System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting an availability of a coin-operated locker in real time; means for storing the collected availability in a central database; means for predicting a future availability using a AI algorithm based on a past usage; means for receiving location information of a user and acquiring an availability of a coin-operated locker around the location; means for providing the acquired availability and the predicted availability to a user device; and means for displaying the acquired availability on the user device.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern urban areas, coin lockers are frequently used by travelers, commuters, students, and others, and there is a need to quickly and accurately grasp their availability. However, conventional systems have difficulty in grasping availability in real time, and it often takes time for users to find the appropriate locker. In addition, due to the lack of predictive functionality, there is a high risk that lockers will become full during busy periods or specific events. Technology is needed to solve this problem and improve the efficiency of coin locker use. [Means for solving the problem]

[0005] The present invention aims to solve the above-mentioned problems by providing a system that utilizes IoT and AI technologies to collect coin locker availability information in real time and predict future availability based on past usage data. The system of the present invention includes: (1) means for collecting coin locker availability information in real time; (2) means for storing the collected data in a central database; (3) means for predicting future availability using an AI algorithm based on past usage data; (4) means for receiving a user's location information and obtaining the availability status of coin lockers in the vicinity of that location; (5) means for providing the obtained availability status and predicted data to a user terminal; and (6) means for displaying the obtained data on the user terminal. This invention allows users to easily find the optimal coin locker, saving time and effort.

[0006] A "coin locker" is a storage device installed in public places that users can use to temporarily store their luggage, either for a fee or free of charge.

[0007] "Real-time" means that information is processed and provided close to the moment it is generated.

[0008] "Availability" is information that indicates whether a coin locker is currently in use or available.

[0009] An "IoT sensor" is a device that converts information from the physical world into digital data and collects and transmits it over a network.

[0010] A "central database" is a database for centrally storing data shared across the entire system.

[0011] "Past usage data" refers to data that includes historical information about how coin lockers have been used in the past.

[0012] An "AI algorithm" is a computational method that mimics human knowledge and experience to analyze data and make predictions and recognize patterns.

[0013] "Future availability" refers to the future availability of coin lockers predicted based on past data and patterns.

[0014] A "user terminal" is a device such as a smartphone or tablet operated by a user of the system.

[0015] "Location information" is data indicating the current location of a user device, and is obtained using technologies such as GPS.

[0016] An "API" is an interface for exchanging functions and data between different software programs.

[0017] "Navigation" means a function that provides route guidance to a destination. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] This invention is a system that utilizes IoT and AI technologies to collect information on coin locker availability in real time and predict future availability based on past usage data. This system consists of a server, a terminal (such as a user's smartphone), and an IoT sensor. Details of each component and specific steps for implementing the invention are explained below.

[0040] System Components

[0041] 1. Server

[0042] The server plays a central role in the system. It collects real-time availability data sent from IoT sensors installed in coin lockers and stores it in a central database. It also uses AI algorithms to analyze past usage data and predict future availability.

[0043] 2. Device (user's smartphone)

[0044] Users can check current and predicted availability using a dedicated app downloaded to their smartphones. The app acquires the user's current location and requests information from the server about the nearest coin locker based on that location.

[0045] 3. IoT Sensors

[0046] Each coin locker is equipped with an IoT sensor that detects the availability of the locker in real time and sends the information to a server.

[0047] System Operation

[0048] The specific operation of the system will be described below.

[0049] Collection and storage of availability

[0050] The server periodically obtains information about the availability of each locker from IoT sensors installed in each locker. For example, it collects data from sensors that detect whether there is luggage in the locker. This data is stored in a central database, which keeps the latest status of each locker.

[0051] Availability analysis

[0052] The server analyzes the latest availability data stored in a central database to determine which lockers are currently vacant or occupied.

[0053] Prediction by AI algorithm

[0054] The server runs an AI algorithm based on past usage data to predict future availability. For example, it learns patterns of locker usage on specific days of the week or at specific times of the day, and uses that to estimate future availability.

[0055] User request processing

[0056] The user launches the smartphone app and inputs their current location, which then sends a request to the server.

[0057] Data provision

[0058] The server receives the user's request, obtains real-time availability and forecast data for the coin lockers closest to the user's current location, and then compiles this information into a packet and sends it to the user's smartphone.

[0059] Data display and guidance

[0060] The device then displays the location of the coin lockers on a map based on the received data. The user can then select the most suitable locker based on this information and begin navigation to their destination.

[0061] Specific examples

[0062] Searching for a coin locker at Tokyo Station

[0063] User: A traveler arrives at Tokyo Station and launches the smartphone app.

[0064] Device (smartphone): The app will display the home screen and select "Coin Locker Search." A pop-up will appear requesting location information.

[0065] User: Allow location information.

[0066] Device: Sends the acquired current location information to the server.

[0067] Server: Based on the user's location, it queries the central database to obtain the availability of coin lockers around Tokyo Station.

[0068] Server: Analyzes the acquired data and generates a list of optimal lockers.

[0069] Server: Returns the generated list to the device via API.

[0070] Terminal: Displays a list of lockers and a map. The user selects the appropriate locker and taps the "Start Navigation" button.

[0071] Device: Launch the map app and begin navigating to the selected locker.

[0072] This invention allows users to efficiently use coin lockers in public places, significantly reducing the time and effort required to search for a locker and providing a more comfortable user experience.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The server periodically collects real-time availability data from IoT sensors installed in coin lockers. The sensors detect whether the locker is in use or vacant and send the status to the server.

[0076] Step 2:

[0077] The server stores the received availability data in a central database, where the latest status of each locker is stored.

[0078] Step 3:

[0079] The server analyzes the availability data stored in the database to determine which lockers are currently available.

[0080] Step 4:

[0081] The server runs AI algorithms based on past usage data to predict future availability, generating a predictive model based on usage patterns, specific dates, and time periods.

[0082] Step 5:

[0083] The user starts the smartphone app and allows the app to obtain their current location information. The allowed location information is then acquired.

[0084] Step 6:

[0085] The terminal (smartphone) sends the acquired location information to the server and requests information about available coin lockers nearby.

[0086] Step 7:

[0087] The server receives the user's location information and retrieves real-time availability and forecast data for coin lockers around that location from the database.

[0088] Step 8:

[0089] The server generates a list of the best available lockers and sends that information to the device via an API.

[0090] Step 9:

[0091] The terminal (smartphone) displays the received list of available lockers on the user interface, along with information on the locker's location, availability, size, and fees on a map.

[0092] Step 10:

[0093] The user selects the most suitable coin locker based on the displayed information and taps the "Start navigation" button.

[0094] Step 11:

[0095] The device (smartphone) will begin navigation to the selected locker, launching a map app and guiding the user to the specified locker.

[0096] Step 12:

[0097] The user follows the navigation to reach the locker and begins using the locker.

[0098] Example 1

[0099] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0100] Conventional coin locker management systems are inefficient because users must check availability only after arriving at the location. Furthermore, they have problems with poor prediction of availability, making it impossible to respond to future demand. This means users have to spend a lot of time searching for a locker, significantly reducing convenience.

[0101] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0102] In this invention, the server includes means for collecting coin locker availability information in real time, means for storing the collected data in a central database, means for predicting future availability using a machine learning algorithm based on past usage data, means for receiving user location information and obtaining availability information for coin lockers around that location, means for providing the obtained availability information and prediction data to a user device, means for displaying the obtained data on the user device, means for analyzing coin locker availability and integrating the real-time data and prediction data, and means for filtering data based on requests sent from the user device and providing information on the most suitable locker. This allows users to efficiently check and use coin locker availability.

[0103] A "coin locker" is an individual storage space installed in a public or commercial facility, and is a storage facility where users can temporarily store their luggage.

[0104] "Availability" is information that indicates whether a coin locker is currently in use or available for use.

[0105] "Real-time collection means" refers to devices and systems that use IoT sensors and other technologies to instantly detect coin locker usage and collect data.

[0106] The "central database" is a data storage system for centrally managing and storing coin locker availability data.

[0107] A "machine learning algorithm" is a type of computer program used to learn patterns from past usage data and predict future availability.

[0108] "User Location Information" means data that describes a user's current geographic location, typically obtained through GPS or other location tracking technologies.

[0109] "User Device" means an electronic device that a user can carry with them, such as a smartphone or tablet, which is used to check the availability of coin lockers through the application.

[0110] "Real-time data" refers to data that indicates the current usage status of coin lockers.

[0111] "Predictive data" is information about future coin locker availability calculated based on past usage data and machine learning algorithms.

[0112] "Analytical means" are the tools and techniques used to process collected data and derive trends and patterns.

[0113] A "filtering means" is a method or system for extracting data based on specific conditions and providing users with only the information they need.

[0114] "Means for displaying on a map" refers to a function that visually displays geographic information on an electronic device, allowing users to determine the location of coin lockers.

[0115] "Route Guidance" is a navigation function that shows users the best route to reach their destination.

[0116] This invention is a system that utilizes IoT and AI technologies to collect information on coin locker availability in real time and predict future availability based on past usage data. This system consists of a server, a terminal (such as a user's smartphone), and an IoT sensor. Details of each component and specific steps for implementing the invention are explained below.

[0117] System Components

[0118] server

[0119] The server plays a central role in this system. It collects availability data sent in real time from IoT sensors installed in coin lockers and stores it in a central database. It also uses machine learning algorithms based on past usage data to predict future availability. Specifically, it performs the following tasks:

[0120] The database engine used is MySQL or PostgreSQL.

[0121] TensorFlow and PyTorch are used as machine learning frameworks.

[0122] Device (user's smartphone)

[0123] Users can check the current and predicted availability of lockers using a dedicated app downloaded to their smartphone. The app obtains the user's current location and requests information about the nearest coin locker based on that location from the server. As the user operates the app, the following steps are performed:

[0124] Location information is obtained using a GPS module.

[0125] The app sends an HTTP request to the server.

[0126] IoT Sensors

[0127] IoT sensors are installed in each coin locker, and these sensors detect the availability of the locker in real time. The sensors send this information to a server. As specific examples, the following sensors could be used:

[0128] Pressure sensor to detect the presence or absence of luggage

[0129] The sensors transmit data using communication protocols such as Wi-Fi and LoRa.

[0130] Specific examples

[0131] Searching for a coin locker at Tokyo Station

[0132] 1. The user arrives at Tokyo Station and launches the smartphone app.

[0133] 2. Device (smartphone): The app home screen will appear. Select "Coin Locker Search." A pop-up will appear requesting your location information.

[0134] 3. The user allows location information to be obtained.

[0135] 4. The device sends the current location information it has acquired to the server.

[0136] 5. The server queries the central database to obtain the availability of coin lockers around Tokyo Station based on the user's location.

[0137] 6. The server analyzes the acquired data and generates a list of optimal lockers.

[0138] 7. The server returns the generated list to the device via API.

[0139] 8. The device (smartphone) displays a list of lockers and a map. The user selects the appropriate locker and taps the "Start Navigation" button.

[0140] 9. The device will launch a map app and begin providing directions to the selected locker.

[0141] Prompt Sentence Examples

[0142] By inputting the following prompt sentences into the generative AI model, you can get an explanation for a specific scenario.

[0143] "Please explain in detail how to arrive at Tokyo Station and use a smartphone app to find the nearest coin locker. Please also explain how to use the app, which displays the current availability and future forecast availability, and the specific steps users take when using it."

[0144] As described above, the system of the present invention can efficiently provide users with information on available coin lockers, thereby significantly improving convenience of use.

[0145] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0146] Step 1:

[0147] The server collects availability data from IoT sensors installed in coin lockers.

[0148] Specific behavior:

[0149] Input: Locker usage status data obtained by IoT sensors (e.g., whether or not luggage is present).

[0150] Data processing: Receives and formats data sent from sensors. Each data is appended with a timestamp and the corresponding locker ID.

[0151] Output: A formatted set of availability data.

[0152] Step 2:

[0153] The server stores the collected data in a central database.

[0154] Specific behavior:

[0155] Input: Formatted availability data.

[0156] Data manipulation: Insert data using a database query.

[0157] Output: Availability data stored in database and acknowledged by query.

[0158] Step 3:

[0159] The server analyzes the latest availability data stored in a central database.

[0160] Specific behavior:

[0161] Input: Availability data in a central database.

[0162] Data Calculation: Uses database queries to identify currently available and occupied lockers, performing aggregation and filtering as needed.

[0163] Output: A list of current availability as a result of the analysis.

[0164] Step 4:

[0165] The server runs a machine learning algorithm based on past usage data to predict future availability.

[0166] Specific behavior:

[0167] Input: Historical usage data in a database.

[0168] Data calculation: Run machine learning algorithms using TensorFlow and PyTorch to predict future availability.

[0169] Output: Predicted future availability data.

[0170] Step 5:

[0171] The user launches the smartphone app and sends their location information to the server.

[0172] Specific behavior:

[0173] Input: Local location information obtained by the smartphone's GPS module.

[0174] Data processing: The location information is sent to the server via an HTTP request.

[0175] Output: The location information sent to the server.

[0176] Step 6:

[0177] The server receives requests from users and obtains real-time and forecast data for the nearest coin lockers.

[0178] Specific behavior:

[0179] Input: The user's location.

[0180] Data calculation: Based on the location information, a geolocation database is queried to identify the nearest coin locker. After identification, real-time data is combined with predictive data.

[0181] Output: Data packets (real-time data + predicted data) provided to the user.

[0182] Step 7:

[0183] The server transmits the processed data packets to the user device.

[0184] Specific behavior:

[0185] Input: Consolidated data packets.

[0186] Data processing: Convert the data packet into JSON format and send it to the user device through the API.

[0187] Output: Data packets received by the user device.

[0188] Step 8:

[0189] The terminal (user's smartphone) displays the location and availability of coin lockers based on the received data.

[0190] Specific behavior:

[0191] Input: Data packet sent by the server.

[0192] Data processing: Analyzes data packets and displays the location and availability of coin lockers on a map.

[0193] Output: A map that the user can view and a display of locker availability.

[0194] Step 9:

[0195] The terminal will begin providing directions to the locker selected by the user.

[0196] Specific behavior:

[0197] Input: The location of the user's selected locker.

[0198] Data calculation: Launches the map app and calls the navigation function to start route guidance.

[0199] Output: Start of navigation, route guidance to help the user reach their destination.

[0200] (Application example 1)

[0201] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0202] With conventional management systems for coin lockers and parts shelves, it is difficult to grasp availability in real time, which increases the workload of users and administrators. In addition, it is not possible to predict future availability, making planned use difficult. As a result, issues include a lack of convenience and efficiency.

[0203] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0204] In this invention, the server includes means for collecting the availability status of coin lockers and parts shelves in real time, means for storing the collected data in a central database, means for predicting future availability using an AI algorithm based on past usage data, means for receiving user location information and obtaining availability status around that location, means for providing the obtained availability status and predicted data to a user terminal, and means for displaying the obtained data on the user terminal.This allows users and administrators to grasp availability status in real time and, by predicting future status, enables planned and efficient use.

[0205] A "coin locker" is a storage device used by users to temporarily store their luggage.

[0206] "Availability" is information that indicates whether storage facilities such as coin lockers and parts shelves are currently available.

[0207] "Real-time" collection means that data is collected immediately, on the spot, without delay.

[0208] A "server" is a central processing unit that collects, stores, and analyzes data.

[0209] A "central database" is a database system in which collected data is centrally stored and managed.

[0210] An "AI algorithm" is a computational procedure or model used to perform data analysis and predictions using artificial intelligence technology.

[0211] "User terminal" means a device (smartphone, tablet, etc.) used by a user to receive and display information.

[0212] "Location information" is data that indicates the geographic location of a user or object.

[0213] A "parts shelf" is a shelf used to store and manage parts in a factory or warehouse.

[0214] "Predicting future availability" means estimating future availability based on analysis of past data.

[0215] "Means for collecting" refers to methods and devices for obtaining data in real time.

[0216] "Storage means" refers to the method or device used to store collected data in a central database.

[0217] A "means for predicting" is a method or device for estimating future states using an AI algorithm.

[0218] The "means for providing" refers to a method or device for transmitting the acquired data or predictions to a user terminal.

[0219] The "display means" refers to a method or device for visually displaying the acquired data or predictions on a user terminal.

[0220] To realize this invention, it is necessary to build a system that collects real-time information on the availability of coin lockers and parts shelves, and uses AI algorithms based on past data to predict future availability. Details of the system and specific implementation methods are described below.

[0221] System Components

[0222] server

[0223] The server is the main data processing device of this system. Specifically, it has the following functions:

[0224] Store the collected data in a central database: The availability status is obtained in real time from IoT sensors installed in each coin locker and parts shelf and stored in a central database.

[0225] Predict future availability using AI algorithms based on past data: Analyze collected data and past usage data using AI algorithms (e.g., machine learning models) to predict future availability.

[0226] Device (user's smartphone)

[0227] Users access the system using a smartphone, which has the following features:

[0228] Check current and predicted availability: The system uses the user's location information to obtain the availability of the nearest coin lockers and parts shelves, and displays it in real time. It also allows users to check the predicted availability in the future.

[0229] IoT Sensors

[0230] IoT sensors are installed in each coin locker and parts shelf to detect vacancy in real time. These sensors have the following functions:

[0231] Collect availability information in real time and send it to the server: The usage status of coin lockers and parts shelves is collected in real time via sensors and sent to the server.

[0232] Specific operation of the system

[0233] Data processing by the server

[0234] The server collects availability data from each IoT sensor, stores the collected data in a central database, and then runs an AI algorithm (e.g., a machine learning model) based on the past data to predict future availability.

[0235] Displaying data on a terminal

[0236] Users can use the app on their smartphone to check current and predicted future availability, and information on the nearest coin lockers and parts shelves based on the user's location is also retrieved and displayed.

[0237] Hardware and software used

[0238] Hardware: IoT sensors, servers, user devices (smartphones)

[0239] Software: Python, Requests library, central database, AI models (e.g., machine learning algorithms)

[0240] As a concrete example, the following describes how to build a system that predicts the availability of parts shelves.

[0241] Examples:

[0242] IoT sensors installed on each parts shelf in the factory collect real-time data, and AI is used to predict future availability based on past data. Users (factory workers) can use their smartphones to check the current availability of parts shelves through the app, allowing them to handle parts efficiently.

[0243] Example prompt sentence:

[0244] "Create a Python program that uses IoT sensors installed on each parts shelf in the factory to obtain real-time data, and uses AI to predict future availability based on past data. The program must have the ability to send the availability status of parts shelves to a server and display the prediction results."

[0245] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0246] Step 1:

[0247] The server collects real-time availability data from IoT sensors installed in each coin locker and parts shelf. This data includes the current usage status of each storage space (vacant or occupied), providing the latest usage information.

[0248] Step 2:

[0249] The server stores the collected data in a central database. The input data is real-time data sent from the IoT sensors, and the output is the latest data stored in the central database. This process allows for centralized management of the usage status of all coin lockers and parts shelves.

[0250] Step 3:

[0251] The server runs an AI algorithm based on past usage data stored in a central database to predict future availability. The input is past usage data, and the output is predicted future availability data. Specifically, it uses a machine learning model to analyze patterns and estimate future usage trends.

[0252] Step 4:

[0253] The terminal (user's smartphone) acquires the user's current location. Based on this location information, it requests the server for information on the availability of coin lockers and parts shelves. The input is the user's location information, and the output is a request to the server.

[0254] Step 5:

[0255] The server receives the user's location information and retrieves the availability of coin lockers and parts shelves around that location. The input is the user's location information, and the output is availability data for the area. This provides the user with information on the nearest available storage space.

[0256] Step 6:

[0257] The server then sends the acquired availability and forecast data to the user's device. The input is availability data and forecast data for the relevant area, and the output is data sent to the user's device, allowing the user to check the current situation and future forecasts.

[0258] Step 7:

[0259] The terminal (user's smartphone) displays the acquired data. The input is availability data and forecast data sent from the server, and the output is information displayed to the user. Specifically, availability is visually displayed in a map app or list format. Using this information, the user can select the appropriate coin locker or parts shelf.

[0260] This series of processes allows users to grasp availability in real time and use coin lockers and parts shelves efficiently.

[0261] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0262] This invention is a system that utilizes IoT and AI technologies and an emotion recognition engine to collect information on coin locker availability in real time, predict future availability based on past usage data, and recognize users' emotions to provide an optimal locker usage experience.The system consists of a server, a terminal (such as a user's smartphone), IoT sensors, and an emotion recognition engine.

[0263] System Components

[0264] 1. Server

[0265] The server plays a central role in this system. It collects availability data sent in real time from IoT sensors installed in coin lockers and stores it in a central database. It also uses AI algorithms to analyze past usage data and predict future availability. It also receives user emotional data and adjusts the locker and guidance method based on the user's emotional state.

[0266] 2. Device (user's smartphone)

[0267] Users can check current and predicted availability using a dedicated app downloaded to their smartphone. The app acquires the user's current location and requests information about the nearest coin locker based on that location from the server. The app also has an emotion engine that analyzes the user's facial expressions and voice to detect the user's emotional state.

[0268] 3. IoT Sensors

[0269] Each coin locker is equipped with an IoT sensor that detects the availability of the locker in real time and sends that information to a server.

[0270] 4. Emotion Engine

[0271] The emotion engine is a software component that recognizes emotions from the user's facial expressions and voice. The emotion engine collects emotional data from the user via the smartphone camera and microphone and sends the analysis results to the server. This allows the system to determine the user's stress level and satisfaction level, and then recommend the most suitable locker and select the appropriate guidance method.

