System

Abstract

A system is provided.SOLUTION: A system comprising: means for accepting vacancy status confirmation requests from users; means for confirming vacancy statuses of lockers in real time using a generated AI model; means for providing users with information on vacant lockers; means for accepting locker booking requests from users; means for registering booking information in a database; and means for notifying users of booking completion information using the generated AI model.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 society, users often have difficulty finding an available locker when they need one. Particularly in facilities with multiple lockers, users must walk around to check availability, wasting time and effort. There is a need for a system that can eliminate this inconvenience and allow users to efficiently reserve and use lockers. [Means for solving the problem]

[0005] The present invention solves the above problem with a system that includes a means for accepting availability confirmation requests from users, a means for checking locker availability in real time using a generative AI model, a means for providing users with information about available lockers, a means for accepting locker reservation requests from users, a means for registering the reservation information in a database, and a means for notifying users of reservation completion information using a generative AI model. Furthermore, by including a generative AI model that allows users and the system to converse in natural language, users can intuitively and efficiently check locker availability and reserve the locker they need. This significantly improves user convenience.

[0006] "User" means an individual or organization that uses the system to check locker availability and make reservations.

[0007] "Availability Check Request" means a request sent by a user to the system to find out which lockers are currently available.

[0008] A "generative AI model" is an artificial intelligence model that enables natural language dialogue with users and is used to provide locker availability and reservation information.

[0009] "Real-time" refers to a state in which information is processed almost immediately with little delay.

[0010] "Locker availability" refers to information about lockers that are available at a particular time.

[0011] A "reservation request" is a request that a user submits to the system to use a specific locker at a specific date and time.

[0012] The "database" is a data management system for managing and storing locker availability and reservation information.

[0013] "Reservation completion information" is information that notifies the user that the locker reservation has been successfully completed.

[0014] An "interaction module" is a system component that manages interactions with users via a generative AI model, accepting requests and generating responses. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The locker reservation system of the present invention provides a specific means for users to check the availability of lockers and make reservations. The processing of the system program will be explained in natural language below.

[0037] Check availability

[0038] User:

[0039] When a user wants to know which lockers are available, they type "Which lockers are currently available?" into the terminal.

[0040] Device:

[0041] The terminal receives the user's input and sends an availability check request to the server.

[0042] server:

[0043] Based on the received request, the server retrieves the current availability of lockers from the database.

[0044] The acquired availability information is passed to a generative AI model to generate an appropriate response.

[0045] After the generative AI model generates a response saying "The currently available lockers are A and C," it sends it to the terminal.

[0046] Device:

[0047] The terminal displays the response received from the server to the user.

[0048] Examples:

[0049] The user types into the terminal, "Which lockers are currently available?"

[0050] The device sends a request to the server, which queries the database and uses a generative AI model to generate a response such as "The currently available lockers are A and C," which is then sent to the device.

[0051] The terminal displays the response to the user, who then checks whether locker A or C is available.

[0052] Locker reservation

[0053] User:

[0054] When a user wants to use a specific locker, they enter "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[0055] Device:

[0056] The terminal receives user input and sends a reservation request to the server.

[0057] server:

[0058] The server registers the reservation information in the database based on the received request.

[0059] Confirm that the registration was successful and have the generative AI model generate a response indicating that the reservation was completed.

[0060] The generative AI model generates a response saying "Reservation completed" and sends it to the device.

[0061] Device:

[0062] The terminal displays the response received from the server to the user.

[0063] Examples:

[0064] The user enters into the terminal, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow."

[0065] The device sends a request to the server, the server updates the database, uses the generative AI model to generate a response saying "Reservation completed," and sends it to the device.

[0066] The terminal displays the response to the user, and the user confirms that the reservation of Locker A has been completed.

[0067] Interactive processing

[0068] User:

[0069] Enter your questions or requests into the terminal in natural language.

[0070] Device:

[0071] Sends user input to the interaction API.

[0072] server:

[0073] Input received through a dialogue API is passed to a generative AI model to generate an appropriate response.

[0074] The generated response is sent to the device via the interaction API.

[0075] Device:

[0076] The generated response is displayed to the user.

[0077] Examples:

[0078] The user types into the terminal, "Please tell me which lockers are available."

[0079] The server passes the received input to a generative AI model, which generates a response and sends it to the device.

[0080] The terminal displays "Currently available lockers are A and C," and the user confirms.

[0081] In this way, the system of the present invention comprehensively realizes a series of processes to significantly improve user convenience. Users can check availability and make reservations interactively through the generative AI model, allowing for efficient locker usage. This system reduces time wasted and increases user satisfaction.

[0082] The processing flow will be explained below.

[0083] Check availability

[0084] Step 1:

[0085] The user inputs a request for the locker they want to check availability for into the terminal (e.g., "Which lockers are currently available?").

[0086] Step 2:

[0087] The terminal receives the user's input and sends an availability check request to the server.

[0088] Step 3:

[0089] The server passes the request to the generative AI model through the dialogue module.

[0090] Step 4:

[0091] A generative AI model analyzes the request and generates an appropriate SQL query to query the database about locker availability.

[0092] Step 5:

[0093] The server receives the availability results from the database.

[0094] Step 6:

[0095] The availability information obtained by the server is then passed back to the generative AI model, which generates a natural language response.

[0096] Step 7:

[0097] The generative AI model generates the response, "The currently available lockers are A and C."

[0098] Step 8:

[0099] The server sends the generated response to the terminal.

[0100] Step 9:

[0101] The terminal displays the response received from the server to the user.

[0102] Locker reservation

[0103] Step 1:

[0104] When a user wants to use a specific locker, they input "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[0105] Step 2:

[0106] The terminal receives the user's input and sends a reservation request to the server.

[0107] Step 3:

[0108] The server passes the request to the generative AI model through the dialogue module.

[0109] Step 4:

[0110] A generative AI model analyzes the request and generates the appropriate SQL query to register the reservation information in the database.

[0111] Step 5:

[0112] The server receives the reservation registration results from the database.

[0113] Step 6:

[0114] The server confirms that the registration was successful and has the generative AI model generate a response indicating that the reservation is complete.

[0115] Step 7:

[0116] The generative AI model generates the response "Reservation completed."

[0117] Step 8:

[0118] The server sends the generated response to the terminal.

[0119] Step 9:

[0120] The terminal displays the response received from the server to the user.

[0121] Interactive processing

[0122] Step 1:

[0123] The user enters a question or request into the terminal in natural language.

[0124] Step 2:

[0125] The device sends input from the user to the interaction API.

[0126] Step 3:

[0127] The server passes the input received through the dialogue API to the generative AI model.

[0128] Step 4:

[0129] A generative AI model analyzes the input and generates an appropriate response.

[0130] Step 5:

[0131] The server sends the generated response to the terminal via the interaction API.

[0132] Step 6:

[0133] The terminal displays the generated response to the user.

[0134] Example 1

[0135] 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."

[0136] Currently, there is no system that allows users to easily check the availability of lockers and quickly make reservations. As a result, users often waste time checking for available lockers and reserving them, which prevents them from using the lockers efficiently. Another issue is that conventional systems require complex interfaces and operations, which reduces user convenience.

[0137] 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.

[0138] In this invention, the server includes means for accepting a vacancy check request from a user, means for sending the vacancy check request to the server, means for the server to obtain vacancy information from a database, means for passing the obtained vacancy information to a generative AI model to generate an appropriate response, means for sending the generated response from the server to a terminal and for the terminal to display the response, means for accepting a locker reservation request from a user, means for sending the reservation request to the server, means for the server to register the reservation information in the database, and means for generating a reservation completion response using the generative AI model and displaying it on the terminal after the reservation registration is successful. This allows users to easily and quickly check for available lockers and reserve them.

[0139] The "means for accepting a request from a user to check availability" is a function for accepting a request input by a user into a terminal to check the availability of a locker.

[0140] The "means for transmitting an availability check request to a server" is a function for transmitting an availability check request from a terminal to a server.

[0141] The "means by which the server obtains availability information from the database" is a function that allows the server to access the internal database and obtain the current availability status of lockers.

[0142] "Means of passing the acquired availability information to the generative AI model and generating an appropriate response" refers to a function that inputs the locker availability information acquired by the server from the database into the generative AI model and creates a response in a format that is easy for the user to understand.

[0143] "Means for transmitting the generated response from the server to the terminal, and for the terminal to display the response" refers to the function by which the server transmits the response obtained from the generative AI model to the terminal, and the terminal displays the response to the user.

[0144] The "means for accepting a locker reservation request from a user" is a function for accepting a request input by a user into a terminal to reserve a specific locker at a specified time.

[0145] The "means for sending a reservation request to the server" is a function for sending a locker reservation request from the terminal to the server.

[0146] "Means for the server to register reservation information in the database" is a function that allows the server to update the database based on a reservation request and reserve a locker at a specified time.

[0147] "After the reservation is successfully registered, a means for generating a response indicating that the reservation is complete using the generative AI model and displaying it on the terminal" is a function that confirms that the server has successfully registered the reservation information in the database, inputs the result into the generative AI model, generates a response such as "The reservation has been completed," and sends it to the terminal to display to the user.

[0148] The present invention relates to a locker reservation system, which provides a specific means for users to check locker availability and make reservations. This system improves user convenience by providing locker availability and reservation information through natural language dialogue with users. To implement this invention, ordinary computer system hardware and software, such as a server, terminal, generative AI model, and database, are used.

[0149] Check availability

[0150] User:

[0151] When a user wants to know which lockers are available, they type "Which lockers are currently available?" into the terminal.

[0152] Device:

[0153] The terminal receives input from the user and transmits the content of the input to the server as a request to check availability.

[0154] server:

[0155] When the server receives a request to check availability, it accesses an internal database to obtain the current availability of lockers. This database stores the usage status of each locker (available, reserved, or in use).

[0156] The server passes the acquired availability information to a generative AI model, which generates a response in a format that is easy for the user to understand.

[0157] After the generative AI model generates a response such as "The currently available lockers are A and C," the server sends the response to the terminal.

[0158] Device:

[0159] The terminal displays the response received from the server to the user, allowing the user to check which lockers are currently available.

[0160] Examples:

[0161] The user types into the terminal, "Which lockers are currently available?"

[0162] The terminal sends the contents to the server, which then queries the database to obtain availability data.

[0163] The server passes the acquired data to the generative AI model, which generates a response such as "The currently available lockers are A and C."

[0164] The terminal displays the received response to the user, and the user checks whether locker A or C is available.

[0165] Locker reservation

[0166] User:

[0167] When a user wants to reserve a specific locker for a specified time, the user inputs into the terminal, "I would like to reserve locker A from 10:00 to 12:00 tomorrow."

[0168] Device:

[0169] The terminal receives input from the user and transmits the contents to the server as a reservation request.

[0170] server:

[0171] When the server receives the reservation request, it accesses the database and registers the reservation information for locker A at the specified time.

[0172] After confirming that the reservation information has been successfully registered, the server has the generative AI model generate a response saying "The reservation has been completed."

[0173] The server sends the response generated by the generative AI model to the terminal.

[0174] Device:

[0175] The terminal displays the response received from the server to the user, allowing the user to confirm that the reservation has been completed.

[0176] Examples:

[0177] The user enters into the terminal, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow."

[0178] The terminal transmits the contents to the server, and the server updates the database and registers the reservation information.

[0179] The server uses the generative AI model to generate a response saying "Reservation completed" and sends it to the terminal.

[0180] The terminal displays the received response to the user, and the user confirms that the reservation of locker A has been completed.

[0181] Interactive processing

[0182] User:

[0183] Enter your questions or requests into the terminal in natural language.

[0184] Device:

[0185] Sends user input to the interaction API.

[0186] server:

[0187] The server passes the input received through the dialogue API to the generative AI model, which then generates an appropriate response.

[0188] The generated response is sent to the device via the interaction API.

[0189] Device:

[0190] The terminal displays the received response to the user.

[0191] Examples:

[0192] The user types into the terminal, "Please tell me which lockers are available."

[0193] The device sends the content to a dialogue API, and the server generates a response using a generative AI model.

[0194] The generated response is sent to the terminal, which displays "The currently available lockers are A and C."

[0195] The user confirms.

[0196] In this way, the system of the present invention comprehensively realizes a series of processes to significantly improve user convenience. Users can check availability and make reservations interactively through the generative AI model, allowing for efficient locker usage. This system reduces time wasted and increases user satisfaction.

[0197] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0198] Availability Check Processing Steps

[0199] Step 1:

[0200] User: A user who wants to know the availability status enters "Which lockers are currently available?" into the terminal. This generates an availability check request from the user.

[0201] Step 2:

[0202] Terminal: The terminal receives input from the user (a request to check availability) and sends it to the server. Specifically, it structures the request content in an appropriate format and sends it to the server as an HTTP request.

[0203] Input: User typed "Which lockers are currently available?"

[0204] Output: Availability check request to the server

[0205] Step 3:

[0206] Server: The server receives the availability check request and queries the database to get the current availability of the locker. The server retrieves the locker status from the database using an SQL query or similar.

[0207] Input: Availability check request from terminal

[0208] Output: Availability data retrieved from the database

[0209] Step 4:

[0210] Server: The server passes the acquired availability data to the generative AI model, which then analyzes the data and generates a natural language response.

[0211] Input: Availability data retrieved from the database

[0212] Output: The response generated by the generative AI model: "The currently available lockers are A and C."

[0213] Step 5:

[0214] Server: The server sends the generated response to the terminal. Specifically, the server returns the response to the terminal as an HTTP response.

[0215] Input: The response generated by the generative AI model

[0216] Output: Response data to the terminal

[0217] Step 6:

[0218] Terminal: The terminal displays the response received from the server to the user, allowing the user to check which lockers are currently available.

[0219] Input: Response data from the server

[0220] Output: Show available lockers

[0221] Locker reservation process steps

[0222] Step 1:

[0223] User: A user who wants to reserve a specific locker enters into the terminal, "I would like to reserve locker A from 10:00 to 12:00 tomorrow." This generates a locker reservation request from the user.

[0224] Step 2:

[0225] Terminal: The terminal receives input (reservation request) from the user and sends it to the server. Specifically, it structures the request content in an appropriate format and sends it to the server as an HTTP request.

[0226] Input: User input: "I would like to reserve Locker A from 10:00 to 12:00 tomorrow."

[0227] Output: Reservation request to server

[0228] Step 3:

[0229] Server: The server receives the reservation request, accesses the database, and registers the reservation information for Locker A at the specified time. The server inserts the locker reservation information into the database using an SQL query or similar.

[0230] Input: Reservation request from terminal

[0231] Output: Reservation information database registration results

[0232] Step 4:

[0233] Server: The server confirms that the reservation information has been successfully registered in the database and has the generative AI model generate a response saying "Reservation completed." The generative AI model analyzes the data and generates a natural language response.

[0234] Input: Reservation information database registration results

[0235] Output: The response "Reservation completed" generated by the generative AI model

[0236] Step 5:

[0237] Server: The server sends the generated response to the terminal. Specifically, the server returns the response to the terminal as an HTTP response.

[0238] Input: The response generated by the generative AI model

[0239] Output: Response data to the terminal

[0240] Step 6:

[0241] Terminal: The terminal displays the response received from the server to the user, allowing the user to confirm that the reservation for Locker A has been completed.

[0242] Input: Response data from the server

[0243] Output: Reservation completed

[0244] Interaction processing steps

[0245] Step 1:

[0246] User: Enters a question or request into the terminal in natural language, which generates a user request.

[0247] Step 2:

[0248] Terminal: The terminal sends user input to the interaction API. Specifically, it structures the request content in an appropriate format and sends it to the server as an HTTP request.

[0249] Input: Natural language input from the user

[0250] Output: Request to the conversation API

[0251] Step 3:

[0252] Server: The server receives input through the dialogue API and passes it to the generative AI model to generate an appropriate response. The generative AI model analyzes the data and generates a natural language response.

[0253] Input: User input received through the interaction API.

[0254] Output: The response generated by the generative AI model

[0255] Step 4:

[0256] Server: The server sends the generated response to the terminal via the interaction API. Specifically, the server returns the response to the terminal as an HTTP response.

[0257] Input: The response generated by the generative AI model

[0258] Output: Response data to the terminal

[0259] Step 5:

[0260] Terminal: The terminal displays the responses it receives to the user, allowing the user to get answers to their questions and requests.

[0261] Input: Response data from the server

[0262] Output: Displaying the response to the user

[0263] (Application example 1)

[0264] 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."

[0265] Conventional locker reservation systems require users to input text into a terminal when checking availability or making a reservation, which is time-consuming. Furthermore, at logistics centers and other such sites, this can lead to inefficient item management and pickup operations. This can lead to reduced efficiency in inventory management and pickup, and can lead to work delays and errors. Therefore, there is a demand for a more intuitive and efficient locker reservation system.

[0266] 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.

[0267] In this invention, the server includes a means for converting a user's voice input into text, a means for communicating a response generated by the generative AI model to the user by voice and visual display, and a means for checking locker availability in real time. This allows users to intuitively check availability and reserve lockers by voice input, significantly improving the efficiency of inventory management and pickup operations, especially in logistics centers.

[0268] "User" refers to a person who uses the locker reservation system to check locker availability and make a reservation.

[0269] "Availability Check Request" refers to a request sent by a User to the System to find out which lockers are currently available.

[0270] A "generative AI model" is a model that uses artificial intelligence to understand natural language and generate appropriate responses.

[0271] "Locker" means an individual storage unit for the temporary storage of goods or personal items.

[0272] "Real-time" means that information is processed immediately and reflects the current situation.

[0273] "Reservation Request" means a request submitted by a User to the System to use a specific Locker at a specific date and time.

[0274] "Database" refers to the electronic information storage system for recording and managing locker availability and reservation status.

[0275] "Voice input" refers to an input method in which the system recognizes the user's speech and converts it into text.

[0276] "Visual display" refers to the method by which system-generated information is visually conveyed to the user through a screen or display.

[0277] "Voice response" refers to a method of conveying system-generated information to users by voice.

[0278] A system for realizing the present invention has the following configuration.

[0279] The system components include smart glasses, a server, a database, and a generative AI model. The smart glasses receive voice input from the user, convert it into text, and send it to the server. The server then passes the received text data to the generative AI model, which generates an appropriate response. The generated response is then sent back to the smart glasses and communicated to the user via voice and visual feedback.

[0280] Specifically, the following processing is performed.

[0281] 1. Voice input and conversion: The user speaks to the smart glasses, saying, "Which lockers are currently available?" This voice data is converted into text by the smart glasses' voice recognition software (e.g., Google Cloud Speech-to-Text).

[0282] 2. Utilizing text processing and generative AI models: The input, "Which lockers are currently available?", is converted into text and sent to the server. The server passes this text to a generative AI model, which generates an appropriate response. An example of the generative AI model used here is the OpenAI API.

[0283] 3. Obtaining and transmitting a response: The generative AI model generates a response such as "The currently available lockers are A and C," and the server sends this information to the smart glasses. The smart glasses then convey this information to the user visually (display) and via voice output (e.g., voice synthesis using the gTTS library).

[0284] 4. Locker reservation request processing: The user then voice-inputs, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow." This voice is also converted into text by the smart glasses and sent to the server. The server registers the reservation information in the database and uses the generative AI model to generate a response saying "Reservation completed" and sends it to the smart glasses.

[0285] Examples of prompt statements

[0286] Below is an example of a prompt to be passed to the generative AI model.

[0287] Run the availability check module of the locker reservation system. User input: Which lockers are currently available?

[0288] With the above configuration, users can intuitively check locker availability and make reservations by using voice input through the smart glasses. This is expected to significantly improve the efficiency of inventory management and pickup operations, especially at logistics centers.

[0289] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0290] Step 1:

[0291] The user speaks into the smart glasses, asking, "Which lockers are currently available?" The smart glasses collect the user's voice data using a built-in microphone. The collected voice data is converted into text data by the voice recognition software inside the smart glasses. The input is voice data, and the output is text data.

[0292] Step 2:

[0293] The terminal (smart glasses) sends the converted text data "Which lockers are currently available?" to the server. This transmission uses wireless communication technology (e.g., Wi-Fi). The input is text data, and the output is the transmission of the text data to the server.

[0294] Step 3:

[0295] The server passes the received user text data to the generative AI model as input. The server then sends the text data to the global AI model API (e.g., OpenAI API) to generate an appropriate response. The input is the user's text data, "Which lockers are currently available?", and the output from the generative AI model is the response text, "The currently available lockers are A and C."

[0296] Step 4:

[0297] The server transmits the response text data obtained from the generative AI model to the smart glasses. Wireless communication technology is used for transmission, and the data reaches the smart glasses in real time. The input is the response text data from the generative AI model, and the output is the data transmitted to the smart glasses.

[0298] Step 5:

[0299] The device (smart glasses) receives the response text data, "The currently available lockers are A and C," and conveys it to the user visually and audibly. The visual display uses the display, and the audio output uses a speech synthesis library (e.g., gTTS). The input is the response text data, and the output is the visual display and audio output.

[0300] Step 6:

[0301] The user again uses voice input and speaks into the smart glasses, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow." The voice data is again collected and converted into text data through voice recognition software. The input is voice data, and the output is text data.

[0302] Step 7:

[0303] The terminal (smart glasses) sends text data, such as "I would like to reserve Locker A from 10:00 to 12:00 tomorrow," to the server. This transmission also uses wireless communication technology. The input is text data, and the output is the transmission of text data to the server.

[0304] Step 8:

[0305] The server queries the reservation information to register the received reservation request in the database, and if availability is confirmed, adds the reservation information to the database. The input is the reservation request text data, and the output is an update of the database.

[0306] Step 9:

[0307] The server passes the successful reservation information to the generative AI model, which then generates a response text saying "Reservation completed." The generative AI model generates an appropriate response and returns it to the server. The input is the successful reservation information, and the output is the response text data.

[0308] Step 10:

[0309] The server sends the generated response text data "Reservation completed" to the device (smart glasses). The input is the response text data from the generative AI model, and the output is the data sent to the smart glasses.

[0310] Step 11:

[0311] The terminal (smart glasses) conveys the received response text data to the user by visual display and audio output. The visual display uses a display, and the audio output uses a speech synthesis library. The input is the response text data, and the output is the visual display and audio output.

[0312] Through the above processing steps, the user can easily check the availability of lockers and make reservations through voice input.

[0313] 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.

