System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving visit destination information input by a user; means for acquiring past congestion data from a database based on the visit destination information; means for acquiring weather data of a current day from an external weather information service based on the visit destination information; means for performing congestion prediction based on the past congestion data and the weather data of the current day; and means for providing a result of the congestion prediction 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] Many users are unable to accurately ascertain congestion information for their destinations in advance, resulting in visits to crowded facilities and reduced satisfaction with their visit. To solve this problem, a system is needed that combines past congestion data with weather information for the day to provide more accurate congestion predictions in real time. It is also important that users can easily obtain congestion prediction information with minimal input and operation. [Means for solving the problem]

[0005] The present invention is a system including a means for receiving destination information input by a user, a means for acquiring past congestion data from a database based on the destination information, a means for acquiring weather data for the current day from an external weather information service based on the destination information, a means for predicting congestion based on the past congestion data and the weather data for the current day, and a means for providing the congestion prediction result to a user.

[0006] Specifically, the server queries a database for destination information received from the user to obtain past congestion data, and obtains weather data for the current day from an external weather information service. Based on this data, it predicts congestion and performs calculations to reduce congestion levels in bad weather and increase congestion levels in good weather. The results are then formatted and provided to the user, allowing the user to grasp the congestion status of the destination in advance and make optimal visit plans. The system also includes a means for displaying the prediction results in an appropriate format, making them easy for users to understand.

[0007] The "means for receiving destination information input by the user" refers to an interface and data receiving function for inputting information about places the user wants to visit and acquiring that information.

[0008] "Means for obtaining past congestion data from a database" refers to the function of obtaining data on past congestion conditions stored in a database through an inquiry.

[0009] "Means of obtaining today's weather data from external weather information services" refers to the function of obtaining data on the weather for the specified location on the day through external APIs or other weather information services.

[0010] "Means for predicting congestion based on past congestion data and weather data for the day" refers to algorithms and calculation functions for predicting congestion at specific locations and times using acquired past congestion data and weather data for the day.

[0011] "Means for providing congestion prediction results to the user" refers to a function for formatting the congestion prediction results in an appropriate format and displaying or notifying the user. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] The present invention is designed to enable users to accurately grasp congestion information for their destinations in advance. Specifically, the system involves a server receiving a request from a user, predicting congestion based on past congestion data and weather data for the day, and providing the results to the user.

[0034] System configuration

[0035] 1. User inputs destination information

[0036] The user inputs information about the place they want to visit on the device, for example, "parks in Tokyo."

[0037] 2. Sending a request by the device

[0038] The device sends the information entered by the user to the server, and the request includes the location to be visited.

[0039] 3. Obtaining past congestion data from the server

[0040] The server queries the database to retrieve historical congestion data for the specified destination, including congestion levels for specific periods in the past and congestion levels during specific events.

[0041] 4. Obtaining weather data from the server

[0042] The server sends a request to an external weather service to retrieve the current day's weather data, including the weather, temperature, and chance of precipitation.

[0043] 5. Congestion prediction by the server

[0044] The server predicts congestion based on past congestion data and weather data. For example, it uses rules such as decreasing congestion on bad weather days and increasing congestion on good weather days.

[0045] 6. Server-based formatting of prediction results

[0046] The server formats the prediction results into a predetermined format, such as JSON. The formatted data includes information such as the predicted congestion level, date, time, and location.

[0047] 7. Sending prediction results from the server to the device

[0048] The server sends the formatted prediction results to the user's device.

[0049] 8. Displaying prediction results on the device

[0050] The device then displays the received prediction results to the user. For example, a message such as "The predicted congestion level for parks in Tokyo is 50%" may be displayed on the screen.

[0051] Specific examples

[0052] For example, consider the case where a user inputs "I plan to go to a park in Tokyo."

[0053] 1. User Input

[0054] The user enters "Tokyo Park" into the input field of the terminal.

[0055] 2. Request transmission by terminal

[0056] The device sends the information "parks in Tokyo" to the server.

[0057] 3. Data Acquisition by the Server

[0058] The server retrieves past congestion data for "Tokyo parks" from a database, and simultaneously retrieves current weather data from a weather information service.

[0059] 4. Server congestion prediction

[0060] The server predicts future congestion levels based on past congestion data and weather data. For example, if the weather is good, it predicts that the congestion level will be 70%.

[0061] 5. Server-based result formatting

[0062] The server formats the prediction results into a format such as "The predicted congestion level of parks in Tokyo is 70%."

[0063] 6. Sending results from the server to the device

[0064] The server sends the formatted prediction results to the terminal.

[0065] 7. Displaying results on a terminal

[0066] The device displays the message "The predicted congestion level for Tokyo parks is 70%."

[0067] In this way, the present invention is a system that helps users understand the congestion situation at their destinations in advance and plan their visits optimally. Crowd predictions are based on predefined rules and a combination of past data and weather information, making highly accurate predictions possible.

[0068] The processing flow will be explained below.

[0069] Program processing steps

[0070] Server Processing Steps

[0071] Step 1:

[0072] The server receives a request from the user containing information about the destinations visited, including the location, date, and time of the destinations entered by the user.

[0073] Step 2:

[0074] The server connects to the database and retrieves past congestion data for the specified destination. For example, it retrieves data such as the past congestion level of a specified "Tokyo park" or the congestion situation during an event.

[0075] Step 3:

[0076] The server sends a request to an external weather information service API to obtain weather data for the current day at the destination, including weather conditions, temperature, and chance of precipitation.

[0077] Step 4:

[0078] The server predicts congestion based on past congestion data and weather data. Specifically, it applies basic rules such as increasing congestion when the weather is sunny and decreasing congestion when it rains.

[0079] Step 5:

[0080] The server formats the congestion prediction results into an appropriate format (e.g., JSON), which includes information such as location, date, time, and predicted congestion level.

[0081] Step 6:

[0082] The server then sends the formatted prediction results to the user's device as an HTTP response.

[0083] Terminal processing steps

[0084] Step 1:

[0085] The user enters information about the destination into the input form on the device. For example, the user enters information such as "a park in Tokyo."

[0086] Step 2:

[0087] The terminal sends a request to the server based on the entered visit destination information. This request includes the location information of the visit destination.

[0088] Step 3:

[0089] The device receives a response from the server, which contains the congestion prediction results in JSON format.

[0090] Step 4:

[0091] The device analyzes the prediction results it receives and displays them in a format that is easy for the user to understand. For example, it displays a message on the screen saying, "The predicted congestion level for parks in Tokyo is 70%."

[0092] Specific examples

[0093] Server Processing Steps

[0094] 1. Step 1: The server receives a request from the user saying, "I have plans to go to a park in Tokyo."

[0095] 2. Step 2: The server references the database to obtain historical crowding data for parks in Tokyo.

[0096] 3. Step 3: The server sends a request to the weather information API to obtain the current day's weather data for Tokyo.

[0097] 4. Step 4: The server uses a congestion prediction algorithm to predict the congestion level based on past data and weather data. For example, if the weather is good, it predicts the congestion level to be 70%.

[0098] 5. Step 5: The server formats the prediction results into JSON format.

[0099] 6. Step 6: The server sends the formatted prediction results to the user's device.

[0100] Terminal processing steps

[0101] 1. Step 1: The user enters "Tokyo Parks" into the input field on the device.

[0102] 2. Step 2: The device sends the information about "Tokyo Parks" to the server.

[0103] 3. Step 3: The device receives the response from the server and obtains the prediction result.

[0104] 4. Step 4: The device will display the message "The predicted congestion level for Tokyo parks is 70%."

[0105] This is the specific flow of program processing by the server and terminal. This allows the user to know in advance how busy the destination will be and to plan the most appropriate visit.

[0106] Example 1

[0107] 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."

[0108] Conventional congestion prediction systems make it difficult for users to accurately predict the congestion situation at their destinations. As a result, users may end up with long waiting times at their destinations and may not be able to plan their visits properly. Another problem is that weather information is not reflected in the congestion predictions, resulting in low prediction accuracy. To solve these problems, a system is needed that allows users to accurately grasp the congestion situation at their destinations and make optimal visit plans.

[0109] 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.

[0110] In this invention, the server includes means for receiving destination information entered by a user, means for acquiring past congestion data from a database, means for acquiring weather data for the current day from an external weather information service, means for predicting congestion based on the past congestion data and weather data using a machine learning model, means for formatting the congestion prediction result in a predetermined format and transmitting it to the user's terminal, and means for displaying the congestion prediction result on the user's terminal. This allows the user to accurately grasp the congestion status of the destination in advance and make an optimal visit plan.

[0111] A "user" is an individual or group that uses this system to obtain congestion information about a destination.

[0112] A "terminal" is an electronic device that a user uses to access the system, input destination information, and display prediction results.

[0113] The "server" is a central computer facility that receives input destination information, acquires past congestion data and weather data, and performs congestion predictions.

[0114] "Destination information" refers to information about a place that the user wishes to visit, and specifically includes the location, place name, and the like.

[0115] "Past congestion data" is data that indicates the past congestion situation at the designated destination, and includes the degree of congestion during a specific period and the congestion situation during an event.

[0116] "Weather data" refers to data about the weather on the day obtained from an external weather information service, and includes the weather, temperature, probability of precipitation, and the like.

[0117] "Crowd prediction" refers to the process of predicting future congestion at a destination based on past congestion data and weather data.

[0118] A "machine learning model" is a mathematical model that learns from given data (past congestion data and weather data) and is used to predict future congestion conditions.

[0119] "Format" refers to the specific way in which data or information is organized and formatted, including, for example, JSON format.

[0120] The "prediction result" is the result data obtained after making a congestion prediction, and includes information such as the predicted congestion level, date and time, and location.

[0121] The present invention provides a system for enabling a user to accurately grasp congestion information about a destination in advance. A specific embodiment of the present invention comprises the following steps.

[0122] First, the user uses the device to input information about the place they want to visit. By entering a specific place name or location into the input field, the device prepares to send the request. For example, the user might enter "parks in Tokyo."

[0123] Next, the device sends the destination information entered by the user to the server. This request includes geographical information of the destination and user identification information. It is sent to the server in the format "location: park in Tokyo, user_id: 12345".

[0124] The server retrieves past congestion data from the database based on the entered destination information. It uses the SQL query "SELECT FROM crowd_data WHERE location = 'Tokyo Parks'" to query the database. The past congestion data includes the level of congestion during specific periods and the congestion situation during events.

[0125] In parallel, the server sends an API request to an external weather service to get the current day's weather data. This request uses the geographic coordinates of the destination. For example, the request is sent to the weather service in the format "GET / weather?lat=35.6895&lon=139.6917". The weather data includes the weather, temperature, chance of precipitation, etc.

[0126] The server performs congestion predictions based on the acquired past congestion data and weather data. A trained model is loaded using a Python machine learning library (e.g., scikit-learn). For example, the model is loaded using "from sklearn.externals import joblib", and predictions are made in the format "model.predict([[temperature, probability of precipitation, past congestion level]])". This takes into account the tendency for congestion to increase on good weather days and decrease on bad weather days.

[0127] The prediction results are formatted by the server in a predetermined format. The formatted data includes information such as the predicted crowd level, date, time, and location. For example, it is formatted in the format "{'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}".

[0128] The server sends the formatted prediction results to the user's device as an HTTP response in JSON format, specifically, "{'status': 'success', 'data': {'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}}".

[0129] The device then displays the received prediction results to the user. At this time, it uses a UI component that parses the prediction results and displays them in a visually easy-to-understand format for the user. For example, a message such as "The predicted congestion level for parks in Tokyo is 70%" is displayed.

[0130] This system allows users to accurately understand the congestion situation at their destinations in advance and plan their visits optimally.

[0131] Specific examples

[0132] Consider the case where a user inputs "I'm planning to go to a park in Tokyo."

[0133] 1. User input: The user enters "Parks in Tokyo" into the input field on the device.

[0134] 2. Request sent by device: The device sends a request to the server: "location: park in Tokyo, user_id: 12345".

[0135] 3. Data retrieval by the server: The server executes the SQL query "SELECT FROM crowd_data WHERE location = 'Tokyo parks'" to retrieve historical crowd data from the database, and simultaneously sends the API request "GET / weather?lat=35.6895&lon=139.6917" to the weather information service to retrieve weather data.

[0136] 4. Congestion prediction by the server: The server executes the code "model.predict([[temperature, probability of precipitation, past congestion level]])" to predict future congestion levels.

[0137] 5. Server formatting: The server formats the prediction results into the format "{'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}".

[0138] 6. Sending results from the server to the device: The server sends the prediction results to the device in JSON format: {'status': 'success', 'data': {'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}}.

[0139] 7. Displaying the results on the device: The device displays on the screen, "The predicted congestion level of Tokyo parks is 70%."

[0140] Prompt Sentence Examples

[0141] "I'd like to know the crowd forecast for parks in Tokyo. Today's weather is sunny."

[0142] This system allows users to understand the congestion situation at their destinations in advance and plan their visits optimally.

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Step 1:

[0145] The user uses the device to input information about the place they want to visit. In this input field, they input a specific place name, such as "Tokyo Park," and the information is displayed on the device screen in real time. The input data is "location: Tokyo Park."

[0146] Step 2:

[0147] The device sends the destination information entered by the user to the server. This request includes the geographical information of the destination and the user identification information, and is sent in the format "location: park in Tokyo, user_id: 12345".

[0148] Step 3:

[0149] The server retrieves past congestion data from the database based on the destination information received from the device. It executes the SQL query "SELECT FROM crowd_data WHERE location = 'Tokyo parks'" to retrieve the relevant congestion data. This data includes the level of congestion over a certain period of time in the past and congestion conditions during events.

[0150] Step 4:

[0151] Based on the visitor information, the server sends an API request to an external weather information service to obtain weather data for the day. The request is sent in the format "GET / weather?lat=35.6895&lon=139.6917", which retrieves data such as weather, temperature, and precipitation probability.

[0152] Step 5:

[0153] The server uses the acquired past congestion data and weather data to make congestion predictions. To do this, it uses a Python machine learning library (e.g., scikit-learn) and a trained model. For example, it executes "model.predict([[temperature, probability of precipitation, past congestion level]])" to predict future congestion levels. The output is the predicted congestion level.

[0154] Step 6:

[0155] The server formats the prediction results in JSON format. The results include the location of the visit, the predicted crowd level, and the predicted date and time. Specifically, the results are formatted in the format "{'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}". The formatted output is the predicted data in JSON format.

[0156] Step 7:

[0157] The server sends the formatted prediction result data to the user's device as an HTTP response. This response is in JSON format, like this: {'status': 'success', 'data': {'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}}.

[0158] Step 8:

[0159] The device analyzes the received prediction results and displays them to the user. The device uses a UI component to parse the prediction results and display them in a visually easy-to-understand format for the user. Specifically, the device displays a message on the screen saying, "The predicted congestion level for parks in Tokyo is 70%."

[0160] (Application example 1)

[0161] 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."

[0162] Conventional methods for finding out the congestion situation at a destination have the problem that it is difficult to accurately grasp the congestion situation at a store you plan to visit in advance. In particular, there is a lack of systems that can provide information on the impact of weather on congestion situations or specific congestion forecasts, so users are often at the mercy of the congestion at their destination.

[0163] 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.

[0164] In this invention, the server includes means for receiving destination information entered by the user, means for retrieving past congestion data from a database based on the destination information, means for retrieving weather data for the current day from an external weather information service based on the destination information, means for predicting congestion based on the past congestion data and the weather data for the current day, and means for providing and displaying the congestion prediction result to the user, thereby enabling the user to grasp the congestion status of the destination in advance and plan the optimal visiting time.

[0165] The "means for receiving destination information input by the user" is a means for the user to input information about places he or she wants to visit and for the system to receive that information.

[0166] The "means for acquiring past congestion data from a database" refers to a means for acquiring the data from a database that stores past data on the congestion status of destinations.

[0167] The "means for obtaining today's weather data from an external weather information service" refers to a means for sending a request to a third-party weather information service and obtaining data regarding the weather of the day.

[0168] The "means for predicting congestion" is a means for predicting future congestion conditions based on acquired past congestion data and weather data for the day.

[0169] The "means for providing and displaying the congestion prediction results to the user" refers to a means for visually displaying the results of the congestion prediction that has been performed to the user in an easy-to-understand manner.

[0170] "Formatting means" refers to a means for converting prediction results into a specified format such as JSON format.

[0171] The present invention is configured to enable users to accurately grasp congestion information for destinations in advance. Specifically, the system is one in which a server receives a request from a user, predicts congestion based on past congestion data and weather data for the day, and provides the result to the user. An embodiment of this system is described in detail below.

[0172] First, the user inputs information about the place they want to visit on their smartphone. For example, the user might input "shopping mall." Next, the user's device (smartphone) sends the input information to the server. The request includes the location of the place to be visited.

[0173] The server first queries the database to obtain past congestion data for the specified destination. This data includes congestion levels during specific periods in the past and congestion conditions during specific events. The server also sends a request to an external weather information service to obtain weather data for the current day. This data includes weather, temperature, and probability of precipitation.

[0174] The server makes congestion predictions based on the acquired past congestion data and weather data. For example, it applies rules such as congestion decreasing on bad weather days and increasing on good weather days. This allows users to understand the future congestion situation at their destination.

[0175] The server then formats the prediction results into a predetermined format, such as JSON. The formatted data includes information such as the predicted congestion level, date, time, and location. The server then sends the formatted prediction results to the user's device. The device visually displays the received prediction results to the user. For example, a message such as "The predicted congestion level of the shopping mall is 70%" may be displayed on the screen.

[0176] As a concrete example, consider the case where a user inputs "I plan to go to the shopping mall." When the user inputs "shopping mall" into the input field, the device sends this information to the server. The server retrieves past congestion data for "shopping mall" from a database, and at the same time retrieves current weather data from a weather information service. The server predicts future congestion levels based on this data, and if the weather is good, for example, predicts that the congestion level will be 70%. The formatted prediction result is converted into the format "The predicted congestion level at the shopping mall is 70%" and sent from the server to the device. The device displays the received data on the screen.

[0177] The following are examples of prompt sentences:

[0178] When a user types "I want to go to the shopping mall," the application predicts the level of congestion based on the mall's past congestion data and weather data, and displays the predicted level of congestion.

[0179] In this way, the system of the present invention helps users understand the congestion situation at their destinations in advance and plan their visits appropriately. It is possible to provide highly accurate congestion forecasts using hardware and software such as servers, smartphones, databases, and weather information services.

[0180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0181] Step 1:

[0182] The user inputs the location of the destination using the smartphone.

[0183] Input: Text input by the user, such as "shopping mall."

[0184] Output: A request by a device to send input information.

[0185] Step 2:

[0186] The user's terminal transmits the entered visiting destination information to the server.

[0187] Input: Visiting destination information from the user.

[0188] Output: Sending a request to the server.

[0189] Step 3:

[0190] The server retrieves past congestion data for the designated destination from the database.

[0191] Input: The destination information submitted (e.g., "shopping mall").

[0192] Output: Historical congestion data retrieved from the database.

[0193] Step 4:

[0194] The server sends a request to an external weather information service to obtain the current day's weather data.

[0195] Input: The destination information submitted (e.g., "shopping mall").

[0196] Output: Weather data obtained from weather information services.

[0197] Step 5:

[0198] Congestion predictions are made based on past congestion data and weather data acquired by the server.

[0199] Input: Obtained historical congestion and weather data.

[0200] Output: Crowd prediction result (e.g., "The predicted crowding level of the shopping mall is 70%").

[0201] Specific operation: The system calculates to increase the congestion level on good weather days and decrease it on bad weather days.

[0202] Step 6:

[0203] The server formats the prediction results into a specified format such as JSON.

[0204] Input: Congestion prediction results.

[0205] Output: Prediction results in JSON format.

[0206] Step 7:

[0207] The server sends the formatted prediction results to the user's device.

[0208] Input: Formatted prediction results (JSON format).

[0209] Output: Prediction results are sent to the user's device.

[0210] Step 8:

[0211] The prediction results received by the user's terminal are displayed on the screen.

[0212] Input: Submitted prediction results (JSON format).

[0213] Output: A screen that displays "The predicted occupancy level of the shopping mall is 70%."

[0214] 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.

[0215] This invention is a system that allows users to create optimal visit plans based on congestion information at destinations and their own emotions. In particular, by combining this system with an emotion engine, it is possible to predict congestion and suggest destinations based on the user's emotional state.

[0216] System configuration

[0217] 1. User inputs destination information

[0218] The user inputs information about the place they want to visit on the terminal, such as "art museum" or "theme park."

[0219] 2. Sending a request by the device

[0220] The terminal sends a request including the information entered by the user to the server. The request also includes information about the destination.

[0221] 3. Obtaining past congestion data from the server

[0222] The server retrieves historical congestion data for the specified destination from the database, including congestion levels over a certain period of time in the past and congestion levels during specific events.

[0223] 4. Obtaining weather data from the server

[0224] The server retrieves the current day's weather data from an external weather information service, including the weather, temperature, and chance of precipitation.