[0272] System Operation

[0273] The specific operation of the system will be described below.

[0274] Collection and storage of availability

[0275] The server periodically obtains the availability status from IoT sensors installed in each coin locker. The sensors detect whether there is any luggage in the locker. This data is stored in a central database, which keeps the latest status of each locker.

[0276] Availability analysis

[0277] The server analyzes the latest availability data stored in a central database to determine which lockers are currently available.

[0278] Prediction by AI algorithm

[0279] The server runs an AI algorithm based on past usage data to predict future availability. For example, it learns patterns of locker usage on specific days of the week or at specific times of the day, and uses that to estimate future availability.

[0280] User request processing

[0281] The user launches the smartphone app and inputs their current location. Based on that information and emotion data, the app sends a request to the server.

[0282] Data provision

[0283] The server receives the user's location information and emotion data, retrieves real-time availability and forecast data for coin lockers in the vicinity of the user's location from a database, and compiles this information into a single packet that is sent to the user's smartphone.

[0284] Data display and guidance

[0285] The device then displays the location of the coin lockers on a map based on the received data. The user can then select the most suitable locker based on that information and begin navigation to their destination. Furthermore, the guidance method and information display are dynamically adjusted based on the emotion data.

[0286] Specific examples

[0287] Searching for a coin locker at Tokyo Station

[0288] User: A traveler arrives at Tokyo Station and launches the smartphone app.

[0289] Device (smartphone): The app will display the home screen and select "Coin Locker Search." A pop-up will appear requesting permission to obtain location information and collect emotional data.

[0290] User: Allow location and emotion data collection.

[0291] Device: Sends the acquired current location information and emotion data to the server.

[0292] Server: Based on the user's location and emotion data, it queries the central database to obtain the availability of coin lockers around Tokyo Station.

[0293] Server: Analyzes the acquired data, generates a list of optimal lockers, and selects a guidance method based on the emotional data.

[0294] Server: Returns the generated list to the device via API.

[0295] Terminal: Display a list of lockers and a map. Based on emotion data, provide specific guidance to the user. The user selects the appropriate locker and taps the "Start Navigation" button.

[0296] Device: Launch the map app and begin navigating to the selected locker.

[0297] The present invention allows users to quickly find the coin locker that best suits their emotional state at the time, thereby increasing convenience and satisfaction.

[0298] The processing flow will be explained below.

[0299] Step 1:

[0300] The server periodically collects real-time availability data from IoT sensors installed in the coin lockers. For example, every minute, the sensor detects whether the locker is in use or vacant, and sends that data to the server.

[0301] Step 2:

[0302] The server stores the received availability data in a central database, which stores the latest status of each locker.

[0303] Step 3:

[0304] The server analyzes the availability data stored in the database to determine which lockers are currently available and creates a list of available lockers.

[0305] Step 4:

[0306] The server runs an AI algorithm based on past usage data to predict future availability. It learns patterns of locker usage on specific days and times, and uses that to estimate future availability.

[0307] Step 5:

[0308] The user launches the smartphone app and allows it to use location and emotional data, which then uses the camera and microphone to capture their current facial expressions and voice.

[0309] Step 6:

[0310] The device (smartphone) sends the acquired location information and emotion data to the server, requesting the availability of coin lockers near the user's current location and appropriate guidance.

[0311] Step 7:

[0312] The server receives the user's location information and emotion data, and retrieves real-time availability and forecast data for coin lockers around that location from the database.

[0313] Step 8:

[0314] The server analyzes the emotional data and, if it determines that the user is feeling stressed, it prioritizes the nearest available locker. The server then sends the list of created lockers to the device via API.

[0315] Step 9:

[0316] The terminal (smartphone) displays the received list of available lockers on the user interface, showing the locker location, availability, size, and fee information on a map, and adjusting the guidance method (e.g., whether to provide audio guidance or highlighting method) based on the user's emotional state.

[0317] Step 10:

[0318] The user selects the most suitable coin locker based on the displayed information and taps the "Start Navigation" button. The system then follows the guidance method based on the emotion data.

[0319] Step 11:

[0320] The device (smartphone) will begin navigation to the selected locker, launching a map app and guiding the user to the specified locker.

[0321] Step 12:

[0322] The user follows the navigation to reach the locker and begins using it. The data at the time of use is again sent to the server via the IoT sensor, and the database is updated.

[0323] Example 2

[0324] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0325] It is difficult to grasp the availability of coin lockers in real time and provide this information to users promptly. In addition, there is a lack of means to predict locker usage patterns, making it impossible to secure the optimal locker when a user needs it. Furthermore, services do not take into account the emotional state of the user, making it a challenge to improve user satisfaction.

[0326] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0327] In this invention, the server includes means for collecting coin locker availability information in real time, means for storing the collected data in a central database, means for predicting future availability using an artificial intelligence algorithm based on past usage data, means for receiving user location information and obtaining information on available coin lockers in the vicinity of that location, means for receiving and analyzing the user's emotional data, means for providing the obtained availability information and predicted data to the user's terminal, means for displaying the obtained data on the user's terminal, and means for adjusting the guidance content based on the user's emotional data. This allows users to quickly and accurately grasp the available coin locker status and select the optimal locker based on predicted usage patterns. Furthermore, providing services tailored to the user's emotional state can improve user satisfaction.

[0328] "Means for collecting vacancy status in real time" refers to a device or method that instantly detects the current vacancy status of lockers and collects data through sensors installed in coin lockers.

[0329] "Central database" refers to a database system for centrally storing and managing all collected locker status data.

[0330] "Artificial intelligence algorithm" refers to a computational method using machine learning or other AI techniques that is used to learn patterns based on past data and predict future availability.

[0331] "Means of receiving the user's location information and obtaining the availability of coin lockers in the vicinity of that location" refers to a function that obtains the user's current location using GPS or other means, and uses that information to search for and obtain the availability of nearby lockers.

[0332] "Means for receiving and analyzing user emotional data" refers to a device or method that senses the user's facial expressions and voice, analyzes their emotional state, and digitizes it.

[0333] "Means for providing acquired availability and forecast data to a user terminal" refers to a communication means for transmitting real-time availability data and forecast data from a server to a user terminal.

[0334] "Means for displaying acquired data on the user's device" refers to an interface that visually displays availability and forecast data collected on the user's device.

[0335] "Means for adjusting guidance content based on the user's emotional data" refers to a system that optimally adjusts the guidance method and message content for the user based on analyzed emotional data.

[0336] This invention is a system that collects information on coin locker availability in real time, predicts future availability based on past usage data, and recognizes user emotions to provide an optimal locker usage experience. This system consists of a server, a terminal (user terminal), IoT sensors, and an emotion engine.

[0337] server

[0338] The server plays a central role in this system and has the following functions:

[0339] 1. Data collection from IoT sensors: The server collects vacancy data in real time from IoT sensors installed in each coin locker. This allows the server to grasp the immediate vacancy status of each locker. For example, Microsoft's Azure IoT Hub can be used.

[0340] 2. Database storage: The collected data is stored in a central database, which stores the latest status information for each locker. For example, a database service such as MySQL is used.

[0341] 3. Data Analysis and Prediction: The server analyzes the data stored in the central database to determine which lockers are available. It also runs AI algorithms based on past usage data to predict future availability. This is done using machine learning frameworks (e.g., TensorFlow or PyTorch).

[0342] 4. User information processing: Receives the user's location information and emotion data, retrieves real-time availability and forecast data for coin lockers around the location from the database, and sends the retrieved information to the user's device.

[0343] User terminal

[0344] The user device (smartphone) connects to the system using a dedicated application. The main functions of the application are as follows:

[0345] 1. Location information acquisition: Obtain the user's current location and send that information to the server. Obtain location information using the GPS module.

[0346] 2. Collecting and analyzing emotional data: The user's facial expressions and voice are analyzed using an emotion engine (for example, Microsoft Azure Cognitive Services' Face API or Speech API), and the emotional data is sent to the server.

[0347] 3. Data display: Display the availability and forecast data of coin lockers received from the server. For example, use the Google Maps API to display the location and availability of lockers on a map.

[0348] 4. Guidance: Provides optimal guidance based on the user's emotional data, including navigation to a specific locker and the ability to change the message content.

[0349] Specific examples

[0350] Searching for a coin locker at Tokyo Station

[0351] User: A traveler arrives at Tokyo Station and launches the smartphone app.

[0352] Device: The app will display the home screen and select "Coin Locker Search." A pop-up will appear requesting permission to obtain location information and collect emotional data.

[0353] User: Allow location and emotion data collection.

[0354] Device: Sends the acquired current location information and emotion data to the server.

[0355] Server: Based on the user's location and emotion data, it queries the central database to obtain the availability of coin lockers around Tokyo Station.

[0356] Server: Analyzes the acquired data, generates a list of optimal lockers, and selects a guidance method based on the emotional data.

[0357] Server: Returns the generated list to the device via API.

[0358] Terminal: Display a list of lockers and a map. Based on emotion data, provide specific guidance to the user. The user selects the appropriate locker and taps the "Start Navigation" button.

[0359] Device: Launch the map app and begin navigating to the selected locker.

[0360] Prompt Sentence Examples

[0361] 1. Sample Prompt 1: "Describe an assistance system that helps travelers find the best coin locker when they're in a hurry."

[0362] 2. Example prompt 2: "Please explain the flow of a system that uses IoT sensors and emotion analysis to grasp the availability of coin lockers at stations in real time and provide the optimal guidance method based on the user's emotion."

[0363] This system allows users to check the availability of coin lockers in real time and receive optimal guidance based on their emotional state, thereby improving convenience and satisfaction.

[0364] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0365] System program processing flow

[0366] Step 1: Collect availability data

[0367] The server collects availability data from IoT sensors installed in each coin locker.

[0368] Input: Real-time availability data (e.g., open, closed) from each IoT sensor

[0369] Processing: The server receives data sent from the IoT sensors and obtains the status of each locker. This involves sending data acquisition requests through the sensor API and receiving responses.

[0370] Output: Availability data list (status information for each locker)

[0371] Specific operation: The server sends a request to the sensor API every 15 minutes to obtain the availability status of the lockers. After collecting data from all the lockers, it proceeds to the next step all at once.

[0372] Step 2: Store in the database

[0373] The server stores the collected data in a central database.

[0374] Input: Availability data list

[0375] Processing: The server saves the retrieved data to a database, which involves opening a database connection and executing an SQL insert query.

[0376] Output: Data stored in a central database

[0377] What happens: The server executes an insert query on the database to store each locker's status information in a table. For example, it executes a query like "INSERT INTO locker_status (locker_id, status, timestamp) VALUES (...)".

[0378] Step 3: Analyze availability data

[0379] The server analyzes data stored in a central database to ascertain the latest availability.

[0380] Input: Data from the central database

[0381] Processing: The server queries the latest availability data from the database and lists the real-time availability. This involves using a SELECT query to get the latest data and then parsing it.

[0382] Output: Latest availability list

[0383] Specific operation: The server executes a query to the database such as "SELECT locker_id, status FROM locker_status WHERE timestamp = (SELECT MAX(timestamp) FROM locker_status)" to get the latest availability status.

[0384] Step 4: Future predictions using AI algorithms

[0385] The server runs an AI algorithm based on past usage data to predict future availability.

[0386] Input: Historical usage data

[0387] Processing: The server inputs historical usage data into the machine learning model to obtain predictions of future availability. This includes loading the prediction model, normalizing the data, and analyzing the predictions.

[0388] Output: Future availability forecast data

[0389] What it does: The server uses the TensorFlow model to predict future availability based on past usage data, for example by using a function like "model.predict(past_usage_data)".

[0390] Step 5: Receiving a request from the user

[0391] Users launch the smartphone app, enter their current location information, and request information about available coin lockers.

[0392] Input: User's current location

[0393] Processing: The user device acquires GPS data and sends the location information to the server. This includes using the location service API to acquire the current location and sending it to the server in JSON format.

[0394] Output: Location request sent to the server

[0395] What it does: When a user opens the app and presses the "Get current location" button, the app uses GPS to get the current location and sends that information to the server, for example by using a function like "current_location = gps.get_current_location()".

[0396] Step 6: Analyze user sentiment data

[0397] The device analyzes the user's facial expressions and voice using an emotion engine and sends the emotion data to the server.

[0398] Input: User's facial expression data, voice data

[0399] Processing: The emotion engine (API) analyzes facial expressions and voice to detect emotional states. This involves collecting data through the camera and microphone and sending it to the analysis engine.

[0400] Output: Emotion data

[0401] Specific operation: The app captures the user's facial expression with the camera, sends it to the API, receives the analysis results, and sends them to the server. For example, execute a function like "emotion_data = emotion_api.analyze(face_image)".

[0402] Step 7: Generate optimal locker information

[0403] The server selects the most suitable locker based on the user's location information, emotional data, and real-time locker availability information.

[0404] Input: Location, emotion data, real-time availability

[0405] Processing: The server combines this data and uses algorithms to select the best locker, including prioritizing based on location and emotion data, and selecting the most appropriate locker based on availability data.

[0406] Output: Optimal locker list

[0407] What it does: The server generates a list of lockers that are closest to the user and that correspond to their emotions based on their location and emotion data. For example, it executes a function like "optimal_lockers = find_optimal_lockers(location, emotion_data, availability_data)".

[0408] Step 8: Provide information to your smartphone

[0409] The terminal receives the information returned from the server and displays it to the user.

[0410] Input: Optimal locker list from server

[0411] Processing: Analyzing the received data and displaying it through the user interface. This includes receiving the data, analyzing it, and displaying it on a map.

[0412] Output: Best locker information provided to user

[0413] What it does: It displays recommended lockers on a map on the user's phone, for example, by mapping the locker locations using the Google Maps API and using a function like "display_lockers_on_map(optimal_lockers)".

[0414] Step 9: Navigation

[0415] The terminal will begin navigating to the locker selected by the user.

[0416] Input: Location of the locker selected by the user

[0417] Processing: Calculate the route to the selected locker and initiate navigation, including using a route calculation algorithm and providing audio and visual guidance to the user.

[0418] Output: Navigation instructions

[0419] Specific behavior: The map app will launch and guide the user to the selected locker. For example, it will execute a function such as "start_navigation(destination_location)".

[0420] Prompt Sentence Examples

[0421] 1. Sample prompt 1: "Describe an assistance system that helps travelers find the best coin locker when they're in a hurry."

[0422] 2. Example prompt 2: "Please explain the flow of a system that uses IoT sensors and emotion analysis to grasp the availability of coin lockers at stations in real time and provide the optimal guidance method based on the user's emotion."

[0423] (Application example 2)

[0424] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0425] Conventional coin locker systems were unable to adequately grasp availability or predict future availability, making it difficult for users to find available lockers during busy times. Furthermore, they did not take into account the user's emotional state, and were unable to suggest the most suitable locker or guide users to shopping areas. This resulted in problems that reduced user convenience and satisfaction.

[0426] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting coin locker availability information in real time, means for storing the collected data in a central database, means for predicting future availability using an AI algorithm based on past usage data, means for receiving user location information and obtaining availability information for coin lockers around that location, means for analyzing emotional data, means for suggesting the most suitable coin locker or shopping space based on the analyzed emotional data, and means for displaying the obtained data on the user terminal. This enables the user to quickly find the most suitable coin locker or shopping space based on their emotional state.

[0427] A "coin locker" is a storage device installed in public places and commercial facilities, etc., and used for short-term storage of luggage.

[0428] "Availability" is information indicating whether or not there are any coin lockers available and how many there are.

[0429] "Collecting in real time" means obtaining the current status and information immediately and updating it without delay.

[0430] A "central database" is a data management system that centrally manages multiple data sets and stores, searches, and analyzes the necessary information.

[0431] An "AI algorithm" is a computational method or procedure that uses artificial intelligence technology to analyze data and make predictions.

[0432] "Means for predicting future availability" refers to technology that estimates the future availability of coin lockers based on past data and current conditions.

[0433] "User location information" is data obtained from a smartphone, GPS device, etc. that indicates the user's current geographic location.

[0434] "Emotional data" is information that indicates the emotional state of the user analyzed from facial expressions, voice, etc.

[0435] "Analyzing" means examining data in detail to understand its structure and meaning.

[0436] "Means to suggest the most suitable coin lockers and shopping spaces" refers to technology that guides users to the most appropriate lockers and stores based on their situation and needs.

[0437] A "user terminal" is a device, such as a smartphone or tablet, that a user uses to check and input information.

[0438] This invention relates to a system that provides users with optimal coin lockers and shopping spaces in shopping centers and public places. This system collects availability information in real time, analyzes past data to predict future availability, and analyzes user sentiment to make optimal suggestions.

[0439] System Components

[0440] 1. Server

[0441] The server plays a central role in this system. Specifically, it performs the following processes:

[0442] 1. Collect real-time availability data from IoT sensors installed in each coin locker and store and store it in a central database.

[0443] 2. Run an AI algorithm based on past usage data to predict future availability.

[0444] 3. Recommend optimal lockers and shopping spaces based on location and emotion data received from the user's device.

[0445] 2. Device (user's smartphone)

[0446] Users can access the system using their smartphones and enjoy the following features:

[0447] 1. Obtaining location information and sending it to the server.

[0448] 2. Emotional data is collected using the smartphone camera and microphone, and the analysis results are sent to the server.

[0449] 3. Receive availability and future forecast data provided by the server, as well as suggestions for optimal lockers and shopping spaces.

[0450] 4. Display the locations of coin lockers and stores on a map and provide navigation.

[0451] 3. IoT Sensors

[0452] IoT sensors installed in each coin locker and store detect current availability in real time and send that information to a server.

[0453] 4. Emotion Engine

[0454] The emotion engine is a component that recognizes the user's emotions from their facial expressions and voice, determining their stress level and satisfaction level and making optimal suggestions.

[0455] System Operation

[0456] Collection and storage

[0457] The server periodically collects occupancy data from IoT sensors installed in each coin locker and store, and stores store this data in a central database, where it is then analyzed using AI algorithms.

[0458] Availability forecast

[0459] The server runs an AI algorithm based on past data to predict future availability, making it possible to know in advance which times and days of the week lockers and stores will be crowded.

[0460] User request processing

[0461] Users use a smartphone app to send location and emotional data to the server, which then understands the user's current location and emotional state and makes the most appropriate suggestions.

[0462] Sentiment Analysis and Recommendations

[0463] The server uses an emotion engine to analyze the user's emotions, and based on the results, combines them with availability and future prediction data to suggest the most suitable locker or shopping space.

[0464] Specific examples

[0465] Shopping mall usage scenario

[0466] When a user enters a shopping mall, the smartphone app automatically launches. The app sends location and emotion data to a server, which then suggests the most suitable store or locker based on that information. The user can then view the suggested store or locker location on a map through the app and begin navigation.

[0467] Prompt Sentence Examples

[0468] I'm interested in developing a smart tourist guide system for shopping malls. This system will recognize the user's current location and emotions in real time and recommend the most suitable stores and tourist attractions. EmotionRecognizer will be used to recognize user emotions, and congestion data will be obtained via an API. Can you give me a concrete code example?

[0469] This allows users to quickly find the most suitable coin locker or shopping space based on their feelings and preferences, improving their shopping experience.

[0470] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0471] Step 1:

[0472] The server collects real-time availability data from IoT sensors installed in each coin locker and in the store. Specifically, the IoT sensors detect the current availability and send that information to the server. The server receives this data and stores it in a central database.

[0473] Input: Availability data sent from IoT sensors

[0474] Data processing: Parse availability data and convert it into a structured data format

[0475] Output: Availability data stored in a central database

[0476] Step 2:

[0477] The server runs an AI algorithm based on past usage data to predict future availability. Specifically, it uses AI technology to analyze data and predict future usage patterns based on specific days of the week and times of day.

[0478] Input: Historical usage data stored in a central database

[0479] Data calculations: Predicting future availability using AI algorithms

[0480] Output: Future availability forecast data

[0481] Step 3:

[0482] The user device sends its current location information to the server via a smartphone app. Specifically, the smartphone's GPS function is used to obtain location information and then the data is sent to the server.

[0483] Input: Current location information obtained from your smartphone

[0484] Data processing: Convert location information into a format to send to the server

[0485] Output: Location information sent to the server

[0486] Step 4:

[0487] The user device collects emotional data using the smartphone camera and microphone, and then uses the emotion engine to analyze the results and send them to the server. Specifically, the emotion engine analyzes the user's facial expressions and voice, and sends the resulting emotional state data to the server.

[0488] Input: Facial expression and voice data acquired from a smartphone camera and microphone

[0489] Data calculation: Emotion data analysis using emotion engine

[0490] Output: Emotional state data sent to the server

[0491] Step 5:

[0492] The server retrieves real-time availability and forecast data from a central database based on the received location information and emotion data, and then proposes the most suitable coin lockers and shopping spaces. Specifically, it selects the lockers and stores that best match the location information and emotion state, and generates a list of them.

[0493] Input: Location, emotional state data, availability data and forecast data from a central database

[0494] Data calculation: Query the database based on location and emotional state to generate optimal suggestions

[0495] Output: A list of the best lockers and stores

[0496] Step 6:

[0497] The user device displays the list of optimal lockers and shopping spaces received from the server to the user. Specifically, it displays the locations of the suggested lockers and stores on a map and provides navigation.