[0314] The locker reservation system of the present invention provides a concrete means for users to check the availability of lockers and make reservations. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide a more personalized service. The program processing of this system is described in detail below in natural language.

[0315] Check availability

[0316] User:

[0317] When a user wants to know which lockers are available, they type "Which lockers are currently available?" into the terminal.

[0318] Device:

[0319] The terminal receives the user's input and sends an availability check request to the server.

[0320] server:

[0321] The server passes the request through the dialogue module to the generative AI model, which analyzes the request and generates an appropriate SQL query to query the database about the availability of the locker.

[0322] server:

[0323] It receives availability results from a database and passes them to a generative AI model to generate natural language responses.

[0324] Generative AI models:

[0325] Generates the response "Currently available lockers are A and C."

[0326] server:

[0327] The generated response is sent to the terminal.

[0328] Device:

[0329] The response received from the server is displayed to the user.

[0330] Examples:

[0331] When a user types "Which lockers are currently available?" into the terminal, the terminal sends a request to the server, which queries the database and uses a generative AI model to generate a response saying "Lockers A and C are currently available," which is sent to the terminal. The terminal displays the response to the user, who can then confirm whether locker A or C is available.

[0332] Locker reservation

[0333] User:

[0334] When a user wants to use a specific locker, they enter "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[0335] Device:

[0336] The terminal receives user input and sends a reservation request to the server.

[0337] server:

[0338] The server passes the request through the dialogue module to the generative AI model, which analyzes the request and generates the appropriate SQL query to register the reservation information in the database.

[0339] server:

[0340] The reservation registration results are received from the database and the generative AI model generates a response indicating the reservation is complete.

[0341] Generative AI models:

[0342] Generates a "Reservation completed" response.

[0343] server:

[0344] The generated response is sent to the terminal.

[0345] Device:

[0346] The response received from the server is displayed to the user.

[0347] Examples:

[0348] When a user enters "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal, the terminal sends a request to the server, the server updates the database, and uses the generative AI model to generate a response saying "Reservation completed" and sends it to the terminal. The terminal displays the response to the user, who confirms that the reservation of Locker A has been completed.

[0349] Emotion Recognition and Dialogue Processing

[0350] User:

[0351] Enter your questions or requests into the terminal in natural language.

[0352] Device:

[0353] Sends user input to the interaction API.

[0354] server:

[0355] Input received through the dialogue API is passed to the generative AI model, which analyzes the input and generates an appropriate response.

[0356] Emotion Engine:

[0357] Analyze emotions from user input and tailor responses based on those emotions.

[0358] Generative AI models:

[0359] Feedback from the emotion engine is used to generate responses with appropriate timing and content.

[0360] server:

[0361] The generated response is sent to the device via the interaction API.

[0362] Device:

[0363] The generated response is displayed to the user.

[0364] Examples:

[0365] When a user types "Please tell me which lockers are available" into the device, the server passes the received input to the generative AI model, and the emotion engine analyzes the user's emotions. Based on feedback from the emotion engine, the generative AI model generates a response saying "Currently available lockers are A and C. Have you found the locker you are looking for?" and sends it to the device. The device displays the response to the user, who confirms it.

[0366] In this way, the system of the present invention comprehensively realizes a series of processes to significantly improve user convenience. Furthermore, by combining it with an emotion engine, interactions with users become more natural and personalized, further increasing user satisfaction.

[0367] The processing flow will be explained below.

[0368] Check availability

[0369] Step 1:

[0370] The user inputs a request for the locker they want to check availability for into the terminal (e.g., "Which lockers are currently available?").

[0371] Step 2:

[0372] The terminal receives the user's input and sends an availability check request to the server.

[0373] Step 3:

[0374] The server passes the request to the generative AI model through the dialogue module.

[0375] Step 4:

[0376] A generative AI model analyzes the request and generates an SQL query to query the database about locker availability.

[0377] Step 5:

[0378] The server retrieves availability information from the database.

[0379] Step 6:

[0380] The server passes the acquired availability information to a generative AI model, which generates a natural language response.

[0381] Step 7:

[0382] The generative AI model generates the response, "The currently available lockers are A and C."

[0383] Step 8:

[0384] The server sends the generated response to the terminal.

[0385] Step 9:

[0386] The terminal displays the response received from the server to the user.

[0387] Locker reservation

[0388] Step 1:

[0389] When a user wants to use a specific locker, they input "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[0390] Step 2:

[0391] The terminal receives the user's input and sends a reservation request to the server.

[0392] Step 3:

[0393] The server passes the request to the generative AI model through the dialogue module.

[0394] Step 4:

[0395] The generative AI model analyzes the request and generates an SQL query to register the reservation information in the database.

[0396] Step 5:

[0397] The server receives the reservation registration results from the database.

[0398] Step 6:

[0399] The server confirms whether the registration was successful and has the generative AI model generate a response indicating that the reservation is complete.

[0400] Step 7:

[0401] The generative AI model generates the response "Reservation completed."

[0402] Step 8:

[0403] The server sends the generated response to the terminal.

[0404] Step 9:

[0405] The terminal displays the response received from the server to the user.

[0406] Emotion Recognition and Dialogue Processing

[0407] Step 1:

[0408] The user types a question or request into the terminal in natural language (e.g., "Please tell me which lockers are available").

[0409] Step 2:

[0410] The device sends input from the user to the interaction API.

[0411] Step 3:

[0412] The server passes the input received through the dialogue API to the generative AI model.

[0413] Step 4:

[0414] The generative AI model analyzes the input and requests emotion analysis from the emotion engine.

[0415] Step 5:

[0416] The emotion engine analyzes emotions from user input and sends the results back to the generative AI model.

[0417] Step 6:

[0418] The generative AI model uses feedback from the emotion engine to generate an appropriate response (e.g., "Currently available lockers are A and C. Did you find the locker you were looking for?").

[0419] Step 7:

[0420] The server sends the generated response to the terminal via the interaction API.

[0421] Step 8:

[0422] The terminal displays the generated response to the user.

[0423] Example 2

[0424] 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."

[0425] The problem that this invention aims to solve is to provide a system that allows users to obtain real-time information when checking locker availability and reserving lockers, and further to make the dialogue with users natural and personalized by using an emotion engine. In conventional systems, checking locker availability and reserving lockers were done manually, which reduced user convenience. Furthermore, because the system was unable to respond in a way that took the user's emotions into consideration, the dialogue was one-sided and user satisfaction was low.

[0426] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting an availability check request from a user, means for checking locker availability in real time using a generative AI model, means for providing information on available lockers to the user, means for accepting a locker reservation request from a user, means for registering the reservation information in a database, means for notifying the user of reservation completion information using a generative AI model, and means for analyzing the user's emotions using an emotion engine and adjusting the response based on the emotions. This not only enables efficient checking of locker availability and reservations, but also makes it possible to have natural and personalized interactions with the user, thereby improving user satisfaction.

[0427] "Availability check request from user" means a request sent by a user to the system to check the availability of a locker.

[0428] A "generative AI model" refers to an artificial intelligence technology that analyzes user input and generates an appropriate response based on the results of that analysis.

[0429] "Means for checking locker availability in real time" refers to methods and technologies for instantly obtaining information about currently available lockers and providing it to users.

[0430] "Means for providing users with information about available lockers" refers to methods or technologies for displaying or notifying users of information about currently available lockers.

[0431] A "locker reservation request from a user" is a request sent to the system by a user wishing to use a specific locker on a specific date and time.

[0432] "Means for registering reservation information in a database" refers to a method or technology for storing reservation-related information in a database based on a reservation request from a user.

[0433] "Means for notifying users of reservation completion information using a generative AI model" refers to methods or technologies for generating a response using a generative AI model to inform users that their reservation has been successfully completed, and for communicating that response to users.

[0434] An "emotion engine" is a technology or module that analyzes emotions from user input and adjusts the system's response based on the analysis results.

[0435] "Means for analyzing user emotions" refers to methods or techniques for identifying emotions from text entered by a user and using the results to help the system generate responses.

[0436] The locker reservation system of the present invention provides a means for users to check locker availability and make reservations. This system combines a generative AI model and an emotion engine to improve user convenience, and is described in detail below.

[0437] System Configuration

[0438] This system includes the following hardware and software:

[0439] Terminal: The device used by the user to input data (smartphone, tablet, PC, etc.)

[0440] Server: A central system that processes input and interfaces with generative AI models and databases

[0441] Database: Data storage for storing locker availability and reservation information

[0442] Generative AI model: AI technology that generates responses through natural language processing

[0443] Emotion Engine: Technology that analyzes user emotions and adjusts responses

[0444] Check availability

[0445] User:

[0446] When a user wants to check the availability of lockers, they type "Which lockers are currently available?" into the terminal.

[0447] Device:

[0448] The terminal receives the user's input and sends an availability check request to the server.

[0449] server:

[0450] The server passes the request through the dialogue module to the generative AI model, which parses the request, generates the appropriate SQL query, and executes it against the database. For example, it generates the query "SELECT FROM lockers WHERE status='empty'".

[0451] Database:

[0452] It receives query results from the database and sends information about available lockers to the server.

[0453] Generative AI models:

[0454] The generative AI model receives the database results from the server and generates a natural language response such as, "The currently available lockers are A and C."

[0455] server:

[0456] The generated response is sent to the terminal.

[0457] Device:

[0458] The terminal displays the response received from the server to the user. Specifically, the terminal screen displays "Currently available lockers are A and C."

[0459] Examples:

[0460] When a user types "Which lockers are currently available?" into the device, the device sends the request to the server. The server executes an SQL query and uses a generative AI model to generate a response, "Lockers A and C are currently available," which is sent to the device. The device then displays the response on its screen for the user to confirm.

[0461] Locker reservation

[0462] User:

[0463] When a user wants to reserve a specific locker, they enter "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[0464] Device:

[0465] The terminal receives the input and sends a reservation request to the server.

[0466] server:

[0467] The server passes the reservation request through the dialogue module to the generative AI model, which parses the request, generates the appropriate SQL query, and executes it against the database. For example, it generates the query "INSERT INTO reservations (locker_id, start_time, end_time) VALUES ('A', '2023-06-01 10:00', '2023-06-01 12:00')".

[0468] Database:

[0469] The database executes queries and registers reservation information.

[0470] Generative AI models:

[0471] The generative AI model receives the registration completion result from the database and generates a natural language response saying, "Your reservation is complete."

[0472] server:

[0473] The generated response is sent to the terminal.

[0474] Device:

[0475] The terminal displays the response received from the server to the user. Specifically, the terminal displays "Reservation completed" on its screen.

[0476] Examples:

[0477] When a user types "I'd like to reserve Locker A from 10:00 to 12:00 tomorrow" into the device, the device sends the request to the server. The server executes an SQL query and uses a generative AI model to generate a response saying "Reservation completed" and sends it to the device. The device then displays the response on its screen for the user to confirm.

[0478] Emotion Recognition and Dialogue Processing

[0479] User:

[0480] Enter your question or request into the terminal in natural language, for example, "Please tell me which lockers are available."

[0481] Device:

[0482] Input from the user is sent to the server via the interaction API.

[0483] server:

[0484] The server passes the request through a dialogue API to the generative AI model, which analyzes the input and uses an emotion engine to analyze the user's emotions.

[0485] Emotion Engine:

[0486] The emotion engine analyzes emotions from user input and feeds the results back to the generative AI model.

[0487] Generative AI models:

[0488] The generative AI model generates a natural language response based on feedback from the emotion engine, for example, "Currently, the available lockers are A and C. Did you find the locker you were looking for?"

[0489] server:

[0490] The generated response is sent to the device via the interaction API.

[0491] Device:

[0492] The terminal displays the response received from the server to the user. Specifically, the terminal screen displays the message "Currently available lockers are A and C. Have you found the locker you are looking for?"

[0493] Examples:

[0494] When a user types "Please tell me which lockers are available" into the device, the server passes the request to the generative AI model, which analyzes the emotion using the emotion engine. The generative AI model generates a response saying "Currently available lockers are A and C. Have you found the locker you are looking for?" and sends it to the device. The device displays the response on the screen for the user to confirm.

[0495] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0496] Availability Check Processing Steps

[0497] Step 1:

[0498] The user inputs information into the terminal to check availability. Specifically, the user inputs "Which lockers are currently available?" into the input field on the terminal. Input: The user's question in natural language.

[0499] Step 2:

[0500] The terminal processes the user's input and sends it to the server as an HTTP request. Input: User's question text. Output: HTTP request to the server.

[0501] Step 3:

[0502] The server passes the received request to the dialogue module and sends the request to the generative AI model. Input: Contents of the HTTP request. Output: Request to the generative AI model.

[0503] Step 4:

[0504] The generative AI model analyzes the user's question and generates an appropriate SQL query. It then returns the query to the server. For example, it generates the query "SELECT FROM lockers WHERE status='empty'". Input: User's question text. Output: SQL query.

[0505] Step 5:

[0506] The server sends the SQL query received from the generative AI model to the database and executes the query. Input: SQL query. Output: Result of executing the database query.

[0507] Step 6:

[0508] The database returns the information of available lockers to the server as a result of the query. For example, information such as "Locker A, Locker C" is returned. Input: Database query. Output: Available locker information.

[0509] Step 7:

[0510] The server passes the information about available lockers received from the database to the generative AI model, which generates a natural language response. Input: Available locker information. Output: Natural language response.

[0511] Step 8:

[0512] The generative AI model generates a natural language response based on the information about available lockers, such as "The currently available lockers are A and C," and returns that response to the server. Input: Available locker information. Output: Response text.

[0513] Step 9:

[0514] The server generates a response and sends it to the device. Input: Response text. Output: HTTP response to the device.

[0515] Step 10:

[0516] The terminal receives the response from the server and displays it to the user. Specifically, it displays "Currently available lockers are A and C" on the terminal screen. Input: HTTP response. Output: Displayed to the user.

[0517] Locker reservation process steps

[0518] Step 1:

[0519] The user inputs information into the terminal to reserve a specific locker. Specifically, the user inputs "I would like to reserve locker A from 10:00 to 12:00 tomorrow." Input: The user's reservation request in natural language.

[0520] Step 2:

[0521] The terminal processes the user's input and sends it to the server as an HTTP request. Input: Reservation request text. Output: HTTP request to the server.

[0522] Step 3:

[0523] The server passes the received request to the dialogue module and sends the request to the generative AI model. Input: Contents of the HTTP request. Output: Request to the generative AI model.

[0524] Step 4:

[0525] The generative AI model analyzes the user's reservation request and generates the appropriate SQL query. It then returns the query to the server. For example, it generates the query "INSERT INTO reservations (locker_id, start_time, end_time) VALUES ('A', '2023-06-01 10:00', '2023-06-01 12:00')". Input: Reservation request text. Output: SQL query.

[0526] Step 5:

[0527] The server sends the SQL query received from the generative AI model to the database and executes the query. Input: SQL query. Output: Result of executing the database query.

[0528] Step 6:

[0529] The database returns the reservation information to the server as a result of the query, e.g., a result of "reservation successful". Input: Database query. Output: Reservation result.

[0530] Step 7:

[0531] The server passes the reservation results received from the database to the generative AI model, which generates a natural language response. Input: Reservation results. Output: Natural language response.

[0532] Step 8:

[0533] The generative AI model generates a natural language response, "Reservation completed," based on the reservation result and returns the response to the server. Input: Reservation result. Output: Response text.

[0534] Step 9:

[0535] The server generates a response and sends it to the device. Input: Response text. Output: HTTP response to the device.

[0536] Step 10:

[0537] The terminal receives the response from the server and displays it to the user. Specifically, it displays "Reservation completed" on the terminal screen. Input: HTTP response. Output: Displayed to the user.

[0538] Processing steps for emotion recognition and dialogue processing

[0539] Step 1:

[0540] The user inputs a question or request into the terminal in natural language. For example, "Please tell me which lockers are available." Input: A user's natural language inquiry.

[0541] Step 2:

[0542] The terminal sends the user's input to the server through the interaction API. Input: Query text. Output: HTTP request to the server.

[0543] Step 3:

[0544] The server sends a request to the generative AI model through the interaction API. Input: HTTP request content. Output: Request to the generative AI model.

[0545] Step 4:

[0546] The generative AI model analyzes the user's input and sends the results to the emotion engine. Input: User's query text. Output: Analyzed data.

[0547] Step 5:

[0548] The emotion engine analyzes emotions from user input and feeds the analysis results back to the generative AI model. For example, it may analyze the input to determine that the emotion is "friendly." Input: Analyzed data. Output: Emotion feedback.

[0549] Step 6:

[0550] The generative AI model uses emotional feedback to generate an appropriate natural language response. For example, it generates a response like, "Currently available lockers are A and C. Did you find the locker you were looking for?" Input: Emotional feedback. Output: Response text.

[0551] Step 7:

[0552] The server sends the generated response to the device via the interaction API. Input: Response text. Output: HTTP response to the device.

[0553] Step 8:

[0554] The terminal receives the response from the server and displays it to the user. Specifically, it displays the message "Currently available lockers are A and C. Have you found the locker you are looking for?" on the terminal screen. Input: HTTP response. Output: Displayed to the user.

[0555] (Application example 2)

[0556] 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."

[0557] In conventional locker reservation systems, when users check availability and make reservations, they are provided with one-sided information that ignores their emotions. This often causes inconvenience and stress for users. In addition, there are problems with the speed and accuracy of real-time availability confirmation and reservation completion notifications, which tends to reduce user satisfaction.

[0558] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0559] In this invention, the server includes means for accepting availability confirmation requests from users, means for checking locker availability in real time using a generative AI model, means for providing users with information on available lockers, means for accepting locker reservation requests from users, means for registering the reservation information in a database, means for notifying users of reservation completion information using a generative AI model, and means for analyzing the user's emotions using an emotion recognition engine and adjusting responses based on the emotions, thereby enabling users to receive more personalized and convenient services.

[0560] "User" refers to an end user who uses the locker reservation system.

[0561] "Availability Check Request" refers to a request made by a User to check information about lockers that are currently available.

[0562] A "generative AI model" refers to an artificial intelligence model that uses technologies such as natural language processing to conduct dialogue and process data within a system.

[0563] An "emotion recognition engine" refers to an engine that analyzes the emotions from the user's input and adjusts the response.

[0564] "Real-time" refers to data processing occurring almost simultaneously with the current time.

[0565] "Locker availability" refers to information indicating whether a locker is currently in use or available.

[0566] "Reservation Request" means a request by a User to make a reservation for use of a particular Locker.

[0567] "Database" refers to structured data storage for managing and storing locker availability and reservation information.

[0568] "Reservation completion information" refers to information that notifies a user that their locker reservation has been successful.

[0569] The present invention provides a concrete example of how to use a locker reservation system. This system allows users to check locker availability, make a reservation, and receive notification of reservation completion, using a generative AI model. It also utilizes an emotion recognition engine to analyze user emotions and tailor responses to provide a more personalized experience for users.

[0570] System configuration

[0571] The system consists of the following main elements:

[0572] 1. Server

[0573] The server accepts requests from users, queries the database, and generates results using a generative AI model.

[0574] 2. Terminal

[0575] The device receives input from the user, sends the request to the server, displays the response, and runs an emotion recognition engine to analyze the user's emotions.

[0576] 3. Database

[0577] The database manages and stores locker availability and reservation information.

[0578] Program processing

[0579] The server includes means for accepting a request from a user to check availability, means for checking locker availability in real time using a generative AI model, means for providing information about available lockers to users, means for accepting a locker reservation request from a user, means for registering the reservation information in a database, means for notifying the user of reservation completion information using a generative AI model, and means for analyzing the user's emotions using an emotion recognition engine and adjusting a response based on the emotions.

[0580] Hardware or software used

[0581] This system uses the following hardware and software:

[0582] Microphone for voice input

[0583] User's device (Android or iOS smartphone)

[0584] Speech-to-text conversion using the speech_recognition library

[0585] Sending API requests to the server using the requests library

[0586] The server-side backend implements the generative AI model and database.

[0587] Specific examples

[0588] When a user speaks to the device, asking, "Which parking spaces are currently available?", the system converts the voice input into text and sends a request to the server. The server queries the database and uses a generative AI model to generate a natural language response. For example, it might respond, "The currently available parking spaces are B2 and D4."

[0589] Example prompt sentence:

[0590] User: What parking spaces are currently available?

[0591] Server: Please wait while we check parking availability...

[0592] Generative AI model: The currently available parking spaces are B2 and D4.

[0593] As described above, this system can provide efficient and personalized services to users.

[0594] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0595] Step 1:

[0596] The user speaks to their smartphone device, "What parking spaces are currently available?" The device converts the voice input into text and generates the user's request. At this time, a speech recognition engine (e.g., Google Speech API) analyzes the voice data and converts it into text data.

[0597] Input: User voice input

[0598] Output: Text data ("What parking spaces are currently available?")

[0599] Step 2:

[0600] The device sends the generated text data to the server, which receives the request and passes it to the generative AI model via the dialogue module. The generative AI model analyzes the request and generates an SQL query to check availability.

[0601] Input: Text data ("What parking spaces are currently available?")

[0602] Output: SQL query

[0603] Step 3:

[0604] The server sends the generated SQL query to the database to inquire about availability, and the database responds by returning the current availability.

[0605] Input: SQL query

[0606] Output: Availability data (e.g. "B2 and D4 are available")

[0607] Step 4:

[0608] The server passes the acquired availability data to the generative AI model, which then generates a response in natural language. The generative AI model uses an emotion recognition engine to analyze the user's emotions and select an appropriate response.

[0609] Input: Availability data ("B2 and D4 are available")

[0610] Output: A natural language response (e.g., "Currently available parking spaces are B2 and D4")

[0611] Step 5:

[0612] The server sends the generated natural language response to the device, which uses a speech synthesis engine (e.g., Google Text-to-Speech) to audibly notify the user of the received response.

[0613] Input: Natural Language Response ("Currently available parking spaces are B2 and D4")

[0614] Output: Audio data (notification to user)

[0615] Step 6:

[0616] If a user wants to reserve a specific parking space, they can speak into the terminal, "I would like to reserve parking space B2 from 2:00 PM to 4:00 PM tomorrow." The terminal converts the voice input into text and generates a reservation request for the user.

[0617] Input: User voice input

[0618] Output: Text data ("I would like to reserve parking space B2 from 2:00 PM to 4:00 PM tomorrow.")