[0225] 5. Acquiring user emotion data using the emotion engine

[0226] The emotion engine analyzes emotions from user input data or voice data, for example, extracting emotional states from user-entered comments or voice messages.

[0227] 6. Congestion prediction by the server

[0228] The server predicts congestion based on past congestion data, weather data, and user emotion data. For example, if the user is depressed, it will predict that less crowded locations will be prioritized.

[0229] 7. Server-suggested destinations

[0230] Based on the results of emotion analysis, the system suggests places to visit based on the user's emotional state. For example, if the user is looking to relax, it will suggest a quiet park.

[0231] 8. Server-based formatting of prediction results

[0232] The server then formats the congestion prediction results and suggested destinations into an appropriate format (e.g., JSON). This data includes the congestion level, date, time, location, and suggested destinations based on the user's emotional state.

[0233] 9. Sending prediction results and suggestions from the server to the device

[0234] The server sends the formatted prediction results and suggested destination data to the user's device.

[0235] 10. Displaying prediction results and suggestions on the device

[0236] The device then displays the received prediction results and suggested places to visit to the user. For example, a message such as "The museum is predicted to be 50% crowded. There is also a quiet park recommended for a change of pace" may be displayed on the screen.

[0237] Specific examples

[0238] Consider the case where a user inputs, "I'm planning to go to a theme park in Tokyo, but I've been feeling tired lately so I'd prefer a quiet place."

[0239] 1. User Input

[0240] The user enters "Tokyo theme park" into the input field on the device and adds the comment "I've been feeling tired lately."

[0241] 2. Request transmission by terminal

[0242] The device sends information including "Tokyo theme parks" and comments to the server.

[0243] 3. Data Acquisition by the Server

[0244] The server retrieves past congestion data for theme parks in Tokyo from a database, and simultaneously retrieves the current day's weather data from a weather information service.

[0245] 4. Emotion analysis using an emotion engine

[0246] The emotion engine analyzes the user's comments and recognizes that the user is "tired."

[0247] 5. Server-based congestion prediction and proposal

[0248] The server uses a congestion prediction algorithm to predict congestion levels based on past data and weather data. It also suggests places to visit based on the user's emotions. For example, if a theme park is 50% crowded, it will suggest a park where you can spend some quiet time.

[0249] 6. Server-based Result Formatting

[0250] The server formats the prediction results into the following format: "The predicted congestion level at Tokyo's theme parks is 50%. There are also quiet parks that are recommended for a change of pace."

[0251] 7. Sending results from the server to the device

[0252] The server sends the formatted prediction results and suggestions to the user's device.

[0253] 8. Displaying results on a terminal

[0254] The device displays the message, "The predicted congestion level at Tokyo's theme parks is 50%. There are also quiet parks that are recommended for a change of pace."

[0255] In this way, the present invention is a system that supports visit plans that result in even higher user satisfaction by predicting congestion and suggesting places to visit that take into account the user's emotional state.

[0256] The processing flow will be explained below.

[0257] Program processing steps

[0258] Server Processing Steps

[0259] Step 1:

[0260] The server receives a request from the user, which includes the destination information and a comment. The comment can convey the user's emotional state, such as if they are tired.

[0261] Step 2:

[0262] The server retrieves past congestion data for the specified destination from the database, including congestion levels during specific periods in the past and congestion conditions during events.

[0263] Step 3:

[0264] The server sends a request to an external weather information service API to obtain weather data for the day at the destination, including weather conditions, temperature, and chance of precipitation.

[0265] Step 4:

[0266] The server uses an emotion engine to analyze the user's comments and determine their emotions. For example, it can recognize that a comment such as "I'm tired" indicates that the user is seeking relaxation.

[0267] Step 5:

[0268] The server predicts congestion based on past congestion data, weather data, and user emotion data. The emotion data is used to determine the user's preferred situation.

[0269] Step 6:

[0270] The server recommends optimal destinations based on the crowding prediction results and the user's emotional state. For example, if the predicted crowding rate for a theme park is 50%, the server may recommend a quiet park.

[0271] Step 7:

[0272] The server then formats the predictions and suggestions into an appropriate format (e.g., JSON). This data includes suggestions based on crowding, date, time, location, and the user's emotional state.

[0273] Step 8:

[0274] The server then sends the formatted prediction results and suggestions to the user's device as an HTTP response.

[0275] Terminal processing steps

[0276] Step 1:

[0277] The user enters information about the place they visited and their emotional state as a comment into the input form on their device. For example, they might enter "a theme park in Tokyo" and comment "I've been feeling tired lately."

[0278] Step 2:

[0279] The device sends a request to the server based on the entered visit information and comments. This request includes the visit destination and emotional state.

[0280] Step 3:

[0281] The device receives a response from the server, which includes the congestion prediction results and suggested places to visit.

[0282] Step 4:

[0283] The device analyzes the prediction results and suggestions received and displays them in a format that is easy for the user to understand. For example, it displays a message on the screen saying, "The predicted congestion level at Tokyo's theme parks is 50%. There are also some quiet parks that we recommend for a change of pace."

[0284] Specific examples

[0285] Server Processing Steps

[0286] 1. Step 1: The server receives a request from the user saying, "I'm planning to go to a theme park in Tokyo, but I'm tired recently and would prefer a quiet place."

[0287] 2. Step 2: The server references the database to obtain historical crowd data for theme parks in Tokyo.

[0288] 3. Step 3: The server sends a request to the weather information API to obtain the current day's weather data for Tokyo.

[0289] 4. Step 4: The server uses the emotion engine to recognize the "tired" state from the user's comments.

[0290] 5. Step 5: The server uses a congestion prediction algorithm to predict the level of congestion based on past data and weather data. For example, if the weather is good, it predicts the level of congestion to be 50%. It also determines from emotional data that people prefer "quiet places."

[0291] 6. Step 6: The server combines the crowd prediction results with the emotion data and suggests, "The predicted crowding level at theme parks in Tokyo is 50%, but there are also quiet parks that are recommended for a change of pace."

[0292] 7. Step 7: The server formats the prediction results and suggestions into JSON format.

[0293] 8. Step 8: The server sends the formatted prediction results and suggestions to the user's device.

[0294] Terminal processing steps

[0295] 1. Step 1: The user types "Tokyo theme parks" into the device's input field and comments "I've been feeling tired lately."

[0296] 2. Step 2: The device sends information including "Tokyo theme parks" and "I've been feeling tired lately" to the server.

[0297] 3. Step 3: The device receives the response from the server and obtains the prediction results and suggestions.

[0298] 4. Step 4: The device will display the following message on the screen: "Tokyo theme parks are expected to be 50% crowded. There are also some quiet parks that are recommended for a change of pace."

[0299] The above is the specific program processing flow of the system that combines the emotion engine. As a result, users can receive congestion predictions and suggested places to visit that reflect their own emotional state, allowing them to create optimal travel plans.

[0300] Example 2

[0301] 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."

[0302] In today's world, predicting crowd levels at specific locations and events is important to users, but existing systems are unable to provide optimal suggestions that take into account the user's emotional state. Furthermore, it is difficult to make advanced predictions that combine weather information and past crowd data, making it difficult to provide visit plans that will satisfy users.

[0303] 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.

[0304] In this invention, the server includes means for receiving destination information and emotional state input by the user, means for retrieving past congestion data from a database, means for retrieving weather data for the current day from an external weather information service, means for analyzing the emotional state and retrieving the user's emotional data, means for predicting congestion based on the past congestion data, weather data, and emotional data, means for providing destination suggestions based on the congestion prediction result and the user's emotional state, means for formatting the prediction result and the suggested destinations into a predetermined format, and means for presenting the prediction result and the suggested destinations to the user. This enables congestion prediction and suggestions of optimal destinations that take the user's emotional state into consideration.

[0305] A "user" is an entity that utilizes the system to input destination information and emotional state.

[0306] "Destination information" is information about places and events that the user plans to visit.

[0307] "Emotional state" is information that indicates the user's current psychological state.

[0308] A "terminal" is a device that allows a user to input and send information, and includes smartphones, personal computers, etc.

[0309] A "request" refers to an operation of sending data, including information entered by a user, to a server.

[0310] A "server" is a computer system that receives a request and processes and provides the required data.

[0311] A "database" is a system that stores and manages information such as past congestion data.

[0312] A "weather information service" is an external service that provides weather information such as the weather, temperature, and probability of precipitation for the day.

[0313] An "emotion engine" is a system that analyzes emotions from user input data and voice data.

[0314] "Crowd prediction" refers to estimating future congestion levels at a destination based on past congestion data, weather data, and emotion data.

[0315] "Suggesting destinations" refers to suggesting optimal destinations based on the congestion prediction results and the user's emotional state.

[0316] "Formatting the prediction results" refers to the operation of converting the congestion prediction results and suggested destinations into a fixed format.

[0317] A "format" is data organized in a specific way, typically JSON format.

[0318] A "generative AI model" is an artificial intelligence model used to analyze data and make predictions.

[0319] "Comment" refers to text information such as an emotional state entered by a user.

[0320] The present invention is a system that allows users to create optimal visit plans based on congestion information at destinations and their own emotions. This system uses an emotion engine to predict congestion and suggest destinations based on the user's emotional state. The specific configuration and operation are as follows.

[0321] System configuration

[0322] Hardware and Software

[0323] 1. User Device:

[0324] Devices such as smartphones, tablets, and personal computers are used as user terminals, providing an interface for users to input visiting information and emotional states.

[0325] 2. Server:

[0326] A server system with powerful computing power is required to manage the database, communicate with weather information services, calculate congestion forecasts, analyze sentiment, and format the results.

[0327] 3. Database:

[0328] A database is used to store and manage past congestion data, including congestion levels over a certain period of time in the past and congestion conditions during specific events.

[0329] 4. Emotion Engine:

[0330] There is a need for an emotion engine that uses NLP (Natural Language Processing) techniques to analyze emotions from user input data and voice data.

[0331] 5. Weather information services:

[0332] Use an API to obtain real-time weather data from external weather information services.

[0333] 6. Generative AI Models:

[0334] A generative AI model used to integrate multiple data sets and make crowd predictions and suggest places to visit.

[0335] Operation explanation

[0336] The user inputs destination information and emotional state using the device. For example, the user inputs, "I'm planning to go to a theme park in Tokyo, but I've been feeling tired lately, so I'd prefer a quiet place." This information is then sent from the device to the server.

[0337] The server does the following:

[0338] 1. Obtain historical crowd data for theme parks in Tokyo from the database.

[0339] 2. Obtain the current day's weather data for Tokyo from an external weather information service.

[0340] 3. Use the emotion engine to analyze the user's emotion data. In this example, it recognizes that the user is in a "tired" state.

[0341] 4. Crowd forecasting based on historical crowd data, weather data, and sentiment data. Generative AI models are used to predict current and future crowd levels at destinations.

[0342] 5. Creates recommendations for places to visit based on the crowding prediction results and the user's emotional state. In this example, the system generates recommendations such as, "The predicted crowding rate at theme parks in Tokyo is 50%. There are also quiet parks recommended for a change of pace."

[0343] 6. Format the prediction results and destination suggestions into an appropriate format (e.g., JSON).

[0344] The formatted data is sent from the server to the device, which then displays it to the user. For example, a message such as "The predicted congestion level at Tokyo's theme parks is 50%. There are also some quiet parks recommended for a change of pace" may be displayed on the device screen.

[0345] Specific examples

[0346] Let's say a user types, "I'm planning to go to a theme park in Tokyo, but I'm tired these days and would prefer a quiet place." In this case, the following prompt sentence is used:

[0347] Example prompt sentence:

[0348] "I'm planning to go to a theme park in Tokyo, but I've been feeling tired lately so I'd prefer a quiet place."

[0349] Based on this information, the server processes the data and proposes an optimal visit plan to the user. In this way, the present invention supports visit plans that increase user satisfaction.

[0350] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0351] Step 1:

[0352] The user enters the destination information and emotional comment into the input fields on the device. Specifically, they enter "Tokyo theme park" and "I've been feeling tired lately," and press the send button. The input information is converted to JSON format on the device. Input: Destination information and emotional comment. Output: Request data in JSON format.

[0353] Step 2:

[0354] The device sends a JSON-formatted request containing the entered information to the server as an HTTP POST request. Specifically, the device parses the user's input into JSON format and sends the data to the specified API endpoint. Input: JSON-formatted request data. Output: HTTP POST request to the server.

[0355] Step 3:

[0356] The server receives the request and retrieves past congestion data from the database based on the destination information. Specifically, it analyzes the destination information in the request and executes an SQL query based on that information to extract congestion data. Input: Destination information. Output: Past congestion data.

[0357] Step 4:

[0358] The server calls the API of an external weather information service to obtain weather data for the day. It sends an API request and analyzes the received data. Specific weather information includes the weather, temperature, and probability of precipitation. Input: API request. Output: Weather data.

[0359] Step 5:

[0360] The server uses an emotion engine to obtain the user's emotion data. Specifically, it analyzes the entered emotion comment and identifies the emotion using natural language processing (NLP) technology. In this case, the comment "I've been tired lately" is analyzed and evaluated as a "tired" state. Input: Emotion comment. Output: Emotion data.

[0361] Step 6:

[0362] The server combines past congestion data, weather data, and emotion data to perform congestion prediction. Using a generative AI model, multiple data sets are input and prediction results are generated. At this time, emotion data is taken into consideration and destinations with low congestion levels are recommended preferentially. Input: Past congestion data, weather data, emotion data. Output: Congestion prediction results.

[0363] Step 7:

[0364] The server generates optimal destination suggestions for the user based on the congestion prediction results and emotion analysis results. Specifically, it makes suggestions that match the user's emotional state, such as less crowded places or quiet parks. Input: Congestion prediction results, emotion analysis results. Output: Destination suggestions.

[0365] Step 8:

[0366] The server formats the congestion prediction results and visit suggestions into an appropriate format (e.g., JSON format). This is the process of converting the results into a format that can be easily analyzed and displayed on the user's device. Input: Congestion prediction results, visit suggestions. Output: Formatted prediction results and suggestion data.

[0367] Step 9:

[0368] The server returns the formatted prediction results and suggestions to the user's device. This is sent in the form of an HTTP response, which the device receives. Input: Formatted prediction results and suggestion data. Output: HTTP response to the device.

[0369] Step 10:

[0370] The device displays the prediction results and suggested places to visit received by the device to the user. For example, a message such as "The predicted congestion level at theme parks in Tokyo is 50%. There are also quiet parks recommended for a change of pace" is displayed on the screen. Input: HTTP response data. Output: Message to be displayed to the user.

[0371] (Application example 2)

[0372] 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."

[0373] Conventional congestion prediction systems for destinations do not take into account the user's emotional state, so the selection of destinations may not be appropriate for the user's psychological state. Furthermore, there is a need for a system that can not only predict congestion but also suggest optimal destinations based on the user's current emotions. Therefore, it is necessary to develop a new system that can predict congestion and recommend optimal destinations while taking into account the user's emotions.

[0374] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving destination information and emotion information input by the user, means for acquiring past congestion data from a database based on the destination information, means for acquiring weather data for the current day from an external weather information service based on the destination information, means for predicting congestion based on the acquired past congestion data, weather data for the current day, and user emotion data, means for recommending optimal destinations based on the user's emotional state, and means for providing the user with the congestion prediction results and recommendation results. This makes it possible to predict congestion and suggest optimal destinations that take the user's emotional state into consideration.

[0375] "Destination information entered by the user" is information about a specific place or facility that the user wishes to visit.

[0376] "Emotion information" is information about the user's current emotional state and psychological situation.

[0377] "Past congestion data" refers to data relating to past congestion at a particular destination.

[0378] A "database" is a system for storing, managing, and searching data.

[0379] A "weather information service" is a service that provides information about weather and climate from an external source.

[0380] "Weather data for the current day" refers to data related to the climate, such as the weather, temperature, and probability of precipitation, for a specific day.

[0381] The "emotion engine" is an engine for analyzing the emotional state from text and voice input by the user.

[0382] A "crowd forecast" means a method or algorithm for predicting the crowding level of a destination based on past data, current weather conditions, and the user's emotional state.

[0383] A "recommendation means" is a method or system for suggesting optimal destinations to a user based on the user's emotional state and congestion prediction results.

[0384] The "means for providing predicted results and recommended results" refers to a method or system for providing the results generated by the server to the user in an easy-to-understand manner.

[0385] A "format" is a rule or method for arranging data or information into a particular structure or form.

[0386] The present invention provides a system for predicting congestion and suggesting optimal destinations that take into account the emotional state of a user. This system is implemented using the following hardware and software.

[0387] Hardware and Software

[0388] Smartphone (terminal): The user enters destination information and emotion information, and the results are displayed.

[0389] Server: Receives information from users and processes the data.

[0390] Database (SQLite): Stores and manages past congestion data.

[0391] External weather information service (Weather API): Obtains the current day's weather data.

[0392] Emotion engine: Analyzes the user's emotional state from input data.

[0393] Programming language (Python) and framework (Flask): Performs server-side processing.

[0394] Program processing

[0395] 1. Receiving user input:

[0396] The server receives destination information and emotion information from the smartphone terminal. The user inputs the place they want to visit and their current emotional state.

[0397] 2. Data Acquisition:

[0398] The server acquires past congestion data from a database based on the received destination information.

[0399] In addition, weather data for the day is obtained using an external weather information service.

[0400] 3. Emotion analysis:

[0401] The emotion engine analyzes the user's input data and extracts the user's current emotional state.

[0402] 4. Congestion prediction and destination suggestions:

[0403] The server makes congestion predictions based on acquired past congestion data, weather data for the day, and user emotion data.

[0404] To suggest the most suitable destination according to a user's emotional state.

[0405] 5. Results provided:

[0406] The server formats the congestion prediction results and suggested places to visit into an appropriate format and sends them to the smartphone device.

[0407] The terminal displays the received results to the user.

[0408] Specific examples

[0409] For example, if a user inputs "I want to go to the shopping mall in Shinjuku, but I've been really tired lately," the server will process the request based on the following prompt:

[0410] "A user types, 'I want to go to a shopping mall in Shinjuku, but I've been feeling really tired lately.' Please predict how crowded it will be and suggest a quiet place so I can relax."

[0411] In this case, the server predicts congestion at shopping malls in Shinjuku based on past congestion data and the weather data for the day, and suggests quiet cafes where users can relax based on their emotional information. These suggestions are displayed on the smartphone device and used as a reference when the user chooses the best place to visit.

[0412] The above is an embodiment of the present invention. This system makes it possible to predict congestion and suggest places to visit while taking into account the emotional state of the user.

[0413] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0414] Step 1:

[0415] The user inputs destination information and emotional information on their smartphone. Specifically, the user inputs "the place they want to visit" (e.g., a shopping mall in Shinjuku) and "emotional state" (e.g., "I've been feeling very tired lately") into the application's input fields. The input data includes destination information and emotional comments. The input data here is sent to the server in the next step.

[0416] Step 2:

[0417] The device sends the entered visit information and emotion information to the server. The device then formats the input data into an appropriate data format, such as JSON, and sends it to the server via an HTTP request. This sent data includes the user name, visit information, and emotion comment. The server receives this and begins processing the next step.

[0418] Step 3:

[0419] Based on the destination information received by the server, past congestion data is retrieved from the database. The server issues an SQL query to retrieve data on the corresponding destination from a pre-stored congestion database. The destination information is the input data, and past congestion data is obtained as output data.

[0420] Step 4:

[0421] The server uses an external weather information service to obtain weather data for the day. The server sends an API request to obtain information such as the weather, temperature, and probability of precipitation for the day. The input data is the area information for the destination, and the output data is the weather information for the day.

[0422] Step 5:

[0423] The server uses an emotion engine to analyze the user's emotional state from the input data. The emotion engine processes the user's emotional comments and extracts emotional data such as "I'm tired" or "I want to relax." The input data is the emotional comments, and the output data is the emotional state.

[0424] Step 6:

[0425] The server predicts congestion based on the acquired past congestion data, weather data for the day, and user emotion data. It then executes a congestion prediction algorithm to predict future congestion levels. The input data are past congestion data, weather data, and emotion data, and the output data is the predicted future congestion level.

[0426] Step 7:

[0427] The server suggests optimal destinations based on the user's emotional state. For example, if congestion is predicted or the user is tired, it suggests a quiet cafe or other place where the user can relax. The input data are congestion prediction data and emotion data, and the output data are the suggested destinations.

[0428] Step 8:

[0429] The server formats the congestion prediction results and recommended destinations and converts them into an appropriate format, such as JSON. The formatted data includes congestion levels, weather information, and optimal destinations. The input data here are the prediction results and recommended destinations, and the output data is the formatted data.

[0430] Step 9:

[0431] The server sends the formatted data to the terminal, and returns data containing the prediction results and suggestions obtained as an HTTP response. This data is to be presented to the user on the terminal.

[0432] Step 10:

[0433] The device then displays the received prediction results and suggestions to the user. For example, a message such as "The predicted congestion level at the Shinjuku shopping mall is 70%. If you want to relax, we recommend a quiet cafe nearby." The user can then decide what to do next based on this information.

