System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a message including a specific character string by a user on a communication terminal; means for receiving and analyzing the message and recognizing the specific character string as a trigger; means for collecting schedule information of the user and determining a common schedule; means for notifying the user of the determined schedule; means for proposing a candidate shop based on preference data of the user; means for reserving the selected shop; means for notifying reservation completion; and means for transmitting a remind message the day before a drinking party.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] Currently, when many people plan a drinking party, it can be difficult to make it happen because of the time and effort required to coordinate schedules, select a restaurant, and make reservations. There is a need for a system that can eliminate this hassle and make it easy to schedule a drinking party. In addition, there are problems such as the difficulty of checking everyone's schedules to find the optimal date, and the time it takes to find a restaurant that suits everyone's preferences. This leads to missed opportunities for drinking parties. [Means for solving the problem]

[0005] The present invention provides a system including a means for a user to input a message containing a specific character string on a communication terminal, a means for receiving and analyzing the message and recognizing the specific character string as a trigger, a means for collecting the user's schedule information and determining a common schedule, a means for notifying the user of the determined schedule, a means for suggesting candidate restaurants based on the user's preference data, a means for making a reservation at the selected restaurant, a means for notifying the user that the reservation is complete, and a means for sending a reminder message the day before the drinking party. This allows the user to automatically arrange the schedule and reserve the restaurant simply by inputting the specific character string, making the drinking party hassle-free. Furthermore, the system simplifies operation by using natural language processing technology to analyze the specific character string and recognize it as a trigger.

[0006] A "communication terminal" is a device that users use to input and receive messages, such as a smartphone, tablet, or PC.

[0007] The "particular character string" refers to a key phrase such as "Let's go drinking!" that a user uses when inputting a proposal for a drinking party.

[0008] A "message" refers to text information that a user sends using a communication terminal.

[0009] "Analysis" refers to the process of analyzing a received message and understanding its content.

[0010] A "trigger" is a condition or signal that initiates a particular action.

[0011] "User's schedule information" refers to information about the user's own schedule provided by the user.

[0012] "Common dates" refer to dates and times available to all participating users.

[0013] "Preference data" refers to data collected based on a user's past history and preferences.

[0014] A "candidate store" refers to a store that is proposed to the user as a location for a drinking party.

[0015] "Reservation" refers to the user making a reservation at a store selected by the user.

[0016] "Notification" refers to a message that notifies the user of decisions or reminders.

[0017] A "reminding message" refers to a message that prompts the user to reconfirm their plans just before a drinking party.

[0018] "System" refers to the overall structure that integrates the above means and automatically sets up drinking parties.

[0019] "Natural language processing technology" refers to computer technology for analyzing and understanding human language. [Brief explanation of the drawings]

[0020] [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 illustrating 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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] This invention is a system for easily setting up drinking parties using communication terminals, and is realized mainly through communication between a server, terminals, and users. This system also uses natural language processing technology to analyze messages from users, obtain necessary information, and proceed with the process.

[0042] System Overview

[0043] 1. User Action:

[0044] User: The user uses a messaging app on their communication device to type a specific string of characters (e.g., "Let's go for a drink!") into a chat screen with a friend.

[0045] Device: The device sends the entered message to the LINE server.

[0046] 2. Message reception and analysis:

[0047] Server: The server receives the message sent by the user and analyzes it to see if it contains a specific string. This analysis uses natural language processing technology.

[0048] Server: When the message is recognized as a trigger, it launches "Let's go drinking! AI."

[0049] 3. Scheduling:

[0050] Server: "Let's go drinking! AI" sends a message to the chat screen to inform all users that it is starting to arrange a date.

[0051] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[0052] Users: Each user replies with their available dates.

[0053] Example: "I'm free on XX / XX and XX / XX."

[0054] Server: The server aggregates all users' inputs, determines common availability, determines the optimal schedule, and notifies all users.

[0055] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[0056] 4. Shop selection:

[0057] Server: "Let's Drink! AI" searches for candidate stores based on the user's past history and preference data.

[0058] Server: Suggests candidate stores to the user.

[0059] Example: "How about the following places? 1. Izakaya A 2. Bar B 3. Restaurant C"

[0060] User: The user chooses one of the suggested stores.

[0061] Example: "Izakaya A is good."

[0062] 5. Restaurant reservations:

[0063] Server: After the user has completed their selection, the server automatically makes an online reservation for the selected restaurant.

