows users to set their destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. Furthermore, this system has the ability to predict and analyze users' preferences and recommend stores and products that they might be interested in while traveling. It also provides users with a summary of real-time information, preventing information overload and supporting appropriate decision-making.

[0105] This system is realized using the following components:

[0106] 1. Terminal: A device that receives the user's destination and intermediate point input. This device can be a smartphone or tablet, for example.

[0107] 2. Server: A central processing unit that processes various data, calculates optimal routes, and generates recommendation information. The software used here is, for example, a platform that implements the Google Maps API or machine learning algorithms.

[0108] The specific implementation steps are as follows:

[0109] Entering destination and stopover points

[0110] The user inputs the starting point, intermediate points, and destination on the terminal. For example, consider traveling from Tokyo Station (35.681236, 139.767125) to Shinjuku Station (35.689487, 139.691706), and set Akihabara (35.698353, 139.773114) as a stopover point along the way.

[0111] System initialization

[0112] The server sets the API key (e.g., Google Maps API key) and database connection information and connects to various services.

[0113] Obtaining real-time traffic and people flow information

[0114] The server sends a request to a dedicated API to obtain the latest traffic and people flow data for the specified location (Tokyo Station, Akihabara, Shinjuku Station).

[0115] Generate optimal routes

[0116] The server uses real-time traffic and people flow information to generate optimal routes, such as calculating alternative routes to avoid the crowds in Akihabara.

[0117] Predictive analytics based on user preferences

[0118] The server uses machine learning algorithms to predict and analyze the user's preferences based on the user's past behavioral data and input data. Based on past data, it analyzes the user's interests in electronic devices and cafes.

[0119] Store and product recommendations

[0120] Based on the results of the predictive analysis, the server generates a recommendation list by listing stores and products that are close to the user's current location and that match their interests, such as electronics stores and cafes near the user's current location.

[0121] Real-time information summary

[0122] The server then summarizes the acquired traffic and pedestrian flow data and converts it into a format that is easy for users to understand, such as "traffic conditions are moderate, and delays of approximately 10 minutes are expected."

[0123] Integration of optimal routes and recommendation information

[0124] The server integrates all acquired information and analysis results to generate optimal routes and recommendation information for users.

[0125] Presenting results to the user

[0126] The terminal displays the final results from the server to the user, such as the optimal route, a summary of the traffic situation in Akihabara along the way, and information on cafes and electronics stores in the area, through a user interface.

[0127] Prompt Sentence Examples

[0128] "Please provide the best route from Tokyo Station to Shinjuku Station. Also check the traffic conditions in Akihabara along the way and provide recommendations for suitable stores and cafes."

[0129] The system allows users to obtain the optimal route based on real-time traffic conditions and simultaneously obtain information on stores and products they are interested in, providing an efficient and comfortable travel experience.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] User: Enter the destination and intermediate points using the terminal. Specifically, the departure point is set to Tokyo Station (35.681236,139.767125), the intermediate point is set to Akihabara (35.698353,139.773114), and the destination is set to Shinjuku Station (35.689487,139.691706).

[0133] Input: User-specified coordinates of the starting point, intermediate point, and destination.

[0134] Output: Data transmission to the server is complete.

[0135] Step 2:

[0136] Server: Performs initialization. Specifically, sets the API key (e.g., Google Maps API key) and database connection information to enable access to each API service.

[0137] Inputs: API key, database connection information.

[0138] Output: Initialization completion status.

[0139] Step 3:

[0140] Server: Obtains real-time traffic and people flow information. Sends a request to a dedicated API to obtain the latest traffic and people flow data for each specified location (e.g., Tokyo Station, Akihabara, Shinjuku Station).

[0141] Input: Coordinate information of the starting point, intermediate point, and destination set by the user.

[0142] Output: Traffic and people flow data obtained from the API.

[0143] Step 4:

[0144] Server: Generates optimal routes based on real-time traffic and people flow information. Algorithms are used to calculate the most efficient route, taking congestion and delays into account.

[0145] Input: Traffic and people flow data obtained from API.

[0146] Output: Optimal route information.

[0147] Step 5:

[0148] Server: Performs predictive analysis based on user preferences. Utilizing machine learning algorithms based on the user's past behavioral data and input data, the server predicts and analyzes the user's interests.

[0149] Input: User's past behavior data, current input data.

[0150] Output: Predictive data about user interests.

[0151] Step 6:

[0152] Server: Based on the results of predictive analysis, the server recommends stores and products that are close to the user's current location and that match their interests. The server references a database of nearby stores and creates a list.

[0153] Input: current user location, predictive analysis results, store database.

[0154] Output: A list of recommended stores and products.

[0155] Step 7:

[0156] Server: Appropriately summarizes the acquired traffic and people flow data, summarizing the information succinctly and converting it into a format that is easy for users to understand.

[0157] Input: Raw data from the API.

[0158] Output: Summarized traffic and people flow information.

[0159] Step 8:

[0160] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[0161] Input: optimal route information, summarized traffic and people flow information, recommendation list.

[0162] Output: The final integrated result data.

[0163] Step 9:

[0164] Device: The results sent from the server are displayed to the user, who can check the optimal route, a summary of traffic conditions, and recommended stores and products on the device screen.

[0165] Input: The final integration result data from the server.

[0166] Output: The screen display that the user sees.

[0167] This process allows users to travel efficiently and comfortably, while also receiving store information tailored to their interests.

[0168] (Application example 1)

[0169] 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."

[0170] Previously, navigation systems for autonomous vehicles provided optimal route guidance based on real-time traffic and pedestrian flow information, but they did not offer recommendations based on user preferences or route suggestions that took into account user behavioral history. Furthermore, they lacked the ability to summarize acquired real-time information or provide appropriate information to users, creating challenges in supporting efficient and comfortable travel. Furthermore, systems for autonomous vehicles that would provide new purchasing opportunities were also underdeveloped.

[0171] 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.

[0172] In this invention, the server includes means for inputting a user's destination and intermediate points, means for acquiring real-time traffic information and people flow information, means for generating an optimal route based on the input destination and intermediate points, means for performing predictive analysis based on the user's preferences, means for recommending related stores or products along the optimal route based on the predictive analysis, means for summarizing the acquired real-time information and providing it to the user, means for integrating data into the navigation system of a related autonomous vehicle based on the user's past behavior data, and means for recommending stores and products that the user may be interested in based on the user's location data. This allows the user to check the optimal route based on real-time information, and makes it possible to provide recommendations and purchasing opportunities according to their preferences.

[0173] The "means for inputting the user's destination and intermediate points" is an interface that allows the user to input their travel destination and intermediate points into the system.

[0174] "Means for obtaining real-time traffic and people flow information" refers to a system for instantly obtaining data on current traffic conditions and people flow.

[0175] The "means for generating an optimal route based on the input destination and intermediate points" is an algorithm that uses the input destination and intermediate point information to calculate the most efficient travel route.

[0176] The "means for performing predictive analysis based on the user's preferences" is a data analysis technique for predicting future behavior based on the user's past behavioral data and preferences.

[0177] "Means for recommending related stores or products along the optimal route based on the predictive analysis" refers to a system that suggests related stores or products located on or near the optimal route based on predicted user preferences and behavior.

[0178] The "means for summarizing the acquired real-time information and providing it to the user" is a function for concisely summarizing the acquired traffic information and people flow information and presenting it to the user.

[0179] "Means for integrating data into the navigation system of the associated autonomous vehicle based on the user's past behavioral data" refers to a technology that reflects data based on the user's past travel history and behavioral patterns in the navigation system of the autonomous vehicle.

[0180] "Means for recommending stores and products that may be of interest to the user based on the user's location data" refers to a system that uses the user's current location information to recommend stores and products that are close to that location.

[0181] An embodiment of the present invention is described in detail below. The present invention is a system that allows users to set their destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. Furthermore, the system has the function of predicting and analyzing the user's preferences and recommending stores and products that the user may be interested in while traveling. Furthermore, by providing a summary of real-time information to the user, the system prevents information overload and supports appropriate decision-making.

[0182] System configuration

[0183] Hardware Configuration

[0184] Server: A server with high-performance computing capabilities that processes real-time data and connects to databases. Specific examples include Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[0185] Device: A device that provides a user interface, such as a smartphone, smart glasses, or head-mounted display.

[0186] Autonomous vehicles: Equipped with advanced navigation systems and capable of connecting with external systems.

[0187] Software Configuration

[0188] User interface application: An application that runs on a smartphone or head-mounted display, where the user inputs destinations and intermediate points.

[0189] Data acquisition module: A module for acquiring real-time traffic and people flow information from various APIs.

[0190] Predictive analytics module: Uses machine learning algorithms to predict and analyze user preferences.

[0191] Recommendation module: Recommends relevant stores and products along the optimal route.

[0192] Data Summarization Module: Summarizes the acquired real-time information and provides it to the user concisely.

[0193] Operation explanation

[0194] 1. User input: The user inputs the destination and intermediate points through an application installed on a smartphone or head-mounted display. For example, the user specifies a trip from Tokyo Station to Shinjuku Station, with Akihabara as the intermediate point.

[0195] 2. Acquisition of real-time data: The server uses APIs to acquire traffic and people flow information. For example, it acquires real-time traffic data from Tokyo Station to Shinjuku Station to understand the congestion situation around Akihabara.

[0196] 3. Generate optimal route: The server generates the optimal route based on the acquired data. It calculates the most efficient route for the user, taking into account traffic conditions and delay information.

[0197] 4. User preference prediction: The server analyzes the user's past behavior data and uses machine learning algorithms to predict the user's preferences. For example, the server knows from past data that the user is interested in cafes and electronics stores.

[0198] 5. Providing recommendation information: Based on the results of predictive analysis, the server recommends related stores and products along the optimal route, for example, providing users with information on cafes and electronics stores in Akihabara.

[0199] 6. Real-time information summary: The server briefly summarizes the real-time information it has acquired and provides it to the user, for example, displaying information such as "Traffic conditions are moderate, and delays of approximately 10 minutes are expected."

[0200] 7. Presenting the results to the user: The application presents the final navigation information to the user, including details of the optimal route, recommendations, and summarized real-time information. For example, route guidance from Tokyo Station to Shinjuku Station and recommended shops around Akihabara are displayed.

[0201] Specific examples

[0202] Example prompt sentence:

[0203] "You are going to create a navigation system that will help a user travel from Tokyo Station to Shinjuku Station and provide information about interesting shops (e.g. electronics stores and cafes) in Akihabara along the way. The system should provide the optimal route based on real-time traffic information and recommend places that may be of interest to the user based on their previous behavior."

[0204] As a result, the system of the present invention can provide users with efficient and comfortable travel and create new purchasing opportunities.

[0205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0206] Step 1:

[0207] The user inputs the destination and intermediate points through an application installed on a smartphone or head-mounted display. This input includes the starting point, destination, and intermediate points (e.g., from Tokyo Station to Shinjuku Station, with Akihabara as the intermediate point). The input data is sent from the application to the server.

[0208] Step 2:

[0209] The server uses the provided API to obtain real-time traffic and pedestrian flow information. Specifically, it obtains traffic conditions and congestion levels from the departure point to the destination through an API request. In this process, it uses the API key and database connection information. The input is the request sent to the API, and the output is the obtained real-time traffic information.

[0210] Step 3:

[0211] The server generates optimal routes based on the acquired real-time traffic information. This involves analyzing traffic data and applying shortest-path algorithms. For example, it calculates the most efficient route from Tokyo Station to Shinjuku Station via Akihabara, using algorithms to avoid congestion and delays. The input is traffic data, and the output is optimal route information.

[0212] Step 4:

[0213] The server analyzes the user's past behavioral data and predicts the user's preferences using a machine learning algorithm. The input is the user's past behavioral data, and the output is the predicted user's interests (e.g., the user is interested in cafes and electronics stores). The algorithm extracts patterns from user behavior and predicts interests.

[0214] Step 5:

[0215] Based on the results of the predictive analysis, the server recommends related stores and products along the optimal route. This process searches for and lists appropriate store information based on the user's predicted interests. For example, a list is generated by searching for information on cafes and electronics stores around Akihabara. The input is the predicted interests and current location information, and the output is a list of recommended stores.

[0216] Step 6:

[0217] The server summarizes the acquired real-time information and provides it to the user in a concise format. This process summarizes traffic conditions and congestion data and generates an easy-to-understand message. For example, it creates a summary such as "Traffic conditions are moderate, with delays expected to be approximately 10 minutes." The input is real-time traffic information, and the output is summarized information.

[0218] Step 7:

[0219] The terminal presents the final results to the user through a user interface, including details of the optimal route, recommended information, and a summary of real-time information. The input is data from the server, and the output is the information displayed to the user. For example, the terminal displays a navigation route from Tokyo Station to Shinjuku Station, recommended store information around Akihabara, and a summary of traffic conditions.

[0220] 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.

[0221] This invention is a system that inputs a user's destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. It also has the ability to predict and analyze the user's preferences and recommend stores and products that the user might be interested in while traveling. By combining it with an emotion engine that recognizes the user's emotions, the system can adjust the recommendations based on the user's current emotional state and past emotional data, providing a more personalized service.

[0222] System implementation

[0223] 1. User's destination and stopover input (terminal):

[0224] User: Enter the destination and intermediate points. For example, consider traveling from Tokyo Station (35.681236,139.767125) to Shinjuku Station (35.689487,139.691706).

[0225] 2. System initialization (server):

[0226] Server: Initializes the system and sets up the necessary API keys and database connection information. The emotion engine is also prepared at this stage.

[0227] 3. Acquisition of real-time traffic and people flow information (server):

[0228] Server: Sends an API request to obtain traffic and people flow information based on the input destination and intermediate points.

[0229] 4. Generate optimal route (server):

[0230] Server: Generates the optimal route from the departure point to the destination based on real-time traffic and people flow information.

[0231] 5. Predictive analysis based on user preferences (server):

[0232] Server: Uses machine learning algorithms to predict user preferences based on the user's past data and current input.

[0233] 6. Use of Emotion Engine (Server):

[0234] Server: Uses an emotion engine to recognize the user's emotions in real time. It recognizes the user's emotions from camera and voice data and obtains the user's current emotional state.

[0235] 7. Emotion-based recommendation adjustment (server):

[0236] Server: Based on the recognized emotional data, it recommends relaxing places if the user is feeling stressed, and active places if the user is feeling excited.

[0237] 8. Recommendation of stores and products along the way (server):

[0238] Server: Based on the results of predictive analysis and the sentiment engine, it recommends stores and products that may be of interest to the user near their current location or along the optimal route.

[0239] 9. Real-time information summary (server):

[0240] Server: Easily compiles the acquired traffic data and pedestrian flow information and summarizes it in a format that is easy for users to understand.

[0241] 10. Integration of optimal routes and recommendation information (server):

[0242] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[0243] 11. Displaying results to the user (terminal):

[0244] Terminal: Displays the final results to the user, including details of the optimal route, real-time traffic information, delay predictions, and recommended stores and products.

[0245] 12. Collecting user responses (server):

[0246] Server: Collects information about how users respond to the information provided to help improve the service and its predictive models.

[0247] As a specific example, consider a user traveling from Tokyo Station to Shinjuku Station and wanting to take a break in Akihabara. The user inputs their destination and stopover points, and the system retrieves real-time traffic and people flow information. After an optimal route is generated, the emotion engine detects the user's stress level and recommends cafes in the Akihabara area where they can relax. This information, along with a summary of traffic conditions, is provided to the user, enabling an efficient and comfortable journey. This series of processes allows users to improve the quality of their journey and receive personalized service.

[0248] The processing flow will be explained below.

[0249] Step 1:

[0250] User: Enter the destination and intermediate points. For example, consider traveling from Tokyo Station (35.681236, 139.767125) to Shinjuku Station (35.689487, 139.691706). Enter that you will pass through Akihabara (35.7033, 139.7745) on the way.

[0251] Step 2:

[0252] Server: Initializes the system and sets up the necessary API keys and database connection information. The emotion engine is also set up at this stage.

[0253] Step 3:

[0254] Server: Sends API requests to obtain traffic and people flow information based on the input destination and intermediate points. Specifically, it constructs a URL and sends a request to an external service to obtain the data.

[0255] Step 4:

[0256] Server: Analyzes real-time traffic and people flow information and generates the optimal route from the departure point to the destination. For example, it calculates routes that avoid congestion and delays, and selects the most efficient route from multiple candidate routes.

[0257] Step 5:

[0258] Server: Uses machine learning algorithms to predict and analyze user preferences based on the user's past history and current input data. For example, it analyzes data on restaurants and cafes the user has chosen in the past to infer new preferences.

[0259] Step 6:

[0260] Server: Uses an emotion engine to recognize the user's emotions. It analyzes camera footage and audio data to obtain the user's current emotional state (stress, excitement, relaxation, etc.). The emotion engine analyzes facial expressions and tone of voice to estimate emotions.

[0261] Step 7:

[0262] Server: Based on the recognized emotional data, the server tailors recommendations to suit the user's current emotional state. For example, if the user is feeling stressed, the server recommends relaxing cafes and parks. Conversely, if the user is excited, the server recommends active activities and events.

[0263] Step 8:

[0264] Server: Based on the results of the emotion engine and predictive analysis, the server recommends relevant stores and products near the user's current location and along the optimal route. For example, it creates a list of electronics stores and cafes near Akihabara and prepares it for the user.

[0265] Step 9:

[0266] Server: The server concisely summarizes the acquired traffic data and pedestrian flow information in a format that is easy for users to understand. Specifically, it generates information such as "traffic conditions are moderate, and delays of approximately 10 minutes are expected."

[0267] Step 10:

[0268] Server: Integrates optimal route information and recommendation information. This integrated information provides a detailed travel plan that assists users in their travels and includes store information that is useful during their trip.

[0269] Step 11:

[0270] Terminal: Presents users with integrated information, including maps of optimal routes, real-time traffic information, delay predictions, and recommended stores and products, through an application or web interface that is easily accessible to users.

[0271] Step 12:

[0272] User: Based on the information provided, the user makes the most optimal trip and stops at recommended stores as needed. Information is updated in real time during the trip, allowing the user to reach their destination efficiently and comfortably.

[0273] As a result, the system takes into account real-time information and the user's emotional state to provide optimal routes and personalized recommendations, significantly improving the quality of travel and providing information tailored to individual needs.

[0274] Example 2

[0275] 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."

[0276] While conventional navigation systems can provide optimal routes based on real-time traffic information, it is difficult to provide optimal services that take into account the user's emotions and individual preferences. Furthermore, they lack a mechanism for collecting user reactions to the information provided and continuously improving the service. This limits the user experience and makes it difficult to increase satisfaction during travel.

[0277] 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.

[0278] In this invention, the server includes means for inputting a user's destination and intermediate points, means for acquiring real-time traffic information and people flow information, means for generating an optimal route based on the input destination and intermediate points, means for performing predictive analysis based on the user's preferences, means for recognizing the user's emotions and adjusting recommendations based on the emotions, means for summarizing the acquired real-time information and providing it to the user, and means for collecting user responses and improving the accuracy of the predictive model. This makes it possible to provide a personalized optimal route based on the user's emotions and preferences, and to continuously improve the service.

[0279] "Means for inputting user's destination and stopover points" is a function that allows the user to input their travel destination and stopover points into the system.

[0280] "Means for obtaining real-time traffic and people flow information" refers to a function for obtaining data on the latest traffic conditions and people flow from external sources.

[0281] The "means for generating optimal routes" is a function that calculates and presents the most efficient route for the user based on acquired real-time traffic and people flow information.

[0282] "Means for predictive analysis" refers to a function that uses a user's past behavioral data and current input data to predict the environment and services that a user will prefer.

[0283] "Recommendation means" is a function that presents facilities and products that are of interest to users along the optimal route based on the results of predictive analysis.

[0284] "Means for recognizing user emotions" refers to a function that uses a camera, audio data, etc. to identify the user's current emotional state.

[0285] "Means for adjusting recommendation content based on emotions" refers to a function that changes or adjusts the recommendation content provided to a user according to the recognized emotional state of the user.

[0286] "Means for summarizing and providing acquired real-time information" refers to a function that summarizes acquired traffic and people flow information in an easy-to-understand format and presents it to the user.

[0287] "Means for collecting user responses" refers to a function that collects information about how users respond to the information and services provided.

[0288] "Means for improving the accuracy of the predictive model" refers to a function for improving the accuracy of the system's predictions and recommendations based on collected user response data.