[0434] These are the processing steps for the entire system. Each step explains how the input data is processed and what output is obtained.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] [Second embodiment]

[0439] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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."

[0451] The present invention is designed to enable users to accurately grasp congestion information for their destinations in advance. Specifically, the system involves a server receiving a request from a user, predicting congestion based on past congestion data and weather data for the day, and providing the results to the user.

[0452] System configuration

[0453] 1. User inputs destination information

[0454] The user inputs information about the place they want to visit on the device, for example, "parks in Tokyo."

[0455] 2. Sending a request by the device

[0456] The device sends the information entered by the user to the server, and the request includes the location to be visited.

[0457] 3. Obtaining past congestion data from the server

[0458] The server queries the database to retrieve historical congestion data for the specified destination, including congestion levels for specific periods in the past and congestion levels during specific events.

[0459] 4. Obtaining weather data from the server

[0460] The server sends a request to an external weather service to retrieve the current day's weather data, including the weather, temperature, and chance of precipitation.

[0461] 5. Congestion prediction by the server

[0462] The server predicts congestion based on past congestion data and weather data. For example, it uses rules such as decreasing congestion on bad weather days and increasing congestion on good weather days.

[0463] 6. Server-based formatting of prediction results

[0464] The server formats the prediction results into a predetermined format, such as JSON. The formatted data includes information such as the predicted congestion level, date, time, and location.

[0465] 7. Sending prediction results from the server to the device

[0466] The server sends the formatted prediction results to the user's device.

[0467] 8. Displaying prediction results on the device

[0468] The device then displays the received prediction results to the user. For example, a message such as "The predicted congestion level for parks in Tokyo is 50%" may be displayed on the screen.

[0469] Specific examples

[0470] For example, consider the case where a user inputs "I plan to go to a park in Tokyo."

[0471] 1. User Input

[0472] The user enters "Tokyo Park" into the input field of the terminal.

[0473] 2. Request transmission by terminal

[0474] The device sends the information "parks in Tokyo" to the server.

[0475] 3. Data Acquisition by the Server

[0476] The server retrieves past congestion data for "Tokyo parks" from a database, and simultaneously retrieves current weather data from a weather information service.

[0477] 4. Server congestion prediction

[0478] The server predicts future congestion levels based on past congestion data and weather data. For example, if the weather is good, it predicts that the congestion level will be 70%.

[0479] 5. Server-based result formatting

[0480] The server formats the prediction results into a format such as "The predicted congestion level of parks in Tokyo is 70%."

[0481] 6. Sending results from the server to the device

[0482] The server sends the formatted prediction results to the terminal.

[0483] 7. Displaying results on a terminal

[0484] The device displays the message "The predicted congestion level for Tokyo parks is 70%."

[0485] In this way, the present invention is a system that helps users understand the congestion situation at their destinations in advance and plan their visits optimally. Crowd predictions are based on predefined rules and a combination of past data and weather information, making highly accurate predictions possible.

[0486] The processing flow will be explained below.

[0487] Program processing steps

[0488] Server Processing Steps

[0489] Step 1:

[0490] The server receives a request from the user containing information about the destinations visited, including the location, date, and time of the destinations entered by the user.

[0491] Step 2:

[0492] The server connects to the database and retrieves past congestion data for the specified destination. For example, it retrieves data such as the past congestion level of a specified "Tokyo park" or the congestion situation during an event.

[0493] Step 3:

[0494] The server sends a request to an external weather information service API to obtain weather data for the current day at the destination, including weather conditions, temperature, and chance of precipitation.

[0495] Step 4:

[0496] The server predicts congestion based on past congestion data and weather data. Specifically, it applies basic rules such as increasing congestion when the weather is sunny and decreasing congestion when it rains.

[0497] Step 5:

[0498] The server formats the congestion prediction results into an appropriate format (e.g., JSON), which includes information such as location, date, time, and predicted congestion level.

[0499] Step 6:

[0500] The server then sends the formatted prediction results to the user's device as an HTTP response.

[0501] Terminal processing steps

[0502] Step 1:

[0503] The user enters information about the destination into the input form on the device. For example, the user enters information such as "a park in Tokyo."

[0504] Step 2:

[0505] The terminal sends a request to the server based on the entered visit destination information. This request includes the location information of the visit destination.

[0506] Step 3:

[0507] The device receives a response from the server, which contains the congestion prediction results in JSON format.

[0508] Step 4:

[0509] The device analyzes the prediction results it receives and displays them in a format that is easy for the user to understand. For example, it displays a message on the screen saying, "The predicted congestion level for parks in Tokyo is 70%."

[0510] Specific examples

[0511] Server Processing Steps

[0512] 1. Step 1: The server receives a request from the user saying, "I have plans to go to a park in Tokyo."

[0513] 2. Step 2: The server references the database to obtain historical crowding data for parks in Tokyo.

[0514] 3. Step 3: The server sends a request to the weather information API to obtain the current day's weather data for Tokyo.

[0515] 4. Step 4: The server uses a congestion prediction algorithm to predict the congestion level based on past data and weather data. For example, if the weather is good, it predicts the congestion level to be 70%.

[0516] 5. Step 5: The server formats the prediction results into JSON format.

[0517] 6. Step 6: The server sends the formatted prediction results to the user's device.

[0518] Terminal processing steps

[0519] 1. Step 1: The user enters "Tokyo Parks" into the input field on the device.

[0520] 2. Step 2: The device sends the information about "Tokyo Parks" to the server.

[0521] 3. Step 3: The device receives the response from the server and obtains the prediction result.

[0522] 4. Step 4: The device will display the message "The predicted congestion level for Tokyo parks is 70%."

[0523] This is the specific flow of program processing by the server and terminal. This allows the user to know in advance how busy the destination will be and to plan the most appropriate visit.

[0524] Example 1

[0525] 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."

[0526] Conventional congestion prediction systems make it difficult for users to accurately predict the congestion situation at their destinations. As a result, users may end up with long waiting times at their destinations and may not be able to plan their visits properly. Another problem is that weather information is not reflected in the congestion predictions, resulting in low prediction accuracy. To solve these problems, a system is needed that allows users to accurately grasp the congestion situation at their destinations and make optimal visit plans.

[0527] 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.

[0528] In this invention, the server includes means for receiving destination information entered by a user, means for acquiring past congestion data from a database, means for acquiring weather data for the current day from an external weather information service, means for predicting congestion based on the past congestion data and weather data using a machine learning model, means for formatting the congestion prediction result in a predetermined format and transmitting it to the user's terminal, and means for displaying the congestion prediction result on the user's terminal. This allows the user to accurately grasp the congestion status of the destination in advance and make an optimal visit plan.

[0529] A "user" is an individual or group that uses this system to obtain congestion information about a destination.

[0530] A "terminal" is an electronic device that a user uses to access the system, input destination information, and display prediction results.

[0531] The "server" is a central computer facility that receives input destination information, acquires past congestion data and weather data, and performs congestion predictions.

[0532] "Destination information" refers to information about a place that the user wishes to visit, and specifically includes the location, place name, and the like.

[0533] "Past congestion data" is data that indicates the past congestion situation at the designated destination, and includes the degree of congestion during a specific period and the congestion situation during an event.

[0534] "Weather data" refers to data about the weather on the day obtained from an external weather information service, and includes the weather, temperature, probability of precipitation, and the like.

[0535] "Crowd prediction" refers to the process of predicting future congestion at a destination based on past congestion data and weather data.

[0536] A "machine learning model" is a mathematical model that learns from given data (past congestion data and weather data) and is used to predict future congestion conditions.

[0537] "Format" refers to the specific way in which data or information is organized and formatted, including, for example, JSON format.

[0538] The "prediction result" is the result data obtained after making a congestion prediction, and includes information such as the predicted congestion level, date and time, and location.

[0539] The present invention provides a system for enabling a user to accurately grasp congestion information about a destination in advance. A specific embodiment of the present invention comprises the following steps.

[0540] First, the user uses the device to input information about the place they want to visit. By entering a specific place name or location into the input field, the device prepares to send the request. For example, the user might enter "parks in Tokyo."

[0541] Next, the device sends the destination information entered by the user to the server. This request includes geographical information of the destination and user identification information. It is sent to the server in the format "location: park in Tokyo, user_id: 12345".

[0542] The server retrieves past congestion data from the database based on the entered destination information. It uses the SQL query "SELECT FROM crowd_data WHERE location = 'Tokyo Parks'" to query the database. The past congestion data includes the level of congestion during specific periods and the congestion situation during events.

[0543] In parallel, the server sends an API request to an external weather service to get the current day's weather data. This request uses the geographic coordinates of the destination. For example, the request is sent to the weather service in the format "GET / weather?lat=35.6895&lon=139.6917". The weather data includes the weather, temperature, chance of precipitation, etc.

[0544] The server performs congestion predictions based on the acquired past congestion data and weather data. A trained model is loaded using a Python machine learning library (e.g., scikit-learn). For example, the model is loaded using "from sklearn.externals import joblib", and predictions are made in the format "model.predict([[temperature, probability of precipitation, past congestion level]])". This takes into account the tendency for congestion to increase on good weather days and decrease on bad weather days.

[0545] The prediction results are formatted by the server in a predetermined format. The formatted data includes information such as the predicted crowd level, date, time, and location. For example, it is formatted in the format "{'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}".

[0546] The server sends the formatted prediction results to the user's device as an HTTP response in JSON format, specifically, "{'status': 'success', 'data': {'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}}".

[0547] The device then displays the received prediction results to the user. At this time, it uses a UI component that parses the prediction results and displays them in a visually easy-to-understand format for the user. For example, a message such as "The predicted congestion level for parks in Tokyo is 70%" is displayed.

[0548] This system allows users to accurately understand the congestion situation at their destinations in advance and plan their visits optimally.

[0549] Specific examples

[0550] Consider the case where a user inputs "I'm planning to go to a park in Tokyo."

[0551] 1. User input: The user enters "Parks in Tokyo" into the input field on the device.

[0552] 2. Request sent by device: The device sends a request to the server: "location: park in Tokyo, user_id: 12345".

[0553] 3. Data retrieval by the server: The server executes the SQL query "SELECT FROM crowd_data WHERE location = 'Tokyo parks'" to retrieve historical crowd data from the database, and simultaneously sends the API request "GET / weather?lat=35.6895&lon=139.6917" to the weather information service to retrieve weather data.

[0554] 4. Congestion prediction by the server: The server executes the code "model.predict([[temperature, probability of precipitation, past congestion level]])" to predict future congestion levels.

[0555] 5. Server formatting: The server formats the prediction results into the format "{'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}".

[0556] 6. Sending results from the server to the device: The server sends the prediction results to the device in JSON format: {'status': 'success', 'data': {'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}}.

[0557] 7. Displaying the results on the device: The device displays on the screen, "The predicted congestion level of Tokyo parks is 70%."

[0558] Prompt Sentence Examples

[0559] "I'd like to know the crowd forecast for parks in Tokyo. Today's weather is sunny."

[0560] This system allows users to understand the congestion situation at their destinations in advance and plan their visits optimally.

[0561] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0562] Step 1:

[0563] The user uses the device to input information about the place they want to visit. In this input field, they input a specific place name, such as "Tokyo Park," and the information is displayed on the device screen in real time. The input data is "location: Tokyo Park."

[0564] Step 2:

[0565] The device sends the destination information entered by the user to the server. This request includes the geographical information of the destination and the user identification information, and is sent in the format "location: park in Tokyo, user_id: 12345".

[0566] Step 3:

[0567] The server retrieves past congestion data from the database based on the destination information received from the device. It executes the SQL query "SELECT FROM crowd_data WHERE location = 'Tokyo parks'" to retrieve the relevant congestion data. This data includes the level of congestion over a certain period of time in the past and congestion conditions during events.

[0568] Step 4:

[0569] Based on the visitor information, the server sends an API request to an external weather information service to obtain weather data for the day. The request is sent in the format "GET / weather?lat=35.6895&lon=139.6917", which retrieves data such as weather, temperature, and precipitation probability.

[0570] Step 5:

[0571] The server uses the acquired past congestion data and weather data to make congestion predictions. To do this, it uses a Python machine learning library (e.g., scikit-learn) and a trained model. For example, it executes "model.predict([[temperature, probability of precipitation, past congestion level]])" to predict future congestion levels. The output is the predicted congestion level.

[0572] Step 6:

[0573] The server formats the prediction results in JSON format. The results include the location of the visit, the predicted crowd level, and the predicted date and time. Specifically, the results are formatted in the format "{'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}". The formatted output is the predicted data in JSON format.

[0574] Step 7:

[0575] The server sends the formatted prediction result data to the user's device as an HTTP response. This response is in JSON format, like this: {'status': 'success', 'data': {'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}}.

[0576] Step 8:

[0577] The device analyzes the received prediction results and displays them to the user. The device uses a UI component to parse the prediction results and display them in a visually easy-to-understand format for the user. Specifically, the device displays a message on the screen saying, "The predicted congestion level for parks in Tokyo is 70%."

[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] Conventional methods for finding out the congestion situation at a destination have the problem that it is difficult to accurately grasp the congestion situation at a store you plan to visit in advance. In particular, there is a lack of systems that can provide information on the impact of weather on congestion situations or specific congestion forecasts, so users are often at the mercy of the congestion at their destination.

[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 receiving destination information entered by the user, means for retrieving past congestion data from a database based on the destination information, means for retrieving weather data for the current day from an external weather information service based on the destination information, means for predicting congestion based on the past congestion data and the weather data for the current day, and means for providing and displaying the congestion prediction result to the user, thereby enabling the user to grasp the congestion status of the destination in advance and plan the optimal visiting time.

[0583] The "means for receiving destination information input by the user" is a means for the user to input information about places he or she wants to visit and for the system to receive that information.

[0584] The "means for acquiring past congestion data from a database" refers to a means for acquiring the data from a database that stores past data on the congestion status of destinations.

[0585] The "means for obtaining today's weather data from an external weather information service" refers to a means for sending a request to a third-party weather information service and obtaining data regarding the weather of the day.

[0586] The "means for predicting congestion" is a means for predicting future congestion conditions based on acquired past congestion data and weather data for the day.

[0587] The "means for providing and displaying the congestion prediction results to the user" refers to a means for visually displaying the results of the congestion prediction that has been performed to the user in an easy-to-understand manner.

[0588] "Formatting means" refers to a means for converting prediction results into a specified format such as JSON format.

[0589] The present invention is configured to enable users to accurately grasp congestion information for destinations in advance. Specifically, the system is one in which a server receives a request from a user, predicts congestion based on past congestion data and weather data for the day, and provides the result to the user. An embodiment of this system is described in detail below.

[0590] First, the user inputs information about the place they want to visit on their smartphone. For example, the user might input "shopping mall." Next, the user's device (smartphone) sends the input information to the server. The request includes the location of the place to be visited.

[0591] The server first queries the database to obtain past congestion data for the specified destination. This data includes congestion levels during specific periods in the past and congestion conditions during specific events. The server also sends a request to an external weather information service to obtain weather data for the current day. This data includes weather, temperature, and probability of precipitation.

[0592] The server makes congestion predictions based on the acquired past congestion data and weather data. For example, it applies rules such as congestion decreasing on bad weather days and increasing on good weather days. This allows users to understand the future congestion situation at their destination.

[0593] The server then formats the prediction results into a predetermined format, such as JSON. The formatted data includes information such as the predicted congestion level, date, time, and location. The server then sends the formatted prediction results to the user's device. The device visually displays the received prediction results to the user. For example, a message such as "The predicted congestion level of the shopping mall is 70%" may be displayed on the screen.

[0594] As a concrete example, consider the case where a user inputs "I plan to go to the shopping mall." When the user inputs "shopping mall" into the input field, the device sends this information to the server. The server retrieves past congestion data for "shopping mall" from a database, and at the same time retrieves current weather data from a weather information service. The server predicts future congestion levels based on this data, and if the weather is good, for example, predicts that the congestion level will be 70%. The formatted prediction result is converted into the format "The predicted congestion level at the shopping mall is 70%" and sent from the server to the device. The device displays the received data on the screen.

[0595] The following are examples of prompt sentences:

[0596] When a user types "I want to go to the shopping mall," the application predicts the level of congestion based on the mall's past congestion data and weather data, and displays the predicted level of congestion.

[0597] In this way, the system of the present invention helps users understand the congestion situation at their destinations in advance and plan their visits appropriately. It is possible to provide highly accurate congestion forecasts using hardware and software such as servers, smartphones, databases, and weather information services.

[0598] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0599] Step 1:

[0600] The user inputs the location of the destination using the smartphone.

[0601] Input: Text input by the user, such as "shopping mall."

[0602] Output: A request by a device to send input information.

[0603] Step 2:

[0604] The user's terminal transmits the entered visiting destination information to the server.

[0605] Input: Visiting destination information from the user.

[0606] Output: Sending a request to the server.

[0607] Step 3:

[0608] The server retrieves past congestion data for the designated destination from the database.

[0609] Input: The destination information submitted (e.g., "shopping mall").

[0610] Output: Historical congestion data retrieved from the database.

[0611] Step 4:

[0612] The server sends a request to an external weather information service to obtain the current day's weather data.

[0613] Input: The destination information submitted (e.g., "shopping mall").

[0614] Output: Weather data obtained from weather information services.

[0615] Step 5:

[0616] Congestion predictions are made based on past congestion data and weather data acquired by the server.

[0617] Input: Obtained historical congestion and weather data.

[0618] Output: Crowd prediction result (e.g., "The predicted crowding level of the shopping mall is 70%").

[0619] Specific operation: The system calculates to increase the congestion level on good weather days and decrease it on bad weather days.

[0620] Step 6:

[0621] The server formats the prediction results into a specified format such as JSON.

[0622] Input: Congestion prediction results.

[0623] Output: Prediction results in JSON format.

[0624] Step 7:

[0625] The server sends the formatted prediction results to the user's device.

[0626] Input: Formatted prediction results (JSON format).

[0627] Output: Prediction results are sent to the user's device.

[0628] Step 8:

[0629] The prediction results received by the user's terminal are displayed on the screen.

[0630] Input: Submitted prediction results (JSON format).

[0631] Output: A screen that displays "The predicted occupancy level of the shopping mall is 70%."

[0632] 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.

[0633] This invention is a system that allows users to create optimal visit plans based on congestion information at destinations and their own emotions. In particular, by combining this system with an emotion engine, it is possible to predict congestion and suggest destinations based on the user's emotional state.

[0634] System configuration

[0635] 1. User inputs destination information

[0636] The user inputs information about the place they want to visit on the terminal, such as "art museum" or "theme park."

[0637] 2. Sending a request by the device

[0638] The terminal sends a request including the information entered by the user to the server. The request also includes information about the destination.

[0639] 3. Obtaining past congestion data from the server

[0640] The server retrieves historical congestion data for the specified destination from the database, including congestion levels over a certain period of time in the past and congestion levels during specific events.

[0641] 4. Obtaining weather data from the server

[0642] The server retrieves the current day's weather data from an external weather information service, including the weather, temperature, and chance of precipitation.

[0643] 5. Acquiring user emotion data using the emotion engine

[0644] The emotion engine analyzes emotions from user input data or voice data, for example, extracting emotional states from user-entered comments or voice messages.

[0645] 6. Congestion prediction by the server

[0646] The server predicts congestion based on past congestion data, weather data, and user emotion data. For example, if the user is depressed, it will predict that less crowded locations will be prioritized.

[0647] 7. Server-suggested destinations

[0648] Based on the results of emotion analysis, the system suggests places to visit based on the user's emotional state. For example, if the user is looking to relax, it will suggest a quiet park.

[0649] 8. Server-based formatting of prediction results

[0650] The server then formats the congestion prediction results and suggested destinations into an appropriate format (e.g., JSON). This data includes the congestion level, date, time, location, and suggested destinations based on the user's emotional state.

[0651] 9. Sending prediction results and suggestions from the server to the device

[0652] The server sends the formatted prediction results and suggested destination data to the user's device.

[0653] 10. Displaying prediction results and suggestions on the device

[0654] The device then displays the received prediction results and suggested places to visit to the user. For example, a message such as "The museum is predicted to be 50% crowded. There is also a quiet park recommended for a change of pace" may be displayed on the screen.

[0655] Specific examples

[0656] Consider the case where a user inputs, "I'm planning to go to a theme park in Tokyo, but I've been feeling tired lately so I'd prefer a quiet place."

[0657] 1. User Input

[0658] The user enters "Tokyo theme park" into the input field on the device and adds the comment "I've been feeling tired lately."

[0659] 2. Request transmission by terminal

[0660] The device sends information including "Tokyo theme parks" and comments to the server.

[0661] 3. Data Acquisition by the Server

[0662] The server retrieves past congestion data for theme parks in Tokyo from a database, and simultaneously retrieves the current day's weather data from a weather information service.

[0663] 4. Emotion analysis using an emotion engine

[0664] The emotion engine analyzes the user's comments and recognizes that the user is "tired."

[0665] 5. Server-based congestion prediction and proposal

[0666] The server uses a congestion prediction algorithm to predict congestion levels based on past data and weather data. It also suggests places to visit based on the user's emotions. For example, if a theme park is 50% crowded, it will suggest a park where you can spend some quiet time.

