system

Abstract

We provide the system. [Solution] An input method for the user to select the facility to visit, A server for collecting and analyzing waiting time information for selected facilities in real time, A display means for displaying analyzed waiting time and recommended visit timing information on the user's terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] When visiting facilities such as government offices, hospitals, and restaurants, many users face the problem of long waiting times, which cause physical and mental burdens and prevent them from effectively using their limited living time. Especially for users who cannot adjust their schedules, the consumption of time on the spot is inevitable, which deteriorates their impression of the facility and results in losses of visiting opportunities and decreases in revenue. Thus, there is a problem of how to shorten the waiting time.

Means for Solving the Problems

[0005] This invention provides a system that allows users to check the waiting time of a facility they plan to visit in real time via their terminal. The system comprises an input means for the user to select a facility, a server means for collecting waiting time information for the selected facility and analyzing it in real time, and a display means for displaying the predicted waiting time and recommended visit timing generated as a result of the analysis to the user. Furthermore, by using a generating AI to predict future waiting times using past and present waiting time data, the system enables users to effectively adjust their visit timing. As a result, users can engage in other meaningful activities, and facilities can also distribute waiting times.

[0006] A "user" is a person who uses the system to check and adjust waiting times at the facilities they visit.

[0007] "Facility" is a general term referring to places such as government offices, hospitals, and restaurants that users plan to visit.

[0008] "Input method" refers to the interface or device that allows the user to select the facility they plan to visit.

[0009] "Server system" refers to a central processing unit used to collect, analyze, and provide facility waiting time information to users.

[0010] "Waiting time information" refers to data on the current waiting situation and predicted waiting times at a facility.

[0011] The "generation processing means" is a processing function for performing analysis to predict waiting times based on past and present data.

[0012] A "display means" is an interface for visually providing users with information collected and analyzed on a terminal.

[0013] "Recommended visit timing" is information that indicates the optimal time for a user to visit, based on predicted waiting times. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system for effectively managing waiting times at facilities visited by users. This system allows users to select a facility and see waiting times in real time. Facility waiting time data is collected by a server, and future waiting times are predicted through analysis by a generative AI based on past data and current conditions. The analysis results are transmitted from the server to the user's terminal, allowing the user to view waiting time information and the optimal timing for visits on the application screen.

[0036] As a concrete example, consider a situation where a user is planning to visit a popular restaurant. The user uses the app to select the restaurant they intend to visit. The device requests wait time information for the selected restaurant from the server. The server analyzes the real-time wait time data provided by the restaurant and inputs historical data and current trends into a generating AI to make predictions. As a result, the server calculates the optimal visit time along with the predicted wait time and provides it to the user. The user can check the times with shorter wait times through the app and adjust the timing of their visit accordingly.

[0037] This system allows users to minimize waiting times and use their time more effectively. Furthermore, it enables facilities to distribute waiting times, promoting more efficient operations.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user launches the smartphone app and selects the facility they wish to visit. The device retrieves the ID of the selected facility.

[0041] Step 2:

[0042] The terminal sends a request to the server containing the acquired facility ID and user information (including location information and current date and time).

[0043] Step 3:

[0044] The server receives requests from terminals and searches the database for waiting time data for the corresponding facilities.

[0045] Step 4:

[0046] The server uses generative AI to analyze the facility's past wait time data and predict future wait times along with current real-time data.

[0047] Step 5:

[0048] The server calculates the recommended visit time for the user, along with a predicted wait time, and then generates a response.

[0049] Step 6:

[0050] The server sends the generated response data to the terminal.

[0051] Step 7:

[0052] The terminal displays received waiting time information and recommended visit timings on the user interface.

[0053] Step 8:

[0054] Based on the displayed information, users adjust the timing of their visit and determine the optimal time to actually visit.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] At facilities visited by users, it is difficult to minimize the time wasted due to the uncertainty of waiting times, which hinders the efficient use of infrastructure. Furthermore, when users plan their visits, there is a lack of effective means to predict the congestion level of their destinations in advance, making it difficult to determine the optimal time to visit.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes an information processing device for collecting and analyzing waiting time information for selected facilities in real time, communication means for calculating and transmitting the generated waiting time prediction results to a terminal, and generation means for predicting current and future waiting times using past waiting time data. This makes it possible for users to predict the congestion level of their destination in advance and select the optimal timing for their visit.

[0060] An "input device" is a device that provides an interface for a user to select a facility to visit.

[0061] An "information processing device" is a computer system that collects waiting time information for selected facilities and analyzes the data in real time.

[0062] A "display device" is a device that visually presents analyzed waiting time and recommended visit timing information to the user's terminal.

[0063] "Communication means" refers to a device that has network functionality for sending and receiving data between a server and a user terminal.

[0064] A "generation means" is a device that executes algorithms and techniques for predicting current and future waiting times based on past waiting time data.

[0065] A "calculating device" is a computer system that uses data, including the user's location information, to calculate the optimal timing for a visit.

[0066] This invention is implemented as a system for effectively managing waiting times at facilities visited by users. The system mainly consists of multiple elements, including a user's terminal, a server, and a generative AI model.

[0067] The user selects the facility they plan to visit using an application on their device. The device has a mechanism to send a request to the server based on this selection information. Specifically, the user can select a facility using the application's interface. The device, as an input means, has a communication mechanism to send this information to the server.

[0068] The server functions as an information processing device, acquiring real-time wait time information from selected facilities. This data is typically obtained through POS systems or reservation management systems. The server inputs the collected data into a generative AI model, which considers historical data and current trends to predict future wait times. As a specific example, the generative AI model can be "OpenAI's GPT-4 (registered trademark)".

[0069] The generated prediction information is transmitted to the user's terminal via a communication method. The user's terminal functions as a display device, visually presenting the received prediction information on waiting times and the optimal visit timing to the user. This allows the user to know the congestion status of their destination in advance and receive support in deciding the optimal visit timing.

[0070] As a concrete example, consider a scenario where a user wants to visit a popular restaurant. The user selects the restaurant through their device, and the device requests real-time wait time data for that restaurant from a server. The server combines the collected current data with past trends and inputs a prompt message into a generating AI model, such as, "Based on data from the past year, predict the wait time for the next three hours and suggest the optimal time to visit." Once the prediction is complete, the optimal visit time is sent to the device and displayed for the user to confirm. In this way, the user can optimize their visit plan and enjoy a smoother experience.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The user opens the application on their device and selects the facility they plan to visit. As input, the user taps the facility name or category to obtain facility ID information. The output is detailed information about the selected facility. This information is saved on the device as request data.

[0074] Step 2:

[0075] The terminal sends a request to the server. The input includes the ID of the selected facility and the user's current location information. Specifically, the terminal's built-in GPS function is used to obtain the current location, and the data is sent to the server via the communication module. The output is an acknowledgment that the server has received the request.

[0076] Step 3:

[0077] The server collects relevant wait time data using an information processing device based on the received facility ID. The input is request data from the terminal, and the output is real-time wait time data. This process integrates with the facility's reservation management system and POS system to obtain the latest data.

[0078] Step 4:

[0079] The server combines collected wait time data with historical data and inputs it into a generating AI model. The input data includes historical wait time information and current trend information. The prompt message includes instructions such as "Predict the wait time for the next 3 hours at the selected facility," and the output is a prediction of future wait times.

[0080] Step 5:

[0081] The server analyzes the output from the generated AI model and calculates the optimal visit time. The input is the AI ​​model's prediction result, and the output is the optimal visit timing. In the calculation process, the system combines the user's desired visit time with the prediction data to avoid congestion.

[0082] Step 6:

[0083] The server sends the optimal visit time to the user's terminal. The input is the calculated optimal visit time, and the result is sent to the terminal using a communication method. The output is data containing the optimal visit time for display on the user's terminal.

[0084] Step 7:

[0085] The optimal visit timing received on the user's device is displayed. Input is data transmitted from the server, and output is visual information provided to the user. The device presents the information to the user in an easy-to-understand format, such as graphs and text. Based on this information, the user can adjust their visit plan.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] In physical stores such as shopping malls and theme parks, it is difficult for users to know in advance how crowded the stores and attractions they plan to visit are, making it difficult to plan an efficient visit. In particular, increased waiting times due to congestion and the choice of unexpected routes can prevent users from visiting the facility as planned, forcing them to use their time inefficiently. This leads to decreased user satisfaction, and the facilities themselves face a decline in operational efficiency due to the concentration of visitors.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes input means for the user to select a facility to visit, server means for collecting and analyzing waiting time information for the selected facility in real time, display means for displaying the analyzed waiting time, recommended visit timing, and optimal travel route on the user terminal, and location information processing means for acquiring the user's location information and optimizing the travel route. As a result, the user can understand the congestion status of the facility or attraction to visit in advance and make an efficient visit with minimal waiting time. This improves user satisfaction and allows facilities to improve operational efficiency.

[0091] An "input method" is an interface used by the user to select the facility they wish to visit.

[0092] A "server system" is a system for collecting facility waiting time data and analyzing it in real time.

[0093] "Display means" refers to a device or software for visually presenting analyzed waiting time information, recommended visit times, and optimal travel routes to the user's terminal.

[0094] "Location information processing means" refers to technology that acquires the user's current location and optimizes the visit route based on that information.

[0095] "Waiting time information" refers to information about the time spent at a facility or waiting in line, based on the current and predicted crowd levels at the facility or attraction.

[0096] "Generation processing means" refers to analytical and computational techniques used to predict current and future situations by utilizing past data.

[0097] "Travel route" refers to the optimal path a user takes from their starting point to their destination.

[0098] The system that implements this application allows users to check the waiting time at a facility they wish to visit in real time and create an efficient visit plan. This system consists of the following hardware and software:

[0099] First, the user operates the application using a smartphone or smart glasses and selects the facility they wish to visit. This selection is made through an input device, and the information is sent to the server.

[0100] Next, the server retrieves waiting time information for the selected facility from the facility and collects it in real time using Firebase. Then, based on a generative AI model using TENSORFLOW® or PyTorch, it predicts waiting times from historical data and current conditions, and calculates the optimal visit time and route based on the user's location. This allows the user to reduce unnecessary waiting times and obtain an efficient travel route.

[0101] The analysis results obtained from the server are transmitted in real time to a smartphone or smart glasses via Flask. Users can then view specific waiting times, optimal visit times, and recommended routes through the application's display method.

[0102] For example, a user might want to visit a specific store in a shopping mall. In this case, the system would suggest less crowded times and routes that are more comfortable to travel along, avoiding peak hours. An example of a prompt message might be: "The store you're looking for is currently crowded. We recommend visiting around 2 PM. Would you like to check the shortest route?"

[0103] This system allows users to identify less crowded times in advance and use facilities efficiently, while also enabling facilities to improve operational efficiency by distributing congestion.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The user selects the facility they wish to visit via their smartphone. This selection information is sent from the device to the server. The input includes the facility's name and category. The output is the server's identification information for the selected facility.

[0107] Step 2:

[0108] The server collects real-time wait time data for the selected facility through the facility's own system or API. Firebase is used for data retrieval and storage. The input is the facility's identification information, and the output is the current wait time data.

[0109] Step 3:

[0110] The server uses a generative AI model in TensorFlow or PyTorch to analyze acquired latency data and historical latency data. The data is input into the AI ​​model, and predicted latency is output. Here, trend analysis of historical data and processing of current latency data are performed to predict future conditions.

[0111] Step 4:

[0112] The server calculates the optimal travel route based on the user's location information. This calculation uses the Google® Maps API to obtain the current location and calculate the shortest path to the destination. The input is the user's location information, and the output is the optimal route.