[0289] This invention is a system that provides users with optimal routes that take into account real-time traffic and pedestrian flow information based on destinations and intermediate points set by the user. The system makes personalized recommendations based on the user's emotions and past behavioral data, and can also collect user responses to improve the accuracy of its predictive model.

[0290] First, the user inputs their destination and intermediate points. This information is entered through a device such as a smartphone or computer. The input method for this system includes a touch screen, voice recognition software, or keyboard input. Specifically, the user inputs using the following prompt sentences:

[0291] example:

[0292] Destination: Shinjuku Station

[0293] Stopover point: Akihabara

[0294] Next, the server initializes the entire system and sets the necessary API key and database connection information. This system uses external information providers to obtain real-time traffic and pedestrian flow information. For example, it uses the Google Maps API and various traffic information APIs.

[0295] The server obtains real-time traffic and pedestrian flow information through these APIs, and then generates optimal routes based on the obtained data using Dijkstra's algorithm and A algorithm.

[0296] The server then predicts the user's preferences based on their past behavioral data. This prediction is made using machine learning algorithms (such as TensorFlow and Scikit-learn). The obtained user preference data is then integrated with the emotion engine, which collects the user's emotion data in real time.

[0297] The emotion engine uses the camera and microphone on the user's smartphone or PC to analyze the user's facial expressions and vocal tone, thereby recognizing the user's current emotional state. For example, if the user is feeling stressed, it will recommend a relaxing cafe or park, and if the user is excited, it will recommend a place to be active.

[0298] The acquired real-time information is easily summarized by the server and provided to the user in an easy-to-understand format, such as "Current traffic conditions are normal, no delays."

[0299] Finally, the server integrates all the analysis results and generates the optimal route and recommendations for the user, which are displayed in real time on the user's device. User responses are also collected and used to improve the accuracy of future prediction models.

[0300] As a specific example, consider the case where a user is traveling from Tokyo Station to Shinjuku Station and wants to take a break in Akihabara on the way. The user first enters "Shinjuku Station" as the destination and "Akihabara" as the stopover point. Based on this, the system obtains real-time traffic information and calculates the optimal route. Based on the user's emotional data, the system recommends cafes in the Akihabara area where people can relax. Providing this information to the user along with a summary of the traffic conditions enables an efficient and comfortable journey.

[0301] The system allows users to enjoy efficient and comfortable travel with real-time traffic information and personalized recommendations, and it also increases long-term satisfaction by continuously improving the service based on user sentiment and reactions.

[0302] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0303] Step 1:

[0304] User's destination and stopover input (terminal)

[0305] User: The user inputs the destination and intermediate points through a dedicated application. Specifically, the user inputs the destination from "Tokyo Station (35.681236,139.767125)" to "Shinjuku Station (35.689487,139.691706)" and the intermediate point "Akihabara."

[0306] Input: Coordinate data of destination, starting point, and intermediate points.

[0307] Output: User-entered destination and stop data.

[0308] Step 2:

[0309] System initialization (server)

[0310] Server: The server initializes the system and sets up initial settings such as Google Maps API and traffic API keys, database access information, etc. It also initializes the emotion engine module, ensuring that API keys and connection information are loaded.

[0311] Input: config file, API key, connection info.

[0312] Output: Initialized system, emotion engine in ready state.

[0313] Step 3:

[0314] Obtaining real-time traffic and people flow information (server)

[0315] Server: The server sends a request to a real-time traffic information API or a people flow information API based on the destination and intermediate points entered by the user. For example, it sends a request to the Google Maps API to get traffic information.

[0316] Input: Coordinate data of destination, starting point, and intermediate points.

[0317] Output: Real-time traffic and people flow data.

[0318] Step 4:

[0319] Generate optimal route (server)

[0320] Server: The server generates the optimal route using Dijkstra's algorithm and A algorithm based on the acquired real-time traffic information and people flow information.

[0321] Input: Real-time traffic and people flow data, destination, origin and intermediate point coordinate data.

[0322] Output: Optimal route information.

[0323] Step 5:

[0324] Predictive analysis based on user preferences (server)

[0325] Server: The server performs predictive analysis using machine learning algorithms such as TensorFlow and Scikit-learn based on past user data and current input data.

[0326] Input: User's past behavior data, current input data.

[0327] Output: Predictive analysis results based on user preferences.

[0328] Step 6:

[0329] Use of emotion engine (server)

[0330] Server: The server sends the user's camera footage and audio data to the emotion engine, which analyzes the user's emotional state in real time using Amazon Rekognition and the Emotion API.

[0331] Input: Camera video data, audio data.

[0332] Output: User's current emotional state data.

[0333] Step 7:

[0334] Adjustment of recommendation content based on emotions (server)

[0335] Server: Based on the recognized emotional data, it recommends places to relax if the user is feeling stressed, or places to be active if the user is feeling excited.

[0336] Input: User emotional state data, predictive analysis results.

[0337] Output: Adjusted recommendations.

[0338] Step 8:

[0339] Recommendations for stores and products along the way (server)

[0340] Server: The server recommends facilities and products that may be of interest to the user that are located near the user's current location or along the optimal route.

[0341] Input: Predictive analytics results based on user preferences, emotional state data, and current location data.

[0342] Output: Information about recommended stores and products.

[0343] Step 9:

[0344] Real-time information summary (server)

[0345] Server: Summarizes the acquired traffic data and people flow information in an easy-to-understand format and provides it to users.

[0346] Input: Real-time traffic information, people flow information, and optimal route information.

[0347] Output: Summarized real-time information.

[0348] Step 10:

[0349] Integration of optimal routes and recommendation information (server)

[0350] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[0351] Input: Optimal route information, adjusted recommendation content.

[0352] Output: Integrated data of optimal routes and recommendation information.

[0353] Step 11:

[0354] Presenting results to the user (device)

[0355] Terminal: The terminal displays the generated optimized route details, real-time traffic information, delay predictions, and recommended stores and products to the user.

[0356] Input: Integrated data of optimal routes and recommendation information.

[0357] Output: Information displayed to the user.

[0358] Step 12:

[0359] Collecting user responses (server)

[0360] Server: The server collects information about how users respond to the information provided, and uses this information to improve the accuracy of the predictive model.

[0361] Input: User response data.

[0362] Output: Collected user response data, data that helps improve the accuracy of the prediction model.

[0363] (Application example 2)

[0364] 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."

[0365] Conventional navigation systems can provide optimal routes based on real-time traffic and people flow information, but they lack the ability to make personalized recommendations based on the user's emotions and preferences. Furthermore, they are unable to recommend appropriate stores and products in response to changes in the user's emotions while traveling, resulting in a suboptimal user travel experience. Therefore, there is a need for a system that can adjust the optimal route based on the user's emotions and recommend stores and products that the user may be interested in while traveling.

[0366] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0367] In this invention, the server includes: means for inputting a user's destination and intermediate points; means for acquiring real-time traffic information and people flow information; means for generating an optimal route based on the input destination and intermediate points; means for performing predictive analysis based on the user's preferences; means for recommending related stores or products along the optimal route based on the predictive analysis; means for summarizing the acquired real-time information and providing it to the user; means for recognizing the user's emotions and adjusting the recommendation content based on the user's emotional state; means for displaying the recommended stores and products to the user; and means for collecting user responses and contributing to improving the accuracy of future predictive models. This makes it possible to personalize the travel experience according to the user's emotions and preferences and provide an environment in which the user can travel comfortably.

[0368] "Means for inputting user's destination and intermediate points" refers to an interface that allows the user to input specific destinations and intermediate points into the system.

[0369] "Means for obtaining real-time traffic information and people flow information" refers to a function for collecting the latest information on traffic conditions and people flow in real time.

[0370] "Means for generating an optimal route based on the input destination and intermediate points" is a function that calculates and generates the most efficient and convenient route based on the destination and intermediate points input by the user.

[0371] "Means for performing predictive analytics based on user preferences" refers to a feature that uses machine learning algorithms to predict future behavior and preferences based on a user's past behavior and preferences.

[0372] "Means for recommending related stores or products along the optimal route" is a function that recommends stores and products that the user may be interested in along the generated optimal route.

[0373] "Means of summarizing acquired real-time information and providing it to users" refers to the function of organizing and summarizing collected traffic and people flow information and providing it to users in a format that is easy to understand.

[0374] "Means for recognizing the user's emotions and adjusting the content of recommendations based on their emotional state" refers to a function that identifies the user's current emotional state in real time and changes the content of recommendations according to that emotion.

[0375] "Means for displaying recommended stores and products to users" refers to an interface for displaying stores and products recommended by the system to users.

[0376] "Means of collecting user responses and contributing to improving the accuracy of future predictive models" refers to a function that collects how users respond to the information provided and uses that data to improve the accuracy of predictive models.

[0377] This invention provides a system that generates optimal routes by integrating real-time traffic and people flow information based on user-specified destinations and intermediate points. It also has the function of recommending appropriate stores and products during travel based on the user's preferences and emotional state.

[0378] System Configuration

[0379] 1. User's destination and stopover input method:

[0380] The user inputs the destination and intermediate points using a smartphone.

[0381] The interface includes a text box and voice input.

[0382] 2. Means of obtaining real-time traffic and people flow information:

[0383] The server obtains real-time traffic and pedestrian flow information using Google Maps APIs and other services.

[0384] The API key is provided during the initial setup of the server.

[0385] 3. How to generate optimal routes:

[0386] The server calculates the optimal route based on the acquired traffic and pedestrian flow information.

[0387] It also takes into account delay information along the way and provides the optimal route.

[0388] 4. Means of predictive analysis based on user preferences:

[0389] The server uses machine learning algorithms (scikit-learn, TensorFlow, etc.) to predict user preferences.

[0390] Past behavioral data and preference information is used.

[0391] 5. Means of recommending related stores or products along the optimal route:

[0392] The server recommends stores and products along the optimal route to the user.

[0393] You can get information about nearby stores using the Yelp and Foursquare APIs.

[0394] 6. Means for summarizing acquired real-time information and providing it to users:

[0395] The server summarizes traffic data and people flow information and displays it in an easy-to-understand manner for users.

[0396] 7. How to recognize user emotions and tailor recommendations based on their emotional state:

[0397] It uses the smartphone's camera and microphone to recognize the user's emotional state.

[0398] Based on the recognized emotional data, it recommends stores where you can relax and facilities where you can be active.

[0399] 8. How to display recommended stores and products to users:

[0400] Recommended stores and products are visually displayed on the smartphone interface.

[0401] 9. How we collect user feedback to help improve future prediction models:

[0402] The server collects data on how users respond to the information provided.

[0403] The collected data is fed back into the predictive model to help improve accuracy.

[0404] Example

[0405] As a specific example, consider the case where a user travels from Tokyo Station to Shinjuku Station and specifies Akihabara as a stopover point. When the user enters this information into their smartphone, the server obtains real-time traffic information using the Google Maps API, generates the optimal route, and provides directions from Tokyo Station to Shinjuku Station via Akihabara.

[0406] During this process, the user's emotional state is recognized and, if it is determined that they are in a stressful state, a relaxing cafe in Akihabara is recommended. Furthermore, the server uses the Yelp API to obtain information about cafes in the Akihabara area and displays it on the smartphone. When the user visits a cafe based on the information provided, their behavior is fed back to the server, helping to improve the accuracy of the prediction model.

[0407] Example prompts to input to a generative AI model:

[0408] What is the best route to travel from Tokyo Station to Shinjuku Station via Akihabara? Also, since the user is feeling stressed, please recommend a cafe in Akihabara where they can relax. Please also take real-time traffic and people flow information into consideration.

[0409] This invention allows users to enjoy a comfortable and personalized travel experience.

[0410] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0411] Step 1:

[0412] The terminal accepts the user's input of destinations and intermediate points. The user inputs that they are traveling from Tokyo Station to Shinjuku Station, passing through Akihabara on the way. This results in the input data being "Tokyo Station, Shinjuku Station, Akihabara."

[0413] Step 2:

[0414] The server obtains real-time traffic and people flow information and sends a request to the Google Maps API based on the input destination and intermediate points. The output is data on traffic conditions and people flow information for each route.

[0415] Step 3:

[0416] The server generates the optimal route based on the acquired traffic and pedestrian flow information, taking into account traffic congestion and real-time road conditions, calculates the shortest and most optimal route, and generates route data to provide to the user.

[0417] Step 4:

[0418] The server performs predictive analysis based on the user's preferences. It analyzes past behavioral and preference data using machine learning algorithms (such as scikit-learn and TensorFlow) to predict the user's current preferences. The predicted results are output as data on categories that the user is likely to be interested in (e.g., cafes or bookstores).

[0419] Step 5:

[0420] The server recommends relevant stores or products along the optimal route based on predictive analysis. It uses the Yelp or Foursquare API to search for and obtain information about stores and products that fit the predicted category. The output is a list of recommended stores and products.

[0421] Step 6:

[0422] The server summarizes the acquired real-time information and recommendation information and prepares it for delivery to the user. It summarizes traffic information, people flow information, recommended stores and products information, and converts them into a format that can be displayed to the user. The output is a summarized information package.

[0423] Step 7:

[0424] The device recognizes the user's emotions. It uses the smartphone's camera and microphone to obtain real-time emotional data from the user through emotion recognition algorithms. The output is the user's current emotional state.

[0425] Step 8:

[0426] The server adjusts the recommendations based on the user's emotional state. For example, if the user is stressed, the server recommends relaxing stores, and if the user is excited, the server recommends active facilities. The output is the adjusted recommendation list.

[0427] Step 9:

[0428] The terminal displays the recommended stores and products to the user. The adjusted recommendations are visually displayed on the smartphone screen. The output is the user's display screen.

[0429] Step 10:

[0430] The server collects responses from users, such as the stores visited and the products purchased, based on the information provided, and stores the collected data in a database.

[0431] Through these steps, users can enjoy an individually optimized travel experience and receive suggestions tailored to their interests and emotions while traveling.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] [Second embodiment]

[0436] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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."

[0448] This invention is a system that allows users to set their destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. Furthermore, this system has the ability to predict and analyze users' preferences and recommend stores and products that they might be interested in while traveling. It also provides users with a summary of real-time information, preventing information overload and supporting appropriate decision-making.

[0449] System implementation

[0450] 1. User's destination and stopover input (terminal):

[0451] User: Enter the destination and intermediate points. For example, consider traveling from Tokyo Station (35.681236,139.767125) to Shinjuku Station (35.689487,139.691706).

[0452] 2. System initialization (server):

[0453] Server: Set the API key and database connection information required during the initialization phase. This setting is required later to obtain real-time traffic and people flow information.

[0454] 3. Acquisition of real-time traffic and people flow information (server):

[0455] Server: Sends requests to a dedicated API to retrieve up-to-date traffic and people flow data for origins, destinations, and intermediate points.

[0456] Example: Obtain traffic information for Akihabara on the way from Tokyo Station to Shinjuku Station to understand the congestion situation.

[0457] 4. Generate optimal route (server):

[0458] Server: Generates the optimal route based on real-time traffic and pedestrian flow information. This route is calculated by taking into account various factors (e.g., traffic conditions, delays, congestion, etc.) from the departure point to the destination.

[0459] Example: Providing the most efficient route from Tokyo Station to Shinjuku Station, passing through Akihabara, avoiding congestion and delays.

[0460] 5. Predictive analysis based on user preferences (server):

[0461] Server: Utilizes machine learning algorithms based on the user's past behavioral data and input data to predict and analyze user preferences.

[0462] Example: Analyzing past data to see if users are interested in electronic devices or cafes.

[0463] 6. Store and product recommendations (server):

[0464] Server: Based on the results of predictive analysis, it lists and recommends stores and products that are close to the user's current location and that match their interests.

[0465] Example: Recommending electronics stores and cafes to users near their current location.

[0466] 7. Real-time information summary (server):

[0467] Server: Concisely summarizes the acquired traffic data and pedestrian flow information and converts it into a format that is easy for users to understand.

[0468] Example: Present summary information to the user, such as "Traffic conditions are moderate, delays expected approximately 10 minutes."

[0469] 8. Integration of optimal routes and recommendation information (server):

[0470] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[0471] Example: Providing users with the optimal route from Tokyo Station to Shinjuku Station, along with information on stores that may be of interest along the way.

[0472] 9. Displaying results to the user (terminal):

[0473] Terminal: Displays the final results to the user, including details of the best route, real-time traffic information, and recommended stores and products.

[0474] Example: Providing a route from Tokyo Station to Shinjuku Station, a summary of the traffic conditions in Akihabara along the way, and information on cafes and electronics stores in the Akihabara area.

[0475] In this way, users can find the optimal route based on real-time information while traveling, and receive store information tailored to their individual preferences. This system not only allows users to travel efficiently and comfortably, but also opens up new shopping opportunities.

[0476] The processing flow will be explained below.

[0477] Step 1:

[0478] User: Enter the destination and intermediate point. This sets the origin (Tokyo Station), destination (Shinjuku Station), and intermediate point (Akihabara).

[0479] Step 2:

[0480] Server: Initializes the system, including setting up any necessary API keys and database connection information.

[0481] Step 3:

[0482] Server: Sends an API request to obtain targeted traffic and people flow information based on the input destination and intermediate points. Specifically, it obtains this information from an external service that provides traffic and people flow data for Akihabara for the route from Tokyo Station to Shinjuku Station.

[0483] Step 4:

[0484] Server: Analyzes real-time traffic and people flow information and generates the optimal route from the departure point to the destination. For example, it calculates a route from Tokyo Station to Shinjuku Station that avoids congestion and delays when passing through Akihabara.

[0485] Step 5:

[0486] Server: Performs predictive analysis based on the user's past data and input preferences, using machine learning algorithms to predict the store category (e.g., electronics store or cafe) that the user might be interested in.

[0487] Step 6:

[0488] Server: Based on the results of predictive analysis, the server recommends stores and products related to the user's current location and the optimal route. For example, it creates a list of electronics stores and cafes near Tokyo Station or Akihabara and prepares the information to provide to the user.

[0489] Step 7:

[0490] Server: Summarizes the acquired real-time information succinctly, organizing it in a way that is easy for users to understand, such as "Traffic conditions are moderate, with delays expected to be approximately 10 minutes."

[0491] Step 8:

[0492] Server: Integrates optimal route information and recommendation information to create a detailed travel plan that allows users to travel efficiently and conveniently.

[0493] Step 9:

[0494] Terminal: Presents users with integrated information, including a map of the optimal route, real-time traffic information, delay predictions, and recommendations for stores and products that may interest them.

[0495] Step 10:

[0496] User: Based on the information provided, the user makes the most efficient and comfortable journey, stopping at recommended stores as needed. Users can understand traffic conditions and options along the way in real time, allowing them to travel efficiently and comfortably.

[0497] Through these steps, users can determine the optimal route based on real-time information and receive recommendations for stores and products they are interested in. This system prevents information overload and helps users make better decisions, greatly improving the user's travel experience.

[0498] Example 1

[0499] 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."

[0500] Current navigation systems are specialized in providing destination guidance, but they do not fully consider real-time traffic conditions and pedestrian flow, and lack the functionality to provide store and product information based on user preferences. As a result, users not only have difficulty traveling efficiently and comfortably, but also have problems obtaining appropriate information while traveling.

[0501] 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.

[0502] In this invention, the server includes means for inputting a user's destination and intermediate points, means for acquiring real-time traffic information and people flow information, means for generating an optimal route based on the input destination and intermediate points, means for performing predictive analysis based on the user's preferences, means for recommending related stores or products along the optimal route based on the predictive analysis, means for summarizing the acquired real-time information and providing it to the user, means for integrating the optimal route and recommendation information based on the user's destination and intermediate point information, and means for displaying the integrated result on the user's terminal. This allows the user to obtain an optimal route based on real-time traffic conditions and simultaneously obtain information on stores and products that interest them.

[0503] "User" means a person who uses the System to set destinations and waypoints.

[0504] "Destinations and intermediate points" refers to the starting point, intermediate points, and destination when a user travels.

[0505] "Real-time traffic information" means data and information that indicates current traffic conditions, including traffic congestion, road closures, traffic accidents, etc.

[0506] "People flow information" refers to data and information about the movement and gathering of people in a particular area.

[0507] An "optimal route" refers to the most efficient route between the departure point and the destination, taking into maximum consideration conditions such as travel time and congestion.

[0508] "Predictive analytics" is an analytical method for predicting future behavior and interests based on a user's past behavior and input data.

[0509] "Related stores or products" refers to stores or products located along the optimal route based on the user's preferences and interests.

[0510] "Recommending" means recommending appropriate stores and products based on the user's interests and needs.