[0667] 6. Server-based Result Formatting

[0668] The server formats the prediction results into the following format: "The predicted congestion level at Tokyo's theme parks is 50%. There are also quiet parks that are recommended for a change of pace."

[0669] 7. Sending results from the server to the device

[0670] The server sends the formatted prediction results and suggestions to the user's device.

[0671] 8. Displaying results on a terminal

[0672] The device displays the message, "The predicted congestion level at Tokyo's theme parks is 50%. There are also quiet parks that are recommended for a change of pace."

[0673] In this way, the present invention is a system that supports visit plans that result in even higher user satisfaction by predicting congestion and suggesting places to visit that take into account the user's emotional state.

[0674] The processing flow will be explained below.

[0675] Program processing steps

[0676] Server Processing Steps

[0677] Step 1:

[0678] The server receives a request from the user, which includes the destination information and a comment. The comment can convey the user's emotional state, such as if they are tired.

[0679] Step 2:

[0680] The server retrieves past congestion data for the specified destination from the database, including congestion levels during specific periods in the past and congestion conditions during events.

[0681] Step 3:

[0682] The server sends a request to an external weather information service API to obtain weather data for the day at the destination, including weather conditions, temperature, and chance of precipitation.

[0683] Step 4:

[0684] The server uses an emotion engine to analyze the user's comments and determine their emotions. For example, it can recognize that a comment such as "I'm tired" indicates that the user is seeking relaxation.

[0685] Step 5:

[0686] The server predicts congestion based on past congestion data, weather data, and user emotion data. The emotion data is used to determine the user's preferred situation.

[0687] Step 6:

[0688] The server recommends optimal destinations based on the crowding prediction results and the user's emotional state. For example, if the predicted crowding rate for a theme park is 50%, the server may recommend a quiet park.

[0689] Step 7:

[0690] The server then formats the predictions and suggestions into an appropriate format (e.g., JSON). This data includes suggestions based on crowding, date, time, location, and the user's emotional state.

[0691] Step 8:

[0692] The server then sends the formatted prediction results and suggestions to the user's device as an HTTP response.

[0693] Terminal processing steps

[0694] Step 1:

[0695] The user enters information about the place they visited and their emotional state as a comment into the input form on their device. For example, they might enter "a theme park in Tokyo" and comment "I've been feeling tired lately."

[0696] Step 2:

[0697] The device sends a request to the server based on the entered visit information and comments. This request includes the visit destination and emotional state.

[0698] Step 3:

[0699] The device receives a response from the server, which includes the congestion prediction results and suggested places to visit.

[0700] Step 4:

[0701] The device analyzes the prediction results and suggestions received and displays them in a format that is easy for the user to understand. For example, it displays a message on the screen saying, "The predicted congestion level at Tokyo's theme parks is 50%. There are also some quiet parks that we recommend for a change of pace."

[0702] Specific examples

[0703] Server Processing Steps

[0704] 1. Step 1: The server receives a request from the user saying, "I'm planning to go to a theme park in Tokyo, but I'm tired recently and would prefer a quiet place."

[0705] 2. Step 2: The server references the database to obtain historical crowd data for theme parks in Tokyo.

[0706] 3. Step 3: The server sends a request to the weather information API to obtain the current day's weather data for Tokyo.

[0707] 4. Step 4: The server uses the emotion engine to recognize the "tired" state from the user's comments.

[0708] 5. Step 5: The server uses a congestion prediction algorithm to predict the level of congestion based on past data and weather data. For example, if the weather is good, it predicts the level of congestion to be 50%. It also determines from emotional data that people prefer "quiet places."

[0709] 6. Step 6: The server combines the crowd prediction results with the emotion data and suggests, "The predicted crowding level at theme parks in Tokyo is 50%, but there are also quiet parks that are recommended for a change of pace."

[0710] 7. Step 7: The server formats the prediction results and suggestions into JSON format.

[0711] 8. Step 8: The server sends the formatted prediction results and suggestions to the user's device.

[0712] Terminal processing steps

[0713] 1. Step 1: The user types "Tokyo theme parks" into the device's input field and comments "I've been feeling tired lately."

[0714] 2. Step 2: The device sends information including "Tokyo theme parks" and "I've been feeling tired lately" to the server.

[0715] 3. Step 3: The device receives the response from the server and obtains the prediction results and suggestions.

[0716] 4. Step 4: The device will display the following message on the screen: "Tokyo theme parks are expected to be 50% crowded. There are also some quiet parks that are recommended for a change of pace."

[0717] The above is the specific program processing flow of the system that combines the emotion engine. As a result, users can receive congestion predictions and suggested places to visit that reflect their own emotional state, allowing them to create optimal travel plans.

[0718] Example 2

[0719] 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."

[0720] In today's world, predicting crowd levels at specific locations and events is important to users, but existing systems are unable to provide optimal suggestions that take into account the user's emotional state. Furthermore, it is difficult to make advanced predictions that combine weather information and past crowd data, making it difficult to provide visit plans that will satisfy users.

[0721] 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.

[0722] In this invention, the server includes means for receiving destination information and emotional state input by the user, means for retrieving past congestion data from a database, means for retrieving weather data for the current day from an external weather information service, means for analyzing the emotional state and retrieving the user's emotional data, means for predicting congestion based on the past congestion data, weather data, and emotional data, means for providing destination suggestions based on the congestion prediction result and the user's emotional state, means for formatting the prediction result and the suggested destinations into a predetermined format, and means for presenting the prediction result and the suggested destinations to the user. This enables congestion prediction and suggestions of optimal destinations that take the user's emotional state into consideration.

[0723] A "user" is an entity that utilizes the system to input destination information and emotional state.

[0724] "Destination information" is information about places and events that the user plans to visit.

[0725] "Emotional state" is information that indicates the user's current psychological state.

[0726] A "terminal" is a device that allows a user to input and send information, and includes smartphones, personal computers, etc.

[0727] A "request" refers to an operation of sending data, including information entered by a user, to a server.

[0728] A "server" is a computer system that receives a request and processes and provides the required data.

[0729] A "database" is a system that stores and manages information such as past congestion data.

[0730] A "weather information service" is an external service that provides weather information such as the weather, temperature, and probability of precipitation for the day.

[0731] An "emotion engine" is a system that analyzes emotions from user input data and voice data.

[0732] "Crowd prediction" refers to estimating future congestion levels at a destination based on past congestion data, weather data, and emotion data.

[0733] "Suggesting destinations" refers to suggesting optimal destinations based on the congestion prediction results and the user's emotional state.

[0734] "Formatting the prediction results" refers to the operation of converting the congestion prediction results and suggested destinations into a fixed format.

[0735] A "format" is data organized in a specific way, typically JSON format.

[0736] A "generative AI model" is an artificial intelligence model used to analyze data and make predictions.

[0737] "Comment" refers to text information such as an emotional state entered by a user.

[0738] The present invention is a system that allows users to create optimal visit plans based on congestion information at destinations and their own emotions. This system uses an emotion engine to predict congestion and suggest destinations based on the user's emotional state. The specific configuration and operation are as follows.

[0739] System configuration

[0740] Hardware and Software

[0741] 1. User Device:

[0742] Devices such as smartphones, tablets, and personal computers are used as user terminals, providing an interface for users to input visiting information and emotional states.

[0743] 2. Server:

[0744] A server system with powerful computing power is required to manage the database, communicate with weather information services, calculate congestion forecasts, analyze sentiment, and format the results.

[0745] 3. Database:

[0746] A database is used to store and manage past congestion data, including congestion levels over a certain period of time in the past and congestion conditions during specific events.

[0747] 4. Emotion Engine:

[0748] There is a need for an emotion engine that uses NLP (Natural Language Processing) techniques to analyze emotions from user input data and voice data.

[0749] 5. Weather information services:

[0750] Use an API to obtain real-time weather data from external weather information services.

[0751] 6. Generative AI Models:

[0752] A generative AI model used to integrate multiple data sets and make crowd predictions and suggest places to visit.

[0753] Operation explanation

[0754] The user inputs destination information and emotional state using the device. For example, the user inputs, "I'm planning to go to a theme park in Tokyo, but I've been feeling tired lately, so I'd prefer a quiet place." This information is then sent from the device to the server.

[0755] The server does the following:

[0756] 1. Obtain historical crowd data for theme parks in Tokyo from the database.

[0757] 2. Obtain the current day's weather data for Tokyo from an external weather information service.

[0758] 3. Use the emotion engine to analyze the user's emotion data. In this example, it recognizes that the user is in a "tired" state.

[0759] 4. Crowd forecasting based on historical crowd data, weather data, and sentiment data. Generative AI models are used to predict current and future crowd levels at destinations.

[0760] 5. Creates recommendations for places to visit based on the crowding prediction results and the user's emotional state. In this example, the system generates recommendations such as, "The predicted crowding rate at theme parks in Tokyo is 50%. There are also quiet parks recommended for a change of pace."

[0761] 6. Format the prediction results and destination suggestions into an appropriate format (e.g., JSON).

[0762] The formatted data is sent from the server to the device, which then displays it to the user. For example, a message such as "The predicted congestion level at Tokyo's theme parks is 50%. There are also some quiet parks recommended for a change of pace" may be displayed on the device screen.

[0763] Specific examples

[0764] Let's say a user types, "I'm planning to go to a theme park in Tokyo, but I'm tired these days and would prefer a quiet place." In this case, the following prompt sentence is used:

[0765] Example prompt sentence:

[0766] "I'm planning to go to a theme park in Tokyo, but I've been feeling tired lately so I'd prefer a quiet place."

[0767] Based on this information, the server processes the data and proposes an optimal visit plan to the user. In this way, the present invention supports visit plans that increase user satisfaction.

[0768] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0769] Step 1:

[0770] The user enters the destination information and emotional comment into the input fields on the device. Specifically, they enter "Tokyo theme park" and "I've been feeling tired lately," and press the send button. The input information is converted to JSON format on the device. Input: Destination information and emotional comment. Output: Request data in JSON format.

[0771] Step 2:

[0772] The device sends a JSON-formatted request containing the entered information to the server as an HTTP POST request. Specifically, the device parses the user's input into JSON format and sends the data to the specified API endpoint. Input: JSON-formatted request data. Output: HTTP POST request to the server.

[0773] Step 3:

[0774] The server receives the request and retrieves past congestion data from the database based on the destination information. Specifically, it analyzes the destination information in the request and executes an SQL query based on that information to extract congestion data. Input: Destination information. Output: Past congestion data.

[0775] Step 4:

[0776] The server calls the API of an external weather information service to obtain weather data for the day. It sends an API request and analyzes the received data. Specific weather information includes the weather, temperature, and probability of precipitation. Input: API request. Output: Weather data.

[0777] Step 5:

[0778] The server uses an emotion engine to obtain the user's emotion data. Specifically, it analyzes the entered emotion comment and identifies the emotion using natural language processing (NLP) technology. In this case, the comment "I've been tired lately" is analyzed and evaluated as a "tired" state. Input: Emotion comment. Output: Emotion data.

[0779] Step 6:

[0780] The server combines past congestion data, weather data, and emotion data to perform congestion prediction. Using a generative AI model, multiple data sets are input and prediction results are generated. At this time, emotion data is taken into consideration and destinations with low congestion levels are recommended preferentially. Input: Past congestion data, weather data, emotion data. Output: Congestion prediction results.

[0781] Step 7:

[0782] The server generates optimal destination suggestions for the user based on the congestion prediction results and emotion analysis results. Specifically, it makes suggestions that match the user's emotional state, such as less crowded places or quiet parks. Input: Congestion prediction results, emotion analysis results. Output: Destination suggestions.

[0783] Step 8:

[0784] The server formats the congestion prediction results and visit suggestions into an appropriate format (e.g., JSON format). This is the process of converting the results into a format that can be easily analyzed and displayed on the user's device. Input: Congestion prediction results, visit suggestions. Output: Formatted prediction results and suggestion data.

[0785] Step 9:

[0786] The server returns the formatted prediction results and suggestions to the user's device. This is sent in the form of an HTTP response, which the device receives. Input: Formatted prediction results and suggestion data. Output: HTTP response to the device.

[0787] Step 10:

[0788] The device displays the prediction results and suggested places to visit received by the device to the user. For example, a message such as "The predicted congestion level at theme parks in Tokyo is 50%. There are also quiet parks recommended for a change of pace" is displayed on the screen. Input: HTTP response data. Output: Message to be displayed to the user.

[0789] (Application example 2)

[0790] 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."

[0791] Conventional congestion prediction systems for destinations do not take into account the user's emotional state, so the selection of destinations may not be appropriate for the user's psychological state. Furthermore, there is a need for a system that can not only predict congestion but also suggest optimal destinations based on the user's current emotions. Therefore, it is necessary to develop a new system that can predict congestion and recommend optimal destinations while taking into account the user's emotions.

[0792] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving destination information and emotion information input by the user, means for acquiring past congestion data from a database based on the destination information, means for acquiring weather data for the current day from an external weather information service based on the destination information, means for predicting congestion based on the acquired past congestion data, weather data for the current day, and user emotion data, means for recommending optimal destinations based on the user's emotional state, and means for providing the user with the congestion prediction results and recommendation results. This makes it possible to predict congestion and suggest optimal destinations that take the user's emotional state into consideration.

[0793] "Destination information entered by the user" is information about a specific place or facility that the user wishes to visit.

[0794] "Emotion information" is information about the user's current emotional state and psychological situation.

[0795] "Past congestion data" refers to data relating to past congestion at a particular destination.

[0796] A "database" is a system for storing, managing, and searching data.

[0797] A "weather information service" is a service that provides information about weather and climate from an external source.

[0798] "Weather data for the current day" refers to data related to the climate, such as the weather, temperature, and probability of precipitation, for a specific day.

[0799] The "emotion engine" is an engine for analyzing the emotional state from text and voice input by the user.

[0800] A "crowd forecast" means a method or algorithm for predicting the crowding level of a destination based on past data, current weather conditions, and the user's emotional state.

[0801] A "recommendation means" is a method or system for suggesting optimal destinations to a user based on the user's emotional state and congestion prediction results.

[0802] The "means for providing predicted results and recommended results" refers to a method or system for providing the results generated by the server to the user in an easy-to-understand manner.

[0803] A "format" is a rule or method for arranging data or information into a particular structure or form.

[0804] The present invention provides a system for predicting congestion and suggesting optimal destinations that take into account the emotional state of a user. This system is implemented using the following hardware and software.

[0805] Hardware and Software

[0806] Smartphone (terminal): The user enters destination information and emotion information, and the results are displayed.

[0807] Server: Receives information from users and processes the data.

[0808] Database (SQLite): Stores and manages past congestion data.

[0809] External weather information service (Weather API): Obtains the current day's weather data.

[0810] Emotion engine: Analyzes the user's emotional state from input data.

[0811] Programming language (Python) and framework (Flask): Performs server-side processing.

[0812] Program processing

[0813] 1. Receiving user input:

[0814] The server receives destination information and emotion information from the smartphone terminal. The user inputs the place they want to visit and their current emotional state.

[0815] 2. Data Acquisition:

[0816] The server acquires past congestion data from a database based on the received destination information.

[0817] In addition, weather data for the day is obtained using an external weather information service.

[0818] 3. Emotion analysis:

[0819] The emotion engine analyzes the user's input data and extracts the user's current emotional state.

[0820] 4. Congestion prediction and destination suggestions:

[0821] The server makes congestion predictions based on acquired past congestion data, weather data for the day, and user emotion data.

[0822] To suggest the most suitable destination according to a user's emotional state.

[0823] 5. Results provided:

[0824] The server formats the congestion prediction results and suggested places to visit into an appropriate format and sends them to the smartphone device.

[0825] The terminal displays the received results to the user.

[0826] Specific examples

[0827] For example, if a user inputs "I want to go to the shopping mall in Shinjuku, but I've been really tired lately," the server will process the request based on the following prompt:

[0828] "A user types, 'I want to go to a shopping mall in Shinjuku, but I've been feeling really tired lately.' Please predict how crowded it will be and suggest a quiet place so I can relax."

[0829] In this case, the server predicts congestion at shopping malls in Shinjuku based on past congestion data and the weather data for the day, and suggests quiet cafes where users can relax based on their emotional information. These suggestions are displayed on the smartphone device and used as a reference when the user chooses the best place to visit.

[0830] The above is an embodiment of the present invention. This system makes it possible to predict congestion and suggest places to visit while taking into account the emotional state of the user.

[0831] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0832] Step 1:

[0833] The user inputs destination information and emotional information on their smartphone. Specifically, the user inputs "the place they want to visit" (e.g., a shopping mall in Shinjuku) and "emotional state" (e.g., "I've been feeling very tired lately") into the application's input fields. The input data includes destination information and emotional comments. The input data here is sent to the server in the next step.

[0834] Step 2:

[0835] The device sends the entered visit information and emotion information to the server. The device then formats the input data into an appropriate data format, such as JSON, and sends it to the server via an HTTP request. This sent data includes the user name, visit information, and emotion comment. The server receives this and begins processing the next step.

[0836] Step 3:

[0837] Based on the destination information received by the server, past congestion data is retrieved from the database. The server issues an SQL query to retrieve data on the corresponding destination from a pre-stored congestion database. The destination information is the input data, and past congestion data is obtained as output data.

[0838] Step 4:

[0839] The server uses an external weather information service to obtain weather data for the day. The server sends an API request to obtain information such as the weather, temperature, and probability of precipitation for the day. The input data is the area information for the destination, and the output data is the weather information for the day.

[0840] Step 5:

[0841] The server uses an emotion engine to analyze the user's emotional state from the input data. The emotion engine processes the user's emotional comments and extracts emotional data such as "I'm tired" or "I want to relax." The input data is the emotional comments, and the output data is the emotional state.

[0842] Step 6:

[0843] The server predicts congestion based on the acquired past congestion data, weather data for the day, and user emotion data. It then executes a congestion prediction algorithm to predict future congestion levels. The input data are past congestion data, weather data, and emotion data, and the output data is the predicted future congestion level.

[0844] Step 7:

[0845] The server suggests optimal destinations based on the user's emotional state. For example, if congestion is predicted or the user is tired, it suggests a quiet cafe or other place where the user can relax. The input data are congestion prediction data and emotion data, and the output data are the suggested destinations.

[0846] Step 8:

[0847] The server formats the congestion prediction results and recommended destinations and converts them into an appropriate format, such as JSON. The formatted data includes congestion levels, weather information, and optimal destinations. The input data here are the prediction results and recommended destinations, and the output data is the formatted data.

[0848] Step 9:

[0849] The server sends the formatted data to the terminal, and returns data containing the prediction results and suggestions obtained as an HTTP response. This data is to be presented to the user on the terminal.

[0850] Step 10:

[0851] The device then displays the received prediction results and suggestions to the user. For example, a message such as "The predicted congestion level at the Shinjuku shopping mall is 70%. If you want to relax, we recommend a quiet cafe nearby." The user can then decide what to do next based on this information.

[0852] These are the processing steps for the entire system. Each step explains how the input data is processed and what output is obtained.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] [Third embodiment]

[0857] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0858] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0859] 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).

[0860] 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.

[0861] 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.

[0862] 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).

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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."

[0869] The present invention is designed to enable users to accurately grasp congestion information for their destinations in advance. Specifically, the system involves a server receiving a request from a user, predicting congestion based on past congestion data and weather data for the day, and providing the results to the user.

[0870] System configuration

[0871] 1. User inputs destination information

[0872] The user inputs information about the place they want to visit on the device, for example, "parks in Tokyo."

[0873] 2. Sending a request by the device

[0874] The device sends the information entered by the user to the server, and the request includes the location to be visited.

[0875] 3. Obtaining past congestion data from the server

[0876] The server queries the database to retrieve historical congestion data for the specified destination, including congestion levels for specific periods in the past and congestion levels during specific events.

[0877] 4. Obtaining weather data from the server

[0878] The server sends a request to an external weather service to retrieve the current day's weather data, including the weather, temperature, and chance of precipitation.

[0879] 5. Congestion prediction by the server

[0880] The server predicts congestion based on past congestion data and weather data. For example, it uses rules such as decreasing congestion on bad weather days and increasing congestion on good weather days.

[0881] 6. Server-based formatting of prediction results

[0882] The server formats the prediction results into a predetermined format, such as JSON. The formatted data includes information such as the predicted congestion level, date, time, and location.

[0883] 7. Sending prediction results from the server to the device

[0884] The server sends the formatted prediction results to the user's device.

[0885] 8. Displaying prediction results on the device

[0886] The device then displays the received prediction results to the user. For example, a message such as "The predicted congestion level for parks in Tokyo is 50%" may be displayed on the screen.

[0887] Specific examples

[0888] For example, consider the case where a user inputs "I plan to go to a park in Tokyo."

[0889] 1. User Input

[0890] The user enters "Tokyo Park" into the input field of the terminal.

[0891] 2. Request transmission by terminal

[0892] The device sends the information "parks in Tokyo" to the server.