[0619] Step 7:

[0620] The device sends the generated text data to the server, which receives the request, analyzes it using a generative AI model, and generates an SQL query for reservation registration.

[0621] Input: Text data ("I would like to reserve parking space B2 from 2:00 PM to 4:00 PM tomorrow.")

[0622] Output: SQL query

[0623] Step 8:

[0624] The server sends the generated SQL query to the database to register the reservation information, and the database returns the registration result to the server.

[0625] Input: SQL query

[0626] Output: Reservation registration results

[0627] Step 9:

[0628] The server passes the reservation registration results from the database to the generative AI model, which then generates a reservation completion notification. The generative AI model then selects an appropriate response for the user.

[0629] Input: Reservation registration result

[0630] Output: A natural language response (e.g., "Your reservation is complete")

[0631] Step 10:

[0632] The server sends the generated natural language response to the device, which uses a speech synthesis engine to audibly notify the user of the received response.

[0633] Input: Natural language response ("Your reservation is complete")

[0634] Output: Audio data (notification to user)

[0635] 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.

[0636] 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.

[0637] 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.

[0638] [Second embodiment]

[0639] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0640] 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.

[0641] 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).

[0642] 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.

[0643] 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.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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."

[0651] The locker reservation system of the present invention provides a specific means for users to check the availability of lockers and make reservations. The processing of the system program will be explained in natural language below.

[0652] Check availability

[0653] User:

[0654] When a user wants to know which lockers are available, they type "Which lockers are currently available?" into the terminal.

[0655] Device:

[0656] The terminal receives the user's input and sends an availability check request to the server.

[0657] server:

[0658] Based on the received request, the server retrieves the current availability of lockers from the database.

[0659] The acquired availability information is passed to a generative AI model to generate an appropriate response.

[0660] After the generative AI model generates a response saying "The currently available lockers are A and C," it sends it to the terminal.

[0661] Device:

[0662] The terminal displays the response received from the server to the user.

[0663] Examples:

[0664] The user types into the terminal, "Which lockers are currently available?"

[0665] The device sends a request to the server, which queries the database and uses a generative AI model to generate a response such as "The currently available lockers are A and C," which is then sent to the device.

[0666] The terminal displays the response to the user, who then checks whether locker A or C is available.

[0667] Locker reservation

[0668] User:

[0669] When a user wants to use a specific locker, they enter "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[0670] Device:

[0671] The terminal receives user input and sends a reservation request to the server.

[0672] server:

[0673] The server registers the reservation information in the database based on the received request.

[0674] Confirm that the registration was successful and have the generative AI model generate a response indicating that the reservation was completed.

[0675] The generative AI model generates a response saying "Reservation completed" and sends it to the device.

[0676] Device:

[0677] The terminal displays the response received from the server to the user.

[0678] Examples:

[0679] The user enters into the terminal, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow."

[0680] The device sends a request to the server, the server updates the database, uses the generative AI model to generate a response saying "Reservation completed," and sends it to the device.

[0681] The terminal displays the response to the user, and the user confirms that the reservation of Locker A has been completed.

[0682] Interactive processing

[0683] User:

[0684] Enter your questions or requests into the terminal in natural language.

[0685] Device:

[0686] Sends user input to the interaction API.

[0687] server:

[0688] Input received through a dialogue API is passed to a generative AI model to generate an appropriate response.

[0689] The generated response is sent to the device via the interaction API.

[0690] Device:

[0691] The generated response is displayed to the user.

[0692] Examples:

[0693] The user types into the terminal, "Please tell me which lockers are available."

[0694] The server passes the received input to a generative AI model, which generates a response and sends it to the device.

[0695] The terminal displays "Currently available lockers are A and C," and the user confirms.

[0696] In this way, the system of the present invention comprehensively realizes a series of processes to significantly improve user convenience. Users can check availability and make reservations interactively through the generative AI model, allowing for efficient locker usage. This system reduces time wasted and increases user satisfaction.

[0697] The processing flow will be explained below.

[0698] Check availability

[0699] Step 1:

[0700] The user inputs a request for the locker they want to check availability for into the terminal (e.g., "Which lockers are currently available?").

[0701] Step 2:

[0702] The terminal receives the user's input and sends an availability check request to the server.

[0703] Step 3:

[0704] The server passes the request to the generative AI model through the dialogue module.

[0705] Step 4:

[0706] A generative AI model analyzes the request and generates an appropriate SQL query to query the database about locker availability.

[0707] Step 5:

[0708] The server receives the availability results from the database.

[0709] Step 6:

[0710] The availability information obtained by the server is then passed back to the generative AI model, which generates a natural language response.

[0711] Step 7:

[0712] The generative AI model generates the response, "The currently available lockers are A and C."

[0713] Step 8:

[0714] The server sends the generated response to the terminal.

[0715] Step 9:

[0716] The terminal displays the response received from the server to the user.

[0717] Locker reservation

[0718] Step 1:

[0719] When a user wants to use a specific locker, they input "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[0720] Step 2:

[0721] The terminal receives the user's input and sends a reservation request to the server.

[0722] Step 3:

[0723] The server passes the request to the generative AI model through the dialogue module.

[0724] Step 4:

[0725] A generative AI model analyzes the request and generates the appropriate SQL query to register the reservation information in the database.

[0726] Step 5:

[0727] The server receives the reservation registration results from the database.

[0728] Step 6:

[0729] The server confirms that the registration was successful and has the generative AI model generate a response indicating that the reservation is complete.

[0730] Step 7:

[0731] The generative AI model generates the response "Reservation completed."

[0732] Step 8:

[0733] The server sends the generated response to the terminal.

[0734] Step 9:

[0735] The terminal displays the response received from the server to the user.

[0736] Interactive processing

[0737] Step 1:

[0738] The user enters a question or request into the terminal in natural language.

[0739] Step 2:

[0740] The device sends input from the user to the interaction API.

[0741] Step 3:

[0742] The server passes the input received through the dialogue API to the generative AI model.

[0743] Step 4:

[0744] A generative AI model analyzes the input and generates an appropriate response.

[0745] Step 5:

[0746] The server sends the generated response to the terminal via the interaction API.

[0747] Step 6:

[0748] The terminal displays the generated response to the user.

[0749] Example 1

[0750] 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."

[0751] Currently, there is no system that allows users to easily check the availability of lockers and quickly make reservations. As a result, users often waste time checking for available lockers and reserving them, which prevents them from using the lockers efficiently. Another issue is that conventional systems require complex interfaces and operations, which reduces user convenience.

[0752] 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.

[0753] In this invention, the server includes means for accepting a vacancy check request from a user, means for sending the vacancy check request to the server, means for the server to obtain vacancy information from a database, means for passing the obtained vacancy information to a generative AI model to generate an appropriate response, means for sending the generated response from the server to a terminal and for the terminal to display the response, means for accepting a locker reservation request from a user, means for sending the reservation request to the server, means for the server to register the reservation information in the database, and means for generating a reservation completion response using the generative AI model and displaying it on the terminal after the reservation registration is successful. This allows users to easily and quickly check for available lockers and reserve them.

[0754] The "means for accepting a request from a user to check availability" is a function for accepting a request input by a user into a terminal to check the availability of a locker.

[0755] The "means for transmitting an availability check request to a server" is a function for transmitting an availability check request from a terminal to a server.

[0756] The "means by which the server obtains availability information from the database" is a function that allows the server to access the internal database and obtain the current availability status of lockers.

[0757] "Means of passing the acquired availability information to the generative AI model and generating an appropriate response" refers to a function that inputs the locker availability information acquired by the server from the database into the generative AI model and creates a response in a format that is easy for the user to understand.

[0758] "Means for transmitting the generated response from the server to the terminal, and for the terminal to display the response" refers to the function by which the server transmits the response obtained from the generative AI model to the terminal, and the terminal displays the response to the user.

[0759] The "means for accepting a locker reservation request from a user" is a function for accepting a request input by a user into a terminal to reserve a specific locker at a specified time.

[0760] The "means for sending a reservation request to the server" is a function for sending a locker reservation request from the terminal to the server.

[0761] "Means for the server to register reservation information in the database" is a function that allows the server to update the database based on a reservation request and reserve a locker at a specified time.

[0762] "After the reservation is successfully registered, a means for generating a response indicating that the reservation is complete using the generative AI model and displaying it on the terminal" is a function that confirms that the server has successfully registered the reservation information in the database, inputs the result into the generative AI model, generates a response such as "The reservation has been completed," and sends it to the terminal to display to the user.

[0763] The present invention relates to a locker reservation system, which provides a specific means for users to check locker availability and make reservations. This system improves user convenience by providing locker availability and reservation information through natural language dialogue with users. To implement this invention, ordinary computer system hardware and software, such as a server, terminal, generative AI model, and database, are used.

[0764] Check availability

[0765] User:

[0766] When a user wants to know which lockers are available, they type "Which lockers are currently available?" into the terminal.

[0767] Device:

[0768] The terminal receives input from the user and transmits the content of the input to the server as a request to check availability.

[0769] server:

[0770] When the server receives a request to check availability, it accesses an internal database to obtain the current availability of lockers. This database stores the usage status of each locker (available, reserved, or in use).

[0771] The server passes the acquired availability information to a generative AI model, which generates a response in a format that is easy for the user to understand.

[0772] After the generative AI model generates a response such as "The currently available lockers are A and C," the server sends the response to the terminal.

[0773] Device:

[0774] The terminal displays the response received from the server to the user, allowing the user to check which lockers are currently available.

[0775] Examples:

[0776] The user types into the terminal, "Which lockers are currently available?"

[0777] The terminal sends the contents to the server, which then queries the database to obtain availability data.

[0778] The server passes the acquired data to the generative AI model, which generates a response such as "The currently available lockers are A and C."

[0779] The terminal displays the received response to the user, and the user checks whether locker A or C is available.

[0780] Locker reservation

[0781] User:

[0782] When a user wants to reserve a specific locker for a specified time, the user inputs into the terminal, "I would like to reserve locker A from 10:00 to 12:00 tomorrow."

[0783] Device:

[0784] The terminal receives input from the user and transmits the contents to the server as a reservation request.

[0785] server:

[0786] When the server receives the reservation request, it accesses the database and registers the reservation information for locker A at the specified time.

[0787] After confirming that the reservation information has been successfully registered, the server has the generative AI model generate a response saying "The reservation has been completed."

[0788] The server sends the response generated by the generative AI model to the terminal.

[0789] Device:

[0790] The terminal displays the response received from the server to the user, allowing the user to confirm that the reservation has been completed.

[0791] Examples:

[0792] The user enters into the terminal, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow."

[0793] The terminal transmits the contents to the server, and the server updates the database and registers the reservation information.

[0794] The server uses the generative AI model to generate a response saying "Reservation completed" and sends it to the terminal.

[0795] The terminal displays the received response to the user, and the user confirms that the reservation of locker A has been completed.

[0796] Interactive processing

[0797] User:

[0798] Enter your questions or requests into the terminal in natural language.

[0799] Device:

[0800] Sends user input to the interaction API.

[0801] server:

[0802] The server passes the input received through the dialogue API to the generative AI model, which then generates an appropriate response.

[0803] The generated response is sent to the device via the interaction API.

[0804] Device:

[0805] The terminal displays the received response to the user.

[0806] Examples:

[0807] The user types into the terminal, "Please tell me which lockers are available."

[0808] The device sends the content to a dialogue API, and the server generates a response using a generative AI model.

[0809] The generated response is sent to the terminal, which displays "The currently available lockers are A and C."

[0810] The user confirms.

[0811] In this way, the system of the present invention comprehensively realizes a series of processes to significantly improve user convenience. Users can check availability and make reservations interactively through the generative AI model, allowing for efficient locker usage. This system reduces time wasted and increases user satisfaction.

[0812] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0813] Availability Check Processing Steps

[0814] Step 1:

[0815] User: A user who wants to know the availability status enters "Which lockers are currently available?" into the terminal. This generates an availability check request from the user.

[0816] Step 2:

[0817] Terminal: The terminal receives input from the user (a request to check availability) and sends it to the server. Specifically, it structures the request content in an appropriate format and sends it to the server as an HTTP request.

[0818] Input: User typed "Which lockers are currently available?"

[0819] Output: Availability check request to the server

[0820] Step 3:

[0821] Server: The server receives the availability check request and queries the database to get the current availability of the locker. The server retrieves the locker status from the database using an SQL query or similar.

[0822] Input: Availability check request from terminal

[0823] Output: Availability data retrieved from the database

[0824] Step 4:

[0825] Server: The server passes the acquired availability data to the generative AI model, which then analyzes the data and generates a natural language response.

[0826] Input: Availability data retrieved from the database

[0827] Output: The response generated by the generative AI model: "The currently available lockers are A and C."

[0828] Step 5:

[0829] Server: The server sends the generated response to the terminal. Specifically, the server returns the response to the terminal as an HTTP response.

[0830] Input: The response generated by the generative AI model

[0831] Output: Response data to the terminal

[0832] Step 6:

[0833] Terminal: The terminal displays the response received from the server to the user, allowing the user to check which lockers are currently available.

[0834] Input: Response data from the server

[0835] Output: Show available lockers

[0836] Locker reservation process steps

[0837] Step 1:

[0838] User: A user who wants to reserve a specific locker enters into the terminal, "I would like to reserve locker A from 10:00 to 12:00 tomorrow." This generates a locker reservation request from the user.

[0839] Step 2:

[0840] Terminal: The terminal receives input (reservation request) from the user and sends it to the server. Specifically, it structures the request content in an appropriate format and sends it to the server as an HTTP request.

[0841] Input: User input: "I would like to reserve Locker A from 10:00 to 12:00 tomorrow."

[0842] Output: Reservation request to server

[0843] Step 3:

[0844] Server: The server receives the reservation request, accesses the database, and registers the reservation information for Locker A at the specified time. The server inserts the locker reservation information into the database using an SQL query or similar.

[0845] Input: Reservation request from terminal

[0846] Output: Reservation information database registration results

[0847] Step 4:

[0848] Server: The server confirms that the reservation information has been successfully registered in the database and has the generative AI model generate a response saying "Reservation completed." The generative AI model analyzes the data and generates a natural language response.

[0849] Input: Reservation information database registration results

[0850] Output: The response "Reservation completed" generated by the generative AI model

[0851] Step 5:

[0852] Server: The server sends the generated response to the terminal. Specifically, the server returns the response to the terminal as an HTTP response.

[0853] Input: The response generated by the generative AI model

[0854] Output: Response data to the terminal

[0855] Step 6:

[0856] Terminal: The terminal displays the response received from the server to the user, allowing the user to confirm that the reservation for Locker A has been completed.

[0857] Input: Response data from the server

[0858] Output: Reservation completed

[0859] Interaction processing steps

[0860] Step 1:

[0861] User: Enters a question or request into the terminal in natural language, which generates a user request.

[0862] Step 2:

[0863] Terminal: The terminal sends user input to the interaction API. Specifically, it structures the request content in an appropriate format and sends it to the server as an HTTP request.

[0864] Input: Natural language input from the user

[0865] Output: Request to the conversation API

[0866] Step 3:

[0867] Server: The server receives input through the dialogue API and passes it to the generative AI model to generate an appropriate response. The generative AI model analyzes the data and generates a natural language response.

[0868] Input: User input received through the interaction API.

[0869] Output: The response generated by the generative AI model

[0870] Step 4:

[0871] Server: The server sends the generated response to the terminal via the interaction API. Specifically, the server returns the response to the terminal as an HTTP response.

[0872] Input: The response generated by the generative AI model

[0873] Output: Response data to the terminal

[0874] Step 5:

[0875] Terminal: The terminal displays the responses it receives to the user, allowing the user to get answers to their questions and requests.

[0876] Input: Response data from the server

[0877] Output: Displaying the response to the user

[0878] (Application example 1)

[0879] 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."

[0880] Conventional locker reservation systems require users to input text into a terminal when checking availability or making a reservation, which is time-consuming. Furthermore, at logistics centers and other such sites, this can lead to inefficient item management and pickup operations. This can lead to reduced efficiency in inventory management and pickup, and can lead to work delays and errors. Therefore, there is a demand for a more intuitive and efficient locker reservation system.

[0881] 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.

[0882] In this invention, the server includes a means for converting a user's voice input into text, a means for communicating a response generated by the generative AI model to the user by voice and visual display, and a means for checking locker availability in real time. This allows users to intuitively check availability and reserve lockers by voice input, significantly improving the efficiency of inventory management and pickup operations, especially in logistics centers.

[0883] "User" refers to a person who uses the locker reservation system to check locker availability and make a reservation.

[0884] "Availability Check Request" refers to a request sent by a User to the System to find out which lockers are currently available.

[0885] A "generative AI model" is a model that uses artificial intelligence to understand natural language and generate appropriate responses.

[0886] "Locker" means an individual storage unit for the temporary storage of goods or personal items.

[0887] "Real-time" means that information is processed immediately and reflects the current situation.

[0888] "Reservation Request" means a request submitted by a User to the System to use a specific Locker at a specific date and time.

[0889] "Database" refers to the electronic information storage system for recording and managing locker availability and reservation status.

[0890] "Voice input" refers to an input method in which the system recognizes the user's speech and converts it into text.

[0891] "Visual display" refers to the method by which system-generated information is visually conveyed to the user through a screen or display.

[0892] "Voice response" refers to a method of conveying system-generated information to users by voice.

[0893] A system for realizing the present invention has the following configuration.

[0894] The system components include smart glasses, a server, a database, and a generative AI model. The smart glasses receive voice input from the user, convert it into text, and send it to the server. The server then passes the received text data to the generative AI model, which generates an appropriate response. The generated response is then sent back to the smart glasses and communicated to the user via voice and visual feedback.

[0895] Specifically, the following processing is performed.

[0896] 1. Voice input and conversion: The user speaks to the smart glasses, saying, "Which lockers are currently available?" This voice data is converted into text by the smart glasses' voice recognition software (e.g., Google Cloud Speech-to-Text).

[0897] 2. Utilizing text processing and generative AI models: The input, "Which lockers are currently available?", is converted into text and sent to the server. The server passes this text to a generative AI model, which generates an appropriate response. An example of the generative AI model used here is the OpenAI API.

[0898] 3. Obtaining and transmitting a response: The generative AI model generates a response such as "The currently available lockers are A and C," and the server sends this information to the smart glasses. The smart glasses then convey this information to the user visually (display) and via voice output (e.g., voice synthesis using the gTTS library).

[0899] 4. Locker reservation request processing: The user then voice-inputs, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow." This voice is also converted into text by the smart glasses and sent to the server. The server registers the reservation information in the database and uses the generative AI model to generate a response saying "Reservation completed" and sends it to the smart glasses.

[0900] Examples of prompt statements

[0901] Below is an example of a prompt to be passed to the generative AI model.

[0902] Run the availability check module of the locker reservation system. User input: Which lockers are currently available?

[0903] With the above configuration, users can intuitively check locker availability and make reservations by using voice input through the smart glasses. This is expected to significantly improve the efficiency of inventory management and pickup operations, especially at logistics centers.

[0904] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0905] Step 1:

[0906] The user speaks into the smart glasses, asking, "Which lockers are currently available?" The smart glasses collect the user's voice data using a built-in microphone. The collected voice data is converted into text data by the voice recognition software inside the smart glasses. The input is voice data, and the output is text data.

[0907] Step 2:

[0908] The terminal (smart glasses) sends the converted text data "Which lockers are currently available?" to the server. This transmission uses wireless communication technology (e.g., Wi-Fi). The input is text data, and the output is the transmission of the text data to the server.

[0909] Step 3:

[0910] The server passes the received user text data to the generative AI model as input. The server then sends the text data to the global AI model API (e.g., OpenAI API) to generate an appropriate response. The input is the user's text data, "Which lockers are currently available?", and the output from the generative AI model is the response text, "The currently available lockers are A and C."

[0911] Step 4:

[0912] The server transmits the response text data obtained from the generative AI model to the smart glasses. Wireless communication technology is used for transmission, and the data reaches the smart glasses in real time. The input is the response text data from the generative AI model, and the output is the data transmitted to the smart glasses.

[0913] Step 5:

[0914] The device (smart glasses) receives the response text data, "The currently available lockers are A and C," and conveys it to the user visually and audibly. The visual display uses the display, and the audio output uses a speech synthesis library (e.g., gTTS). The input is the response text data, and the output is the visual display and audio output.

[0915] Step 6:

[0916] The user again uses voice input and speaks into the smart glasses, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow." The voice data is again collected and converted into text data through voice recognition software. The input is voice data, and the output is text data.

[0917] Step 7:

[0918] The terminal (smart glasses) sends text data, such as "I would like to reserve Locker A from 10:00 to 12:00 tomorrow," to the server. This transmission also uses wireless communication technology. The input is text data, and the output is the transmission of text data to the server.

[0919] Step 8:

[0920] The server queries the reservation information to register the received reservation request in the database, and if availability is confirmed, adds the reservation information to the database. The input is the reservation request text data, and the output is an update of the database.

[0921] Step 9:

[0922] The server passes the successful reservation information to the generative AI model, which then generates a response text saying "Reservation completed." The generative AI model generates an appropriate response and returns it to the server. The input is the successful reservation information, and the output is the response text data.

[0923] Step 10:

[0924] The server sends the generated response text data "Reservation completed" to the device (smart glasses). The input is the response text data from the generative AI model, and the output is the data sent to the smart glasses.

[0925] Step 11:

[0926] The terminal (smart glasses) conveys the received response text data to the user by visual display and audio output. The visual display uses a display, and the audio output uses a speech synthesis library. The input is the response text data, and the output is the visual display and audio output.

[0927] Through the above processing steps, the user can easily check the availability of lockers and make reservations through voice input.

[0928] 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.

[0929] The locker reservation system of the present invention provides a concrete means for users to check the availability of lockers and make reservations. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide a more personalized service. The program processing of this system is described in detail below in natural language.

[0930] Check availability

[0931] User:

[0932] When a user wants to know which lockers are available, they type "Which lockers are currently available?" into the terminal.

[0933] Device:

[0934] The terminal receives the user's input and sends an availability check request to the server.

[0935] server:

[0936] The server passes the request through the dialogue module to the generative AI model, which analyzes the request and generates an appropriate SQL query to query the database about the availability of the locker.

[0937] server:

[0938] It receives availability results from a database and passes them to a generative AI model to generate natural language responses.