[0511] "Summarization" refers to the process of concisely summarizing large amounts of acquired data and converting it into a format that is easy for users to understand.

[0512] "Integrating" means bringing together different pieces of information or data to generate a complete whole.

[0513] "Terminal" means a device that allows a user to operate the system and input or view information.

[0514] This invention is a system that allows users to set their destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. Furthermore, this system has the ability to predict and analyze users' preferences and recommend stores and products that they might be interested in while traveling. It also provides users with a summary of real-time information, preventing information overload and supporting appropriate decision-making.

[0515] This system is realized using the following components:

[0516] 1. Terminal: A device that receives the user's destination and intermediate point input. This device can be a smartphone or tablet, for example.

[0517] 2. Server: A central processing unit that processes various data, calculates optimal routes, and generates recommendation information. The software used here is, for example, a platform that implements the Google Maps API or machine learning algorithms.

[0518] The specific implementation steps are as follows:

[0519] Entering destination and stopover points

[0520] The user inputs the starting point, intermediate points, and destination on the terminal. For example, consider traveling from Tokyo Station (35.681236, 139.767125) to Shinjuku Station (35.689487, 139.691706), and set Akihabara (35.698353, 139.773114) as a stopover point along the way.

[0521] System initialization

[0522] The server sets the API key (e.g., Google Maps API key) and database connection information and connects to various services.

[0523] Obtaining real-time traffic and people flow information

[0524] The server sends a request to a dedicated API to obtain the latest traffic and people flow data for the specified location (Tokyo Station, Akihabara, Shinjuku Station).

[0525] Generate optimal routes

[0526] The server uses real-time traffic and people flow information to generate optimal routes, such as calculating alternative routes to avoid the crowds in Akihabara.

[0527] Predictive analytics based on user preferences

[0528] The server uses machine learning algorithms to predict and analyze the user's preferences based on the user's past behavioral data and input data. Based on past data, it analyzes the user's interests in electronic devices and cafes.

[0529] Store and product recommendations

[0530] Based on the results of the predictive analysis, the server generates a recommendation list by listing stores and products that are close to the user's current location and that match their interests, such as electronics stores and cafes near the user's current location.

[0531] Real-time information summary

[0532] The server then summarizes the acquired traffic and pedestrian flow data and converts it into a format that is easy for users to understand, such as "traffic conditions are moderate, and delays of approximately 10 minutes are expected."

[0533] Integration of optimal routes and recommendation information

[0534] The server integrates all acquired information and analysis results to generate optimal routes and recommendation information for users.

[0535] Presenting results to the user

[0536] The terminal displays the final results from the server to the user, such as the optimal route, a summary of the traffic situation in Akihabara along the way, and information on cafes and electronics stores in the area, through a user interface.

[0537] Prompt Sentence Examples

[0538] "Please provide the best route from Tokyo Station to Shinjuku Station. Also check the traffic conditions in Akihabara along the way and provide recommendations for suitable stores and cafes."

[0539] The system allows users to obtain the optimal route based on real-time traffic conditions and simultaneously obtain information on stores and products they are interested in, providing an efficient and comfortable travel experience.

[0540] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0541] Step 1:

[0542] User: Enter the destination and intermediate points using the terminal. Specifically, the departure point is set to Tokyo Station (35.681236,139.767125), the intermediate point is set to Akihabara (35.698353,139.773114), and the destination is set to Shinjuku Station (35.689487,139.691706).

[0543] Input: User-specified coordinates of the starting point, intermediate point, and destination.

[0544] Output: Data transmission to the server is complete.

[0545] Step 2:

[0546] Server: Performs initialization. Specifically, sets the API key (e.g., Google Maps API key) and database connection information to enable access to each API service.

[0547] Inputs: API key, database connection information.

[0548] Output: Initialization completion status.

[0549] Step 3:

[0550] Server: Obtains real-time traffic and people flow information. Sends a request to a dedicated API to obtain the latest traffic and people flow data for each specified location (e.g., Tokyo Station, Akihabara, Shinjuku Station).

[0551] Input: Coordinate information of the starting point, intermediate point, and destination set by the user.

[0552] Output: Traffic and people flow data obtained from the API.

[0553] Step 4:

[0554] Server: Generates optimal routes based on real-time traffic and people flow information. Algorithms are used to calculate the most efficient route, taking congestion and delays into account.

[0555] Input: Traffic and people flow data obtained from API.

[0556] Output: Optimal route information.

[0557] Step 5:

[0558] Server: Performs predictive analysis based on user preferences. Utilizing machine learning algorithms based on the user's past behavioral data and input data, the server predicts and analyzes the user's interests.

[0559] Input: User's past behavior data, current input data.

[0560] Output: Predictive data about user interests.

[0561] Step 6:

[0562] Server: Based on the results of predictive analysis, the server recommends stores and products that are close to the user's current location and that match their interests. The server references a database of nearby stores and creates a list.

[0563] Input: current user location, predictive analysis results, store database.

[0564] Output: A list of recommended stores and products.

[0565] Step 7:

[0566] Server: Appropriately summarizes the acquired traffic and people flow data, summarizing the information succinctly and converting it into a format that is easy for users to understand.

[0567] Input: Raw data from the API.

[0568] Output: Summarized traffic and people flow information.

[0569] Step 8:

[0570] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[0571] Input: optimal route information, summarized traffic and people flow information, recommendation list.

[0572] Output: The final integrated result data.

[0573] Step 9:

[0574] Device: The results sent from the server are displayed to the user, who can check the optimal route, a summary of traffic conditions, and recommended stores and products on the device screen.

[0575] Input: The final integration result data from the server.

[0576] Output: The screen display that the user sees.

[0577] This process allows users to travel efficiently and comfortably, while also receiving store information tailored to their interests.

[0578] (Application example 1)

[0579] 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."

[0580] Previously, navigation systems for autonomous vehicles provided optimal route guidance based on real-time traffic and pedestrian flow information, but they did not offer recommendations based on user preferences or route suggestions that took into account user behavioral history. Furthermore, they lacked the ability to summarize acquired real-time information or provide appropriate information to users, creating challenges in supporting efficient and comfortable travel. Furthermore, systems for autonomous vehicles that would provide new purchasing opportunities were also underdeveloped.

[0581] 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.

[0582] In this invention, the server includes means for inputting a user's destination and intermediate points, means for acquiring real-time traffic information and people flow information, means for generating an optimal route based on the input destination and intermediate points, means for performing predictive analysis based on the user's preferences, means for recommending related stores or products along the optimal route based on the predictive analysis, means for summarizing the acquired real-time information and providing it to the user, means for integrating data into the navigation system of a related autonomous vehicle based on the user's past behavior data, and means for recommending stores and products that the user may be interested in based on the user's location data. This allows the user to check the optimal route based on real-time information, and makes it possible to provide recommendations and purchasing opportunities according to their preferences.

[0583] The "means for inputting the user's destination and intermediate points" is an interface that allows the user to input their travel destination and intermediate points into the system.

[0584] "Means for obtaining real-time traffic and people flow information" refers to a system for instantly obtaining data on current traffic conditions and people flow.

[0585] The "means for generating an optimal route based on the input destination and intermediate points" is an algorithm that uses the input destination and intermediate point information to calculate the most efficient travel route.

[0586] The "means for performing predictive analysis based on the user's preferences" is a data analysis technique for predicting future behavior based on the user's past behavioral data and preferences.

[0587] "Means for recommending related stores or products along the optimal route based on the predictive analysis" refers to a system that suggests related stores or products located on or near the optimal route based on predicted user preferences and behavior.

[0588] The "means for summarizing the acquired real-time information and providing it to the user" is a function for concisely summarizing the acquired traffic information and people flow information and presenting it to the user.

[0589] "Means for integrating data into the navigation system of the associated autonomous vehicle based on the user's past behavioral data" refers to a technology that reflects data based on the user's past travel history and behavioral patterns in the navigation system of the autonomous vehicle.

[0590] "Means for recommending stores and products that may be of interest to the user based on the user's location data" refers to a system that uses the user's current location information to recommend stores and products that are close to that location.

[0591] An embodiment of the present invention is described in detail below. The present invention is a system that allows users to set their destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. Furthermore, the system has the function of predicting and analyzing the user's preferences and recommending stores and products that the user may be interested in while traveling. Furthermore, by providing a summary of real-time information to the user, the system prevents information overload and supports appropriate decision-making.

[0592] System configuration

[0593] Hardware Configuration

[0594] Server: A server with high-performance computing capabilities that processes real-time data and connects to databases. Specific examples include Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[0595] Device: A device that provides a user interface, such as a smartphone, smart glasses, or head-mounted display.

[0596] Autonomous vehicles: Equipped with advanced navigation systems and capable of connecting with external systems.

[0597] Software Configuration

[0598] User interface application: An application that runs on a smartphone or head-mounted display, where the user inputs destinations and intermediate points.

[0599] Data acquisition module: A module for acquiring real-time traffic and people flow information from various APIs.

[0600] Predictive analytics module: Uses machine learning algorithms to predict and analyze user preferences.

[0601] Recommendation module: Recommends relevant stores and products along the optimal route.

[0602] Data Summarization Module: Summarizes the acquired real-time information and provides it to the user concisely.

[0603] Operation explanation

[0604] 1. User input: The user inputs the destination and intermediate points through an application installed on a smartphone or head-mounted display. For example, the user specifies a trip from Tokyo Station to Shinjuku Station, with Akihabara as the intermediate point.

[0605] 2. Acquisition of real-time data: The server uses APIs to acquire traffic and people flow information. For example, it acquires real-time traffic data from Tokyo Station to Shinjuku Station to understand the congestion situation around Akihabara.

[0606] 3. Generate optimal route: The server generates the optimal route based on the acquired data. It calculates the most efficient route for the user, taking into account traffic conditions and delay information.

[0607] 4. User preference prediction: The server analyzes the user's past behavior data and uses machine learning algorithms to predict the user's preferences. For example, the server knows from past data that the user is interested in cafes and electronics stores.

[0608] 5. Providing recommendation information: Based on the results of predictive analysis, the server recommends related stores and products along the optimal route, for example, providing users with information on cafes and electronics stores in Akihabara.

[0609] 6. Real-time information summary: The server briefly summarizes the real-time information it has acquired and provides it to the user, for example, displaying information such as "Traffic conditions are moderate, and delays of approximately 10 minutes are expected."

[0610] 7. Presenting the results to the user: The application presents the final navigation information to the user, including details of the optimal route, recommendations, and summarized real-time information. For example, route guidance from Tokyo Station to Shinjuku Station and recommended shops around Akihabara are displayed.

[0611] Specific examples

[0612] Example prompt sentence:

[0613] "You are going to create a navigation system that will help a user travel from Tokyo Station to Shinjuku Station and provide information about interesting shops (e.g. electronics stores and cafes) in Akihabara along the way. The system should provide the optimal route based on real-time traffic information and recommend places that may be of interest to the user based on their previous behavior."

[0614] As a result, the system of the present invention can provide users with efficient and comfortable travel and create new purchasing opportunities.

[0615] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0616] Step 1:

[0617] The user inputs the destination and intermediate points through an application installed on a smartphone or head-mounted display. This input includes the starting point, destination, and intermediate points (e.g., from Tokyo Station to Shinjuku Station, with Akihabara as the intermediate point). The input data is sent from the application to the server.

[0618] Step 2:

[0619] The server uses the provided API to obtain real-time traffic and pedestrian flow information. Specifically, it obtains traffic conditions and congestion levels from the departure point to the destination through an API request. In this process, it uses the API key and database connection information. The input is the request sent to the API, and the output is the obtained real-time traffic information.

[0620] Step 3:

[0621] The server generates optimal routes based on the acquired real-time traffic information. This involves analyzing traffic data and applying shortest-path algorithms. For example, it calculates the most efficient route from Tokyo Station to Shinjuku Station via Akihabara, using algorithms to avoid congestion and delays. The input is traffic data, and the output is optimal route information.

[0622] Step 4:

[0623] The server analyzes the user's past behavioral data and predicts the user's preferences using a machine learning algorithm. The input is the user's past behavioral data, and the output is the predicted user's interests (e.g., the user is interested in cafes and electronics stores). The algorithm extracts patterns from user behavior and predicts interests.

[0624] Step 5:

[0625] Based on the results of the predictive analysis, the server recommends related stores and products along the optimal route. This process searches for and lists appropriate store information based on the user's predicted interests. For example, a list is generated by searching for information on cafes and electronics stores around Akihabara. The input is the predicted interests and current location information, and the output is a list of recommended stores.

[0626] Step 6:

[0627] The server summarizes the acquired real-time information and provides it to the user in a concise format. This process summarizes traffic conditions and congestion data and generates an easy-to-understand message. For example, it creates a summary such as "Traffic conditions are moderate, with delays expected to be approximately 10 minutes." The input is real-time traffic information, and the output is summarized information.

[0628] Step 7:

[0629] The terminal presents the final results to the user through a user interface, including details of the optimal route, recommended information, and a summary of real-time information. The input is data from the server, and the output is the information displayed to the user. For example, the terminal displays a navigation route from Tokyo Station to Shinjuku Station, recommended store information around Akihabara, and a summary of traffic conditions.

[0630] 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.

[0631] This invention is a system that inputs a user's destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. It also has the ability to predict and analyze the user's preferences and recommend stores and products that the user might be interested in while traveling. By combining it with an emotion engine that recognizes the user's emotions, the system can adjust the recommendations based on the user's current emotional state and past emotional data, providing a more personalized service.

[0632] System implementation

[0633] 1. User's destination and stopover input (terminal):

[0634] User: Enter the destination and intermediate points. For example, consider traveling from Tokyo Station (35.681236,139.767125) to Shinjuku Station (35.689487,139.691706).

[0635] 2. System initialization (server):

[0636] Server: Initializes the system and sets up the necessary API keys and database connection information. The emotion engine is also prepared at this stage.

[0637] 3. Acquisition of real-time traffic and people flow information (server):

[0638] Server: Sends an API request to obtain traffic and people flow information based on the input destination and intermediate points.

[0639] 4. Generate optimal route (server):

[0640] Server: Generates the optimal route from the departure point to the destination based on real-time traffic and people flow information.

[0641] 5. Predictive analysis based on user preferences (server):

[0642] Server: Uses machine learning algorithms to predict user preferences based on the user's past data and current input.

[0643] 6. Use of Emotion Engine (Server):

[0644] Server: Uses an emotion engine to recognize the user's emotions in real time. It recognizes the user's emotions from camera and voice data and obtains the user's current emotional state.

[0645] 7. Emotion-based recommendation adjustment (server):

[0646] Server: Based on the recognized emotional data, it recommends relaxing places if the user is feeling stressed, and active places if the user is feeling excited.

[0647] 8. Recommendation of stores and products along the way (server):

[0648] Server: Based on the results of predictive analysis and the sentiment engine, it recommends stores and products that may be of interest to the user near their current location or along the optimal route.

[0649] 9. Real-time information summary (server):

[0650] Server: Easily compiles the acquired traffic data and pedestrian flow information and summarizes it in a format that is easy for users to understand.

[0651] 10. Integration of optimal routes and recommendation information (server):

[0652] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[0653] 11. Displaying results to the user (terminal):

[0654] Terminal: Displays the final results to the user, including details of the optimal route, real-time traffic information, delay predictions, and recommended stores and products.

[0655] 12. Collecting user responses (server):

[0656] Server: Collects information about how users respond to the information provided to help improve the service and its predictive models.

[0657] As a specific example, consider a user traveling from Tokyo Station to Shinjuku Station and wanting to take a break in Akihabara. The user inputs their destination and stopover points, and the system retrieves real-time traffic and people flow information. After an optimal route is generated, the emotion engine detects the user's stress level and recommends cafes in the Akihabara area where they can relax. This information, along with a summary of traffic conditions, is provided to the user, enabling an efficient and comfortable journey. This series of processes allows users to improve the quality of their journey and receive personalized service.

[0658] The processing flow will be explained below.

[0659] Step 1:

[0660] User: Enter the destination and intermediate points. For example, consider traveling from Tokyo Station (35.681236, 139.767125) to Shinjuku Station (35.689487, 139.691706). Enter that you will pass through Akihabara (35.7033, 139.7745) on the way.

[0661] Step 2:

[0662] Server: Initializes the system and sets up the necessary API keys and database connection information. The emotion engine is also set up at this stage.

[0663] Step 3:

[0664] Server: Sends API requests to obtain traffic and people flow information based on the input destination and intermediate points. Specifically, it constructs a URL and sends a request to an external service to obtain the data.

[0665] Step 4:

[0666] Server: Analyzes real-time traffic and people flow information and generates the optimal route from the departure point to the destination. For example, it calculates routes that avoid congestion and delays, and selects the most efficient route from multiple candidate routes.

[0667] Step 5:

[0668] Server: Uses machine learning algorithms to predict and analyze user preferences based on the user's past history and current input data. For example, it analyzes data on restaurants and cafes the user has chosen in the past to infer new preferences.

[0669] Step 6:

[0670] Server: Uses an emotion engine to recognize the user's emotions. It analyzes camera footage and audio data to obtain the user's current emotional state (stress, excitement, relaxation, etc.). The emotion engine analyzes facial expressions and tone of voice to estimate emotions.

[0671] Step 7:

[0672] Server: Based on the recognized emotional data, the server tailors recommendations to suit the user's current emotional state. For example, if the user is feeling stressed, the server recommends relaxing cafes and parks. Conversely, if the user is excited, the server recommends active activities and events.

[0673] Step 8:

[0674] Server: Based on the results of the emotion engine and predictive analysis, the server recommends relevant stores and products near the user's current location and along the optimal route. For example, it creates a list of electronics stores and cafes near Akihabara and prepares it for the user.

[0675] Step 9:

[0676] Server: The server concisely summarizes the acquired traffic data and pedestrian flow information in a format that is easy for users to understand. Specifically, it generates information such as "traffic conditions are moderate, and delays of approximately 10 minutes are expected."

[0677] Step 10:

[0678] Server: Integrates optimal route information and recommendation information. This integrated information provides a detailed travel plan that assists users in their travels and includes store information that is useful during their trip.

[0679] Step 11:

[0680] Terminal: Presents users with integrated information, including maps of optimal routes, real-time traffic information, delay predictions, and recommended stores and products, through an application or web interface that is easily accessible to users.

[0681] Step 12:

[0682] User: Based on the information provided, the user makes the most optimal trip and stops at recommended stores as needed. Information is updated in real time during the trip, allowing the user to reach their destination efficiently and comfortably.

[0683] As a result, the system takes into account real-time information and the user's emotional state to provide optimal routes and personalized recommendations, significantly improving the quality of travel and providing information tailored to individual needs.

[0684] Example 2

[0685] 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."

[0686] While conventional navigation systems can provide optimal routes based on real-time traffic information, it is difficult to provide optimal services that take into account the user's emotions and individual preferences. Furthermore, they lack a mechanism for collecting user reactions to the information provided and continuously improving the service. This limits the user experience and makes it difficult to increase satisfaction during travel.

[0687] 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.

[0688] In this invention, the server includes means for inputting a user's destination and intermediate points, means for acquiring real-time traffic information and people flow information, means for generating an optimal route based on the input destination and intermediate points, means for performing predictive analysis based on the user's preferences, means for recognizing the user's emotions and adjusting recommendations based on the emotions, means for summarizing the acquired real-time information and providing it to the user, and means for collecting user responses and improving the accuracy of the predictive model. This makes it possible to provide a personalized optimal route based on the user's emotions and preferences, and to continuously improve the service.

[0689] "Means for inputting user's destination and stopover points" is a function that allows the user to input their travel destination and stopover points into the system.

[0690] "Means for obtaining real-time traffic and people flow information" refers to a function for obtaining data on the latest traffic conditions and people flow from external sources.

[0691] The "means for generating optimal routes" is a function that calculates and presents the most efficient route for the user based on acquired real-time traffic and people flow information.

[0692] "Means for predictive analysis" refers to a function that uses a user's past behavioral data and current input data to predict the environment and services that a user will prefer.

[0693] "Recommendation means" is a function that presents facilities and products that are of interest to users along the optimal route based on the results of predictive analysis.

[0694] "Means for recognizing user emotions" refers to a function that uses a camera, audio data, etc. to identify the user's current emotional state.

[0695] "Means for adjusting recommendation content based on emotions" refers to a function that changes or adjusts the recommendation content provided to a user according to the recognized emotional state of the user.

[0696] "Means for summarizing and providing acquired real-time information" refers to a function that summarizes acquired traffic and people flow information in an easy-to-understand format and presents it to the user.