[0498] Input: A list of the best lockers and stores sent from the server

[0499] Data processing: Converting data into a format for display on a map

[0500] Output: Locker and store locations and navigation information displayed on the user's device

[0501] As described above, each processing step works in conjunction with the other steps, allowing users to quickly find the most suitable coin locker or shopping space based on their emotional state at the time.

[0502] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0503] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0504] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0505] [Second embodiment]

[0506] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0507] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0508] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0509] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0510] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0511] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0512] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0513] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0514] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0515] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0516] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0517] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0518] This invention is a system that utilizes IoT and AI technologies to collect information on coin locker availability in real time and predict future availability based on past usage data. This system consists of a server, a terminal (such as a user's smartphone), and an IoT sensor. Details of each component and specific steps for implementing the invention are explained below.

[0519] System Components

[0520] 1. Server

[0521] The server plays a central role in the system. It collects real-time availability data sent from IoT sensors installed in coin lockers and stores it in a central database. It also uses AI algorithms to analyze past usage data and predict future availability.

[0522] 2. Device (user's smartphone)

[0523] Users can check current and predicted availability using a dedicated app downloaded to their smartphones. The app acquires the user's current location and requests information from the server about the nearest coin locker based on that location.

[0524] 3. IoT Sensors

[0525] Each coin locker is equipped with an IoT sensor that detects the availability of the locker in real time and sends the information to a server.

[0526] System Operation

[0527] The specific operation of the system will be described below.

[0528] Collection and storage of availability

[0529] The server periodically obtains information about the availability of each locker from IoT sensors installed in each locker. For example, it collects data from sensors that detect whether there is luggage in the locker. This data is stored in a central database, which keeps the latest status of each locker.

[0530] Availability analysis

[0531] The server analyzes the latest availability data stored in a central database to determine which lockers are currently vacant or occupied.

[0532] Prediction by AI algorithm

[0533] The server runs an AI algorithm based on past usage data to predict future availability. For example, it learns patterns of locker usage on specific days of the week or at specific times of the day, and uses that to estimate future availability.

[0534] User request processing

[0535] The user launches the smartphone app and inputs their current location, which then sends a request to the server.

[0536] Data provision

[0537] The server receives the user's request, obtains real-time availability and forecast data for the coin lockers closest to the user's current location, and then compiles this information into a packet and sends it to the user's smartphone.

[0538] Data display and guidance

[0539] The device then displays the location of the coin lockers on a map based on the received data. The user can then select the most suitable locker based on this information and begin navigation to their destination.

[0540] Specific examples

[0541] Searching for a coin locker at Tokyo Station

[0542] User: A traveler arrives at Tokyo Station and launches the smartphone app.

[0543] Device (smartphone): The app will display the home screen and select "Coin Locker Search." A pop-up will appear requesting location information.

[0544] User: Allow location information.

[0545] Device: Sends the acquired current location information to the server.

[0546] Server: Based on the user's location, it queries the central database to obtain the availability of coin lockers around Tokyo Station.

[0547] Server: Analyzes the acquired data and generates a list of optimal lockers.

[0548] Server: Returns the generated list to the device via API.

[0549] Terminal: Displays a list of lockers and a map. The user selects the appropriate locker and taps the "Start Navigation" button.

[0550] Device: Launch the map app and begin navigating to the selected locker.

[0551] This invention allows users to efficiently use coin lockers in public places, significantly reducing the time and effort required to search for a locker and providing a more comfortable user experience.

[0552] The processing flow will be explained below.

[0553] Step 1:

[0554] The server periodically collects real-time availability data from IoT sensors installed in coin lockers. The sensors detect whether the locker is in use or vacant and send the status to the server.

[0555] Step 2:

[0556] The server stores the received availability data in a central database, where the latest status of each locker is stored.

[0557] Step 3:

[0558] The server analyzes the availability data stored in the database to determine which lockers are currently available.

[0559] Step 4:

[0560] The server runs AI algorithms based on past usage data to predict future availability, generating a predictive model based on usage patterns, specific dates, and time periods.

[0561] Step 5:

[0562] The user starts the smartphone app and allows the app to obtain their current location information. The allowed location information is then acquired.

[0563] Step 6:

[0564] The terminal (smartphone) sends the acquired location information to the server and requests information about available coin lockers nearby.

[0565] Step 7:

[0566] The server receives the user's location information and retrieves real-time availability and forecast data for coin lockers around that location from the database.

[0567] Step 8:

[0568] The server generates a list of the best available lockers and sends that information to the device via an API.

[0569] Step 9:

[0570] The terminal (smartphone) displays the received list of available lockers on the user interface, along with information on the locker's location, availability, size, and fees on a map.

[0571] Step 10:

[0572] The user selects the most suitable coin locker based on the displayed information and taps the "Start navigation" button.

[0573] Step 11:

[0574] The device (smartphone) will begin navigation to the selected locker, launching a map app and guiding the user to the specified locker.

[0575] Step 12:

[0576] The user follows the navigation to reach the locker and begins using the locker.

[0577] Example 1

[0578] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0579] Conventional coin locker management systems are inefficient because users must check availability only after arriving at the location. Furthermore, they have problems with poor prediction of availability, making it impossible to respond to future demand. This means users have to spend a lot of time searching for a locker, significantly reducing convenience.

[0580] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0581] In this invention, the server includes means for collecting coin locker availability information in real time, means for storing the collected data in a central database, means for predicting future availability using a machine learning algorithm based on past usage data, means for receiving user location information and obtaining availability information for coin lockers around that location, means for providing the obtained availability information and prediction data to a user device, means for displaying the obtained data on the user device, means for analyzing coin locker availability and integrating the real-time data and prediction data, and means for filtering data based on requests sent from the user device and providing information on the most suitable locker. This allows users to efficiently check and use coin locker availability.

[0582] A "coin locker" is an individual storage space installed in a public or commercial facility, and is a storage facility where users can temporarily store their luggage.

[0583] "Availability" is information that indicates whether a coin locker is currently in use or available for use.

[0584] "Real-time collection means" refers to devices and systems that use IoT sensors and other technologies to instantly detect coin locker usage and collect data.

[0585] The "central database" is a data storage system for centrally managing and storing coin locker availability data.

[0586] A "machine learning algorithm" is a type of computer program used to learn patterns from past usage data and predict future availability.

[0587] "User Location Information" means data that describes a user's current geographic location, typically obtained through GPS or other location tracking technologies.

[0588] "User Device" means an electronic device that a user can carry with them, such as a smartphone or tablet, which is used to check the availability of coin lockers through the application.

[0589] "Real-time data" refers to data that indicates the current usage status of coin lockers.

[0590] "Predictive data" is information about future coin locker availability calculated based on past usage data and machine learning algorithms.

[0591] "Analytical means" are the tools and techniques used to process collected data and derive trends and patterns.

[0592] A "filtering means" is a method or system for extracting data based on specific conditions and providing users with only the information they need.

[0593] "Means for displaying on a map" refers to a function that visually displays geographic information on an electronic device, allowing users to determine the location of coin lockers.

[0594] "Route Guidance" is a navigation function that shows users the best route to reach their destination.

[0595] This invention is a system that utilizes IoT and AI technologies to collect information on coin locker availability in real time and predict future availability based on past usage data. This system consists of a server, a terminal (such as a user's smartphone), and an IoT sensor. Details of each component and specific steps for implementing the invention are explained below.

[0596] System Components

[0597] server

[0598] The server plays a central role in this system. It collects availability data sent in real time from IoT sensors installed in coin lockers and stores it in a central database. It also uses machine learning algorithms based on past usage data to predict future availability. Specifically, it performs the following tasks:

[0599] The database engine used is MySQL or PostgreSQL.

[0600] TensorFlow and PyTorch are used as machine learning frameworks.

[0601] Device (user's smartphone)

[0602] Users can check the current and predicted availability of lockers using a dedicated app downloaded to their smartphone. The app obtains the user's current location and requests information about the nearest coin locker based on that location from the server. As the user operates the app, the following steps are performed:

[0603] Location information is obtained using a GPS module.

[0604] The app sends an HTTP request to the server.

[0605] IoT Sensors

[0606] IoT sensors are installed in each coin locker, and these sensors detect the availability of the locker in real time. The sensors send this information to a server. As specific examples, the following sensors could be used:

[0607] Pressure sensor to detect the presence or absence of luggage

[0608] The sensors transmit data using communication protocols such as Wi-Fi and LoRa.

[0609] Specific examples

[0610] Searching for a coin locker at Tokyo Station

[0611] 1. The user arrives at Tokyo Station and launches the smartphone app.

[0612] 2. Device (smartphone): The app home screen will appear. Select "Coin Locker Search." A pop-up will appear requesting your location information.

[0613] 3. The user allows location information to be obtained.

[0614] 4. The device sends the current location information it has acquired to the server.

[0615] 5. The server queries the central database to obtain the availability of coin lockers around Tokyo Station based on the user's location.

[0616] 6. The server analyzes the acquired data and generates a list of optimal lockers.

[0617] 7. The server returns the generated list to the device via API.

[0618] 8. The device (smartphone) displays a list of lockers and a map. The user selects the appropriate locker and taps the "Start Navigation" button.

[0619] 9. The device will launch a map app and begin providing directions to the selected locker.

[0620] Prompt Sentence Examples

[0621] By inputting the following prompt sentences into the generative AI model, you can get an explanation for a specific scenario.

[0622] "Please explain in detail how to arrive at Tokyo Station and use a smartphone app to find the nearest coin locker. Please also explain how to use the app, which displays the current availability and future forecast availability, and the specific steps users take when using it."

[0623] As described above, the system of the present invention can efficiently provide users with information on available coin lockers, thereby significantly improving convenience of use.

[0624] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0625] Step 1:

[0626] The server collects availability data from IoT sensors installed in coin lockers.

[0627] Specific behavior:

[0628] Input: Locker usage status data obtained by IoT sensors (e.g., whether or not luggage is present).

[0629] Data processing: Receives and formats data sent from sensors. Each data is appended with a timestamp and the corresponding locker ID.

[0630] Output: A formatted set of availability data.

[0631] Step 2:

[0632] The server stores the collected data in a central database.

[0633] Specific behavior:

[0634] Input: Formatted availability data.

[0635] Data manipulation: Insert data using a database query.

[0636] Output: Availability data stored in database and acknowledged by query.

[0637] Step 3:

[0638] The server analyzes the latest availability data stored in a central database.

[0639] Specific behavior:

[0640] Input: Availability data in a central database.

[0641] Data Calculation: Uses database queries to identify currently available and occupied lockers, performing aggregation and filtering as needed.

[0642] Output: A list of current availability as a result of the analysis.

[0643] Step 4:

[0644] The server runs a machine learning algorithm based on past usage data to predict future availability.

[0645] Specific behavior:

[0646] Input: Historical usage data in a database.

[0647] Data calculation: Run machine learning algorithms using TensorFlow and PyTorch to predict future availability.

[0648] Output: Predicted future availability data.

[0649] Step 5:

[0650] The user launches the smartphone app and sends their location information to the server.

[0651] Specific behavior:

[0652] Input: Local location information obtained by the smartphone's GPS module.

[0653] Data processing: The location information is sent to the server via an HTTP request.

[0654] Output: The location information sent to the server.

[0655] Step 6:

[0656] The server receives requests from users and obtains real-time and forecast data for the nearest coin lockers.

[0657] Specific behavior:

[0658] Input: The user's location.

[0659] Data calculation: Based on the location information, a geolocation database is queried to identify the nearest coin locker. After identification, real-time data is combined with predictive data.

[0660] Output: Data packets (real-time data + predicted data) provided to the user.

[0661] Step 7:

[0662] The server transmits the processed data packets to the user device.

[0663] Specific behavior:

[0664] Input: Consolidated data packets.

[0665] Data processing: Convert the data packet into JSON format and send it to the user device through the API.

[0666] Output: Data packets received by the user device.

[0667] Step 8:

[0668] The terminal (user's smartphone) displays the location and availability of coin lockers based on the received data.

[0669] Specific behavior:

[0670] Input: Data packet sent by the server.

[0671] Data processing: Analyzes data packets and displays the location and availability of coin lockers on a map.

[0672] Output: A map that the user can view and a display of locker availability.

[0673] Step 9:

[0674] The terminal will begin providing directions to the locker selected by the user.

[0675] Specific behavior:

[0676] Input: The location of the user's selected locker.

[0677] Data calculation: Launches the map app and calls the navigation function to start route guidance.

[0678] Output: Start of navigation, route guidance to help the user reach their destination.

[0679] (Application example 1)

[0680] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0681] With conventional management systems for coin lockers and parts shelves, it is difficult to grasp availability in real time, which increases the workload of users and administrators. In addition, it is not possible to predict future availability, making planned use difficult. As a result, issues include a lack of convenience and efficiency.

[0682] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0683] In this invention, the server includes means for collecting the availability status of coin lockers and parts shelves in real time, means for storing the collected data in a central database, means for predicting future availability using an AI algorithm based on past usage data, means for receiving user location information and obtaining availability status around that location, means for providing the obtained availability status and predicted data to a user terminal, and means for displaying the obtained data on the user terminal.This allows users and administrators to grasp availability status in real time and, by predicting future status, enables planned and efficient use.

[0684] A "coin locker" is a storage device used by users to temporarily store their luggage.

[0685] "Availability" is information that indicates whether storage facilities such as coin lockers and parts shelves are currently available.

[0686] "Real-time" collection means that data is collected immediately, on the spot, without delay.

[0687] A "server" is a central processing unit that collects, stores, and analyzes data.

[0688] A "central database" is a database system in which collected data is centrally stored and managed.

[0689] An "AI algorithm" is a computational procedure or model used to perform data analysis and predictions using artificial intelligence technology.

[0690] "User terminal" means a device (smartphone, tablet, etc.) used by a user to receive and display information.

[0691] "Location information" is data that indicates the geographic location of a user or object.

[0692] A "parts shelf" is a shelf used to store and manage parts in a factory or warehouse.

[0693] "Predicting future availability" means estimating future availability based on analysis of past data.

[0694] "Means for collecting" refers to methods and devices for obtaining data in real time.

[0695] "Storage means" refers to the method or device used to store collected data in a central database.

[0696] A "means for predicting" is a method or device for estimating future states using an AI algorithm.

[0697] The "means for providing" refers to a method or device for transmitting the acquired data or predictions to a user terminal.

[0698] The "display means" refers to a method or device for visually displaying the acquired data or predictions on a user terminal.

[0699] To realize this invention, it is necessary to build a system that collects real-time information on the availability of coin lockers and parts shelves, and uses AI algorithms based on past data to predict future availability. Details of the system and specific implementation methods are described below.

[0700] System Components

[0701] server

[0702] The server is the main data processing device of this system. Specifically, it has the following functions:

[0703] Store the collected data in a central database: The availability status is obtained in real time from IoT sensors installed in each coin locker and parts shelf and stored in a central database.

[0704] Predict future availability using AI algorithms based on past data: Analyze collected data and past usage data using AI algorithms (e.g., machine learning models) to predict future availability.

[0705] Device (user's smartphone)

[0706] Users access the system using a smartphone, which has the following features:

[0707] Check current and predicted availability: The system uses the user's location information to obtain the availability of the nearest coin lockers and parts shelves, and displays it in real time. It also allows users to check the predicted availability in the future.

[0708] IoT Sensors

[0709] IoT sensors are installed in each coin locker and parts shelf to detect vacancy in real time. These sensors have the following functions:

[0710] Collect availability information in real time and send it to the server: The usage status of coin lockers and parts shelves is collected in real time via sensors and sent to the server.

[0711] Specific operation of the system

[0712] Data processing by the server

[0713] The server collects availability data from each IoT sensor, stores the collected data in a central database, and then runs an AI algorithm (e.g., a machine learning model) based on the past data to predict future availability.

[0714] Displaying data on a terminal

[0715] Users can use the app on their smartphone to check current and predicted future availability, and information on the nearest coin lockers and parts shelves based on the user's location is also retrieved and displayed.

[0716] Hardware and software used

[0717] Hardware: IoT sensors, servers, user devices (smartphones)

[0718] Software: Python, Requests library, central database, AI models (e.g., machine learning algorithms)

[0719] As a concrete example, the following describes how to build a system that predicts the availability of parts shelves.

[0720] Examples:

[0721] IoT sensors installed on each parts shelf in the factory collect real-time data, and AI is used to predict future availability based on past data. Users (factory workers) can use their smartphones to check the current availability of parts shelves through the app, allowing them to handle parts efficiently.

[0722] Example prompt sentence:

[0723] "Create a Python program that uses IoT sensors installed on each parts shelf in the factory to obtain real-time data, and uses AI to predict future availability based on past data. The program must have the ability to send the availability status of parts shelves to a server and display the prediction results."

[0724] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0725] Step 1:

[0726] The server collects real-time availability data from IoT sensors installed in each coin locker and parts shelf. This data includes the current usage status of each storage space (vacant or occupied), providing the latest usage information.

[0727] Step 2:

[0728] The server stores the collected data in a central database. The input data is real-time data sent from the IoT sensors, and the output is the latest data stored in the central database. This process allows for centralized management of the usage status of all coin lockers and parts shelves.

[0729] Step 3:

[0730] The server runs an AI algorithm based on past usage data stored in a central database to predict future availability. The input is past usage data, and the output is predicted future availability data. Specifically, it uses a machine learning model to analyze patterns and estimate future usage trends.

[0731] Step 4:

[0732] The terminal (user's smartphone) acquires the user's current location. Based on this location information, it requests the server for information on the availability of coin lockers and parts shelves. The input is the user's location information, and the output is a request to the server.

[0733] Step 5:

[0734] The server receives the user's location information and retrieves the availability of coin lockers and parts shelves around that location. The input is the user's location information, and the output is availability data for the area. This provides the user with information on the nearest available storage space.

[0735] Step 6:

[0736] The server then sends the acquired availability and forecast data to the user's device. The input is availability data and forecast data for the relevant area, and the output is data sent to the user's device, allowing the user to check the current situation and future forecasts.

[0737] Step 7:

[0738] The terminal (user's smartphone) displays the acquired data. The input is availability data and forecast data sent from the server, and the output is information displayed to the user. Specifically, availability is visually displayed in a map app or list format. Using this information, the user can select the appropriate coin locker or parts shelf.

[0739] This series of processes allows users to grasp availability in real time and use coin lockers and parts shelves efficiently.

[0740] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0741] This invention is a system that utilizes IoT and AI technologies and an emotion recognition engine to collect information on coin locker availability in real time, predict future availability based on past usage data, and recognize users' emotions to provide an optimal locker usage experience.The system consists of a server, a terminal (such as a user's smartphone), IoT sensors, and an emotion recognition engine.

[0742] System Components

[0743] 1. Server

[0744] The server plays a central role in this system. It collects availability data sent in real time from IoT sensors installed in coin lockers and stores it in a central database. It also uses AI algorithms to analyze past usage data and predict future availability. It also receives user emotional data and adjusts the locker and guidance method based on the user's emotional state.

[0745] 2. Device (user's smartphone)

[0746] Users can check current and predicted availability using a dedicated app downloaded to their smartphone. The app acquires the user's current location and requests information about the nearest coin locker based on that location from the server. The app also has an emotion engine that analyzes the user's facial expressions and voice to detect the user's emotional state.

[0747] 3. IoT Sensors

[0748] Each coin locker is equipped with an IoT sensor that detects the availability of the locker in real time and sends that information to a server.

[0749] 4. Emotion Engine

[0750] The emotion engine is a software component that recognizes emotions from the user's facial expressions and voice. The emotion engine collects emotional data from the user via the smartphone camera and microphone and sends the analysis results to the server. This allows the system to determine the user's stress level and satisfaction level, and then recommend the most suitable locker and select the appropriate guidance method.

[0751] System Operation

[0752] The specific operation of the system will be described below.

[0753] Collection and storage of availability

[0754] The server periodically obtains the availability status from IoT sensors installed in each coin locker. The sensors detect whether there is any luggage in the locker. This data is stored in a central database, which keeps the latest status of each locker.

[0755] Availability analysis

[0756] The server analyzes the latest availability data stored in a central database to determine which lockers are currently available.

[0757] Prediction by AI algorithm

[0758] The server runs an AI algorithm based on past usage data to predict future availability. For example, it learns patterns of locker usage on specific days of the week or at specific times of the day, and uses that to estimate future availability.

[0759] User request processing

[0760] The user launches the smartphone app and inputs their current location. Based on that information and emotion data, the app sends a request to the server.

[0761] Data provision

[0762] The server receives the user's location information and emotion data, retrieves real-time availability and forecast data for coin lockers in the vicinity of the user's location from a database, and compiles this information into a single packet that is sent to the user's smartphone.

[0763] Data display and guidance

[0764] The device then displays the location of the coin lockers on a map based on the received data. The user can then select the most suitable locker based on that information and begin navigation to their destination. Furthermore, the guidance method and information display are dynamically adjusted based on the emotion data.

[0765] Specific examples

[0766] Searching for a coin locker at Tokyo Station

[0767] User: A traveler arrives at Tokyo Station and launches the smartphone app.

[0768] Device (smartphone): The app will display the home screen and select "Coin Locker Search." A pop-up will appear requesting permission to obtain location information and collect emotional data.

[0769] User: Allow location and emotion data collection.

[0770] Device: Sends the acquired current location information and emotion data to the server.

[0771] Server: Based on the user's location and emotion data, it queries the central database to obtain the availability of coin lockers around Tokyo Station.