[0939] Generative AI models:

[0940] Generates the response "Currently available lockers are A and C."

[0941] server:

[0942] The generated response is sent to the terminal.

[0943] Device:

[0944] The response received from the server is displayed to the user.

[0945] Examples:

[0946] When a user types "Which lockers are currently available?" into the terminal, the terminal sends a request to the server, which queries the database and uses a generative AI model to generate a response saying "Lockers A and C are currently available," which is sent to the terminal. The terminal displays the response to the user, who can then confirm whether locker A or C is available.

[0947] Locker reservation

[0948] User:

[0949] When a user wants to use a specific locker, they enter "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[0950] Device:

[0951] The terminal receives user input and sends a reservation request to the server.

[0952] server:

[0953] The server passes the request through the dialogue module to the generative AI model, which analyzes the request and generates the appropriate SQL query to register the reservation information in the database.

[0954] server:

[0955] The reservation registration results are received from the database and the generative AI model generates a response indicating the reservation is complete.

[0956] Generative AI models:

[0957] Generates a "Reservation completed" response.

[0958] server:

[0959] The generated response is sent to the terminal.

[0960] Device:

[0961] The response received from the server is displayed to the user.

[0962] Examples:

[0963] When a user enters "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal, the terminal sends a request to the server, the server updates the database, and uses the generative AI model to generate a response saying "Reservation completed" and sends it to the terminal. The terminal displays the response to the user, who confirms that the reservation of Locker A has been completed.

[0964] Emotion Recognition and Dialogue Processing

[0965] User:

[0966] Enter your questions or requests into the terminal in natural language.

[0967] Device:

[0968] Sends user input to the interaction API.

[0969] server:

[0970] Input received through the dialogue API is passed to the generative AI model, which analyzes the input and generates an appropriate response.

[0971] Emotion Engine:

[0972] Analyze emotions from user input and tailor responses based on those emotions.

[0973] Generative AI models:

[0974] Feedback from the emotion engine is used to generate responses with appropriate timing and content.

[0975] server:

[0976] The generated response is sent to the device via the interaction API.

[0977] Device:

[0978] The generated response is displayed to the user.

[0979] Examples:

[0980] When a user types "Please tell me which lockers are available" into the device, the server passes the received input to the generative AI model, and the emotion engine analyzes the user's emotions. Based on feedback from the emotion engine, the generative AI model generates a response saying "Currently available lockers are A and C. Have you found the locker you are looking for?" and sends it to the device. The device displays the response to the user, who confirms it.

[0981] In this way, the system of the present invention comprehensively realizes a series of processes to significantly improve user convenience. Furthermore, by combining it with an emotion engine, interactions with users become more natural and personalized, further increasing user satisfaction.

[0982] The processing flow will be explained below.

[0983] Check availability

[0984] Step 1:

[0985] The user inputs a request for the locker they want to check availability for into the terminal (e.g., "Which lockers are currently available?").

[0986] Step 2:

[0987] The terminal receives the user's input and sends an availability check request to the server.

[0988] Step 3:

[0989] The server passes the request to the generative AI model through the dialogue module.

[0990] Step 4:

[0991] A generative AI model analyzes the request and generates an SQL query to query the database about locker availability.

[0992] Step 5:

[0993] The server retrieves availability information from the database.

[0994] Step 6:

[0995] The server passes the acquired availability information to a generative AI model, which generates a natural language response.

[0996] Step 7:

[0997] The generative AI model generates the response, "The currently available lockers are A and C."

[0998] Step 8:

[0999] The server sends the generated response to the terminal.

[1000] Step 9:

[1001] The terminal displays the response received from the server to the user.

[1002] Locker reservation

[1003] Step 1:

[1004] When a user wants to use a specific locker, they input "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[1005] Step 2:

[1006] The terminal receives the user's input and sends a reservation request to the server.

[1007] Step 3:

[1008] The server passes the request to the generative AI model through the dialogue module.

[1009] Step 4:

[1010] The generative AI model analyzes the request and generates an SQL query to register the reservation information in the database.

[1011] Step 5:

[1012] The server receives the reservation registration results from the database.

[1013] Step 6:

[1014] The server confirms whether the registration was successful and has the generative AI model generate a response indicating that the reservation is complete.

[1015] Step 7:

[1016] The generative AI model generates the response "Reservation completed."

[1017] Step 8:

[1018] The server sends the generated response to the terminal.

[1019] Step 9:

[1020] The terminal displays the response received from the server to the user.

[1021] Emotion Recognition and Dialogue Processing

[1022] Step 1:

[1023] The user types a question or request into the terminal in natural language (e.g., "Please tell me which lockers are available").

[1024] Step 2:

[1025] The device sends input from the user to the interaction API.

[1026] Step 3:

[1027] The server passes the input received through the dialogue API to the generative AI model.

[1028] Step 4:

[1029] The generative AI model analyzes the input and requests emotion analysis from the emotion engine.

[1030] Step 5:

[1031] The emotion engine analyzes emotions from user input and sends the results back to the generative AI model.

[1032] Step 6:

[1033] The generative AI model uses feedback from the emotion engine to generate an appropriate response (e.g., "Currently available lockers are A and C. Did you find the locker you were looking for?").

[1034] Step 7:

[1035] The server sends the generated response to the terminal via the interaction API.

[1036] Step 8:

[1037] The terminal displays the generated response to the user.

[1038] Example 2

[1039] 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."

[1040] The problem that this invention aims to solve is to provide a system that allows users to obtain real-time information when checking locker availability and reserving lockers, and further to make the dialogue with users natural and personalized by using an emotion engine. In conventional systems, checking locker availability and reserving lockers were done manually, which reduced user convenience. Furthermore, because the system was unable to respond in a way that took the user's emotions into consideration, the dialogue was one-sided and user satisfaction was low.

[1041] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting an availability check request from a user, means for checking locker availability in real time using a generative AI model, means for providing information on available lockers to the user, means for accepting a locker reservation request from a user, means for registering the reservation information in a database, means for notifying the user of reservation completion information using a generative AI model, and means for analyzing the user's emotions using an emotion engine and adjusting the response based on the emotions. This not only enables efficient checking of locker availability and reservations, but also makes it possible to have natural and personalized interactions with the user, thereby improving user satisfaction.

[1042] "Availability check request from user" means a request sent by a user to the system to check the availability of a locker.

[1043] A "generative AI model" refers to an artificial intelligence technology that analyzes user input and generates an appropriate response based on the results of that analysis.

[1044] "Means for checking locker availability in real time" refers to methods and technologies for instantly obtaining information about currently available lockers and providing it to users.

[1045] "Means for providing users with information about available lockers" refers to methods or technologies for displaying or notifying users of information about currently available lockers.

[1046] A "locker reservation request from a user" is a request sent to the system by a user wishing to use a specific locker on a specific date and time.

[1047] "Means for registering reservation information in a database" refers to a method or technology for storing reservation-related information in a database based on a reservation request from a user.

[1048] "Means for notifying users of reservation completion information using a generative AI model" refers to methods or technologies for generating a response using a generative AI model to inform users that their reservation has been successfully completed, and for communicating that response to users.

[1049] An "emotion engine" is a technology or module that analyzes emotions from user input and adjusts the system's response based on the analysis results.

[1050] "Means for analyzing user emotions" refers to methods or techniques for identifying emotions from text entered by a user and using the results to help the system generate responses.

[1051] The locker reservation system of the present invention provides a means for users to check locker availability and make reservations. This system combines a generative AI model and an emotion engine to improve user convenience, and is described in detail below.

[1052] System Configuration

[1053] This system includes the following hardware and software:

[1054] Terminal: The device used by the user to input data (smartphone, tablet, PC, etc.)

[1055] Server: A central system that processes input and interfaces with generative AI models and databases

[1056] Database: Data storage for storing locker availability and reservation information

[1057] Generative AI model: AI technology that generates responses through natural language processing

[1058] Emotion Engine: Technology that analyzes user emotions and adjusts responses

[1059] Check availability

[1060] User:

[1061] When a user wants to check the availability of lockers, they type "Which lockers are currently available?" into the terminal.

[1062] Device:

[1063] The terminal receives the user's input and sends an availability check request to the server.

[1064] server:

[1065] The server passes the request through the dialogue module to the generative AI model, which parses the request, generates the appropriate SQL query, and executes it against the database. For example, it generates the query "SELECT FROM lockers WHERE status='empty'".

[1066] Database:

[1067] It receives query results from the database and sends information about available lockers to the server.

[1068] Generative AI models:

[1069] The generative AI model receives the database results from the server and generates a natural language response such as, "The currently available lockers are A and C."

[1070] server:

[1071] The generated response is sent to the terminal.

[1072] Device:

[1073] The terminal displays the response received from the server to the user. Specifically, the terminal screen displays "Currently available lockers are A and C."

[1074] Examples:

[1075] When a user types "Which lockers are currently available?" into the device, the device sends the request to the server. The server executes an SQL query and uses a generative AI model to generate a response, "Lockers A and C are currently available," which is sent to the device. The device then displays the response on its screen for the user to confirm.

[1076] Locker reservation

[1077] User:

[1078] When a user wants to reserve a specific locker, they enter "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[1079] Device:

[1080] The terminal receives the input and sends a reservation request to the server.

[1081] server:

[1082] The server passes the reservation request through the dialogue module to the generative AI model, which parses the request, generates the appropriate SQL query, and executes it against the database. For example, it generates the query "INSERT INTO reservations (locker_id, start_time, end_time) VALUES ('A', '2023-06-01 10:00', '2023-06-01 12:00')".

[1083] Database:

[1084] The database executes queries and registers reservation information.

[1085] Generative AI models:

[1086] The generative AI model receives the registration completion result from the database and generates a natural language response saying, "Your reservation is complete."

[1087] server:

[1088] The generated response is sent to the terminal.

[1089] Device:

[1090] The terminal displays the response received from the server to the user. Specifically, the terminal displays "Reservation completed" on its screen.

[1091] Examples:

[1092] When a user types "I'd like to reserve Locker A from 10:00 to 12:00 tomorrow" into the device, the device sends the request to the server. The server executes an SQL query and uses a generative AI model to generate a response saying "Reservation completed" and sends it to the device. The device then displays the response on its screen for the user to confirm.

[1093] Emotion Recognition and Dialogue Processing

[1094] User:

[1095] Enter your question or request into the terminal in natural language, for example, "Please tell me which lockers are available."

[1096] Device:

[1097] Input from the user is sent to the server via the interaction API.

[1098] server:

[1099] The server passes the request through a dialogue API to the generative AI model, which analyzes the input and uses an emotion engine to analyze the user's emotions.

[1100] Emotion Engine:

[1101] The emotion engine analyzes emotions from user input and feeds the results back to the generative AI model.

[1102] Generative AI models:

[1103] The generative AI model generates a natural language response based on feedback from the emotion engine, for example, "Currently, the available lockers are A and C. Did you find the locker you were looking for?"

[1104] server:

[1105] The generated response is sent to the device via the interaction API.

[1106] Device:

[1107] The terminal displays the response received from the server to the user. Specifically, the terminal screen displays the message "Currently available lockers are A and C. Have you found the locker you are looking for?"

[1108] Examples:

[1109] When a user types "Please tell me which lockers are available" into the device, the server passes the request to the generative AI model, which analyzes the emotion using the emotion engine. The generative AI model generates a response saying "Currently available lockers are A and C. Have you found the locker you are looking for?" and sends it to the device. The device displays the response on the screen for the user to confirm.

[1110] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1111] Availability Check Processing Steps

[1112] Step 1:

[1113] The user inputs information into the terminal to check availability. Specifically, the user inputs "Which lockers are currently available?" into the input field on the terminal. Input: The user's question in natural language.

[1114] Step 2:

[1115] The terminal processes the user's input and sends it to the server as an HTTP request. Input: User's question text. Output: HTTP request to the server.

[1116] Step 3:

[1117] The server passes the received request to the dialogue module and sends the request to the generative AI model. Input: Contents of the HTTP request. Output: Request to the generative AI model.

[1118] Step 4:

[1119] The generative AI model analyzes the user's question and generates an appropriate SQL query. It then returns the query to the server. For example, it generates the query "SELECT FROM lockers WHERE status='empty'". Input: User's question text. Output: SQL query.

[1120] Step 5:

[1121] The server sends the SQL query received from the generative AI model to the database and executes the query. Input: SQL query. Output: Result of executing the database query.

[1122] Step 6:

[1123] The database returns the information of available lockers to the server as a result of the query. For example, information such as "Locker A, Locker C" is returned. Input: Database query. Output: Available locker information.

[1124] Step 7:

[1125] The server passes the information about available lockers received from the database to the generative AI model, which generates a natural language response. Input: Available locker information. Output: Natural language response.

[1126] Step 8:

[1127] The generative AI model generates a natural language response based on the information about available lockers, such as "The currently available lockers are A and C," and returns that response to the server. Input: Available locker information. Output: Response text.

[1128] Step 9:

[1129] The server generates a response and sends it to the device. Input: Response text. Output: HTTP response to the device.

[1130] Step 10:

[1131] The terminal receives the response from the server and displays it to the user. Specifically, it displays "Currently available lockers are A and C" on the terminal screen. Input: HTTP response. Output: Displayed to the user.

[1132] Locker reservation process steps

[1133] Step 1:

[1134] The user inputs information into the terminal to reserve a specific locker. Specifically, the user inputs "I would like to reserve locker A from 10:00 to 12:00 tomorrow." Input: The user's reservation request in natural language.

[1135] Step 2:

[1136] The terminal processes the user's input and sends it to the server as an HTTP request. Input: Reservation request text. Output: HTTP request to the server.

[1137] Step 3:

[1138] The server passes the received request to the dialogue module and sends the request to the generative AI model. Input: Contents of the HTTP request. Output: Request to the generative AI model.

[1139] Step 4:

[1140] The generative AI model analyzes the user's reservation request and generates the appropriate SQL query. It then returns the query to the server. For example, it generates the query "INSERT INTO reservations (locker_id, start_time, end_time) VALUES ('A', '2023-06-01 10:00', '2023-06-01 12:00')". Input: Reservation request text. Output: SQL query.

[1141] Step 5:

[1142] The server sends the SQL query received from the generative AI model to the database and executes the query. Input: SQL query. Output: Result of executing the database query.

[1143] Step 6:

[1144] The database returns the reservation information to the server as a result of the query, e.g., a result of "reservation successful". Input: Database query. Output: Reservation result.

[1145] Step 7:

[1146] The server passes the reservation results received from the database to the generative AI model, which generates a natural language response. Input: Reservation results. Output: Natural language response.

[1147] Step 8:

[1148] The generative AI model generates a natural language response, "Reservation completed," based on the reservation result and returns the response to the server. Input: Reservation result. Output: Response text.

[1149] Step 9:

[1150] The server generates a response and sends it to the device. Input: Response text. Output: HTTP response to the device.

[1151] Step 10:

[1152] The terminal receives the response from the server and displays it to the user. Specifically, it displays "Reservation completed" on the terminal screen. Input: HTTP response. Output: Displayed to the user.

[1153] Processing steps for emotion recognition and dialogue processing

[1154] Step 1:

[1155] The user inputs a question or request into the terminal in natural language. For example, "Please tell me which lockers are available." Input: A user's natural language inquiry.

[1156] Step 2:

[1157] The terminal sends the user's input to the server through the interaction API. Input: Query text. Output: HTTP request to the server.

[1158] Step 3:

[1159] The server sends a request to the generative AI model through the interaction API. Input: HTTP request content. Output: Request to the generative AI model.

[1160] Step 4:

[1161] The generative AI model analyzes the user's input and sends the results to the emotion engine. Input: User's query text. Output: Analyzed data.

[1162] Step 5:

[1163] The emotion engine analyzes emotions from user input and feeds the analysis results back to the generative AI model. For example, it may analyze the input to determine that the emotion is "friendly." Input: Analyzed data. Output: Emotion feedback.

[1164] Step 6:

[1165] The generative AI model uses emotional feedback to generate an appropriate natural language response. For example, it generates a response like, "Currently available lockers are A and C. Did you find the locker you were looking for?" Input: Emotional feedback. Output: Response text.

[1166] Step 7:

[1167] The server sends the generated response to the device via the interaction API. Input: Response text. Output: HTTP response to the device.

[1168] Step 8:

[1169] The terminal receives the response from the server and displays it to the user. Specifically, it displays the message "Currently available lockers are A and C. Have you found the locker you are looking for?" on the terminal screen. Input: HTTP response. Output: Displayed to the user.

[1170] (Application example 2)

[1171] 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."

[1172] In conventional locker reservation systems, when users check availability and make reservations, they are provided with one-sided information that ignores their emotions. This often causes inconvenience and stress for users. In addition, there are problems with the speed and accuracy of real-time availability confirmation and reservation completion notifications, which tends to reduce user satisfaction.

[1173] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1174] In this invention, the server includes means for accepting availability confirmation requests from users, means for checking locker availability in real time using a generative AI model, means for providing users with information on available lockers, means for accepting locker reservation requests from users, means for registering the reservation information in a database, means for notifying users of reservation completion information using a generative AI model, and means for analyzing the user's emotions using an emotion recognition engine and adjusting responses based on the emotions, thereby enabling users to receive more personalized and convenient services.

[1175] "User" refers to an end user who uses the locker reservation system.

[1176] "Availability Check Request" refers to a request made by a User to check information about lockers that are currently available.

[1177] A "generative AI model" refers to an artificial intelligence model that uses technologies such as natural language processing to conduct dialogue and process data within a system.

[1178] An "emotion recognition engine" refers to an engine that analyzes the emotions from the user's input and adjusts the response.

[1179] "Real-time" refers to data processing occurring almost simultaneously with the current time.

[1180] "Locker availability" refers to information indicating whether a locker is currently in use or available.

[1181] "Reservation Request" means a request by a User to make a reservation for use of a particular Locker.

[1182] "Database" refers to structured data storage for managing and storing locker availability and reservation information.

[1183] "Reservation completion information" refers to information that notifies a user that their locker reservation has been successful.

[1184] The present invention provides a concrete example of how to use a locker reservation system. This system allows users to check locker availability, make a reservation, and receive notification of reservation completion, using a generative AI model. It also utilizes an emotion recognition engine to analyze user emotions and tailor responses to provide a more personalized experience for users.

[1185] System configuration

[1186] The system consists of the following main elements:

[1187] 1. Server

[1188] The server accepts requests from users, queries the database, and generates results using a generative AI model.

[1189] 2. Terminal

[1190] The device receives input from the user, sends the request to the server, displays the response, and runs an emotion recognition engine to analyze the user's emotions.

[1191] 3. Database

[1192] The database manages and stores locker availability and reservation information.

[1193] Program processing

[1194] The server includes means for accepting a request from a user to check availability, means for checking locker availability in real time using a generative AI model, means for providing information about available lockers to users, means for accepting a locker reservation request from a user, means for registering the reservation information in a database, means for notifying the user of reservation completion information using a generative AI model, and means for analyzing the user's emotions using an emotion recognition engine and adjusting a response based on the emotions.

[1195] Hardware or software used

[1196] This system uses the following hardware and software:

[1197] Microphone for voice input

[1198] User's device (Android or iOS smartphone)

[1199] Speech-to-text conversion using the speech_recognition library

[1200] Sending API requests to the server using the requests library

[1201] The server-side backend implements the generative AI model and database.

[1202] Specific examples

[1203] When a user speaks to the device, asking, "Which parking spaces are currently available?", the system converts the voice input into text and sends a request to the server. The server queries the database and uses a generative AI model to generate a natural language response. For example, it might respond, "The currently available parking spaces are B2 and D4."

[1204] Example prompt sentence:

[1205] User: What parking spaces are currently available?

[1206] Server: Please wait while we check parking availability...

[1207] Generative AI model: The currently available parking spaces are B2 and D4.

[1208] As described above, this system can provide efficient and personalized services to users.

[1209] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1210] Step 1:

[1211] The user speaks to their smartphone device, "What parking spaces are currently available?" The device converts the voice input into text and generates the user's request. At this time, a speech recognition engine (e.g., Google Speech API) analyzes the voice data and converts it into text data.

[1212] Input: User voice input

[1213] Output: Text data ("What parking spaces are currently available?")

[1214] Step 2:

[1215] The device sends the generated text data to the server, which receives the request and passes it to the generative AI model via the dialogue module. The generative AI model analyzes the request and generates an SQL query to check availability.

[1216] Input: Text data ("What parking spaces are currently available?")

[1217] Output: SQL query

[1218] Step 3:

[1219] The server sends the generated SQL query to the database to inquire about availability, and the database responds by returning the current availability.

[1220] Input: SQL query

[1221] Output: Availability data (e.g. "B2 and D4 are available")

[1222] Step 4:

[1223] The server passes the acquired availability data to the generative AI model, which then generates a response in natural language. The generative AI model uses an emotion recognition engine to analyze the user's emotions and select an appropriate response.

[1224] Input: Availability data ("B2 and D4 are available")

[1225] Output: A natural language response (e.g., "Currently available parking spaces are B2 and D4")

[1226] Step 5:

[1227] The server sends the generated natural language response to the device, which uses a speech synthesis engine (e.g., Google Text-to-Speech) to audibly notify the user of the received response.

[1228] Input: Natural Language Response ("Currently available parking spaces are B2 and D4")

[1229] Output: Audio data (notification to user)

[1230] Step 6:

[1231] If a user wants to reserve a specific parking space, they can speak into the terminal, "I would like to reserve parking space B2 from 2:00 PM to 4:00 PM tomorrow." The terminal converts the voice input into text and generates a reservation request for the user.

[1232] Input: User voice input

[1233] Output: Text data ("I would like to reserve parking space B2 from 2:00 PM to 4:00 PM tomorrow.")

[1234] Step 7:

[1235] The device sends the generated text data to the server, which receives the request, analyzes it using a generative AI model, and generates an SQL query for reservation registration.

[1236] Input: Text data ("I would like to reserve parking space B2 from 2:00 PM to 4:00 PM tomorrow.")

[1237] Output: SQL query

[1238] Step 8:

[1239] The server sends the generated SQL query to the database to register the reservation information, and the database returns the registration result to the server.

[1240] Input: SQL query

[1241] Output: Reservation registration results

[1242] Step 9:

[1243] The server passes the reservation registration results from the database to the generative AI model, which then generates a reservation completion notification. The generative AI model then selects an appropriate response for the user.