[0697] "Means for collecting user responses" refers to a function that collects information about how users respond to the information and services provided.

[0698] "Means for improving the accuracy of the predictive model" refers to a function for improving the accuracy of the system's predictions and recommendations based on collected user response data.

[0699] This invention is a system that provides users with optimal routes that take into account real-time traffic and pedestrian flow information based on destinations and intermediate points set by the user. The system makes personalized recommendations based on the user's emotions and past behavioral data, and can also collect user responses to improve the accuracy of its predictive model.

[0700] First, the user inputs their destination and intermediate points. This information is entered through a device such as a smartphone or computer. The input method for this system includes a touch screen, voice recognition software, or keyboard input. Specifically, the user inputs using the following prompt sentences:

[0701] example:

[0702] Destination: Shinjuku Station

[0703] Stopover point: Akihabara

[0704] Next, the server initializes the entire system and sets the necessary API key and database connection information. This system uses external information providers to obtain real-time traffic and pedestrian flow information, such as the Google Maps API and various traffic information APIs.

[0705] The server obtains real-time traffic and pedestrian flow information through these APIs, and then generates optimal routes based on the obtained data using Dijkstra's algorithm and A algorithm.

[0706] The server then predicts the user's preferences based on their past behavioral data. This prediction is made using machine learning algorithms (such as TensorFlow and Scikit-learn). The obtained user preference data is then integrated with the emotion engine, which collects the user's emotion data in real time.

[0707] The emotion engine uses the camera and microphone on the user's smartphone or PC to analyze the user's facial expressions and vocal tone, thereby recognizing the user's current emotional state. For example, if the user is feeling stressed, it will recommend a relaxing cafe or park, and if the user is excited, it will recommend a place to be active.

[0708] The acquired real-time information is easily summarized by the server and provided to the user in an easy-to-understand format, such as "Current traffic conditions are normal, no delays."

[0709] Finally, the server integrates all the analysis results and generates the optimal route and recommendations for the user, which are displayed in real time on the user's device. User responses are also collected and used to improve the accuracy of future prediction models.

[0710] As a specific example, consider the case where a user is traveling from Tokyo Station to Shinjuku Station and wants to take a break in Akihabara on the way. The user first enters "Shinjuku Station" as the destination and "Akihabara" as the stopover point. Based on this, the system obtains real-time traffic information and calculates the optimal route. Based on the user's emotional data, the system recommends cafes in the Akihabara area where people can relax. Providing this information to the user along with a summary of the traffic conditions enables an efficient and comfortable journey.

[0711] The system allows users to enjoy efficient and comfortable travel with real-time traffic information and personalized recommendations, and it also increases long-term satisfaction by continuously improving the service based on user sentiment and reactions.

[0712] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0713] Step 1:

[0714] User's destination and stopover input (terminal)

[0715] User: The user inputs the destination and intermediate points through a dedicated application. Specifically, the user inputs the destination from "Tokyo Station (35.681236,139.767125)" to "Shinjuku Station (35.689487,139.691706)" and the intermediate point "Akihabara."

[0716] Input: Coordinate data of destination, starting point, and intermediate points.

[0717] Output: User-entered destination and stop data.

[0718] Step 2:

[0719] System initialization (server)

[0720] Server: The server initializes the system and sets up initial settings such as Google Maps API and traffic API keys, database access information, etc. It also initializes the emotion engine module, ensuring that API keys and connection information are loaded.

[0721] Input: config file, API key, connection info.

[0722] Output: Initialized system, emotion engine in ready state.

[0723] Step 3:

[0724] Obtaining real-time traffic and people flow information (server)

[0725] Server: The server sends a request to a real-time traffic information API or a people flow information API based on the destination and intermediate points entered by the user. For example, it sends a request to the Google Maps API to get traffic information.

[0726] Input: Coordinate data of destination, starting point, and intermediate points.

[0727] Output: Real-time traffic and people flow data.

[0728] Step 4:

[0729] Generate optimal route (server)

[0730] Server: The server generates the optimal route using Dijkstra's algorithm and A algorithm based on the acquired real-time traffic information and people flow information.

[0731] Input: Real-time traffic and people flow data, destination, origin and intermediate point coordinate data.

[0732] Output: Optimal route information.

[0733] Step 5:

[0734] Predictive analysis based on user preferences (server)

[0735] Server: The server performs predictive analysis using machine learning algorithms such as TensorFlow and Scikit-learn based on past user data and current input data.

[0736] Input: User's past behavior data, current input data.

[0737] Output: Predictive analysis results based on user preferences.

[0738] Step 6:

[0739] Use of emotion engine (server)

[0740] Server: The server sends the user's camera footage and audio data to the emotion engine, which analyzes the user's emotional state in real time using Amazon Rekognition and the Emotion API.

[0741] Input: Camera video data, audio data.

[0742] Output: User's current emotional state data.

[0743] Step 7:

[0744] Adjustment of recommendation content based on emotions (server)

[0745] Server: Based on the recognized emotional data, it recommends places to relax if the user is feeling stressed, or places to be active if the user is feeling excited.

[0746] Input: User emotional state data, predictive analysis results.

[0747] Output: Adjusted recommendations.

[0748] Step 8:

[0749] Recommendations for stores and products along the way (server)

[0750] Server: The server recommends facilities and products that may be of interest to the user that are located near the user's current location or along the optimal route.

[0751] Input: Predictive analytics results based on user preferences, emotional state data, and current location data.

[0752] Output: Information about recommended stores and products.

[0753] Step 9:

[0754] Real-time information summary (server)

[0755] Server: Summarizes the acquired traffic data and people flow information in an easy-to-understand format and provides it to users.

[0756] Input: Real-time traffic information, people flow information, and optimal route information.

[0757] Output: Summarized real-time information.

[0758] Step 10:

[0759] Integration of optimal routes and recommendation information (server)

[0760] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[0761] Input: Optimal route information, adjusted recommendation content.

[0762] Output: Integrated data of optimal routes and recommendation information.

[0763] Step 11:

[0764] Presenting results to the user (device)

[0765] Terminal: The terminal displays the generated optimized route details, real-time traffic information, delay predictions, and recommended stores and products to the user.

[0766] Input: Integrated data of optimal routes and recommendation information.

[0767] Output: Information displayed to the user.

[0768] Step 12:

[0769] Collecting user responses (server)

[0770] Server: The server collects information about how users respond to the information provided, and uses this information to improve the accuracy of the predictive model.

[0771] Input: User response data.

[0772] Output: Collected user response data, data that helps improve the accuracy of the prediction model.

[0773] (Application example 2)

[0774] 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."

[0775] Conventional navigation systems can provide optimal routes based on real-time traffic and people flow information, but they lack the ability to make personalized recommendations based on the user's emotions and preferences. Furthermore, they are unable to recommend appropriate stores and products in response to changes in the user's emotions while traveling, resulting in a suboptimal user travel experience. Therefore, there is a need for a system that can adjust the optimal route based on the user's emotions and recommend stores and products that the user may be interested in while traveling.

[0776] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0777] In this invention, the server includes: means for inputting a user's destination and intermediate points; means for acquiring real-time traffic information and people flow information; means for generating an optimal route based on the input destination and intermediate points; means for performing predictive analysis based on the user's preferences; means for recommending related stores or products along the optimal route based on the predictive analysis; means for summarizing the acquired real-time information and providing it to the user; means for recognizing the user's emotions and adjusting the recommendation content based on the user's emotional state; means for displaying the recommended stores and products to the user; and means for collecting user responses and contributing to improving the accuracy of future predictive models. This makes it possible to personalize the travel experience according to the user's emotions and preferences and provide an environment in which the user can travel comfortably.

[0778] "Means for inputting user's destination and intermediate points" refers to an interface that allows the user to input specific destinations and intermediate points into the system.

[0779] "Means for obtaining real-time traffic information and people flow information" refers to a function for collecting the latest information on traffic conditions and people flow in real time.

[0780] "Means for generating an optimal route based on the input destination and intermediate points" is a function that calculates and generates the most efficient and convenient route based on the destination and intermediate points input by the user.

[0781] "Means for performing predictive analytics based on user preferences" refers to a feature that uses machine learning algorithms to predict future behavior and preferences based on a user's past behavior and preferences.

[0782] "Means for recommending related stores or products along the optimal route" is a function that recommends stores and products that the user may be interested in along the generated optimal route.

[0783] "Means of summarizing acquired real-time information and providing it to users" refers to the function of organizing and summarizing collected traffic and people flow information and providing it to users in a format that is easy to understand.

[0784] "Means for recognizing the user's emotions and adjusting the content of recommendations based on their emotional state" refers to a function that identifies the user's current emotional state in real time and changes the content of recommendations according to that emotion.

[0785] "Means for displaying recommended stores and products to users" refers to an interface for displaying stores and products recommended by the system to users.

[0786] "Means of collecting user responses and contributing to improving the accuracy of future predictive models" refers to a function that collects how users respond to the information provided and uses that data to improve the accuracy of predictive models.

[0787] This invention provides a system that generates optimal routes by integrating real-time traffic and people flow information based on user-specified destinations and intermediate points. It also has the function of recommending appropriate stores and products during travel based on the user's preferences and emotional state.

[0788] System Configuration

[0789] 1. User's destination and stopover input method:

[0790] The user inputs the destination and intermediate points using a smartphone.

[0791] The interface includes a text box and voice input.

[0792] 2. Means of obtaining real-time traffic and people flow information:

[0793] The server obtains real-time traffic and pedestrian flow information using Google Maps APIs and other services.

[0794] The API key is provided during the initial setup of the server.

[0795] 3. How to generate optimal routes:

[0796] The server calculates the optimal route based on the acquired traffic and pedestrian flow information.

[0797] It also takes into account delay information along the way and provides the optimal route.

[0798] 4. Means of predictive analysis based on user preferences:

[0799] The server uses machine learning algorithms (scikit-learn, TensorFlow, etc.) to predict user preferences.

[0800] Past behavioral data and preference information is used.

[0801] 5. Means of recommending related stores or products along the optimal route:

[0802] The server recommends stores and products along the optimal route to the user.

[0803] You can get information about nearby stores using the Yelp and Foursquare APIs.

[0804] 6. Means for summarizing acquired real-time information and providing it to users:

[0805] The server summarizes traffic data and people flow information and displays it in an easy-to-understand manner for users.

[0806] 7. How to recognize user emotions and tailor recommendations based on their emotional state:

[0807] It uses the smartphone's camera and microphone to recognize the user's emotional state.

[0808] Based on the recognized emotional data, it recommends stores where you can relax and facilities where you can be active.

[0809] 8. How to display recommended stores and products to users:

[0810] Recommended stores and products are visually displayed on the smartphone interface.

[0811] 9. How we collect user feedback to help improve future prediction models:

[0812] The server collects data on how users respond to the information provided.

[0813] The collected data is fed back into the predictive model to help improve accuracy.

[0814] Example

[0815] As a specific example, consider the case where a user travels from Tokyo Station to Shinjuku Station and specifies Akihabara as a stopover point. When the user enters this information into their smartphone, the server obtains real-time traffic information using the Google Maps API, generates the optimal route, and provides directions from Tokyo Station to Shinjuku Station via Akihabara.

[0816] During this process, the user's emotional state is recognized and, if it is determined that they are in a stressful state, a relaxing cafe in Akihabara is recommended. Furthermore, the server uses the Yelp API to obtain information about cafes in the Akihabara area and displays it on the smartphone. When the user visits a cafe based on the information provided, their behavior is fed back to the server, helping to improve the accuracy of the prediction model.

[0817] Example prompts to input to a generative AI model:

[0818] What is the best route to travel from Tokyo Station to Shinjuku Station via Akihabara? Also, since the user is feeling stressed, please recommend a cafe in Akihabara where they can relax. Please also take real-time traffic and people flow information into consideration.

[0819] This invention allows users to enjoy a comfortable and personalized travel experience.

[0820] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0821] Step 1:

[0822] The terminal accepts input of the user's destination and intermediate points. The user inputs that they are traveling from Tokyo Station to Shinjuku Station, passing through Akihabara on the way. This results in the input data being "Tokyo Station, Shinjuku Station, Akihabara."

[0823] Step 2:

[0824] The server obtains real-time traffic and people flow information and sends a request to the Google Maps API based on the input destination and intermediate points. The output is data on traffic conditions and people flow information for each route.

[0825] Step 3:

[0826] The server generates the optimal route based on the acquired traffic and pedestrian flow information, taking into account traffic congestion and real-time road conditions, calculates the shortest and most optimal route, and generates route data to provide to the user.

[0827] Step 4:

[0828] The server performs predictive analysis based on the user's preferences. It analyzes past behavioral and preference data using machine learning algorithms (such as scikit-learn and TensorFlow) to predict the user's current preferences. The predicted results are output as data on categories that the user is likely to be interested in (e.g., cafes or bookstores).

[0829] Step 5:

[0830] The server recommends relevant stores or products along the optimal route based on predictive analysis. It uses the Yelp or Foursquare API to search for and obtain information about stores and products that fit the predicted category. The output is a list of recommended stores and products.

[0831] Step 6:

[0832] The server summarizes the acquired real-time information and recommendation information and prepares it for delivery to the user. It summarizes traffic information, people flow information, recommended stores and products information, and converts them into a format that can be displayed to the user. The output is a summarized information package.

[0833] Step 7:

[0834] The device recognizes the user's emotions. It uses the smartphone's camera and microphone to obtain real-time emotional data from the user through emotion recognition algorithms. The output is the user's current emotional state.

[0835] Step 8:

[0836] The server adjusts the recommendations based on the user's emotional state. For example, if the user is stressed, the server recommends relaxing stores, and if the user is excited, the server recommends active facilities. The output is the adjusted recommendation list.

[0837] Step 9:

[0838] The terminal displays the recommended stores and products to the user. The adjusted recommendations are visually displayed on the smartphone screen. The output is the user's display screen.

[0839] Step 10:

[0840] The server collects responses from users, such as the stores they visited and the products they purchased, based on the information they provided, and stores this information in a database. The output is the collected user response data.

[0841] Through these steps, users can enjoy an individually optimized travel experience and receive suggestions tailored to their interests and emotions while traveling.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] [Third embodiment]

[0846] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0847] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0848] 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).

[0849] 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.

[0850] 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.

[0851] 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).

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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."

[0858] This invention is a system that allows users to set their destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. Furthermore, this system has the ability to predict and analyze users' preferences and recommend stores and products that they might be interested in while traveling. It also provides users with a summary of real-time information, preventing information overload and supporting appropriate decision-making.

[0859] System implementation

[0860] 1. User's destination and stopover input (terminal):

[0861] User: Enter the destination and intermediate points. For example, consider traveling from Tokyo Station (35.681236,139.767125) to Shinjuku Station (35.689487,139.691706).

[0862] 2. System initialization (server):

[0863] Server: Set the API key and database connection information required during the initialization phase. This setting is required later to obtain real-time traffic and people flow information.

[0864] 3. Acquisition of real-time traffic and people flow information (server):

[0865] Server: Sends requests to a dedicated API to retrieve up-to-date traffic and people flow data for origins, destinations, and intermediate points.

[0866] Example: Obtain traffic information for Akihabara on the way from Tokyo Station to Shinjuku Station to understand the congestion situation.

[0867] 4. Generate optimal route (server):

[0868] Server: Generates the optimal route based on real-time traffic and pedestrian flow information. This route is calculated by taking into account various factors (e.g., traffic conditions, delays, congestion, etc.) from the departure point to the destination.

[0869] Example: Providing the most efficient route from Tokyo Station to Shinjuku Station, passing through Akihabara, avoiding congestion and delays.

[0870] 5. Predictive analysis based on user preferences (server):

[0871] Server: Utilizes machine learning algorithms based on the user's past behavioral data and input data to predict and analyze user preferences.

[0872] Example: Analyzing past data to see if users are interested in electronic devices or cafes.

[0873] 6. Store and product recommendations (server):

[0874] Server: Based on the results of predictive analysis, it lists and recommends stores and products that are close to the user's current location and that match their interests.

[0875] Example: Recommending electronics stores and cafes to users near their current location.

[0876] 7. Real-time information summary (server):

[0877] Server: Concisely summarizes the acquired traffic data and pedestrian flow information and converts it into a format that is easy for users to understand.

[0878] Example: Present summary information to the user, such as "Traffic conditions are moderate, delays expected approximately 10 minutes."

[0879] 8. Integration of optimal routes and recommendation information (server):

[0880] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[0881] Example: Providing users with the optimal route from Tokyo Station to Shinjuku Station, along with information on stores that may be of interest along the way.

[0882] 9. Displaying results to the user (terminal):

[0883] Terminal: Displays the final results to the user, including details of the best route, real-time traffic information, and recommended stores and products.

[0884] Example: Providing a route from Tokyo Station to Shinjuku Station, a summary of the traffic conditions in Akihabara along the way, and information on cafes and electronics stores in the Akihabara area.

[0885] In this way, users can find the optimal route based on real-time information while traveling, and receive store information tailored to their individual preferences. This system not only allows users to travel efficiently and comfortably, but also opens up new shopping opportunities.

[0886] The processing flow will be explained below.

[0887] Step 1:

[0888] User: Enter the destination and intermediate point. This sets the origin (Tokyo Station), destination (Shinjuku Station), and intermediate point (Akihabara).

[0889] Step 2:

[0890] Server: Initializes the system, including setting up any necessary API keys and database connection information.

[0891] Step 3:

[0892] Server: Sends an API request to obtain targeted traffic and people flow information based on the input destination and intermediate points. Specifically, it obtains this information from an external service that provides traffic and people flow data for Akihabara for the route from Tokyo Station to Shinjuku Station.

[0893] Step 4:

[0894] Server: Analyzes real-time traffic and people flow information and generates the optimal route from the departure point to the destination. For example, it calculates a route from Tokyo Station to Shinjuku Station that avoids congestion and delays when passing through Akihabara.

[0895] Step 5:

[0896] Server: Performs predictive analysis based on the user's past data and input preferences, using machine learning algorithms to predict the store category (e.g., electronics store or cafe) that the user might be interested in.

[0897] Step 6:

[0898] Server: Based on the results of predictive analysis, the server recommends stores and products related to the user's current location and the optimal route. For example, it creates a list of electronics stores and cafes near Tokyo Station or Akihabara and prepares the information to provide to the user.

[0899] Step 7:

[0900] Server: Summarizes the acquired real-time information succinctly, organizing it in a way that is easy for users to understand, such as "Traffic conditions are moderate, with delays expected of approximately 10 minutes."

[0901] Step 8:

[0902] Server: Integrates optimal route information and recommendation information to create a detailed travel plan that allows users to travel efficiently and conveniently.

[0903] Step 9:

[0904] Terminal: Presents users with integrated information, including a map of the optimal route, real-time traffic information, delay predictions, and recommendations for stores and products that may interest them.

[0905] Step 10:

[0906] User: Based on the information provided, the user makes the most efficient and comfortable journey, stopping at recommended stores as needed. Users can understand traffic conditions and options along the way in real time, allowing them to travel efficiently and comfortably.

[0907] Through these steps, users can determine the optimal route based on real-time information and receive recommendations for stores and products they are interested in. This system prevents information overload and helps users make better decisions, greatly improving the user's travel experience.

[0908] Example 1

[0909] 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."

[0910] Current navigation systems are specialized in providing destination guidance, but they do not fully consider real-time traffic conditions and pedestrian flow, and lack the functionality to provide store and product information based on user preferences. As a result, users not only have difficulty traveling efficiently and comfortably, but also have problems obtaining appropriate information while traveling.

[0911] 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.

[0912] In this invention, the server includes means for inputting a user's destination and intermediate points, means for acquiring real-time traffic information and people flow information, means for generating an optimal route based on the input destination and intermediate points, means for performing predictive analysis based on the user's preferences, means for recommending related stores or products along the optimal route based on the predictive analysis, means for summarizing the acquired real-time information and providing it to the user, means for integrating the optimal route and recommendation information based on the user's destination and intermediate point information, and means for displaying the integrated result on the user's terminal. This allows the user to obtain an optimal route based on real-time traffic conditions and simultaneously obtain information on stores and products that interest them.

[0913] "User" means a person who uses the System to set destinations and waypoints.

[0914] "Destinations and intermediate points" refers to the starting point, intermediate points, and destination when a user travels.

[0915] "Real-time traffic information" means data and information that indicates current traffic conditions, including traffic congestion, road closures, traffic accidents, etc.

[0916] "People flow information" refers to data and information about the movement and gathering of people in a particular area.