[0893] 3. Data Acquisition by the Server

[0894] The server retrieves past congestion data for "Tokyo parks" from a database, and simultaneously retrieves current weather data from a weather information service.

[0895] 4. Server congestion prediction

[0896] The server predicts future congestion levels based on past congestion data and weather data. For example, if the weather is good, it predicts that the congestion level will be 70%.

[0897] 5. Server-based result formatting

[0898] The server formats the prediction results into a format such as "The predicted congestion level of parks in Tokyo is 70%."

[0899] 6. Sending results from the server to the device

[0900] The server sends the formatted prediction results to the terminal.

[0901] 7. Displaying results on a terminal

[0902] The device displays the message "The predicted congestion level for Tokyo parks is 70%."

[0903] In this way, the present invention is a system that helps users understand the congestion situation at their destinations in advance and plan their visits optimally. Crowd predictions are based on predefined rules and a combination of past data and weather information, making highly accurate predictions possible.

[0904] The processing flow will be explained below.

[0905] Program processing steps

[0906] Server Processing Steps

[0907] Step 1:

[0908] The server receives a request from the user containing information about the destinations visited, including the location, date, and time of the destinations entered by the user.

[0909] Step 2:

[0910] The server connects to the database and retrieves past congestion data for the specified destination. For example, it retrieves data such as the past congestion level of a specified "Tokyo park" or the congestion situation during an event.

[0911] Step 3:

[0912] The server sends a request to an external weather information service API to obtain weather data for the current day at the destination, including weather conditions, temperature, and chance of precipitation.

[0913] Step 4:

[0914] The server predicts congestion based on past congestion data and weather data. Specifically, it applies basic rules such as increasing congestion when the weather is sunny and decreasing congestion when it rains.

[0915] Step 5:

[0916] The server formats the congestion prediction results into an appropriate format (e.g., JSON), which includes information such as location, date, time, and predicted congestion level.

[0917] Step 6:

[0918] The server then sends the formatted prediction results to the user's device as an HTTP response.

[0919] Terminal processing steps

[0920] Step 1:

[0921] The user enters information about the destination into the input form on the device. For example, the user enters information such as "a park in Tokyo."

[0922] Step 2:

[0923] The terminal sends a request to the server based on the entered visit destination information. This request includes the location information of the visit destination.

[0924] Step 3:

[0925] The device receives a response from the server, which contains the congestion prediction results in JSON format.

[0926] Step 4:

[0927] The device analyzes the prediction results it receives and displays them in a format that is easy for the user to understand. For example, it displays a message on the screen saying, "The predicted congestion level for parks in Tokyo is 70%."

[0928] Specific examples

[0929] Server Processing Steps

[0930] 1. Step 1: The server receives a request from the user saying, "I have plans to go to a park in Tokyo."

[0931] 2. Step 2: The server references the database to obtain historical crowding data for parks in Tokyo.

[0932] 3. Step 3: The server sends a request to the weather information API to obtain the current day's weather data for Tokyo.

[0933] 4. Step 4: The server uses a congestion prediction algorithm to predict the congestion level based on past data and weather data. For example, if the weather is good, it predicts the congestion level to be 70%.

[0934] 5. Step 5: The server formats the prediction results into JSON format.

[0935] 6. Step 6: The server sends the formatted prediction results to the user's device.

[0936] Terminal processing steps

[0937] 1. Step 1: The user enters "Tokyo Parks" into the input field on the device.

[0938] 2. Step 2: The device sends the information about "Tokyo Parks" to the server.

[0939] 3. Step 3: The device receives the response from the server and obtains the prediction result.

[0940] 4. Step 4: The device will display the message "The predicted congestion level for Tokyo parks is 70%."

[0941] This is the specific flow of program processing by the server and terminal. This allows the user to know in advance how busy the destination will be and to plan the most appropriate visit.

[0942] Example 1

[0943] 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."

[0944] Conventional congestion prediction systems make it difficult for users to accurately predict the congestion situation at their destinations. As a result, users may end up with long waiting times at their destinations and may not be able to plan their visits properly. Another problem is that weather information is not reflected in the congestion predictions, resulting in low prediction accuracy. To solve these problems, a system is needed that allows users to accurately grasp the congestion situation at their destinations and make optimal visit plans.

[0945] 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.

[0946] In this invention, the server includes means for receiving destination information entered by a user, means for acquiring past congestion data from a database, means for acquiring weather data for the current day from an external weather information service, means for predicting congestion based on the past congestion data and weather data using a machine learning model, means for formatting the congestion prediction result in a predetermined format and transmitting it to the user's terminal, and means for displaying the congestion prediction result on the user's terminal. This allows the user to accurately grasp the congestion status of the destination in advance and make an optimal visit plan.

[0947] A "user" is an individual or group that uses this system to obtain congestion information about a destination.

[0948] A "terminal" is an electronic device that a user uses to access the system, input destination information, and display prediction results.

[0949] The "server" is a central computer facility that receives input destination information, acquires past congestion data and weather data, and performs congestion predictions.

[0950] "Destination information" refers to information about a place that the user wishes to visit, and specifically includes the location, place name, and the like.

[0951] "Past congestion data" is data that indicates the past congestion situation at the designated destination, and includes the degree of congestion during a specific period and the congestion situation during an event.

[0952] "Weather data" refers to data about the weather on the day obtained from an external weather information service, and includes the weather, temperature, probability of precipitation, and the like.

[0953] "Crowd prediction" refers to the process of predicting future congestion at a destination based on past congestion data and weather data.

[0954] A "machine learning model" is a mathematical model that learns from given data (past congestion data and weather data) and is used to predict future congestion conditions.

[0955] "Format" refers to the specific way in which data or information is organized and formatted, including, for example, JSON format.

[0956] The "prediction result" is the result data obtained after making a congestion prediction, and includes information such as the predicted congestion level, date and time, and location.

[0957] The present invention provides a system for enabling a user to accurately grasp congestion information about a destination in advance. A specific embodiment of the present invention comprises the following steps.

[0958] First, the user uses the device to input information about the place they want to visit. By entering a specific place name or location into the input field, the device prepares to send the request. For example, the user might enter "parks in Tokyo."

[0959] Next, the device sends the destination information entered by the user to the server. This request includes geographical information of the destination and user identification information. It is sent to the server in the format "location: park in Tokyo, user_id: 12345".

[0960] The server retrieves past congestion data from the database based on the entered destination information. It uses the SQL query "SELECT FROM crowd_data WHERE location = 'Tokyo Parks'" to query the database. The past congestion data includes the level of congestion during specific periods and the congestion situation during events.

[0961] In parallel, the server sends an API request to an external weather service to get the current day's weather data. This request uses the geographic coordinates of the destination. For example, the request is sent to the weather service in the format "GET / weather?lat=35.6895&lon=139.6917". The weather data includes the weather, temperature, chance of precipitation, etc.

[0962] The server performs congestion predictions based on the acquired past congestion data and weather data. A trained model is loaded using a Python machine learning library (e.g., scikit-learn). For example, the model is loaded using "from sklearn.externals import joblib", and predictions are made in the format "model.predict([[temperature, probability of precipitation, past congestion level]])". This takes into account the tendency for congestion to increase on good weather days and decrease on bad weather days.

[0963] The prediction results are formatted by the server in a predetermined format. The formatted data includes information such as the predicted crowd level, date, time, and location. For example, it is formatted in the format "{'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}".

[0964] The server sends the formatted prediction results to the user's device as an HTTP response in JSON format, specifically, "{'status': 'success', 'data': {'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}}".

[0965] The device then displays the received prediction results to the user. At this time, it uses a UI component that parses the prediction results and displays them in a visually easy-to-understand format for the user. For example, a message such as "The predicted congestion level for parks in Tokyo is 70%" is displayed.

[0966] This system allows users to accurately understand the congestion situation at their destinations in advance and plan their visits optimally.

[0967] Specific examples

[0968] Consider the case where a user inputs "I'm planning to go to a park in Tokyo."

[0969] 1. User input: The user enters "Parks in Tokyo" into the input field on the device.

[0970] 2. Request sent by device: The device sends a request to the server: "location: park in Tokyo, user_id: 12345".

[0971] 3. Data retrieval by the server: The server executes the SQL query "SELECT FROM crowd_data WHERE location = 'Tokyo parks'" to retrieve historical crowd data from the database, and simultaneously sends the API request "GET / weather?lat=35.6895&lon=139.6917" to the weather information service to retrieve weather data.

[0972] 4. Congestion prediction by the server: The server executes the code "model.predict([[temperature, probability of precipitation, past congestion level]])" to predict future congestion levels.

[0973] 5. Server formatting: The server formats the prediction results into the format "{'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}".

[0974] 6. Sending results from the server to the device: The server sends the prediction results to the device in JSON format: {'status': 'success', 'data': {'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}}.

[0975] 7. Displaying the results on the device: The device displays on the screen, "The predicted congestion level of Tokyo parks is 70%."

[0976] Prompt Sentence Examples

[0977] "I'd like to know the crowd forecast for parks in Tokyo. Today's weather is sunny."

[0978] This system allows users to understand the congestion situation at their destinations in advance and plan their visits optimally.

[0979] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0980] Step 1:

[0981] The user uses the device to input information about the place they want to visit. In this input field, they input a specific place name, such as "Tokyo Park," and the information is displayed on the device screen in real time. The input data is "location: Tokyo Park."

[0982] Step 2:

[0983] The device sends the destination information entered by the user to the server. This request includes the geographical information of the destination and the user identification information, and is sent in the format "location: park in Tokyo, user_id: 12345".

[0984] Step 3:

[0985] The server retrieves past congestion data from the database based on the destination information received from the device. It executes the SQL query "SELECT FROM crowd_data WHERE location = 'Tokyo parks'" to retrieve the relevant congestion data. This data includes the level of congestion over a certain period of time in the past and congestion conditions during events.

[0986] Step 4:

[0987] Based on the visitor information, the server sends an API request to an external weather information service to obtain weather data for the day. The request is sent in the format "GET / weather?lat=35.6895&lon=139.6917", which retrieves data such as weather, temperature, and precipitation probability.

[0988] Step 5:

[0989] The server uses the acquired past congestion data and weather data to make congestion predictions. To do this, it uses a Python machine learning library (e.g., scikit-learn) and a trained model. For example, it executes "model.predict([[temperature, probability of precipitation, past congestion level]])" to predict future congestion levels. The output is the predicted congestion level.

[0990] Step 6:

[0991] The server formats the prediction results in JSON format. The results include the location of the visit, the predicted crowd level, and the predicted date and time. Specifically, the results are formatted in the format "{'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}". The formatted output is the predicted data in JSON format.

[0992] Step 7:

[0993] The server sends the formatted prediction result data to the user's device as an HTTP response. This response is in JSON format, like this: {'status': 'success', 'data': {'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}}.

[0994] Step 8:

[0995] The device analyzes the received prediction results and displays them to the user. The device uses a UI component to parse the prediction results and display them in a visually easy-to-understand format for the user. Specifically, the device displays a message on the screen saying, "The predicted congestion level for parks in Tokyo is 70%."

[0996] (Application example 1)

[0997] 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."

[0998] Conventional methods for finding out the congestion situation at a destination have the problem that it is difficult to accurately grasp the congestion situation at a store you plan to visit in advance. In particular, there is a lack of systems that can provide information on the impact of weather on congestion situations or specific congestion forecasts, so users are often at the mercy of the congestion at their destination.

[0999] 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.

[1000] In this invention, the server includes means for receiving destination information entered by the user, means for retrieving past congestion data from a database based on the destination information, means for retrieving weather data for the current day from an external weather information service based on the destination information, means for predicting congestion based on the past congestion data and the weather data for the current day, and means for providing and displaying the congestion prediction result to the user, thereby enabling the user to grasp the congestion status of the destination in advance and plan the optimal visiting time.

[1001] The "means for receiving destination information input by the user" is a means for the user to input information about places he or she wants to visit and for the system to receive that information.

[1002] The "means for acquiring past congestion data from a database" refers to a means for acquiring the data from a database that stores past data on the congestion status of destinations.

[1003] The "means for obtaining today's weather data from an external weather information service" refers to a means for sending a request to a third-party weather information service and obtaining data regarding the weather of the day.

[1004] The "means for predicting congestion" is a means for predicting future congestion conditions based on acquired past congestion data and weather data for the day.

[1005] The "means for providing and displaying the congestion prediction results to the user" refers to a means for visually displaying the results of the congestion prediction that has been performed to the user in an easy-to-understand manner.

[1006] "Formatting means" refers to a means for converting prediction results into a specified format such as JSON format.

[1007] The present invention is configured to enable users to accurately grasp congestion information for destinations in advance. Specifically, the system is one in which a server receives a request from a user, predicts congestion based on past congestion data and weather data for the day, and provides the result to the user. An embodiment of this system is described in detail below.

[1008] First, the user inputs information about the place they want to visit on their smartphone. For example, the user might input "shopping mall." Next, the user's device (smartphone) sends the input information to the server. The request includes the location of the place to be visited.

[1009] The server first queries the database to obtain past congestion data for the specified destination. This data includes congestion levels during specific periods in the past and congestion conditions during specific events. The server also sends a request to an external weather information service to obtain weather data for the current day. This data includes weather, temperature, and probability of precipitation.

[1010] The server makes congestion predictions based on the acquired past congestion data and weather data. For example, it applies rules such as congestion decreasing on bad weather days and increasing on good weather days. This allows users to understand the future congestion situation at their destination.

[1011] The server then formats the prediction results into a predetermined format, such as JSON. The formatted data includes information such as the predicted congestion level, date, time, and location. The server then sends the formatted prediction results to the user's device. The device visually displays the received prediction results to the user. For example, a message such as "The predicted congestion level of the shopping mall is 70%" may be displayed on the screen.

[1012] As a concrete example, consider the case where a user inputs "I plan to go to the shopping mall." When the user inputs "shopping mall" into the input field, the device sends this information to the server. The server retrieves past congestion data for "shopping mall" from a database, and at the same time retrieves current weather data from a weather information service. The server predicts future congestion levels based on this data, and if the weather is good, for example, predicts that the congestion level will be 70%. The formatted prediction result is converted into the format "The predicted congestion level at the shopping mall is 70%" and sent from the server to the device. The device displays the received data on the screen.

[1013] The following are examples of prompt sentences:

[1014] When a user types "I want to go to the shopping mall," the application predicts the level of congestion based on the mall's past congestion data and weather data, and displays the predicted level of congestion.

[1015] In this way, the system of the present invention helps users understand the congestion situation at their destinations in advance and plan their visits appropriately. It is possible to provide highly accurate congestion forecasts using hardware and software such as servers, smartphones, databases, and weather information services.

[1016] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1017] Step 1:

[1018] The user inputs the location of the destination using the smartphone.

[1019] Input: Text input by the user, such as "shopping mall."

[1020] Output: A request by a device to send input information.

[1021] Step 2:

[1022] The user's terminal transmits the entered visiting destination information to the server.

[1023] Input: Visiting destination information from the user.

[1024] Output: Sending a request to the server.

[1025] Step 3:

[1026] The server retrieves past congestion data for the designated destination from the database.

[1027] Input: The destination information submitted (e.g., "shopping mall").

[1028] Output: Historical congestion data retrieved from the database.

[1029] Step 4:

[1030] The server sends a request to an external weather information service to obtain the current day's weather data.

[1031] Input: The destination information submitted (e.g., "shopping mall").

[1032] Output: Weather data obtained from weather information services.

[1033] Step 5:

[1034] Congestion predictions are made based on past congestion data and weather data acquired by the server.

[1035] Input: Obtained historical congestion and weather data.

[1036] Output: Crowd prediction result (e.g., "The predicted crowding level of the shopping mall is 70%").

[1037] Specific operation: The system calculates to increase the congestion level on good weather days and decrease it on bad weather days.

[1038] Step 6:

[1039] The server formats the prediction results into a specified format such as JSON.

[1040] Input: Congestion prediction results.

[1041] Output: Prediction results in JSON format.

[1042] Step 7:

[1043] The server sends the formatted prediction results to the user's device.

[1044] Input: Formatted prediction results (JSON format).

[1045] Output: Prediction results are sent to the user's device.

[1046] Step 8:

[1047] The prediction results received by the user's terminal are displayed on the screen.

[1048] Input: Submitted prediction results (JSON format).

[1049] Output: A screen that displays "The predicted occupancy level of the shopping mall is 70%."

[1050] 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.

[1051] This invention is a system that allows users to create optimal visit plans based on congestion information at destinations and their own emotions. In particular, by combining this system with an emotion engine, it is possible to predict congestion and suggest destinations based on the user's emotional state.

[1052] System configuration

[1053] 1. User inputs destination information

[1054] The user inputs information about the place they want to visit on the terminal, such as "art museum" or "theme park."

[1055] 2. Sending a request by the device

[1056] The terminal sends a request including the information entered by the user to the server. The request also includes information about the destination.

[1057] 3. Obtaining past congestion data from the server

[1058] The server retrieves historical congestion data for the specified destination from the database, including congestion levels over a certain period of time in the past and congestion levels during specific events.

[1059] 4. Obtaining weather data from the server

[1060] The server retrieves the current day's weather data from an external weather information service, including the weather, temperature, and chance of precipitation.

[1061] 5. Acquiring user emotion data using the emotion engine

[1062] The emotion engine analyzes emotions from user input data or voice data, for example, extracting emotional states from user-entered comments or voice messages.

[1063] 6. Congestion prediction by the server

[1064] The server predicts congestion based on past congestion data, weather data, and user emotion data. For example, if the user is depressed, it will predict that less crowded locations will be prioritized.

[1065] 7. Server-suggested destinations

[1066] Based on the results of emotion analysis, the system suggests places to visit based on the user's emotional state. For example, if the user is looking to relax, it will suggest a quiet park.

[1067] 8. Server-based formatting of prediction results

[1068] The server then formats the congestion prediction results and suggested destinations into an appropriate format (e.g., JSON). This data includes the congestion level, date, time, location, and suggested destinations based on the user's emotional state.

[1069] 9. Sending prediction results and suggestions from the server to the device

[1070] The server sends the formatted prediction results and suggested destination data to the user's device.

[1071] 10. Displaying prediction results and suggestions on the device

[1072] The device then displays the received prediction results and suggested places to visit to the user. For example, a message such as "The museum is predicted to be 50% crowded. There is also a quiet park recommended for a change of pace" may be displayed on the screen.

[1073] Specific examples

[1074] Consider the case where a user inputs, "I'm planning to go to a theme park in Tokyo, but I've been feeling tired lately so I'd prefer a quiet place."

[1075] 1. User Input

[1076] The user enters "Tokyo theme park" into the input field on the device and adds the comment "I've been feeling tired lately."

[1077] 2. Request transmission by terminal

[1078] The device sends information including "Tokyo theme parks" and comments to the server.

[1079] 3. Data Acquisition by the Server

[1080] The server retrieves past congestion data for theme parks in Tokyo from a database, and simultaneously retrieves the current day's weather data from a weather information service.

[1081] 4. Emotion analysis using an emotion engine

[1082] The emotion engine analyzes the user's comments and recognizes that the user is "tired."

[1083] 5. Server-based congestion prediction and proposal

[1084] The server uses a congestion prediction algorithm to predict congestion levels based on past data and weather data. It also suggests places to visit based on the user's emotions. For example, if a theme park is 50% crowded, it will suggest a park where you can spend some quiet time.

[1085] 6. Server-based Result Formatting

[1086] The server formats the prediction results into the following format: "The predicted congestion level at Tokyo's theme parks is 50%. There are also quiet parks that are recommended for a change of pace."

[1087] 7. Sending results from the server to the device

[1088] The server sends the formatted prediction results and suggestions to the user's device.

[1089] 8. Displaying results on a terminal

[1090] The device displays the message, "The predicted congestion level at Tokyo's theme parks is 50%. There are also quiet parks that are recommended for a change of pace."

[1091] In this way, the present invention is a system that supports visit plans that result in even higher user satisfaction by predicting congestion and suggesting places to visit that take into account the user's emotional state.

[1092] The processing flow will be explained below.

[1093] Program processing steps

[1094] Server Processing Steps

[1095] Step 1:

[1096] The server receives a request from the user, which includes the destination information and a comment. The comment can convey the user's emotional state, such as if they are tired.

[1097] Step 2:

[1098] The server retrieves past congestion data for the specified destination from the database, including congestion levels during specific periods in the past and congestion conditions during events.

[1099] Step 3:

[1100] The server sends a request to an external weather information service API to obtain weather data for the day at the destination, including weather conditions, temperature, and chance of precipitation.

[1101] Step 4:

[1102] The server uses an emotion engine to analyze the user's comments and determine their emotions. For example, it can recognize that a comment such as "I'm tired" indicates that the user is seeking relaxation.

[1103] Step 5:

[1104] The server predicts congestion based on past congestion data, weather data, and user emotion data. The emotion data is used to determine the user's preferred situation.

[1105] Step 6:

[1106] The server recommends optimal destinations based on the crowding prediction results and the user's emotional state. For example, if the predicted crowding rate for a theme park is 50%, the server may recommend a quiet park.