[0113] Step 5:

[0114] The server sends information, including the calculated optimal visit time and route, to the terminal via Flask. This transmission includes real-time data updates and is optimized for fast response times. The input is the predicted wait time and route information, and the output is the visit guidance information displayed on the user's terminal.

[0115] Step 6:

[0116] The terminal displays the received information to the user. Using the display method, specific waiting times, optimal visit times, and recommended travel routes are visually presented. Input is data from the server, and output is a visit plan that is visually understandable to the user.

[0117] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0118] This invention combines a system for effectively managing waiting times at facilities visited by users with an emotion engine that recognizes user emotions. During the process of a user selecting a facility and monitoring waiting times in real time, the emotion engine analyzes the user's facial expressions, tone of voice, and other factors to recognize their emotions. This recognized emotion information is then analyzed by a server to determine how stressed the user is experiencing during the current waiting time.

[0119] As a concrete example, consider a scenario where a user attempts to visit a crowded hospital. The user selects a hospital through the app and sets a planned visit time on their device. The server retrieves and analyzes hospital waiting time information, calculating a waiting time prediction and recommended visit timing based on real-time data. Here, the emotion engine uses data obtained from the user's camera and microphone to analyze the user's emotions from their facial expressions and voice.

[0120] For example, if a user's facial expression indicates displeasure, the emotion engine determines a high stress level. The server uses this information to provide the user with special recommendations for faster service or alternative options to reduce waiting times (e.g., other less busy times or different facilities).

[0121] Furthermore, the device can notify users of recommended activities during their waiting time (for example, recommendations for relaxing music or guidance for simple exercises) based on their emotional state. This allows users to spend their waiting time more productively and improves the operational efficiency of the facility.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The user launches the smartphone app and selects the facility they wish to visit. The device records the ID of the selected facility and the user's location information.

[0125] Step 2:

[0126] The terminal sends a request to the server that includes the facility ID and user information. This request also includes a signal to trigger analysis by the emotion engine.

[0127] Step 3:

[0128] The server receives the request and retrieves the real-time wait time for the specified facility from the database. Simultaneously, a generating AI uses historical data to predict future wait times.

[0129] Step 4:

[0130] The emotion engine built into the device analyzes the user's facial expressions and voice through the camera and microphone to determine their emotional state (e.g., anxiety, stress, relaxation, etc.).

[0131] Step 5:

[0132] The server receives emotional data from the emotion engine and evaluates the user's stress level. This evaluation is treated as a factor influencing the waiting status.

[0133] Step 6:

[0134] The server combines wait time predictions, stress level assessments, and recommended visit timings to generate the best visit options for the user. This may include suggestions for alternative time slots or facilities.

[0135] Step 7:

[0136] The device receives the calculated information and displays the waiting time, optimal visit timing, and recommended activities for mood improvement on the user interface.

[0137] Step 8:

[0138] Based on this information, users can adjust their visit plans and proceed with the visit process while minimizing waiting times and managing their emotions.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] Users often experience unnecessary stress and wasted time due to unclear waiting times at places they visit. Furthermore, when waiting times do occur, there is a lack of information on how to best utilize that time. Flexible waiting time management and suggestions that take users' emotional states into account are needed.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes receiving means for selecting a location for the user to visit, information processing means for collecting and storing waiting time information for the selected location, analysis means for analyzing the user's emotional state and generating emotional information, integrated analysis means for integrating the analyzed emotional information and waiting time, evaluating the burden felt by the user, and suggesting improvement plans, and presentation means for presenting the integrated information to the user terminal. This makes it possible to optimize and effectively utilize waiting time based on the user's emotional state.

[0144] "Receiving means" refers to a device or program for a user to select a location to visit and transmit that selection information to the system.

[0145] "Information processing means" refers to a device or program for collecting waiting time information related to a selected location and storing it statistically or in real time.

[0146] "Analysis means" refers to a device or program for acquiring data such as the user's facial expressions and voice, and for recognizing and analyzing their emotional state.

[0147] An "integrated analysis means" is a device or program that integrates analyzed emotional information and collected waiting time information to evaluate the stress felt by the user and derive appropriate improvement suggestions.

[0148] "Presentation means" refers to a device or program that displays integrated and analyzed information on a user terminal in an intuitive and easy-to-understand manner, and provides feedback to the user.

[0149] This invention is a system for optimizing waiting times and improving the user experience during those times. Users select the location they wish to visit via a terminal with a dedicated application installed. They select a facility using the terminal's input screen, and this information is communicated to a server.

[0150] The server utilizes real-time API or database connections with facilities to collect waiting time information for selected locations. Standard protocols via the internet are used for real-time data collection, while data analysis software is used for data storage and analysis. Machine learning algorithms are employed to predict waiting times, and historical data is used to estimate future congestion levels.

[0151] The camera and microphone on the user's device collect data for emotion analysis. Emotion analysis uses commonly available emotion engine software. This system analyzes the user's emotional state from, for example, their facial expressions and voice tone, and the resulting emotional information is sent to a server.

[0152] The server integrates collected emotional and waiting time information to assess the stress users experience during waiting times. Based on this analysis, it generates suggestions for further stress reduction. A generative AI model is used to provide the option best suited to the user's emotions.

[0153] As a concrete example, consider a user visiting a hospital who uses the app. Once the user selects a hospital and decides on a visit date, the waiting time evaluation begins. If the user's facial expression indicates high stress, the server will suggest an earlier visit time or another available clinic.

[0154] Additionally, as an option during waiting times, the device can be notified with a list of relaxing music or suggestions for short exercises. Users can select the information and suggestions provided, allowing them to spend their time more effectively.

[0155] In this way, we achieve waiting time management and optimization based on the user's emotional state, thereby improving the quality of the user experience.

[0156] Example prompt: "We anticipate long waiting times at the hospital. What relaxation techniques would you suggest to alleviate stress in this situation?"

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The user launches the application from their device and selects the location they wish to visit. This input sends the selected location and planned visit time from the device to the server. The server receives this information and records the location selection information in its database.

[0160] Step 2:

[0161] The server collects waiting time information for the selected location. Specifically, the server obtains real-time data via the facility's API and analyzes waiting times and congestion levels. The output of this process is the predicted waiting time and recommended visit time.

[0162] Step 3:

[0163] The device collects video and audio data using the user's camera and microphone. This data is input into the emotion engine. The emotion engine analyzes the user's facial expressions and voice tone and outputs their emotional state.

[0164] Step 4:

[0165] The server integrates emotional information obtained from the emotion engine with waiting time information. Specifically, it evaluates the level of stress the user feels based on their emotional state and analyzes its impact. As a result of this analysis, it outputs visit timings and alternative suggestions that take the user's emotional state into consideration.

[0166] Step 5:

[0167] The server generates suggestions for the user based on the integrated analysis results. Using a generation AI model, it determines recommended activities during waiting times (e.g., suggestions for relaxing music or exercise). These suggestions are then sent to the user's device.

[0168] Step 6:

[0169] The device notifies the user of suggestions received from the server. Based on this, the user can readjust their visit time or select activities to make the most of their waiting time. This notification reflects the user's choices in their actions.

[0170] (Application Example 2)

[0171] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0172] Waiting times when visiting a facility often cause stress for users and can lead to a decline in the customer experience for the facility. Furthermore, appropriate responses that address the user's emotional state are frequently required. However, conventional waiting time management systems fail to consider the emotional state of users and are insufficient for improving user satisfaction.

[0173] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0174] In this invention, the server includes input means for the user to select a facility to visit, information processing means for collecting and analyzing waiting time information for the selected facility in real time, output means for displaying the analyzed waiting time and recommended visit timing information on the user's device, emotion analysis means for analyzing the user's facial expressions and voice and recognizing their emotions, and recommendation means for providing recommended entertainment during the waiting time based on the recognized emotions. This makes it possible to manage waiting times while taking the user's emotions into consideration, thereby reducing user stress and providing a comfortable way to spend waiting time.

[0175] An "input method" refers to a device or interface that allows a user to select the facility they wish to visit.

[0176] "Information processing means" refers to a technological device that collects waiting time information for selected facilities and performs real-time analysis.

[0177] "Output means" refers to a display device that conveys analyzed waiting times and optimal timing for visits to the user.

[0178] "Emotional analysis methods" refer to technological processes that analyze a user's facial expressions and voice to recognize their emotional state.

[0179] "Recommended methods" refer to methods for providing entertainment and other activities that users can engage in while waiting, based on their perceived emotions.

[0180] This invention is a system for managing waiting times at facilities visited by users and for analyzing users' emotions to reduce stress. This system mainly consists of input means, information processing means, output means, emotion analysis means, and recommendation means.

[0181] The server collects and analyzes facility waiting time information in real time as an information processing tool. Specifically, this process involves obtaining waiting time data from facility management systems via the internet and storing it in a database. The analysis uses a waiting time prediction algorithm based on historical data. In the case of facilities with long waiting times, the server calculates a recommended visit time and notifies the user.

[0182] The user's device collects data to identify the user's emotions using a camera and microphone as means of emotion analysis. Specifically, it captures facial expressions through the camera and analyzes voice tone through the microphone. This data is then analyzed by an emotion analysis engine, such as commonly used image processing software or voice analysis solutions.

[0183] Subsequently, based on the recognized emotional information, the user's device implements recommended measures to provide stress-reducing activities during waiting times. The device may play relaxing music using streaming technology or display exercise guides on the screen. This allows users to spend their waiting time more comfortably.

[0184] As a concrete example, when a user heads to a store in a shopping mall, they may anticipate a wait. For instance, suppose a particular cafe is extremely crowded on a Saturday afternoon. In this case, the terminal would display a recommended visit time and a list of music suitable for listening to while waiting. An example of a prompt message in this scenario would be: "In a shopping mall wait time management application, how can we analyze customer emotions in real time and suggest optimal music or exercise to reduce stress?" This implementation would improve the shopping experience.

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The user uses a terminal to select the facility they wish to visit. The input is the facility selection information, which is transmitted by the terminal and sent to the server. The server receives this data and identifies the facility ID necessary for the next process.

[0188] Step 2:

[0189] The server collects wait time information for selected facilities and performs real-time analysis. The input is the facility ID, and the output is the latest analyzed wait time data and recommended visit time. The server accesses the facility database, retrieves the current wait time, and compares it with historical data to make predictions.

[0190] Step 3:

[0191] The device collects user emotion data. The input consists of the user's facial image and voice data, which are collected using the camera and microphone. The output is the emotion data before processing. The device then sends the collected data to an emotion analysis engine.

[0192] Step 4:

[0193] The emotion analysis engine analyzes the user's emotions and sends the results to the server. The input is facial expression images and audio data, and the output is the analyzed emotional state. The emotion analysis engine uses image processing algorithms and audio analysis models to generate a numerical representation of the emotion.

[0194] Step 5:

[0195] The server determines activities to reduce user stress based on emotional state and waiting time data. The input is the analyzed emotional state and recommended visit time, and the output is recommended entertainment content. The server generates and selects a list of music and exercises best suited to the emotional state.

[0196] Step 6:

[0197] The device notifies the user of recommended activities. The input is entertainment content sent from the server, and the output is the most suitable content to display on the user's device. The device performs actions such as playing a music list or displaying an exercise guide through the user interface.

[0198] By following these steps, users will be able to spend their waiting time productively.