[1244] Input: Reservation registration result

[1245] Output: A natural language response (e.g., "Your reservation is complete")

[1246] Step 10:

[1247] The server sends the generated natural language response to the device, which uses a speech synthesis engine to audibly notify the user of the received response.

[1248] Input: Natural language response ("Your reservation is complete")

[1249] Output: Audio data (notification to user)

[1250] 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.

[1251] 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.

[1252] 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.

[1253] [Third embodiment]

[1254] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1255] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1256] 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).

[1257] 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.

[1258] 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.

[1259] 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).

[1260] 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.

[1261] 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.

[1262] 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.

[1263] 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.

[1264] 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.

[1265] 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."

[1266] The locker reservation system of the present invention provides a specific means for users to check the availability of lockers and make reservations. The processing of the system program will be explained in natural language below.

[1267] Check availability

[1268] User:

[1269] When a user wants to know which lockers are available, they type "Which lockers are currently available?" into the terminal.

[1270] Device:

[1271] The terminal receives the user's input and sends an availability check request to the server.

[1272] server:

[1273] Based on the received request, the server retrieves the current availability of lockers from the database.

[1274] The acquired availability information is passed to a generative AI model to generate an appropriate response.

[1275] After the generative AI model generates a response saying "The currently available lockers are A and C," it sends it to the terminal.

[1276] Device:

[1277] The terminal displays the response received from the server to the user.

[1278] Examples:

[1279] The user types into the terminal, "Which lockers are currently available?"

[1280] The device sends a request to the server, which queries the database and uses a generative AI model to generate a response such as "The currently available lockers are A and C," which is then sent to the device.

[1281] The terminal displays the response to the user, who then checks whether locker A or C is available.

[1282] Locker reservation

[1283] User:

[1284] When a user wants to use a specific locker, they enter "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[1285] Device:

[1286] The terminal receives user input and sends a reservation request to the server.

[1287] server:

[1288] The server registers the reservation information in the database based on the received request.

[1289] Confirm that the registration was successful and have the generative AI model generate a response indicating that the reservation was completed.

[1290] The generative AI model generates a response saying "Reservation completed" and sends it to the device.

[1291] Device:

[1292] The terminal displays the response received from the server to the user.

[1293] Examples:

[1294] The user enters into the terminal, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow."

[1295] The device sends a request to the server, the server updates the database, uses the generative AI model to generate a response saying "Reservation completed," and sends it to the device.

[1296] The terminal displays the response to the user, and the user confirms that the reservation of Locker A has been completed.

[1297] Interactive processing

[1298] User:

[1299] Enter your questions or requests into the terminal in natural language.

[1300] Device:

[1301] Sends user input to the interaction API.

[1302] server:

[1303] Input received through a dialogue API is passed to a generative AI model to generate an appropriate response.

[1304] The generated response is sent to the device via the interaction API.

[1305] Device:

[1306] The generated response is displayed to the user.

[1307] Examples:

[1308] The user types into the terminal, "Please tell me which lockers are available."

[1309] The server passes the received input to a generative AI model, which generates a response and sends it to the device.

[1310] The terminal displays "Currently available lockers are A and C," and the user confirms.

[1311] In this way, the system of the present invention comprehensively realizes a series of processes to significantly improve user convenience. Users can check availability and make reservations interactively through the generative AI model, allowing for efficient locker usage. This system reduces time wasted and increases user satisfaction.

[1312] The processing flow will be explained below.

[1313] Check availability

[1314] Step 1:

[1315] The user inputs a request for the locker they want to check availability for into the terminal (e.g., "Which lockers are currently available?").

[1316] Step 2:

[1317] The terminal receives the user's input and sends an availability check request to the server.

[1318] Step 3:

[1319] The server passes the request to the generative AI model through the dialogue module.

[1320] Step 4:

[1321] A generative AI model analyzes the request and generates an appropriate SQL query to query the database about locker availability.

[1322] Step 5:

[1323] The server receives the availability results from the database.

[1324] Step 6:

[1325] The availability information obtained by the server is then passed back to the generative AI model, which generates a natural language response.

[1326] Step 7:

[1327] The generative AI model generates the response, "The currently available lockers are A and C."

[1328] Step 8:

[1329] The server sends the generated response to the terminal.

[1330] Step 9:

[1331] The terminal displays the response received from the server to the user.

[1332] Locker reservation

[1333] Step 1:

[1334] When a user wants to use a specific locker, they input "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[1335] Step 2:

[1336] The terminal receives the user's input and sends a reservation request to the server.

[1337] Step 3:

[1338] The server passes the request to the generative AI model through the dialogue module.

[1339] Step 4:

[1340] A generative AI model analyzes the request and generates the appropriate SQL query to register the reservation information in the database.

[1341] Step 5:

[1342] The server receives the reservation registration results from the database.

[1343] Step 6:

[1344] The server confirms that the registration was successful and has the generative AI model generate a response indicating that the reservation is complete.

[1345] Step 7:

[1346] The generative AI model generates the response "Reservation completed."

[1347] Step 8:

[1348] The server sends the generated response to the terminal.

[1349] Step 9:

[1350] The terminal displays the response received from the server to the user.

[1351] Interactive processing

[1352] Step 1:

[1353] The user enters a question or request into the terminal in natural language.

[1354] Step 2:

[1355] The device sends input from the user to the interaction API.

[1356] Step 3:

[1357] The server passes the input received through the dialogue API to the generative AI model.

[1358] Step 4:

[1359] A generative AI model analyzes the input and generates an appropriate response.

[1360] Step 5:

[1361] The server sends the generated response to the terminal via the interaction API.

[1362] Step 6:

[1363] The terminal displays the generated response to the user.

[1364] Example 1

[1365] 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."

[1366] Currently, there is no system that allows users to easily check the availability of lockers and quickly make reservations. As a result, users often waste time checking for available lockers and reserving them, which prevents them from using the lockers efficiently. Another issue is that conventional systems require complex interfaces and operations, which reduces user convenience.

[1367] 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.

[1368] In this invention, the server includes means for accepting a vacancy check request from a user, means for sending the vacancy check request to the server, means for the server to obtain vacancy information from a database, means for passing the obtained vacancy information to a generative AI model to generate an appropriate response, means for sending the generated response from the server to a terminal and for the terminal to display the response, means for accepting a locker reservation request from a user, means for sending the reservation request to the server, means for the server to register the reservation information in the database, and means for generating a reservation completion response using the generative AI model and displaying it on the terminal after the reservation registration is successful. This allows users to easily and quickly check for available lockers and reserve them.

[1369] The "means for accepting a request from a user to check availability" is a function for accepting a request input by a user into a terminal to check the availability of a locker.

[1370] The "means for transmitting an availability check request to a server" is a function for transmitting an availability check request from a terminal to a server.

[1371] The "means by which the server obtains availability information from the database" is a function that allows the server to access the internal database and obtain the current availability status of lockers.

[1372] "Means of passing the acquired availability information to the generative AI model and generating an appropriate response" refers to a function that inputs the locker availability information acquired by the server from the database into the generative AI model and creates a response in a format that is easy for the user to understand.

[1373] "Means for transmitting the generated response from the server to the terminal, and for the terminal to display the response" refers to the function by which the server transmits the response obtained from the generative AI model to the terminal, and the terminal displays the response to the user.

[1374] The "means for accepting a locker reservation request from a user" is a function for accepting a request input by a user into a terminal to reserve a specific locker at a specified time.

[1375] The "means for sending a reservation request to the server" is a function for sending a locker reservation request from the terminal to the server.

[1376] "Means for the server to register reservation information in the database" is a function that allows the server to update the database based on a reservation request and reserve a locker at a specified time.

[1377] "After the reservation is successfully registered, a means for generating a response indicating that the reservation is complete using the generative AI model and displaying it on the terminal" is a function that confirms that the server has successfully registered the reservation information in the database, inputs the result into the generative AI model, generates a response such as "The reservation has been completed," and sends it to the terminal to display to the user.

[1378] The present invention relates to a locker reservation system, which provides a specific means for users to check locker availability and make reservations. This system improves user convenience by providing locker availability and reservation information through natural language dialogue with users. To implement this invention, ordinary computer system hardware and software, such as a server, terminal, generative AI model, and database, are used.

[1379] Check availability

[1380] User:

[1381] When a user wants to know which lockers are available, they type "Which lockers are currently available?" into the terminal.

[1382] Device:

[1383] The terminal receives input from the user and transmits the content of the input to the server as a request to check availability.

[1384] server:

[1385] When the server receives a request to check availability, it accesses an internal database to obtain the current availability of lockers. This database stores the usage status of each locker (available, reserved, or in use).

[1386] The server passes the acquired availability information to a generative AI model, which generates a response in a format that is easy for the user to understand.

[1387] After the generative AI model generates a response such as "The currently available lockers are A and C," the server sends the response to the terminal.

[1388] Device:

[1389] The terminal displays the response received from the server to the user, allowing the user to check which lockers are currently available.

[1390] Examples:

[1391] The user types into the terminal, "Which lockers are currently available?"

[1392] The terminal sends the contents to the server, which then queries the database to obtain availability data.

[1393] The server passes the acquired data to the generative AI model, which generates a response such as "The currently available lockers are A and C."

[1394] The terminal displays the received response to the user, and the user checks whether locker A or C is available.

[1395] Locker reservation

[1396] User:

[1397] When a user wants to reserve a specific locker for a specified time, the user inputs into the terminal, "I would like to reserve locker A from 10:00 to 12:00 tomorrow."

[1398] Device:

[1399] The terminal receives input from the user and transmits the contents to the server as a reservation request.

[1400] server:

[1401] When the server receives the reservation request, it accesses the database and registers the reservation information for locker A at the specified time.

[1402] After confirming that the reservation information has been successfully registered, the server has the generative AI model generate a response saying "The reservation has been completed."

[1403] The server sends the response generated by the generative AI model to the terminal.

[1404] Device:

[1405] The terminal displays the response received from the server to the user, allowing the user to confirm that the reservation has been completed.

[1406] Examples:

[1407] The user enters into the terminal, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow."

[1408] The terminal transmits the contents to the server, and the server updates the database and registers the reservation information.

[1409] The server uses the generative AI model to generate a response saying "Reservation completed" and sends it to the terminal.

[1410] The terminal displays the received response to the user, and the user confirms that the reservation of locker A has been completed.

[1411] Interactive processing

[1412] User:

[1413] Enter your questions or requests into the terminal in natural language.

[1414] Device:

[1415] Sends user input to the interaction API.

[1416] server:

[1417] The server passes the input received through the dialogue API to the generative AI model, which then generates an appropriate response.

[1418] The generated response is sent to the device via the interaction API.

[1419] Device:

[1420] The terminal displays the received response to the user.

[1421] Examples:

[1422] The user types into the terminal, "Please tell me which lockers are available."

[1423] The device sends the content to a dialogue API, and the server generates a response using a generative AI model.

[1424] The generated response is sent to the terminal, which displays "The currently available lockers are A and C."

[1425] The user confirms.

[1426] In this way, the system of the present invention comprehensively realizes a series of processes to significantly improve user convenience. Users can check availability and make reservations interactively through the generative AI model, allowing for efficient locker usage. This system reduces time wasted and increases user satisfaction.

[1427] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1428] Availability Check Processing Steps

[1429] Step 1:

[1430] User: A user who wants to know the availability status enters "Which lockers are currently available?" into the terminal. This generates an availability check request from the user.

[1431] Step 2:

[1432] Terminal: The terminal receives input from the user (a request to check availability) and sends it to the server. Specifically, it structures the request content in an appropriate format and sends it to the server as an HTTP request.

[1433] Input: User typed "Which lockers are currently available?"

[1434] Output: Availability check request to the server

[1435] Step 3:

[1436] Server: The server receives the availability check request and queries the database to get the current availability of the locker. The server retrieves the locker status from the database using an SQL query or similar.

[1437] Input: Availability check request from terminal

[1438] Output: Availability data retrieved from the database

[1439] Step 4:

[1440] Server: The server passes the acquired availability data to the generative AI model, which then analyzes the data and generates a natural language response.

[1441] Input: Availability data retrieved from the database

[1442] Output: The response generated by the generative AI model: "The currently available lockers are A and C."

[1443] Step 5:

[1444] Server: The server sends the generated response to the terminal. Specifically, the server returns the response to the terminal as an HTTP response.

[1445] Input: The response generated by the generative AI model

[1446] Output: Response data to the terminal

[1447] Step 6:

[1448] Terminal: The terminal displays the response received from the server to the user, allowing the user to check which lockers are currently available.

[1449] Input: Response data from the server

[1450] Output: Show available lockers

[1451] Locker reservation process steps

[1452] Step 1:

[1453] User: A user who wants to reserve a specific locker enters into the terminal, "I would like to reserve locker A from 10:00 to 12:00 tomorrow." This generates a locker reservation request from the user.

[1454] Step 2:

[1455] Terminal: The terminal receives input (reservation request) from the user and sends it to the server. Specifically, it structures the request content in an appropriate format and sends it to the server as an HTTP request.

[1456] Input: User input: "I would like to reserve Locker A from 10:00 to 12:00 tomorrow."

[1457] Output: Reservation request to server

[1458] Step 3:

[1459] Server: The server receives the reservation request, accesses the database, and registers the reservation information for Locker A at the specified time. The server inserts the locker reservation information into the database using an SQL query or similar.

[1460] Input: Reservation request from terminal

[1461] Output: Reservation information database registration results

[1462] Step 4:

[1463] Server: The server confirms that the reservation information has been successfully registered in the database and has the generative AI model generate a response saying "Reservation completed." The generative AI model analyzes the data and generates a natural language response.

[1464] Input: Reservation information database registration results

[1465] Output: The response "Reservation completed" generated by the generative AI model

[1466] Step 5:

[1467] Server: The server sends the generated response to the terminal. Specifically, the server returns the response to the terminal as an HTTP response.

[1468] Input: The response generated by the generative AI model

[1469] Output: Response data to the terminal

[1470] Step 6:

[1471] Terminal: The terminal displays the response received from the server to the user, allowing the user to confirm that the reservation for Locker A has been completed.

[1472] Input: Response data from the server

[1473] Output: Reservation completed

[1474] Interaction processing steps

[1475] Step 1:

[1476] User: Enters a question or request into the terminal in natural language, which generates a user request.

[1477] Step 2:

[1478] Terminal: The terminal sends user input to the interaction API. Specifically, it structures the request content in an appropriate format and sends it to the server as an HTTP request.

[1479] Input: Natural language input from the user

[1480] Output: Request to the conversation API

[1481] Step 3:

[1482] Server: The server receives input through the dialogue API and passes it to the generative AI model to generate an appropriate response. The generative AI model analyzes the data and generates a natural language response.

[1483] Input: User input received through the interaction API.

[1484] Output: The response generated by the generative AI model

[1485] Step 4:

[1486] Server: The server sends the generated response to the terminal via the interaction API. Specifically, the server returns the response to the terminal as an HTTP response.

[1487] Input: The response generated by the generative AI model

[1488] Output: Response data to the terminal

[1489] Step 5:

[1490] Terminal: The terminal displays the responses it receives to the user, allowing the user to get answers to their questions and requests.

[1491] Input: Response data from the server

[1492] Output: Displaying the response to the user

[1493] (Application example 1)

[1494] 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."

[1495] Conventional locker reservation systems require users to input text into a terminal when checking availability or making a reservation, which is time-consuming. Furthermore, at logistics centers and other such sites, this can lead to inefficient item management and pickup operations. This can lead to reduced efficiency in inventory management and pickup, and can lead to work delays and errors. Therefore, there is a demand for a more intuitive and efficient locker reservation system.

[1496] 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.

[1497] In this invention, the server includes a means for converting a user's voice input into text, a means for communicating a response generated by the generative AI model to the user by voice and visual display, and a means for checking locker availability in real time. This allows users to intuitively check availability and reserve lockers by voice input, significantly improving the efficiency of inventory management and pickup operations, especially in logistics centers.

[1498] "User" refers to a person who uses the locker reservation system to check locker availability and make a reservation.

[1499] "Availability Check Request" refers to a request sent by a User to the System to find out which lockers are currently available.

[1500] A "generative AI model" is a model that uses artificial intelligence to understand natural language and generate appropriate responses.

[1501] "Locker" means an individual storage unit for the temporary storage of goods or personal items.

[1502] "Real-time" means that information is processed immediately and reflects the current situation.

[1503] "Reservation Request" means a request submitted by a User to the System to use a specific Locker at a specific date and time.

[1504] "Database" refers to the electronic information storage system for recording and managing locker availability and reservation status.

[1505] "Voice input" refers to an input method in which the system recognizes the user's speech and converts it into text.

[1506] "Visual display" refers to the method by which system-generated information is visually conveyed to the user through a screen or display.

[1507] "Voice response" refers to a method of conveying system-generated information to users by voice.

[1508] A system for realizing the present invention has the following configuration.

[1509] The system components include smart glasses, a server, a database, and a generative AI model. The smart glasses receive voice input from the user, convert it into text, and send it to the server. The server then passes the received text data to the generative AI model, which generates an appropriate response. The generated response is then sent back to the smart glasses and communicated to the user via voice and visual feedback.

[1510] Specifically, the following processing is performed.

[1511] 1. Voice input and conversion: The user speaks to the smart glasses, saying, "Which lockers are currently available?" This voice data is converted into text by the smart glasses' voice recognition software (e.g., Google Cloud Speech-to-Text).

[1512] 2. Utilizing text processing and generative AI models: The input, "Which lockers are currently available?", is converted into text and sent to the server. The server passes this text to a generative AI model, which generates an appropriate response. An example of the generative AI model used here is the OpenAI API.

[1513] 3. Obtaining and transmitting a response: The generative AI model generates a response such as "The currently available lockers are A and C," and the server sends this information to the smart glasses. The smart glasses then convey this information to the user visually (display) and via voice output (e.g., voice synthesis using the gTTS library).

[1514] 4. Locker reservation request processing: The user then voice-inputs, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow." This voice is also converted into text by the smart glasses and sent to the server. The server registers the reservation information in the database and uses the generative AI model to generate a response saying "Reservation completed" and sends it to the smart glasses.

[1515] Examples of prompt statements

[1516] Below is an example of a prompt to be passed to the generative AI model.

[1517] Run the availability check module of the locker reservation system. User input: Which lockers are currently available?

[1518] With the above configuration, users can intuitively check locker availability and make reservations by using voice input through the smart glasses. This is expected to significantly improve the efficiency of inventory management and pickup operations, especially at logistics centers.

[1519] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1520] Step 1:

[1521] The user speaks into the smart glasses, asking, "Which lockers are currently available?" The smart glasses collect the user's voice data using a built-in microphone. The collected voice data is converted into text data by the voice recognition software inside the smart glasses. The input is voice data, and the output is text data.

[1522] Step 2:

[1523] The terminal (smart glasses) sends the converted text data "Which lockers are currently available?" to the server. This transmission uses wireless communication technology (e.g., Wi-Fi). The input is text data, and the output is the transmission of the text data to the server.

[1524] Step 3:

[1525] The server passes the received user text data to the generative AI model as input. The server then sends the text data to the global AI model API (e.g., OpenAI API) to generate an appropriate response. The input is the user's text data, "Which lockers are currently available?", and the output from the generative AI model is the response text, "The currently available lockers are A and C."

[1526] Step 4:

[1527] The server transmits the response text data obtained from the generative AI model to the smart glasses. Wireless communication technology is used for transmission, and the data reaches the smart glasses in real time. The input is the response text data from the generative AI model, and the output is the data transmitted to the smart glasses.

[1528] Step 5:

[1529] The device (smart glasses) receives the response text data, "The currently available lockers are A and C," and conveys it to the user visually and audibly. The visual display uses the display, and the audio output uses a speech synthesis library (e.g., gTTS). The input is the response text data, and the output is the visual display and audio output.

[1530] Step 6:

[1531] The user again uses voice input and speaks into the smart glasses, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow." The voice data is again collected and converted into text data through voice recognition software. The input is voice data, and the output is text data.

[1532] Step 7:

[1533] The terminal (smart glasses) sends text data, such as "I would like to reserve Locker A from 10:00 to 12:00 tomorrow," to the server. This transmission also uses wireless communication technology. The input is text data, and the output is the transmission of text data to the server.

[1534] Step 8:

[1535] The server queries the reservation information to register the received reservation request in the database, and if availability is confirmed, adds the reservation information to the database. The input is the reservation request text data, and the output is an update of the database.

[1536] Step 9:

[1537] The server passes the successful reservation information to the generative AI model, which then generates a response text saying "Reservation completed." The generative AI model generates an appropriate response and returns it to the server. The input is the successful reservation information, and the output is the response text data.

[1538] Step 10:

[1539] The server sends the generated response text data "Reservation completed" to the device (smart glasses). The input is the response text data from the generative AI model, and the output is the data sent to the smart glasses.

[1540] Step 11:

[1541] The terminal (smart glasses) conveys the received response text data to the user by visual display and audio output. The visual display uses a display, and the audio output uses a speech synthesis library. The input is the response text data, and the output is the visual display and audio output.

[1542] Through the above processing steps, the user can easily check the availability of lockers and make reservations through voice input.

[1543] 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.

[1544] The locker reservation system of the present invention provides a concrete means for users to check the availability of lockers and make reservations. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide a more personalized service. The program processing of this system is described in detail below in natural language.

[1545] Check availability

[1546] User:

[1547] When a user wants to know which lockers are available, they type "Which lockers are currently available?" into the terminal.

[1548] Device:

[1549] The terminal receives the user's input and sends an availability check request to the server.

[1550] server:

[1551] The server passes the request through the dialogue module to the generative AI model, which analyzes the request and generates an appropriate SQL query to query the database about the availability of the locker.

[1552] server:

[1553] It receives availability results from a database and passes them to a generative AI model to generate natural language responses.

[1554] Generative AI models:

[1555] Generates the response "Currently available lockers are A and C."

[1556] server:

[1557] The generated response is sent to the terminal.

[1558] Device:

[1559] The response received from the server is displayed to the user.

[1560] Examples:

[1561] When a user types "Which lockers are currently available?" into the terminal, the terminal sends a request to the server, which queries the database and uses a generative AI model to generate a response saying "Lockers A and C are currently available," which is sent to the terminal. The terminal displays the response to the user, who can then confirm whether locker A or C is available.