[1107] Step 7:

[1108] The server then formats the predictions and suggestions into an appropriate format (e.g., JSON). This data includes suggestions based on crowding, date, time, location, and the user's emotional state.

[1109] Step 8:

[1110] The server then sends the formatted prediction results and suggestions to the user's device as an HTTP response.

[1111] Terminal processing steps

[1112] Step 1:

[1113] The user enters information about the place they visited and their emotional state as a comment into the input form on their device. For example, they might enter "a theme park in Tokyo" and comment "I've been feeling tired lately."

[1114] Step 2:

[1115] The device sends a request to the server based on the entered visit information and comments. This request includes the visit destination and emotional state.

[1116] Step 3:

[1117] The device receives a response from the server, which includes the congestion prediction results and suggested places to visit.

[1118] Step 4:

[1119] The device analyzes the prediction results and suggestions received and displays them in a format that is easy for the user to understand. For example, it displays a message on the screen saying, "The predicted congestion level at Tokyo's theme parks is 50%. There are also some quiet parks that we recommend for a change of pace."

[1120] Specific examples

[1121] Server Processing Steps

[1122] 1. Step 1: The server receives a request from the user saying, "I'm planning to go to a theme park in Tokyo, but I'm tired recently and would prefer a quiet place."

[1123] 2. Step 2: The server references the database to obtain historical crowd data for theme parks in Tokyo.

[1124] 3. Step 3: The server sends a request to the weather information API to obtain the current day's weather data for Tokyo.

[1125] 4. Step 4: The server uses the emotion engine to recognize the "tired" state from the user's comments.

[1126] 5. Step 5: The server uses a congestion prediction algorithm to predict the level of congestion based on past data and weather data. For example, if the weather is good, it predicts the level of congestion to be 50%. It also determines from emotional data that people prefer "quiet places."

[1127] 6. Step 6: The server combines the crowd prediction results with the emotion data and suggests, "The predicted crowding level at theme parks in Tokyo is 50%, but there are also quiet parks that are recommended for a change of pace."

[1128] 7. Step 7: The server formats the prediction results and suggestions into JSON format.

[1129] 8. Step 8: The server sends the formatted prediction results and suggestions to the user's device.

[1130] Terminal processing steps

[1131] 1. Step 1: The user types "Tokyo theme parks" into the device's input field and comments "I've been feeling tired lately."

[1132] 2. Step 2: The device sends information including "Tokyo theme parks" and "I've been feeling tired lately" to the server.

[1133] 3. Step 3: The device receives the response from the server and obtains the prediction results and suggestions.

[1134] 4. Step 4: The device will display the following message on the screen: "Tokyo theme parks are expected to be 50% crowded. There are also some quiet parks that are recommended for a change of pace."

[1135] The above is the specific program processing flow of the system that combines the emotion engine. As a result, users can receive congestion predictions and suggested places to visit that reflect their own emotional state, allowing them to create optimal travel plans.

[1136] Example 2

[1137] 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."

[1138] In today's world, predicting crowd levels at specific locations and events is important to users, but existing systems are unable to provide optimal suggestions that take into account the user's emotional state. Furthermore, it is difficult to make advanced predictions that combine weather information and past crowd data, making it difficult to provide visit plans that will satisfy users.

[1139] 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.

[1140] In this invention, the server includes means for receiving destination information and emotional state input by the user, means for retrieving past congestion data from a database, means for retrieving weather data for the current day from an external weather information service, means for analyzing the emotional state and retrieving the user's emotional data, means for predicting congestion based on the past congestion data, weather data, and emotional data, means for providing destination suggestions based on the congestion prediction result and the user's emotional state, means for formatting the prediction result and the suggested destinations into a predetermined format, and means for presenting the prediction result and the suggested destinations to the user. This enables congestion prediction and suggestions of optimal destinations that take the user's emotional state into consideration.

[1141] A "user" is an entity that utilizes the system to input destination information and emotional state.

[1142] "Destination information" is information about places and events that the user plans to visit.

[1143] "Emotional state" is information that indicates the user's current psychological state.

[1144] A "terminal" is a device that allows a user to input and send information, and includes smartphones, personal computers, etc.

[1145] A "request" refers to an operation of sending data, including information entered by a user, to a server.

[1146] A "server" is a computer system that receives a request and processes and provides the required data.

[1147] A "database" is a system that stores and manages information such as past congestion data.

[1148] A "weather information service" is an external service that provides weather information such as the weather, temperature, and probability of precipitation for the day.

[1149] An "emotion engine" is a system that analyzes emotions from user input data and voice data.

[1150] "Crowd prediction" refers to estimating future congestion levels at a destination based on past congestion data, weather data, and emotion data.

[1151] "Suggesting destinations" refers to suggesting optimal destinations based on the congestion prediction results and the user's emotional state.

[1152] "Formatting the prediction results" refers to the operation of converting the congestion prediction results and suggested destinations into a fixed format.

[1153] A "format" is data organized in a specific way, typically JSON format.

[1154] A "generative AI model" is an artificial intelligence model used to analyze data and make predictions.

[1155] "Comment" refers to text information such as an emotional state entered by a user.

[1156] The present invention is a system that allows users to create optimal visit plans based on congestion information at destinations and their own emotions. This system uses an emotion engine to predict congestion and suggest destinations based on the user's emotional state. The specific configuration and operation are as follows.

[1157] System configuration

[1158] Hardware and Software

[1159] 1. User Device:

[1160] Devices such as smartphones, tablets, and personal computers are used as user terminals, providing an interface for users to input visiting information and emotional states.

[1161] 2. Server:

[1162] A server system with powerful computing power is required to manage the database, communicate with weather information services, calculate congestion forecasts, analyze sentiment, and format the results.

[1163] 3. Database:

[1164] A database is used to store and manage past congestion data, including congestion levels over a certain period of time in the past and congestion conditions during specific events.

[1165] 4. Emotion Engine:

[1166] There is a need for an emotion engine that uses NLP (Natural Language Processing) techniques to analyze emotions from user input data and voice data.

[1167] 5. Weather information services:

[1168] Use an API to obtain real-time weather data from external weather information services.

[1169] 6. Generative AI Models:

[1170] A generative AI model used to integrate multiple data sets and make crowd predictions and suggest places to visit.

[1171] Operation explanation

[1172] The user inputs destination information and emotional state using the device. For example, the user inputs, "I'm planning to go to a theme park in Tokyo, but I've been feeling tired lately, so I'd prefer a quiet place." This information is then sent from the device to the server.

[1173] The server does the following:

[1174] 1. Obtain historical crowd data for theme parks in Tokyo from the database.

[1175] 2. Obtain the current day's weather data for Tokyo from an external weather information service.

[1176] 3. Use the emotion engine to analyze the user's emotion data. In this example, it recognizes that the user is in a "tired" state.

[1177] 4. Crowd forecasting based on historical crowd data, weather data, and sentiment data. Generative AI models are used to predict current and future crowd levels at destinations.

[1178] 5. Creates recommendations for places to visit based on the crowding prediction results and the user's emotional state. In this example, the system generates recommendations such as, "The predicted crowding rate at theme parks in Tokyo is 50%. There are also quiet parks recommended for a change of pace."

[1179] 6. Format the prediction results and destination suggestions into an appropriate format (e.g., JSON).

[1180] The formatted data is sent from the server to the device, which then displays it to the user. For example, a message such as "The predicted congestion level at Tokyo's theme parks is 50%. There are also some quiet parks recommended for a change of pace" may be displayed on the device screen.

[1181] Specific examples

[1182] Let's say a user types, "I'm planning to go to a theme park in Tokyo, but I'm tired these days and would prefer a quiet place." In this case, the following prompt sentence is used:

[1183] Example prompt sentence:

[1184] "I'm planning to go to a theme park in Tokyo, but I've been feeling tired lately so I'd prefer a quiet place."

[1185] Based on this information, the server processes the data and proposes an optimal visit plan to the user. In this way, the present invention supports visit plans that increase user satisfaction.

[1186] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1187] Step 1:

[1188] The user enters the destination information and emotional comment into the input fields on the device. Specifically, they enter "Tokyo theme park" and "I've been feeling tired lately," and press the send button. The input information is converted to JSON format on the device. Input: Destination information and emotional comment. Output: Request data in JSON format.

[1189] Step 2:

[1190] The device sends a JSON-formatted request containing the entered information to the server as an HTTP POST request. Specifically, the device parses the user's input into JSON format and sends the data to the specified API endpoint. Input: JSON-formatted request data. Output: HTTP POST request to the server.

[1191] Step 3:

[1192] The server receives the request and retrieves past congestion data from the database based on the destination information. Specifically, it analyzes the destination information in the request and executes an SQL query based on that information to extract congestion data. Input: Destination information. Output: Past congestion data.

[1193] Step 4:

[1194] The server calls the API of an external weather information service to obtain weather data for the day. It sends an API request and analyzes the received data. Specific weather information includes the weather, temperature, and probability of precipitation. Input: API request. Output: Weather data.

[1195] Step 5:

[1196] The server uses an emotion engine to obtain the user's emotion data. Specifically, it analyzes the entered emotion comment and identifies the emotion using natural language processing (NLP) technology. In this case, the comment "I've been tired lately" is analyzed and evaluated as a "tired" state. Input: Emotion comment. Output: Emotion data.

[1197] Step 6:

[1198] The server combines past congestion data, weather data, and emotion data to perform congestion prediction. Using a generative AI model, multiple data sets are input and prediction results are generated. At this time, emotion data is taken into consideration and destinations with low congestion levels are recommended preferentially. Input: Past congestion data, weather data, emotion data. Output: Congestion prediction results.

[1199] Step 7:

[1200] The server generates optimal destination suggestions for the user based on the congestion prediction results and emotion analysis results. Specifically, it makes suggestions that match the user's emotional state, such as less crowded places or quiet parks. Input: Congestion prediction results, emotion analysis results. Output: Destination suggestions.

[1201] Step 8:

[1202] The server formats the congestion prediction results and visit suggestions into an appropriate format (e.g., JSON format). This is the process of converting the results into a format that can be easily analyzed and displayed on the user's device. Input: Congestion prediction results, visit suggestions. Output: Formatted prediction results and suggestion data.

[1203] Step 9:

[1204] The server returns the formatted prediction results and suggestions to the user's device. This is sent in the form of an HTTP response, which the device receives. Input: Formatted prediction results and suggestion data. Output: HTTP response to the device.

[1205] Step 10:

[1206] The device displays the prediction results and suggested places to visit received by the device to the user. For example, a message such as "The predicted congestion level at theme parks in Tokyo is 50%. There are also quiet parks recommended for a change of pace" is displayed on the screen. Input: HTTP response data. Output: Message to be displayed to the user.

[1207] (Application example 2)

[1208] 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."

[1209] Conventional congestion prediction systems for destinations do not take into account the user's emotional state, so the selection of destinations may not be appropriate for the user's psychological state. Furthermore, there is a need for a system that can not only predict congestion but also suggest optimal destinations based on the user's current emotions. Therefore, it is necessary to develop a new system that can predict congestion and recommend optimal destinations while taking into account the user's emotions.

[1210] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving destination information and emotion information input by the user, means for acquiring past congestion data from a database based on the destination information, means for acquiring weather data for the current day from an external weather information service based on the destination information, means for predicting congestion based on the acquired past congestion data, weather data for the current day, and user emotion data, means for recommending optimal destinations based on the user's emotional state, and means for providing the user with the congestion prediction results and recommendation results. This makes it possible to predict congestion and suggest optimal destinations that take the user's emotional state into consideration.

[1211] "Destination information entered by the user" is information about a specific place or facility that the user wishes to visit.

[1212] "Emotion information" is information about the user's current emotional state and psychological situation.

[1213] "Past congestion data" refers to data relating to past congestion at a particular destination.

[1214] A "database" is a system for storing, managing, and searching data.

[1215] A "weather information service" is a service that provides information about weather and climate from an external source.

[1216] "Weather data for the current day" refers to data related to the climate, such as the weather, temperature, and probability of precipitation, for a specific day.

[1217] The "emotion engine" is an engine for analyzing the emotional state from text and voice input by the user.

[1218] A "crowd forecast" means a method or algorithm for predicting the crowding level of a destination based on past data, current weather conditions, and the user's emotional state.

[1219] A "recommendation means" is a method or system for suggesting optimal destinations to a user based on the user's emotional state and congestion prediction results.

[1220] The "means for providing predicted results and recommended results" refers to a method or system for providing the results generated by the server to the user in an easy-to-understand manner.

[1221] A "format" is a rule or method for arranging data or information into a particular structure or form.

[1222] The present invention provides a system for predicting congestion and suggesting optimal destinations that take into account the emotional state of a user. This system is implemented using the following hardware and software.

[1223] Hardware and Software

[1224] Smartphone (terminal): The user enters destination information and emotion information, and the results are displayed.

[1225] Server: Receives information from users and processes the data.

[1226] Database (SQLite): Stores and manages past congestion data.

[1227] External weather information service (Weather API): Obtains the current day's weather data.

[1228] Emotion engine: Analyzes the user's emotional state from input data.

[1229] Programming language (Python) and framework (Flask): Performs server-side processing.

[1230] Program processing

[1231] 1. Receiving user input:

[1232] The server receives destination information and emotion information from the smartphone terminal. The user inputs the place they want to visit and their current emotional state.

[1233] 2. Data Acquisition:

[1234] The server acquires past congestion data from a database based on the received destination information.

[1235] In addition, weather data for the day is obtained using an external weather information service.

[1236] 3. Emotion analysis:

[1237] The emotion engine analyzes the user's input data and extracts the user's current emotional state.

[1238] 4. Congestion prediction and destination suggestions:

[1239] The server makes congestion predictions based on acquired past congestion data, weather data for the day, and user emotion data.

[1240] To suggest the most suitable destination according to a user's emotional state.

[1241] 5. Results provided:

[1242] The server formats the congestion prediction results and suggested places to visit into an appropriate format and sends them to the smartphone device.

[1243] The terminal displays the received results to the user.

[1244] Specific examples

[1245] For example, if a user inputs "I want to go to the shopping mall in Shinjuku, but I've been really tired lately," the server will process the request based on the following prompt:

[1246] "A user types, 'I want to go to a shopping mall in Shinjuku, but I've been feeling really tired lately.' Please predict how crowded it will be and suggest a quiet place so I can relax."

[1247] In this case, the server predicts congestion at shopping malls in Shinjuku based on past congestion data and the weather data for the day, and suggests quiet cafes where users can relax based on their emotional information. These suggestions are displayed on the smartphone device and used as a reference when the user chooses the best place to visit.

[1248] The above is an embodiment of the present invention. This system makes it possible to predict congestion and suggest places to visit while taking into account the emotional state of the user.

[1249] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1250] Step 1:

[1251] The user inputs destination information and emotional information on their smartphone. Specifically, the user inputs "the place they want to visit" (e.g., a shopping mall in Shinjuku) and "emotional state" (e.g., "I've been feeling very tired lately") into the application's input fields. The input data includes destination information and emotional comments. The input data here is sent to the server in the next step.

[1252] Step 2:

[1253] The device sends the entered visit information and emotion information to the server. The device then formats the input data into an appropriate data format, such as JSON, and sends it to the server via an HTTP request. This sent data includes the user name, visit information, and emotion comment. The server receives this and begins processing the next step.

[1254] Step 3:

[1255] Based on the destination information received by the server, past congestion data is retrieved from the database. The server issues an SQL query to retrieve data on the corresponding destination from a pre-stored congestion database. The destination information is the input data, and past congestion data is obtained as output data.

[1256] Step 4:

[1257] The server uses an external weather information service to obtain weather data for the day. The server sends an API request to obtain information such as the weather, temperature, and probability of precipitation for the day. The input data is the area information for the destination, and the output data is the weather information for the day.

[1258] Step 5:

[1259] The server uses an emotion engine to analyze the user's emotional state from the input data. The emotion engine processes the user's emotional comments and extracts emotional data such as "I'm tired" or "I want to relax." The input data is the emotional comments, and the output data is the emotional state.

[1260] Step 6:

[1261] The server predicts congestion based on the acquired past congestion data, weather data for the day, and user emotion data. It then executes a congestion prediction algorithm to predict future congestion levels. The input data are past congestion data, weather data, and emotion data, and the output data is the predicted future congestion level.

[1262] Step 7:

[1263] The server suggests optimal destinations based on the user's emotional state. For example, if congestion is predicted or the user is tired, it suggests a quiet cafe or other place where the user can relax. The input data are congestion prediction data and emotion data, and the output data are the suggested destinations.

[1264] Step 8:

[1265] The server formats the congestion prediction results and recommended destinations and converts them into an appropriate format, such as JSON. The formatted data includes congestion levels, weather information, and optimal destinations. The input data here are the prediction results and recommended destinations, and the output data is the formatted data.

[1266] Step 9:

[1267] The server sends the formatted data to the terminal, and returns data containing the prediction results and suggestions obtained as an HTTP response. This data is to be presented to the user on the terminal.

[1268] Step 10:

[1269] The device then displays the received prediction results and suggestions to the user. For example, a message such as "The predicted congestion level at the Shinjuku shopping mall is 70%. If you want to relax, we recommend a quiet cafe nearby." The user can then decide what to do next based on this information.

[1270] These are the processing steps for the entire system. Each step explains how the input data is processed and what output is obtained.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] [Fourth embodiment]

[1275] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1276] 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.

[1277] 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).

[1278] 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.

[1279] 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.

[1280] 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).

[1281] 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.

[1282] 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.

[1283] 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.

[1284] 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.

[1285] 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.

[1286] 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.

[1287] 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."

[1288] The present invention is designed to enable users to accurately grasp congestion information for their destinations in advance. Specifically, the system involves a server receiving a request from a user, predicting congestion based on past congestion data and weather data for the day, and providing the results to the user.

[1289] System configuration

[1290] 1. User inputs destination information

[1291] The user inputs information about the place they want to visit on the device, for example, "parks in Tokyo."

[1292] 2. Sending a request by the device

[1293] The device sends the information entered by the user to the server, and the request includes the location to be visited.

[1294] 3. Obtaining past congestion data from the server

[1295] The server queries the database to retrieve historical congestion data for the specified destination, including congestion levels for specific periods in the past and congestion levels during specific events.

[1296] 4. Obtaining weather data from the server

[1297] The server sends a request to an external weather service to retrieve the current day's weather data, including the weather, temperature, and chance of precipitation.

[1298] 5. Congestion prediction by the server

[1299] The server predicts congestion based on past congestion data and weather data. For example, it uses rules such as decreasing congestion on bad weather days and increasing congestion on good weather days.

[1300] 6. Server-based formatting of prediction results

[1301] The server formats the prediction results into a predetermined format, such as JSON. The formatted data includes information such as the predicted congestion level, date, time, and location.

[1302] 7. Sending prediction results from the server to the device

[1303] The server sends the formatted prediction results to the user's device.

[1304] 8. Displaying prediction results on the device

[1305] The device then displays the received prediction results to the user. For example, a message such as "The predicted congestion level for parks in Tokyo is 50%" may be displayed on the screen.

[1306] Specific examples

[1307] For example, consider the case where a user inputs "I plan to go to a park in Tokyo."

[1308] 1. User Input

[1309] The user enters "Tokyo Park" into the input field of the terminal.

[1310] 2. Request transmission by terminal

[1311] The device sends the information "parks in Tokyo" to the server.

[1312] 3. Data Acquisition by the Server

[1313] The server retrieves past congestion data for "Tokyo parks" from a database, and simultaneously retrieves current weather data from a weather information service.

[1314] 4. Server congestion prediction

[1315] The server predicts future congestion levels based on past congestion data and weather data. For example, if the weather is good, it predicts that the congestion level will be 70%.

[1316] 5. Server-based result formatting

[1317] The server formats the prediction results into a format such as "The predicted congestion level of parks in Tokyo is 70%."

[1318] 6. Sending results from the server to the device

[1319] The server sends the formatted prediction results to the terminal.

[1320] 7. Displaying results on a terminal

[1321] The device displays the message "The predicted congestion level for Tokyo parks is 70%."

[1322] In this way, the present invention is a system that helps users understand the congestion situation at their destinations in advance and plan their visits optimally. Crowd predictions are based on predefined rules and a combination of past data and weather information, making highly accurate predictions possible.

[1323] The processing flow will be explained below.

[1324] Program processing steps

[1325] Server Processing Steps

[1326] Step 1:

[1327] The server receives a request from the user containing information about the destinations visited, including the location, date, and time of the destinations entered by the user.

[1328] Step 2:

[1329] The server connects to the database and retrieves past congestion data for the specified destination. For example, it retrieves data such as the past congestion level of a specified "Tokyo park" or the congestion situation during an event.

[1330] Step 3:

[1331] The server sends a request to an external weather information service API to obtain weather data for the current day at the destination, including weather conditions, temperature, and chance of precipitation.

[1332] Step 4:

[1333] The server predicts congestion based on past congestion data and weather data. Specifically, it applies basic rules such as increasing congestion when the weather is sunny and decreasing congestion when it rains.