[0772] Server: Analyzes the acquired data, generates a list of optimal lockers, and selects a guidance method based on the emotional data.

[0773] Server: Returns the generated list to the device via API.

[0774] Terminal: Display a list of lockers and a map. Based on emotion data, provide specific guidance to the user. The user selects the appropriate locker and taps the "Start Navigation" button.

[0775] Device: Launch the map app and begin navigating to the selected locker.

[0776] The present invention allows users to quickly find the coin locker that best suits their emotional state at the time, thereby increasing convenience and satisfaction.

[0777] The processing flow will be explained below.

[0778] Step 1:

[0779] The server periodically collects real-time availability data from IoT sensors installed in the coin lockers. For example, every minute, the sensor detects whether the locker is in use or vacant, and sends that data to the server.

[0780] Step 2:

[0781] The server stores the received availability data in a central database, which stores the latest status of each locker.

[0782] Step 3:

[0783] The server analyzes the availability data stored in the database to determine which lockers are currently available and creates a list of available lockers.

[0784] Step 4:

[0785] The server runs an AI algorithm based on past usage data to predict future availability. It learns patterns of locker usage on specific days and times, and uses that to estimate future availability.

[0786] Step 5:

[0787] The user launches the smartphone app and allows it to use location and emotional data, which then uses the camera and microphone to capture their current facial expressions and voice.

[0788] Step 6:

[0789] The device (smartphone) sends the acquired location information and emotion data to the server, requesting the availability of coin lockers near the user's current location and appropriate guidance.

[0790] Step 7:

[0791] The server receives the user's location information and emotion data, and retrieves real-time availability and forecast data for coin lockers around that location from the database.

[0792] Step 8:

[0793] The server analyzes the emotional data and, if it determines that the user is feeling stressed, it prioritizes the nearest available locker. The server then sends the list of created lockers to the device via API.

[0794] Step 9:

[0795] The terminal (smartphone) displays the received list of available lockers on the user interface, showing the locker location, availability, size, and fee information on a map, and adjusting the guidance method (e.g., whether to provide audio guidance or highlighting method) based on the user's emotional state.

[0796] Step 10:

[0797] The user selects the most suitable coin locker based on the displayed information and taps the "Start Navigation" button. The system then follows the guidance method based on the emotion data.

[0798] Step 11:

[0799] The device (smartphone) will begin navigation to the selected locker, launching a map app and guiding the user to the specified locker.

[0800] Step 12:

[0801] The user follows the navigation to reach the locker and begins using it. The data at the time of use is again sent to the server via the IoT sensor, and the database is updated.

[0802] Example 2

[0803] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0804] It is difficult to grasp the availability of coin lockers in real time and provide this information to users promptly. In addition, there is a lack of means to predict locker usage patterns, making it impossible to secure the optimal locker when a user needs it. Furthermore, services do not take into account the emotional state of the user, making it a challenge to improve user satisfaction.

[0805] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0806] In this invention, the server includes means for collecting coin locker availability information in real time, means for storing the collected data in a central database, means for predicting future availability using an artificial intelligence algorithm based on past usage data, means for receiving user location information and obtaining information on available coin lockers in the vicinity of that location, means for receiving and analyzing the user's emotional data, means for providing the obtained availability information and predicted data to the user's terminal, means for displaying the obtained data on the user's terminal, and means for adjusting the guidance content based on the user's emotional data. This allows users to quickly and accurately grasp the available coin locker status and select the optimal locker based on predicted usage patterns. Furthermore, providing services tailored to the user's emotional state can improve user satisfaction.

[0807] "Means for collecting vacancy status in real time" refers to a device or method that instantly detects the current vacancy status of lockers and collects data through sensors installed in coin lockers.

[0808] "Central database" refers to a database system for centrally storing and managing all collected locker status data.

[0809] "Artificial intelligence algorithm" refers to a computational method using machine learning or other AI techniques that is used to learn patterns based on past data and predict future availability.

[0810] "Means of receiving the user's location information and obtaining the availability of coin lockers in the vicinity of that location" refers to a function that obtains the user's current location using GPS or other means, and uses that information to search for and obtain the availability of nearby lockers.

[0811] "Means for receiving and analyzing user emotional data" refers to a device or method that senses the user's facial expressions and voice, analyzes their emotional state, and digitizes it.

[0812] "Means for providing acquired availability and forecast data to a user terminal" refers to a communication means for transmitting real-time availability data and forecast data from a server to a user terminal.

[0813] "Means for displaying acquired data on the user's device" refers to an interface that visually displays availability and forecast data collected on the user's device.

[0814] "Means for adjusting guidance content based on the user's emotional data" refers to a system that optimally adjusts the guidance method and message content for the user based on analyzed emotional data.

[0815] This invention is a system that collects information on coin locker availability in real time, predicts future availability based on past usage data, and recognizes user emotions to provide an optimal locker usage experience. This system consists of a server, a terminal (user terminal), IoT sensors, and an emotion engine.

[0816] server

[0817] The server plays a central role in this system and has the following functions:

[0818] 1. Data collection from IoT sensors: The server collects vacancy data in real time from IoT sensors installed in each coin locker. This allows the server to grasp the immediate vacancy status of each locker. For example, Microsoft's Azure IoT Hub can be used.

[0819] 2. Database storage: The collected data is stored in a central database, which stores the latest status information for each locker. For example, a database service such as MySQL is used.

[0820] 3. Data Analysis and Prediction: The server analyzes the data stored in the central database to determine which lockers are available. It also runs AI algorithms based on past usage data to predict future availability. This is done using machine learning frameworks (e.g., TensorFlow or PyTorch).

[0821] 4. User information processing: Receives the user's location information and emotion data, retrieves real-time availability and forecast data for coin lockers around the location from the database, and sends the retrieved information to the user's device.

[0822] User terminal

[0823] The user device (smartphone) connects to the system using a dedicated application. The main functions of the application are as follows:

[0824] 1. Location information acquisition: Obtain the user's current location and send that information to the server. Obtain location information using the GPS module.

[0825] 2. Collecting and analyzing emotional data: The user's facial expressions and voice are analyzed using an emotion engine (for example, Microsoft Azure Cognitive Services' Face API or Speech API), and the emotional data is sent to the server.

[0826] 3. Data display: Display the availability and forecast data of coin lockers received from the server. For example, use the Google Maps API to display the location and availability of lockers on a map.

[0827] 4. Guidance: Provides optimal guidance based on the user's emotional data, including navigation to a specific locker and the ability to change the message content.

[0828] Specific examples

[0829] Searching for a coin locker at Tokyo Station

[0830] User: A traveler arrives at Tokyo Station and launches the smartphone app.

[0831] Device: The app will display the home screen and select "Coin Locker Search." A pop-up will appear requesting permission to obtain location information and collect emotional data.

[0832] User: Allow location and emotion data collection.

[0833] Device: Sends the acquired current location information and emotion data to the server.

[0834] Server: Based on the user's location and emotion data, it queries the central database to obtain the availability of coin lockers around Tokyo Station.

[0835] Server: Analyzes the acquired data, generates a list of optimal lockers, and selects a guidance method based on the emotional data.

[0836] Server: Returns the generated list to the device via API.

[0837] Terminal: Display a list of lockers and a map. Based on emotion data, provide specific guidance to the user. The user selects the appropriate locker and taps the "Start Navigation" button.

[0838] Device: Launch the map app and begin navigating to the selected locker.

[0839] Prompt Sentence Examples

[0840] 1. Sample Prompt 1: "Describe an assistance system that helps travelers find the best coin locker when they're in a hurry."

[0841] 2. Example prompt 2: "Please explain the flow of a system that uses IoT sensors and emotion analysis to grasp the availability of coin lockers at stations in real time and provide the optimal guidance method based on the user's emotion."

[0842] This system allows users to check the availability of coin lockers in real time and receive optimal guidance based on their emotional state, thereby improving convenience and satisfaction.

[0843] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0844] System program processing flow

[0845] Step 1: Collect availability data

[0846] The server collects availability data from IoT sensors installed in each coin locker.

[0847] Input: Real-time availability data (e.g., open, closed) from each IoT sensor

[0848] Processing: The server receives data sent from the IoT sensors and obtains the status of each locker. This involves sending data acquisition requests through the sensor API and receiving responses.

[0849] Output: Availability data list (status information for each locker)

[0850] Specific operation: The server sends a request to the sensor API every 15 minutes to obtain the availability status of the lockers. After collecting data from all the lockers, it proceeds to the next step all at once.

[0851] Step 2: Store in the database

[0852] The server stores the collected data in a central database.

[0853] Input: Availability data list

[0854] Processing: The server saves the retrieved data to a database, which involves opening a database connection and executing an SQL insert query.

[0855] Output: Data stored in a central database

[0856] What happens: The server executes an insert query on the database to store each locker's status information in a table. For example, it executes a query like "INSERT INTO locker_status (locker_id, status, timestamp) VALUES (...)".

[0857] Step 3: Analyze availability data

[0858] The server analyzes data stored in a central database to ascertain the latest availability.

[0859] Input: Data from the central database

[0860] Processing: The server queries the latest availability data from the database and lists the real-time availability. This involves using a SELECT query to get the latest data and then parsing it.

[0861] Output: Latest availability list

[0862] Specific operation: The server executes a query to the database such as "SELECT locker_id, status FROM locker_status WHERE timestamp = (SELECT MAX(timestamp) FROM locker_status)" to get the latest availability status.

[0863] Step 4: Future predictions using AI algorithms

[0864] The server runs an AI algorithm based on past usage data to predict future availability.

[0865] Input: Historical usage data

[0866] Processing: The server inputs historical usage data into the machine learning model to obtain predictions of future availability. This includes loading the prediction model, normalizing the data, and analyzing the predictions.

[0867] Output: Future availability forecast data

[0868] What it does: The server uses the TensorFlow model to predict future availability based on past usage data, for example by using a function like "model.predict(past_usage_data)".

[0869] Step 5: Receiving a request from the user

[0870] Users launch the smartphone app, enter their current location information, and request information about available coin lockers.

[0871] Input: User's current location

[0872] Processing: The user device acquires GPS data and sends the location information to the server. This includes using the location service API to acquire the current location and sending it to the server in JSON format.

[0873] Output: Location request sent to the server

[0874] What it does: When a user opens the app and presses the "Get current location" button, the app uses GPS to get the current location and sends that information to the server, for example by using a function like "current_location = gps.get_current_location()".

[0875] Step 6: Analyze user sentiment data

[0876] The device analyzes the user's facial expressions and voice using an emotion engine and sends the emotion data to the server.

[0877] Input: User's facial expression data, voice data

[0878] Processing: The emotion engine (API) analyzes facial expressions and voice to detect emotional states. This involves collecting data through the camera and microphone and sending it to the analysis engine.

[0879] Output: Emotion data

[0880] Specific operation: The app captures the user's facial expression with the camera, sends it to the API, receives the analysis results, and sends them to the server. For example, execute a function like "emotion_data = emotion_api.analyze(face_image)".

[0881] Step 7: Generate optimal locker information

[0882] The server selects the most suitable locker based on the user's location information, emotional data, and real-time locker availability information.

[0883] Input: Location, emotion data, real-time availability

[0884] Processing: The server combines this data and uses algorithms to select the best locker, including prioritizing based on location and emotion data, and selecting the most appropriate locker based on availability data.

[0885] Output: Optimal locker list

[0886] What it does: The server generates a list of lockers that are closest to the user and that correspond to their emotions based on their location and emotion data. For example, it executes a function like "optimal_lockers = find_optimal_lockers(location, emotion_data, availability_data)".

[0887] Step 8: Provide information to your smartphone

[0888] The terminal receives the information returned from the server and displays it to the user.

[0889] Input: Optimal locker list from server

[0890] Processing: Analyzing the received data and displaying it through the user interface. This includes receiving the data, analyzing it, and displaying it on a map.

[0891] Output: Best locker information provided to user

[0892] What it does: It displays recommended lockers on a map on the user's phone, for example, by mapping the locker locations using the Google Maps API and using a function like "display_lockers_on_map(optimal_lockers)".

[0893] Step 9: Navigation

[0894] The terminal will begin navigating to the locker selected by the user.

[0895] Input: Location of the locker selected by the user

[0896] Processing: Calculate the route to the selected locker and initiate navigation, including using a route calculation algorithm and providing audio and visual guidance to the user.

[0897] Output: Navigation instructions

[0898] Specific behavior: The map app will launch and guide the user to the selected locker. For example, it will execute a function such as "start_navigation(destination_location)".

[0899] Prompt Sentence Examples

[0900] 1. Sample prompt 1: "Describe an assistance system that helps travelers find the best coin locker when they're in a hurry."

[0901] 2. Example prompt 2: "Please explain the flow of a system that uses IoT sensors and emotion analysis to grasp the availability of coin lockers at stations in real time and provide the optimal guidance method based on the user's emotion."

[0902] (Application example 2)

[0903] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0904] Conventional coin locker systems were unable to adequately grasp availability or predict future availability, making it difficult for users to find available lockers during busy times. Furthermore, they did not take into account the user's emotional state, and were unable to suggest the most suitable locker or guide users to shopping areas. This resulted in problems that reduced user convenience and satisfaction.

[0905] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting coin locker availability information in real time, means for storing the collected data in a central database, means for predicting future availability using an AI algorithm based on past usage data, means for receiving user location information and obtaining availability information for coin lockers around that location, means for analyzing emotional data, means for suggesting the most suitable coin locker or shopping space based on the analyzed emotional data, and means for displaying the obtained data on the user terminal. This enables the user to quickly find the most suitable coin locker or shopping space based on their emotional state.

[0906] A "coin locker" is a storage device installed in public places and commercial facilities, etc., and used for short-term storage of luggage.

[0907] "Availability" is information indicating whether or not there are any coin lockers available and how many there are.

[0908] "Collecting in real time" means obtaining the current status and information immediately and updating it without delay.

[0909] A "central database" is a data management system that centrally manages multiple data sets and stores, searches, and analyzes the necessary information.

[0910] An "AI algorithm" is a computational method or procedure that uses artificial intelligence technology to analyze data and make predictions.

[0911] "Means for predicting future availability" refers to technology that estimates the future availability of coin lockers based on past data and current conditions.

[0912] "User location information" is data obtained from a smartphone, GPS device, etc. that indicates the user's current geographic location.

[0913] "Emotional data" is information that indicates the emotional state of the user analyzed from facial expressions, voice, etc.

[0914] "Analyzing" means examining data in detail to understand its structure and meaning.

[0915] "Means to suggest the most suitable coin lockers and shopping spaces" refers to technology that guides users to the most appropriate lockers and stores based on their situation and needs.

[0916] A "user terminal" is a device, such as a smartphone or tablet, that a user uses to check and input information.

[0917] This invention relates to a system that provides users with optimal coin lockers and shopping spaces in shopping centers and public places. This system collects availability information in real time, analyzes past data to predict future availability, and analyzes user sentiment to make optimal suggestions.

[0918] System Components

[0919] 1. Server

[0920] The server plays a central role in this system. Specifically, it performs the following processes:

[0921] 1. Collect real-time availability data from IoT sensors installed in each coin locker and store and store it in a central database.

[0922] 2. Run an AI algorithm based on past usage data to predict future availability.

[0923] 3. Recommend optimal lockers and shopping spaces based on location and emotion data received from the user's device.

[0924] 2. Device (user's smartphone)

[0925] Users can access the system using their smartphones and enjoy the following features:

[0926] 1. Obtaining location information and sending it to the server.

[0927] 2. Emotional data is collected using the smartphone camera and microphone, and the analysis results are sent to the server.

[0928] 3. Receive availability and future forecast data provided by the server, as well as suggestions for optimal lockers and shopping spaces.

[0929] 4. Display the locations of coin lockers and stores on a map and provide navigation.

[0930] 3. IoT Sensors

[0931] IoT sensors installed in each coin locker and store detect current availability in real time and send that information to a server.

[0932] 4. Emotion Engine

[0933] The emotion engine is a component that recognizes the user's emotions from their facial expressions and voice, determining their stress level and satisfaction level and making optimal suggestions.

[0934] System Operation

[0935] Collection and storage

[0936] The server periodically collects occupancy data from IoT sensors installed in each coin locker and store, and stores store this data in a central database, where it is then analyzed using AI algorithms.

[0937] Availability forecast

[0938] The server runs an AI algorithm based on past data to predict future availability, making it possible to know in advance which times and days of the week lockers and stores will be crowded.

[0939] User request processing

[0940] Users use a smartphone app to send location and emotional data to the server, which then understands the user's current location and emotional state and makes the most appropriate suggestions.

[0941] Sentiment Analysis and Recommendations

[0942] The server uses an emotion engine to analyze the user's emotions, and based on the results, combines them with availability and future prediction data to suggest the most suitable locker or shopping space.

[0943] Specific examples

[0944] Shopping mall usage scenario

[0945] When a user enters a shopping mall, the smartphone app automatically launches. The app sends location and emotion data to a server, which then suggests the most suitable store or locker based on that information. The user can then view the suggested store or locker location on a map through the app and begin navigation.

[0946] Prompt Sentence Examples

[0947] I'm interested in developing a smart tourist guide system for shopping malls. This system will recognize the user's current location and emotions in real time and recommend the most suitable stores and tourist attractions. EmotionRecognizer will be used to recognize user emotions, and congestion data will be obtained via an API. Can you give me a concrete code example?

[0948] This allows users to quickly find the most suitable coin locker or shopping space based on their feelings and preferences, improving their shopping experience.

[0949] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0950] Step 1:

[0951] The server collects real-time availability data from IoT sensors installed in each coin locker and in the store. Specifically, the IoT sensors detect the current availability and send that information to the server. The server receives this data and stores it in a central database.

[0952] Input: Availability data sent from IoT sensors

[0953] Data processing: Parse availability data and convert it into a structured data format

[0954] Output: Availability data stored in a central database

[0955] Step 2:

[0956] The server runs an AI algorithm based on past usage data to predict future availability. Specifically, it uses AI technology to analyze data and predict future usage patterns based on specific days of the week and times of day.

[0957] Input: Historical usage data stored in a central database

[0958] Data calculations: Predicting future availability using AI algorithms

[0959] Output: Future availability forecast data

[0960] Step 3:

[0961] The user device sends its current location information to the server via a smartphone app. Specifically, the smartphone's GPS function is used to obtain location information and then the data is sent to the server.

[0962] Input: Current location information obtained from your smartphone

[0963] Data processing: Convert location information into a format to send to the server

[0964] Output: Location information sent to the server

[0965] Step 4:

[0966] The user device collects emotional data using the smartphone camera and microphone, and then uses the emotion engine to analyze the results and send them to the server. Specifically, the emotion engine analyzes the user's facial expressions and voice, and sends the resulting emotional state data to the server.

[0967] Input: Facial expression and voice data acquired from a smartphone camera and microphone

[0968] Data calculation: Emotion data analysis using emotion engine

[0969] Output: Emotional state data sent to the server

[0970] Step 5:

[0971] The server retrieves real-time availability and forecast data from a central database based on the received location information and emotion data, and then proposes the most suitable coin lockers and shopping spaces. Specifically, it selects the lockers and stores that best match the location information and emotion state, and generates a list of them.

[0972] Input: Location, emotional state data, availability data and forecast data from a central database

[0973] Data calculation: Query the database based on location and emotional state to generate optimal suggestions

[0974] Output: A list of the best lockers and stores

[0975] Step 6:

[0976] The user device displays the list of optimal lockers and shopping spaces received from the server to the user. Specifically, it displays the locations of the suggested lockers and stores on a map and provides navigation.

[0977] Input: A list of the best lockers and stores sent from the server

[0978] Data processing: Converting data into a format for display on a map

[0979] Output: Locker and store locations and navigation information displayed on the user's device

[0980] As described above, each processing step works in conjunction with the other steps, allowing users to quickly find the most suitable coin locker or shopping space based on their emotional state at the time.

[0981] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0982] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0983] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0984] [Third embodiment]

[0985] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0986] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0987] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0988] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0989] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0990] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0991] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0992] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0993] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0994] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0995] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0996] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0997] This invention is a system that utilizes IoT and AI technologies to collect information on coin locker availability in real time and predict future availability based on past usage data. This system consists of a server, a terminal (such as a user's smartphone), and an IoT sensor. Details of each component and specific steps for implementing the invention are explained below.

[0998] System Components

[0999] 1. Server

[1000] The server plays a central role in the system. It collects real-time availability data sent from IoT sensors installed in coin lockers and stores it in a central database. It also uses AI algorithms to analyze past usage data and predict future availability.

[1001] 2. Device (user's smartphone)

[1002] Users can check current and predicted availability using a dedicated app downloaded to their smartphones. The app acquires the user's current location and requests information from the server about the nearest coin locker based on that location.

[1003] 3. IoT Sensors

[1004] Each coin locker is equipped with an IoT sensor that detects the availability of the locker in real time and sends the information to a server.

[1005] System Operation

[1006] The specific operation of the system will be described below.

[1007] Collection and storage of availability

[1008] The server periodically obtains information about the availability of each locker from IoT sensors installed in each locker. For example, it collects data from sensors that detect whether there is luggage in the locker. This data is stored in a central database, which keeps the latest status of each locker.

[1009] Availability analysis

[1010] The server analyzes the latest availability data stored in a central database to determine which lockers are currently vacant or occupied.

[1011] Prediction by AI algorithm

[1012] The server runs an AI algorithm based on past usage data to predict future availability. For example, it learns patterns of locker usage on specific days of the week or at specific times of the day, and uses that to estimate future availability.