[1562] Locker reservation

[1563] User:

[1564] When a user wants to use a specific locker, they enter "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[1565] Device:

[1566] The terminal receives user input and sends a reservation request to the server.

[1567] server:

[1568] The server passes the request through the dialogue module to the generative AI model, which analyzes the request and generates the appropriate SQL query to register the reservation information in the database.

[1569] server:

[1570] The reservation registration results are received from the database and the generative AI model generates a response indicating the reservation is complete.

[1571] Generative AI models:

[1572] Generates a "Reservation completed" response.

[1573] server:

[1574] The generated response is sent to the terminal.

[1575] Device:

[1576] The response received from the server is displayed to the user.

[1577] Examples:

[1578] When a user enters "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal, the terminal sends a request to the server, the server updates the database, and uses the generative AI model to generate a response saying "Reservation completed" and sends it to the terminal. The terminal displays the response to the user, who confirms that the reservation of Locker A has been completed.

[1579] Emotion Recognition and Dialogue Processing

[1580] User:

[1581] Enter your questions or requests into the terminal in natural language.

[1582] Device:

[1583] Sends user input to the interaction API.

[1584] server:

[1585] Input received through the dialogue API is passed to the generative AI model, which analyzes the input and generates an appropriate response.

[1586] Emotion Engine:

[1587] Analyze emotions from user input and tailor responses based on those emotions.

[1588] Generative AI models:

[1589] Feedback from the emotion engine is used to generate responses with appropriate timing and content.

[1590] server:

[1591] The generated response is sent to the device via the interaction API.

[1592] Device:

[1593] The generated response is displayed to the user.

[1594] Examples:

[1595] When a user types "Please tell me which lockers are available" into the device, the server passes the received input to the generative AI model, and the emotion engine analyzes the user's emotions. Based on feedback from the emotion engine, the generative AI model generates a response saying "Currently available lockers are A and C. Have you found the locker you are looking for?" and sends it to the device. The device displays the response to the user, who confirms it.

[1596] In this way, the system of the present invention comprehensively realizes a series of processes to significantly improve user convenience. Furthermore, by combining it with an emotion engine, interactions with users become more natural and personalized, further increasing user satisfaction.

[1597] The processing flow will be explained below.

[1598] Check availability

[1599] Step 1:

[1600] The user inputs a request for the locker they want to check availability for into the terminal (e.g., "Which lockers are currently available?").

[1601] Step 2:

[1602] The terminal receives the user's input and sends an availability check request to the server.

[1603] Step 3:

[1604] The server passes the request to the generative AI model through the dialogue module.

[1605] Step 4:

[1606] A generative AI model analyzes the request and generates an SQL query to query the database about locker availability.

[1607] Step 5:

[1608] The server retrieves availability information from the database.

[1609] Step 6:

[1610] The server passes the acquired availability information to a generative AI model, which generates a natural language response.

[1611] Step 7:

[1612] The generative AI model generates the response, "The currently available lockers are A and C."

[1613] Step 8:

[1614] The server sends the generated response to the terminal.

[1615] Step 9:

[1616] The terminal displays the response received from the server to the user.

[1617] Locker reservation

[1618] Step 1:

[1619] When a user wants to use a specific locker, they input "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[1620] Step 2:

[1621] The terminal receives the user's input and sends a reservation request to the server.

[1622] Step 3:

[1623] The server passes the request to the generative AI model through the dialogue module.

[1624] Step 4:

[1625] The generative AI model analyzes the request and generates an SQL query to register the reservation information in the database.

[1626] Step 5:

[1627] The server receives the reservation registration results from the database.

[1628] Step 6:

[1629] The server confirms whether the registration was successful and has the generative AI model generate a response indicating that the reservation is complete.

[1630] Step 7:

[1631] The generative AI model generates the response "Reservation completed."

[1632] Step 8:

[1633] The server sends the generated response to the terminal.

[1634] Step 9:

[1635] The terminal displays the response received from the server to the user.

[1636] Emotion Recognition and Dialogue Processing

[1637] Step 1:

[1638] The user types a question or request into the terminal in natural language (e.g., "Please tell me which lockers are available").

[1639] Step 2:

[1640] The device sends input from the user to the interaction API.

[1641] Step 3:

[1642] The server passes the input received through the dialogue API to the generative AI model.

[1643] Step 4:

[1644] The generative AI model analyzes the input and requests emotion analysis from the emotion engine.

[1645] Step 5:

[1646] The emotion engine analyzes emotions from user input and sends the results back to the generative AI model.

[1647] Step 6:

[1648] The generative AI model uses feedback from the emotion engine to generate an appropriate response (e.g., "Currently available lockers are A and C. Did you find the locker you were looking for?").

[1649] Step 7:

[1650] The server sends the generated response to the terminal via the interaction API.

[1651] Step 8:

[1652] The terminal displays the generated response to the user.

[1653] Example 2

[1654] 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."

[1655] The problem that this invention aims to solve is to provide a system that allows users to obtain real-time information when checking locker availability and reserving lockers, and further to make the dialogue with users natural and personalized by using an emotion engine. In conventional systems, checking locker availability and reserving lockers were done manually, which reduced user convenience. Furthermore, because the system was unable to respond in a way that took the user's emotions into consideration, the dialogue was one-sided and user satisfaction was low.

[1656] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting an availability check request from a user, means for checking locker availability in real time using a generative AI model, means for providing information on available lockers to the user, means for accepting a locker reservation request from a user, means for registering the reservation information in a database, means for notifying the user of reservation completion information using a generative AI model, and means for analyzing the user's emotions using an emotion engine and adjusting the response based on the emotions. This not only enables efficient checking of locker availability and reservations, but also makes it possible to have natural and personalized interactions with the user, thereby improving user satisfaction.

[1657] "Availability check request from user" means a request sent by a user to the system to check the availability of a locker.

[1658] A "generative AI model" refers to an artificial intelligence technology that analyzes user input and generates an appropriate response based on the results of that analysis.

[1659] "Means for checking locker availability in real time" refers to methods and technologies for instantly obtaining information about currently available lockers and providing it to users.

[1660] "Means for providing users with information about available lockers" refers to methods or technologies for displaying or notifying users of information about currently available lockers.

[1661] A "locker reservation request from a user" is a request sent to the system by a user wishing to use a specific locker on a specific date and time.

[1662] "Means for registering reservation information in a database" refers to a method or technology for storing reservation-related information in a database based on a reservation request from a user.

[1663] "Means for notifying users of reservation completion information using a generative AI model" refers to methods or technologies for generating a response using a generative AI model to inform users that their reservation has been successfully completed, and for communicating that response to users.

[1664] An "emotion engine" is a technology or module that analyzes emotions from user input and adjusts the system's response based on the analysis results.

[1665] "Means for analyzing user emotions" refers to methods or techniques for identifying emotions from text entered by a user and using the results to help the system generate responses.

[1666] The locker reservation system of the present invention provides a means for users to check locker availability and make reservations. This system combines a generative AI model and an emotion engine to improve user convenience, and is described in detail below.

[1667] System Configuration

[1668] This system includes the following hardware and software:

[1669] Terminal: The device used by the user to input data (smartphone, tablet, PC, etc.)

[1670] Server: A central system that processes input and interfaces with generative AI models and databases

[1671] Database: Data storage for storing locker availability and reservation information

[1672] Generative AI model: AI technology that generates responses through natural language processing

[1673] Emotion Engine: Technology that analyzes user emotions and adjusts responses

[1674] Check availability

[1675] User:

[1676] When a user wants to check the availability of lockers, they type "Which lockers are currently available?" into the terminal.

[1677] Device:

[1678] The terminal receives the user's input and sends an availability check request to the server.

[1679] server:

[1680] The server passes the request through the dialogue module to the generative AI model, which parses the request, generates the appropriate SQL query, and executes it against the database. For example, it generates the query "SELECT FROM lockers WHERE status='empty'".

[1681] Database:

[1682] It receives query results from the database and sends information about available lockers to the server.

[1683] Generative AI models:

[1684] The generative AI model receives the database results from the server and generates a natural language response such as, "The currently available lockers are A and C."

[1685] server:

[1686] The generated response is sent to the terminal.

[1687] Device:

[1688] The terminal displays the response received from the server to the user. Specifically, the terminal screen displays "Currently available lockers are A and C."

[1689] Examples:

[1690] When a user types "Which lockers are currently available?" into the device, the device sends the request to the server. The server executes an SQL query and uses a generative AI model to generate a response, "Lockers A and C are currently available," which is sent to the device. The device then displays the response on its screen for the user to confirm.

[1691] Locker reservation

[1692] User:

[1693] When a user wants to reserve a specific locker, they enter "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[1694] Device:

[1695] The terminal receives the input and sends a reservation request to the server.

[1696] server:

[1697] The server passes the reservation request through the dialogue module to the generative AI model, which parses the request, generates the appropriate SQL query, and executes it against the database. For example, it generates the query "INSERT INTO reservations (locker_id, start_time, end_time) VALUES ('A', '2023-06-01 10:00', '2023-06-01 12:00')".

[1698] Database:

[1699] The database executes queries and registers reservation information.

[1700] Generative AI models:

[1701] The generative AI model receives the registration completion result from the database and generates a natural language response saying, "Your reservation is complete."

[1702] server:

[1703] The generated response is sent to the terminal.

[1704] Device:

[1705] The terminal displays the response received from the server to the user. Specifically, the terminal displays "Reservation completed" on its screen.

[1706] Examples:

[1707] When a user types "I'd like to reserve Locker A from 10:00 to 12:00 tomorrow" into the device, the device sends the request to the server. The server executes an SQL query and uses a generative AI model to generate a response saying "Reservation completed" and sends it to the device. The device then displays the response on its screen for the user to confirm.

[1708] Emotion Recognition and Dialogue Processing

[1709] User:

[1710] Enter your question or request into the terminal in natural language, for example, "Please tell me which lockers are available."

[1711] Device:

[1712] Input from the user is sent to the server via the interaction API.

[1713] server:

[1714] The server passes the request through a dialogue API to the generative AI model, which analyzes the input and uses an emotion engine to analyze the user's emotions.

[1715] Emotion Engine:

[1716] The emotion engine analyzes emotions from user input and feeds the results back to the generative AI model.

[1717] Generative AI models:

[1718] The generative AI model generates a natural language response based on feedback from the emotion engine, for example, "Currently, the available lockers are A and C. Did you find the locker you were looking for?"

[1719] server:

[1720] The generated response is sent to the device via the interaction API.

[1721] Device:

[1722] The terminal displays the response received from the server to the user. Specifically, the terminal screen displays the message "Currently available lockers are A and C. Have you found the locker you are looking for?"

[1723] Examples:

[1724] When a user types "Please tell me which lockers are available" into the device, the server passes the request to the generative AI model, which analyzes the emotion using the emotion engine. The generative AI model generates a response saying "Currently available lockers are A and C. Have you found the locker you are looking for?" and sends it to the device. The device displays the response on the screen for the user to confirm.

[1725] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1726] Availability Check Processing Steps

[1727] Step 1:

[1728] The user inputs information into the terminal to check availability. Specifically, the user inputs "Which lockers are currently available?" into the input field on the terminal. Input: The user's question in natural language.

[1729] Step 2:

[1730] The terminal processes the user's input and sends it to the server as an HTTP request. Input: User's question text. Output: HTTP request to the server.

[1731] Step 3:

[1732] The server passes the received request to the dialogue module and sends the request to the generative AI model. Input: Contents of the HTTP request. Output: Request to the generative AI model.

[1733] Step 4:

[1734] The generative AI model analyzes the user's question and generates an appropriate SQL query. It then returns the query to the server. For example, it generates the query "SELECT FROM lockers WHERE status='empty'". Input: User's question text. Output: SQL query.

[1735] Step 5:

[1736] The server sends the SQL query received from the generative AI model to the database and executes the query. Input: SQL query. Output: Result of executing the database query.

[1737] Step 6:

[1738] The database returns the information of available lockers to the server as a result of the query. For example, information such as "Locker A, Locker C" is returned. Input: Database query. Output: Available locker information.

[1739] Step 7:

[1740] The server passes the information about available lockers received from the database to the generative AI model, which generates a natural language response. Input: Available locker information. Output: Natural language response.

[1741] Step 8:

[1742] The generative AI model generates a natural language response based on the information about available lockers, such as "The currently available lockers are A and C," and returns that response to the server. Input: Available locker information. Output: Response text.

[1743] Step 9:

[1744] The server generates a response and sends it to the device. Input: Response text. Output: HTTP response to the device.

[1745] Step 10:

[1746] The terminal receives the response from the server and displays it to the user. Specifically, it displays "Currently available lockers are A and C" on the terminal screen. Input: HTTP response. Output: Displayed to the user.

[1747] Locker reservation process steps

[1748] Step 1:

[1749] The user inputs information into the terminal to reserve a specific locker. Specifically, the user inputs "I would like to reserve locker A from 10:00 to 12:00 tomorrow." Input: The user's reservation request in natural language.

[1750] Step 2:

[1751] The terminal processes the user's input and sends it to the server as an HTTP request. Input: Reservation request text. Output: HTTP request to the server.

[1752] Step 3:

[1753] The server passes the received request to the dialogue module and sends the request to the generative AI model. Input: Contents of the HTTP request. Output: Request to the generative AI model.

[1754] Step 4:

[1755] The generative AI model analyzes the user's reservation request and generates the appropriate SQL query. It then returns the query to the server. For example, it generates the query "INSERT INTO reservations (locker_id, start_time, end_time) VALUES ('A', '2023-06-01 10:00', '2023-06-01 12:00')". Input: Reservation request text. Output: SQL query.

[1756] Step 5:

[1757] The server sends the SQL query received from the generative AI model to the database and executes the query. Input: SQL query. Output: Result of executing the database query.

[1758] Step 6:

[1759] The database returns the reservation information to the server as a result of the query, e.g., a result of "reservation successful". Input: Database query. Output: Reservation result.

[1760] Step 7:

[1761] The server passes the reservation results received from the database to the generative AI model, which generates a natural language response. Input: Reservation results. Output: Natural language response.

[1762] Step 8:

[1763] The generative AI model generates a natural language response, "Reservation completed," based on the reservation result and returns the response to the server. Input: Reservation result. Output: Response text.

[1764] Step 9:

[1765] The server generates a response and sends it to the device. Input: Response text. Output: HTTP response to the device.

[1766] Step 10:

[1767] The terminal receives the response from the server and displays it to the user. Specifically, it displays "Reservation completed" on the terminal screen. Input: HTTP response. Output: Displayed to the user.

[1768] Processing steps for emotion recognition and dialogue processing

[1769] Step 1:

[1770] The user inputs a question or request into the terminal in natural language. For example, "Please tell me which lockers are available." Input: A user's natural language inquiry.

[1771] Step 2:

[1772] The terminal sends the user's input to the server through the interaction API. Input: Query text. Output: HTTP request to the server.

[1773] Step 3:

[1774] The server sends a request to the generative AI model through the interaction API. Input: HTTP request content. Output: Request to the generative AI model.

[1775] Step 4:

[1776] The generative AI model analyzes the user's input and sends the results to the emotion engine. Input: User's query text. Output: Analyzed data.

[1777] Step 5:

[1778] The emotion engine analyzes emotions from user input and feeds the analysis results back to the generative AI model. For example, it may analyze the input to determine that the emotion is "friendly." Input: Analyzed data. Output: Emotion feedback.

[1779] Step 6:

[1780] The generative AI model uses emotional feedback to generate an appropriate natural language response. For example, it generates a response like, "Currently available lockers are A and C. Did you find the locker you were looking for?" Input: Emotional feedback. Output: Response text.

[1781] Step 7:

[1782] The server sends the generated response to the device via the interaction API. Input: Response text. Output: HTTP response to the device.

[1783] Step 8:

[1784] The terminal receives the response from the server and displays it to the user. Specifically, it displays the message "Currently available lockers are A and C. Have you found the locker you are looking for?" on the terminal screen. Input: HTTP response. Output: Displayed to the user.

[1785] (Application example 2)

[1786] 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."

[1787] In conventional locker reservation systems, when users check availability and make reservations, they are provided with one-sided information that ignores their emotions. This often causes inconvenience and stress for users. In addition, there are problems with the speed and accuracy of real-time availability confirmation and reservation completion notifications, which tends to reduce user satisfaction.

[1788] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1789] In this invention, the server includes means for accepting availability confirmation requests from users, means for checking locker availability in real time using a generative AI model, means for providing users with information on available lockers, means for accepting locker reservation requests from users, means for registering the reservation information in a database, means for notifying users of reservation completion information using a generative AI model, and means for analyzing the user's emotions using an emotion recognition engine and adjusting responses based on the emotions, thereby enabling users to receive more personalized and convenient services.

[1790] "User" refers to an end user who uses the locker reservation system.

[1791] "Availability Check Request" refers to a request made by a User to check information about lockers that are currently available.

[1792] A "generative AI model" refers to an artificial intelligence model that uses technologies such as natural language processing to conduct dialogue and process data within a system.

[1793] An "emotion recognition engine" refers to an engine that analyzes the emotions from the user's input and adjusts the response.

[1794] "Real-time" refers to data processing occurring almost simultaneously with the current time.

[1795] "Locker availability" refers to information indicating whether a locker is currently in use or available.

[1796] "Reservation Request" means a request by a User to make a reservation for use of a particular Locker.

[1797] "Database" refers to structured data storage for managing and storing locker availability and reservation information.

[1798] "Reservation completion information" refers to information that notifies a user that their locker reservation has been successful.

[1799] The present invention provides a concrete example of how to use a locker reservation system. This system allows users to check locker availability, make a reservation, and receive notification of reservation completion, using a generative AI model. It also utilizes an emotion recognition engine to analyze user emotions and tailor responses to provide a more personalized experience for users.

[1800] System configuration

[1801] The system consists of the following main elements:

[1802] 1. Server

[1803] The server accepts requests from users, queries the database, and generates results using a generative AI model.

[1804] 2. Terminal

[1805] The device receives input from the user, sends the request to the server, displays the response, and runs an emotion recognition engine to analyze the user's emotions.

[1806] 3. Database

[1807] The database manages and stores locker availability and reservation information.

[1808] Program processing

[1809] The server includes means for accepting a request from a user to check availability, means for checking locker availability in real time using a generative AI model, means for providing information about available lockers to users, means for accepting a locker reservation request from a user, means for registering the reservation information in a database, means for notifying the user of reservation completion information using a generative AI model, and means for analyzing the user's emotions using an emotion recognition engine and adjusting a response based on the emotions.

[1810] Hardware or software used

[1811] This system uses the following hardware and software:

[1812] Microphone for voice input

[1813] User's device (Android or iOS smartphone)

[1814] Speech-to-text conversion using the speech_recognition library

[1815] Sending API requests to the server using the requests library

[1816] The server-side backend implements the generative AI model and database.

[1817] Specific examples

[1818] When a user speaks to the device, asking, "Which parking spaces are currently available?", the system converts the voice input into text and sends a request to the server. The server queries the database and uses a generative AI model to generate a natural language response. For example, it might respond, "The currently available parking spaces are B2 and D4."

[1819] Example prompt sentence:

[1820] User: What parking spaces are currently available?

[1821] Server: Please wait while we check parking availability...

[1822] Generative AI model: The currently available parking spaces are B2 and D4.

[1823] As described above, this system can provide efficient and personalized services to users.

[1824] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1825] Step 1:

[1826] The user speaks to their smartphone device, "What parking spaces are currently available?" The device converts the voice input into text and generates the user's request. At this time, a speech recognition engine (e.g., Google Speech API) analyzes the voice data and converts it into text data.

[1827] Input: User voice input

[1828] Output: Text data ("What parking spaces are currently available?")

[1829] Step 2:

[1830] The device sends the generated text data to the server, which receives the request and passes it to the generative AI model via the dialogue module. The generative AI model analyzes the request and generates an SQL query to check availability.

[1831] Input: Text data ("What parking spaces are currently available?")

[1832] Output: SQL query

[1833] Step 3:

[1834] The server sends the generated SQL query to the database to inquire about availability, and the database responds by returning the current availability.

[1835] Input: SQL query

[1836] Output: Availability data (e.g. "B2 and D4 are available")

[1837] Step 4:

[1838] The server passes the acquired availability data to the generative AI model, which then generates a response in natural language. The generative AI model uses an emotion recognition engine to analyze the user's emotions and select an appropriate response.

[1839] Input: Availability data ("B2 and D4 are available")

[1840] Output: A natural language response (e.g., "Currently available parking spaces are B2 and D4")

[1841] Step 5:

[1842] The server sends the generated natural language response to the device, which uses a speech synthesis engine (e.g., Google Text-to-Speech) to audibly notify the user of the received response.

[1843] Input: Natural Language Response ("Currently available parking spaces are B2 and D4")

[1844] Output: Audio data (notification to user)

[1845] Step 6:

[1846] If a user wants to reserve a specific parking space, they can speak into the terminal, "I would like to reserve parking space B2 from 2:00 PM to 4:00 PM tomorrow." The terminal converts the voice input into text and generates a reservation request for the user.

[1847] Input: User voice input

[1848] Output: Text data ("I would like to reserve parking space B2 from 2:00 PM to 4:00 PM tomorrow.")

[1849] Step 7:

[1850] The device sends the generated text data to the server, which receives the request, analyzes it using a generative AI model, and generates an SQL query for reservation registration.

[1851] Input: Text data ("I would like to reserve parking space B2 from 2:00 PM to 4:00 PM tomorrow.")

[1852] Output: SQL query

[1853] Step 8:

[1854] The server sends the generated SQL query to the database to register the reservation information, and the database returns the registration result to the server.

[1855] Input: SQL query

[1856] Output: Reservation registration results

[1857] Step 9:

[1858] The server passes the reservation registration results from the database to the generative AI model, which then generates a reservation completion notification. The generative AI model then selects an appropriate response for the user.

[1859] Input: Reservation registration result

[1860] Output: A natural language response (e.g., "Your reservation is complete")

[1861] Step 10:

[1862] The server sends the generated natural language response to the device, which uses a speech synthesis engine to audibly notify the user of the received response.

[1863] Input: Natural language response ("Your reservation is complete")

[1864] Output: Audio data (notification to user)

[1865] 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.

[1866] 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.

[1867] 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.

[1868] [Fourth embodiment]

[1869] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1870] 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.

[1871] 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).

[1872] 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.

[1873] 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.

[1874] 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).

[1875] 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.