[0199] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0200] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0201] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0205] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0206] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0207] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0208] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0209] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0210] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0211] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0212] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0213] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0214] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0215] This invention is a system for effectively managing waiting times at facilities visited by users. This system allows users to select a facility and see waiting times in real time. Facility waiting time data is collected by a server, and future waiting times are predicted through analysis by a generative AI based on past data and current conditions. The analysis results are transmitted from the server to the user's terminal, allowing the user to view waiting time information and the optimal timing for visits on the application screen.

[0216] As a concrete example, consider a situation where a user is planning to visit a popular restaurant. The user uses the app to select the restaurant they intend to visit. The device requests wait time information for the selected restaurant from the server. The server analyzes the real-time wait time data provided by the restaurant and inputs historical data and current trends into a generating AI to make predictions. As a result, the server calculates the optimal visit time along with the predicted wait time and provides it to the user. The user can check the times with shorter wait times through the app and adjust the timing of their visit accordingly.

[0217] This system allows users to minimize waiting times and use their time more effectively. Furthermore, it enables facilities to distribute waiting times, promoting more efficient operations.

[0218] The following describes the processing flow.

[0219] Step 1:

[0220] The user launches the smartphone app and selects the facility they wish to visit. The device retrieves the ID of the selected facility.

[0221] Step 2:

[0222] The terminal sends a request to the server containing the acquired facility ID and user information (including location information and current date and time).

[0223] Step 3:

[0224] The server receives requests from terminals and searches the database for waiting time data for the corresponding facilities.

[0225] Step 4:

[0226] The server uses generative AI to analyze the facility's past wait time data and predict future wait times along with current real-time data.

[0227] Step 5:

[0228] The server calculates the recommended visit time for the user, along with a predicted wait time, and then generates a response.

[0229] Step 6:

[0230] The server sends the generated response data to the terminal.

[0231] Step 7:

[0232] The terminal displays received waiting time information and recommended visit timings on the user interface.

[0233] Step 8:

[0234] Based on the displayed information, users adjust the timing of their visit and determine the optimal time to actually visit.

[0235] (Example 1)

[0236] Next, we will describe Example 1. 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."

[0237] At facilities visited by users, it is difficult to minimize the time wasted due to the uncertainty of waiting times, which hinders the efficient use of infrastructure. Furthermore, when users plan their visits, there is a lack of effective means to predict the congestion level of their destinations in advance, making it difficult to determine the optimal time to visit.

[0238] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0239] In this invention, the server includes an information processing device for collecting and analyzing waiting time information for selected facilities in real time, communication means for calculating and transmitting the generated waiting time prediction results to a terminal, and generation means for predicting current and future waiting times using past waiting time data. This makes it possible for users to predict the congestion level of their destination in advance and select the optimal timing for their visit.

[0240] An "input device" is a device that provides an interface for a user to select a facility to visit.

[0241] An "information processing device" is a computer system that collects waiting time information for selected facilities and analyzes the data in real time.

[0242] A "display device" is a device that visually presents analyzed waiting time and recommended visit timing information to the user's terminal.

[0243] "Communication means" refers to a device that has network functionality for sending and receiving data between a server and a user terminal.

[0244] A "generation means" is a device that executes algorithms and techniques for predicting current and future waiting times based on past waiting time data.

[0245] A "calculating device" is a computer system that uses data, including the user's location information, to calculate the optimal timing for a visit.

[0246] This invention is implemented as a system for effectively managing waiting times at facilities visited by users. The system mainly consists of multiple elements, including a user's terminal, a server, and a generative AI model.

[0247] The user selects the facility they plan to visit using an application on their device. The device has a mechanism to send a request to the server based on this selection information. Specifically, the user can select a facility using the application's interface. The device, as an input means, has a communication mechanism to send this information to the server.

[0248] The server functions as an information processing device, acquiring real-time wait time information from selected facilities. This data is typically obtained through POS systems or reservation management systems. The server inputs the collected data into a generative AI model, which considers historical data and current trends to predict future wait times. As a specific example, "OpenAI's GPT-4" can be used as the generative AI model.

[0249] The generated prediction information is transmitted to the user's terminal via a communication method. The user's terminal functions as a display device, visually presenting the received prediction information on waiting times and the optimal visit timing to the user. This allows the user to know the congestion status of their destination in advance and receive support in deciding the optimal visit timing.

[0250] As a concrete example, consider a scenario where a user wants to visit a popular restaurant. The user selects the restaurant through their device, and the device requests real-time wait time data for that restaurant from a server. The server combines the collected current data with past trends and inputs a prompt message into a generating AI model, such as, "Based on data from the past year, predict the wait time for the next three hours and suggest the optimal time to visit." Once the prediction is complete, the optimal visit time is sent to the device and displayed for the user to confirm. In this way, the user can optimize their visit plan and enjoy a smoother experience.

[0251] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0252] Step 1:

[0253] The user opens the application on their device and selects the facility they plan to visit. As input, the user taps the facility name or category to obtain facility ID information. The output is detailed information about the selected facility. This information is saved on the device as request data.

[0254] Step 2:

[0255] The terminal sends a request to the server. The input includes the ID of the selected facility and the user's current location information. Specifically, the terminal's built-in GPS function is used to obtain the current location, and the data is sent to the server via the communication module. The output is an acknowledgment that the server has received the request.

[0256] Step 3:

[0257] The server collects relevant wait time data using an information processing device based on the received facility ID. The input is request data from the terminal, and the output is real-time wait time data. This process integrates with the facility's reservation management system and POS system to obtain the latest data.

[0258] Step 4:

[0259] The server combines collected wait time data with historical data and inputs it into a generating AI model. The input data includes historical wait time information and current trend information. The prompt message includes instructions such as "Predict the wait time for the next 3 hours at the selected facility," and the output is a prediction of future wait times.

[0260] Step 5:

[0261] The server analyzes the output from the generated AI model and calculates the optimal visit time. The input is the AI ​​model's prediction result, and the output is the optimal visit timing. In the calculation process, the system combines the user's desired visit time with the prediction data to avoid congestion.

[0262] Step 6:

[0263] The server sends the optimal visit time to the user's terminal. The input is the calculated optimal visit time, and the result is sent to the terminal using a communication method. The output is data containing the optimal visit time for display on the user's terminal.

[0264] Step 7:

[0265] The optimal visit timing received on the user's device is displayed. Input is data transmitted from the server, and output is visual information provided to the user. The device presents the information to the user in an easy-to-understand format, such as graphs and text. Based on this information, the user can adjust their visit plan.

[0266] (Application Example 1)

[0267] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0268] In physical stores such as shopping malls and theme parks, it is difficult for users to know in advance how crowded the stores and attractions they plan to visit are, making it difficult to plan an efficient visit. In particular, increased waiting times due to congestion and the choice of unexpected routes can prevent users from visiting the facility as planned, forcing them to use their time inefficiently. This leads to decreased user satisfaction, and the facilities themselves face a decline in operational efficiency due to the concentration of visitors.

[0269] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0270] In this invention, the server includes input means for the user to select a facility to visit, server means for collecting and analyzing waiting time information for the selected facility in real time, display means for displaying the analyzed waiting time, recommended visit timing, and optimal travel route on the user terminal, and location information processing means for acquiring the user's location information and optimizing the travel route. As a result, the user can understand the congestion status of the facility or attraction to visit in advance and make an efficient visit with minimal waiting time. This improves user satisfaction and allows facilities to improve operational efficiency.

[0271] An "input method" is an interface used by the user to select the facility they wish to visit.

[0272] A "server system" is a system for collecting facility waiting time data and analyzing it in real time.

[0273] "Display means" refers to a device or software for visually presenting analyzed waiting time information, recommended visit times, and optimal travel routes to the user's terminal.

[0274] "Location information processing means" refers to technology that acquires the user's current location and optimizes the visit route based on that information.

[0275] "Waiting time information" refers to information about the time spent at a facility or waiting in line, based on the current and predicted crowd levels at the facility or attraction.

[0276] "Generation processing means" refers to analytical and computational techniques used to predict current and future situations by utilizing past data.

[0277] "Travel route" refers to the optimal path a user takes from their starting point to their destination.

[0278] The system that implements this application allows users to check the waiting time at a facility they wish to visit in real time and create an efficient visit plan. This system consists of the following hardware and software:

[0279] First, the user operates the application using a smartphone or smart glasses and selects the facility they wish to visit. This selection is made through an input device, and the information is sent to the server.

[0280] Next, the server retrieves wait time information for the selected facility and collects it in real time using Firebase. Then, based on a generative AI model using TensorFlow or PyTorch, it predicts wait times from historical data and current conditions, and calculates the optimal visit time and route based on the user's location. This allows the user to reduce unnecessary wait times and obtain an efficient travel route.

[0281] The analysis results obtained from the server are transmitted in real time to a smartphone or smart glasses via Flask. Users can then view specific waiting times, optimal visit times, and recommended routes through the application's display method.

[0282] For example, a user might want to visit a specific store in a shopping mall. In this case, the system would suggest less crowded times and routes that are more comfortable to travel along, avoiding peak hours. An example of a prompt message might be: "The store you're looking for is currently crowded. We recommend visiting around 2 PM. Would you like to check the shortest route?"

[0283] This system allows users to identify less crowded times in advance and use facilities efficiently, while also enabling facilities to improve operational efficiency by distributing congestion.

[0284] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The user selects a facility that they wish to visit through a smartphone. This selection information is sent from the terminal to the server. The input includes the name and category of the facility. The output is the identification information of the selected facility on the server.

[0287] Step 2:

[0288] The server collects real-time waiting time data for the selected facility through the facility's own system or API. At this time, Firebase is used to obtain and save the data. The input is the identification information of the facility, and the output is the current waiting time data.

[0289] Step 3:

[0290] The server analyzes the obtained waiting time data and past waiting time history using a generative AI model of TensorFlow or PyTorch. The data is input into the AI model, and the predicted waiting time is output. Here, trend analysis of past data and current waiting time data are processed, and calculations for predicting future situations are performed.

[0291] Step 4:

[0292] The server calculates the optimal travel route based on the user's location information. For this calculation, the Google Maps API is used to obtain the current location and calculate the shortest route to the destination. The input is the user's location information, and the output is the optimal route.

[0293] Step 5:

[0294] The server sends information, including the calculated optimal visit time and route, to the terminal via Flask. This transmission includes real-time data updates and is optimized for fast response times. The input is the predicted wait time and route information, and the output is the visit guidance information displayed on the user's terminal.

[0295] Step 6:

[0296] The terminal displays the received information to the user. Using the display method, specific waiting times, optimal visit times, and recommended travel routes are visually presented. Input is data from the server, and output is a visit plan that is visually understandable to the user.

[0297] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0298] This invention combines a system for effectively managing waiting times at facilities visited by users with an emotion engine that recognizes user emotions. During the process of a user selecting a facility and monitoring waiting times in real time, the emotion engine analyzes the user's facial expressions, tone of voice, and other factors to recognize their emotions. This recognized emotion information is then analyzed by a server to determine how stressed the user is experiencing during the current waiting time.

[0299] As a concrete example, consider a scenario where a user attempts to visit a crowded hospital. The user selects a hospital through the app and sets a planned visit time on their device. The server retrieves and analyzes hospital waiting time information, calculating a waiting time prediction and recommended visit timing based on real-time data. Here, the emotion engine uses data obtained from the user's camera and microphone to analyze the user's emotions from their facial expressions and voice.

[0300] For example, when the user's expression shows annoyance, the emotion engine determines a high stress level. The server uses this information to provide the user with special recommendations for a quicker response or alternative options (e.g., other available time slots or different facilities) to shorten the waiting time.