[0064] Server: Once the reservation is complete, all users will be notified of the details.

[0065] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[0066] 6. Reminder:

[0067] Server: When the date of the drinking party approaches, the server automatically sends a reminder message.

[0068] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[0069] Specific examples

[0070] For example, a user types "Let's go for a drink!" into a chat screen with a friend on LINE. When the device sends this message to the LINE server, the server receives and analyzes the message. As a result of the analysis, the phrase "Let's go for a drink!" is recognized as a specific trigger, activating the "Let's go for a drink! AI." The AI ​​then sends a message to arrange a date, and the user replies with their available dates. The server collects this information, decides on a common date, and notifies the user.

[0071] Next, "Let's Drink! AI" searches for candidate restaurants based on the user's preference data and suggests several options to the user. The user selects Izakaya A from these, and the server automatically makes the reservation. Once the reservation is complete, all users are notified of the details and a reminder message is sent the day before the drinking party.

[0072] This allows users to automatically plan a drinking party simply by entering a specific string of characters.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] User: Using a communication device, type "Let's go for a drink!" into the chat screen with a friend.

[0076] Step 2:

[0077] Device: Sends the user's input to the LINE server.

[0078] Step 3:

[0079] Server: The LINE server analyzes the received message and recognizes the phrase "Let's go for a drink!" as a specific trigger.

[0080] Step 4:

[0081] Server: After the trigger is recognized, launch "Let's go drinking! AI" and send a message to the chat screen notifying the start of schedule adjustment.

[0082] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[0083] Step 5:

[0084] User: Each user replies with their available dates.

[0085] Example: "I'm free on XX / XX and XX / XX."

[0086] Step 6:

[0087] Server: Aggregates schedule information sent by all users and determines a common schedule.

[0088] Step 7:

[0089] Server: Determines the optimal schedule and notifies all users of that schedule.

[0090] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[0091] Step 8:

[0092] Server: Searches for store candidates based on the user's past history and preference data.

[0093] Step 9:

[0094] Server: Selects candidate stores and suggests them to the user.

[0095] Example: "How about the following places? 1. Izakaya A 2. Bar B 3. Restaurant C"

[0096] Step 10:

[0097] User: The user chooses one of the suggested stores.

[0098] Example: "Izakaya A is good."

[0099] Step 11:

[0100] Server: Automatically make online reservations at selected restaurants.

[0101] Step 12:

[0102] Server: Once the reservation is complete, the details will be sent to all users.

[0103] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[0104] Step 13:

[0105] Server: Automatically send reminder messages when the date of a drinking party approaches.

[0106] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[0107] Example 1

[0108] 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."

[0109] When planning an event such as a drinking party, it takes time and effort to adjust the date, select a restaurant, and confirm reservations, which can be particularly tedious when there are multiple participants. It is also easy to forget to send reminders to all participants. This system solves these problems and allows users to easily schedule a drinking party.

[0110] 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.

[0111] In this invention, the server includes means for a user to input a message containing a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate restaurants based on the user's preference data, means for reserving the selected restaurant, means for notifying the user that the reservation is complete, means for sending a reminder message the day before the drinking party, means for analyzing the message using a natural language processing engine and detecting the trigger, means for analyzing the optimal available date and time from the aggregated schedule information, and means for automatically making a restaurant reservation online. This allows users to easily adjust schedules, select restaurants, and confirm reservations through the system.

[0112] A "communication terminal" is an electronic device that a user uses to input and send messages, and includes smartphones, personal computers, and the like.

[0113] A "specific string" refers to a group of letters or words that the system is configured to recognize as a trigger.

[0114] A "message" refers to text information that a user inputs via a communication terminal and sends to other users or a server.

[0115] A "natural language processing engine" is a software technology that analyzes messages entered by users and recognizes specific strings of characters that act as triggers.

[0116] "Schedule information" refers to data regarding the dates and times when a user can participate.

[0117] "Preference data" is information about a user's past history and preferences, and is used to suggest candidate stores.

[0118] "Candidate store" refers to a store that the system suggests to the user as a location for a drinking party.

[0119] "Reservation" refers to the act of reserving a candidate store selected by a user and making it available for use on a specific date and time.

[0120] A "reminding message" is a message that notifies the user of the details of the event again when the date of the drinking party approaches.