[1876] 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.

[1877] 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.

[1878] 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.

[1879] 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.

[1880] 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.

[1881] 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."

[1882] The locker reservation system of the present invention provides a specific means for users to check the availability of lockers and make reservations. The processing of the system program will be explained in natural language below.

[1883] Check availability

[1884] User:

[1885] When a user wants to know which lockers are available, they type "Which lockers are currently available?" into the terminal.

[1886] Device:

[1887] The terminal receives the user's input and sends an availability check request to the server.

[1888] server:

[1889] Based on the received request, the server retrieves the current availability of lockers from the database.

[1890] The acquired availability information is passed to a generative AI model to generate an appropriate response.

[1891] After the generative AI model generates a response saying "The currently available lockers are A and C," it sends it to the terminal.

[1892] Device:

[1893] The terminal displays the response received from the server to the user.

[1894] Examples:

[1895] The user types into the terminal, "Which lockers are currently available?"

[1896] The device sends a request to the server, which queries the database and uses a generative AI model to generate a response such as "The currently available lockers are A and C," which is then sent to the device.

[1897] The terminal displays the response to the user, who then checks whether locker A or C is available.

[1898] Locker reservation

[1899] User:

[1900] When a user wants to use a specific locker, they enter "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[1901] Device:

[1902] The terminal receives user input and sends a reservation request to the server.

[1903] server:

[1904] The server registers the reservation information in the database based on the received request.

[1905] Confirm that the registration was successful and have the generative AI model generate a response indicating that the reservation was completed.

[1906] The generative AI model generates a response saying "Reservation completed" and sends it to the device.

[1907] Device:

[1908] The terminal displays the response received from the server to the user.

[1909] Examples:

[1910] The user enters into the terminal, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow."

[1911] The device sends a request to the server, the server updates the database, uses the generative AI model to generate a response saying "Reservation completed," and sends it to the device.

[1912] The terminal displays the response to the user, and the user confirms that the reservation of Locker A has been completed.

[1913] Interactive processing

[1914] User:

[1915] Enter your questions or requests into the terminal in natural language.

[1916] Device:

[1917] Sends user input to the interaction API.

[1918] server:

[1919] Input received through a dialogue API is passed to a generative AI model to generate an appropriate response.

[1920] The generated response is sent to the device via the interaction API.

[1921] Device:

[1922] The generated response is displayed to the user.

[1923] Examples:

[1924] The user types into the terminal, "Please tell me which lockers are available."

[1925] The server passes the received input to a generative AI model, which generates a response and sends it to the device.

[1926] The terminal displays "Currently available lockers are A and C," and the user confirms.

[1927] In this way, the system of the present invention comprehensively realizes a series of processes to significantly improve user convenience. Users can check availability and make reservations interactively through the generative AI model, allowing for efficient locker usage. This system reduces time wasted and increases user satisfaction.

[1928] The processing flow will be explained below.

[1929] Check availability

[1930] Step 1:

[1931] The user inputs a request for the locker they want to check availability for into the terminal (e.g., "Which lockers are currently available?").

[1932] Step 2:

[1933] The terminal receives the user's input and sends an availability check request to the server.

[1934] Step 3:

[1935] The server passes the request to the generative AI model through the dialogue module.

[1936] Step 4:

[1937] A generative AI model analyzes the request and generates an appropriate SQL query to query the database about locker availability.

[1938] Step 5:

[1939] The server receives the availability results from the database.

[1940] Step 6:

[1941] The availability information obtained by the server is then passed back to the generative AI model, which generates a natural language response.

[1942] Step 7:

[1943] The generative AI model generates the response, "The currently available lockers are A and C."

[1944] Step 8:

[1945] The server sends the generated response to the terminal.

[1946] Step 9:

[1947] The terminal displays the response received from the server to the user.

[1948] Locker reservation

[1949] Step 1:

[1950] When a user wants to use a specific locker, they input "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[1951] Step 2:

[1952] The terminal receives the user's input and sends a reservation request to the server.

[1953] Step 3:

[1954] The server passes the request to the generative AI model through the dialogue module.

[1955] Step 4:

[1956] A generative AI model analyzes the request and generates the appropriate SQL query to register the reservation information in the database.

[1957] Step 5:

[1958] The server receives the reservation registration results from the database.

[1959] Step 6:

[1960] The server confirms that the registration was successful and has the generative AI model generate a response indicating that the reservation is complete.

[1961] Step 7:

[1962] The generative AI model generates the response "Reservation completed."

[1963] Step 8:

[1964] The server sends the generated response to the terminal.

[1965] Step 9:

[1966] The terminal displays the response received from the server to the user.

[1967] Interactive processing

[1968] Step 1:

[1969] The user enters a question or request into the terminal in natural language.

[1970] Step 2:

[1971] The device sends input from the user to the interaction API.

[1972] Step 3:

[1973] The server passes the input received through the dialogue API to the generative AI model.

[1974] Step 4:

[1975] A generative AI model analyzes the input and generates an appropriate response.

[1976] Step 5:

[1977] The server sends the generated response to the terminal via the interaction API.

[1978] Step 6:

[1979] The terminal displays the generated response to the user.

[1980] Example 1

[1981] 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."

[1982] Currently, there is no system that allows users to easily check the availability of lockers and quickly make reservations. As a result, users often waste time checking for available lockers and reserving them, which prevents them from using the lockers efficiently. Another issue is that conventional systems require complex interfaces and operations, which reduces user convenience.

[1983] 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.

[1984] In this invention, the server includes means for accepting a vacancy check request from a user, means for sending the vacancy check request to the server, means for the server to obtain vacancy information from a database, means for passing the obtained vacancy information to a generative AI model to generate an appropriate response, means for sending the generated response from the server to a terminal and for the terminal to display the response, means for accepting a locker reservation request from a user, means for sending the reservation request to the server, means for the server to register the reservation information in the database, and means for generating a reservation completion response using the generative AI model and displaying it on the terminal after the reservation registration is successful. This allows users to easily and quickly check for available lockers and reserve them.

[1985] The "means for accepting a request from a user to check availability" is a function for accepting a request input by a user into a terminal to check the availability of a locker.

[1986] The "means for transmitting an availability check request to a server" is a function for transmitting an availability check request from a terminal to a server.

[1987] The "means by which the server obtains availability information from the database" is a function that allows the server to access the internal database and obtain the current availability status of lockers.

[1988] "Means of passing the acquired availability information to the generative AI model and generating an appropriate response" refers to a function that inputs the locker availability information acquired by the server from the database into the generative AI model and creates a response in a format that is easy for the user to understand.

[1989] "Means for transmitting the generated response from the server to the terminal, and for the terminal to display the response" refers to the function by which the server transmits the response obtained from the generative AI model to the terminal, and the terminal displays the response to the user.

[1990] The "means for accepting a locker reservation request from a user" is a function for accepting a request input by a user into a terminal to reserve a specific locker at a specified time.

[1991] The "means for sending a reservation request to the server" is a function for sending a locker reservation request from the terminal to the server.

[1992] "Means for the server to register reservation information in the database" is a function that allows the server to update the database based on a reservation request and reserve a locker at a specified time.

[1993] "After the reservation is successfully registered, a means for generating a response indicating that the reservation is complete using the generative AI model and displaying it on the terminal" is a function that confirms that the server has successfully registered the reservation information in the database, inputs the result into the generative AI model, generates a response such as "The reservation has been completed," and sends it to the terminal to display to the user.

[1994] The present invention relates to a locker reservation system, which provides a specific means for users to check locker availability and make reservations. This system improves user convenience by providing locker availability and reservation information through natural language dialogue with users. To implement this invention, ordinary computer system hardware and software, such as a server, terminal, generative AI model, and database, are used.

[1995] Check availability

[1996] User:

[1997] When a user wants to know which lockers are available, they type "Which lockers are currently available?" into the terminal.

[1998] Device:

[1999] The terminal receives input from the user and transmits the content of the input to the server as a request to check availability.

[2000] server:

[2001] When the server receives a request to check availability, it accesses an internal database to obtain the current availability of lockers. This database stores the usage status of each locker (available, reserved, or in use).

[2002] The server passes the acquired availability information to a generative AI model, which generates a response in a format that is easy for the user to understand.

[2003] After the generative AI model generates a response such as "The currently available lockers are A and C," the server sends the response to the terminal.

[2004] Device:

[2005] The terminal displays the response received from the server to the user, allowing the user to check which lockers are currently available.

[2006] Examples:

[2007] The user types into the terminal, "Which lockers are currently available?"

[2008] The terminal sends the contents to the server, which then queries the database to obtain availability data.

[2009] The server passes the acquired data to the generative AI model, which generates a response such as "The currently available lockers are A and C."

[2010] The terminal displays the received response to the user, and the user checks whether locker A or C is available.

[2011] Locker reservation

[2012] User:

[2013] When a user wants to reserve a specific locker for a specified time, the user inputs into the terminal, "I would like to reserve locker A from 10:00 to 12:00 tomorrow."

[2014] Device:

[2015] The terminal receives input from the user and transmits the contents to the server as a reservation request.

[2016] server:

[2017] When the server receives the reservation request, it accesses the database and registers the reservation information for locker A at the specified time.

[2018] After confirming that the reservation information has been successfully registered, the server has the generative AI model generate a response saying "The reservation has been completed."

[2019] The server sends the response generated by the generative AI model to the terminal.

[2020] Device:

[2021] The terminal displays the response received from the server to the user, allowing the user to confirm that the reservation has been completed.

[2022] Examples:

[2023] The user enters into the terminal, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow."

[2024] The terminal transmits the contents to the server, and the server updates the database and registers the reservation information.

[2025] The server uses the generative AI model to generate a response saying "Reservation completed" and sends it to the terminal.

[2026] The terminal displays the received response to the user, and the user confirms that the reservation of locker A has been completed.

[2027] Interactive processing

[2028] User:

[2029] Enter your questions or requests into the terminal in natural language.

[2030] Device:

[2031] Sends user input to the interaction API.

[2032] server:

[2033] The server passes the input received through the dialogue API to the generative AI model, which then generates an appropriate response.

[2034] The generated response is sent to the device via the interaction API.

[2035] Device:

[2036] The terminal displays the received response to the user.

[2037] Examples:

[2038] The user types into the terminal, "Please tell me which lockers are available."

[2039] The device sends the content to a dialogue API, and the server generates a response using a generative AI model.

[2040] The generated response is sent to the terminal, which displays "The currently available lockers are A and C."

[2041] The user confirms.

[2042] In this way, the system of the present invention comprehensively realizes a series of processes to significantly improve user convenience. Users can check availability and make reservations interactively through the generative AI model, allowing for efficient locker usage. This system reduces time wasted and increases user satisfaction.

[2043] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2044] Availability Check Processing Steps

[2045] Step 1:

[2046] User: A user who wants to know the availability status enters "Which lockers are currently available?" into the terminal. This generates an availability check request from the user.

[2047] Step 2:

[2048] Terminal: The terminal receives input from the user (a request to check availability) and sends it to the server. Specifically, it structures the request content in an appropriate format and sends it to the server as an HTTP request.

[2049] Input: User typed "Which lockers are currently available?"

[2050] Output: Availability check request to the server

[2051] Step 3:

[2052] Server: The server receives the availability check request and queries the database to get the current availability of the locker. The server retrieves the locker status from the database using an SQL query or similar.

[2053] Input: Availability check request from terminal

[2054] Output: Availability data retrieved from the database

[2055] Step 4:

[2056] Server: The server passes the acquired availability data to the generative AI model, which then analyzes the data and generates a natural language response.

[2057] Input: Availability data retrieved from the database

[2058] Output: The response generated by the generative AI model: "The currently available lockers are A and C."

[2059] Step 5:

[2060] Server: The server sends the generated response to the terminal. Specifically, the server returns the response to the terminal as an HTTP response.

[2061] Input: The response generated by the generative AI model

[2062] Output: Response data to the terminal

[2063] Step 6:

[2064] Terminal: The terminal displays the response received from the server to the user, allowing the user to check which lockers are currently available.

[2065] Input: Response data from the server

[2066] Output: Show available lockers

[2067] Locker reservation process steps

[2068] Step 1:

[2069] User: A user who wants to reserve a specific locker enters into the terminal, "I would like to reserve locker A from 10:00 to 12:00 tomorrow." This generates a locker reservation request from the user.

[2070] Step 2:

[2071] Terminal: The terminal receives input (reservation request) from the user and sends it to the server. Specifically, it structures the request content in an appropriate format and sends it to the server as an HTTP request.

[2072] Input: User input: "I would like to reserve Locker A from 10:00 to 12:00 tomorrow."

[2073] Output: Reservation request to server

[2074] Step 3:

[2075] Server: The server receives the reservation request, accesses the database, and registers the reservation information for Locker A at the specified time. The server inserts the locker reservation information into the database using an SQL query or similar.

[2076] Input: Reservation request from terminal

[2077] Output: Reservation information database registration results

[2078] Step 4:

[2079] Server: The server confirms that the reservation information has been successfully registered in the database and has the generative AI model generate a response saying "Reservation completed." The generative AI model analyzes the data and generates a natural language response.

[2080] Input: Reservation information database registration results

[2081] Output: The response "Reservation completed" generated by the generative AI model

[2082] Step 5:

[2083] Server: The server sends the generated response to the terminal. Specifically, the server returns the response to the terminal as an HTTP response.

[2084] Input: The response generated by the generative AI model

[2085] Output: Response data to the terminal

[2086] Step 6:

[2087] Terminal: The terminal displays the response received from the server to the user, allowing the user to confirm that the reservation for Locker A has been completed.

[2088] Input: Response data from the server

[2089] Output: Reservation completed

[2090] Interaction processing steps

[2091] Step 1:

[2092] User: Enters a question or request into the terminal in natural language, which generates a user request.

[2093] Step 2:

[2094] Terminal: The terminal sends user input to the interaction API. Specifically, it structures the request content in an appropriate format and sends it to the server as an HTTP request.

[2095] Input: Natural language input from the user

[2096] Output: Request to the conversation API

[2097] Step 3:

[2098] Server: The server receives input through the dialogue API and passes it to the generative AI model to generate an appropriate response. The generative AI model analyzes the data and generates a natural language response.

[2099] Input: User input received through the interaction API.

[2100] Output: The response generated by the generative AI model

[2101] Step 4:

[2102] Server: The server sends the generated response to the terminal via the interaction API. Specifically, the server returns the response to the terminal as an HTTP response.

[2103] Input: The response generated by the generative AI model

[2104] Output: Response data to the terminal

[2105] Step 5:

[2106] Terminal: The terminal displays the responses it receives to the user, allowing the user to get answers to their questions and requests.

[2107] Input: Response data from the server

[2108] Output: Displaying the response to the user

[2109] (Application example 1)

[2110] 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."

[2111] Conventional locker reservation systems require users to input text into a terminal when checking availability or making a reservation, which is time-consuming. Furthermore, at logistics centers and other such sites, this can lead to inefficient item management and pickup operations. This can lead to reduced efficiency in inventory management and pickup, and can lead to work delays and errors. Therefore, there is a demand for a more intuitive and efficient locker reservation system.

[2112] 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.

[2113] In this invention, the server includes a means for converting a user's voice input into text, a means for communicating a response generated by the generative AI model to the user by voice and visual display, and a means for checking locker availability in real time. This allows users to intuitively check availability and reserve lockers by voice input, significantly improving the efficiency of inventory management and pickup operations, especially in logistics centers.

[2114] "User" refers to a person who uses the locker reservation system to check locker availability and make a reservation.

[2115] "Availability Check Request" refers to a request sent by a User to the System to find out which lockers are currently available.

[2116] A "generative AI model" is a model that uses artificial intelligence to understand natural language and generate appropriate responses.

[2117] "Locker" means an individual storage unit for the temporary storage of goods or personal items.

[2118] "Real-time" means that information is processed immediately and reflects the current situation.

[2119] "Reservation Request" means a request submitted by a User to the System to use a specific Locker at a specific date and time.

[2120] "Database" refers to the electronic information storage system for recording and managing locker availability and reservation status.

[2121] "Voice input" refers to an input method in which the system recognizes the user's speech and converts it into text.

[2122] "Visual display" refers to the method by which system-generated information is visually conveyed to the user through a screen or display.

[2123] "Voice response" refers to a method of conveying system-generated information to users by voice.

[2124] A system for realizing the present invention has the following configuration.

[2125] The system components include smart glasses, a server, a database, and a generative AI model. The smart glasses receive voice input from the user, convert it into text, and send it to the server. The server then passes the received text data to the generative AI model, which generates an appropriate response. The generated response is then sent back to the smart glasses and communicated to the user via voice and visual feedback.

[2126] Specifically, the following processing is performed.

[2127] 1. Voice input and conversion: The user speaks to the smart glasses, saying, "Which lockers are currently available?" This voice data is converted into text by the smart glasses' voice recognition software (e.g., Google Cloud Speech-to-Text).

[2128] 2. Utilizing text processing and generative AI models: The input, "Which lockers are currently available?", is converted into text and sent to the server. The server passes this text to a generative AI model, which generates an appropriate response. An example of the generative AI model used here is the OpenAI API.

[2129] 3. Obtaining and transmitting a response: The generative AI model generates a response such as "The currently available lockers are A and C," and the server sends this information to the smart glasses. The smart glasses then convey this information to the user visually (display) and via voice output (e.g., voice synthesis using the gTTS library).

[2130] 4. Locker reservation request processing: The user then voice-inputs, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow." This voice is also converted into text by the smart glasses and sent to the server. The server registers the reservation information in the database and uses the generative AI model to generate a response saying "Reservation completed" and sends it to the smart glasses.

[2131] Examples of prompt statements

[2132] Below is an example of a prompt to be passed to the generative AI model.

[2133] Run the availability check module of the locker reservation system. User input: Which lockers are currently available?

[2134] With the above configuration, users can intuitively check locker availability and make reservations by using voice input through the smart glasses. This is expected to significantly improve the efficiency of inventory management and pickup operations, especially at logistics centers.

[2135] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2136] Step 1:

[2137] The user speaks into the smart glasses, asking, "Which lockers are currently available?" The smart glasses collect the user's voice data using a built-in microphone. The collected voice data is converted into text data by the voice recognition software inside the smart glasses. The input is voice data, and the output is text data.

[2138] Step 2:

[2139] The terminal (smart glasses) sends the converted text data "Which lockers are currently available?" to the server. This transmission uses wireless communication technology (e.g., Wi-Fi). The input is text data, and the output is the transmission of the text data to the server.

[2140] Step 3:

[2141] The server passes the received user text data to the generative AI model as input. The server then sends the text data to the global AI model API (e.g., OpenAI API) to generate an appropriate response. The input is the user's text data, "Which lockers are currently available?", and the output from the generative AI model is the response text, "The currently available lockers are A and C."

[2142] Step 4:

[2143] The server transmits the response text data obtained from the generative AI model to the smart glasses. Wireless communication technology is used for transmission, and the data reaches the smart glasses in real time. The input is the response text data from the generative AI model, and the output is the data transmitted to the smart glasses.

[2144] Step 5:

[2145] The device (smart glasses) receives the response text data, "The currently available lockers are A and C," and conveys it to the user visually and audibly. The visual display uses the display, and the audio output uses a speech synthesis library (e.g., gTTS). The input is the response text data, and the output is the visual display and audio output.

[2146] Step 6:

[2147] The user again uses voice input and speaks into the smart glasses, "I would like to reserve Locker A from 10:00 to 12:00 tomorrow." The voice data is again collected and converted into text data through voice recognition software. The input is voice data, and the output is text data.

[2148] Step 7:

[2149] The terminal (smart glasses) sends text data, such as "I would like to reserve Locker A from 10:00 to 12:00 tomorrow," to the server. This transmission also uses wireless communication technology. The input is text data, and the output is the transmission of text data to the server.

[2150] Step 8:

[2151] The server queries the reservation information to register the received reservation request in the database, and if availability is confirmed, adds the reservation information to the database. The input is the reservation request text data, and the output is an update of the database.

[2152] Step 9:

[2153] The server passes the successful reservation information to the generative AI model, which then generates a response text saying "Reservation completed." The generative AI model generates an appropriate response and returns it to the server. The input is the successful reservation information, and the output is the response text data.

[2154] Step 10:

[2155] The server sends the generated response text data "Reservation completed" to the device (smart glasses). The input is the response text data from the generative AI model, and the output is the data sent to the smart glasses.

[2156] Step 11:

[2157] The terminal (smart glasses) conveys the received response text data to the user by visual display and audio output. The visual display uses a display, and the audio output uses a speech synthesis library. The input is the response text data, and the output is the visual display and audio output.

[2158] Through the above processing steps, the user can easily check the availability of lockers and make reservations through voice input.

[2159] 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.

[2160] The locker reservation system of the present invention provides a concrete means for users to check the availability of lockers and make reservations. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide a more personalized service. The program processing of this system is described in detail below in natural language.

[2161] Check availability

[2162] User:

[2163] When a user wants to know which lockers are available, they type "Which lockers are currently available?" into the terminal.

[2164] Device:

[2165] The terminal receives the user's input and sends an availability check request to the server.

[2166] server:

[2167] The server passes the request through the dialogue module to the generative AI model, which analyzes the request and generates an appropriate SQL query to query the database about the availability of the locker.

[2168] server:

[2169] It receives availability results from a database and passes them to a generative AI model to generate natural language responses.

[2170] Generative AI models:

[2171] Generates the response "Currently available lockers are A and C."

[2172] server:

[2173] The generated response is sent to the terminal.

[2174] Device:

[2175] The response received from the server is displayed to the user.

[2176] Examples:

[2177] When a user types "Which lockers are currently available?" into the terminal, the terminal sends a request to the server, which queries the database and uses a generative AI model to generate a response saying "Lockers A and C are currently available," which is sent to the terminal. The terminal displays the response to the user, who can then confirm whether locker A or C is available.

[2178] Locker reservation

[2179] User:

[2180] When a user wants to use a specific locker, they enter "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[2181] Device:

[2182] The terminal receives user input and sends a reservation request to the server.

[2183] server:

[2184] The server passes the request through the dialogue module to the generative AI model, which analyzes the request and generates the appropriate SQL query to register the reservation information in the database.

[2185] server:

[2186] The reservation registration results are received from the database and the generative AI model generates a response indicating the reservation is complete.

[2187] Generative AI models:

[2188] Generates a "Reservation completed" response.