[0301] Also, depending on the user's emotional state, the terminal can notify the recommended activities (e.g., recommendations for relaxing music or guidance on simple exercises) during the waiting time. This enables the user to spend the waiting time meaningfully and also improves the operational efficiency of the facility.

[0302] The following explains the process flow.

[0303] Step 1:

[0304] The user launches the smartphone app and selects the facility to visit. The terminal records the ID of the selected facility and the user's location information.

[0305] Step 2:

[0306] The terminal sends a request including the facility ID and user information to the server. This request also includes a signal to trigger analysis by the emotion engine.

[0307] Step 3:

[0308] The server receives the request and obtains the real-time waiting time of the specified facility from the database. At the same time, an AI generated using past data predicts the future waiting time.

[0309] Step 4:

[0310] The emotion engine installed on the terminal analyzes the user's expression and voice through the camera and microphone, and determines the emotional state (e.g., anxiety, stress, relaxation, etc.).

[0311] Step 5:

[0312] The server receives emotional data from the emotion engine and evaluates the user's stress level. This evaluation is treated as a factor influencing the waiting status.

[0313] Step 6:

[0314] The server combines wait time predictions, stress level assessments, and recommended visit timings to generate the best visit options for the user. This may include suggestions for alternative time slots or facilities.

[0315] Step 7:

[0316] The device receives the calculated information and displays the waiting time, optimal visit timing, and recommended activities for mood improvement on the user interface.

[0317] Step 8:

[0318] Based on this information, users can adjust their visit plans and proceed with the visit process while minimizing waiting times and managing their emotions.

[0319] (Example 2)

[0320] Next, we will describe Example 2. 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".

[0321] Users often experience unnecessary stress and wasted time due to unclear waiting times at places they visit. Furthermore, when waiting times do occur, there is a lack of information on how to best utilize that time. Flexible waiting time management and suggestions that take users' emotional states into account are needed.

[0322] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0323] In this invention, the server includes receiving means for selecting a location for the user to visit, information processing means for collecting and storing waiting time information for the selected location, analysis means for analyzing the user's emotional state and generating emotional information, integrated analysis means for integrating the analyzed emotional information and waiting time, evaluating the burden felt by the user, and suggesting improvement plans, and presentation means for presenting the integrated information to the user terminal. This makes it possible to optimize and effectively utilize waiting time based on the user's emotional state.

[0324] "Receiving means" refers to a device or program for a user to select a location to visit and transmit that selection information to the system.

[0325] "Information processing means" refers to a device or program for collecting waiting time information related to a selected location and storing it statistically or in real time.

[0326] "Analysis means" refers to a device or program for acquiring data such as the user's facial expressions and voice, and for recognizing and analyzing their emotional state.

[0327] An "integrated analysis means" is a device or program that integrates analyzed emotional information and collected waiting time information to evaluate the stress felt by the user and derive appropriate improvement suggestions.

[0328] "Presentation means" refers to a device or program that displays integrated and analyzed information on a user terminal in an intuitive and easy-to-understand manner, and provides feedback to the user.

[0329] This invention is a system for optimizing waiting times and improving the user experience during those times. Users select the location they wish to visit via a terminal with a dedicated application installed. They select a facility using the terminal's input screen, and this information is communicated to a server.

[0330] The server utilizes real-time API or database connections with facilities to collect waiting time information for selected locations. Standard protocols via the internet are used for real-time data collection, while data analysis software is used for data storage and analysis. Machine learning algorithms are employed to predict waiting times, and historical data is used to estimate future congestion levels.

[0331] The camera and microphone on the user's device collect data for emotion analysis. Emotion analysis uses commonly available emotion engine software. This system analyzes the user's emotional state from, for example, their facial expressions and voice tone, and the resulting emotional information is sent to a server.

[0332] The server integrates collected emotional and waiting time information to assess the stress users experience during waiting times. Based on this analysis, it generates suggestions for further stress reduction. A generative AI model is used to provide the option best suited to the user's emotions.

[0333] As a concrete example, consider a user visiting a hospital who uses the app. Once the user selects a hospital and decides on a visit date, the waiting time evaluation begins. If the user's facial expression indicates high stress, the server will suggest an earlier visit time or another available clinic.

[0334] Additionally, as an option during waiting times, the device can be notified with a list of relaxing music or suggestions for short exercises. Users can select the information and suggestions provided, allowing them to spend their time more effectively.

[0335] In this way, we achieve waiting time management and optimization based on the user's emotional state, thereby improving the quality of the user experience.

[0336] Example prompt: "We anticipate long waiting times at the hospital. What relaxation techniques would you suggest to alleviate stress in this situation?"

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1:

[0339] The user launches the application from their device and selects the location they wish to visit. This input sends the selected location and planned visit time from the device to the server. The server receives this information and records the location selection information in its database.

[0340] Step 2:

[0341] The server collects waiting time information for the selected location. Specifically, the server obtains real-time data via the facility's API and analyzes waiting times and congestion levels. The output of this process is the predicted waiting time and recommended visit time.

[0342] Step 3:

[0343] The device collects video and audio data using the user's camera and microphone. This data is input into the emotion engine. The emotion engine analyzes the user's facial expressions and voice tone and outputs their emotional state.

[0344] Step 4:

[0345] The server integrates emotional information obtained from the emotion engine with waiting time information. Specifically, it evaluates the level of stress the user feels based on their emotional state and analyzes its impact. As a result of this analysis, it outputs visit timings and alternative suggestions that take the user's emotional state into consideration.

[0346] Step 5:

[0347] The server generates suggestions for the user based on the integrated analysis results. Using a generation AI model, it determines recommended activities during waiting times (e.g., suggestions for relaxing music or exercise). These suggestions are then sent to the user's device.

[0348] Step 6:

[0349] The device notifies the user of suggestions received from the server. Based on this, the user can readjust their visit time or select activities to make the most of their waiting time. This notification reflects the user's choices in their actions.

[0350] (Application Example 2)

[0351] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0352] Waiting times when visiting a facility often cause stress for users and can lead to a decline in the customer experience for the facility. Furthermore, appropriate responses that address the user's emotional state are frequently required. However, conventional waiting time management systems fail to consider the emotional state of users and are insufficient for improving user satisfaction.

[0353] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0354] In this invention, the server includes input means for the user to select a facility to visit, information processing means for collecting and analyzing waiting time information for the selected facility in real time, output means for displaying the analyzed waiting time and recommended visit timing information on the user's device, emotion analysis means for analyzing the user's facial expressions and voice and recognizing their emotions, and recommendation means for providing recommended entertainment during the waiting time based on the recognized emotions. This makes it possible to manage waiting times while taking the user's emotions into consideration, thereby reducing user stress and providing a comfortable way to spend waiting time.

[0355] An "input method" refers to a device or interface that allows a user to select the facility they wish to visit.

[0356] "Information processing means" refers to a technological device that collects waiting time information for selected facilities and performs real-time analysis.

[0357] "Output means" refers to a display device that conveys analyzed waiting times and optimal timing for visits to the user.

[0358] "Emotional analysis methods" refer to technological processes that analyze a user's facial expressions and voice to recognize their emotional state.

[0359] "Recommended methods" refer to methods for providing entertainment and other activities that users can engage in while waiting, based on their perceived emotions.

[0360] This invention is a system for managing waiting times at facilities visited by users and for analyzing users' emotions to reduce stress. This system mainly consists of input means, information processing means, output means, emotion analysis means, and recommendation means.

[0361] The server collects and analyzes facility waiting time information in real time as an information processing tool. Specifically, this process involves obtaining waiting time data from facility management systems via the internet and storing it in a database. The analysis uses a waiting time prediction algorithm based on historical data. In the case of facilities with long waiting times, the server calculates a recommended visit time and notifies the user.

[0362] The user's device collects data to identify the user's emotions using a camera and microphone as means of emotion analysis. Specifically, it captures facial expressions through the camera and analyzes voice tone through the microphone. This data is then analyzed by an emotion analysis engine, such as commonly used image processing software or voice analysis solutions.

[0363] Subsequently, based on the recognized emotional information, the user's device implements recommended measures to provide stress-reducing activities during waiting times. The device may play relaxing music using streaming technology or display exercise guides on the screen. This allows users to spend their waiting time more comfortably.

[0364] As a concrete example, when a user heads to a store in a shopping mall, they may anticipate a wait. For instance, suppose a particular cafe is extremely crowded on a Saturday afternoon. In this case, the terminal would display a recommended visit time and a list of music suitable for listening to while waiting. An example of a prompt message in this scenario would be: "In a shopping mall wait time management application, how can we analyze customer emotions in real time and suggest optimal music or exercise to reduce stress?" This implementation would improve the shopping experience.

[0365] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0366] Step 1:

[0367] The user uses a terminal to select the facility they wish to visit. The input is the facility selection information, which is transmitted by the terminal and sent to the server. The server receives this data and identifies the facility ID necessary for the next process.

[0368] Step 2:

[0369] The server collects wait time information for selected facilities and performs real-time analysis. The input is the facility ID, and the output is the latest analyzed wait time data and recommended visit time. The server accesses the facility database, retrieves the current wait time, and compares it with historical data to make predictions.

[0370] Step 3:

[0371] The device collects user emotion data. The input consists of the user's facial image and voice data, which are collected using the camera and microphone. The output is the emotion data before processing. The device then sends the collected data to an emotion analysis engine.

[0372] Step 4:

[0373] The emotion analysis engine analyzes the user's emotions and sends the results to the server. The input is facial expression images and audio data, and the output is the analyzed emotional state. The emotion analysis engine uses image processing algorithms and audio analysis models to generate a numerical representation of the emotion.

[0374] Step 5:

[0375] The server determines activities to reduce user stress based on emotional state and waiting time data. The input is the analyzed emotional state and recommended visit time, and the output is recommended entertainment content. The server generates and selects a list of music and exercises best suited to the emotional state.

[0376] Step 6:

[0377] The device notifies the user of recommended activities. The input is entertainment content sent from the server, and the output is the most suitable content to display on the user's device. The device performs actions such as playing a music list or displaying an exercise guide through the user interface.

[0378] By following these steps, users will be able to spend their waiting time productively.

[0379] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0380] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0381] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0382] [Third Embodiment]

[0383] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0384] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0385] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0386] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0387] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0388] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0389] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0390] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0391] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0392] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0393] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0394] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0395] This invention is a system for effectively managing waiting times at facilities visited by users. This system allows users to select a facility and see waiting times in real time. Facility waiting time data is collected by a server, and future waiting times are predicted through analysis by a generative AI based on past data and current conditions. The analysis results are transmitted from the server to the user's terminal, allowing the user to view waiting time information and the optimal timing for visits on the application screen.

[0396] As a concrete example, consider a situation where a user is planning to visit a popular restaurant. The user uses the app to select the restaurant they intend to visit. The device requests wait time information for the selected restaurant from the server. The server analyzes the real-time wait time data provided by the restaurant and inputs historical data and current trends into a generating AI to make predictions. As a result, the server calculates the optimal visit time along with the predicted wait time and provides it to the user. The user can check the times with shorter wait times through the app and adjust the timing of their visit accordingly.

[0397] This system allows users to minimize waiting times and use their time more effectively. Furthermore, it enables facilities to distribute waiting times, promoting more efficient operations.

[0398] The following describes the processing flow.

[0399] Step 1:

[0400] The user launches the smartphone app and selects the facility they wish to visit. The device retrieves the ID of the selected facility.

[0401] Step 2:

[0402] The terminal sends a request to the server containing the acquired facility ID and user information (including location information and current date and time).

[0403] Step 3:

[0404] The server receives requests from terminals and searches the database for waiting time data for the corresponding facilities.