[1013] User request processing

[1014] The user launches the smartphone app and inputs their current location, which then sends a request to the server.

[1015] Data provision

[1016] The server receives the user's request, obtains real-time availability and forecast data for the coin lockers closest to the user's current location, and then compiles this information into a packet and sends it to the user's smartphone.

[1017] Data display and guidance

[1018] The device then displays the location of the coin lockers on a map based on the received data. The user can then select the most suitable locker based on this information and begin navigation to their destination.

[1019] Specific examples

[1020] Searching for a coin locker at Tokyo Station

[1021] User: A traveler arrives at Tokyo Station and launches the smartphone app.

[1022] Device (smartphone): The app will display the home screen and select "Coin Locker Search." A pop-up will appear requesting location information.

[1023] User: Allow location information.

[1024] Device: Sends the acquired current location information to the server.

[1025] Server: Based on the user's location, it queries the central database to obtain the availability of coin lockers around Tokyo Station.

[1026] Server: Analyzes the acquired data and generates a list of optimal lockers.

[1027] Server: Returns the generated list to the device via API.

[1028] Terminal: Displays a list of lockers and a map. The user selects the appropriate locker and taps the "Start Navigation" button.

[1029] Device: Launch the map app and begin navigating to the selected locker.

[1030] This invention allows users to efficiently use coin lockers in public places, significantly reducing the time and effort required to search for a locker and providing a more comfortable user experience.

[1031] The processing flow will be explained below.

[1032] Step 1:

[1033] The server periodically collects real-time availability data from IoT sensors installed in coin lockers. The sensors detect whether the locker is in use or vacant and send the status to the server.

[1034] Step 2:

[1035] The server stores the received availability data in a central database, where the latest status of each locker is stored.

[1036] Step 3:

[1037] The server analyzes the availability data stored in the database to determine which lockers are currently available.

[1038] Step 4:

[1039] The server runs AI algorithms based on past usage data to predict future availability, generating a predictive model based on usage patterns, specific dates, and time periods.

[1040] Step 5:

[1041] The user starts the smartphone app and allows the app to obtain their current location information. The allowed location information is then acquired.

[1042] Step 6:

[1043] The terminal (smartphone) sends the acquired location information to the server and requests information about available coin lockers nearby.

[1044] Step 7:

[1045] The server receives the user's location information and retrieves real-time availability and forecast data for coin lockers around that location from the database.

[1046] Step 8:

[1047] The server generates a list of the best available lockers and sends that information to the device via an API.

[1048] Step 9:

[1049] The terminal (smartphone) displays the received list of available lockers on the user interface, along with information on the locker's location, availability, size, and fees on a map.

[1050] Step 10:

[1051] The user selects the most suitable coin locker based on the displayed information and taps the "Start navigation" button.

[1052] Step 11:

[1053] The device (smartphone) will begin navigation to the selected locker, launching a map app and guiding the user to the specified locker.

[1054] Step 12:

[1055] The user follows the navigation to reach the locker and begins using the locker.

[1056] Example 1

[1057] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1058] Conventional coin locker management systems are inefficient because users must check availability only after arriving at the location. Furthermore, they have problems with poor prediction of availability, making it impossible to respond to future demand. This means users have to spend a lot of time searching for a locker, significantly reducing convenience.

[1059] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1060] In this invention, the server includes means for collecting coin locker availability information in real time, means for storing the collected data in a central database, means for predicting future availability using a machine learning algorithm based on past usage data, means for receiving user location information and obtaining availability information for coin lockers around that location, means for providing the obtained availability information and prediction data to a user device, means for displaying the obtained data on the user device, means for analyzing coin locker availability and integrating the real-time data and prediction data, and means for filtering data based on requests sent from the user device and providing information on the most suitable locker. This allows users to efficiently check and use coin locker availability.

[1061] A "coin locker" is an individual storage space installed in a public or commercial facility, and is a storage facility where users can temporarily store their luggage.

[1062] "Availability" is information that indicates whether a coin locker is currently in use or available for use.

[1063] "Real-time collection means" refers to devices and systems that use IoT sensors and other technologies to instantly detect coin locker usage and collect data.

[1064] The "central database" is a data storage system for centrally managing and storing coin locker availability data.

[1065] A "machine learning algorithm" is a type of computer program used to learn patterns from past usage data and predict future availability.

[1066] "User Location Information" means data that describes a user's current geographic location, typically obtained through GPS or other location tracking technologies.

[1067] "User Device" means an electronic device that a user can carry with them, such as a smartphone or tablet, which is used to check the availability of coin lockers through the application.

[1068] "Real-time data" refers to data that indicates the current usage status of coin lockers.

[1069] "Predictive data" is information about future coin locker availability calculated based on past usage data and machine learning algorithms.

[1070] "Analytical means" are the tools and techniques used to process collected data and derive trends and patterns.

[1071] A "filtering means" is a method or system for extracting data based on specific conditions and providing users with only the information they need.

[1072] "Means for displaying on a map" refers to a function that visually displays geographic information on an electronic device, allowing users to determine the location of coin lockers.

[1073] "Route Guidance" is a navigation function that shows users the best route to reach their destination.

[1074] This invention is a system that utilizes IoT and AI technologies to collect information on coin locker availability in real time and predict future availability based on past usage data. This system consists of a server, a terminal (such as a user's smartphone), and an IoT sensor. Details of each component and specific steps for implementing the invention are explained below.

[1075] System Components

[1076] server

[1077] The server plays a central role in this system. It collects availability data sent in real time from IoT sensors installed in coin lockers and stores it in a central database. It also uses machine learning algorithms based on past usage data to predict future availability. Specifically, it performs the following tasks:

[1078] The database engine used is MySQL or PostgreSQL.

[1079] TensorFlow and PyTorch are used as machine learning frameworks.

[1080] Device (user's smartphone)

[1081] Users can check the current and predicted availability of lockers using a dedicated app downloaded to their smartphone. The app obtains the user's current location and requests information about the nearest coin locker based on that location from the server. As the user operates the app, the following steps are performed:

[1082] Location information is obtained using a GPS module.

[1083] The app sends an HTTP request to the server.

[1084] IoT Sensors

[1085] IoT sensors are installed in each coin locker, and these sensors detect the availability of the locker in real time. The sensors send this information to a server. As specific examples, the following sensors could be used:

[1086] Pressure sensor to detect the presence or absence of luggage

[1087] The sensors transmit data using communication protocols such as Wi-Fi and LoRa.

[1088] Specific examples

[1089] Searching for a coin locker at Tokyo Station

[1090] 1. The user arrives at Tokyo Station and launches the smartphone app.

[1091] 2. Device (smartphone): The app home screen will appear. Select "Coin Locker Search." A pop-up will appear requesting your location information.

[1092] 3. The user allows location information to be obtained.

[1093] 4. The device sends the current location information it has acquired to the server.

[1094] 5. The server queries the central database to obtain the availability of coin lockers around Tokyo Station based on the user's location.

[1095] 6. The server analyzes the acquired data and generates a list of optimal lockers.

[1096] 7. The server returns the generated list to the device via API.

[1097] 8. The device (smartphone) displays a list of lockers and a map. The user selects the appropriate locker and taps the "Start Navigation" button.

[1098] 9. The device will launch a map app and begin providing directions to the selected locker.

[1099] Prompt Sentence Examples

[1100] By inputting the following prompt sentences into the generative AI model, you can get an explanation for a specific scenario.

[1101] "Please explain in detail how to arrive at Tokyo Station and use a smartphone app to find the nearest coin locker. Please also explain how to use the app, which displays the current availability and future forecast availability, and the specific steps users take when using it."

[1102] As described above, the system of the present invention can efficiently provide users with information on available coin lockers, thereby significantly improving convenience of use.

[1103] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1104] Step 1:

[1105] The server collects availability data from IoT sensors installed in coin lockers.

[1106] Specific behavior:

[1107] Input: Locker usage status data obtained by IoT sensors (e.g., whether or not luggage is present).

[1108] Data processing: Receives and formats data sent from sensors. Each data is appended with a timestamp and the corresponding locker ID.

[1109] Output: A formatted set of availability data.

[1110] Step 2:

[1111] The server stores the collected data in a central database.

[1112] Specific behavior:

[1113] Input: Formatted availability data.

[1114] Data manipulation: Insert data using a database query.

[1115] Output: Availability data stored in database and acknowledged by query.

[1116] Step 3:

[1117] The server analyzes the latest availability data stored in a central database.

[1118] Specific behavior:

[1119] Input: Availability data in a central database.

[1120] Data Calculation: Uses database queries to identify currently available and occupied lockers, performing aggregation and filtering as needed.

[1121] Output: A list of current availability as a result of the analysis.

[1122] Step 4:

[1123] The server runs a machine learning algorithm based on past usage data to predict future availability.

[1124] Specific behavior:

[1125] Input: Historical usage data in a database.

[1126] Data calculation: Run machine learning algorithms using TensorFlow and PyTorch to predict future availability.

[1127] Output: Predicted future availability data.

[1128] Step 5:

[1129] The user launches the smartphone app and sends their location information to the server.

[1130] Specific behavior:

[1131] Input: Local location information obtained by the smartphone's GPS module.

[1132] Data processing: The location information is sent to the server via an HTTP request.

[1133] Output: The location information sent to the server.

[1134] Step 6:

[1135] The server receives requests from users and obtains real-time and forecast data for the nearest coin lockers.

[1136] Specific behavior:

[1137] Input: The user's location.

[1138] Data calculation: Based on the location information, a geolocation database is queried to identify the nearest coin locker. After identification, real-time data is combined with predictive data.

[1139] Output: Data packets (real-time data + predicted data) provided to the user.

[1140] Step 7:

[1141] The server transmits the processed data packets to the user device.

[1142] Specific behavior:

[1143] Input: Consolidated data packets.

[1144] Data processing: Convert the data packet into JSON format and send it to the user device through the API.

[1145] Output: Data packets received by the user device.

[1146] Step 8:

[1147] The terminal (user's smartphone) displays the location and availability of coin lockers based on the received data.

[1148] Specific behavior:

[1149] Input: Data packet sent by the server.

[1150] Data processing: Analyzes data packets and displays the location and availability of coin lockers on a map.

[1151] Output: A map that the user can view and a display of locker availability.

[1152] Step 9:

[1153] The terminal will begin providing directions to the locker selected by the user.

[1154] Specific behavior:

[1155] Input: The location of the user's selected locker.

[1156] Data calculation: Launches the map app and calls the navigation function to start route guidance.

[1157] Output: Start of navigation, route guidance to help the user reach their destination.

[1158] (Application example 1)

[1159] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1160] With conventional management systems for coin lockers and parts shelves, it is difficult to grasp availability in real time, which increases the workload of users and administrators. In addition, it is not possible to predict future availability, making planned use difficult. As a result, issues include a lack of convenience and efficiency.

[1161] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1162] In this invention, the server includes means for collecting the availability status of coin lockers and parts shelves in real time, means for storing the collected data in a central database, means for predicting future availability using an AI algorithm based on past usage data, means for receiving user location information and obtaining availability status around that location, means for providing the obtained availability status and predicted data to a user terminal, and means for displaying the obtained data on the user terminal.This allows users and administrators to grasp availability status in real time and, by predicting future status, enables planned and efficient use.

[1163] A "coin locker" is a storage device used by users to temporarily store their luggage.

[1164] "Availability" is information that indicates whether storage facilities such as coin lockers and parts shelves are currently available.

[1165] "Real-time" collection means that data is collected immediately, on the spot, without delay.

[1166] A "server" is a central processing unit that collects, stores, and analyzes data.

[1167] A "central database" is a database system in which collected data is centrally stored and managed.

[1168] An "AI algorithm" is a computational procedure or model used to perform data analysis and predictions using artificial intelligence technology.

[1169] "User terminal" means a device (smartphone, tablet, etc.) used by a user to receive and display information.

[1170] "Location information" is data that indicates the geographic location of a user or object.

[1171] A "parts shelf" is a shelf used to store and manage parts in a factory or warehouse.

[1172] "Predicting future availability" means estimating future availability based on analysis of past data.

[1173] "Means for collecting" refers to methods and devices for obtaining data in real time.

[1174] "Storage means" refers to the method or device used to store collected data in a central database.

[1175] A "means for predicting" is a method or device for estimating future states using an AI algorithm.

[1176] The "means for providing" refers to a method or device for transmitting the acquired data or predictions to a user terminal.

[1177] The "display means" refers to a method or device for visually displaying the acquired data or predictions on a user terminal.

[1178] To realize this invention, it is necessary to build a system that collects real-time information on the availability of coin lockers and parts shelves, and uses AI algorithms based on past data to predict future availability. Details of the system and specific implementation methods are described below.

[1179] System Components

[1180] server

[1181] The server is the main data processing device of this system. Specifically, it has the following functions:

[1182] Store the collected data in a central database: The availability status is obtained in real time from IoT sensors installed in each coin locker and parts shelf and stored in a central database.

[1183] Predict future availability using AI algorithms based on past data: Analyze collected data and past usage data using AI algorithms (e.g., machine learning models) to predict future availability.

[1184] Device (user's smartphone)

[1185] Users access the system using a smartphone, which has the following features:

[1186] Check current and predicted availability: The system uses the user's location information to obtain the availability of the nearest coin lockers and parts shelves, and displays it in real time. It also allows users to check the predicted availability in the future.

[1187] IoT Sensors

[1188] IoT sensors are installed in each coin locker and parts shelf to detect vacancy in real time. These sensors have the following functions:

[1189] Collect availability information in real time and send it to the server: The usage status of coin lockers and parts shelves is collected in real time via sensors and sent to the server.

[1190] Specific operation of the system

[1191] Data processing by the server

[1192] The server collects availability data from each IoT sensor, stores the collected data in a central database, and then runs an AI algorithm (e.g., a machine learning model) based on the past data to predict future availability.

[1193] Displaying data on a terminal

[1194] Users can use the app on their smartphone to check current and predicted future availability, and information on the nearest coin lockers and parts shelves based on the user's location is also retrieved and displayed.

[1195] Hardware and software used

[1196] Hardware: IoT sensors, servers, user devices (smartphones)

[1197] Software: Python, Requests library, central database, AI models (e.g., machine learning algorithms)

[1198] As a concrete example, the following describes how to build a system that predicts the availability of parts shelves.

[1199] Examples:

[1200] IoT sensors installed on each parts shelf in the factory collect real-time data, and AI is used to predict future availability based on past data. Users (factory workers) can use their smartphones to check the current availability of parts shelves through the app, allowing them to handle parts efficiently.

[1201] Example prompt sentence:

[1202] "Create a Python program that uses IoT sensors installed on each parts shelf in the factory to obtain real-time data, and uses AI to predict future availability based on past data. The program must have the ability to send the availability status of parts shelves to a server and display the prediction results."

[1203] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1204] Step 1:

[1205] The server collects real-time availability data from IoT sensors installed in each coin locker and parts shelf. This data includes the current usage status of each storage space (vacant or occupied), providing the latest usage information.

[1206] Step 2:

[1207] The server stores the collected data in a central database. The input data is real-time data sent from the IoT sensors, and the output is the latest data stored in the central database. This process allows for centralized management of the usage status of all coin lockers and parts shelves.

[1208] Step 3:

[1209] The server runs an AI algorithm based on past usage data stored in a central database to predict future availability. The input is past usage data, and the output is predicted future availability data. Specifically, it uses a machine learning model to analyze patterns and estimate future usage trends.

[1210] Step 4:

[1211] The terminal (user's smartphone) acquires the user's current location. Based on this location information, it requests the server for information on the availability of coin lockers and parts shelves. The input is the user's location information, and the output is a request to the server.

[1212] Step 5:

[1213] The server receives the user's location information and retrieves the availability of coin lockers and parts shelves around that location. The input is the user's location information, and the output is availability data for the area. This provides the user with information on the nearest available storage space.

[1214] Step 6:

[1215] The server then sends the acquired availability and forecast data to the user's device. The input is availability data and forecast data for the relevant area, and the output is data sent to the user's device, allowing the user to check the current situation and future forecasts.

[1216] Step 7:

[1217] The terminal (user's smartphone) displays the acquired data. The input is availability data and forecast data sent from the server, and the output is information displayed to the user. Specifically, availability is visually displayed in a map app or list format. Using this information, the user can select the appropriate coin locker or parts shelf.

[1218] This series of processes allows users to grasp availability in real time and use coin lockers and parts shelves efficiently.

[1219] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1220] This invention is a system that utilizes IoT and AI technologies and an emotion recognition engine to collect information on coin locker availability in real time, predict future availability based on past usage data, and recognize users' emotions to provide an optimal locker usage experience.The system consists of a server, a terminal (such as a user's smartphone), IoT sensors, and an emotion recognition engine.

[1221] System Components

[1222] 1. Server

[1223] The server plays a central role in this system. It collects availability data sent in real time from IoT sensors installed in coin lockers and stores it in a central database. It also uses AI algorithms to analyze past usage data and predict future availability. It also receives user emotional data and adjusts the locker and guidance method based on the user's emotional state.

[1224] 2. Device (user's smartphone)

[1225] Users can check current and predicted availability using a dedicated app downloaded to their smartphone. The app acquires the user's current location and requests information about the nearest coin locker based on that location from the server. The app also has an emotion engine that analyzes the user's facial expressions and voice to detect the user's emotional state.

[1226] 3. IoT Sensors

[1227] Each coin locker is equipped with an IoT sensor that detects the availability of the locker in real time and sends that information to a server.

[1228] 4. Emotion Engine

[1229] The emotion engine is a software component that recognizes emotions from the user's facial expressions and voice. The emotion engine collects emotional data from the user via the smartphone camera and microphone and sends the analysis results to the server. This allows the system to determine the user's stress level and satisfaction level, and then recommend the most suitable locker and select the appropriate guidance method.

[1230] System Operation

[1231] The specific operation of the system will be described below.

[1232] Collection and storage of availability

[1233] The server periodically obtains the availability status from IoT sensors installed in each coin locker. The sensors detect whether there is any luggage in the locker. This data is stored in a central database, which keeps the latest status of each locker.

[1234] Availability analysis

[1235] The server analyzes the latest availability data stored in a central database to determine which lockers are currently available.

[1236] Prediction by AI algorithm

[1237] The server runs an AI algorithm based on past usage data to predict future availability. For example, it learns patterns of locker usage on specific days of the week or at specific times of the day, and uses that to estimate future availability.

[1238] User request processing

[1239] The user launches the smartphone app and inputs their current location. Based on that information and emotion data, the app sends a request to the server.

[1240] Data provision

[1241] The server receives the user's location information and emotion data, retrieves real-time availability and forecast data for coin lockers in the vicinity of the user's location from a database, and compiles this information into a single packet that is sent to the user's smartphone.

[1242] Data display and guidance

[1243] The device then displays the location of the coin lockers on a map based on the received data. The user can then select the most suitable locker based on that information and begin navigation to their destination. Furthermore, the guidance method and information display are dynamically adjusted based on the emotion data.

[1244] Specific examples

[1245] Searching for a coin locker at Tokyo Station

[1246] User: A traveler arrives at Tokyo Station and launches the smartphone app.

[1247] Device (smartphone): The app will display the home screen and select "Coin Locker Search." A pop-up will appear requesting permission to obtain location information and collect emotional data.

[1248] User: Allow location and emotion data collection.

[1249] Device: Sends the acquired current location information and emotion data to the server.

[1250] Server: Based on the user's location and emotion data, it queries the central database to obtain the availability of coin lockers around Tokyo Station.

[1251] Server: Analyzes the acquired data, generates a list of optimal lockers, and selects a guidance method based on the emotional data.

[1252] Server: Returns the generated list to the device via API.

[1253] Terminal: Display a list of lockers and a map. Based on emotion data, provide specific guidance to the user. The user selects the appropriate locker and taps the "Start Navigation" button.

[1254] Device: Launch the map app and begin navigating to the selected locker.

[1255] The present invention allows users to quickly find the coin locker that best suits their emotional state at the time, thereby increasing convenience and satisfaction.

[1256] The processing flow will be explained below.

[1257] Step 1:

[1258] The server periodically collects real-time availability data from IoT sensors installed in the coin lockers. For example, every minute, the sensor detects whether the locker is in use or vacant, and sends that data to the server.

[1259] Step 2:

[1260] The server stores the received availability data in a central database, which stores the latest status of each locker.

[1261] Step 3:

[1262] The server analyzes the availability data stored in the database to determine which lockers are currently available and creates a list of available lockers.

[1263] Step 4:

[1264] The server runs an AI algorithm based on past usage data to predict future availability. It learns patterns of locker usage on specific days and times, and uses that to estimate future availability.

[1265] Step 5:

[1266] The user launches the smartphone app and allows it to use location and emotional data, which then uses the camera and microphone to capture their current facial expressions and voice.

[1267] Step 6:

[1268] The device (smartphone) sends the acquired location information and emotion data to the server, requesting the availability of coin lockers near the user's current location and appropriate guidance.

[1269] Step 7:

[1270] The server receives the user's location information and emotion data, and retrieves real-time availability and forecast data for coin lockers around that location from the database.

[1271] Step 8:

[1272] The server analyzes the emotional data and, if it determines that the user is feeling stressed, it prioritizes the nearest available locker. The server then sends the list of created lockers to the device via API.