[0917] An "optimal route" refers to the most efficient route between the departure point and the destination, taking into maximum consideration conditions such as travel time and congestion.

[0918] "Predictive analytics" is an analytical method for predicting future behavior and interests based on a user's past behavior and input data.

[0919] "Related stores or products" refers to stores or products located along the optimal route based on the user's preferences and interests.

[0920] "Recommending" means recommending appropriate stores and products based on the user's interests and needs.

[0921] "Summarization" refers to the process of concisely summarizing large amounts of acquired data and converting it into a format that is easy for users to understand.

[0922] "Integrating" means bringing together different pieces of information or data to generate a complete whole.

[0923] "Terminal" means a device that allows a user to operate the system and input or view information.

[0924] This invention is a system that allows users to set their destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. Furthermore, this system has the ability to predict and analyze users' preferences and recommend stores and products that they might be interested in while traveling. It also provides users with a summary of real-time information, preventing information overload and supporting appropriate decision-making.

[0925] This system is realized using the following components:

[0926] 1. Terminal: A device that receives the user's destination and intermediate point input. This device can be a smartphone or tablet, for example.

[0927] 2. Server: A central processing unit that processes various data, calculates optimal routes, and generates recommendation information. The software used here is, for example, a platform that implements the Google Maps API or machine learning algorithms.

[0928] The specific implementation steps are as follows:

[0929] Entering destination and stopover points

[0930] The user inputs the starting point, intermediate points, and destination on the terminal. For example, consider traveling from Tokyo Station (35.681236, 139.767125) to Shinjuku Station (35.689487, 139.691706), and set Akihabara (35.698353, 139.773114) as a stopover point along the way.

[0931] System initialization

[0932] The server sets the API key (e.g., Google Maps API key) and database connection information and connects to various services.

[0933] Obtaining real-time traffic and people flow information

[0934] The server sends a request to a dedicated API to obtain the latest traffic and people flow data for the specified location (Tokyo Station, Akihabara, Shinjuku Station).

[0935] Generate optimal routes

[0936] The server uses real-time traffic and people flow information to generate optimal routes, such as calculating alternative routes to avoid the crowds in Akihabara.

[0937] Predictive analytics based on user preferences

[0938] The server uses machine learning algorithms to predict and analyze the user's preferences based on the user's past behavioral data and input data. Based on past data, it analyzes the user's interests in electronic devices and cafes.

[0939] Store and product recommendations

[0940] Based on the results of the predictive analysis, the server generates a recommendation list by listing stores and products that are close to the user's current location and that match their interests, such as electronics stores and cafes near the user's current location.

[0941] Real-time information summary

[0942] The server then summarizes the acquired traffic and pedestrian flow data and converts it into a format that is easy for users to understand, such as "traffic conditions are moderate, and delays of approximately 10 minutes are expected."

[0943] Integration of optimal routes and recommendation information

[0944] The server integrates all acquired information and analysis results to generate optimal routes and recommendation information for users.

[0945] Presenting results to the user

[0946] The device displays the final results from the server to the user, such as the optimal route, a summary of the traffic situation in Akihabara along the way, and information on cafes and electronics stores in the area, through a user interface.

[0947] Prompt Sentence Examples

[0948] "Please provide the best route from Tokyo Station to Shinjuku Station. Also check the traffic conditions in Akihabara along the way and provide recommendations for suitable stores and cafes."

[0949] The system allows users to obtain the optimal route based on real-time traffic conditions and simultaneously obtain information on stores and products they are interested in, providing an efficient and comfortable travel experience.

[0950] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0951] Step 1:

[0952] User: Enter the destination and intermediate points using the terminal. Specifically, the departure point is set to Tokyo Station (35.681236,139.767125), the intermediate point is set to Akihabara (35.698353,139.773114), and the destination is set to Shinjuku Station (35.689487,139.691706).

[0953] Input: User-specified coordinates of the starting point, intermediate point, and destination.

[0954] Output: Data transmission to the server is complete.

[0955] Step 2:

[0956] Server: Performs initialization. Specifically, sets the API key (e.g., Google Maps API key) and database connection information to enable access to each API service.

[0957] Inputs: API key, database connection information.

[0958] Output: Initialization completion status.

[0959] Step 3:

[0960] Server: Obtains real-time traffic and people flow information. Sends a request to a dedicated API to obtain the latest traffic and people flow data for each specified location (e.g., Tokyo Station, Akihabara, Shinjuku Station).

[0961] Input: Coordinate information of the starting point, intermediate point, and destination set by the user.

[0962] Output: Traffic and people flow data obtained from the API.

[0963] Step 4:

[0964] Server: Generates optimal routes based on real-time traffic and people flow information. Algorithms are used to calculate the most efficient route, taking congestion and delays into account.

[0965] Input: Traffic and people flow data obtained from API.

[0966] Output: Optimal route information.

[0967] Step 5:

[0968] Server: Performs predictive analysis based on user preferences. Utilizing machine learning algorithms based on the user's past behavioral data and input data, predicts and analyzes the user's interests.

[0969] Input: User's past behavior data, current input data.

[0970] Output: Predictive data about user interests.

[0971] Step 6:

[0972] Server: Based on the results of predictive analysis, the server recommends stores and products that are close to the user's current location and that match their interests. The server references a database of nearby stores and creates a list.

[0973] Input: current user location, predictive analysis results, store database.

[0974] Output: A list of recommended stores and products.

[0975] Step 7:

[0976] Server: Appropriately summarizes the acquired traffic and people flow data, summarizing the information succinctly and converting it into a format that is easy for users to understand.

[0977] Input: Raw data from the API.

[0978] Output: Summarized traffic and people flow information.

[0979] Step 8:

[0980] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[0981] Input: optimal route information, summarized traffic and people flow information, recommendation list.

[0982] Output: The final integrated result data.

[0983] Step 9:

[0984] Device: The results sent from the server are displayed to the user, who can check the optimal route, a summary of traffic conditions, and recommended stores and products on the device screen.

[0985] Input: The final integration result data from the server.

[0986] Output: The screen display that the user sees.

[0987] This process allows users to travel efficiently and comfortably, while also receiving store information tailored to their interests.

[0988] (Application example 1)

[0989] 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."

[0990] Previously, navigation systems for autonomous vehicles provided optimal route guidance based on real-time traffic and pedestrian flow information, but they did not offer recommendations based on user preferences or route suggestions that took into account user behavioral history. Furthermore, they lacked the ability to summarize acquired real-time information or provide appropriate information to users, creating challenges in supporting efficient and comfortable travel. Furthermore, systems for autonomous vehicles that would provide new purchasing opportunities were also underdeveloped.

[0991] 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.

[0992] In this invention, the server includes means for inputting a user's destination and intermediate points, means for acquiring real-time traffic information and people flow information, means for generating an optimal route based on the input destination and intermediate points, means for performing predictive analysis based on the user's preferences, means for recommending related stores or products along the optimal route based on the predictive analysis, means for summarizing the acquired real-time information and providing it to the user, means for integrating data into the navigation system of a related autonomous vehicle based on the user's past behavior data, and means for recommending stores and products that the user may be interested in based on the user's location data. This allows the user to check the optimal route based on real-time information, and makes it possible to provide recommendations and purchasing opportunities according to their preferences.

[0993] The "means for inputting the user's destination and intermediate points" is an interface that allows the user to input their travel destination and intermediate points into the system.

[0994] "Means for obtaining real-time traffic and people flow information" refers to a system for instantly obtaining data on current traffic conditions and people flow.

[0995] The "means for generating an optimal route based on the input destination and intermediate points" is an algorithm that uses the input destination and intermediate point information to calculate the most efficient travel route.

[0996] The "means for performing predictive analysis based on the user's preferences" is a data analysis technique for predicting future behavior based on the user's past behavioral data and preferences.

[0997] "Means for recommending related stores or products along the optimal route based on the predictive analysis" refers to a system that suggests related stores or products located on or near the optimal route based on predicted user preferences and behavior.

[0998] The "means for summarizing the acquired real-time information and providing it to the user" is a function for concisely summarizing the acquired traffic information and people flow information and presenting it to the user.

[0999] "Means for integrating data into the navigation system of the associated autonomous vehicle based on the user's past behavioral data" refers to a technology that reflects data based on the user's past travel history and behavioral patterns in the navigation system of the autonomous vehicle.

[1000] "Means for recommending stores and products that may be of interest to the user based on the user's location data" refers to a system that uses the user's current location information to recommend stores and products that are close to that location.

[1001] An embodiment of the present invention is described in detail below. The present invention is a system that allows users to set their destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. Furthermore, the system has the function of predicting and analyzing the user's preferences and recommending stores and products that the user may be interested in while traveling. Furthermore, by providing a summary of real-time information to the user, the system prevents information overload and supports appropriate decision-making.

[1002] System configuration

[1003] Hardware Configuration

[1004] Server: A server with high-performance computing capabilities that processes real-time data and connects to databases. Specific examples include Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[1005] Device: A device that provides a user interface, such as a smartphone, smart glasses, or a head-mounted display.

[1006] Autonomous vehicles: Equipped with advanced navigation systems and capable of connecting with external systems.

[1007] Software Configuration

[1008] User interface application: An application that runs on a smartphone or head-mounted display, where the user inputs destinations and intermediate points.

[1009] Data acquisition module: A module for acquiring real-time traffic and people flow information from various APIs.

[1010] Predictive analysis module: Uses machine learning algorithms to predict and analyze user preferences.

[1011] Recommendation module: Recommends relevant stores and products along the optimal route.

[1012] Data Summarization Module: Summarizes the acquired real-time information and provides it to the user concisely.

[1013] Operation explanation

[1014] 1. User input: The user inputs the destination and intermediate points through an application installed on a smartphone or head-mounted display. For example, the user specifies a trip from Tokyo Station to Shinjuku Station, with Akihabara as the intermediate point.

[1015] 2. Acquisition of real-time data: The server uses APIs to acquire traffic and people flow information. For example, it acquires real-time traffic data from Tokyo Station to Shinjuku Station to understand the congestion situation around Akihabara.

[1016] 3. Generate optimal route: The server generates the optimal route based on the acquired data. It takes into account traffic conditions and delay information to calculate the most efficient route for the user.

[1017] 4. User preference prediction: The server analyzes the user's past behavior data and uses machine learning algorithms to predict the user's preferences. For example, the server knows from past data that the user is interested in cafes and electronics stores.

[1018] 5. Providing recommendation information: Based on the results of predictive analysis, the server recommends related stores and products along the optimal route, for example, providing users with information on cafes and electronics stores in Akihabara.

[1019] 6. Real-time information summary: The server briefly summarizes the real-time information it has acquired and provides it to the user, for example, displaying information such as "Traffic conditions are moderate, and delays of approximately 10 minutes are expected."

[1020] 7. Presenting the results to the user: The application presents the final navigation information to the user, including details of the optimal route, recommendations, and summarized real-time information. For example, route guidance from Tokyo Station to Shinjuku Station and recommended shops around Akihabara are displayed.

[1021] Specific examples

[1022] Example prompt sentence:

[1023] "You are going to create a navigation system that will help a user travel from Tokyo Station to Shinjuku Station and provide information about interesting shops (e.g. electronics stores and cafes) in Akihabara along the way. The system should provide the optimal route based on real-time traffic information and recommend places that may be of interest to the user based on their previous behavior."

[1024] As a result, the system of the present invention can provide users with efficient and comfortable travel and create new purchasing opportunities.

[1025] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1026] Step 1:

[1027] The user inputs the destination and intermediate points through an application installed on a smartphone or head-mounted display. This input includes the starting point, destination, and intermediate points (e.g., from Tokyo Station to Shinjuku Station, with Akihabara as the intermediate point). The input data is sent from the application to the server.

[1028] Step 2:

[1029] The server uses the provided API to obtain real-time traffic and pedestrian flow information. Specifically, it obtains traffic conditions and congestion levels from the departure point to the destination through an API request. In this process, it uses the API key and database connection information. The input is the request sent to the API, and the output is the obtained real-time traffic information.

[1030] Step 3:

[1031] The server generates optimal routes based on the acquired real-time traffic information. This involves analyzing traffic data and applying shortest-path algorithms. For example, it calculates the most efficient route from Tokyo Station to Shinjuku Station via Akihabara, using algorithms to avoid congestion and delays. The input is traffic data, and the output is optimal route information.

[1032] Step 4:

[1033] The server analyzes the user's past behavioral data and predicts the user's preferences using a machine learning algorithm. The input is the user's past behavioral data, and the output is the predicted user's interests (e.g., the user is interested in cafes and electronics stores). The algorithm extracts patterns from user behavior and predicts interests.

[1034] Step 5:

[1035] Based on the results of the predictive analysis, the server recommends related stores and products along the optimal route. This process searches for and lists appropriate store information based on the user's predicted interests. For example, a list is generated by searching for information on cafes and electronics stores around Akihabara. The input is the predicted interests and current location information, and the output is a list of recommended stores.

[1036] Step 6:

[1037] The server summarizes the acquired real-time information and provides it to the user in a concise format. This process summarizes traffic conditions and congestion data and generates an easy-to-understand message. For example, it creates a summary such as "Traffic conditions are moderate, with delays expected to be approximately 10 minutes." The input is real-time traffic information, and the output is summarized information.

[1038] Step 7:

[1039] The terminal presents the final results to the user through a user interface, including details of the optimal route, recommended information, and a summary of real-time information. The input is data from the server, and the output is the information displayed to the user. For example, the terminal displays a navigation route from Tokyo Station to Shinjuku Station, recommended store information around Akihabara, and a summary of traffic conditions.

[1040] 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.

[1041] This invention is a system that inputs a user's destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. It also has the ability to predict and analyze the user's preferences and recommend stores and products that the user might be interested in while traveling. By combining it with an emotion engine that recognizes the user's emotions, the system can adjust the recommendations based on the user's current emotional state and past emotional data, providing a more personalized service.

[1042] System implementation

[1043] 1. User's destination and stopover input (terminal):

[1044] User: Enter the destination and intermediate points. For example, consider traveling from Tokyo Station (35.681236,139.767125) to Shinjuku Station (35.689487,139.691706).

[1045] 2. System initialization (server):

[1046] Server: Initializes the system and sets up the necessary API keys and database connection information. The emotion engine is also prepared at this stage.

[1047] 3. Acquisition of real-time traffic and people flow information (server):

[1048] Server: Sends an API request to obtain traffic and people flow information based on the input destination and intermediate points.

[1049] 4. Generate optimal route (server):

[1050] Server: Generates the optimal route from the departure point to the destination based on real-time traffic and people flow information.

[1051] 5. Predictive analysis based on user preferences (server):

[1052] Server: Uses machine learning algorithms to predict user preferences based on the user's past data and current input.

[1053] 6. Use of Emotion Engine (Server):

[1054] Server: Uses an emotion engine to recognize the user's emotions in real time. It recognizes the user's emotions from camera and voice data and obtains the user's current emotional state.

[1055] 7. Emotion-based recommendation adjustment (server):

[1056] Server: Based on the recognized emotional data, it recommends relaxing places if the user is feeling stressed, and active places if the user is feeling excited.

[1057] 8. Recommendation of stores and products along the way (server):

[1058] Server: Based on the results of predictive analysis and the sentiment engine, it recommends stores and products that may be of interest to the user near their current location or along the optimal route.

[1059] 9. Real-time information summary (server):

[1060] Server: Easily compiles the acquired traffic data and pedestrian flow information and summarizes it in a format that is easy for users to understand.

[1061] 10. Integration of optimal routes and recommendation information (server):

[1062] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[1063] 11. Displaying results to the user (terminal):

[1064] Terminal: Displays the final results to the user, including details of the optimal route, real-time traffic information, delay predictions, and recommended stores and products.

[1065] 12. Collecting user responses (server):

[1066] Server: Collects information about how users respond to the information provided to help improve the service and its predictive models.

[1067] As a specific example, consider a user traveling from Tokyo Station to Shinjuku Station and wanting to take a break in Akihabara. The user inputs their destination and stopover points, and the system retrieves real-time traffic and people flow information. After an optimal route is generated, the emotion engine detects the user's stress level and recommends cafes in the Akihabara area where they can relax. This information, along with a summary of traffic conditions, is provided to the user, enabling an efficient and comfortable journey. This series of processes allows users to improve the quality of their journey and receive personalized service.

[1068] The processing flow will be explained below.

[1069] Step 1:

[1070] User: Enter the destination and intermediate points. For example, consider traveling from Tokyo Station (35.681236, 139.767125) to Shinjuku Station (35.689487, 139.691706). Enter that you will pass through Akihabara (35.7033, 139.7745) on the way.

[1071] Step 2:

[1072] Server: Initializes the system and sets up the necessary API keys and database connection information. The emotion engine is also set up at this stage.

[1073] Step 3:

[1074] Server: Sends API requests to obtain traffic and people flow information based on the input destination and intermediate points. Specifically, it constructs a URL and sends a request to an external service to obtain the data.

[1075] Step 4:

[1076] Server: Analyzes real-time traffic and people flow information and generates the optimal route from the departure point to the destination. For example, it calculates routes that avoid congestion and delays, and selects the most efficient route from multiple candidate routes.

[1077] Step 5:

[1078] Server: Uses machine learning algorithms to predict and analyze user preferences based on the user's past history and current input data. For example, it analyzes data on restaurants and cafes the user has chosen in the past to infer new preferences.

[1079] Step 6:

[1080] Server: Uses an emotion engine to recognize the user's emotions. It analyzes camera footage and audio data to obtain the user's current emotional state (stress, excitement, relaxation, etc.). The emotion engine analyzes facial expressions and tone of voice to estimate emotions.

[1081] Step 7:

[1082] Server: Based on the recognized emotional data, the server tailors recommendations to suit the user's current emotional state. For example, if the user is feeling stressed, the server recommends relaxing cafes and parks. Conversely, if the user is excited, the server recommends active activities and events.

[1083] Step 8:

[1084] Server: Based on the results of the emotion engine and predictive analysis, the server recommends relevant stores and products near the user's current location and along the optimal route. For example, it creates a list of electronics stores and cafes near Akihabara and prepares it for the user.

[1085] Step 9:

[1086] Server: The server concisely summarizes the acquired traffic data and pedestrian flow information in a format that is easy for users to understand. Specifically, it generates information such as "traffic conditions are moderate, and delays of approximately 10 minutes are expected."

[1087] Step 10:

[1088] Server: Integrates optimal route information and recommendation information. This integrated information provides a detailed travel plan that assists users in their travels and includes store information that is useful during their trip.

[1089] Step 11:

[1090] Terminal: Presents users with integrated information, including maps of optimal routes, real-time traffic information, delay predictions, and recommended stores and products, through an application or web interface that is easily accessible to users.

[1091] Step 12:

[1092] User: Based on the information provided, the user makes the most optimal trip and stops at recommended stores as needed. Information is updated in real time during the trip, allowing the user to reach their destination efficiently and comfortably.

[1093] As a result, the system takes into account real-time information and the user's emotional state to provide optimal routes and personalized recommendations, significantly improving the quality of travel and providing information tailored to individual needs.

[1094] Example 2

[1095] 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."

[1096] While conventional navigation systems can provide optimal routes based on real-time traffic information, it is difficult to provide optimal services that take into account the user's emotions and individual preferences. Furthermore, they lack a mechanism for collecting user reactions to the information provided and continuously improving the service. This limits the user experience and makes it difficult to increase satisfaction during travel.

[1097] 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.

[1098] In this invention, the server includes means for inputting a user's destination and intermediate points, means for acquiring real-time traffic information and people flow information, means for generating an optimal route based on the input destination and intermediate points, means for performing predictive analysis based on the user's preferences, means for recognizing the user's emotions and adjusting recommendations based on the emotions, means for summarizing the acquired real-time information and providing it to the user, and means for collecting user responses and improving the accuracy of the predictive model. This makes it possible to provide a personalized optimal route based on the user's emotions and preferences, and to continuously improve the service.

[1099] "Means for inputting user's destination and stopover points" is a function that allows the user to input their travel destination and stopover points into the system.

[1100] "Means for obtaining real-time traffic and people flow information" refers to a function for obtaining data on the latest traffic conditions and people flow from external sources.

[1101] The "means for generating optimal routes" is a function that calculates and presents the most efficient route for the user based on acquired real-time traffic and people flow information.

[1102] "Means for predictive analysis" refers to a function that uses a user's past behavioral data and current input data to predict the environment and services that a user will prefer.

[1103] "Recommendation means" is a function that presents facilities and products that are of interest to users along the optimal route based on the results of predictive analysis.