[0121] "Generative AI models" refer to algorithms and programs that use AI to analyze data and make predictions.

[0122] A "prompt sentence" is a standard sentence to be input into a generative AI model, and contains instructions for the AI ​​to process appropriately.

[0123] The present invention is a system that allows users to easily schedule drinking parties using communication terminals, and is implemented primarily through communications between a server, terminals, and users. This system utilizes a natural language processing engine and a generative AI model to automatically perform message analysis, schedule adjustment, restaurant selection, and reminders. The following describes in detail an embodiment of the present invention.

[0124] System configuration

[0125] 1. Communication terminal

[0126] A communication device is used by users to input and send messages. Typically, this is a smartphone or a PC. Using this communication device, users input specific characters into the chat screen with friends and send them.

[0127] 2. Server

[0128] The server plays a central role in receiving and analyzing messages. It has a built-in natural language processing engine, which analyzes messages sent by users and detects whether they contain specific strings that act as triggers. This allows subsequent processing to proceed automatically.

[0129] 3. Natural Language Processing Engine

[0130] The natural language processing engine analyzes the message entered by the user and recognizes specific triggers. If the engine detects a trigger, the server launches "Let's Drink! AI" and the scheduling process begins.

[0131] 4. Generative AI Models

[0132] A generative AI model refers to an algorithm or program that analyzes data and makes predictions during processes such as scheduling, selecting restaurants, and sending reminders. This model suggests optimal restaurants and dates based on the user's past history and preference data.

[0133] System operation explanation

[0134] When a user operates a communication device and sends a specific string of characters, such as "Let's go for a drink!", the message is sent over the Internet to a server. The server uses a natural language processing engine to analyze the message and detects the specific string that acts as a trigger. This activates the "Let's go for a drink! AI," which then begins the process of arranging a date and selecting a restaurant.

[0135] Specifically, the system operates as follows: The server sends a message to all users to arrange a date and collects the available date information replies from each user. The generative AI model aggregates this date information and analyzes common available dates and times. It then notifies all users of the optimal date. After that, candidate restaurants are suggested based on the user's preference data and past history, and the restaurant selected by the user is automatically reserved online. Once the reservation is complete, the details are notified to all users, and a reminder message is sent as the date of the drinking party approaches.

[0136] Specific examples

[0137] For example, a user types "Let's go for a drink!" into the LINE chat screen and sends it. When the device sends this message to the server, the server analyzes the message. As a result of the analysis, the phrase "Let's go for a drink!" is recognized as a trigger, and the "Let's go for a drink! AI" is activated.

[0138] Next, the server sends a message to the chat screen saying, "We'll start arranging dates, so please let us know when you can join." Each user replies with their available dates, and the server aggregates this information, determines common availability, and decides on the optimal date. Next, "Let's go drinking! AI" searches for candidate restaurants based on the user's preference data and suggests several options to the user. The user selects a restaurant from these, and the server automatically reserves the restaurant and notifies all users of the details. Finally, the server sends a reminder message the day before the drinking party.

[0139] Prompt Sentence Examples

[0140] 1. When the phrase "Let's go for a drink!" is sent via LINE, "Let's go for a drink! AI" will start arranging a date.

[0141] "We will begin arranging the schedule. Please let us know the dates that you can attend."

[0142] 2. After the date of the drinking party is decided, a prompt to suggest a possible restaurant:

[0143] "How about the following establishments? 1. Izakaya 2. Bar 3. Restaurant"

[0144] 3. Once the user has selected a store, a prompt will automatically reserve that store:

[0145] "Reservation completed at 7pm on XX month."

[0146] As described above, the system of the present invention provides an environment in which users can easily set up drinking parties.

[0147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0148] Step 1:

[0149] The user inputs a specific string into the communication terminal and sends it.

[0150] Input: The user enters the string "Let's go for a drink!" on a communication device such as a smartphone or PC.

[0151] Behavior: The user presses the send button on the chat screen to send this string.

[0152] Output: The communication terminal sends this message to the server.

[0153] Step 2:

[0154] The device sends a message to the server

[0155] Input: A message sent by the user: "Let's go for a drink!"

[0156] How it works: The device sends this message to the server via the internet. This process is carried out through the LINE server.

[0157] Output: The server receives this message.

[0158] Step 3:

[0159] The server parses the message

[0160] Input: The message "Let's go for a drink!" received by the server.