[2189] server:

[2190] The generated response is sent to the terminal.

[2191] Device:

[2192] The response received from the server is displayed to the user.

[2193] Examples:

[2194] When a user enters "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal, the terminal sends a request to the server, the server updates the database, and uses the generative AI model to generate a response saying "Reservation completed" and sends it to the terminal. The terminal displays the response to the user, who confirms that the reservation of Locker A has been completed.

[2195] Emotion Recognition and Dialogue Processing

[2196] User:

[2197] Enter your questions or requests into the terminal in natural language.

[2198] Device:

[2199] Sends user input to the interaction API.

[2200] server:

[2201] Input received through the dialogue API is passed to the generative AI model, which analyzes the input and generates an appropriate response.

[2202] Emotion Engine:

[2203] Analyze emotions from user input and tailor responses based on those emotions.

[2204] Generative AI models:

[2205] Feedback from the emotion engine is used to generate responses with appropriate timing and content.

[2206] server:

[2207] The generated response is sent to the device via the interaction API.

[2208] Device:

[2209] The generated response is displayed to the user.

[2210] Examples:

[2211] When a user types "Please tell me which lockers are available" into the device, the server passes the received input to the generative AI model, and the emotion engine analyzes the user's emotions. Based on feedback from the emotion engine, the generative AI model generates a response saying "Currently available lockers are A and C. Have you found the locker you are looking for?" and sends it to the device. The device displays the response to the user, who confirms it.

[2212] In this way, the system of the present invention comprehensively realizes a series of processes to significantly improve user convenience. Furthermore, by combining it with an emotion engine, interactions with users become more natural and personalized, further increasing user satisfaction.

[2213] The processing flow will be explained below.

[2214] Check availability

[2215] Step 1:

[2216] The user inputs a request for the locker they want to check availability for into the terminal (e.g., "Which lockers are currently available?").

[2217] Step 2:

[2218] The terminal receives the user's input and sends an availability check request to the server.

[2219] Step 3:

[2220] The server passes the request to the generative AI model through the dialogue module.

[2221] Step 4:

[2222] A generative AI model analyzes the request and generates an SQL query to query the database about locker availability.

[2223] Step 5:

[2224] The server retrieves availability information from the database.

[2225] Step 6:

[2226] The server passes the acquired availability information to a generative AI model, which generates a natural language response.

[2227] Step 7:

[2228] The generative AI model generates the response, "The currently available lockers are A and C."

[2229] Step 8:

[2230] The server sends the generated response to the terminal.

[2231] Step 9:

[2232] The terminal displays the response received from the server to the user.

[2233] Locker reservation

[2234] Step 1:

[2235] When a user wants to use a specific locker, they input "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[2236] Step 2:

[2237] The terminal receives the user's input and sends a reservation request to the server.

[2238] Step 3:

[2239] The server passes the request to the generative AI model through the dialogue module.

[2240] Step 4:

[2241] The generative AI model analyzes the request and generates an SQL query to register the reservation information in the database.

[2242] Step 5:

[2243] The server receives the reservation registration results from the database.

[2244] Step 6:

[2245] The server confirms whether the registration was successful and has the generative AI model generate a response indicating that the reservation is complete.

[2246] Step 7:

[2247] The generative AI model generates the response "Reservation completed."

[2248] Step 8:

[2249] The server sends the generated response to the terminal.

[2250] Step 9:

[2251] The terminal displays the response received from the server to the user.

[2252] Emotion Recognition and Dialogue Processing

[2253] Step 1:

[2254] The user types a question or request into the terminal in natural language (e.g., "Please tell me which lockers are available").

[2255] Step 2:

[2256] The device sends input from the user to the interaction API.

[2257] Step 3:

[2258] The server passes the input received through the dialogue API to the generative AI model.

[2259] Step 4:

[2260] The generative AI model analyzes the input and requests emotion analysis from the emotion engine.

[2261] Step 5:

[2262] The emotion engine analyzes emotions from user input and sends the results back to the generative AI model.

[2263] Step 6:

[2264] The generative AI model uses feedback from the emotion engine to generate an appropriate response (e.g., "Currently available lockers are A and C. Did you find the locker you were looking for?").

[2265] Step 7:

[2266] The server sends the generated response to the terminal via the interaction API.

[2267] Step 8:

[2268] The terminal displays the generated response to the user.

[2269] Example 2

[2270] 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."

[2271] The problem that this invention aims to solve is to provide a system that allows users to obtain real-time information when checking locker availability and reserving lockers, and further to make the dialogue with users natural and personalized by using an emotion engine. In conventional systems, checking locker availability and reserving lockers were done manually, which reduced user convenience. Furthermore, because the system was unable to respond in a way that took the user's emotions into consideration, the dialogue was one-sided and user satisfaction was low.

[2272] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting an availability check request from a user, means for checking locker availability in real time using a generative AI model, means for providing information on available lockers to the user, means for accepting a locker reservation request from a user, means for registering the reservation information in a database, means for notifying the user of reservation completion information using a generative AI model, and means for analyzing the user's emotions using an emotion engine and adjusting the response based on the emotions. This not only enables efficient checking of locker availability and reservations, but also makes it possible to have natural and personalized interactions with the user, thereby improving user satisfaction.

[2273] "Availability check request from user" means a request sent by a user to the system to check the availability of a locker.

[2274] A "generative AI model" refers to an artificial intelligence technology that analyzes user input and generates an appropriate response based on the results of that analysis.

[2275] "Means for checking locker availability in real time" refers to methods and technologies for instantly obtaining information about currently available lockers and providing it to users.

[2276] "Means for providing users with information about available lockers" refers to methods or technologies for displaying or notifying users of information about currently available lockers.

[2277] A "locker reservation request from a user" is a request sent to the system by a user wishing to use a specific locker on a specific date and time.

[2278] "Means for registering reservation information in a database" refers to a method or technology for storing reservation-related information in a database based on a reservation request from a user.

[2279] "Means for notifying users of reservation completion information using a generative AI model" refers to methods or technologies for generating a response using a generative AI model to inform users that their reservation has been successfully completed, and for communicating that response to users.

[2280] An "emotion engine" is a technology or module that analyzes emotions from user input and adjusts the system's response based on the analysis results.

[2281] "Means for analyzing user emotions" refers to methods or techniques for identifying emotions from text entered by a user and using the results to help the system generate responses.

[2282] The locker reservation system of the present invention provides a means for users to check locker availability and make reservations. This system combines a generative AI model and an emotion engine to improve user convenience, and is described in detail below.

[2283] System Configuration

[2284] This system includes the following hardware and software:

[2285] Terminal: The device used by the user to input data (smartphone, tablet, PC, etc.)

[2286] Server: A central system that processes input and interfaces with generative AI models and databases

[2287] Database: Data storage for storing locker availability and reservation information

[2288] Generative AI model: AI technology that generates responses through natural language processing

[2289] Emotion Engine: Technology that analyzes user emotions and adjusts responses

[2290] Check availability

[2291] User:

[2292] When a user wants to check the availability of lockers, they type "Which lockers are currently available?" into the terminal.

[2293] Device:

[2294] The terminal receives the user's input and sends an availability check request to the server.

[2295] server:

[2296] The server passes the request through the dialogue module to the generative AI model, which parses the request, generates the appropriate SQL query, and executes it against the database. For example, it generates the query "SELECT FROM lockers WHERE status='empty'".

[2297] Database:

[2298] It receives query results from the database and sends information about available lockers to the server.

[2299] Generative AI models:

[2300] The generative AI model receives the database results from the server and generates a natural language response such as, "The currently available lockers are A and C."

[2301] server:

[2302] The generated response is sent to the terminal.

[2303] Device:

[2304] The terminal displays the response received from the server to the user. Specifically, the terminal screen displays "Currently available lockers are A and C."

[2305] Examples:

[2306] When a user types "Which lockers are currently available?" into the device, the device sends the request to the server. The server executes an SQL query and uses a generative AI model to generate a response, "Lockers A and C are currently available," which is sent to the device. The device then displays the response on its screen for the user to confirm.

[2307] Locker reservation

[2308] User:

[2309] When a user wants to reserve a specific locker, they enter "I would like to reserve Locker A from 10:00 to 12:00 tomorrow" into the terminal.

[2310] Device:

[2311] The terminal receives the input and sends a reservation request to the server.

[2312] server:

[2313] The server passes the reservation request through the dialogue module to the generative AI model, which parses the request, generates the appropriate SQL query, and executes it against the database. For example, it generates the query "INSERT INTO reservations (locker_id, start_time, end_time) VALUES ('A', '2023-06-01 10:00', '2023-06-01 12:00')".

[2314] Database:

[2315] The database executes queries and registers reservation information.

[2316] Generative AI models:

[2317] The generative AI model receives the registration completion result from the database and generates a natural language response saying, "Your reservation is complete."

[2318] server:

[2319] The generated response is sent to the terminal.

[2320] Device:

[2321] The terminal displays the response received from the server to the user. Specifically, the terminal displays "Reservation completed" on its screen.

[2322] Examples:

[2323] When a user types "I'd like to reserve Locker A from 10:00 to 12:00 tomorrow" into the device, the device sends the request to the server. The server executes an SQL query and uses a generative AI model to generate a response saying "Reservation completed" and sends it to the device. The device then displays the response on its screen for the user to confirm.

[2324] Emotion Recognition and Dialogue Processing

[2325] User:

[2326] Enter your question or request into the terminal in natural language, for example, "Please tell me which lockers are available."

[2327] Device:

[2328] Input from the user is sent to the server via the interaction API.

[2329] server:

[2330] The server passes the request through a dialogue API to the generative AI model, which analyzes the input and uses an emotion engine to analyze the user's emotions.

[2331] Emotion Engine:

[2332] The emotion engine analyzes emotions from user input and feeds the results back to the generative AI model.

[2333] Generative AI models:

[2334] The generative AI model generates a natural language response based on feedback from the emotion engine, for example, "Currently, the available lockers are A and C. Did you find the locker you were looking for?"

[2335] server:

[2336] The generated response is sent to the device via the interaction API.

[2337] Device:

[2338] The terminal displays the response received from the server to the user. Specifically, the terminal screen displays the message "Currently available lockers are A and C. Have you found the locker you are looking for?"

[2339] Examples:

[2340] When a user types "Please tell me which lockers are available" into the device, the server passes the request to the generative AI model, which analyzes the emotion using the emotion engine. The generative AI model generates a response saying "Currently available lockers are A and C. Have you found the locker you are looking for?" and sends it to the device. The device displays the response on the screen for the user to confirm.

[2341] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2342] Availability Check Processing Steps

[2343] Step 1:

[2344] The user inputs information into the terminal to check availability. Specifically, the user inputs "Which lockers are currently available?" into the input field on the terminal. Input: The user's question in natural language.

[2345] Step 2:

[2346] The terminal processes the user's input and sends it to the server as an HTTP request. Input: User's question text. Output: HTTP request to the server.

[2347] Step 3:

[2348] The server passes the received request to the dialogue module and sends the request to the generative AI model. Input: Contents of the HTTP request. Output: Request to the generative AI model.

[2349] Step 4:

[2350] The generative AI model analyzes the user's question and generates an appropriate SQL query. It then returns the query to the server. For example, it generates the query "SELECT FROM lockers WHERE status='empty'". Input: User's question text. Output: SQL query.

[2351] Step 5:

[2352] The server sends the SQL query received from the generative AI model to the database and executes the query. Input: SQL query. Output: Result of executing the database query.

[2353] Step 6:

[2354] The database returns the information of available lockers to the server as a result of the query. For example, information such as "Locker A, Locker C" is returned. Input: Database query. Output: Available locker information.

[2355] Step 7:

[2356] The server passes the information about available lockers received from the database to the generative AI model, which generates a natural language response. Input: Available locker information. Output: Natural language response.

[2357] Step 8:

[2358] The generative AI model generates a natural language response based on the information about available lockers, such as "The currently available lockers are A and C," and returns that response to the server. Input: Available locker information. Output: Response text.

[2359] Step 9:

[2360] The server generates a response and sends it to the device. Input: Response text. Output: HTTP response to the device.

[2361] Step 10:

[2362] The terminal receives the response from the server and displays it to the user. Specifically, it displays "Currently available lockers are A and C" on the terminal screen. Input: HTTP response. Output: Displayed to the user.

[2363] Locker reservation process steps

[2364] Step 1:

[2365] The user inputs information into the terminal to reserve a specific locker. Specifically, the user inputs "I would like to reserve locker A from 10:00 to 12:00 tomorrow." Input: The user's reservation request in natural language.

[2366] Step 2:

[2367] The terminal processes the user's input and sends it to the server as an HTTP request. Input: Reservation request text. Output: HTTP request to the server.

[2368] Step 3:

[2369] The server passes the received request to the dialogue module and sends the request to the generative AI model. Input: Contents of the HTTP request. Output: Request to the generative AI model.

[2370] Step 4:

[2371] The generative AI model analyzes the user's reservation request and generates the appropriate SQL query. It then returns the query to the server. For example, it generates the query "INSERT INTO reservations (locker_id, start_time, end_time) VALUES ('A', '2023-06-01 10:00', '2023-06-01 12:00')". Input: Reservation request text. Output: SQL query.

[2372] Step 5:

[2373] The server sends the SQL query received from the generative AI model to the database and executes the query. Input: SQL query. Output: Result of executing the database query.

[2374] Step 6:

[2375] The database returns the reservation information to the server as a result of the query, e.g., a result of "reservation successful". Input: Database query. Output: Reservation result.

[2376] Step 7:

[2377] The server passes the reservation results received from the database to the generative AI model, which generates a natural language response. Input: Reservation results. Output: Natural language response.

[2378] Step 8:

[2379] The generative AI model generates a natural language response, "Reservation completed," based on the reservation result and returns the response to the server. Input: Reservation result. Output: Response text.

[2380] Step 9:

[2381] The server generates a response and sends it to the device. Input: Response text. Output: HTTP response to the device.

[2382] Step 10:

[2383] The terminal receives the response from the server and displays it to the user. Specifically, it displays "Reservation completed" on the terminal screen. Input: HTTP response. Output: Displayed to the user.

[2384] Processing steps for emotion recognition and dialogue processing

[2385] Step 1:

[2386] The user inputs a question or request into the terminal in natural language. For example, "Please tell me which lockers are available." Input: A user's natural language inquiry.

[2387] Step 2:

[2388] The terminal sends the user's input to the server through the interaction API. Input: Query text. Output: HTTP request to the server.

[2389] Step 3:

[2390] The server sends a request to the generative AI model through the interaction API. Input: HTTP request content. Output: Request to the generative AI model.

[2391] Step 4:

[2392] The generative AI model analyzes the user's input and sends the results to the emotion engine. Input: User's query text. Output: Analyzed data.

[2393] Step 5:

[2394] The emotion engine analyzes emotions from user input and feeds the analysis results back to the generative AI model. For example, it may analyze the input to determine that the emotion is "friendly." Input: Analyzed data. Output: Emotion feedback.

[2395] Step 6:

[2396] The generative AI model uses emotional feedback to generate an appropriate natural language response. For example, it generates a response like, "Currently available lockers are A and C. Did you find the locker you were looking for?" Input: Emotional feedback. Output: Response text.

[2397] Step 7:

[2398] The server sends the generated response to the device via the interaction API. Input: Response text. Output: HTTP response to the device.

[2399] Step 8:

[2400] The terminal receives the response from the server and displays it to the user. Specifically, it displays the message "Currently available lockers are A and C. Have you found the locker you are looking for?" on the terminal screen. Input: HTTP response. Output: Displayed to the user.

[2401] (Application example 2)

[2402] 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."

[2403] In conventional locker reservation systems, when users check availability and make reservations, they are provided with one-sided information that ignores their emotions. This often causes inconvenience and stress for users. In addition, there are problems with the speed and accuracy of real-time availability confirmation and reservation completion notifications, which tends to reduce user satisfaction.

[2404] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2405] In this invention, the server includes means for accepting availability confirmation requests from users, means for checking locker availability in real time using a generative AI model, means for providing users with information on available lockers, means for accepting locker reservation requests from users, means for registering the reservation information in a database, means for notifying users of reservation completion information using a generative AI model, and means for analyzing the user's emotions using an emotion recognition engine and adjusting responses based on the emotions, thereby enabling users to receive more personalized and convenient services.

[2406] "User" refers to an end user who uses the locker reservation system.

[2407] "Availability Check Request" refers to a request made by a User to check information about lockers that are currently available.

[2408] A "generative AI model" refers to an artificial intelligence model that uses technologies such as natural language processing to conduct dialogue and process data within a system.

[2409] An "emotion recognition engine" refers to an engine that analyzes the emotions from the user's input and adjusts the response.

[2410] "Real-time" refers to data processing occurring almost simultaneously with the current time.

[2411] "Locker availability" refers to information indicating whether a locker is currently in use or available.

[2412] "Reservation Request" means a request by a User to make a reservation for use of a particular Locker.

[2413] "Database" refers to structured data storage for managing and storing locker availability and reservation information.

[2414] "Reservation completion information" refers to information that notifies a user that their locker reservation has been successful.

[2415] The present invention provides a concrete example of how to use a locker reservation system. This system allows users to check locker availability, make a reservation, and receive notification of reservation completion, using a generative AI model. It also utilizes an emotion recognition engine to analyze user emotions and tailor responses to provide a more personalized experience for users.

[2416] System configuration

[2417] The system consists of the following main elements:

[2418] 1. Server

[2419] The server accepts requests from users, queries the database, and generates results using a generative AI model.

[2420] 2. Terminal

[2421] The device receives input from the user, sends the request to the server, displays the response, and runs an emotion recognition engine to analyze the user's emotions.

[2422] 3. Database

[2423] The database manages and stores locker availability and reservation information.

[2424] Program processing

[2425] The server includes means for accepting a request from a user to check availability, means for checking locker availability in real time using a generative AI model, means for providing information about available lockers to users, means for accepting a locker reservation request from a user, means for registering the reservation information in a database, means for notifying the user of reservation completion information using a generative AI model, and means for analyzing the user's emotions using an emotion recognition engine and adjusting a response based on the emotions.

[2426] Hardware or software used

[2427] This system uses the following hardware and software:

[2428] Microphone for voice input

[2429] User's device (Android or iOS smartphone)

[2430] Speech-to-text conversion using the speech_recognition library

[2431] Sending API requests to the server using the requests library

[2432] The server-side backend implements the generative AI model and database.

[2433] Specific examples

[2434] When a user speaks to the device, asking, "Which parking spaces are currently available?", the system converts the voice input into text and sends a request to the server. The server queries the database and uses a generative AI model to generate a natural language response. For example, it might respond, "The currently available parking spaces are B2 and D4."

[2435] Example prompt sentence:

[2436] User: What parking spaces are currently available?

[2437] Server: Please wait while we check parking availability...

[2438] Generative AI model: The currently available parking spaces are B2 and D4.

[2439] As described above, this system can provide efficient and personalized services to users.

[2440] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2441] Step 1:

[2442] The user speaks to their smartphone device, "What parking spaces are currently available?" The device converts the voice input into text and generates the user's request. At this time, a speech recognition engine (e.g., Google Speech API) analyzes the voice data and converts it into text data.

[2443] Input: User voice input

[2444] Output: Text data ("What parking spaces are currently available?")

[2445] Step 2:

[2446] The device sends the generated text data to the server, which receives the request and passes it to the generative AI model via the dialogue module. The generative AI model analyzes the request and generates an SQL query to check availability.

[2447] Input: Text data ("What parking spaces are currently available?")

[2448] Output: SQL query

[2449] Step 3:

[2450] The server sends the generated SQL query to the database to inquire about availability, and the database responds by returning the current availability.

[2451] Input: SQL query

[2452] Output: Availability data (e.g. "B2 and D4 are available")

[2453] Step 4:

[2454] The server passes the acquired availability data to the generative AI model, which then generates a response in natural language. The generative AI model uses an emotion recognition engine to analyze the user's emotions and select an appropriate response.

[2455] Input: Availability data ("B2 and D4 are available")

[2456] Output: A natural language response (e.g., "Currently available parking spaces are B2 and D4")

[2457] Step 5:

[2458] The server sends the generated natural language response to the device, which uses a speech synthesis engine (e.g., Google Text-to-Speech) to audibly notify the user of the received response.

[2459] Input: Natural Language Response ("Currently available parking spaces are B2 and D4")

[2460] Output: Audio data (notification to user)

[2461] Step 6:

[2462] If a user wants to reserve a specific parking space, they can speak into the terminal, "I would like to reserve parking space B2 from 2:00 PM to 4:00 PM tomorrow." The terminal converts the voice input into text and generates a reservation request for the user.

[2463] Input: User voice input

[2464] Output: Text data ("I would like to reserve parking space B2 from 2:00 PM to 4:00 PM tomorrow.")

[2465] Step 7:

[2466] The device sends the generated text data to the server, which receives the request, analyzes it using a generative AI model, and generates an SQL query for reservation registration.

[2467] Input: Text data ("I would like to reserve parking space B2 from 2:00 PM to 4:00 PM tomorrow.")

[2468] Output: SQL query

[2469] Step 8:

[2470] The server sends the generated SQL query to the database to register the reservation information, and the database returns the registration result to the server.

[2471] Input: SQL query

[2472] Output: Reservation registration results

[2473] Step 9:

[2474] The server passes the reservation registration results from the database to the generative AI model, which then generates a reservation completion notification. The generative AI model then selects an appropriate response for the user.

[2475] Input: Reservation registration result

[2476] Output: A natural language response (e.g., "Your reservation is complete")

[2477] Step 10:

[2478] The server sends the generated natural language response to the device, which uses a speech synthesis engine to audibly notify the user of the received response.

[2479] Input: Natural language response ("Your reservation is complete")

[2480] Output: Audio data (notification to user)

[2481] 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.

[2482] 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.

[2483] In the above embodiment, an example was given in which the specific processing is performed by the data processi...

Claims

1. A means for receiving availability confirmation requests from users; A means to check locker availability in real time using generative AI models, A means for providing users with information about available lockers; A means for accepting locker reservation requests from users; a means for registering reservation information in a database; A means for notifying the user of reservation completion information using a generation AI model; A system including:

2. 10. The system of claim 1, including a generative AI model that allows a user and the system to interact in natural language.

3. 2. The system according to claim 1, further comprising means for reserving an available locker at a specified date and time based on a reservation request from a user.

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