[1273] Step 9:

[1274] The terminal (smartphone) displays the received list of available lockers on the user interface, showing the locker location, availability, size, and fee information on a map, and adjusting the guidance method (e.g., whether to provide audio guidance or highlighting method) based on the user's emotional state.

[1275] Step 10:

[1276] The user selects the most suitable coin locker based on the displayed information and taps the "Start Navigation" button. The system then follows the guidance method based on the emotion data.

[1277] Step 11:

[1278] The device (smartphone) will begin navigation to the selected locker, launching a map app and guiding the user to the specified locker.

[1279] Step 12:

[1280] The user follows the navigation to reach the locker and begins using it. The data at the time of use is again sent to the server via the IoT sensor, and the database is updated.

[1281] Example 2

[1282] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1283] It is difficult to grasp the availability of coin lockers in real time and provide this information to users promptly. In addition, there is a lack of means to predict locker usage patterns, making it impossible to secure the optimal locker when a user needs it. Furthermore, services do not take into account the emotional state of the user, making it a challenge to improve user satisfaction.

[1284] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1285] In this invention, the server includes means for collecting coin locker availability information in real time, means for storing the collected data in a central database, means for predicting future availability using an artificial intelligence algorithm based on past usage data, means for receiving user location information and obtaining information on available coin lockers in the vicinity of that location, means for receiving and analyzing the user's emotional data, means for providing the obtained availability information and predicted data to the user's terminal, means for displaying the obtained data on the user's terminal, and means for adjusting the guidance content based on the user's emotional data. This allows users to quickly and accurately grasp the available coin locker status and select the optimal locker based on predicted usage patterns. Furthermore, providing services tailored to the user's emotional state can improve user satisfaction.

[1286] "Means for collecting vacancy status in real time" refers to a device or method that instantly detects the current vacancy status of lockers and collects data through sensors installed in coin lockers.

[1287] "Central database" refers to a database system for centrally storing and managing all collected locker status data.

[1288] "Artificial intelligence algorithm" refers to a computational method using machine learning or other AI techniques that is used to learn patterns based on past data and predict future availability.

[1289] "Means of receiving the user's location information and obtaining the availability of coin lockers in the vicinity of that location" refers to a function that obtains the user's current location using GPS or other means, and uses that information to search for and obtain the availability of nearby lockers.

[1290] "Means for receiving and analyzing user emotional data" refers to a device or method that senses the user's facial expressions and voice, analyzes their emotional state, and digitizes it.

[1291] "Means for providing acquired availability and forecast data to a user terminal" refers to a communication means for transmitting real-time availability data and forecast data from a server to a user terminal.

[1292] "Means for displaying acquired data on the user's device" refers to an interface that visually displays availability and forecast data collected on the user's device.

[1293] "Means for adjusting guidance content based on the user's emotional data" refers to a system that optimally adjusts the guidance method and message content for the user based on analyzed emotional data.

[1294] This invention is a system that collects information on coin locker availability in real time, predicts future availability based on past usage data, and recognizes user emotions to provide an optimal locker usage experience. This system consists of a server, a terminal (user terminal), IoT sensors, and an emotion engine.

[1295] server

[1296] The server plays a central role in this system and has the following functions:

[1297] 1. Data collection from IoT sensors: The server collects vacancy data in real time from IoT sensors installed in each coin locker. This allows the server to grasp the immediate vacancy status of each locker. For example, Microsoft's Azure IoT Hub can be used.

[1298] 2. Database storage: The collected data is stored in a central database, which stores the latest status information for each locker. For example, a database service such as MySQL is used.

[1299] 3. Data Analysis and Prediction: The server analyzes the data stored in the central database to determine which lockers are available. It also runs AI algorithms based on past usage data to predict future availability. This is done using machine learning frameworks (e.g., TensorFlow or PyTorch).

[1300] 4. User information processing: Receives the user's location information and emotion data, retrieves real-time availability and forecast data for coin lockers around the location from the database, and sends the retrieved information to the user's device.

[1301] User terminal

[1302] The user device (smartphone) connects to the system using a dedicated application. The main functions of the application are as follows:

[1303] 1. Location information acquisition: Obtain the user's current location and send that information to the server. Obtain location information using the GPS module.

[1304] 2. Collecting and analyzing emotional data: The user's facial expressions and voice are analyzed using an emotion engine (for example, Microsoft Azure Cognitive Services' Face API or Speech API), and the emotional data is sent to the server.

[1305] 3. Data display: Display the availability and forecast data of coin lockers received from the server. For example, use the Google Maps API to display the location and availability of lockers on a map.

[1306] 4. Guidance: Provides optimal guidance based on the user's emotional data, including navigation to a specific locker and the ability to change the message content.

[1307] Specific examples

[1308] Searching for a coin locker at Tokyo Station

[1309] User: A traveler arrives at Tokyo Station and launches the smartphone app.

[1310] Device: The app will display the home screen and select "Coin Locker Search." A pop-up will appear requesting permission to obtain location information and collect emotional data.

[1311] User: Allow location and emotion data collection.

[1312] Device: Sends the acquired current location information and emotion data to the server.

[1313] Server: Based on the user's location and emotion data, it queries the central database to obtain the availability of coin lockers around Tokyo Station.

[1314] Server: Analyzes the acquired data, generates a list of optimal lockers, and selects a guidance method based on the emotional data.

[1315] Server: Returns the generated list to the device via API.

[1316] Terminal: Display a list of lockers and a map. Based on emotion data, provide specific guidance to the user. The user selects the appropriate locker and taps the "Start Navigation" button.

[1317] Device: Launch the map app and begin navigating to the selected locker.

[1318] Prompt Sentence Examples

[1319] 1. Sample Prompt 1: "Describe an assistance system that helps travelers find the best coin locker when they're in a hurry."

[1320] 2. Example prompt 2: "Please explain the flow of a system that uses IoT sensors and emotion analysis to grasp the availability of coin lockers at stations in real time and provide the optimal guidance method based on the user's emotion."

[1321] This system allows users to check the availability of coin lockers in real time and receive optimal guidance based on their emotional state, thereby improving convenience and satisfaction.

[1322] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1323] System program processing flow

[1324] Step 1: Collect availability data

[1325] The server collects availability data from IoT sensors installed in each coin locker.

[1326] Input: Real-time availability data (e.g., open, closed) from each IoT sensor

[1327] Processing: The server receives data sent from the IoT sensors and obtains the status of each locker. This involves sending data acquisition requests through the sensor API and receiving responses.

[1328] Output: Availability data list (status information for each locker)

[1329] Specific operation: The server sends a request to the sensor API every 15 minutes to obtain the availability status of the lockers. After collecting data from all the lockers, it proceeds to the next step all at once.

[1330] Step 2: Store in the database

[1331] The server stores the collected data in a central database.

[1332] Input: Availability data list

[1333] Processing: The server saves the retrieved data to a database, which involves opening a database connection and executing an SQL insert query.

[1334] Output: Data stored in a central database

[1335] What happens: The server executes an insert query on the database to store each locker's status information in a table. For example, it executes a query like "INSERT INTO locker_status (locker_id, status, timestamp) VALUES (...)".

[1336] Step 3: Analyze availability data

[1337] The server analyzes data stored in a central database to ascertain the latest availability.

[1338] Input: Data from the central database

[1339] Processing: The server queries the latest availability data from the database and lists the real-time availability. This involves using a SELECT query to get the latest data and then parsing it.

[1340] Output: Latest availability list

[1341] Specific operation: The server executes a query to the database such as "SELECT locker_id, status FROM locker_status WHERE timestamp = (SELECT MAX(timestamp) FROM locker_status)" to get the latest availability status.

[1342] Step 4: Future predictions using AI algorithms

[1343] The server runs an AI algorithm based on past usage data to predict future availability.

[1344] Input: Historical usage data

[1345] Processing: The server inputs historical usage data into the machine learning model to obtain predictions of future availability. This includes loading the prediction model, normalizing the data, and analyzing the predictions.

[1346] Output: Future availability forecast data

[1347] What it does: The server uses the TensorFlow model to predict future availability based on past usage data, for example by using a function like "model.predict(past_usage_data)".

[1348] Step 5: Receiving a request from the user

[1349] Users launch the smartphone app, enter their current location information, and request information about available coin lockers.

[1350] Input: User's current location

[1351] Processing: The user device acquires GPS data and sends the location information to the server. This includes using the location service API to acquire the current location and sending it to the server in JSON format.

[1352] Output: Location request sent to the server

[1353] What it does: When a user opens the app and presses the "Get current location" button, the app uses GPS to get the current location and sends that information to the server, for example by using a function like "current_location = gps.get_current_location()".

[1354] Step 6: Analyze user sentiment data

[1355] The device analyzes the user's facial expressions and voice using an emotion engine and sends the emotion data to the server.

[1356] Input: User's facial expression data, voice data

[1357] Processing: The emotion engine (API) analyzes facial expressions and voice to detect emotional states. This involves collecting data through the camera and microphone and sending it to the analysis engine.

[1358] Output: Emotion data

[1359] Specific operation: The app captures the user's facial expression with the camera, sends it to the API, receives the analysis results, and sends them to the server. For example, execute a function like "emotion_data = emotion_api.analyze(face_image)".

[1360] Step 7: Generate optimal locker information

[1361] The server selects the most suitable locker based on the user's location information, emotional data, and real-time locker availability information.

[1362] Input: Location, emotion data, real-time availability

[1363] Processing: The server combines this data and uses algorithms to select the best locker, including prioritizing based on location and emotion data, and selecting the most appropriate locker based on availability data.

[1364] Output: Optimal locker list

[1365] What it does: The server generates a list of lockers that are closest to the user and that correspond to their emotions based on their location and emotion data. For example, it executes a function like "optimal_lockers = find_optimal_lockers(location, emotion_data, availability_data)".

[1366] Step 8: Provide information to your smartphone

[1367] The terminal receives the information returned from the server and displays it to the user.

[1368] Input: Optimal locker list from server

[1369] Processing: Analyzing the received data and displaying it through the user interface. This includes receiving the data, analyzing it, and displaying it on a map.

[1370] Output: Best locker information provided to user

[1371] What it does: It displays recommended lockers on a map on the user's phone, for example, by mapping the locker locations using the Google Maps API and using a function like "display_lockers_on_map(optimal_lockers)".

[1372] Step 9: Navigation

[1373] The terminal will begin navigating to the locker selected by the user.

[1374] Input: Location of the locker selected by the user

[1375] Processing: Calculate the route to the selected locker and initiate navigation, including using a route calculation algorithm and providing audio and visual guidance to the user.

[1376] Output: Navigation instructions

[1377] Specific behavior: The map app will launch and guide the user to the selected locker. For example, it will execute a function such as "start_navigation(destination_location)".

[1378] Prompt Sentence Examples

[1379] 1. Sample prompt 1: "Describe an assistance system that helps travelers find the best coin locker when they're in a hurry."

[1380] 2. Example prompt 2: "Please explain the flow of a system that uses IoT sensors and emotion analysis to grasp the availability of coin lockers at stations in real time and provide the optimal guidance method based on the user's emotion."

[1381] (Application example 2)

[1382] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1383] Conventional coin locker systems were unable to adequately grasp availability or predict future availability, making it difficult for users to find available lockers during busy times. Furthermore, they did not take into account the user's emotional state, and were unable to suggest the most suitable locker or guide users to shopping areas. This resulted in problems that reduced user convenience and satisfaction.

[1384] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting coin locker availability information in real time, means for storing the collected data in a central database, means for predicting future availability using an AI algorithm based on past usage data, means for receiving user location information and obtaining availability information for coin lockers around that location, means for analyzing emotional data, means for suggesting the most suitable coin locker or shopping space based on the analyzed emotional data, and means for displaying the obtained data on the user terminal. This enables the user to quickly find the most suitable coin locker or shopping space based on their emotional state.

[1385] A "coin locker" is a storage device installed in public places and commercial facilities, etc., and used for short-term storage of luggage.

[1386] "Availability" is information indicating whether or not there are any coin lockers available and how many there are.

[1387] "Collecting in real time" means obtaining the current status and information immediately and updating it without delay.

[1388] A "central database" is a data management system that centrally manages multiple data sets and stores, searches, and analyzes the necessary information.

[1389] An "AI algorithm" is a computational method or procedure that uses artificial intelligence technology to analyze data and make predictions.

[1390] "Means for predicting future availability" refers to technology that estimates the future availability of coin lockers based on past data and current conditions.

[1391] "User location information" is data obtained from a smartphone, GPS device, etc. that indicates the user's current geographic location.

[1392] "Emotional data" is information that indicates the emotional state of the user analyzed from facial expressions, voice, etc.

[1393] "Analyzing" means examining data in detail to understand its structure and meaning.

[1394] "Means to suggest the most suitable coin lockers and shopping spaces" refers to technology that guides users to the most appropriate lockers and stores based on their situation and needs.

[1395] A "user terminal" is a device, such as a smartphone or tablet, that a user uses to check and input information.

[1396] This invention relates to a system that provides users with optimal coin lockers and shopping spaces in shopping centers and public places. This system collects availability information in real time, analyzes past data to predict future availability, and analyzes user sentiment to make optimal suggestions.

[1397] System Components

[1398] 1. Server

[1399] The server plays a central role in this system. Specifically, it performs the following processes:

[1400] 1. Collect real-time availability data from IoT sensors installed in each coin locker and store and store it in a central database.

[1401] 2. Run an AI algorithm based on past usage data to predict future availability.

[1402] 3. Recommend optimal lockers and shopping spaces based on location and emotion data received from the user's device.

[1403] 2. Device (user's smartphone)

[1404] Users can access the system using their smartphones and enjoy the following features:

[1405] 1. Obtaining location information and sending it to the server.

[1406] 2. Emotional data is collected using the smartphone camera and microphone, and the analysis results are sent to the server.

[1407] 3. Receive availability and future forecast data provided by the server, as well as suggestions for optimal lockers and shopping spaces.

[1408] 4. Display the locations of coin lockers and stores on a map and provide navigation.

[1409] 3. IoT Sensors

[1410] IoT sensors installed in each coin locker and store detect current availability in real time and send that information to a server.

[1411] 4. Emotion Engine

[1412] The emotion engine is a component that recognizes the user's emotions from their facial expressions and voice, determining their stress level and satisfaction level and making optimal suggestions.

[1413] System Operation

[1414] Collection and storage

[1415] The server periodically collects occupancy data from IoT sensors installed in each coin locker and store, and stores store this data in a central database, where it is then analyzed using AI algorithms.

[1416] Availability forecast

[1417] The server runs an AI algorithm based on past data to predict future availability, making it possible to know in advance which times and days of the week lockers and stores will be crowded.

[1418] User request processing

[1419] Users use a smartphone app to send location and emotional data to the server, which then understands the user's current location and emotional state and makes the most appropriate suggestions.

[1420] Sentiment Analysis and Recommendations

[1421] The server uses an emotion engine to analyze the user's emotions, and based on the results, combines them with availability and future prediction data to suggest the most suitable locker or shopping space.

[1422] Specific examples

[1423] Shopping mall usage scenario

[1424] When a user enters a shopping mall, the smartphone app automatically launches. The app sends location and emotion data to a server, which then suggests the most suitable store or locker based on that information. The user can then view the suggested store or locker location on a map through the app and begin navigation.

[1425] Prompt Sentence Examples

[1426] I'm interested in developing a smart tourist guide system for shopping malls. This system will recognize the user's current location and emotions in real time and recommend the most suitable stores and tourist attractions. EmotionRecognizer will be used to recognize user emotions, and congestion data will be obtained via an API. Can you give me a concrete code example?

[1427] This allows users to quickly find the most suitable coin locker or shopping space based on their feelings and preferences, improving their shopping experience.

[1428] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1429] Step 1:

[1430] The server collects real-time availability data from IoT sensors installed in each coin locker and in the store. Specifically, the IoT sensors detect the current availability and send that information to the server. The server receives this data and stores it in a central database.

[1431] Input: Availability data sent from IoT sensors

[1432] Data processing: Parse availability data and convert it into a structured data format

[1433] Output: Availability data stored in a central database

[1434] Step 2:

[1435] The server runs an AI algorithm based on past usage data to predict future availability. Specifically, it uses AI technology to analyze data and predict future usage patterns based on specific days of the week and times of day.

[1436] Input: Historical usage data stored in a central database

[1437] Data calculations: Predicting future availability using AI algorithms

[1438] Output: Future availability forecast data

[1439] Step 3:

[1440] The user device sends its current location information to the server via a smartphone app. Specifically, the smartphone's GPS function is used to obtain location information and then the data is sent to the server.

[1441] Input: Current location information obtained from your smartphone

[1442] Data processing: Convert location information into a format to send to the server

[1443] Output: Location information sent to the server

[1444] Step 4:

[1445] The user device collects emotional data using the smartphone camera and microphone, and then uses the emotion engine to analyze the results and send them to the server. Specifically, the emotion engine analyzes the user's facial expressions and voice, and sends the resulting emotional state data to the server.

[1446] Input: Facial expression and voice data acquired from a smartphone camera and microphone

[1447] Data calculation: Emotion data analysis using emotion engine

[1448] Output: Emotional state data sent to the server

[1449] Step 5:

[1450] The server retrieves real-time availability and forecast data from a central database based on the received location information and emotion data, and then proposes the most suitable coin lockers and shopping spaces. Specifically, it selects the lockers and stores that best match the location information and emotion state, and generates a list of them.

[1451] Input: Location, emotional state data, availability data and forecast data from a central database

[1452] Data calculation: Query the database based on location and emotional state to generate optimal suggestions

[1453] Output: A list of the best lockers and stores

[1454] Step 6:

[1455] The user device displays the list of optimal lockers and shopping spaces received from the server to the user. Specifically, it displays the locations of the suggested lockers and stores on a map and provides navigation.

[1456] Input: A list of the best lockers and stores sent from the server

[1457] Data processing: Converting data into a format for display on a map

[1458] Output: Locker and store locations and navigation information displayed on the user's device

[1459] As described above, each processing step works in conjunction with the other steps, allowing users to quickly find the most suitable coin locker or shopping space based on their emotional state at the time.

[1460] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1461] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1462] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1463] [Fourth embodiment]

[1464] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1465] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1466] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1467] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1468] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1469] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1470] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1471] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1472] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1473] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1474] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1475] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1476] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1477] This invention is a system that utilizes IoT and AI technologies to collect information on coin locker availability in real time and predict future availability based on past usage data. This system consists of a server, a terminal (such as a user's smartphone), and an IoT sensor. Details of each component and specific steps for implementing the invention are explained below.

[1478] System Components

[1479] 1. Server

[1480] The server plays a central role in the system. It collects real-time availability data sent from IoT sensors installed in coin lockers and stores it in a central database. It also uses AI algorithms to analyze past usage data and predict future availability.

[1481] 2. Device (user's smartphone)

[1482] Users can check current and predicted availability using a dedicated app downloaded to their smartphones. The app acquires the user's current location and requests information from the server about the nearest coin locker based on that location.

[1483] 3. IoT Sensors

[1484] Each coin locker is equipped with an IoT sensor that detects the availability of the locker in real time and sends the information to a server.

[1485] System Operation

[1486] The specific operation of the system will be described below.

[1487] Collection and storage of availability

[1488] The server periodically obtains information about the availability of each locker from IoT sensors installed in each locker. For example, it collects data from sensors that detect whether there is luggage in the locker. This data is stored in a central database, which keeps the latest status of each locker.

[1489] Availability analysis

[1490] The server analyzes the latest availability data stored in a central database to determine which lockers are currently vacant or occupied.

[1491] Prediction by AI algorithm

[1492] The server runs an AI algorithm based on past usage data to predict future availability. For example, it learns patterns of locker usage on specific days of the week or at specific times of the day, and uses that to estimate future availability.

[1493] User request processing

[1494] The user launches the smartphone app and inputs their current location, which then sends a request to the server.

[1495] Data provision

[1496] The server receives the user's request, obtains real-time availability and forecast data for the coin lockers closest to the user's current location, and then compiles this information into a packet and sends it to the user's smartphone.

[1497] Data display and guidance

[1498] The device then displays the location of the coin lockers on a map based on the received data. The user can then select the most suitable locker based on this information and begin navigation to their destination.

[1499] Specific examples

[1500] Searching for a coin locker at Tokyo Station

[1501] User: A traveler arrives at Tokyo Station and launches the smartphone app.

[1502] Device (smartphone): The app will display the home screen and select "Coin Locker Search." A pop-up will appear requesting location information.

[1503] User: Allow location information.

[1504] Device: Sends the acquired current location information to the server.

[1505] Server: Based on the user's location, it queries the central database to obtain the availability of coin lockers around Tokyo Station.

[1506] Server: Analyzes the acquired data and generates a list of optimal lockers.

[1507] Server: Returns the generated list to the device via API.

[1508] Terminal: Displays a list of lockers and a map. The user selects the appropriate locker and taps the "Start Navigation" button.

[1509] Device: Launch the map app and begin navigating to the selected locker.

[1510] This invention allows users to efficiently use coin lockers in public places, significantly reducing the time and effort required to search for a locker and providing a more comfortable user experience.

[1511] The processing flow will be explained below.

[1512] Step 1:

[1513] The server periodically collects real-time availability data from IoT sensors installed in coin lockers. The sensors detect whether the locker is in use or vacant and send the status to the server.

[1514] Step 2:

[1515] The server stores the received availability data in a central database, where the latest status of each locker is stored.

[1516] Step 3:

[1517] The server analyzes the availability data stored in the database to determine which lockers are currently available.