[0161] How it works: The server's natural language processing engine parses the message to see if it contains a specific string. This process uses a natural language processing model.

[0162] Output: When a specific trigger string is detected, "Let's go drinking! AI" will be launched.

[0163] Step 4:

[0164] The server notifies the user that scheduling is about to begin.

[0165] Input: The natural language processing engine detects the trigger "Let's go for a drink!"

[0166] How it works: The server sends a message to all users saying, "We're starting to schedule. Please let us know when you can join."

[0167] Output: A message is displayed to all users informing them that scheduling has begun.

[0168] Step 5:

[0169] The user replies with their available dates

[0170] Input: The rescheduling message sent by the server.

[0171] How it works: Each user replies with the date and time they are available. For example, they might reply, "I'm free on XX / XX and XX / XX."

[0172] Output: The server collects information about the dates that users can attend.

[0173] Step 6:

[0174] The server decides the best date

[0175] Input: Available dates and times provided by each user.

[0176] How it works: The server analyzes the aggregated schedule information and identifies common free dates and times. A generative AI model assists in this analysis.

[0177] Output: The optimal date is determined and the result is notified to all users from the server. For example, a message such as "We will hold a drinking party on XX day at 7 PM" is sent.

[0178] Step 7:

[0179] The server suggests candidate stores

[0180] Input: Best dates and user preference data.

[0181] How it works: The server uses a generative AI model to search for candidate stores based on the user's past history and preference data.

[0182] Output: A list of candidate stores is generated, and the server sends a suggestion message to the user saying, "How about the following stores? 1. Izakaya 2. Bar 3. Restaurant."

[0183] Step 8:

[0184] The user selects a store

[0185] Input: A list of candidate stores sent from the server.

[0186] Operation: The user selects one of the suggested restaurants and replies, for example, "Izakaya is good."

[0187] Output: The selected store information is sent to the server.

[0188] Step 9:

[0189] The server reserves the selected store

[0190] Input: Store information selected by the user.

[0191] How it works: The server automatically reserves the selected store online using an online reservation service.

[0192] Output: The reservation is completed and the details are sent to all users from the server. For example, a message like "Your reservation at the izakaya on the ____ date at 7pm has been completed. Thank you!" is sent.

[0193] Step 10:

[0194] The server sends a reminder message

[0195] Input: Time information and store information after completing reservation.

[0196] How it works: When the date of the drinking party approaches, the server automatically sends a reminder message to the user.

[0197] Output: A reminder message is sent to the user saying "There's a drinking party at the izakaya tomorrow at 7pm. Don't forget!"

[0198] The above specific processing steps realize a system that allows users to easily set up drinking parties.

[0199] (Application example 1)

[0200] 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."

[0201] Traditionally, setting up a drinking party requires a lot of time and effort, including coordinating participants' schedules and reserving restaurants. Furthermore, in order to efficiently complete these tasks, it is necessary to gather the opinions of all participants, which can make the process cumbersome. This reduces the time participants can spend enjoying the party. Furthermore, while there is a demand for easy operation via voice commands, traditional systems do not adequately achieve this.

[0202] 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.

[0203] In this invention, the server includes means for a user to input a message containing a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate restaurants based on the user's preference data, means for making a reservation at the selected restaurant, means for notifying the user that the reservation is complete, means for sending a reminder message the day before the drinking party, means for activating the system by voice command, and means having a natural language processing device for analyzing the voice command. This allows the user to quickly and efficiently set up a drinking party using simple voice commands, improving the convenience of the entire system.

[0204] "Communication terminal" refers to any electronic device that allows a user to input and send messages.

[0205] A "specific character string" refers to a uniquely identifiable phrase or word that a user inputs into a communication terminal.

[0206] A "trigger" is a string or command that causes the system to start a specific process.

[0207] "Date information" refers to data provided by a user regarding available dates and times for participation.

[0208] The "common schedule" refers to a date and time when all users can participate among the available dates and times for participation.

[0209] "Preference data" refers to information based on a user's past behavioral history and preference trends.

[0210] "Candidate stores" refer to multiple restaurants that the user may visit.

[0211] "Reservation" refers to a user reserving one of the candidate stores in advance for a specific date and time.

[0212] A "reminder message" refers to a confirmation notification sent when the date and time of a drinking party approaches.