[0405] Step 4:

[0406] The server uses generative AI to analyze the facility's past wait time data and predict future wait times along with current real-time data.

[0407] Step 5:

[0408] The server calculates the recommended visit time for the user, along with a predicted wait time, and then generates a response.

[0409] Step 6:

[0410] The server sends the generated response data to the terminal.

[0411] Step 7:

[0412] The terminal displays received waiting time information and recommended visit timings on the user interface.

[0413] Step 8:

[0414] Based on the displayed information, users adjust the timing of their visit and determine the optimal time to actually visit.

[0415] (Example 1)

[0416] Next, we will describe Example 1. 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."

[0417] At facilities visited by users, it is difficult to minimize the time wasted due to the uncertainty of waiting times, which hinders the efficient use of infrastructure. Furthermore, when users plan their visits, there is a lack of effective means to predict the congestion level of their destinations in advance, making it difficult to determine the optimal time to visit.

[0418] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0419] In this invention, the server includes an information processing device for collecting and analyzing waiting time information for selected facilities in real time, communication means for calculating and transmitting the generated waiting time prediction results to a terminal, and generation means for predicting current and future waiting times using past waiting time data. This makes it possible for users to predict the congestion level of their destination in advance and select the optimal timing for their visit.

[0420] An "input device" is a device that provides an interface for a user to select a facility to visit.

[0421] An "information processing device" is a computer system that collects waiting time information for selected facilities and analyzes the data in real time.

[0422] A "display device" is a device that visually presents analyzed waiting time and recommended visit timing information to the user's terminal.

[0423] "Communication means" refers to a device that has network functionality for sending and receiving data between a server and a user terminal.

[0424] A "generation means" is a device that executes algorithms and techniques for predicting current and future waiting times based on past waiting time data.

[0425] A "calculating device" is a computer system that uses data, including the user's location information, to calculate the optimal timing for a visit.

[0426] This invention is implemented as a system for effectively managing waiting times at facilities visited by users. The system mainly consists of multiple elements, including a user's terminal, a server, and a generative AI model.

[0427] The user selects the facility they plan to visit using an application on their device. The device has a mechanism to send a request to the server based on this selection information. Specifically, the user can select a facility using the application's interface. The device, as an input means, has a communication mechanism to send this information to the server.

[0428] The server functions as an information processing device, acquiring real-time wait time information from selected facilities. This data is typically obtained through POS systems or reservation management systems. The server inputs the collected data into a generative AI model, which considers historical data and current trends to predict future wait times. As a specific example, "OpenAI's GPT-4" can be used as the generative AI model.

[0429] The generated prediction information is transmitted to the user's terminal via a communication method. The user's terminal functions as a display device, visually presenting the received prediction information on waiting times and the optimal visit timing to the user. This allows the user to know the congestion status of their destination in advance and receive support in deciding the optimal visit timing.

[0430] As a concrete example, consider a scenario where a user wants to visit a popular restaurant. The user selects the restaurant through their device, and the device requests real-time wait time data for that restaurant from a server. The server combines the collected current data with past trends and inputs a prompt message into a generating AI model, such as, "Based on data from the past year, predict the wait time for the next three hours and suggest the optimal time to visit." Once the prediction is complete, the optimal visit time is sent to the device and displayed for the user to confirm. In this way, the user can optimize their visit plan and enjoy a smoother experience.

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] The user opens the application on their device and selects the facility they plan to visit. As input, the user taps the facility name or category to obtain facility ID information. The output is detailed information about the selected facility. This information is saved on the device as request data.

[0434] Step 2:

[0435] The terminal sends a request to the server. The input includes the ID of the selected facility and the user's current location information. Specifically, the terminal's built-in GPS function is used to obtain the current location, and the data is sent to the server via the communication module. The output is an acknowledgment that the server has received the request.

[0436] Step 3:

[0437] The server collects relevant wait time data using an information processing device based on the received facility ID. The input is request data from the terminal, and the output is real-time wait time data. This process integrates with the facility's reservation management system and POS system to obtain the latest data.

[0438] Step 4:

[0439] The server combines collected wait time data with historical data and inputs it into a generating AI model. The input data includes historical wait time information and current trend information. The prompt message includes instructions such as "Predict the wait time for the next 3 hours at the selected facility," and the output is a prediction of future wait times.

[0440] Step 5:

[0441] The server analyzes the output from the generated AI model and calculates the optimal visit time. The input is the AI ​​model's prediction result, and the output is the optimal visit timing. In the calculation process, the system combines the user's desired visit time with the prediction data to avoid congestion.

[0442] Step 6:

[0443] The server sends the optimal visit time to the user's terminal. The input is the calculated optimal visit time, and the result is sent to the terminal using a communication method. The output is data containing the optimal visit time for display on the user's terminal.

[0444] Step 7:

[0445] The optimal visit timing received on the user's device is displayed. Input is data transmitted from the server, and output is visual information provided to the user. The device presents the information to the user in an easy-to-understand format, such as graphs and text. Based on this information, the user can adjust their visit plan.

[0446] (Application Example 1)

[0447] Next, we will explain Application Example 1. In the following explanation, 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."

[0448] In physical stores such as shopping malls and theme parks, it is difficult for users to know in advance how crowded the stores and attractions they plan to visit are, making it difficult to plan an efficient visit. In particular, increased waiting times due to congestion and the choice of unexpected routes can prevent users from visiting the facility as planned, forcing them to use their time inefficiently. This leads to decreased user satisfaction, and the facilities themselves face a decline in operational efficiency due to the concentration of visitors.

[0449] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0450] In this invention, the server includes input means for the user to select a facility to visit, server means for collecting and analyzing waiting time information for the selected facility in real time, display means for displaying the analyzed waiting time, recommended visit timing, and optimal travel route on the user terminal, and location information processing means for acquiring the user's location information and optimizing the travel route. As a result, the user can understand the congestion status of the facility or attraction to visit in advance and make an efficient visit with minimal waiting time. This improves user satisfaction and allows facilities to improve operational efficiency.

[0451] An "input method" is an interface used by the user to select the facility they wish to visit.

[0452] A "server system" is a system for collecting facility waiting time data and analyzing it in real time.

[0453] "Display means" refers to a device or software for visually presenting analyzed waiting time information, recommended visit times, and optimal travel routes to the user's terminal.

[0454] "Location information processing means" refers to technology that acquires the user's current location and optimizes the visit route based on that information.

[0455] "Waiting time information" refers to information about the time spent at a facility or waiting in line, based on the current and predicted crowd levels at the facility or attraction.

[0456] "Generation processing means" refers to analytical and computational techniques used to predict current and future situations by utilizing past data.

[0457] "Travel route" refers to the optimal path a user takes from their starting point to their destination.

[0458] The system that implements this application allows users to check the waiting time at a facility they wish to visit in real time and create an efficient visit plan. This system consists of the following hardware and software:

[0459] First, the user operates the application using a smartphone or smart glasses and selects the facility they wish to visit. This selection is made through an input device, and the information is sent to the server.

[0460] Next, the server retrieves wait time information for the selected facility and collects it in real time using Firebase. Then, based on a generative AI model using TensorFlow or PyTorch, it predicts wait times from historical data and current conditions, and calculates the optimal visit time and route based on the user's location. This allows the user to reduce unnecessary wait times and obtain an efficient travel route.

[0461] The analysis results obtained from the server are transmitted in real time to a smartphone or smart glasses via Flask. Users can then view specific waiting times, optimal visit times, and recommended routes through the application's display method.

[0462] For example, a user might want to visit a specific store in a shopping mall. In this case, the system would suggest less crowded times and routes that are more comfortable to travel along, avoiding peak hours. An example of a prompt message might be: "The store you're looking for is currently crowded. We recommend visiting around 2 PM. Would you like to check the shortest route?"

[0463] This system allows users to identify less crowded times in advance and use facilities efficiently, while also enabling facilities to improve operational efficiency by distributing congestion.

[0464] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0465] Step 1:

[0466] The user selects the facility they wish to visit via their smartphone. This selection information is sent from the device to the server. The input includes the facility's name and category. The output is the server's identification information for the selected facility.

[0467] Step 2:

[0468] The server collects real-time wait time data for the selected facility through the facility's own system or API. Firebase is used for data retrieval and storage. The input is the facility's identification information, and the output is the current wait time data.

[0469] Step 3:

[0470] The server uses a generative AI model in TensorFlow or PyTorch to analyze acquired latency data and historical latency data. The data is input into the AI ​​model, and predicted latency is output. Here, trend analysis of historical data and processing of current latency data are performed to predict future conditions.

[0471] Step 4:

[0472] The server calculates the optimal travel route based on the user's location information. This calculation uses the Google Maps API to obtain the current location and determine the shortest path to the destination. The input is the user's location information, and the output is the optimal route.

[0473] Step 5:

[0474] The server sends information, including the calculated optimal visit time and route, to the terminal via Flask. This transmission includes real-time data updates and is optimized for fast response times. The input is the predicted wait time and route information, and the output is the visit guidance information displayed on the user's terminal.

[0475] Step 6:

[0476] The terminal displays the received information to the user. Using the display method, specific waiting times, optimal visit times, and recommended travel routes are visually presented. Input is data from the server, and output is a visit plan that is visually understandable to the user.

[0477] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0478] This invention combines a system for effectively managing waiting times at facilities visited by users with an emotion engine that recognizes user emotions. During the process of a user selecting a facility and monitoring waiting times in real time, the emotion engine analyzes the user's facial expressions, tone of voice, and other factors to recognize their emotions. This recognized emotion information is then analyzed by a server to determine how stressed the user is experiencing during the current waiting time.

[0479] As a concrete example, consider a scenario where a user attempts to visit a crowded hospital. The user selects a hospital through the app and sets a planned visit time on their device. The server retrieves and analyzes hospital waiting time information, calculating a waiting time prediction and recommended visit timing based on real-time data. Here, the emotion engine uses data obtained from the user's camera and microphone to analyze the user's emotions from their facial expressions and voice.

[0480] For example, if a user's facial expression indicates displeasure, the emotion engine determines a high stress level. The server uses this information to provide the user with special recommendations for faster service or alternative options to reduce waiting times (e.g., other less busy times or different facilities).

[0481] Furthermore, the device can notify users of recommended activities during their waiting time (for example, recommendations for relaxing music or guidance for simple exercises) based on their emotional state. This allows users to spend their waiting time more productively and improves the operational efficiency of the facility.

[0482] The following describes the processing flow.

[0483] Step 1:

[0484] The user launches the smartphone app and selects the facility they wish to visit. The device records the ID of the selected facility and the user's location information.

[0485] Step 2:

[0486] The terminal sends a request to the server that includes the facility ID and user information. This request also includes a signal to trigger analysis by the emotion engine.

[0487] Step 3:

[0488] The server receives the request and retrieves the real-time wait time for the specified facility from the database. Simultaneously, a generating AI uses historical data to predict future wait times.

[0489] Step 4:

[0490] The emotion engine built into the device analyzes the user's facial expressions and voice through the camera and microphone to determine their emotional state (e.g., anxiety, stress, relaxation, etc.).

[0491] Step 5:

[0492] The server receives emotional data from the emotion engine and evaluates the user's stress level. This evaluation is treated as a factor influencing the waiting status.

[0493] Step 6:

[0494] The server combines wait time predictions, stress level assessments, and recommended visit timings to generate the best visit options for the user. This may include suggestions for alternative time slots or facilities.

[0495] Step 7:

[0496] The device receives the calculated information and displays the waiting time, optimal visit timing, and recommended activities for mood improvement on the user interface.