[1104] "Means for recognizing user emotions" refers to a function that uses a camera, audio data, etc. to identify the user's current emotional state.

[1105] "Means for adjusting recommendation content based on emotions" refers to a function that changes or adjusts the recommendation content provided to a user according to the recognized emotional state of the user.

[1106] "Means for summarizing and providing acquired real-time information" refers to a function that summarizes acquired traffic and people flow information in an easy-to-understand format and presents it to the user.

[1107] "Means for collecting user responses" refers to a function that collects information about how users respond to the information and services provided.

[1108] "Means for improving the accuracy of the predictive model" refers to a function for improving the accuracy of the system's predictions and recommendations based on collected user response data.

[1109] This invention is a system that provides users with optimal routes that take into account real-time traffic and pedestrian flow information based on destinations and intermediate points set by the user. The system makes personalized recommendations based on the user's emotions and past behavioral data, and can also collect user responses to improve the accuracy of its predictive model.

[1110] First, the user inputs their destination and intermediate points. This information is entered through a device such as a smartphone or computer. The input method for this system includes a touch screen, voice recognition software, or keyboard input. Specifically, the user inputs using the following prompt sentences:

[1111] example:

[1112] Destination: Shinjuku Station

[1113] Stopover point: Akihabara

[1114] Next, the server initializes the entire system and sets the necessary API key and database connection information. This system uses external information providers to obtain real-time traffic and pedestrian flow information. For example, it uses the Google Maps API and various traffic information APIs.

[1115] The server obtains real-time traffic and pedestrian flow information through these APIs, and then generates optimal routes based on the obtained data using Dijkstra's algorithm and A algorithm.

[1116] The server then predicts the user's preferences based on their past behavioral data. This prediction is made using machine learning algorithms (such as TensorFlow and Scikit-learn). The obtained user preference data is then integrated with the emotion engine, which collects the user's emotion data in real time.

[1117] The emotion engine uses the camera and microphone on the user's smartphone or PC to analyze the user's facial expressions and vocal tone, thereby recognizing the user's current emotional state. For example, if the user is feeling stressed, it will recommend a relaxing cafe or park, and if the user is excited, it will recommend a place to be active.

[1118] The acquired real-time information is easily summarized by the server and provided to the user in an easy-to-understand format, such as "Current traffic conditions are normal, no delays."

[1119] Finally, the server integrates all the analysis results and generates the optimal route and recommendations for the user, which are displayed in real time on the user's device. User responses are also collected and used to improve the accuracy of future prediction models.

[1120] As a specific example, consider the case where a user is traveling from Tokyo Station to Shinjuku Station and wants to take a break in Akihabara on the way. The user first enters "Shinjuku Station" as the destination and "Akihabara" as the stopover point. Based on this, the system obtains real-time traffic information and calculates the optimal route. Based on the user's emotional data, the system recommends cafes in the Akihabara area where people can relax. Providing this information to the user along with a summary of the traffic conditions enables an efficient and comfortable journey.

[1121] The system allows users to enjoy efficient and comfortable travel with real-time traffic information and personalized recommendations, and it also increases long-term satisfaction by continuously improving the service based on user sentiment and reactions.

[1122] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1123] Step 1:

[1124] User's destination and stopover input (terminal)

[1125] User: The user inputs the destination and intermediate points through a dedicated application. Specifically, the user inputs the destination from "Tokyo Station (35.681236,139.767125)" to "Shinjuku Station (35.689487,139.691706)" and the intermediate point "Akihabara."

[1126] Input: Coordinate data of destination, starting point, and intermediate points.

[1127] Output: User-entered destination and stop data.

[1128] Step 2:

[1129] System initialization (server)

[1130] Server: The server initializes the system and sets up initial settings such as Google Maps API and traffic API keys, database access information, etc. It also initializes the emotion engine module, ensuring that API keys and connection information are loaded.

[1131] Input: config file, API key, connection info.

[1132] Output: Initialized system, emotion engine in ready state.

[1133] Step 3:

[1134] Obtaining real-time traffic and people flow information (server)

[1135] Server: The server sends a request to a real-time traffic information API or a people flow information API based on the destination and intermediate points entered by the user. For example, it sends a request to the Google Maps API to get traffic information.

[1136] Input: Coordinate data of destination, starting point, and intermediate points.

[1137] Output: Real-time traffic and people flow data.

[1138] Step 4:

[1139] Generate optimal route (server)

[1140] Server: The server generates the optimal route using Dijkstra's algorithm and A algorithm based on the acquired real-time traffic information and people flow information.

[1141] Input: Real-time traffic and people flow data, destination, origin and intermediate point coordinate data.

[1142] Output: Optimal route information.

[1143] Step 5:

[1144] Predictive analysis based on user preferences (server)

[1145] Server: The server performs predictive analysis using machine learning algorithms such as TensorFlow and Scikit-learn based on past user data and current input data.

[1146] Input: User's past behavior data, current input data.

[1147] Output: Predictive analysis results based on user preferences.

[1148] Step 6:

[1149] Use of emotion engine (server)

[1150] Server: The server sends the user's camera footage and audio data to the emotion engine, which analyzes the user's emotional state in real time using Amazon Rekognition and the Emotion API.

[1151] Input: Camera video data, audio data.

[1152] Output: User's current emotional state data.

[1153] Step 7:

[1154] Adjustment of recommendation content based on emotions (server)

[1155] Server: Based on the recognized emotional data, it recommends places to relax if the user is feeling stressed, or places to be active if the user is feeling excited.

[1156] Input: User emotional state data, predictive analysis results.

[1157] Output: Adjusted recommendations.

[1158] Step 8:

[1159] Recommendations for stores and products along the way (server)

[1160] Server: The server recommends facilities and products that may be of interest to the user that are located near the user's current location or along the optimal route.

[1161] Input: Predictive analytics results based on user preferences, emotional state data, and current location data.

[1162] Output: Information about recommended stores and products.

[1163] Step 9:

[1164] Real-time information summary (server)

[1165] Server: Summarizes the acquired traffic data and people flow information in an easy-to-understand format and provides it to users.

[1166] Input: Real-time traffic information, people flow information, and optimal route information.

[1167] Output: Summarized real-time information.

[1168] Step 10:

[1169] Integration of optimal routes and recommendation information (server)

[1170] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[1171] Input: Optimal route information, adjusted recommendation content.

[1172] Output: Integrated data of optimal routes and recommendation information.

[1173] Step 11:

[1174] Presenting results to the user (device)

[1175] Terminal: The terminal displays the generated optimized route details, real-time traffic information, delay predictions, and recommended stores and products to the user.

[1176] Input: Integrated data of optimal routes and recommendation information.

[1177] Output: Information displayed to the user.

[1178] Step 12:

[1179] Collecting user responses (server)

[1180] Server: The server collects information about how users respond to the information provided, and uses this information to improve the accuracy of the predictive model.

[1181] Input: User response data.

[1182] Output: Collected user response data, data that helps improve the accuracy of the prediction model.

[1183] (Application example 2)

[1184] 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."

[1185] Conventional navigation systems can provide optimal routes based on real-time traffic and people flow information, but they lack the ability to make personalized recommendations based on the user's emotions and preferences. Furthermore, they are unable to recommend appropriate stores and products in response to changes in the user's emotions while traveling, resulting in a suboptimal user travel experience. Therefore, there is a need for a system that can adjust the optimal route based on the user's emotions and recommend stores and products that the user may be interested in while traveling.

[1186] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1187] In this invention, the server includes: means for inputting a user's destination and intermediate points; means for acquiring real-time traffic information and people flow information; means for generating an optimal route based on the input destination and intermediate points; means for performing predictive analysis based on the user's preferences; means for recommending related stores or products along the optimal route based on the predictive analysis; means for summarizing the acquired real-time information and providing it to the user; means for recognizing the user's emotions and adjusting the recommendation content based on the user's emotional state; means for displaying the recommended stores and products to the user; and means for collecting user responses and contributing to improving the accuracy of future predictive models. This makes it possible to personalize the travel experience according to the user's emotions and preferences and provide an environment in which the user can travel comfortably.

[1188] "Means for inputting user's destination and intermediate points" refers to an interface that allows the user to input specific destinations and intermediate points into the system.

[1189] "Means for obtaining real-time traffic and people flow information" refers to a function for collecting the latest information on traffic conditions and people flow in real time.

[1190] "Means for generating an optimal route based on the input destination and intermediate points" is a function that calculates and generates the most efficient and convenient route based on the destination and intermediate points input by the user.

[1191] "Means for performing predictive analytics based on user preferences" refers to a feature that uses machine learning algorithms to predict future behavior and preferences based on a user's past behavior and preferences.

[1192] "Means for recommending related stores or products along the optimal route" is a function that recommends stores and products that the user may be interested in along the generated optimal route.

[1193] "Means of summarizing acquired real-time information and providing it to users" refers to the function of organizing and summarizing collected traffic and people flow information and providing it to users in a format that is easy to understand.

[1194] "Means for recognizing the user's emotions and adjusting the content of recommendations based on their emotional state" refers to a function that identifies the user's current emotional state in real time and changes the content of recommendations according to those emotions.

[1195] "Means for displaying recommended stores and products to users" refers to an interface for displaying stores and products recommended by the system to users.

[1196] "Means of collecting user responses and contributing to improving the accuracy of future predictive models" is a function that collects how users respond to the information provided and uses that data to improve the accuracy of predictive models.

[1197] This invention provides a system that generates optimal routes by integrating real-time traffic and people flow information based on user-specified destinations and intermediate points. It also has the function of recommending appropriate stores and products during travel based on the user's preferences and emotional state.

[1198] System Configuration

[1199] 1. User's destination and stopover input method:

[1200] The user inputs the destination and intermediate points using a smartphone.

[1201] The interface includes a text box and voice input.

[1202] 2. Means of obtaining real-time traffic and people flow information:

[1203] The server obtains real-time traffic and pedestrian flow information using Google Maps APIs and other services.

[1204] The API key is provided during the initial setup of the server.

[1205] 3. How to generate optimal routes:

[1206] The server calculates the optimal route based on the acquired traffic and pedestrian flow information.

[1207] It also takes into account delay information along the way and provides the optimal route.

[1208] 4. Means of predictive analysis based on user preferences:

[1209] The server uses machine learning algorithms (scikit-learn, TensorFlow, etc.) to predict user preferences.

[1210] Past behavioral data and preference information is used.

[1211] 5. Means of recommending related stores or products along the optimal route:

[1212] The server recommends stores and products along the optimal route to the user.

[1213] You can get information about nearby stores using the Yelp and Foursquare APIs.

[1214] 6. Means for summarizing acquired real-time information and providing it to users:

[1215] The server summarizes traffic data and people flow information and displays it in an easy-to-understand manner for users.

[1216] 7. How to recognize user emotions and tailor recommendations based on their emotional state:

[1217] It uses the smartphone's camera and microphone to recognize the user's emotional state.

[1218] Based on the recognized emotional data, it recommends stores where you can relax and facilities where you can be active.

[1219] 8. How to display recommended stores and products to users:

[1220] Recommended stores and products are visually displayed on the smartphone interface.

[1221] 9. How we collect user feedback to help improve future prediction models:

[1222] The server collects data on how users respond to the information provided.

[1223] The collected data is fed back into the predictive model to help improve accuracy.

[1224] Example

[1225] As a specific example, consider the case where a user travels from Tokyo Station to Shinjuku Station and specifies Akihabara as a stopover point. When the user enters this information into their smartphone, the server obtains real-time traffic information using the Google Maps API, generates the optimal route, and provides directions from Tokyo Station to Shinjuku Station via Akihabara.

[1226] During this process, the user's emotional state is recognized and, if it is determined that they are in a stressful state, a relaxing cafe in Akihabara is recommended. Furthermore, the server uses the Yelp API to obtain information about cafes in the Akihabara area and displays it on the smartphone. When the user visits a cafe based on the information provided, their behavior is fed back to the server, helping to improve the accuracy of the prediction model.

[1227] Example prompts to input to a generative AI model:

[1228] What is the best route to travel from Tokyo Station to Shinjuku Station via Akihabara? Also, since the user is feeling stressed, please recommend a cafe in Akihabara where they can relax. Please also take real-time traffic and people flow information into consideration.

[1229] This invention allows users to enjoy a comfortable and personalized travel experience.

[1230] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1231] Step 1:

[1232] The terminal accepts the user's input of destinations and intermediate points. The user inputs that they are traveling from Tokyo Station to Shinjuku Station, passing through Akihabara on the way. This results in the input data being "Tokyo Station, Shinjuku Station, Akihabara."

[1233] Step 2:

[1234] The server obtains real-time traffic and people flow information and sends a request to the Google Maps API based on the input destination and intermediate points. The output is data on traffic conditions and people flow information for each route.

[1235] Step 3:

[1236] The server generates the optimal route based on the acquired traffic and pedestrian flow information, taking into account traffic congestion and real-time road conditions, calculates the shortest and most optimal route, and generates route data to provide to the user.

[1237] Step 4:

[1238] The server performs predictive analysis based on the user's preferences. It analyzes past behavioral and preference data using machine learning algorithms (such as scikit-learn and TensorFlow) to predict the user's current preferences. The predicted results are output as data on categories that the user is likely to be interested in (e.g., cafes or bookstores).

[1239] Step 5:

[1240] The server recommends relevant stores or products along the optimal route based on predictive analysis. It uses the Yelp or Foursquare API to search for and obtain information about stores and products that fit the predicted category. The output is a list of recommended stores and products.

[1241] Step 6:

[1242] The server summarizes the acquired real-time information and recommendation information and prepares it for delivery to the user. It summarizes traffic information, people flow information, recommended stores and products information, and converts them into a format that can be displayed to the user. The output is a summarized information package.

[1243] Step 7:

[1244] The device recognizes the user's emotions. It uses the smartphone's camera and microphone to obtain real-time emotional data from the user through emotion recognition algorithms. The output is the user's current emotional state.

[1245] Step 8:

[1246] The server adjusts the recommendations based on the user's emotional state. For example, if the user is stressed, the server recommends relaxing stores, and if the user is excited, the server recommends active facilities. The output is the adjusted recommendation list.

[1247] Step 9:

[1248] The terminal displays the recommended stores and products to the user. The adjusted recommendations are visually displayed on the smartphone screen. The output is the user's display screen.

[1249] Step 10:

[1250] The server collects responses from users, such as the stores visited and the products purchased, based on the information provided, and stores the collected data in a database.

[1251] Through these steps, users can enjoy an individually optimized travel experience and receive suggestions tailored to their interests and emotions while traveling.

[1252] 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.

[1253] 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.

[1254] 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.

[1255] [Fourth embodiment]

[1256] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1257] 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.

[1258] 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).

[1259] 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.

[1260] 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.

[1261] 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).

[1262] 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.

[1263] 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.

[1264] 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.

[1265] 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.

[1266] 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.

[1267] 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.

[1268] 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."

[1269] This invention is a system that allows users to set their destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. Furthermore, this system has the ability to predict and analyze users' preferences and recommend stores and products that they might be interested in while traveling. It also provides users with a summary of real-time information, preventing information overload and supporting appropriate decision-making.

[1270] System implementation

[1271] 1. User's destination and stopover input (terminal):

[1272] User: Enter the destination and intermediate points. For example, consider traveling from Tokyo Station (35.681236,139.767125) to Shinjuku Station (35.689487,139.691706).

[1273] 2. System initialization (server):

[1274] Server: Set the API key and database connection information required during the initialization phase. This setting is required later to obtain real-time traffic and people flow information.

[1275] 3. Acquisition of real-time traffic and people flow information (server):

[1276] Server: Sends requests to a dedicated API to retrieve up-to-date traffic and people flow data for origins, destinations, and intermediate points.

[1277] Example: Obtain traffic information for Akihabara on the way from Tokyo Station to Shinjuku Station to understand the congestion situation.

[1278] 4. Generate optimal route (server):

[1279] Server: Generates the optimal route based on real-time traffic and pedestrian flow information. This route is calculated by taking into account various factors (e.g., traffic conditions, delays, congestion, etc.) from the departure point to the destination.

[1280] Example: Providing the most efficient route from Tokyo Station to Shinjuku Station, passing through Akihabara, avoiding congestion and delays.

[1281] 5. Predictive analysis based on user preferences (server):

[1282] Server: Utilizes machine learning algorithms based on the user's past behavioral data and input data to predict and analyze user preferences.

[1283] Example: Analyzing past data to see if users are interested in electronic devices or cafes.

[1284] 6. Store and product recommendations (server):

[1285] Server: Based on the results of predictive analysis, it lists and recommends stores and products that are close to the user's current location and that match their interests.

[1286] Example: Recommending electronics stores and cafes to users near their current location.

[1287] 7. Real-time information summary (server):

[1288] Server: Concisely summarizes the acquired traffic data and pedestrian flow information and converts it into a format that is easy for users to understand.

[1289] Example: Present summary information to the user, such as "Traffic conditions are moderate, delays expected approximately 10 minutes."

[1290] 8. Integration of optimal routes and recommendation information (server):

[1291] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[1292] Example: Providing users with the optimal route from Tokyo Station to Shinjuku Station, along with information on stores that may be of interest along the way.

[1293] 9. Displaying results to the user (terminal):

[1294] Terminal: Displays the final results to the user, including details of the best route, real-time traffic information, and recommended stores and products.

[1295] Example: Providing a route from Tokyo Station to Shinjuku Station, a summary of the traffic conditions in Akihabara along the way, and information on cafes and electronics stores in the Akihabara area.

[1296] In this way, users can find the optimal route based on real-time information while traveling, and receive store information tailored to their individual preferences. This system not only allows users to travel efficiently and comfortably, but also opens up new shopping opportunities.

[1297] The processing flow will be explained below.

[1298] Step 1:

[1299] User: Enter the destination and intermediate point. This sets the origin (Tokyo Station), destination (Shinjuku Station), and intermediate point (Akihabara).

[1300] Step 2:

[1301] Server: Initializes the system, including setting up any necessary API keys and database connection information.

[1302] Step 3:

[1303] Server: Sends an API request to obtain targeted traffic and people flow information based on the input destination and intermediate points. Specifically, it obtains this information from an external service that provides traffic and people flow data for Akihabara for the route from Tokyo Station to Shinjuku Station.

[1304] Step 4:

[1305] Server: Analyzes real-time traffic and people flow information and generates the optimal route from the departure point to the destination. For example, it calculates a route from Tokyo Station to Shinjuku Station that avoids congestion and delays when passing through Akihabara.

[1306] Step 5:

[1307] Server: Performs predictive analysis based on the user's past data and input preferences, using machine learning algorithms to predict the store category (e.g., electronics store or cafe) that the user might be interested in.

[1308] Step 6:

[1309] Server: Based on the results of predictive analysis, the server recommends stores and products related to the user's current location and the optimal route. For example, it creates a list of electronics stores and cafes near Tokyo Station or Akihabara and prepares the information to provide to the user.

[1310] Step 7:

[1311] Server: Summarizes the acquired real-time information succinctly, organizing it in a way that is easy for users to understand, such as "Traffic conditions are moderate, with delays expected of approximately 10 minutes."

[1312] Step 8:

[1313] Server: Integrates optimal route information and recommendation information to create a detailed travel plan that allows users to travel efficiently and conveniently.

[1314] Step 9:

[1315] Terminal: Presents users with integrated information, including a map of the optimal route, real-time traffic information, delay predictions, and recommendations for stores and products that may interest them.

[1316] Step 10:

[1317] User: Based on the information provided, the user makes the most efficient and comfortable journey, stopping at recommended stores as needed. Users can understand traffic conditions and options along the way in real time, allowing them to travel efficiently and comfortably.

[1318] Through these steps, users can determine the optimal route based on real-time information and receive recommendations for stores and products they are interested in. This system prevents information overload and helps users make better decisions, greatly improving the user's travel experience.

[1319] Example 1

[1320] 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."

[1321] Current navigation systems are specialized in providing destination guidance, but they do not fully consider real-time traffic conditions and pedestrian flow, and lack the functionality to provide store and product information based on user preferences. As a result, users not only have difficulty traveling efficiently and comfortably, but also have problems obtaining appropriate information while traveling.

[1322] 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.

[1323] In this invention, the server includes means for inputting a user's destination and intermediate points, means for acquiring real-time traffic information and people flow information, means for generating an optimal route based on the input destination and intermediate points, means for performing predictive analysis based on the user's preferences, means for recommending related stores or products along the optimal route based on the predictive analysis, means for summarizing the acquired real-time information and providing it to the user, means for integrating the optimal route and recommendation information based on the user's destination and intermediate point information, and means for displaying the integrated result on the user's terminal. This allows the user to obtain an optimal route based on real-time traffic conditions and simultaneously obtain information on stores and products that interest them.