[0213] "Voice command" refers to a verbal instruction given by a user to a system.

[0214] "Natural language processing device" refers to a technical device that analyzes a user's voice commands or messages and takes appropriate action.

[0215] This invention is a system for easily setting up drinking parties using communication terminals, and is realized through mutual communication between users, terminals, and a server.

[0216] A communication device includes a means for users to input messages containing specific strings of characters and a means for receiving voice commands. Specifically, this is an electronic device such as a smartphone or smart glasses. It may also be a device equipped with a voice recognition function. For example, the speech_recognition library can be used to convert a user's voice commands into text.

[0217] The server is equipped with a means for receiving messages and voice commands sent by users and analyzing them using natural language processing technology, such as the Google Natural Language API. The server also has built-in means for collecting users' schedule information and determining a common schedule.

[0218] Once the date is decided, the server sends a notification to the user and then suggests candidate stores based on the user's preference data. This data includes past behavioral history and user preferences. The server uses map services such as Google Maps API to suggest candidate stores.

[0219] After selecting a restaurant, the server automatically completes the reservation process and notifies the user when it is complete. The reservation system can use a payment system such as the Stripe API. It also has a function to send a reminder message the day before the party.

[0220] As a concrete example, consider the case where a user gives a voice command to a communication device saying, "Schedule a drinking party." The device receives this voice command and converts it to text using the speech_recognition library. The server receives and analyzes this voice command and starts the schedule adjustment process. The server then aggregates the available dates and times for all users and decides on a common date. Next, the Google Maps API is used to suggest candidate restaurants, and the user selects one. Finally, the restaurant is reserved via the Stripe API, and a completion notification and reminder message are sent.

[0221] An example prompt might look like this:

[0222] Please generate a program that will schedule a drinking party, select a restaurant, and make a reservation in response to the following voice commands.

[0223] User: Set up a drinking party

[0224] System: What dates are you available for?

[0225] User: October 15, 2023 is available

[0226] System: How about the following establishments? 1. Izakaya A 2. Bar B 3. Restaurant C

[0227] User: Izakaya A is good

[0228] System: Izakaya A has been reserved for October 15, 2023

[0229] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0230] Step 1:

[0231] The user inputs a voice command into a communication device (smartphone or smart glasses) saying, "Set up a drinking party." The device converts this voice command into text using the speech_recognition library. The input is the user's voice, and the output is a text command. This converted text is sent to the server.

[0232] Step 2:

[0233] The server analyzes the received text data using natural language processing technology such as the Google Natural Language API. The input is a voice command in text format, and the output is the analysis result (the intent of the command). As a result of the analysis, the instruction "Schedule a drinking party" is recognized, and the process proceeds to the next step.

[0234] Step 3:

[0235] After recognizing the intent of the voice command, the server generates a message to collect the user's schedule information and sends it to all participants. The input is the analysis result, and the output is a message to collect schedule information. Specifically, the server sends a message to the user such as "Please tell us your free dates."

[0236] Step 4:

[0237] Users reply with their available dates via their communication terminals. The terminals then collect and send these reply data to the server. The input is the user's schedule information, and the output is the aggregated schedule data sent to the server.

[0238] Step 5:

[0239] The server aggregates the schedule information of all users and determines a common schedule. The input is the aggregated schedule data, and the output is a common schedule that works for everyone. As part of the data processing, the server calculates the schedule information of all users and selects the most appropriate schedule.

[0240] Step 6:

[0241] The server notifies all users of the decided common schedule. The input is the common schedule information, and the output is a notification message. Specifically, it sends a message such as "The date has been decided. A drinking party will be held at 7pm on XX month."

[0242] Step 7:

[0243] The server collects user preference data and suggests candidate restaurants based on past history and preferences. The input is the user's preference data, and the output is a list of candidate restaurants. Data processing uses map services such as Google Maps API to search for suitable restaurants.

[0244] Step 8:

[0245] The user selects one of the proposed candidate stores and sends the result to the server. The input is the candidate store list, and the output is the selected store. The selection result is notified to the server via the communication terminal.

[0246] Step 9:

[0247] The server automatically reserves the selected store online and notifies all users that the reservation is complete. The input is the selected store information, and the output is a reservation confirmation notification message. The reservation procedure is carried out using a payment system such as Stripe API.