[1518] Step 4:

[1519] The server runs AI algorithms based on past usage data to predict future availability, generating a predictive model based on usage patterns, specific dates, and time periods.

[1520] Step 5:

[1521] The user starts the smartphone app and allows the app to obtain their current location information. The allowed location information is then acquired.

[1522] Step 6:

[1523] The terminal (smartphone) sends the acquired location information to the server and requests information about available coin lockers nearby.

[1524] Step 7:

[1525] The server receives the user's location information and retrieves real-time availability and forecast data for coin lockers around that location from the database.

[1526] Step 8:

[1527] The server generates a list of the best available lockers and sends that information to the device via an API.

[1528] Step 9:

[1529] The terminal (smartphone) displays the received list of available lockers on the user interface, along with information on the locker's location, availability, size, and fees on a map.

[1530] Step 10:

[1531] The user selects the most suitable coin locker based on the displayed information and taps the "Start navigation" button.

[1532] Step 11:

[1533] The device (smartphone) will begin navigation to the selected locker, launching a map app and guiding the user to the specified locker.

[1534] Step 12:

[1535] The user follows the navigation to reach the locker and begins using the locker.

[1536] Example 1

[1537] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1538] Conventional coin locker management systems are inefficient because users must check availability only after arriving at the location. Furthermore, they have problems with poor prediction of availability, making it impossible to respond to future demand. This means users have to spend a lot of time searching for a locker, significantly reducing convenience.

[1539] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1540] In this invention, the server includes means for collecting coin locker availability information in real time, means for storing the collected data in a central database, means for predicting future availability using a machine learning algorithm based on past usage data, means for receiving user location information and obtaining availability information for coin lockers around that location, means for providing the obtained availability information and prediction data to a user device, means for displaying the obtained data on the user device, means for analyzing coin locker availability and integrating the real-time data and prediction data, and means for filtering data based on requests sent from the user device and providing information on the most suitable locker. This allows users to efficiently check and use coin locker availability.

[1541] A "coin locker" is an individual storage space installed in a public or commercial facility, and is a storage facility where users can temporarily store their luggage.

[1542] "Availability" is information that indicates whether a coin locker is currently in use or available for use.

[1543] "Real-time collection means" refers to devices and systems that use IoT sensors and other technologies to instantly detect coin locker usage and collect data.

[1544] The "central database" is a data storage system for centrally managing and storing coin locker availability data.

[1545] A "machine learning algorithm" is a type of computer program used to learn patterns from past usage data and predict future availability.

[1546] "User Location Information" means data that describes a user's current geographic location, typically obtained through GPS or other location tracking technologies.

[1547] "User Device" means an electronic device that a user can carry with them, such as a smartphone or tablet, which is used to check the availability of coin lockers through the application.

[1548] "Real-time data" refers to data that indicates the current usage status of coin lockers.

[1549] "Predictive data" is information about future coin locker availability calculated based on past usage data and machine learning algorithms.

[1550] "Analytical means" are the tools and techniques used to process collected data and derive trends and patterns.

[1551] A "filtering means" is a method or system for extracting data based on specific conditions and providing users with only the information they need.

[1552] "Means for displaying on a map" refers to a function that visually displays geographic information on an electronic device, allowing users to determine the location of coin lockers.

[1553] "Route Guidance" is a navigation function that shows users the best route to reach their destination.

[1554] This invention is a system that utilizes IoT and AI technologies to collect information on coin locker availability in real time and predict future availability based on past usage data. This system consists of a server, a terminal (such as a user's smartphone), and an IoT sensor. Details of each component and specific steps for implementing the invention are explained below.

[1555] System Components

[1556] server

[1557] The server plays a central role in this system. It collects availability data sent in real time from IoT sensors installed in coin lockers and stores it in a central database. It also uses machine learning algorithms based on past usage data to predict future availability. Specifically, it performs the following tasks:

[1558] The database engine used is MySQL or PostgreSQL.

[1559] TensorFlow and PyTorch are used as machine learning frameworks.

[1560] Device (user's smartphone)

[1561] Users can check the current and predicted availability of lockers using a dedicated app downloaded to their smartphone. The app obtains the user's current location and requests information about the nearest coin locker based on that location from the server. As the user operates the app, the following steps are performed:

[1562] Location information is obtained using a GPS module.

[1563] The app sends an HTTP request to the server.

[1564] IoT Sensors

[1565] IoT sensors are installed in each coin locker, and these sensors detect the availability of the locker in real time. The sensors send this information to a server. As specific examples, the following sensors could be used:

[1566] Pressure sensor to detect the presence or absence of luggage

[1567] The sensors transmit data using communication protocols such as Wi-Fi and LoRa.

[1568] Specific examples

[1569] Searching for a coin locker at Tokyo Station

[1570] 1. The user arrives at Tokyo Station and launches the smartphone app.

[1571] 2. Device (smartphone): The app home screen will appear. Select "Coin Locker Search." A pop-up will appear requesting your location information.

[1572] 3. The user allows location information to be obtained.

[1573] 4. The device sends the current location information it has acquired to the server.

[1574] 5. The server queries the central database to obtain the availability of coin lockers around Tokyo Station based on the user's location.

[1575] 6. The server analyzes the acquired data and generates a list of optimal lockers.

[1576] 7. The server returns the generated list to the device via API.

[1577] 8. The device (smartphone) displays a list of lockers and a map. The user selects the appropriate locker and taps the "Start Navigation" button.

[1578] 9. The device will launch a map app and begin providing directions to the selected locker.

[1579] Prompt Sentence Examples

[1580] By inputting the following prompt sentences into the generative AI model, you can get an explanation for a specific scenario.

[1581] "Please explain in detail how to arrive at Tokyo Station and use a smartphone app to find the nearest coin locker. Please also explain how to use the app, which displays the current availability and future forecast availability, and the specific steps users take when using it."

[1582] As described above, the system of the present invention can efficiently provide users with information on available coin lockers, thereby significantly improving convenience of use.

[1583] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1584] Step 1:

[1585] The server collects availability data from IoT sensors installed in coin lockers.

[1586] Specific behavior:

[1587] Input: Locker usage status data obtained by IoT sensors (e.g., whether or not luggage is present).

[1588] Data processing: Receives and formats data sent from sensors. Each data is appended with a timestamp and the corresponding locker ID.

[1589] Output: A formatted set of availability data.

[1590] Step 2:

[1591] The server stores the collected data in a central database.

[1592] Specific behavior:

[1593] Input: Formatted availability data.

[1594] Data manipulation: Insert data using a database query.

[1595] Output: Availability data stored in database and acknowledged by query.

[1596] Step 3:

[1597] The server analyzes the latest availability data stored in a central database.

[1598] Specific behavior:

[1599] Input: Availability data in a central database.

[1600] Data Calculation: Uses database queries to identify currently available and occupied lockers, performing aggregation and filtering as needed.

[1601] Output: A list of current availability as a result of the analysis.

[1602] Step 4:

[1603] The server runs a machine learning algorithm based on past usage data to predict future availability.

[1604] Specific behavior:

[1605] Input: Historical usage data in a database.

[1606] Data calculation: Run machine learning algorithms using TensorFlow and PyTorch to predict future availability.

[1607] Output: Predicted future availability data.

[1608] Step 5:

[1609] The user launches the smartphone app and sends their location information to the server.

[1610] Specific behavior:

[1611] Input: Local location information obtained by the smartphone's GPS module.

[1612] Data processing: The location information is sent to the server via an HTTP request.

[1613] Output: The location information sent to the server.

[1614] Step 6:

[1615] The server receives requests from users and obtains real-time and forecast data for the nearest coin lockers.

[1616] Specific behavior:

[1617] Input: The user's location.

[1618] Data calculation: Based on the location information, a geolocation database is queried to identify the nearest coin locker. After identification, real-time data is combined with predictive data.

[1619] Output: Data packets (real-time data + predicted data) provided to the user.

[1620] Step 7:

[1621] The server transmits the processed data packets to the user device.

[1622] Specific behavior:

[1623] Input: Consolidated data packets.

[1624] Data processing: Convert the data packet into JSON format and send it to the user device through the API.

[1625] Output: Data packets received by the user device.

[1626] Step 8:

[1627] The terminal (user's smartphone) displays the location and availability of coin lockers based on the received data.

[1628] Specific behavior:

[1629] Input: Data packet sent by the server.

[1630] Data processing: Analyzes data packets and displays the location and availability of coin lockers on a map.

[1631] Output: A map that the user can view and a display of locker availability.

[1632] Step 9:

[1633] The terminal will begin providing directions to the locker selected by the user.

[1634] Specific behavior:

[1635] Input: The location of the user's selected locker.

[1636] Data calculation: Launches the map app and calls the navigation function to start route guidance.

[1637] Output: Start of navigation, route guidance to help the user reach their destination.

[1638] (Application example 1)

[1639] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1640] With conventional management systems for coin lockers and parts shelves, it is difficult to grasp availability in real time, which increases the workload of users and administrators. In addition, it is not possible to predict future availability, making planned use difficult. As a result, issues include a lack of convenience and efficiency.

[1641] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1642] In this invention, the server includes means for collecting the availability status of coin lockers and parts shelves in real time, means for storing the collected data in a central database, means for predicting future availability using an AI algorithm based on past usage data, means for receiving user location information and obtaining availability status around that location, means for providing the obtained availability status and predicted data to a user terminal, and means for displaying the obtained data on the user terminal.This allows users and administrators to grasp availability status in real time and, by predicting future status, enables planned and efficient use.

[1643] A "coin locker" is a storage device used by users to temporarily store their luggage.

[1644] "Availability" is information that indicates whether storage facilities such as coin lockers and parts shelves are currently available.

[1645] "Real-time" collection means that data is collected immediately, on the spot, without delay.

[1646] A "server" is a central processing unit that collects, stores, and analyzes data.

[1647] A "central database" is a database system in which collected data is centrally stored and managed.

[1648] An "AI algorithm" is a computational procedure or model used to perform data analysis and predictions using artificial intelligence technology.

[1649] "User terminal" means a device (smartphone, tablet, etc.) used by a user to receive and display information.

[1650] "Location information" is data that indicates the geographic location of a user or object.

[1651] A "parts shelf" is a shelf used to store and manage parts in a factory or warehouse.

[1652] "Predicting future availability" means estimating future availability based on analysis of past data.

[1653] "Means for collecting" refers to methods and devices for obtaining data in real time.

[1654] "Storage means" refers to the method or device used to store collected data in a central database.

[1655] A "means for predicting" is a method or device for estimating future states using an AI algorithm.

[1656] The "means for providing" refers to a method or device for transmitting the acquired data or predictions to a user terminal.

[1657] The "display means" refers to a method or device for visually displaying the acquired data or predictions on a user terminal.

[1658] To realize this invention, it is necessary to build a system that collects real-time information on the availability of coin lockers and parts shelves, and uses AI algorithms based on past data to predict future availability. Details of the system and specific implementation methods are described below.

[1659] System Components

[1660] server

[1661] The server is the main data processing device of this system. Specifically, it has the following functions:

[1662] Store the collected data in a central database: The availability status is obtained in real time from IoT sensors installed in each coin locker and parts shelf and stored in a central database.

[1663] Predict future availability using AI algorithms based on past data: Analyze collected data and past usage data using AI algorithms (e.g., machine learning models) to predict future availability.

[1664] Device (user's smartphone)

[1665] Users access the system using a smartphone, which has the following features:

[1666] Check current and predicted availability: The system uses the user's location information to obtain the availability of the nearest coin lockers and parts shelves, and displays it in real time. It also allows users to check the predicted availability in the future.

[1667] IoT Sensors

[1668] IoT sensors are installed in each coin locker and parts shelf to detect vacancy in real time. These sensors have the following functions:

[1669] Collect availability information in real time and send it to the server: The usage status of coin lockers and parts shelves is collected in real time via sensors and sent to the server.

[1670] Specific operation of the system

[1671] Data processing by the server

[1672] The server collects availability data from each IoT sensor, stores the collected data in a central database, and then runs an AI algorithm (e.g., a machine learning model) based on the past data to predict future availability.

[1673] Displaying data on a terminal

[1674] Users can use the app on their smartphone to check current and predicted future availability, and information on the nearest coin lockers and parts shelves based on the user's location is also retrieved and displayed.

[1675] Hardware and software used

[1676] Hardware: IoT sensors, servers, user devices (smartphones)

[1677] Software: Python, Requests library, central database, AI models (e.g., machine learning algorithms)

[1678] As a concrete example, the following describes how to build a system that predicts the availability of parts shelves.

[1679] Examples:

[1680] IoT sensors installed on each parts shelf in the factory collect real-time data, and AI is used to predict future availability based on past data. Users (factory workers) can use their smartphones to check the current availability of parts shelves through the app, allowing them to handle parts efficiently.

[1681] Example prompt sentence:

[1682] "Create a Python program that uses IoT sensors installed on each parts shelf in the factory to obtain real-time data, and uses AI to predict future availability based on past data. The program must have the ability to send the availability status of parts shelves to a server and display the prediction results."

[1683] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1684] Step 1:

[1685] The server collects real-time availability data from IoT sensors installed in each coin locker and parts shelf. This data includes the current usage status of each storage space (vacant or occupied), providing the latest usage information.

[1686] Step 2:

[1687] The server stores the collected data in a central database. The input data is real-time data sent from the IoT sensors, and the output is the latest data stored in the central database. This process allows for centralized management of the usage status of all coin lockers and parts shelves.

[1688] Step 3:

[1689] The server runs an AI algorithm based on past usage data stored in a central database to predict future availability. The input is past usage data, and the output is predicted future availability data. Specifically, it uses a machine learning model to analyze patterns and estimate future usage trends.

[1690] Step 4:

[1691] The terminal (user's smartphone) acquires the user's current location. Based on this location information, it requests the server for information on the availability of coin lockers and parts shelves. The input is the user's location information, and the output is a request to the server.

[1692] Step 5:

[1693] The server receives the user's location information and retrieves the availability of coin lockers and parts shelves around that location. The input is the user's location information, and the output is availability data for the area. This provides the user with information on the nearest available storage space.

[1694] Step 6:

[1695] The server then sends the acquired availability and forecast data to the user's device. The input is availability data and forecast data for the relevant area, and the output is data sent to the user's device, allowing the user to check the current situation and future forecasts.

[1696] Step 7:

[1697] The terminal (user's smartphone) displays the acquired data. The input is availability data and forecast data sent from the server, and the output is information displayed to the user. Specifically, availability is visually displayed in a map app or list format. Using this information, the user can select the appropriate coin locker or parts shelf.

[1698] This series of processes allows users to grasp availability in real time and use coin lockers and parts shelves efficiently.

[1699] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1700] This invention is a system that utilizes IoT and AI technologies and an emotion recognition engine to collect information on coin locker availability in real time, predict future availability based on past usage data, and recognize users' emotions to provide an optimal locker usage experience.The system consists of a server, a terminal (such as a user's smartphone), IoT sensors, and an emotion recognition engine.

[1701] System Components

[1702] 1. Server

[1703] The server plays a central role in this system. It collects availability data sent in real time from IoT sensors installed in coin lockers and stores it in a central database. It also uses AI algorithms to analyze past usage data and predict future availability. It also receives user emotional data and adjusts the locker and guidance method based on the user's emotional state.

[1704] 2. Device (user's smartphone)

[1705] Users can check current and predicted availability using a dedicated app downloaded to their smartphone. The app acquires the user's current location and requests information about the nearest coin locker based on that location from the server. The app also has an emotion engine that analyzes the user's facial expressions and voice to detect the user's emotional state.

[1706] 3. IoT Sensors

[1707] Each coin locker is equipped with an IoT sensor that detects the availability of the locker in real time and sends that information to a server.

[1708] 4. Emotion Engine

[1709] The emotion engine is a software component that recognizes emotions from the user's facial expressions and voice. The emotion engine collects emotional data from the user via the smartphone camera and microphone and sends the analysis results to the server. This allows the system to determine the user's stress level and satisfaction level, and then recommend the most suitable locker and select the appropriate guidance method.

[1710] System Operation

[1711] The specific operation of the system will be described below.

[1712] Collection and storage of availability

[1713] The server periodically obtains the availability status from IoT sensors installed in each coin locker. The sensors detect whether there is any luggage in the locker. This data is stored in a central database, which keeps the latest status of each locker.

[1714] Availability analysis

[1715] The server analyzes the latest availability data stored in a central database to determine which lockers are currently available.

[1716] Prediction by AI algorithm

[1717] The server runs an AI algorithm based on past usage data to predict future availability. For example, it learns patterns of locker usage on specific days of the week or at specific times of the day, and uses that to estimate future availability.

[1718] User request processing

[1719] The user launches the smartphone app and inputs their current location. Based on that information and emotion data, the app sends a request to the server.

[1720] Data provision

[1721] The server receives the user's location information and emotion data, retrieves real-time availability and forecast data for coin lockers in the vicinity of the user's location from a database, and compiles this information into a single packet that is sent to the user's smartphone.

[1722] Data display and guidance

[1723] The device then displays the location of the coin lockers on a map based on the received data. The user can then select the most suitable locker based on that information and begin navigation to their destination. Furthermore, the guidance method and information display are dynamically adjusted based on the emotion data.

[1724] Specific examples

[1725] Searching for a coin locker at Tokyo Station

[1726] User: A traveler arrives at Tokyo Station and launches the smartphone app.

[1727] Device (smartphone): The app will display the home screen and select "Coin Locker Search." A pop-up will appear requesting permission to obtain location information and collect emotional data.

[1728] User: Allow location and emotion data collection.

[1729] Device: Sends the acquired current location information and emotion data to the server.

[1730] Server: Based on the user's location and emotion data, it queries the central database to obtain the availability of coin lockers around Tokyo Station.

[1731] Server: Analyzes the acquired data, generates a list of optimal lockers, and selects a guidance method based on the emotional data.

[1732] Server: Returns the generated list to the device via API.

[1733] Terminal: Display a list of lockers and a map. Based on emotion data, provide specific guidance to the user. The user selects the appropriate locker and taps the "Start Navigation" button.

[1734] Device: Launch the map app and begin navigating to the selected locker.

[1735] The present invention allows users to quickly find the coin locker that best suits their emotional state at the time, thereby increasing convenience and satisfaction.

[1736] The processing flow will be explained below.

[1737] Step 1:

[1738] The server periodically collects real-time availability data from IoT sensors installed in the coin lockers. For example, every minute, the sensor detects whether the locker is in use or vacant, and sends that data to the server.

[1739] Step 2:

[1740] The server stores the received availability data in a central database, which stores the latest status of each locker.

[1741] Step 3:

[1742] The server analyzes the availability data stored in the database to determine which lockers are currently available and creates a list of available lockers.

[1743] Step 4:

[1744] The server runs an AI algorithm based on past usage data to predict future availability. It learns patterns of locker usage on specific days and times, and uses that to estimate future availability.

[1745] Step 5:

[1746] The user launches the smartphone app and allows it to use location and emotional data, which then uses the camera and microphone to capture their current facial expressions and voice.

[1747] Step 6:

[1748] The device (smartphone) sends the acquired location information and emotion data to the server, requesting the availability of coin lockers near the user's current location and appropriate guidance.

[1749] Step 7:

[1750] The server receives the user's location information and emotion data, and retrieves real-time availability and forecast data for coin lockers around that location from the database.

[1751] Step 8:

[1752] The server analyzes the emotional data and, if it determines that the user is feeling stressed, it prioritizes the nearest available locker. The server then sends the list of created lockers to the device via API.

[1753] Step 9:

[1754] The terminal (smartphone) displays the received list of available lockers on the user interface, showing the locker location, availability, size, and fee information on a map, and adjusting the guidance method (e.g., whether to provide audio guidance or highlighting method) based on the user's emotional state.

[1755] Step 10:

[1756] The user selects the most suitable coin locker based on the displayed information and taps the "Start Navigation" button. The system then follows the guidance method based on the emotion data.

[1757] Step 11:

[1758] The device (smartphone) will begin navigation to the selected locker, launching a map app and guiding the user to the specified locker.

[1759] Step 12:

[1760] The user follows the navigation to reach the locker and begins using it. The data at the time of use is again sent to the server via the IoT sensor, and the database is updated.

[1761] Example 2

[1762] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1763] It is difficult to grasp the availability of coin lockers in real time and provide this information to users promptly. In addition, there is a lack of means to predict locker usage patterns, making it impossible to secure the optimal locker when a user needs it. Furthermore, services do not take into account the emotional state of the user, making it a challenge to improve user satisfaction.

[1764] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1765] In this invention, the server includes means for collecting coin locker availability information in real time, means for storing the collected data in a central database, means for predicting future availability using an artificial intelligence algorithm based on past usage data, means for receiving user location information and obtaining information on available coin lockers in the vicinity of that location, means for receiving and analyzing the user's emotional data, means for providing the obtained availability information and predicted data to the user's terminal, means for displaying the obtained data on the user's terminal, and means for adjusting the guidance content based on the user's emotional data. This allows users to quickly and accurately grasp the available coin locker status and select the optimal locker based on predicted usage patterns. Furthermore, providing services tailored to the user's emotional state can improve user satisfaction.

[1766] "Means for collecting vacancy status in real time" refers to a device or method that instantly detects the current vacancy status of lockers and collects data through sensors installed in coin lockers.

[1767] "Central database" refers to a database system for centrally storing and managing all collected locker status data.