[0497] Step 8:

[0498] Based on this information, users can adjust their visit plans and proceed with the visit process while minimizing waiting times and managing their emotions.

[0499] (Example 2)

[0500] Next, we will describe Example 2. 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."

[0501] Users often experience unnecessary stress and wasted time due to unclear waiting times at places they visit. Furthermore, when waiting times do occur, there is a lack of information on how to best utilize that time. Flexible waiting time management and suggestions that take users' emotional states into account are needed.

[0502] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0503] In this invention, the server includes receiving means for selecting a location for the user to visit, information processing means for collecting and storing waiting time information for the selected location, analysis means for analyzing the user's emotional state and generating emotional information, integrated analysis means for integrating the analyzed emotional information and waiting time, evaluating the burden felt by the user, and suggesting improvement plans, and presentation means for presenting the integrated information to the user terminal. This makes it possible to optimize and effectively utilize waiting time based on the user's emotional state.

[0504] "Receiving means" refers to a device or program for a user to select a location to visit and transmit that selection information to the system.

[0505] "Information processing means" refers to a device or program for collecting waiting time information related to a selected location and storing it statistically or in real time.

[0506] "Analysis means" refers to a device or program for acquiring data such as the user's facial expressions and voice, and for recognizing and analyzing their emotional state.

[0507] An "integrated analysis means" is a device or program that integrates analyzed emotional information and collected waiting time information to evaluate the stress felt by the user and derive appropriate improvement suggestions.

[0508] "Presentation means" refers to a device or program that displays integrated and analyzed information on a user terminal in an intuitive and easy-to-understand manner, and provides feedback to the user.

[0509] This invention is a system for optimizing waiting times and improving the user experience during those times. Users select the location they wish to visit via a terminal with a dedicated application installed. They select a facility using the terminal's input screen, and this information is communicated to a server.

[0510] The server utilizes real-time API or database connections with facilities to collect waiting time information for selected locations. Standard protocols via the internet are used for real-time data collection, while data analysis software is used for data storage and analysis. Machine learning algorithms are employed to predict waiting times, and historical data is used to estimate future congestion levels.

[0511] The camera and microphone on the user's device collect data for emotion analysis. Emotion analysis uses commonly available emotion engine software. This system analyzes the user's emotional state from, for example, their facial expressions and voice tone, and the resulting emotional information is sent to a server.

[0512] The server integrates collected emotional and waiting time information to assess the stress users experience during waiting times. Based on this analysis, it generates suggestions for further stress reduction. A generative AI model is used to provide the option best suited to the user's emotions.

[0513] As a concrete example, consider a user visiting a hospital who uses the app. Once the user selects a hospital and decides on a visit date, the waiting time evaluation begins. If the user's facial expression indicates high stress, the server will suggest an earlier visit time or another available clinic.

[0514] Additionally, as an option during waiting times, the device can be notified with a list of relaxing music or suggestions for short exercises. Users can select the information and suggestions provided, allowing them to spend their time more effectively.

[0515] In this way, we achieve waiting time management and optimization based on the user's emotional state, thereby improving the quality of the user experience.

[0516] Example prompt: "We anticipate long waiting times at the hospital. What relaxation techniques would you suggest to alleviate stress in this situation?"

[0517] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0518] Step 1:

[0519] The user launches the application from their device and selects the location they wish to visit. This input sends the selected location and planned visit time from the device to the server. The server receives this information and records the location selection information in its database.

[0520] Step 2:

[0521] The server collects waiting time information for the selected location. Specifically, the server obtains real-time data via the facility's API and analyzes waiting times and congestion levels. The output of this process is the predicted waiting time and recommended visit time.

[0522] Step 3:

[0523] The device collects video and audio data using the user's camera and microphone. This data is input into the emotion engine. The emotion engine analyzes the user's facial expressions and voice tone and outputs their emotional state.

[0524] Step 4:

[0525] The server integrates emotional information obtained from the emotion engine with waiting time information. Specifically, it evaluates the level of stress the user feels based on their emotional state and analyzes its impact. As a result of this analysis, it outputs visit timings and alternative suggestions that take the user's emotional state into consideration.

[0526] Step 5:

[0527] The server generates suggestions for the user based on the integrated analysis results. Using a generation AI model, it determines recommended activities during waiting times (e.g., suggestions for relaxing music or exercise). These suggestions are then sent to the user's device.

[0528] Step 6:

[0529] The device notifies the user of suggestions received from the server. Based on this, the user can readjust their visit time or select activities to make the most of their waiting time. This notification reflects the user's choices in their actions.

[0530] (Application Example 2)

[0531] Next, we will explain application example 2. In the following explanation, 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."

[0532] Waiting times when visiting a facility often cause stress for users and can lead to a decline in the customer experience for the facility. Furthermore, appropriate responses that address the user's emotional state are frequently required. However, conventional waiting time management systems fail to consider the emotional state of users and are insufficient for improving user satisfaction.

[0533] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0534] In this invention, the server includes input means for the user to select a facility to visit, information processing means for collecting and analyzing waiting time information for the selected facility in real time, output means for displaying the analyzed waiting time and recommended visit timing information on the user's device, emotion analysis means for analyzing the user's facial expressions and voice and recognizing their emotions, and recommendation means for providing recommended entertainment during the waiting time based on the recognized emotions. This makes it possible to manage waiting times while taking the user's emotions into consideration, thereby reducing user stress and providing a comfortable way to spend waiting time.

[0535] An "input method" refers to a device or interface that allows a user to select the facility they wish to visit.

[0536] "Information processing means" refers to a technological device that collects waiting time information for selected facilities and performs real-time analysis.

[0537] "Output means" refers to a display device that conveys analyzed waiting times and optimal timing for visits to the user.

[0538] "Emotional analysis methods" refer to technological processes that analyze a user's facial expressions and voice to recognize their emotional state.

[0539] "Recommended methods" refer to methods for providing entertainment and other activities that users can engage in while waiting, based on their perceived emotions.

[0540] This invention is a system for managing waiting times at facilities visited by users and for analyzing users' emotions to reduce stress. This system mainly consists of input means, information processing means, output means, emotion analysis means, and recommendation means.

[0541] The server collects and analyzes facility waiting time information in real time as an information processing tool. Specifically, this process involves obtaining waiting time data from facility management systems via the internet and storing it in a database. The analysis uses a waiting time prediction algorithm based on historical data. In the case of facilities with long waiting times, the server calculates a recommended visit time and notifies the user.

[0542] The user's device collects data to identify the user's emotions using a camera and microphone as means of emotion analysis. Specifically, it captures facial expressions through the camera and analyzes voice tone through the microphone. This data is then analyzed by an emotion analysis engine, such as commonly used image processing software or voice analysis solutions.

[0543] Subsequently, based on the recognized emotional information, the user's device implements recommended measures to provide stress-reducing activities during waiting times. The device may play relaxing music using streaming technology or display exercise guides on the screen. This allows users to spend their waiting time more comfortably.

[0544] As a concrete example, when a user heads to a store in a shopping mall, they may anticipate a wait. For instance, suppose a particular cafe is extremely crowded on a Saturday afternoon. In this case, the terminal would display a recommended visit time and a list of music suitable for listening to while waiting. An example of a prompt message in this scenario would be: "In a shopping mall wait time management application, how can we analyze customer emotions in real time and suggest optimal music or exercise to reduce stress?" This implementation would improve the shopping experience.

[0545] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0546] Step 1:

[0547] The user uses a terminal to select the facility they wish to visit. The input is the facility selection information, which is transmitted by the terminal and sent to the server. The server receives this data and identifies the facility ID necessary for the next process.

[0548] Step 2:

[0549] The server collects wait time information for selected facilities and performs real-time analysis. The input is the facility ID, and the output is the latest analyzed wait time data and recommended visit time. The server accesses the facility database, retrieves the current wait time, and compares it with historical data to make predictions.

[0550] Step 3:

[0551] The device collects user emotion data. The input consists of the user's facial image and voice data, which are collected using the camera and microphone. The output is the emotion data before processing. The device then sends the collected data to an emotion analysis engine.

[0552] Step 4:

[0553] The emotion analysis engine analyzes the user's emotions and sends the results to the server. The input is facial expression images and audio data, and the output is the analyzed emotional state. The emotion analysis engine uses image processing algorithms and audio analysis models to generate a numerical representation of the emotion.

[0554] Step 5:

[0555] The server determines activities to reduce user stress based on emotional state and waiting time data. The input is the analyzed emotional state and recommended visit time, and the output is recommended entertainment content. The server generates and selects a list of music and exercises best suited to the emotional state.

[0556] Step 6:

[0557] The device notifies the user of recommended activities. The input is entertainment content sent from the server, and the output is the most suitable content to display on the user's device. The device performs actions such as playing a music list or displaying an exercise guide through the user interface.

[0558] By following these steps, users will be able to spend their waiting time productively.

[0559] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0560] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0561] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0562] [Fourth Embodiment]

[0563] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0564] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0565] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0566] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0567] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0568] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0569] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0570] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0571] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0572] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0573] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0574] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0575] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0576] This invention is a system for effectively managing waiting times at facilities visited by users. This system allows users to select a facility and see waiting times in real time. Facility waiting time data is collected by a server, and future waiting times are predicted through analysis by a generative AI based on past data and current conditions. The analysis results are transmitted from the server to the user's terminal, allowing the user to view waiting time information and the optimal timing for visits on the application screen.

[0577] As a concrete example, consider a situation where a user is planning to visit a popular restaurant. The user uses the app to select the restaurant they intend to visit. The device requests wait time information for the selected restaurant from the server. The server analyzes the real-time wait time data provided by the restaurant and inputs historical data and current trends into a generating AI to make predictions. As a result, the server calculates the optimal visit time along with the predicted wait time and provides it to the user. The user can check the times with shorter wait times through the app and adjust the timing of their visit accordingly.

[0578] This system allows users to minimize waiting times and use their time more effectively. Furthermore, it enables facilities to distribute waiting times, promoting more efficient operations.

[0579] The following describes the processing flow.

[0580] Step 1:

[0581] The user launches the smartphone app and selects the facility they wish to visit. The device retrieves the ID of the selected facility.

[0582] Step 2:

[0583] The terminal sends a request to the server containing the acquired facility ID and user information (including location information and current date and time).

[0584] Step 3:

[0585] The server receives requests from terminals and searches the database for waiting time data for the corresponding facilities.

[0586] Step 4:

[0587] The server uses generative AI to analyze the facility's past wait time data and predict future wait times along with current real-time data.

[0588] Step 5:

[0589] The server calculates the recommended visit time for the user, along with a predicted wait time, and then generates a response.

[0590] Step 6:

[0591] The server sends the generated response data to the terminal.

[0592] Step 7:

[0593] The terminal displays received waiting time information and recommended visit timings on the user interface.

[0594] Step 8:

[0595] Based on the displayed information, users adjust the timing of their visit and determine the optimal time to actually visit.

[0596] (Example 1)

[0597] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0598] At facilities visited by users, it is difficult to minimize the time wasted due to the uncertainty of waiting times, which hinders the efficient use of infrastructure. Furthermore, when users plan their visits, there is a lack of effective means to predict the congestion level of their destinations in advance, making it difficult to determine the optimal time to visit.

[0599] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0600] In this invention, the server includes an information processing device for collecting and analyzing waiting time information for selected facilities in real time, communication means for calculating and transmitting the generated waiting time prediction results to a terminal, and generation means for predicting current and future waiting times using past waiting time data. This makes it possible for users to predict the congestion level of their destination in advance and select the optimal timing for their visit.