[1334] Step 5:

[1335] The server formats the congestion prediction results into an appropriate format (e.g., JSON), which includes information such as location, date, time, and predicted congestion level.

[1336] Step 6:

[1337] The server then sends the formatted prediction results to the user's device as an HTTP response.

[1338] Terminal processing steps

[1339] Step 1:

[1340] The user enters information about the destination into the input form on the device. For example, the user enters information such as "a park in Tokyo."

[1341] Step 2:

[1342] The terminal sends a request to the server based on the entered visit destination information. This request includes the location information of the visit destination.

[1343] Step 3:

[1344] The device receives a response from the server, which contains the congestion prediction results in JSON format.

[1345] Step 4:

[1346] The device analyzes the prediction results it receives and displays them in a format that is easy for the user to understand. For example, it displays a message on the screen saying, "The predicted congestion level for parks in Tokyo is 70%."

[1347] Specific examples

[1348] Server Processing Steps

[1349] 1. Step 1: The server receives a request from the user saying, "I have plans to go to a park in Tokyo."

[1350] 2. Step 2: The server references the database to obtain historical crowding data for parks in Tokyo.

[1351] 3. Step 3: The server sends a request to the weather information API to obtain the current day's weather data for Tokyo.

[1352] 4. Step 4: The server uses a congestion prediction algorithm to predict the congestion level based on past data and weather data. For example, if the weather is good, it predicts the congestion level to be 70%.

[1353] 5. Step 5: The server formats the prediction results into JSON format.

[1354] 6. Step 6: The server sends the formatted prediction results to the user's device.

[1355] Terminal processing steps

[1356] 1. Step 1: The user enters "Tokyo Parks" into the input field on the device.

[1357] 2. Step 2: The device sends the information about "Tokyo Parks" to the server.

[1358] 3. Step 3: The device receives the response from the server and obtains the prediction result.

[1359] 4. Step 4: The device will display the message "The predicted congestion level for Tokyo parks is 70%."

[1360] This is the specific flow of program processing by the server and terminal. This allows the user to know in advance how busy the destination will be and to plan the most appropriate visit.

[1361] Example 1

[1362] 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."

[1363] Conventional congestion prediction systems make it difficult for users to accurately predict the congestion situation at their destinations. As a result, users may end up with long waiting times at their destinations and may not be able to plan their visits properly. Another problem is that weather information is not reflected in the congestion predictions, resulting in low prediction accuracy. To solve these problems, a system is needed that allows users to accurately grasp the congestion situation at their destinations and make optimal visit plans.

[1364] 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.

[1365] In this invention, the server includes means for receiving destination information entered by a user, means for acquiring past congestion data from a database, means for acquiring weather data for the current day from an external weather information service, means for predicting congestion based on the past congestion data and weather data using a machine learning model, means for formatting the congestion prediction result in a predetermined format and transmitting it to the user's terminal, and means for displaying the congestion prediction result on the user's terminal. This allows the user to accurately grasp the congestion status of the destination in advance and make an optimal visit plan.

[1366] A "user" is an individual or group that uses this system to obtain congestion information about a destination.

[1367] A "terminal" is an electronic device that a user uses to access the system, input destination information, and display prediction results.

[1368] The "server" is a central computer facility that receives input destination information, acquires past congestion data and weather data, and performs congestion predictions.

[1369] "Destination information" refers to information about a place that the user wishes to visit, and specifically includes the location, place name, and the like.

[1370] "Past congestion data" is data that indicates the past congestion situation at the designated destination, and includes the degree of congestion during a specific period and the congestion situation during an event.

[1371] "Weather data" refers to data about the weather on the day obtained from an external weather information service, and includes the weather, temperature, probability of precipitation, and the like.

[1372] "Crowd prediction" refers to the process of predicting future congestion at a destination based on past congestion data and weather data.

[1373] A "machine learning model" is a mathematical model that learns from given data (past congestion data and weather data) and is used to predict future congestion conditions.

[1374] "Format" refers to the specific way in which data or information is organized and formatted, including, for example, JSON format.

[1375] The "prediction result" is the result data obtained after making a congestion prediction, and includes information such as the predicted congestion level, date and time, and location.

[1376] The present invention provides a system for enabling a user to accurately grasp congestion information about a destination in advance. A specific embodiment of the present invention comprises the following steps.

[1377] First, the user uses the device to input information about the place they want to visit. By entering a specific place name or location into the input field, the device prepares to send the request. For example, the user might enter "parks in Tokyo."

[1378] Next, the device sends the destination information entered by the user to the server. This request includes geographical information of the destination and user identification information. It is sent to the server in the format "location: park in Tokyo, user_id: 12345".

[1379] The server retrieves past congestion data from the database based on the entered destination information. It uses the SQL query "SELECT FROM crowd_data WHERE location = 'Tokyo Parks'" to query the database. The past congestion data includes the level of congestion during specific periods and the congestion situation during events.

[1380] In parallel, the server sends an API request to an external weather service to get the current day's weather data. This request uses the geographic coordinates of the destination. For example, the request is sent to the weather service in the format "GET / weather?lat=35.6895&lon=139.6917". The weather data includes the weather, temperature, chance of precipitation, etc.

[1381] The server performs congestion predictions based on the acquired past congestion data and weather data. A trained model is loaded using a Python machine learning library (e.g., scikit-learn). For example, the model is loaded using "from sklearn.externals import joblib", and predictions are made in the format "model.predict([[temperature, probability of precipitation, past congestion level]])". This takes into account the tendency for congestion to increase on good weather days and decrease on bad weather days.

[1382] The prediction results are formatted by the server in a predetermined format. The formatted data includes information such as the predicted crowd level, date, time, and location. For example, it is formatted in the format "{'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}".

[1383] The server sends the formatted prediction results to the user's device as an HTTP response in JSON format, specifically, "{'status': 'success', 'data': {'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}}".

[1384] The device then displays the received prediction results to the user. At this time, it uses a UI component that parses the prediction results and displays them in a visually easy-to-understand format for the user. For example, a message such as "The predicted congestion level for parks in Tokyo is 70%" is displayed.

[1385] This system allows users to accurately understand the congestion situation at their destinations in advance and plan their visits optimally.

[1386] Specific examples

[1387] Consider the case where a user inputs "I'm planning to go to a park in Tokyo."

[1388] 1. User input: The user enters "Parks in Tokyo" into the input field on the device.

[1389] 2. Request sent by device: The device sends a request to the server: "location: park in Tokyo, user_id: 12345".

[1390] 3. Data retrieval by the server: The server executes the SQL query "SELECT FROM crowd_data WHERE location = 'Tokyo parks'" to retrieve historical crowd data from the database, and simultaneously sends the API request "GET / weather?lat=35.6895&lon=139.6917" to the weather information service to retrieve weather data.

[1391] 4. Congestion prediction by the server: The server executes the code "model.predict([[temperature, probability of precipitation, past congestion level]])" to predict future congestion levels.

[1392] 5. Server formatting: The server formats the prediction results into the format "{'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}".

[1393] 6. Sending results from the server to the device: The server sends the prediction results to the device in JSON format: {'status': 'success', 'data': {'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}}.

[1394] 7. Displaying the results on the device: The device displays on the screen, "The predicted congestion level of Tokyo parks is 70%."

[1395] Prompt Sentence Examples

[1396] "I'd like to know the crowd forecast for parks in Tokyo. Today's weather is sunny."

[1397] This system allows users to understand the congestion situation at their destinations in advance and plan their visits optimally.

[1398] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1399] Step 1:

[1400] The user uses the device to input information about the place they want to visit. In this input field, they input a specific place name, such as "Tokyo Park," and the information is displayed on the device screen in real time. The input data is "location: Tokyo Park."

[1401] Step 2:

[1402] The device sends the destination information entered by the user to the server. This request includes the geographical information of the destination and the user identification information, and is sent in the format "location: park in Tokyo, user_id: 12345".

[1403] Step 3:

[1404] The server retrieves past congestion data from the database based on the destination information received from the device. It executes the SQL query "SELECT FROM crowd_data WHERE location = 'Tokyo parks'" to retrieve the relevant congestion data. This data includes the level of congestion over a certain period of time in the past and congestion conditions during events.

[1405] Step 4:

[1406] Based on the visitor information, the server sends an API request to an external weather information service to obtain weather data for the day. The request is sent in the format "GET / weather?lat=35.6895&lon=139.6917", which retrieves data such as weather, temperature, and precipitation probability.

[1407] Step 5:

[1408] The server uses the acquired past congestion data and weather data to make congestion predictions. To do this, it uses a Python machine learning library (e.g., scikit-learn) and a trained model. For example, it executes "model.predict([[temperature, probability of precipitation, past congestion level]])" to predict future congestion levels. The output is the predicted congestion level.

[1409] Step 6:

[1410] The server formats the prediction results in JSON format. The results include the location of the visit, the predicted crowd level, and the predicted date and time. Specifically, the results are formatted in the format "{'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}". The formatted output is the predicted data in JSON format.

[1411] Step 7:

[1412] The server sends the formatted prediction result data to the user's device as an HTTP response. This response is in JSON format, like this: {'status': 'success', 'data': {'location': 'Park in Tokyo', 'predicted_crowd_level': 70, 'date': '2023-10-10'}}.

[1413] Step 8:

[1414] The device analyzes the received prediction results and displays them to the user. The device uses a UI component to parse the prediction results and display them in a visually easy-to-understand format for the user. Specifically, the device displays a message on the screen saying, "The predicted congestion level for parks in Tokyo is 70%."

[1415] (Application example 1)

[1416] 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."

[1417] Conventional methods for finding out the congestion situation at a destination have the problem that it is difficult to accurately grasp the congestion situation at a store you plan to visit in advance. In particular, there is a lack of systems that can provide information on the impact of weather on congestion situations or specific congestion forecasts, so users are often at the mercy of the congestion at their destination.

[1418] 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.

[1419] In this invention, the server includes means for receiving destination information entered by the user, means for retrieving past congestion data from a database based on the destination information, means for retrieving weather data for the current day from an external weather information service based on the destination information, means for predicting congestion based on the past congestion data and the weather data for the current day, and means for providing and displaying the congestion prediction result to the user, thereby enabling the user to grasp the congestion status of the destination in advance and plan the optimal visiting time.

[1420] The "means for receiving destination information input by the user" is a means for the user to input information about places he or she wants to visit and for the system to receive that information.

[1421] The "means for acquiring past congestion data from a database" refers to a means for acquiring the data from a database that stores past data on the congestion status of destinations.

[1422] The "means for obtaining today's weather data from an external weather information service" refers to a means for sending a request to a third-party weather information service and obtaining data regarding the weather of the day.

[1423] The "means for predicting congestion" is a means for predicting future congestion conditions based on acquired past congestion data and weather data for the day.

[1424] The "means for providing and displaying the congestion prediction results to the user" refers to a means for visually displaying the results of the congestion prediction that has been performed to the user in an easy-to-understand manner.

[1425] "Formatting means" refers to a means for converting prediction results into a specified format such as JSON format.

[1426] The present invention is configured to enable users to accurately grasp congestion information for destinations in advance. Specifically, the system is one in which a server receives a request from a user, predicts congestion based on past congestion data and weather data for the day, and provides the result to the user. An embodiment of this system is described in detail below.

[1427] First, the user inputs information about the place they want to visit on their smartphone. For example, the user might input "shopping mall." Next, the user's device (smartphone) sends the input information to the server. The request includes the location of the place to be visited.

[1428] The server first queries the database to obtain past congestion data for the specified destination. This data includes congestion levels during specific periods in the past and congestion conditions during specific events. The server also sends a request to an external weather information service to obtain weather data for the current day. This data includes weather, temperature, and probability of precipitation.

[1429] The server makes congestion predictions based on the acquired past congestion data and weather data. For example, it applies rules such as congestion decreasing on bad weather days and increasing on good weather days. This allows users to understand the future congestion situation at their destination.

[1430] The server then formats the prediction results into a predetermined format, such as JSON. The formatted data includes information such as the predicted congestion level, date, time, and location. The server then sends the formatted prediction results to the user's device. The device visually displays the received prediction results to the user. For example, a message such as "The predicted congestion level of the shopping mall is 70%" may be displayed on the screen.

[1431] As a concrete example, consider the case where a user inputs "I plan to go to the shopping mall." When the user inputs "shopping mall" into the input field, the device sends this information to the server. The server retrieves past congestion data for "shopping mall" from a database, and at the same time retrieves current weather data from a weather information service. The server predicts future congestion levels based on this data, and if the weather is good, for example, predicts that the congestion level will be 70%. The formatted prediction result is converted into the format "The predicted congestion level at the shopping mall is 70%" and sent from the server to the device. The device displays the received data on the screen.

[1432] The following are examples of prompt sentences:

[1433] When a user types "I want to go to the shopping mall," the application predicts the level of congestion based on the mall's past congestion data and weather data, and displays the predicted level of congestion.

[1434] In this way, the system of the present invention helps users understand the congestion situation at their destinations in advance and plan their visits appropriately. It is possible to provide highly accurate congestion forecasts using hardware and software such as servers, smartphones, databases, and weather information services.

[1435] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1436] Step 1:

[1437] The user inputs the location of the destination using the smartphone.

[1438] Input: Text input by the user, such as "shopping mall."

[1439] Output: A request by a device to send input information.

[1440] Step 2:

[1441] The user's terminal transmits the entered visiting destination information to the server.

[1442] Input: Visiting destination information from the user.

[1443] Output: Sending a request to the server.

[1444] Step 3:

[1445] The server retrieves past congestion data for the designated destination from the database.

[1446] Input: The destination information submitted (e.g., "shopping mall").

[1447] Output: Historical congestion data retrieved from the database.

[1448] Step 4:

[1449] The server sends a request to an external weather information service to obtain the current day's weather data.

[1450] Input: The destination information submitted (e.g., "shopping mall").

[1451] Output: Weather data obtained from weather information services.

[1452] Step 5:

[1453] Congestion predictions are made based on past congestion data and weather data acquired by the server.

[1454] Input: Obtained historical congestion and weather data.

[1455] Output: Crowd prediction result (e.g., "The predicted crowding level of the shopping mall is 70%").

[1456] Specific operation: The system calculates to increase the congestion level on good weather days and decrease it on bad weather days.

[1457] Step 6:

[1458] The server formats the prediction results into a specified format such as JSON.

[1459] Input: Congestion prediction results.

[1460] Output: Prediction results in JSON format.

[1461] Step 7:

[1462] The server sends the formatted prediction results to the user's device.

[1463] Input: Formatted prediction results (JSON format).

[1464] Output: Prediction results are sent to the user's device.

[1465] Step 8:

[1466] The prediction results received by the user's terminal are displayed on the screen.

[1467] Input: Submitted prediction results (JSON format).

[1468] Output: A screen that displays "The predicted occupancy level of the shopping mall is 70%."

[1469] 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.

[1470] This invention is a system that allows users to create optimal visit plans based on congestion information at destinations and their own emotions. In particular, by combining this system with an emotion engine, it is possible to predict congestion and suggest destinations based on the user's emotional state.

[1471] System configuration

[1472] 1. User inputs destination information

[1473] The user inputs information about the place they want to visit on the terminal, such as "art museum" or "theme park."

[1474] 2. Sending a request by the device

[1475] The terminal sends a request including the information entered by the user to the server. The request also includes information about the destination.

[1476] 3. Obtaining past congestion data from the server

[1477] The server retrieves historical congestion data for the specified destination from the database, including congestion levels over a certain period of time in the past and congestion levels during specific events.

[1478] 4. Obtaining weather data from the server

[1479] The server retrieves the current day's weather data from an external weather information service, including the weather, temperature, and chance of precipitation.

[1480] 5. Acquiring user emotion data using the emotion engine

[1481] The emotion engine analyzes emotions from user input data or voice data, for example, extracting emotional states from user-entered comments or voice messages.

[1482] 6. Congestion prediction by the server

[1483] The server predicts congestion based on past congestion data, weather data, and user emotion data. For example, if the user is depressed, it will predict that less crowded locations will be prioritized.

[1484] 7. Server-suggested destinations

[1485] Based on the results of emotion analysis, the system suggests places to visit based on the user's emotional state. For example, if the user is looking to relax, it will suggest a quiet park.

[1486] 8. Server-based formatting of prediction results

[1487] The server then formats the congestion prediction results and suggested destinations into an appropriate format (e.g., JSON). This data includes the congestion level, date, time, location, and suggested destinations based on the user's emotional state.

[1488] 9. Sending prediction results and suggestions from the server to the device

[1489] The server sends the formatted prediction results and suggested destination data to the user's device.

[1490] 10. Displaying prediction results and suggestions on the device

[1491] The device then displays the received prediction results and suggested places to visit to the user. For example, a message such as "The museum is predicted to be 50% crowded. There is also a quiet park recommended for a change of pace" may be displayed on the screen.

[1492] Specific examples

[1493] Consider the case where a user inputs, "I'm planning to go to a theme park in Tokyo, but I've been feeling tired lately so I'd prefer a quiet place."

[1494] 1. User Input

[1495] The user enters "Tokyo theme park" into the input field on the device and adds the comment "I've been feeling tired lately."

[1496] 2. Request transmission by terminal

[1497] The device sends information including "Tokyo theme parks" and comments to the server.

[1498] 3. Data Acquisition by the Server

[1499] The server retrieves past congestion data for theme parks in Tokyo from a database, and simultaneously retrieves the current day's weather data from a weather information service.

[1500] 4. Emotion analysis using an emotion engine

[1501] The emotion engine analyzes the user's comments and recognizes that the user is "tired."

[1502] 5. Server-based congestion prediction and proposal

[1503] The server uses a congestion prediction algorithm to predict congestion levels based on past data and weather data. It also suggests places to visit based on the user's emotions. For example, if a theme park is 50% crowded, it will suggest a park where you can spend some quiet time.

[1504] 6. Server-based Result Formatting

[1505] The server formats the prediction results into the following format: "The predicted congestion level at Tokyo's theme parks is 50%. There are also quiet parks that are recommended for a change of pace."

[1506] 7. Sending results from the server to the device

[1507] The server sends the formatted prediction results and suggestions to the user's device.

[1508] 8. Displaying results on a terminal

[1509] The device displays the message, "The predicted congestion level at Tokyo's theme parks is 50%. There are also quiet parks that are recommended for a change of pace."

[1510] In this way, the present invention is a system that supports visit plans that result in even higher user satisfaction by predicting congestion and suggesting places to visit that take into account the user's emotional state.

[1511] The processing flow will be explained below.

[1512] Program processing steps

[1513] Server Processing Steps

[1514] Step 1:

[1515] The server receives a request from the user, which includes the destination information and a comment. The comment can convey the user's emotional state, such as if they are tired.

[1516] Step 2:

[1517] The server retrieves past congestion data for the specified destination from the database, including congestion levels during specific periods in the past and congestion conditions during events.

[1518] Step 3:

[1519] The server sends a request to an external weather information service API to obtain weather data for the day at the destination, including weather conditions, temperature, and chance of precipitation.

[1520] Step 4:

[1521] The server uses an emotion engine to analyze the user's comments and determine their emotions. For example, it can recognize that a comment such as "I'm tired" indicates that the user is seeking relaxation.

[1522] Step 5:

[1523] The server predicts congestion based on past congestion data, weather data, and user emotion data. The emotion data is used to determine the user's preferred situation.

[1524] Step 6:

[1525] The server recommends optimal destinations based on the crowding prediction results and the user's emotional state. For example, if the predicted crowding rate for a theme park is 50%, the server may recommend a quiet park.

[1526] Step 7:

[1527] The server then formats the predictions and suggestions into an appropriate format (e.g., JSON). This data includes suggestions based on crowding, date, time, location, and the user's emotional state.

[1528] Step 8:

[1529] The server then sends the formatted prediction results and suggestions to the user's device as an HTTP response.

[1530] Terminal processing steps

[1531] Step 1:

[1532] The user enters information about the place they visited and their emotional state as a comment into the input form on their device. For example, they might enter "a theme park in Tokyo" and comment "I've been feeling tired lately."

[1533] Step 2:

[1534] The device sends a request to the server based on the entered visit information and comments. This request includes the visit destination and emotional state.

[1535] Step 3:

[1536] The device receives a response from the server, which includes the congestion prediction results and suggested places to visit.

[1537] Step 4:

[1538] The device analyzes the prediction results and suggestions received and displays them in a format that is easy for the user to understand. For example, it displays a message on the screen saying, "The predicted congestion level at Tokyo's theme parks is 50%. There are also some quiet parks that we recommend for a change of pace."

[1539] Specific examples

[1540] Server Processing Steps

[1541] 1. Step 1: The server receives a request from the user saying, "I'm planning to go to a theme park in Tokyo, but I'm tired recently and would prefer a quiet place."

[1542] 2. Step 2: The server references the database to obtain historical crowd data for theme parks in Tokyo.

[1543] 3. Step 3: The server sends a request to the weather information API to obtain the current day's weather data for Tokyo.

[1544] 4. Step 4: The server uses the emotion engine to recognize the "tired" state from the user's comments.