[0248] Step 10:

[0249] The server automatically sends a reminder message the day before the drinking party. The input is the reservation date and time and restaurant information, and the output is the reminder message. Specifically, it sends a message such as, "The drinking party will be at Izakaya A tomorrow at 7pm. Don't forget!"

[0250] 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.

[0251] This system allows users to easily schedule drinking parties using communication devices, and is implemented primarily through communication between a server, devices, and users. By combining natural language processing technology with an emotion engine, the system makes optimal suggestions based on the user's emotions and preferences.

[0252] System Overview

[0253] 1. User Action:

[0254] User: The user uses a messaging app on their communication device to type a specific string of characters (e.g., "Let's go for a drink!") into a chat screen with a friend.

[0255] Terminal: The terminal sends the entered message to the server.

[0256] 2. Message reception and analysis:

[0257] Server: The server receives the message sent by the user and analyzes it to see if it contains a specific string. This analysis uses natural language processing technology.

[0258] Server: When the message is recognized as a trigger, it launches "Let's go drinking! AI."

[0259] 3. Emotion recognition:

[0260] Server: "Let's Drink! AI" has an emotion engine built in that recognizes the user's emotions from the words and context contained in the message.

[0261] Server: The emotion engine analyzes the user's emotions (e.g., joy, sadness, stress, etc.) and prepares appropriate suggestions based on the results.

[0262] 4. Scheduling:

[0263] Server: "Let's go drinking! AI" sends a message to the chat screen to inform all users that it is starting to arrange a date.

[0264] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[0265] Users: Each user replies with their available dates.

[0266] Example: "I'm free on XX / XX and XX / XX."

[0267] Server: The server aggregates all user inputs, determines a common schedule, determines the optimal schedule, and notifies all users.

[0268] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[0269] 5. Shop selection:

[0270] Server: "Let's Drink! AI" searches for candidate restaurants based on the user's past history, preference data, and the results of emotion analysis by the emotion engine.

[0271] Server: Suggests candidate stores to the user, including stores that suit the user's current emotional state.

[0272] Example: "How about these places? 1. Izakaya A (relaxing atmosphere) 2. Bar B (fun events) 3. Restaurant C (quiet and relaxing place)"

[0273] User: The user chooses one of the suggested stores.

[0274] Example: "Izakaya A is good."

[0275] 6. Restaurant Reservations:

[0276] Server: After the user has completed their selection, the server automatically makes an online reservation for the selected restaurant.

[0277] Server: Once the reservation is complete, the details will be sent to all users.

[0278] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[0279] 7. Reminder:

[0280] Server: When the date of the drinking party approaches, the server automatically sends a reminder message.

[0281] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[0282] Specific examples

[0283] For example, a user types "Let's go for a drink!" in a chat screen with a friend. When the device sends this message to the server, the server receives and analyzes the message. As a result of the analysis, the phrase "Let's go for a drink!" is recognized as a specific trigger, and the "Let's go for a drink! AI" is activated. Furthermore, the emotion engine recognizes the emotion from the user's message and considers a response based on that emotion.

[0284] Next, the AI ​​sends a message to arrange a date, and the user replies with their available dates. The server collects this information, decides on a common date, and notifies the user. The AI ​​then takes into account the results of sentiment analysis and suggests the most suitable restaurant for the user. The user selects "Izakaya A," and the server automatically makes the reservation. Once the reservation is complete, the details are notified, and a reminder message is sent the day before the party.

[0285] This allows users to simply enter a specific string of characters, and the system will automatically set up the optimal drinking party based on their emotions and preferences.

[0286] The processing flow will be explained below.

[0287] Step 1:

[0288] User: Using a communication device, type "Let's go for a drink!" into the chat screen with a friend.

[0289] Step 2:

[0290] Terminal: Sends messages entered by the user to the communication server.

[0291] Step 3:

[0292] Server: Analyzes the received message and recognizes the phrase "Let's go for a drink!" as a specific trigger. This analysis uses natural language processing technology.

[0293] Step 4:

[0294] Server: Once trigger recognition is complete, launch "Let's go drinking! AI" and send a message to the chat screen notifying the start of schedule adjustments.

[0295] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[0296] Step 5:

[0297] User: Each user replies with their available dates.

[0298] Example: "I'm free on XX / XX and XX / XX."

[0299] Step 6:

[0300] Server: Aggregates schedule information sent by all users and determines a common schedule.