[0601] An "input device" is a device that provides an interface for a user to select a facility to visit.

[0602] An "information processing device" is a computer system that collects waiting time information for selected facilities and analyzes the data in real time.

[0603] A "display device" is a device that visually presents analyzed waiting time and recommended visit timing information to the user's terminal.

[0604] "Communication means" refers to a device that has network functionality for sending and receiving data between a server and a user terminal.

[0605] A "generation means" is a device that executes algorithms and techniques for predicting current and future waiting times based on past waiting time data.

[0606] A "calculating device" is a computer system that uses data, including the user's location information, to calculate the optimal timing for a visit.

[0607] This invention is implemented as a system for effectively managing waiting times at facilities visited by users. The system mainly consists of multiple elements, including a user's terminal, a server, and a generative AI model.

[0608] The user selects the facility they plan to visit using an application on their device. The device has a mechanism to send a request to the server based on this selection information. Specifically, the user can select a facility using the application's interface. The device, as an input means, has a communication mechanism to send this information to the server.

[0609] The server functions as an information processing device, acquiring real-time wait time information from selected facilities. This data is typically obtained through POS systems or reservation management systems. The server inputs the collected data into a generative AI model, which considers historical data and current trends to predict future wait times. As a specific example, "OpenAI's GPT-4" can be used as the generative AI model.

[0610] The generated prediction information is transmitted to the user's terminal via a communication method. The user's terminal functions as a display device, visually presenting the received prediction information on waiting times and the optimal visit timing to the user. This allows the user to know the congestion status of their destination in advance and receive support in deciding the optimal visit timing.

[0611] As a concrete example, consider a scenario where a user wants to visit a popular restaurant. The user selects the restaurant through their device, and the device requests real-time wait time data for that restaurant from a server. The server combines the collected current data with past trends and inputs a prompt message into a generating AI model, such as, "Based on data from the past year, predict the wait time for the next three hours and suggest the optimal time to visit." Once the prediction is complete, the optimal visit time is sent to the device and displayed for the user to confirm. In this way, the user can optimize their visit plan and enjoy a smoother experience.

[0612] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0613] Step 1:

[0614] The user opens the application on their device and selects the facility they plan to visit. As input, the user taps the facility name or category to obtain facility ID information. The output is detailed information about the selected facility. This information is saved on the device as request data.

[0615] Step 2:

[0616] The terminal sends a request to the server. The input includes the ID of the selected facility and the user's current location information. Specifically, the terminal's built-in GPS function is used to obtain the current location, and the data is sent to the server via the communication module. The output is an acknowledgment that the server has received the request.

[0617] Step 3:

[0618] The server collects relevant wait time data using an information processing device based on the received facility ID. The input is request data from the terminal, and the output is real-time wait time data. This process integrates with the facility's reservation management system and POS system to obtain the latest data.

[0619] Step 4:

[0620] The server combines collected wait time data with historical data and inputs it into a generating AI model. The input data includes historical wait time information and current trend information. The prompt message includes instructions such as "Predict the wait time for the next 3 hours at the selected facility," and the output is a prediction of future wait times.

[0621] Step 5:

[0622] The server analyzes the output from the generated AI model and calculates the optimal visit time. The input is the AI ​​model's prediction result, and the output is the optimal visit timing. In the calculation process, the system combines the user's desired visit time with the prediction data to avoid congestion.

[0623] Step 6:

[0624] The server sends the optimal visit time to the user's terminal. The input is the calculated optimal visit time, and the result is sent to the terminal using a communication method. The output is data containing the optimal visit time for display on the user's terminal.

[0625] Step 7:

[0626] The optimal visit timing received on the user's device is displayed. Input is data transmitted from the server, and output is visual information provided to the user. The device presents the information to the user in an easy-to-understand format, such as graphs and text. Based on this information, the user can adjust their visit plan.

[0627] (Application Example 1)

[0628] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0629] In physical stores such as shopping malls and theme parks, it is difficult for users to know in advance how crowded the stores and attractions they plan to visit are, making it difficult to plan an efficient visit. In particular, increased waiting times due to congestion and the choice of unexpected routes can prevent users from visiting the facility as planned, forcing them to use their time inefficiently. This leads to decreased user satisfaction, and the facilities themselves face a decline in operational efficiency due to the concentration of visitors.

[0630] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0631] In this invention, the server includes input means for the user to select a facility to visit, server means for collecting and analyzing waiting time information for the selected facility in real time, display means for displaying the analyzed waiting time, recommended visit timing, and optimal travel route on the user terminal, and location information processing means for acquiring the user's location information and optimizing the travel route. As a result, the user can understand the congestion status of the facility or attraction to visit in advance and make an efficient visit with minimal waiting time. This improves user satisfaction and allows facilities to improve operational efficiency.

[0632] An "input method" is an interface used by the user to select the facility they wish to visit.

[0633] A "server system" is a system for collecting facility waiting time data and analyzing it in real time.

[0634] "Display means" refers to a device or software for visually presenting analyzed waiting time information, recommended visit times, and optimal travel routes to the user's terminal.

[0635] "Location information processing means" refers to technology that acquires the user's current location and optimizes the visit route based on that information.

[0636] "Waiting time information" refers to information about the time spent at a facility or waiting in line, based on the current and predicted crowd levels at the facility or attraction.

[0637] "Generation processing means" refers to analytical and computational techniques used to predict current and future situations by utilizing past data.

[0638] "Travel route" refers to the optimal path a user takes from their starting point to their destination.

[0639] The system that implements this application allows users to check the waiting time at a facility they wish to visit in real time and create an efficient visit plan. This system consists of the following hardware and software:

[0640] First, the user operates the application using a smartphone or smart glasses and selects the facility they wish to visit. This selection is made through an input device, and the information is sent to the server.

[0641] Next, the server retrieves wait time information for the selected facility and collects it in real time using Firebase. Then, based on a generative AI model using TensorFlow or PyTorch, it predicts wait times from historical data and current conditions, and calculates the optimal visit time and route based on the user's location. This allows the user to reduce unnecessary wait times and obtain an efficient travel route.

[0642] The analysis results obtained from the server are transmitted in real time to a smartphone or smart glasses via Flask. Users can then view specific waiting times, optimal visit times, and recommended routes through the application's display method.

[0643] For example, a user might want to visit a specific store in a shopping mall. In this case, the system would suggest less crowded times and routes that are more comfortable to travel along, avoiding peak hours. An example of a prompt message might be: "The store you're looking for is currently crowded. We recommend visiting around 2 PM. Would you like to check the shortest route?"

[0644] This system allows users to identify less crowded times in advance and use facilities efficiently, while also enabling facilities to improve operational efficiency by distributing congestion.

[0645] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0646] Step 1:

[0647] The user selects the facility they wish to visit via their smartphone. This selection information is sent from the device to the server. The input includes the facility's name and category. The output is the server's identification information for the selected facility.

[0648] Step 2:

[0649] The server collects real-time wait time data for the selected facility through the facility's own system or API. Firebase is used for data retrieval and storage. The input is the facility's identification information, and the output is the current wait time data.

[0650] Step 3:

[0651] The server uses a generative AI model in TensorFlow or PyTorch to analyze acquired latency data and historical latency data. The data is input into the AI ​​model, and predicted latency is output. Here, trend analysis of historical data and processing of current latency data are performed to predict future conditions.

[0652] Step 4:

[0653] The server calculates the optimal travel route based on the user's location information. This calculation uses the Google Maps API to obtain the current location and determine the shortest path to the destination. The input is the user's location information, and the output is the optimal route.

[0654] Step 5:

[0655] The server sends information, including the calculated optimal visit time and route, to the terminal via Flask. This transmission includes real-time data updates and is optimized for fast response times. The input is the predicted wait time and route information, and the output is the visit guidance information displayed on the user's terminal.

[0656] Step 6:

[0657] The terminal displays the received information to the user. Using the display method, specific waiting times, optimal visit times, and recommended travel routes are visually presented. Input is data from the server, and output is a visit plan that is visually understandable to the user.

[0658] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0659] This invention combines a system for effectively managing waiting times at facilities visited by users with an emotion engine that recognizes user emotions. During the process of a user selecting a facility and monitoring waiting times in real time, the emotion engine analyzes the user's facial expressions, tone of voice, and other factors to recognize their emotions. This recognized emotion information is then analyzed by a server to determine how stressed the user is experiencing during the current waiting time.

[0660] As a concrete example, consider a scenario where a user attempts to visit a crowded hospital. The user selects a hospital through the app and sets a planned visit time on their device. The server retrieves and analyzes hospital waiting time information, calculating a waiting time prediction and recommended visit timing based on real-time data. Here, the emotion engine uses data obtained from the user's camera and microphone to analyze the user's emotions from their facial expressions and voice.

[0661] For example, if a user's facial expression indicates displeasure, the emotion engine determines a high stress level. The server uses this information to provide the user with special recommendations for faster service or alternative options to reduce waiting times (e.g., other less busy times or different facilities).

[0662] Furthermore, the device can notify users of recommended activities during their waiting time (for example, recommendations for relaxing music or guidance for simple exercises) based on their emotional state. This allows users to spend their waiting time more productively and improves the operational efficiency of the facility.

[0663] The following describes the processing flow.

[0664] Step 1:

[0665] The user launches the smartphone app and selects the facility they wish to visit. The device records the ID of the selected facility and the user's location information.

[0666] Step 2:

[0667] The terminal sends a request to the server that includes the facility ID and user information. This request also includes a signal to trigger analysis by the emotion engine.

[0668] Step 3:

[0669] The server receives the request and retrieves the real-time wait time for the specified facility from the database. Simultaneously, a generating AI uses historical data to predict future wait times.

[0670] Step 4:

[0671] The emotion engine built into the device analyzes the user's facial expressions and voice through the camera and microphone to determine their emotional state (e.g., anxiety, stress, relaxation, etc.).

[0672] Step 5:

[0673] The server receives emotional data from the emotion engine and evaluates the user's stress level. This evaluation is treated as a factor influencing the waiting status.

[0674] Step 6:

[0675] The server combines wait time predictions, stress level assessments, and recommended visit timings to generate the best visit options for the user. This may include suggestions for alternative time slots or facilities.

[0676] Step 7:

[0677] The device receives the calculated information and displays the waiting time, optimal visit timing, and recommended activities for mood improvement on the user interface.

[0678] Step 8:

[0679] Based on this information, users can adjust their visit plans and proceed with the visit process while minimizing waiting times and managing their emotions.

[0680] (Example 2)

[0681] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0682] Users often experience unnecessary stress and wasted time due to unclear waiting times at places they visit. Furthermore, when waiting times do occur, there is a lack of information on how to best utilize that time. Flexible waiting time management and suggestions that take users' emotional states into account are needed.

[0683] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0684] In this invention, the server includes receiving means for selecting a location for the user to visit, information processing means for collecting and storing waiting time information for the selected location, analysis means for analyzing the user's emotional state and generating emotional information, integrated analysis means for integrating the analyzed emotional information and waiting time, evaluating the burden felt by the user, and suggesting improvement plans, and presentation means for presenting the integrated information to the user terminal. This makes it possible to optimize and effectively utilize waiting time based on the user's emotional state.

[0685] "Receiving means" refers to a device or program for a user to select a location to visit and transmit that selection information to the system.

[0686] "Information processing means" refers to a device or program for collecting waiting time information related to a selected location and storing it statistically or in real time.

[0687] "Analysis means" refers to a device or program for acquiring data such as the user's facial expressions and voice, and for recognizing and analyzing their emotional state.