[1545] 5. Step 5: The server uses a congestion prediction algorithm to predict the level of congestion based on past data and weather data. For example, if the weather is good, it predicts the level of congestion to be 50%. It also determines from emotional data that people prefer "quiet places."

[1546] 6. Step 6: The server combines the crowd prediction results with the emotion data and suggests, "The predicted crowding level at theme parks in Tokyo is 50%, but there are also quiet parks that are recommended for a change of pace."

[1547] 7. Step 7: The server formats the prediction results and suggestions into JSON format.

[1548] 8. Step 8: The server sends the formatted prediction results and suggestions to the user's device.

[1549] Terminal processing steps

[1550] 1. Step 1: The user types "Tokyo theme parks" into the device's input field and comments "I've been feeling tired lately."

[1551] 2. Step 2: The device sends information including "Tokyo theme parks" and "I've been feeling tired lately" to the server.

[1552] 3. Step 3: The device receives the response from the server and obtains the prediction results and suggestions.

[1553] 4. Step 4: The device will display the following message on the screen: "Tokyo theme parks are expected to be 50% crowded. There are also some quiet parks that are recommended for a change of pace."

[1554] The above is the specific program processing flow of the system that combines the emotion engine. As a result, users can receive congestion predictions and suggested places to visit that reflect their own emotional state, allowing them to create optimal travel plans.

[1555] Example 2

[1556] 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."

[1557] In today's world, predicting crowd levels at specific locations and events is important to users, but existing systems are unable to provide optimal suggestions that take into account the user's emotional state. Furthermore, it is difficult to make advanced predictions that combine weather information and past crowd data, making it difficult to provide visit plans that will satisfy users.

[1558] 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.

[1559] In this invention, the server includes means for receiving destination information and emotional state input by the user, means for retrieving past congestion data from a database, means for retrieving weather data for the current day from an external weather information service, means for analyzing the emotional state and retrieving the user's emotional data, means for predicting congestion based on the past congestion data, weather data, and emotional data, means for providing destination suggestions based on the congestion prediction result and the user's emotional state, means for formatting the prediction result and the suggested destinations into a predetermined format, and means for presenting the prediction result and the suggested destinations to the user. This enables congestion prediction and suggestions of optimal destinations that take the user's emotional state into consideration.

[1560] A "user" is an entity that utilizes the system to input destination information and emotional state.

[1561] "Destination information" is information about places and events that the user plans to visit.

[1562] "Emotional state" is information that indicates the user's current psychological state.

[1563] A "terminal" is a device that allows a user to input and send information, and includes smartphones, personal computers, etc.

[1564] A "request" refers to an operation of sending data, including information entered by a user, to a server.

[1565] A "server" is a computer system that receives a request and processes and provides the required data.

[1566] A "database" is a system that stores and manages information such as past congestion data.

[1567] A "weather information service" is an external service that provides weather information such as the weather, temperature, and probability of precipitation for the day.

[1568] An "emotion engine" is a system that analyzes emotions from user input data and voice data.

[1569] "Crowd prediction" refers to estimating future congestion levels at a destination based on past congestion data, weather data, and emotion data.

[1570] "Suggesting destinations" refers to suggesting optimal destinations based on the congestion prediction results and the user's emotional state.

[1571] "Formatting the prediction results" refers to the operation of converting the congestion prediction results and suggested destinations into a fixed format.

[1572] A "format" is data organized in a specific way, typically JSON format.

[1573] A "generative AI model" is an artificial intelligence model used to analyze data and make predictions.

[1574] "Comment" refers to text information such as an emotional state entered by a user.

[1575] The present invention is a system that allows users to create optimal visit plans based on congestion information at destinations and their own emotions. This system uses an emotion engine to predict congestion and suggest destinations based on the user's emotional state. The specific configuration and operation are as follows.

[1576] System configuration

[1577] Hardware and Software

[1578] 1. User Device:

[1579] Devices such as smartphones, tablets, and personal computers are used as user terminals, providing an interface for users to input visiting information and emotional states.

[1580] 2. Server:

[1581] A server system with powerful computing power is required to manage the database, communicate with weather information services, calculate congestion forecasts, analyze sentiment, and format the results.

[1582] 3. Database:

[1583] A database is used to store and manage past congestion data, including congestion levels over a certain period of time in the past and congestion conditions during specific events.

[1584] 4. Emotion Engine:

[1585] There is a need for an emotion engine that uses NLP (Natural Language Processing) techniques to analyze emotions from user input data and voice data.

[1586] 5. Weather information services:

[1587] Use an API to obtain real-time weather data from external weather information services.

[1588] 6. Generative AI Models:

[1589] A generative AI model used to integrate multiple data sets and make crowd predictions and suggest places to visit.

[1590] Operation explanation

[1591] The user inputs destination information and emotional state using the device. For example, the user inputs, "I'm planning to go to a theme park in Tokyo, but I've been feeling tired lately, so I'd prefer a quiet place." This information is then sent from the device to the server.

[1592] The server does the following:

[1593] 1. Obtain historical crowd data for theme parks in Tokyo from the database.

[1594] 2. Obtain the current day's weather data for Tokyo from an external weather information service.

[1595] 3. Use the emotion engine to analyze the user's emotion data. In this example, it recognizes that the user is in a "tired" state.

[1596] 4. Crowd forecasting based on historical crowd data, weather data, and sentiment data. Generative AI models are used to predict current and future crowd levels at destinations.

[1597] 5. Creates recommendations for places to visit based on the crowding prediction results and the user's emotional state. In this example, the system generates recommendations such as, "The predicted crowding rate at theme parks in Tokyo is 50%. There are also quiet parks recommended for a change of pace."

[1598] 6. Format the prediction results and destination suggestions into an appropriate format (e.g., JSON).

[1599] The formatted data is sent from the server to the device, which then displays it to the user. For example, a message such as "The predicted congestion level at Tokyo's theme parks is 50%. There are also some quiet parks recommended for a change of pace" may be displayed on the device screen.

[1600] Specific examples

[1601] Let's say a user types, "I'm planning to go to a theme park in Tokyo, but I'm tired these days and would prefer a quiet place." In this case, the following prompt sentence is used:

[1602] Example prompt sentence:

[1603] "I'm planning to go to a theme park in Tokyo, but I've been feeling tired lately so I'd prefer a quiet place."

[1604] Based on this information, the server processes the data and proposes an optimal visit plan to the user. In this way, the present invention supports visit plans that increase user satisfaction.

[1605] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1606] Step 1:

[1607] The user enters the destination information and emotional comment into the input fields on the device. Specifically, they enter "Tokyo theme park" and "I've been feeling tired lately," and press the send button. The input information is converted to JSON format on the device. Input: Destination information and emotional comment. Output: Request data in JSON format.

[1608] Step 2:

[1609] The device sends a JSON-formatted request containing the entered information to the server as an HTTP POST request. Specifically, the device parses the user's input into JSON format and sends the data to the specified API endpoint. Input: JSON-formatted request data. Output: HTTP POST request to the server.

[1610] Step 3:

[1611] The server receives the request and retrieves past congestion data from the database based on the destination information. Specifically, it analyzes the destination information in the request and executes an SQL query based on that information to extract congestion data. Input: Destination information. Output: Past congestion data.

[1612] Step 4:

[1613] The server calls the API of an external weather information service to obtain weather data for the day. It sends an API request and analyzes the received data. Specific weather information includes the weather, temperature, and probability of precipitation. Input: API request. Output: Weather data.

[1614] Step 5:

[1615] The server uses an emotion engine to obtain the user's emotion data. Specifically, it analyzes the entered emotion comment and identifies the emotion using natural language processing (NLP) technology. In this case, the comment "I've been tired lately" is analyzed and evaluated as a "tired" state. Input: Emotion comment. Output: Emotion data.

[1616] Step 6:

[1617] The server combines past congestion data, weather data, and emotion data to perform congestion prediction. Using a generative AI model, multiple data sets are input and prediction results are generated. At this time, emotion data is taken into consideration and destinations with low congestion levels are recommended preferentially. Input: Past congestion data, weather data, emotion data. Output: Congestion prediction results.

[1618] Step 7:

[1619] The server generates optimal destination suggestions for the user based on the congestion prediction results and emotion analysis results. Specifically, it makes suggestions that match the user's emotional state, such as less crowded places or quiet parks. Input: Congestion prediction results, emotion analysis results. Output: Destination suggestions.

[1620] Step 8:

[1621] The server formats the congestion prediction results and visit suggestions into an appropriate format (e.g., JSON format). This is the process of converting the results into a format that can be easily analyzed and displayed on the user's device. Input: Congestion prediction results, visit suggestions. Output: Formatted prediction results and suggestion data.

[1622] Step 9:

[1623] The server returns the formatted prediction results and suggestions to the user's device. This is sent in the form of an HTTP response, which the device receives. Input: Formatted prediction results and suggestion data. Output: HTTP response to the device.

[1624] Step 10:

[1625] The device displays the prediction results and suggested places to visit received by the device to the user. For example, a message such as "The predicted congestion level at theme parks in Tokyo is 50%. There are also quiet parks recommended for a change of pace" is displayed on the screen. Input: HTTP response data. Output: Message to be displayed to the user.

[1626] (Application example 2)

[1627] 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."

[1628] Conventional congestion prediction systems for destinations do not take into account the user's emotional state, so the selection of destinations may not be appropriate for the user's psychological state. Furthermore, there is a need for a system that can not only predict congestion but also suggest optimal destinations based on the user's current emotions. Therefore, it is necessary to develop a new system that can predict congestion and recommend optimal destinations while taking into account the user's emotions.

[1629] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving destination information and emotion information input by the user, means for acquiring past congestion data from a database based on the destination information, means for acquiring weather data for the current day from an external weather information service based on the destination information, means for predicting congestion based on the acquired past congestion data, weather data for the current day, and user emotion data, means for recommending optimal destinations based on the user's emotional state, and means for providing the user with the congestion prediction results and recommendation results. This makes it possible to predict congestion and suggest optimal destinations that take the user's emotional state into consideration.

[1630] "Destination information entered by the user" is information about a specific place or facility that the user wishes to visit.

[1631] "Emotion information" is information about the user's current emotional state and psychological situation.

[1632] "Past congestion data" refers to data relating to past congestion at a particular destination.

[1633] A "database" is a system for storing, managing, and searching data.

[1634] A "weather information service" is a service that provides information about weather and climate from an external source.

[1635] "Weather data for the current day" refers to data related to the climate, such as the weather, temperature, and probability of precipitation, for a specific day.

[1636] The "emotion engine" is an engine for analyzing the emotional state from text and voice input by the user.

[1637] A "crowd forecast" means a method or algorithm for predicting the crowding level of a destination based on past data, current weather conditions, and the user's emotional state.

[1638] A "recommendation means" is a method or system for suggesting optimal destinations to a user based on the user's emotional state and congestion prediction results.

[1639] The "means for providing predicted results and recommended results" refers to a method or system for providing the results generated by the server to the user in an easy-to-understand manner.

[1640] A "format" is a rule or method for arranging data or information into a particular structure or form.

[1641] The present invention provides a system for predicting congestion and suggesting optimal destinations that take into account the emotional state of a user. This system is implemented using the following hardware and software.

[1642] Hardware and Software

[1643] Smartphone (terminal): The user enters destination information and emotion information, and the results are displayed.

[1644] Server: Receives information from users and processes the data.

[1645] Database (SQLite): Stores and manages past congestion data.

[1646] External weather information service (Weather API): Obtains the current day's weather data.

[1647] Emotion engine: Analyzes the user's emotional state from input data.

[1648] Programming language (Python) and framework (Flask): Performs server-side processing.

[1649] Program processing

[1650] 1. Receiving user input:

[1651] The server receives destination information and emotion information from the smartphone terminal. The user inputs the place they want to visit and their current emotional state.

[1652] 2. Data Acquisition:

[1653] The server acquires past congestion data from a database based on the received destination information.

[1654] In addition, weather data for the day is obtained using an external weather information service.

[1655] 3. Emotion analysis:

[1656] The emotion engine analyzes the user's input data and extracts the user's current emotional state.

[1657] 4. Congestion prediction and destination suggestions:

[1658] The server makes congestion predictions based on acquired past congestion data, weather data for the day, and user emotion data.

[1659] To suggest the most suitable destination according to a user's emotional state.

[1660] 5. Results provided:

[1661] The server formats the congestion prediction results and suggested places to visit into an appropriate format and sends them to the smartphone device.

[1662] The terminal displays the received results to the user.

[1663] Specific examples

[1664] For example, if a user inputs "I want to go to the shopping mall in Shinjuku, but I've been really tired lately," the server will process the request based on the following prompt:

[1665] "A user types, 'I want to go to a shopping mall in Shinjuku, but I've been feeling really tired lately.' Please predict how crowded it will be and suggest a quiet place so I can relax."

[1666] In this case, the server predicts congestion at shopping malls in Shinjuku based on past congestion data and the weather data for the day, and suggests quiet cafes where users can relax based on their emotional information. These suggestions are displayed on the smartphone device and used as a reference when the user chooses the best place to visit.

[1667] The above is an embodiment of the present invention. This system makes it possible to predict congestion and suggest places to visit while taking into account the emotional state of the user.

[1668] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1669] Step 1:

[1670] The user inputs destination information and emotional information on their smartphone. Specifically, the user inputs "the place they want to visit" (e.g., a shopping mall in Shinjuku) and "emotional state" (e.g., "I've been feeling very tired lately") into the application's input fields. The input data includes destination information and emotional comments. The input data here is sent to the server in the next step.

[1671] Step 2:

[1672] The device sends the entered visit information and emotion information to the server. The device then formats the input data into an appropriate data format, such as JSON, and sends it to the server via an HTTP request. This sent data includes the user name, visit information, and emotion comment. The server receives this and begins processing the next step.

[1673] Step 3:

[1674] Based on the destination information received by the server, past congestion data is retrieved from the database. The server issues an SQL query to retrieve data on the corresponding destination from a pre-stored congestion database. The destination information is the input data, and past congestion data is obtained as output data.

[1675] Step 4:

[1676] The server uses an external weather information service to obtain weather data for the day. The server sends an API request to obtain information such as the weather, temperature, and probability of precipitation for the day. The input data is the area information for the destination, and the output data is the weather information for the day.

[1677] Step 5:

[1678] The server uses an emotion engine to analyze the user's emotional state from the input data. The emotion engine processes the user's emotional comments and extracts emotional data such as "I'm tired" or "I want to relax." The input data is the emotional comments, and the output data is the emotional state.

[1679] Step 6:

[1680] The server predicts congestion based on the acquired past congestion data, weather data for the day, and user emotion data. It then executes a congestion prediction algorithm to predict future congestion levels. The input data are past congestion data, weather data, and emotion data, and the output data is the predicted future congestion level.

[1681] Step 7:

[1682] The server suggests optimal destinations based on the user's emotional state. For example, if congestion is predicted or the user is tired, it suggests a quiet cafe or other place where the user can relax. The input data are congestion prediction data and emotion data, and the output data are the suggested destinations.

[1683] Step 8:

[1684] The server formats the congestion prediction results and recommended destinations and converts them into an appropriate format, such as JSON. The formatted data includes congestion levels, weather information, and optimal destinations. The input data here are the prediction results and recommended destinations, and the output data is the formatted data.

[1685] Step 9:

[1686] The server sends the formatted data to the terminal, and returns data containing the prediction results and suggestions obtained as an HTTP response. This data is to be presented to the user on the terminal.

[1687] Step 10:

[1688] The device then displays the received prediction results and suggestions to the user. For example, a message such as "The predicted congestion level at the Shinjuku shopping mall is 70%. If you want to relax, we recommend a quiet cafe nearby." The user can then decide what to do next based on this information.

[1689] These are the processing steps for the entire system. Each step explains how the input data is processed and what output is obtained.

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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.

[1695] 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.

[1696] 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).

[1697] 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.

[1698] 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."

[1699] 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.

[1700] 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).

[1701] 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.

[1702] 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.

[1703] 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.

[1704] 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.

[1705] 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.

[1706] 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.

[1707] 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.

[1708] 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.

[1709] 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.

[1710] 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.

[1711] The following is further disclosed regarding the above embodiment.

[1712] (Claim 1)

[1713] means for receiving visiting destination information input by a user;

[1714] means for acquiring past congestion data from a database based on the destination information;

[1715] means for acquiring weather data for the day from an external weather information service based on the destination information;

[1716] A means for predicting congestion based on the past congestion data and weather data for the relevant day;

[1717] means for providing the congestion prediction result to a user;

[1718] A system including:

[1719] (Claim 2)

[1720] 2. The system according to claim 1, wherein the congestion prediction means performs a calculation to decrease the congestion level when the weather data indicates bad weather and to increase the congestion level when the weather data indicates good weather.

[1721] (Claim 3)

[1722] 2. The system according to claim 1, further comprising means for formatting the congestion prediction result in a predetermined format and displaying it to a user.

[1723] "Example 1"

[1724] (Claim 1)

[1725] means for receiving visiting destination information input by a user;

[1726] means for acquiring past congestion data from a database based on the destination information;

[1727] means for acquiring weather data for the day from an external weather information service based on the destination information;

[1728] A means for predicting congestion based on the past congestion data and weather data for the relevant day;

[1729] means for converting the congestion prediction result into a predetermined format and transmitting the result to a user terminal;

[1730] means for displaying the congestion prediction result on a user's terminal;

[1731] The congestion prediction means is a means for making a prediction based on past congestion data and weather data using a machine learning model;

[1732] A system including:

[1733] (Claim 2)

[1734] 2. The system according to claim 1, wherein the congestion prediction means performs a calculation to decrease the congestion level when the weather data indicates bad weather and to increase the congestion level when the weather data indicates good weather.

[1735] (Claim 3)

[1736] 2. The system according to claim 1, further comprising means for receiving the congestion prediction result and displaying it on a user's terminal.

[1737] "Application Example 1"

[1738] (Claim 1)

[1739] means for receiving visiting destination information input by a user;

[1740] means for acquiring past congestion data from a database based on the destination information;

[1741] means for acquiring weather data for the day from an external weather information service based on the destination information;

[1742] A means for predicting congestion based on the past congestion data and weather data for the relevant day;

[1743] means for providing the congestion prediction result to a user;

[1744] A means for displaying the congestion prediction result;

[1745] A system including:

[1746] (Claim 2)

[1747] 2. The system according to claim 1, wherein the congestion prediction means performs a calculation to decrease the congestion level when the weather data indicates bad weather and to increase the congestion level when the weather data indicates good weather.

[1748] (Claim 3)

[1749] The system according to claim 1, further comprising means for formatting the congestion prediction result in a predetermined format such as JSON format.

[1750] "Example 2: Combining Emotion Engines"

[1751] (Claim 1)

[1752] means for receiving visiting destination information and emotional state input by a user;

[1753] means for acquiring past congestion data from a database based on the destination information;

[1754] means for acquiring weather data for the day from an external weather information service based on the destination information;

[1755] means for analyzing the emotional state and acquiring emotional data of the user;

[1756] means for predicting congestion based on the past congestion data, the weather data, and the emotion data;

[1757] a means for providing suggestions of destinations to visit according to the congestion prediction result and the emotional state of the user;

[1758] means for formatting the congestion prediction result and the visiting destination suggestions into a predetermined format;

[1759] means for presenting the prediction results and suggested destinations to the user;

[1760] A system including:

[1761] (Claim 2)

[1762] 2. The system according to claim 1, wherein the congestion prediction means predicts the degree of congestion taking into consideration the weather data and the emotion data.

[1763] (Claim 3)

[1764] 10. The system of claim 1, further comprising means for using a generative AI model to analyze the emotion data.

[1765] "Application example 2 when combining emotion engines"

[1766] (Claim 1)

[1767] means for receiving destination information and emotion information input by a user;

[1768] means for acquiring past congestion data from a database based on the destination information;

[1769] means for acquiring weather data for the day from an external weather information service based on the destination information;

[1770] A means for predicting congestion based on the acquired past congestion data, weather data for the day, and user emotion data;

[1771] A means for recommending optimal destinations according to the user's emotional state;

[1772] means for providing the congestion prediction result and recommendation result to a user;

[1773] A system including:

[1774] (Claim 2)

[1775] 2. The system according to claim 1, wherein the congestion prediction means performs a calculation to decrease the congestion level when the weather data indicates bad weather and to increase the congestion level when the weather data indicates good weather.

[1776] (Claim 3)

[1777] 2. The system according to claim 1, further comprising means for formatting the congestion prediction result and the recommendation result in a predetermined format and displaying them to the user. [Explanation of symbols]

[1778] 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 receiving visiting destination information input by a user; means for acquiring past congestion data from a database based on the destination information; means for acquiring weather data for the day from an external weather information service based on the destination information; A means for predicting congestion based on the past congestion data and weather data for the relevant day; means for providing the congestion prediction result to a user; A system including:

2. 2. The system according to claim 1, wherein said congestion prediction means performs a calculation to decrease the congestion level when said weather data indicates bad weather and to increase the congestion level when said weather data indicates good weather.

3. 2. The system according to claim 1, further comprising means for formatting the congestion prediction result in a predetermined format and displaying it to a user.

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