[1768] "Artificial intelligence algorithm" refers to a computational method using machine learning or other AI techniques that is used to learn patterns based on past data and predict future availability.

[1769] "Means of receiving the user's location information and obtaining the availability of coin lockers in the vicinity of that location" refers to a function that obtains the user's current location using GPS or other means, and uses that information to search for and obtain the availability of nearby lockers.

[1770] "Means for receiving and analyzing user emotional data" refers to a device or method that senses the user's facial expressions and voice, analyzes their emotional state, and digitizes it.

[1771] "Means for providing acquired availability and forecast data to a user terminal" refers to a communication means for transmitting real-time availability data and forecast data from a server to a user terminal.

[1772] "Means for displaying acquired data on the user's device" refers to an interface that visually displays availability and forecast data collected on the user's device.

[1773] "Means for adjusting guidance content based on the user's emotional data" refers to a system that optimally adjusts the guidance method and message content for the user based on analyzed emotional data.

[1774] This invention is a system that collects information on coin locker availability in real time, predicts future availability based on past usage data, and recognizes user emotions to provide an optimal locker usage experience. This system consists of a server, a terminal (user terminal), IoT sensors, and an emotion engine.

[1775] server

[1776] The server plays a central role in this system and has the following functions:

[1777] 1. Data collection from IoT sensors: The server collects vacancy data in real time from IoT sensors installed in each coin locker. This allows the server to grasp the immediate vacancy status of each locker. For example, Microsoft's Azure IoT Hub can be used.

[1778] 2. Database storage: The collected data is stored in a central database, which stores the latest status information for each locker. For example, a database service such as MySQL is used.

[1779] 3. Data Analysis and Prediction: The server analyzes the data stored in the central database to determine which lockers are available. It also runs AI algorithms based on past usage data to predict future availability. This is done using machine learning frameworks (e.g., TensorFlow or PyTorch).

[1780] 4. User information processing: Receives the user's location information and emotion data, retrieves real-time availability and forecast data for coin lockers around the location from the database, and sends the retrieved information to the user's device.

[1781] User terminal

[1782] The user device (smartphone) connects to the system using a dedicated application. The main functions of the application are as follows:

[1783] 1. Location information acquisition: Obtain the user's current location and send that information to the server. Obtain location information using the GPS module.

[1784] 2. Collecting and analyzing emotional data: The user's facial expressions and voice are analyzed using an emotion engine (for example, Microsoft Azure Cognitive Services' Face API or Speech API), and the emotional data is sent to the server.

[1785] 3. Data display: Display the availability and forecast data of coin lockers received from the server. For example, use the Google Maps API to display the location and availability of lockers on a map.

[1786] 4. Guidance: Provides optimal guidance based on the user's emotional data, including navigation to a specific locker and the ability to change the message content.

[1787] Specific examples

[1788] Searching for a coin locker at Tokyo Station

[1789] User: A traveler arrives at Tokyo Station and launches the smartphone app.

[1790] Device: The app will display the home screen and select "Coin Locker Search." A pop-up will appear requesting permission to obtain location information and collect emotional data.

[1791] User: Allow location and emotion data collection.

[1792] Device: Sends the acquired current location information and emotion data to the server.

[1793] Server: Based on the user's location and emotion data, it queries the central database to obtain the availability of coin lockers around Tokyo Station.

[1794] Server: Analyzes the acquired data, generates a list of optimal lockers, and selects a guidance method based on the emotional data.

[1795] Server: Returns the generated list to the device via API.

[1796] Terminal: Display a list of lockers and a map. Based on emotion data, provide specific guidance to the user. The user selects the appropriate locker and taps the "Start Navigation" button.

[1797] Device: Launch the map app and begin navigating to the selected locker.

[1798] Prompt Sentence Examples

[1799] 1. Sample Prompt 1: "Describe an assistance system that helps travelers find the best coin locker when they're in a hurry."

[1800] 2. Example prompt 2: "Please explain the flow of a system that uses IoT sensors and emotion analysis to grasp the availability of coin lockers at stations in real time and provide the optimal guidance method based on the user's emotion."

[1801] This system allows users to check the availability of coin lockers in real time and receive optimal guidance based on their emotional state, thereby improving convenience and satisfaction.

[1802] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1803] System program processing flow

[1804] Step 1: Collect availability data

[1805] The server collects availability data from IoT sensors installed in each coin locker.

[1806] Input: Real-time availability data (e.g., open, closed) from each IoT sensor

[1807] Processing: The server receives data sent from the IoT sensors and obtains the status of each locker. This involves sending data acquisition requests through the sensor API and receiving responses.

[1808] Output: Availability data list (status information for each locker)

[1809] Specific operation: The server sends a request to the sensor API every 15 minutes to obtain the availability status of the lockers. After collecting data from all the lockers, it proceeds to the next step all at once.

[1810] Step 2: Store in the database

[1811] The server stores the collected data in a central database.

[1812] Input: Availability data list

[1813] Processing: The server saves the retrieved data to a database, which involves opening a database connection and executing an SQL insert query.

[1814] Output: Data stored in a central database

[1815] What happens: The server executes an insert query on the database to store each locker's status information in a table. For example, it executes a query like "INSERT INTO locker_status (locker_id, status, timestamp) VALUES (...)".

[1816] Step 3: Analyze availability data

[1817] The server analyzes data stored in a central database to ascertain the latest availability.

[1818] Input: Data from the central database

[1819] Processing: The server queries the latest availability data from the database and lists the real-time availability. This involves using a SELECT query to get the latest data and then parsing it.

[1820] Output: Latest availability list

[1821] Specific operation: The server executes a query to the database such as "SELECT locker_id, status FROM locker_status WHERE timestamp = (SELECT MAX(timestamp) FROM locker_status)" to get the latest availability status.

[1822] Step 4: Future predictions using AI algorithms

[1823] The server runs an AI algorithm based on past usage data to predict future availability.

[1824] Input: Historical usage data

[1825] Processing: The server inputs historical usage data into the machine learning model to obtain predictions of future availability. This includes loading the prediction model, normalizing the data, and analyzing the predictions.

[1826] Output: Future availability forecast data

[1827] What it does: The server uses the TensorFlow model to predict future availability based on past usage data, for example by using a function like "model.predict(past_usage_data)".

[1828] Step 5: Receiving a request from the user

[1829] Users launch the smartphone app, enter their current location information, and request information about available coin lockers.

[1830] Input: User's current location

[1831] Processing: The user device acquires GPS data and sends the location information to the server. This includes using the location service API to acquire the current location and sending it to the server in JSON format.

[1832] Output: Location request sent to the server

[1833] What it does: When a user opens the app and presses the "Get current location" button, the app uses GPS to get the current location and sends that information to the server, for example by using a function like "current_location = gps.get_current_location()".

[1834] Step 6: Analyze user sentiment data

[1835] The device analyzes the user's facial expressions and voice using an emotion engine and sends the emotion data to the server.

[1836] Input: User's facial expression data, voice data

[1837] Processing: The emotion engine (API) analyzes facial expressions and voice to detect emotional states. This involves collecting data through the camera and microphone and sending it to the analysis engine.

[1838] Output: Emotion data

[1839] Specific operation: The app captures the user's facial expression with the camera, sends it to the API, receives the analysis results, and sends them to the server. For example, execute a function like "emotion_data = emotion_api.analyze(face_image)".

[1840] Step 7: Generate optimal locker information

[1841] The server selects the most suitable locker based on the user's location information, emotional data, and real-time locker availability information.

[1842] Input: Location, emotion data, real-time availability

[1843] Processing: The server combines this data and uses algorithms to select the best locker, including prioritizing based on location and emotion data, and selecting the most appropriate locker based on availability data.

[1844] Output: Optimal locker list

[1845] What it does: The server generates a list of lockers that are closest to the user and that correspond to their emotions based on their location and emotion data. For example, it executes a function like "optimal_lockers = find_optimal_lockers(location, emotion_data, availability_data)".

[1846] Step 8: Provide information to your smartphone

[1847] The terminal receives the information returned from the server and displays it to the user.

[1848] Input: Optimal locker list from server

[1849] Processing: Analyzing the received data and displaying it through the user interface. This includes receiving the data, analyzing it, and displaying it on a map.

[1850] Output: Best locker information provided to user

[1851] What it does: It displays recommended lockers on a map on the user's phone, for example, by mapping the locker locations using the Google Maps API and using a function like "display_lockers_on_map(optimal_lockers)".

[1852] Step 9: Navigation

[1853] The terminal will begin navigating to the locker selected by the user.

[1854] Input: Location of the locker selected by the user

[1855] Processing: Calculate the route to the selected locker and initiate navigation, including using a route calculation algorithm and providing audio and visual guidance to the user.

[1856] Output: Navigation instructions

[1857] Specific behavior: The map app will launch and guide the user to the selected locker. For example, it will execute a function such as "start_navigation(destination_location)".

[1858] Prompt Sentence Examples

[1859] 1. Sample prompt 1: "Describe an assistance system that helps travelers find the best coin locker when they're in a hurry."

[1860] 2. Example prompt 2: "Please explain the flow of a system that uses IoT sensors and emotion analysis to grasp the availability of coin lockers at stations in real time and provide the optimal guidance method based on the user's emotion."

[1861] (Application example 2)

[1862] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1863] Conventional coin locker systems were unable to adequately grasp availability or predict future availability, making it difficult for users to find available lockers during busy times. Furthermore, they did not take into account the user's emotional state, and were unable to suggest the most suitable locker or guide users to shopping areas. This resulted in problems that reduced user convenience and satisfaction.

[1864] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting coin locker availability information in real time, means for storing the collected data in a central database, means for predicting future availability using an AI algorithm based on past usage data, means for receiving user location information and obtaining availability information for coin lockers around that location, means for analyzing emotional data, means for suggesting the most suitable coin locker or shopping space based on the analyzed emotional data, and means for displaying the obtained data on the user terminal. This enables the user to quickly find the most suitable coin locker or shopping space based on their emotional state.

[1865] A "coin locker" is a storage device installed in public places and commercial facilities, etc., and used for short-term storage of luggage.

[1866] "Availability" is information indicating whether or not there are any coin lockers available and how many there are.

[1867] "Collecting in real time" means obtaining the current status and information immediately and updating it without delay.

[1868] A "central database" is a data management system that centrally manages multiple data sets and stores, searches, and analyzes the necessary information.

[1869] An "AI algorithm" is a computational method or procedure that uses artificial intelligence technology to analyze data and make predictions.

[1870] "Means for predicting future availability" refers to technology that estimates the future availability of coin lockers based on past data and current conditions.

[1871] "User location information" is data obtained from a smartphone, GPS device, etc. that indicates the user's current geographic location.

[1872] "Emotional data" is information that indicates the emotional state of the user analyzed from facial expressions, voice, etc.

[1873] "Analyzing" means examining data in detail to understand its structure and meaning.

[1874] "Means to suggest the most suitable coin lockers and shopping spaces" refers to technology that guides users to the most appropriate lockers and stores based on their situation and needs.

[1875] A "user terminal" is a device, such as a smartphone or tablet, that a user uses to check and input information.

[1876] This invention relates to a system that provides users with optimal coin lockers and shopping spaces in shopping centers and public places. This system collects availability information in real time, analyzes past data to predict future availability, and analyzes user sentiment to make optimal suggestions.

[1877] System Components

[1878] 1. Server

[1879] The server plays a central role in this system. Specifically, it performs the following processes:

[1880] 1. Collect real-time availability data from IoT sensors installed in each coin locker and store and store it in a central database.

[1881] 2. Run an AI algorithm based on past usage data to predict future availability.

[1882] 3. Recommend optimal lockers and shopping spaces based on location and emotion data received from the user's device.

[1883] 2. Device (user's smartphone)

[1884] Users can access the system using their smartphones and enjoy the following features:

[1885] 1. Obtaining location information and sending it to the server.

[1886] 2. Emotional data is collected using the smartphone camera and microphone, and the analysis results are sent to the server.

[1887] 3. Receive availability and future forecast data provided by the server, as well as suggestions for optimal lockers and shopping spaces.

[1888] 4. Display the locations of coin lockers and stores on a map and provide navigation.

[1889] 3. IoT Sensors

[1890] IoT sensors installed in each coin locker and store detect current availability in real time and send that information to a server.

[1891] 4. Emotion Engine

[1892] The emotion engine is a component that recognizes the user's emotions from their facial expressions and voice, determining their stress level and satisfaction level and making optimal suggestions.

[1893] System Operation

[1894] Collection and storage

[1895] The server periodically collects occupancy data from IoT sensors installed in each coin locker and store, and stores store this data in a central database, where it is then analyzed using AI algorithms.

[1896] Availability forecast

[1897] The server runs an AI algorithm based on past data to predict future availability, making it possible to know in advance which times and days of the week lockers and stores will be crowded.

[1898] User request processing

[1899] Users use a smartphone app to send location and emotional data to the server, which then understands the user's current location and emotional state and makes the most appropriate suggestions.

[1900] Sentiment Analysis and Recommendations

[1901] The server uses an emotion engine to analyze the user's emotions, and based on the results, combines them with availability and future prediction data to suggest the most suitable locker or shopping space.

[1902] Specific examples

[1903] Shopping mall usage scenario

[1904] When a user enters a shopping mall, the smartphone app automatically launches. The app sends location and emotion data to a server, which then suggests the most suitable store or locker based on that information. The user can then view the suggested store or locker location on a map through the app and begin navigation.

[1905] Prompt Sentence Examples

[1906] I'm interested in developing a smart tourist guide system for shopping malls. This system will recognize the user's current location and emotions in real time and recommend the most suitable stores and tourist attractions. EmotionRecognizer will be used to recognize user emotions, and congestion data will be obtained via an API. Can you give me a concrete code example?

[1907] This allows users to quickly find the most suitable coin locker or shopping space based on their feelings and preferences, improving their shopping experience.

[1908] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1909] Step 1:

[1910] The server collects real-time availability data from IoT sensors installed in each coin locker and in the store. Specifically, the IoT sensors detect the current availability and send that information to the server. The server receives this data and stores it in a central database.

[1911] Input: Availability data sent from IoT sensors

[1912] Data processing: Parse availability data and convert it into a structured data format

[1913] Output: Availability data stored in a central database

[1914] Step 2:

[1915] The server runs an AI algorithm based on past usage data to predict future availability. Specifically, it uses AI technology to analyze data and predict future usage patterns based on specific days of the week and times of day.

[1916] Input: Historical usage data stored in a central database

[1917] Data calculations: Predicting future availability using AI algorithms

[1918] Output: Future availability forecast data

[1919] Step 3:

[1920] The user device sends its current location information to the server via a smartphone app. Specifically, the smartphone's GPS function is used to obtain location information and then the data is sent to the server.

[1921] Input: Current location information obtained from your smartphone

[1922] Data processing: Convert location information into a format to send to the server

[1923] Output: Location information sent to the server

[1924] Step 4:

[1925] The user device collects emotional data using the smartphone camera and microphone, and then uses the emotion engine to analyze the results and send them to the server. Specifically, the emotion engine analyzes the user's facial expressions and voice, and sends the resulting emotional state data to the server.

[1926] Input: Facial expression and voice data acquired from a smartphone camera and microphone

[1927] Data calculation: Emotion data analysis using emotion engine

[1928] Output: Emotional state data sent to the server

[1929] Step 5:

[1930] The server retrieves real-time availability and forecast data from a central database based on the received location information and emotion data, and then proposes the most suitable coin lockers and shopping spaces. Specifically, it selects the lockers and stores that best match the location information and emotion state, and generates a list of them.

[1931] Input: Location, emotional state data, availability data and forecast data from a central database

[1932] Data calculation: Query the database based on location and emotional state to generate optimal suggestions

[1933] Output: A list of the best lockers and stores

[1934] Step 6:

[1935] The user device displays the list of optimal lockers and shopping spaces received from the server to the user. Specifically, it displays the locations of the suggested lockers and stores on a map and provides navigation.

[1936] Input: A list of the best lockers and stores sent from the server

[1937] Data processing: Converting data into a format for display on a map

[1938] Output: Locker and store locations and navigation information displayed on the user's device

[1939] As described above, each processing step works in conjunction with the other steps, allowing users to quickly find the most suitable coin locker or shopping space based on their emotional state at the time.

[1940] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1941] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1942] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1943] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1944] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1945] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1946] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1947] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1948] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1949] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1950] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1951] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1952] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1953] 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.

[1954] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1955] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1956] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1957] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1958] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1959] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1960] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1961] The following is further disclosed regarding the above embodiment.

[1962] (Claim 1)

[1963] A means of collecting information on coin locker availability in real time,

[1964] means for storing the collected data in a central database;

[1965] A method to predict future availability using AI algorithms based on past usage data,

[1966] A means for receiving location information of a user and obtaining information on available coin lockers around that location;

[1967] a means for providing the acquired availability and forecast data to a user terminal;

[1968] a means for displaying the acquired data on a user terminal;

[1969] A system including:

[1970] (Claim 2)

[1971] 10. The system of claim 1, further comprising means for analyzing data stored in the central database to determine the current availability of each coin locker.

[1972] (Claim 3)

[1973] The system of claim 1, further comprising means for displaying the location of coin lockers on a map on the user terminal and means for providing navigation to the optimal locker.

[1974] "Example 1"

[1975] (Claim 1)

[1976] A means of collecting information on coin locker availability in real time,

[1977] means for storing the collected data in a central database;

[1978] A means of predicting future availability using machine learning algorithms based on past usage data;

[1979] A means for receiving location information of a user and obtaining information on available coin lockers around that location;

[1980] means for providing the obtained availability and forecast data to a user device;

[1981] means for displaying the acquired data on a user device;

[1982] A means to analyze coin locker availability and integrate real-time and forecast data,

[1983] means for filtering data based on a request sent from a user device and providing information on the most suitable locker;

[1984] A system including:

[1985] (Claim 2)

[1986] 10. The system of claim 1, further comprising means for analyzing data stored in the central database to determine the current availability of each coin locker.

[1987] (Claim 3)

[1988] 10. The system of claim 1, further comprising means for displaying the locations of coin lockers on a map on the user device and means for providing route guidance to the optimal locker.

[1989] "Application Example 1"

[1990] (Claim 1)

[1991] A means of collecting information on coin locker availability in real time,

[1992] means for storing the collected data in a central database;

[1993] A method to predict future availability using AI algorithms based on past usage data,

[1994] A means for receiving location information of a user and obtaining information on available coin lockers around that location;

[1995] a means for providing the acquired availability and forecast data to a user terminal;

[1996] a means for displaying the acquired data on a user terminal;

[1997] A means for collecting and transmitting the availability status of parts shelves in real time;

[1998] A method to predict future availability using AI algorithms based on past parts shelf data,

[1999] A system including:

[2000] (Claim 2)

[2001] 10. The system of claim 1, further comprising means for analyzing data stored in the central database to determine the current availability of each coin locker.

[2002] (Claim 3)

[2003] The system of claim 1, further comprising means for displaying the location of coin lockers on a map on the user terminal and means for providing navigation to the optimal locker.

[2004] "Example 2: Combining Emotion Engines"

[2005] (Claim 1)

[2006] A means of collecting information on coin locker availability in real time,

[2007] means for storing the collected data in a central database;

[2008] A means of predicting future availability using artificial intelligence algorithms based on past usage data;

[2009] A means for receiving location information of a user and obtaining information on available coin lockers around that location;

[2010] means for receiving and analyzing user emotion data;

[2011] a means for providing the acquired availability and forecast data to a user terminal;

[2012] a means for displaying the acquired data on a user terminal;

[2013] A means for adjusting the guidance content based on the user's emotion data;

[2014] A system including:

[2015] (Claim 2)

[2016] 10. The system of claim 1, further comprising means for analyzing data stored in the central database to determine the current availability of each coin locker.

[2017] (Claim 3)

[2018] The system of claim 1, further comprising means for displaying the location of coin lockers on a map on the user terminal and means for providing navigation to the optimal locker.

[2019] "Application example 2 when combining emotion engines"

[2020] (Claim 1)

[2021] A means of collecting information on coin locker availability in real time,

[2022] means for storing the collected data in a central database;

[2023] A method to predict future availability using AI algorithms based on past usage data,

[2024] A means for receiving location information of a user and obtaining information on available coin lockers around that location;

[2025] a means for providing the acquired availability and forecast data to a user terminal;

[2026] A means of analyzing user emotions,

[2027] A method to suggest the best coin lockers and shopping spaces based on the analyzed emotional data,

[2028] a means for displaying the acquired data on a user terminal;

[2029] A system including:

[2030] (Claim 2)

[2031] The system of claim 1 further comprising means for analyzing data stored in the central database to determine the current availability of each coin locker, and means for combining emotion data and congestion data to implement optimal recommendations.

[2032] (Claim 3)

[2033] The system according to claim 1, further comprising means for displaying the location of coin lockers on a map on the user terminal and means for providing navigation to the most suitable locker or shopping space. [Explanation of symbols]

[2034] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting information on coin locker availability in real time, means for storing the collected data in a central database; A method to predict future availability using AI algorithms based on past usage data, A means for receiving location information of a user and obtaining information on available coin lockers around that location; a means for providing the acquired availability and forecast data to a user terminal; a means for displaying the acquired data on a user terminal; A system including:

2. 10. The system of claim 1, further comprising means for analyzing data stored in the central database to determine the current availability of each coin locker.

3. The system according to claim 1, further comprising means for displaying the locations of coin lockers on a map on the user terminal and means for providing navigation to the most suitable locker.

Images