[0688] An "integrated analysis means" is a device or program that integrates analyzed emotional information and collected waiting time information to evaluate the stress felt by the user and derive appropriate improvement suggestions.

[0689] "Presentation means" refers to a device or program that displays integrated and analyzed information on a user terminal in an intuitive and easy-to-understand manner, and provides feedback to the user.

[0690] This invention is a system for optimizing waiting times and improving the user experience during those times. Users select the location they wish to visit via a terminal with a dedicated application installed. They select a facility using the terminal's input screen, and this information is communicated to a server.

[0691] The server utilizes real-time API or database connections with facilities to collect waiting time information for selected locations. Standard protocols via the internet are used for real-time data collection, while data analysis software is used for data storage and analysis. Machine learning algorithms are employed to predict waiting times, and historical data is used to estimate future congestion levels.

[0692] The camera and microphone on the user's device collect data for emotion analysis. Emotion analysis uses commonly available emotion engine software. This system analyzes the user's emotional state from, for example, their facial expressions and voice tone, and the resulting emotional information is sent to a server.

[0693] The server integrates collected emotional and waiting time information to assess the stress users experience during waiting times. Based on this analysis, it generates suggestions for further stress reduction. A generative AI model is used to provide the option best suited to the user's emotions.

[0694] As a concrete example, consider a user visiting a hospital who uses the app. Once the user selects a hospital and decides on a visit date, the waiting time evaluation begins. If the user's facial expression indicates high stress, the server will suggest an earlier visit time or another available clinic.

[0695] Additionally, as an option during waiting times, the device can be notified with a list of relaxing music or suggestions for short exercises. Users can select the information and suggestions provided, allowing them to spend their time more effectively.

[0696] In this way, we achieve waiting time management and optimization based on the user's emotional state, thereby improving the quality of the user experience.

[0697] Example prompt: "We anticipate long waiting times at the hospital. What relaxation techniques would you suggest to alleviate stress in this situation?"

[0698] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0699] Step 1:

[0700] The user launches the application from their device and selects the location they wish to visit. This input sends the selected location and planned visit time from the device to the server. The server receives this information and records the location selection information in its database.

[0701] Step 2:

[0702] The server collects waiting time information for the selected location. Specifically, the server obtains real-time data via the facility's API and analyzes waiting times and congestion levels. The output of this process is the predicted waiting time and recommended visit time.

[0703] Step 3:

[0704] The device collects video and audio data using the user's camera and microphone. This data is input into the emotion engine. The emotion engine analyzes the user's facial expressions and voice tone and outputs their emotional state.

[0705] Step 4:

[0706] The server integrates emotional information obtained from the emotion engine with waiting time information. Specifically, it evaluates the level of stress the user feels based on their emotional state and analyzes its impact. As a result of this analysis, it outputs visit timings and alternative suggestions that take the user's emotional state into consideration.

[0707] Step 5:

[0708] The server generates suggestions for the user based on the integrated analysis results. Using a generation AI model, it determines recommended activities during waiting times (e.g., suggestions for relaxing music or exercise). These suggestions are then sent to the user's device.

[0709] Step 6:

[0710] The device notifies the user of suggestions received from the server. Based on this, the user can readjust their visit time or select activities to make the most of their waiting time. This notification reflects the user's choices in their actions.

[0711] (Application Example 2)

[0712] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0713] Waiting times when visiting a facility often cause stress for users and can lead to a decline in the customer experience for the facility. Furthermore, appropriate responses that address the user's emotional state are frequently required. However, conventional waiting time management systems fail to consider the emotional state of users and are insufficient for improving user satisfaction.

[0714] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0715] In this invention, the server includes input means for the user to select a facility to visit, information processing means for collecting and analyzing waiting time information for the selected facility in real time, output means for displaying the analyzed waiting time and recommended visit timing information on the user's device, emotion analysis means for analyzing the user's facial expressions and voice and recognizing their emotions, and recommendation means for providing recommended entertainment during the waiting time based on the recognized emotions. This makes it possible to manage waiting times while taking the user's emotions into consideration, thereby reducing user stress and providing a comfortable way to spend waiting time.

[0716] An "input method" refers to a device or interface that allows a user to select the facility they wish to visit.

[0717] "Information processing means" refers to a technological device that collects waiting time information for selected facilities and performs real-time analysis.

[0718] "Output means" refers to a display device that conveys analyzed waiting times and optimal timing for visits to the user.

[0719] "Emotional analysis methods" refer to technological processes that analyze a user's facial expressions and voice to recognize their emotional state.

[0720] "Recommended methods" refer to methods for providing entertainment and other activities that users can engage in while waiting, based on their perceived emotions.

[0721] This invention is a system for managing waiting times at facilities visited by users and for analyzing users' emotions to reduce stress. This system mainly consists of input means, information processing means, output means, emotion analysis means, and recommendation means.

[0722] The server collects and analyzes facility waiting time information in real time as an information processing tool. Specifically, this process involves obtaining waiting time data from facility management systems via the internet and storing it in a database. The analysis uses a waiting time prediction algorithm based on historical data. In the case of facilities with long waiting times, the server calculates a recommended visit time and notifies the user.

[0723] The user's device collects data to identify the user's emotions using a camera and microphone as means of emotion analysis. Specifically, it captures facial expressions through the camera and analyzes voice tone through the microphone. This data is then analyzed by an emotion analysis engine, such as commonly used image processing software or voice analysis solutions.

[0724] Subsequently, based on the recognized emotional information, the user's device implements recommended measures to provide stress-reducing activities during waiting times. The device may play relaxing music using streaming technology or display exercise guides on the screen. This allows users to spend their waiting time more comfortably.

[0725] As a concrete example, when a user heads to a store in a shopping mall, they may anticipate a wait. For instance, suppose a particular cafe is extremely crowded on a Saturday afternoon. In this case, the terminal would display a recommended visit time and a list of music suitable for listening to while waiting. An example of a prompt message in this scenario would be: "In a shopping mall wait time management application, how can we analyze customer emotions in real time and suggest optimal music or exercise to reduce stress?" This implementation would improve the shopping experience.

[0726] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0727] Step 1:

[0728] The user uses a terminal to select the facility they wish to visit. The input is the facility selection information, which is transmitted by the terminal and sent to the server. The server receives this data and identifies the facility ID necessary for the next process.

[0729] Step 2:

[0730] The server collects wait time information for selected facilities and performs real-time analysis. The input is the facility ID, and the output is the latest analyzed wait time data and recommended visit time. The server accesses the facility database, retrieves the current wait time, and compares it with historical data to make predictions.

[0731] Step 3:

[0732] The device collects user emotion data. The input consists of the user's facial image and voice data, which are collected using the camera and microphone. The output is the emotion data before processing. The device then sends the collected data to an emotion analysis engine.

[0733] Step 4:

[0734] The emotion analysis engine analyzes the user's emotions and sends the results to the server. The input is facial expression images and audio data, and the output is the analyzed emotional state. The emotion analysis engine uses image processing algorithms and audio analysis models to generate a numerical representation of the emotion.

[0735] Step 5:

[0736] The server determines activities to reduce user stress based on emotional state and waiting time data. The input is the analyzed emotional state and recommended visit time, and the output is recommended entertainment content. The server generates and selects a list of music and exercises best suited to the emotional state.

[0737] Step 6:

[0738] The device notifies the user of recommended activities. The input is entertainment content sent from the server, and the output is the most suitable content to display on the user's device. The device performs actions such as playing a music list or displaying an exercise guide through the user interface.

[0739] By following these steps, users will be able to spend their waiting time productively.

[0740] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0741] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0742] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0743] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0744] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0745] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0746] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0747] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0748] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0749] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0750] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0751] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0752] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0753] 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.

[0754] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0755] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0756] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0757] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0758] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0759] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0760] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0761] The following is further disclosed regarding the embodiments described above.

[0762] (Claim 1)

[0763] An input method for the user to select the facility to visit,

[0764] A server for collecting and analyzing waiting time information for selected facilities in real time,

[0765] A display means for displaying analyzed waiting time and recommended visit timing information on the user's terminal,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, further comprising a generation processing means for predicting current and future waiting times using past waiting time data.

[0769] (Claim 3)

[0770] The system according to claim 1, comprising means for utilizing the user's location information to calculate the optimal visit timing, taking into account the time it takes for the user to arrive at the location.

[0771] "Example 1"

[0772] (Claim 1)

[0773] An input method for the user to select the facility to visit,

[0774] An information processing device for collecting and analyzing waiting time information for selected facilities in real time,

[0775] A display device for displaying analyzed waiting time and recommended visit timing information on the user terminal,

[0776] A communication means for calculating the generated waiting time prediction result and transmitting it to the terminal,

[0777] A system that includes this.

[0778] (Claim 2)

[0779] The system according to claim 1, comprising generation means for predicting current and future waiting times using past waiting time data.

[0780] (Claim 3)

[0781] The system according to claim 1, comprising a calculation device that utilizes the user's current location information to calculate the optimal visit timing, taking into account the time it takes for the user to arrive at the location.

[0782] "Application Example 1"

[0783] (Claim 1)

[0784] An input method for the user to select the facility to visit,

[0785] A server for collecting and analyzing waiting time information for selected facilities in real time,

[0786] A display means for displaying the analyzed waiting time, recommended visit timing, and optimal travel route on the user's terminal,

[0787] Location information processing means for acquiring user location information and optimizing travel routes,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, comprising generation processing means for predicting current and future waiting times using past waiting time data and for suggesting the optimal visit time and route based on the user's location information.

[0791] (Claim 3)

[0792] The system according to claim 1, comprising means for optimizing the waiting time and visit route until the user reaches the location using the user's location information, and for calculating the most efficient timing for a visit.

[0793] "Example 2 of combining an emotion engine"

[0794] (Claim 1)

[0795] A means of receiving information for the user to select a place to visit,

[0796] Information processing means for collecting and storing waiting time information for selected locations,

[0797] An analytical means for analyzing a user's emotional state and generating emotional information,

[0798] An integrated analysis method for combining analyzed emotional information and waiting time to evaluate the burden felt by the user and to suggest improvement measures,

[0799] A presentation means for presenting integrated information to the user terminal,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, further comprising a determination means for calculating recommended activities during waiting time based on the user's emotional state through a generation process.

[0803] (Claim 3)

[0804] The system according to claim 1, further comprising suggestion means for suggesting alternative visit timings or different locations based on the user's current emotional state.

[0805] "Application example 2 when combining with an emotional engine"

[0806] (Claim 1)

[0807] An input method for the user to select the facility to visit,

[0808] Information processing means for collecting waiting time information for selected facilities and analyzing it in real time,

[0809] Output means for displaying analyzed waiting time and recommended visit timing information on the user's device,

[0810] An emotion analysis method for analyzing the user's facial expressions and voice to recognize emotions,

[0811] Based on recognized emotions, recommended means for providing entertainment during waiting times,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, further comprising a prediction processing means for predicting current and future waiting times based on past waiting time information.

[0815] (Claim 3)

[0816] The system according to claim 1, comprising means for utilizing the user's location information to calculate the optimal visit timing, taking into account the time it takes for the user to arrive at their destination. [Explanation of Symbols]

[0817] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An input method for the user to select the facility to visit, A server for collecting and analyzing waiting time information for selected facilities in real time, A display means for displaying analyzed waiting time and recommended visit timing information on the user's terminal, A system that includes this.

2. The system according to claim 1, further comprising a generation processing means for predicting current and future waiting times using past waiting time data.

3. The system according to claim 1, comprising means for utilizing the user's location information to calculate the optimal visit timing, taking into account the time it takes for the user to arrive at the location.

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