[1324] "User" means a person who uses the System to set destinations and waypoints.

[1325] "Destinations and intermediate points" refers to the starting point, intermediate points, and destination when a user travels.

[1326] "Real-time traffic information" means data and information that indicates current traffic conditions, including traffic congestion, road closures, traffic accidents, etc.

[1327] "People flow information" refers to data and information about the movement and gathering of people in a particular area.

[1328] An "optimal route" refers to the most efficient route between the departure point and the destination, taking into maximum consideration conditions such as travel time and congestion.

[1329] "Predictive analytics" is an analytical method for predicting future behavior and interests based on a user's past behavior and input data.

[1330] "Related stores or products" refers to stores or products located along the optimal route based on the user's preferences and interests.

[1331] "Recommending" means recommending appropriate stores and products based on the user's interests and needs.

[1332] "Summarization" refers to the process of concisely summarizing large amounts of acquired data and converting it into a format that is easy for users to understand.

[1333] "Integrating" means bringing together different pieces of information or data to generate a complete whole.

[1334] "Terminal" means a device that allows a user to operate the system and input or view information.

[1335] This invention is a system that allows users to set their destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. Furthermore, this system has the ability to predict and analyze users' preferences and recommend stores and products that they might be interested in while traveling. It also provides users with a summary of real-time information, preventing information overload and supporting appropriate decision-making.

[1336] This system is realized using the following components:

[1337] 1. Terminal: A device that receives the user's destination and intermediate point input. This device can be a smartphone or tablet, for example.

[1338] 2. Server: A central processing unit that processes various data, calculates optimal routes, and generates recommendation information. The software used here is, for example, a platform that implements the Google Maps API or machine learning algorithms.

[1339] The specific implementation steps are as follows:

[1340] Entering destination and stopover points

[1341] The user inputs the starting point, intermediate points, and destination on the terminal. For example, consider traveling from Tokyo Station (35.681236, 139.767125) to Shinjuku Station (35.689487, 139.691706), and set Akihabara (35.698353, 139.773114) as a stopover point along the way.

[1342] System initialization

[1343] The server sets the API key (e.g., Google Maps API key) and database connection information and connects to various services.

[1344] Obtaining real-time traffic and people flow information

[1345] The server sends a request to a dedicated API to obtain the latest traffic and people flow data for the specified location (Tokyo Station, Akihabara, Shinjuku Station).

[1346] Generate optimal routes

[1347] The server uses real-time traffic and people flow information to generate optimal routes, such as calculating alternative routes to avoid the crowds in Akihabara.

[1348] Predictive analytics based on user preferences

[1349] The server uses machine learning algorithms to predict and analyze the user's preferences based on the user's past behavioral data and input data. Based on past data, it analyzes the user's interests in electronic devices and cafes.

[1350] Store and product recommendations

[1351] Based on the results of the predictive analysis, the server generates a recommendation list by listing stores and products that are close to the user's current location and that match their interests, such as electronics stores and cafes near the user's current location.

[1352] Real-time information summary

[1353] The server then summarizes the acquired traffic and pedestrian flow data and converts it into a format that is easy for users to understand, such as "traffic conditions are moderate, and delays of approximately 10 minutes are expected."

[1354] Integration of optimal routes and recommendation information

[1355] The server integrates all acquired information and analysis results to generate optimal routes and recommendation information for users.

[1356] Presenting results to the user

[1357] The device displays the final results from the server to the user, such as the optimal route, a summary of the traffic situation in Akihabara along the way, and information on cafes and electronics stores in the area, through a user interface.

[1358] Prompt Sentence Examples

[1359] "Please provide the best route from Tokyo Station to Shinjuku Station. Also check the traffic conditions in Akihabara along the way and provide recommendations for suitable stores and cafes."

[1360] The system allows users to obtain the optimal route based on real-time traffic conditions and simultaneously obtain information on stores and products they are interested in, providing an efficient and comfortable travel experience.

[1361] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1362] Step 1:

[1363] User: Enter the destination and intermediate points using the terminal. Specifically, the departure point is set to Tokyo Station (35.681236,139.767125), the intermediate point is set to Akihabara (35.698353,139.773114), and the destination is set to Shinjuku Station (35.689487,139.691706).

[1364] Input: User-specified coordinates of the starting point, intermediate point, and destination.

[1365] Output: Data transmission to the server is complete.

[1366] Step 2:

[1367] Server: Performs initialization. Specifically, sets the API key (e.g., Google Maps API key) and database connection information to enable access to each API service.

[1368] Inputs: API key, database connection information.

[1369] Output: Initialization completion status.

[1370] Step 3:

[1371] Server: Obtains real-time traffic and people flow information. Sends a request to a dedicated API to obtain the latest traffic and people flow data for each specified location (e.g., Tokyo Station, Akihabara, Shinjuku Station).

[1372] Input: Coordinate information of the starting point, intermediate point, and destination set by the user.

[1373] Output: Traffic and people flow data obtained from the API.

[1374] Step 4:

[1375] Server: Generates optimal routes based on real-time traffic and people flow information. Algorithms are used to calculate the most efficient route, taking congestion and delays into account.

[1376] Input: Traffic and people flow data obtained from API.

[1377] Output: Optimal route information.

[1378] Step 5:

[1379] Server: Performs predictive analysis based on user preferences. Utilizing machine learning algorithms based on the user's past behavioral data and input data, predicts and analyzes the user's interests.

[1380] Input: User's past behavior data, current input data.

[1381] Output: Predictive data about user interests.

[1382] Step 6:

[1383] Server: Based on the results of predictive analysis, the server recommends stores and products that are close to the user's current location and that match their interests. The server references a database of nearby stores and creates a list.

[1384] Input: current user location, predictive analysis results, store database.

[1385] Output: A list of recommended stores and products.

[1386] Step 7:

[1387] Server: Appropriately summarizes the acquired traffic and people flow data, summarizing the information succinctly and converting it into a format that is easy for users to understand.

[1388] Input: Raw data from the API.

[1389] Output: Summarized traffic and people flow information.

[1390] Step 8:

[1391] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[1392] Input: optimal route information, summarized traffic and people flow information, recommendation list.

[1393] Output: The final integrated result data.

[1394] Step 9:

[1395] Device: The results sent from the server are displayed to the user, who can check the optimal route, a summary of traffic conditions, and recommended stores and products on the device screen.

[1396] Input: The final integration result data from the server.

[1397] Output: The screen display that the user sees.

[1398] This process allows users to travel efficiently and comfortably, while also receiving store information tailored to their interests.

[1399] (Application example 1)

[1400] 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."

[1401] Previously, navigation systems for autonomous vehicles provided optimal route guidance based on real-time traffic and pedestrian flow information, but they did not offer recommendations based on user preferences or route suggestions that took into account user behavioral history. Furthermore, they lacked the ability to summarize acquired real-time information or provide appropriate information to users, creating challenges in supporting efficient and comfortable travel. Furthermore, systems for autonomous vehicles that would provide new purchasing opportunities were also underdeveloped.

[1402] 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.

[1403] In this invention, the server includes means for inputting a user's destination and intermediate points, means for acquiring real-time traffic information and people flow information, means for generating an optimal route based on the input destination and intermediate points, means for performing predictive analysis based on the user's preferences, means for recommending related stores or products along the optimal route based on the predictive analysis, means for summarizing the acquired real-time information and providing it to the user, means for integrating data into the navigation system of a related autonomous vehicle based on the user's past behavior data, and means for recommending stores and products that the user may be interested in based on the user's location data. This allows the user to check the optimal route based on real-time information, and makes it possible to provide recommendations and purchasing opportunities according to their preferences.

[1404] The "means for inputting the user's destination and intermediate points" is an interface that allows the user to input their travel destination and intermediate points into the system.

[1405] "Means for obtaining real-time traffic and people flow information" refers to a system for instantly obtaining data on current traffic conditions and people flow.

[1406] The "means for generating an optimal route based on the input destination and intermediate points" is an algorithm that uses the input destination and intermediate point information to calculate the most efficient travel route.

[1407] The "means for performing predictive analysis based on the user's preferences" is a data analysis technique for predicting future behavior based on the user's past behavioral data and preferences.

[1408] "Means for recommending related stores or products along the optimal route based on the predictive analysis" refers to a system that suggests related stores or products located on or near the optimal route based on predicted user preferences and behavior.

[1409] The "means for summarizing the acquired real-time information and providing it to the user" is a function for concisely summarizing the acquired traffic information and people flow information and presenting it to the user.

[1410] "Means for integrating data into the navigation system of the associated autonomous vehicle based on the user's past behavioral data" refers to a technology that reflects data based on the user's past travel history and behavioral patterns in the navigation system of the autonomous vehicle.

[1411] "Means for recommending stores and products that may be of interest to the user based on the user's location data" refers to a system that uses the user's current location information to recommend stores and products that are close to that location.

[1412] An embodiment of the present invention is described in detail below. The present invention is a system that allows users to set their destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. Furthermore, the system has the function of predicting and analyzing the user's preferences and recommending stores and products that the user may be interested in while traveling. Furthermore, by providing a summary of real-time information to the user, the system prevents information overload and supports appropriate decision-making.

[1413] System configuration

[1414] Hardware Configuration

[1415] Server: A server with high-performance computing capabilities that processes real-time data and connects to databases. Specific examples include Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[1416] Device: A device that provides a user interface, such as a smartphone, smart glasses, or a head-mounted display.

[1417] Autonomous vehicles: Equipped with advanced navigation systems and capable of connecting with external systems.

[1418] Software Configuration

[1419] User interface application: An application that runs on a smartphone or head-mounted display, where the user inputs destinations and intermediate points.

[1420] Data acquisition module: A module for acquiring real-time traffic and people flow information from various APIs.

[1421] Predictive analysis module: Uses machine learning algorithms to predict and analyze user preferences.

[1422] Recommendation module: Recommends relevant stores and products along the optimal route.

[1423] Data Summarization Module: Summarizes the acquired real-time information and provides it to the user concisely.

[1424] Operation explanation

[1425] 1. User input: The user inputs the destination and intermediate points through an application installed on a smartphone or head-mounted display. For example, the user specifies a trip from Tokyo Station to Shinjuku Station, with Akihabara as the intermediate point.

[1426] 2. Acquisition of real-time data: The server uses APIs to acquire traffic and people flow information. For example, it acquires real-time traffic data from Tokyo Station to Shinjuku Station to understand the congestion situation around Akihabara.

[1427] 3. Generate optimal route: The server generates the optimal route based on the acquired data. It takes into account traffic conditions and delay information to calculate the most efficient route for the user.

[1428] 4. User preference prediction: The server analyzes the user's past behavior data and uses machine learning algorithms to predict the user's preferences. For example, the server knows from past data that the user is interested in cafes and electronics stores.

[1429] 5. Providing recommendation information: Based on the results of predictive analysis, the server recommends related stores and products along the optimal route, for example, providing users with information on cafes and electronics stores in Akihabara.

[1430] 6. Real-time information summary: The server briefly summarizes the real-time information it has acquired and provides it to the user, for example, displaying information such as "Traffic conditions are moderate, and delays of approximately 10 minutes are expected."

[1431] 7. Presenting the results to the user: The application presents the final navigation information to the user, including details of the optimal route, recommendations, and summarized real-time information. For example, route guidance from Tokyo Station to Shinjuku Station and recommended shops around Akihabara are displayed.

[1432] Specific examples

[1433] Example prompt sentence:

[1434] "You are going to create a navigation system that will help a user travel from Tokyo Station to Shinjuku Station and provide information about interesting shops (e.g. electronics stores and cafes) in Akihabara along the way. The system should provide the optimal route based on real-time traffic information and recommend places that may be of interest to the user based on their previous behavior."

[1435] As a result, the system of the present invention can provide users with efficient and comfortable travel and create new purchasing opportunities.

[1436] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1437] Step 1:

[1438] The user inputs the destination and intermediate points through an application installed on a smartphone or head-mounted display. This input includes the starting point, destination, and intermediate points (e.g., from Tokyo Station to Shinjuku Station, with Akihabara as the intermediate point). The input data is sent from the application to the server.

[1439] Step 2:

[1440] The server uses the provided API to obtain real-time traffic and pedestrian flow information. Specifically, it obtains traffic conditions and congestion levels from the departure point to the destination through an API request. In this process, it uses the API key and database connection information. The input is the request sent to the API, and the output is the obtained real-time traffic information.

[1441] Step 3:

[1442] The server generates optimal routes based on the acquired real-time traffic information. This involves analyzing traffic data and applying shortest-path algorithms. For example, it calculates the most efficient route from Tokyo Station to Shinjuku Station via Akihabara, using algorithms to avoid congestion and delays. The input is traffic data, and the output is optimal route information.

[1443] Step 4:

[1444] The server analyzes the user's past behavioral data and predicts the user's preferences using a machine learning algorithm. The input is the user's past behavioral data, and the output is the predicted user's interests (e.g., the user is interested in cafes and electronics stores). The algorithm extracts patterns from user behavior and predicts interests.

[1445] Step 5:

[1446] Based on the results of the predictive analysis, the server recommends related stores and products along the optimal route. This process searches for and lists appropriate store information based on the user's predicted interests. For example, a list is generated by searching for information on cafes and electronics stores around Akihabara. The input is the predicted interests and current location information, and the output is a list of recommended stores.

[1447] Step 6:

[1448] The server summarizes the acquired real-time information and provides it to the user in a concise format. This process summarizes traffic conditions and congestion data and generates an easy-to-understand message. For example, it creates a summary such as "Traffic conditions are moderate, with delays expected to be approximately 10 minutes." The input is real-time traffic information, and the output is summarized information.

[1449] Step 7:

[1450] The terminal presents the final results to the user through a user interface, including details of the optimal route, recommended information, and a summary of real-time information. The input is data from the server, and the output is the information displayed to the user. For example, the terminal displays a navigation route from Tokyo Station to Shinjuku Station, recommended store information around Akihabara, and a summary of traffic conditions.

[1451] 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.

[1452] This invention is a system that inputs a user's destination and intermediate points and provides optimal route guidance based on real-time traffic and people flow information. It also has the ability to predict and analyze the user's preferences and recommend stores and products that the user might be interested in while traveling. By combining it with an emotion engine that recognizes the user's emotions, the system can adjust the recommendations based on the user's current emotional state and past emotional data, providing a more personalized service.

[1453] System implementation

[1454] 1. User's destination and stopover input (terminal):

[1455] User: Enter the destination and intermediate points. For example, consider traveling from Tokyo Station (35.681236,139.767125) to Shinjuku Station (35.689487,139.691706).

[1456] 2. System initialization (server):

[1457] Server: Initializes the system and sets up the necessary API keys and database connection information. The emotion engine is also prepared at this stage.

[1458] 3. Acquisition of real-time traffic and people flow information (server):

[1459] Server: Sends an API request to obtain traffic and people flow information based on the input destination and intermediate points.

[1460] 4. Generate optimal route (server):

[1461] Server: Generates the optimal route from the departure point to the destination based on real-time traffic and people flow information.

[1462] 5. Predictive analysis based on user preferences (server):

[1463] Server: Uses machine learning algorithms to predict user preferences based on the user's past data and current input.

[1464] 6. Use of Emotion Engine (Server):

[1465] Server: Uses an emotion engine to recognize the user's emotions in real time. It recognizes the user's emotions from camera and voice data and obtains the user's current emotional state.

[1466] 7. Emotion-based recommendation adjustment (server):

[1467] Server: Based on the recognized emotional data, it recommends relaxing places if the user is feeling stressed, and active places if the user is feeling excited.

[1468] 8. Recommendation of stores and products along the way (server):

[1469] Server: Based on the results of predictive analysis and the sentiment engine, it recommends stores and products that may be of interest to the user near their current location or along the optimal route.

[1470] 9. Real-time information summary (server):

[1471] Server: Easily compiles the acquired traffic data and pedestrian flow information and summarizes it in a format that is easy for users to understand.

[1472] 10. Integration of optimal routes and recommendation information (server):

[1473] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[1474] 11. Displaying results to the user (terminal):

[1475] Terminal: Displays the final results to the user, including details of the optimal route, real-time traffic information, delay predictions, and recommended stores and products.

[1476] 12. Collecting user responses (server):

[1477] Server: Collects information about how users respond to the information provided to help improve the service and its predictive models.

[1478] As a specific example, consider a user traveling from Tokyo Station to Shinjuku Station and wanting to take a break in Akihabara. The user inputs their destination and stopover points, and the system retrieves real-time traffic and people flow information. After an optimal route is generated, the emotion engine detects the user's stress level and recommends cafes in the Akihabara area where they can relax. This information, along with a summary of traffic conditions, is provided to the user, enabling an efficient and comfortable journey. This series of processes allows users to improve the quality of their journey and receive personalized service.

[1479] The processing flow will be explained below.

[1480] Step 1:

[1481] User: Enter the destination and intermediate points. For example, consider traveling from Tokyo Station (35.681236, 139.767125) to Shinjuku Station (35.689487, 139.691706). Enter that you will pass through Akihabara (35.7033, 139.7745) on the way.

[1482] Step 2:

[1483] Server: Initializes the system and sets up the necessary API keys and database connection information. The emotion engine is also set up at this stage.

[1484] Step 3:

[1485] Server: Sends API requests to obtain traffic and people flow information based on the input destination and intermediate points. Specifically, it constructs a URL and sends a request to an external service to obtain the data.

[1486] Step 4:

[1487] Server: Analyzes real-time traffic and people flow information and generates the optimal route from the departure point to the destination. For example, it calculates routes that avoid congestion and delays, and selects the most efficient route from multiple candidate routes.

[1488] Step 5:

[1489] Server: Uses machine learning algorithms to predict and analyze user preferences based on the user's past history and current input data. For example, it analyzes data on restaurants and cafes the user has chosen in the past to infer new preferences.

[1490] Step 6:

[1491] Server: Uses an emotion engine to recognize the user's emotions. It analyzes camera footage and audio data to obtain the user's current emotional state (stress, excitement, relaxation, etc.). The emotion engine analyzes facial expressions and tone of voice to estimate emotions.

[1492] Step 7:

[1493] Server: Based on the recognized emotional data, the server tailors recommendations to suit the user's current emotional state. For example, if the user is feeling stressed, the server recommends relaxing cafes and parks. Conversely, if the user is excited, the server recommends active activities and events.

[1494] Step 8:

[1495] Server: Based on the results of the emotion engine and predictive analysis, the server recommends relevant stores and products near the user's current location and along the optimal route. For example, it creates a list of electronics stores and cafes near Akihabara and prepares it for the user.

[1496] Step 9:

[1497] Server: The server concisely summarizes the acquired traffic data and pedestrian flow information in a format that is easy for users to understand. Specifically, it generates information such as "traffic conditions are moderate, and delays of approximately 10 minutes are expected."

[1498] Step 10:

[1499] Server: Integrates optimal route information and recommendation information. This integrated information provides a detailed travel plan that assists users in their travels and includes store information that is useful during their trip.

[1500] Step 11:

[1501] Terminal: Presents users with integrated information, including maps of optimal routes, real-time traffic information, delay predictions, and recommended stores and products, through an application or web interface that is easily accessible to users.

[1502] Step 12:

[1503] User: Based on the information provided, the user makes the most optimal trip and stops at recommended stores as needed. Information is updated in real time during the trip, allowing the user to reach their destination efficiently and comfortably.

[1504] As a result, the system takes into account real-time information and the user's emotional state to provide optimal routes and personalized recommendations, significantly improving the quality of travel and providing information tailored to individual needs.

[1505] Example 2

[1506] 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."

[1507] While conventional navigation systems can provide optimal routes based on real-time traffic information, it is difficult to provide optimal services that take into account the user's emotions and individual preferences. Furthermore, they lack a mechanism for collecting user reactions to the information provided and continuously improving the service. This limits the user experience and makes it difficult to increase satisfaction during travel.

[1508] 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.

[1509] In this invention, the server includes means for inputting a user's destination and intermediate points, means for acquiring real-time traffic information and people flow information, means for generating an optimal route based on the input destination and intermediate points, means for performing predictive analysis based on the user's preferences, means for recognizing the user's emotions and adjusting recommendations based on the emotions, means for summarizing the acquired real-time information and providing it to the user, and means for collecting user responses and improving the accuracy of the predictive model. This makes it possible to provide a personalized optimal route based on the user's emotions and preferences, and to continuously improve the service.

[1510] "Means for inputting user's destination and stopover points" is a function that allows the user to input their travel destination and stopover points into the system.

[1511] "Means for obtaining real-time traffic and people flow information" refers to a function for obtaining data on the latest traffic conditions and people flow from external sources.

[1512] The "means for generating optimal routes" is a function that calculates and presents the most efficient route for the user based on acquired real-time traffic and people flow information.

[1513] "Means for predictive analysis" refers to a function that uses a user's past behavioral data and current input data to predict the environment and services that a user will prefer.

[1514] "Recommendation means" is a function that presents facilities and products that are of interest to users along the optimal route based on the results of predictive analysis.

[1515] "Means for recognizing user emotions" refers to a function that uses a camera, audio data, etc. to identify the user's current emotional state.

[1516] "Means for adjusting recommendation content based on emotions" refers to a function that changes or adjusts the recommendation content provided to a user according to the recognized emotional state of the user.

[1517] "Means for summarizing and providing acquired real-time information" refers to a function that summarizes acquired traffic and people flow information in an easy-to-understand format and presents it to the user.

[1518] "Means for collecting user responses" refers to a function that collects information about how users respond to the information and services provided.

[1519] "Means for improving the accuracy of the predictive model" refers to a function for improving the accuracy of the system's predictions and recommendations based on collected user response data.

[1520] This invention is a system that provides users with optimal routes that take into account real-time traffic and pedestrian flow information based on destinations and intermediate points set by the user. The system makes personalized recommendations based on the user's emotions and past behavioral data, and can also collect user responses to improve the accuracy of its predictive model.

[1521] First, the user inputs their destination and intermediate points. This information is entered through a device such as a smartphone or computer. The input method for this system includes a touch screen, voice recognition software, or keyboard input. Specifically, the user inputs using the following prompt sentences:

[1522] example:

[1523] Destination: Shinjuku Station

[1524] Stopover point: Akihabara

[1525] Next, the server initializes the entire system and sets the necessary API key and database connection information. This system uses external information providers to obtain real-time traffic and pedestrian flow information. For example, it uses the Google Maps API and various traffic information APIs.

[1526] The server obtains real-time traffic and pedestrian flow information through these APIs, and then generates optimal routes based on the obtained data using Dijkstra's algorithm and A algorithm.

[1527] The server then predicts the user's preferences based on their past behavioral data. This prediction is made using machine learning algorithms (such as TensorFlow and Scikit-learn). The obtained user preference data is then integrated with the emotion engine, which collects the user's emotion data in real time.

[1528] The emotion engine uses the camera and microphone on the user's smartphone or PC to analyze the user's facial expressions and vocal tone, thereby recognizing the user's current emotional state. For example, if the user is feeling stressed, it will recommend a relaxing cafe or park, and if the user is excited, it will recommend a place to be active.

[1529] The acquired real-time information is easily summarized by the server and provided to the user in an easy-to-understand format, such as "Current traffic conditions are normal, no delays."

[1530] Finally, the server integrates all the analysis results and generates the optimal route and recommendations for the user, which are displayed in real time on the user's device. User responses are also collected and used to improve the accuracy of future prediction models.

[1531] As a specific example, consider the case where a user is traveling from Tokyo Station to Shinjuku Station and wants to take a break in Akihabara on the way. The user first enters "Shinjuku Station" as the destination and "Akihabara" as the stopover point. Based on this, the system obtains real-time traffic information and calculates the optimal route. Based on the user's emotional data, the system recommends cafes in the Akihabara area where people can relax. Providing this information to the user along with a summary of the traffic conditions enables an efficient and comfortable journey.

[1532] The system allows users to enjoy efficient and comfortable travel with real-time traffic information and personalized recommendations, and it also increases long-term satisfaction by continuously improving the service based on user sentiment and reactions.

[1533] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1534] Step 1:

[1535] User's destination and stopover input (terminal)

[1536] User: The user inputs the destination and intermediate points through a dedicated application. Specifically, the user inputs the destination from "Tokyo Station (35.681236,139.767125)" to "Shinjuku Station (35.689487,139.691706)" and the intermediate point "Akihabara."

[1537] Input: Coordinate data of destination, starting point, and intermediate points.

[1538] Output: User-entered destination and stop data.

[1539] Step 2:

[1540] System initialization (server)

[1541] Server: The server initializes the system and sets up initial settings such as Google Maps API and traffic API keys, database access information, etc. It also initializes the emotion engine module, ensuring that API keys and connection information are loaded.

[1542] Input: config file, API key, connection info.

[1543] Output: Initialized system, emotion engine in ready state.

[1544] Step 3:

[1545] Obtaining real-time traffic and people flow information (server)

[1546] Server: The server sends a request to a real-time traffic information API or a people flow information API based on the destination and intermediate points entered by the user. For example, it sends a request to the Google Maps API to get traffic information.

[1547] Input: Coordinate data of destination, starting point, and intermediate points.

[1548] Output: Real-time traffic and people flow data.

[1549] Step 4:

[1550] Generate optimal route (server)

[1551] Server: The server generates the optimal route using Dijkstra's algorithm and A algorithm based on the acquired real-time traffic information and people flow information.

[1552] Input: Real-time traffic and people flow data, destination, origin and intermediate point coordinate data.

[1553] Output: Optimal route information.

[1554] Step 5:

[1555] Predictive analysis based on user preferences (server)

[1556] Server: The server performs predictive analysis using machine learning algorithms such as TensorFlow and Scikit-learn based on past user data and current input data.

[1557] Input: User's past behavior data, current input data.

[1558] Output: Predictive analysis results based on user preferences.

[1559] Step 6:

[1560] Use of emotion engine (server)

[1561] Server: The server sends the user's camera footage and audio data to the emotion engine, which analyzes the user's emotional state in real time using Amazon Rekognition and the Emotion API.

[1562] Input: Camera video data, audio data.

[1563] Output: User's current emotional state data.

[1564] Step 7:

[1565] Adjustment of recommendation content based on emotions (server)

[1566] Server: Based on the recognized emotional data, it recommends places to relax if the user is feeling stressed, or places to be active if the user is feeling excited.

[1567] Input: User emotional state data, predictive analysis results.

[1568] Output: Adjusted recommendations.

[1569] Step 8:

[1570] Recommendations for stores and products along the way (server)

[1571] Server: The server recommends facilities and products that may be of interest to the user that are located near the user's current location or along the optimal route.

[1572] Input: Predictive analytics results based on user preferences, emotional state data, and current location data.

[1573] Output: Information about recommended stores and products.

[1574] Step 9:

[1575] Real-time information summary (server)

[1576] Server: Summarizes the acquired traffic data and people flow information in an easy-to-understand format and provides it to users.

[1577] Input: Real-time traffic information, people flow information, and optimal route information.

[1578] Output: Summarized real-time information.

[1579] Step 10:

[1580] Integration of optimal routes and recommendation information (server)

[1581] Server: Integrates all acquired information and analysis results to generate optimal routes and recommendations for users.

[1582] Input: Optimal route information, adjusted recommendation content.

[1583] Output: Integrated data of optimal routes and recommendation information.

[1584] Step 11:

[1585] Presenting results to the user (device)

[1586] Terminal: The terminal displays the generated optimized route details, real-time traffic information, delay predictions, and recommended stores and products to the user.

[1587] Input: Integrated data of optimal routes and recommendation information.

[1588] Output: Information displayed to the user.

[1589] Step 12:

[1590] Collecting user responses (server)

[1591] Server: The server collects information about how users respond to the information provided, and uses this information to improve the accuracy of the predictive model.

[1592] Input: User response data.

[1593] Output: Collected user response data, data that helps improve the accuracy of the prediction model.

[1594] (Application example 2)

[1595] 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."

[1596] Conventional navigation systems can provide optimal routes based on real-time traffic and people flow information, but they lack the ability to make personalized recommendations based on the user's emotions and preferences. Furthermore, they are unable to recommend appropriate stores and products in response to changes in the user's emotions while traveling, resulting in a suboptimal user travel experience. Therefore, there is a need for a system that can adjust the optimal route based on the user's emotions and recommend stores and products that the user may be interested in while traveling.

[1597] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1598] In this invention, the server includes: means for inputting a user's destination and intermediate points; means for acquiring real-time traffic information and people flow information; means for generating an optimal route based on the input destination and intermediate points; means for performing predictive analysis based on the user's preferences; means for recommending related stores or products along the optimal route based on the predictive analysis; means for summarizing the acquired real-time information and providing it to the user; means for recognizing the user's emotions and adjusting the recommendation content based on the user's emotional state; means for displaying the recommended stores and products to the user; and means for collecting user responses and contributing to improving the accuracy of future predictive models. This makes it possible to personalize the travel experience according to the user's emotions and preferences and provide an environment in which the user can travel comfortably.

[1599] "Means for inputting user's destination and intermediate points" refers to an interface that allows the user to input specific destinations and intermediate points into the system.

[1600] "Means for obtaining real-time traffic and people flow information" refers to a function for collecting the latest information on traffic conditions and people flow in real time.

[1601] "Means for generating an optimal route based on the input destination and intermediate points" is a function that calculates and generates the most efficient and convenient route based on the destination and intermediate points input by the user.

[1602] "Means for performing predictive analytics based on user preferences" refers to a feature that uses machine learning algorithms to predict future behavior and preferences based on a user's past behavior and preferences.

[1603] "Means for recommending related stores or products along the optimal route" is a function that recommends stores and products that the user may be interested in along the generated optimal route.

[1604] "Means of summarizing acquired real-time information and providing it to users" refers to the function of organizing and summarizing collected traffic and people flow information and providing it to users in a format that is easy to understand.

[1605] "Means for recognizing the user's emotions and adjusting the content of recommendations based on their emotional state" refers to a function that identifies the user's current emotional state in real time and changes the content of recommendations according to those emotions.

[1606] "Means for displaying recommended stores and products to users" refers to an interface for displaying stores and products recommended by the system to users.

[1607] "Means of collecting user responses and contributing to improving the accuracy of future predictive models" is a function that collects how users respond to the information provided and uses that data to improve the accuracy of predictive models.

[1608] This invention provides a system that generates optimal routes by integrating real-time traffic and people flow information based on user-specified destinations and intermediate points. It also has the function of recommending appropriate stores and products during travel based on the user's preferences and emotional state.

[1609] System Configuration

[1610] 1. User's destination and stopover input method:

[1611] The user inputs the destination and intermediate points using a smartphone.

[1612] The interface includes a text box and voice input.

[1613] 2. Means of obtaining real-time traffic and people flow information:

[1614] The server obtains real-time traffic and pedestrian flow information using Google Maps APIs and other services.

[1615] The API key is provided during the initial setup of the server.

[1616] 3. How to generate optimal routes:

[1617] The server calculates the optimal route based on the acquired traffic and pedestrian flow information.

[1618] It also takes into account delay information along the way and provides the optimal route.

[1619] 4. Means of predictive analysis based on user preferences:

[1620] The server uses machine learning algorithms (scikit-learn, TensorFlow, etc.) to predict user preferences.

[1621] Past behavioral data and preference information is used.

[1622] 5. Means of recommending related stores or products along the optimal route:

[1623] The server recommends stores and products along the optimal route to the user.

[1624] You can get information about nearby stores using the Yelp and Foursquare APIs.

[1625] 6. Means for summarizing acquired real-time information and providing it to users:

[1626] The server summarizes traffic data and people flow information and displays it in an easy-to-understand manner for users.

[1627] 7. How to recognize user emotions and tailor recommendations based on their emotional state:

[1628] It uses the smartphone's camera and microphone to recognize the user's emotional state.

[1629] Based on the recognized emotional data, it recommends stores where you can relax and facilities where you can be active.

[1630] 8. How to display recommended stores and products to users:

[1631] Recommended stores and products are visually displayed on the smartphone interface.

[1632] 9. How we collect user feedback to help improve future prediction models:

[1633] The server collects data on how users respond to the information provided.

[1634] The collected data is fed back into the predictive model to help improve accuracy.

[1635] Example

[1636] As a specific example, consider the case where a user travels from Tokyo Station to Shinjuku Station and specifies Akihabara as a stopover point. When the user enters this information into their smartphone, the server obtains real-time traffic information using the Google Maps API, generates the optimal route, and provides directions from Tokyo Station to Shinjuku Station via Akihabara.

[1637] During this process, the user's emotional state is recognized and, if it is determined that they are in a stressful state, a relaxing cafe in Akihabara is recommended. Furthermore, the server uses the Yelp API to obtain information about cafes in the Akihabara area and displays it on the smartphone. When the user visits a cafe based on the information provided, their behavior is fed back to the server, helping to improve the accuracy of the prediction model.

[1638] Example prompts to input to a generative AI model:

[1639] What is the best route to travel from Tokyo Station to Shinjuku Station via Akihabara? Also, since the user is feeling stressed, please recommend a cafe in Akihabara where they can relax. Please also take real-time traffic and people flow information into consideration.

[1640] This invention allows users to enjoy a comfortable and personalized travel experience.

[1641] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1642] Step 1:

[1643] The terminal accepts the user's input of destinations and intermediate points. The user inputs that they are traveling from Tokyo Station to Shinjuku Station, passing through Akihabara on the way. This results in the input data being "Tokyo Station, Shinjuku Station, Akihabara."

[1644] Step 2:

[1645] The server obtains real-time traffic and people flow information and sends a request to the Google Maps API based on the input destination and intermediate points. The output is data on traffic conditions and people flow information for each route.

[1646] Step 3:

[1647] The server generates the optimal route based on the acquired traffic and pedestrian flow information, taking into account traffic congestion and real-time road conditions, calculates the shortest and most optimal route, and generates route data to provide to the user.

[1648] Step 4:

[1649] The server performs predictive analysis based on the user's preferences. It analyzes past behavioral and preference data using machine learning algorithms (such as scikit-learn and TensorFlow) to predict the user's current preferences. The predicted results are output as data on categories that the user is likely to be interested in (e.g., cafes or bookstores).

[1650] Step 5:

[1651] The server recommends relevant stores or products along the optimal route based on predictive analysis. It uses the Yelp or Foursquare API to search for and obtain information about stores and products that fit the predicted category. The output is a list of recommended stores and products.

[1652] Step 6:

[1653] The server summarizes the acquired real-time information and recommendation information and prepares it for delivery to the user. It summarizes traffic information, people flow information, recommended stores and products information, and converts them into a format that can be displayed to the user. The output is a summarized information package.

[1654] Step 7:

[1655] The device recognizes the user's emotions. It uses the smartphone's camera and microphone to obtain real-time emotional data from the user through emotion recognition algorithms. The output is the user's current emotional state.

[1656] Step 8:

[1657] The server adjusts the recommendations based on the user's emotional state. For example, if the user is stressed, the server recommends relaxing stores, and if the user is excited, the server recommends active facilities. The output is the adjusted recommendation list.

[1658] Step 9:

[1659] The terminal displays the recommended stores and products to the user. The adjusted recommendations are visually displayed on the smartphone screen. The output is the user's display screen.

[1660] Step 10:

[1661] The server collects responses from users, such as the stores visited and the products purchased, based on the information provided, and stores the collected data in a database.

[1662] Through these steps, users can enjoy an individually optimized travel experience and receive suggestions tailored to their interests and emotions while traveling.

[1663] 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.

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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.

[1669] 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).

[1670] 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.

[1671] 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."

[1672] 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.

[1673] 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).

[1674] 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.

[1675] 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.

[1676] 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.

[1677] 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.

[1678] 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.

[1679] 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.

[1680] 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.

[1681] 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.

[1682] 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.

[1683] 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.

[1684] The following is further disclosed regarding the above embodiment.

[1685] (Claim 1)

[1686] means for inputting a user's destination and intermediate points;

[1687] a means for obtaining real-time traffic and people flow information;

[1688] means for generating an optimum route based on the input destination and relay points;

[1689] means for performing predictive analysis based on the user's preferences;

[1690] means for recommending relevant stores or products along the optimal route based on the predictive analysis;

[1691] means for summarizing the acquired real-time information and providing it to a user;

[1692] A system including:

[1693] (Claim 2)

[1694] 2. The system according to claim 1, wherein delay information for the optimum route is provided based on the real-time traffic information and people flow information.

[1695] (Claim 3)

[1696] 10. The system of claim 1, further comprising a machine learning algorithm for analyzing the user preferences.

[1697] "Example 1"

[1698] (Claim 1)

[1699] means for inputting a user's destination and intermediate points;

[1700] a means for obtaining real-time traffic and people flow information;

[1701] means for generating an optimum route based on the input destination and relay points;

[1702] means for performing predictive analysis based on the user's preferences;

[1703] means for recommending relevant stores or products along the optimal route based on the predictive analysis;

[1704] means for summarizing the acquired real-time information and providing it to a user;

[1705] A means for integrating an optimal route and recommendation information based on information on the user's destination and intermediate points;

[1706] means for displaying the integrated results on a user's terminal;

[1707] A system including:

[1708] (Claim 2)

[1709] 2. The system according to claim 1, wherein delay information for the optimum route is provided based on the real-time traffic information and people flow information.

[1710] (Claim 3)

[1711] 10. The system of claim 1, further comprising a machine learning algorithm for analyzing the user preferences.

[1712] "Application Example 1"

[1713] (Claim 1)

[1714] means for inputting a user's destination and intermediate points;

[1715] a means for obtaining real-time traffic and people flow information;

[1716] means for generating an optimum route based on the input destination and relay points;

[1717] means for performing predictive analysis based on the user's preferences;

[1718] means for recommending relevant stores or products along the optimal route based on the predictive analysis;

[1719] means for summarizing the acquired real-time information and providing it to a user;

[1720] means for integrating data into a navigation system of an associated autonomous vehicle based on the user's past behavior data;

[1721] A means for recommending stores and products that may be of interest to the user based on the user's location data;

[1722] A system including:

[1723] (Claim 2)

[1724] 2. The system according to claim 1, wherein delay information for the optimum route is provided based on the real-time traffic information and people flow information.

[1725] (Claim 3)

[1726] 10. The system of claim 1, further comprising a machine learning algorithm for analyzing the user preferences.

[1727] "Example 2: Combining Emotion Engines"

[1728] (Claim 1)

[1729] means for inputting a user's destination and intermediate points;

[1730] a means for obtaining real-time traffic and people flow information;

[1731] means for generating an optimum route based on the input destination and relay points;

[1732] means for performing predictive analysis based on the user's preferences;

[1733] means for recommending relevant facilities or products along the optimal route based on the predictive analysis;

[1734] means for recognizing the user's emotions and adjusting recommendations based on the emotions;

[1735] means for summarizing the acquired real-time information and providing it to a user;

[1736] A means of collecting user responses and improving the accuracy of the predictive model;

[1737] A system including:

[1738] (Claim 2)

[1739] 2. The system according to claim 1, wherein delay information for the optimum route is provided based on the real-time traffic information and people flow information.

[1740] (Claim 3)

[1741] 10. The system of claim 1, further comprising a machine learning algorithm for analyzing the user preferences.

[1742] "Application example 2 when combining emotion engines"

[1743] (Claim 1)

[1744] means for inputting a user's destination and intermediate points;

[1745] a means for obtaining real-time traffic and people flow information;

[1746] means for generating an optimum route based on the input destination and relay points;

[1747] means for performing predictive analysis based on the user's preferences;

[1748] means for recommending relevant stores or products along the optimal route based on the predictive analysis;

[1749] means for summarizing the acquired real-time information and providing it to a user;

[1750] means for recognizing the user's emotions and adjusting the recommendation content based on the user's emotional state;

[1751] a means for displaying the recommended stores and products to a user;

[1752] A means to collect user responses and contribute to improving the accuracy of future predictive models;

[1753] A system including:

[1754] (Claim 2)

[1755] 2. The system according to claim 1, wherein delay information for the optimum route is provided based on the real-time traffic information and people flow information.

[1756] (Claim 3)

[1757] 10. The system of claim 1, further comprising a machine learning algorithm for analyzing the user preferences. [Explanation of symbols]

[1758] 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 inputting a user's destination and intermediate points; a means for obtaining real-time traffic and people flow information; means for generating an optimum route based on the input destination and relay points; means for performing predictive analysis based on the user's preferences; means for recommending relevant stores or products along the optimal route based on the predictive analysis; means for summarizing the acquired real-time information and providing it to a user; A system including:

2. 2. The system according to claim 1, wherein delay information for the optimum route is provided based on the real-time traffic information and people flow information.

3. The system of claim 1 , further comprising a machine learning algorithm for analyzing the user preferences.

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