[0301] Step 7:

[0302] Server: Determines the optimal schedule and notifies all users of that schedule.

[0303] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[0304] Step 8:

[0305] Server: At the same time, the emotion engine built into "Let's Drink! AI" recognizes emotions from the user's message and outputs the analysis results.

[0306] Step 9:

[0307] Server: The emotion engine analyzes the user's emotional state (e.g., joy, stress, fatigue, etc.) and searches for store candidates based on the results.

[0308] Step 10:

[0309] Server: Based on the search results, selects candidate restaurants taking into account the user's past history and preference data, and makes suggestions to all users.

[0310] Example: "How about these places? 1. Izakaya A (relaxing place) 2. Bar B (fun atmosphere) 3. Restaurant C (quiet and relaxing place)"

[0311] Step 11:

[0312] User: The user chooses one of the suggested stores.

[0313] Example: "Izakaya A is good."

[0314] Step 12:

[0315] Server: After the user has completed their selection, the server automatically makes an online reservation for the selected restaurant.

[0316] Step 13:

[0317] Server: Once the reservation is complete, the details will be sent to all users.

[0318] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[0319] Step 14:

[0320] Server: Automatically send reminder messages when the date of a drinking party approaches.

[0321] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[0322] This allows users to simply type "Let's go out for drinks!" and the system will automatically arrange dates, select restaurants, make reservations, and even send reminders based on their emotions and preferences.

[0323] Example 2

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

[0325] Few existing drinking party scheduling systems are able to take into account the user's emotions and preferences. Furthermore, it is difficult to automate all stages of scheduling, restaurant selection, and reservations, requiring a lot of manual work from the user. This increases the burden on users and makes it difficult to realize the optimal drinking party. Furthermore, the lack of advance reminders means that some people may miss out on the party.

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

[0327] In this invention, the server includes means for a user to input a message containing a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for analyzing the user's emotions and making suggestions based on the results, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate restaurants based on the user's preference data, means for making a reservation at the selected restaurant, means for notifying the user that the reservation is complete, and means for sending a reminder message the day before the drinking party. As a result, the user only needs to input the specific character string to automatically set up the optimal drinking party based on their emotions and preferences, reducing the user's burden and enabling the optimal drinking party to be realized.

[0328] "Communication terminal" is a general term for electronic devices that allow users to input and send messages.

[0329] The "specific string" is a specified phrase that the system recognizes as a trigger to start setting up a drinking party.

[0330] The "means for receiving messages" is a function for receiving messages from users via the Internet.

[0331] The "analysis means" is a technique for analyzing a received message and determining whether it contains a specific character string.

[0332] A "means for recognizing as a trigger" is a mechanism that initiates a specific action based on the results of the analysis.

[0333] "Means for analyzing emotions" refers to technology that extracts and classifies emotions from users' messages.

[0334] The "means for making suggestions" is a function that provides the user with the optimal options based on the analysis results.

[0335] The "means for collecting schedule information" is a function for collecting the user's free schedule.

[0336] The "means for determining a common schedule" is a mechanism for selecting a date on which all users can participate based on the collected schedule information.

[0337] The "means for notifying the schedule" is a function for notifying the user of the determined schedule.

[0338] "Preference data" is a general term for data that indicates a user's past preference history and interests.

[0339] The "means for suggesting candidate restaurants" is a mechanism that provides users with a selection of restaurants that are suitable for them based on their preference data.

[0340] The "means of reserving a store" is the technology for reserving a selected store online.

[0341] The "means for notifying the user that a reservation has been completed" is a function for notifying the user that a reservation for a store has been completed.

[0342] The "means for sending a reminder message" is a mechanism for sending a reminder to the user the day before the drinking party.

[0343] This invention is a system for easily setting up drinking parties via communication terminals. This system is realized mainly through communication between a server, terminals, and users, and combines natural language processing technology and an emotion engine to make optimal suggestions based on the user's emotions and preferences.

[0344] The system uses the following hardware and software: The hardware consists of communication devices such as smartphones and PCs used by users, and a cloud server. The software includes natural language processing technology using the NLTK library, Sentiment Analysis API, Google Calendar API, Yelp API, and OpenTable API.

[0345] Specifically, a user types "Let's go for a drink!" using a messaging app on a communication device. This message is sent from the device to the server. Example: "Let's go for a drink!"

[0346] The server receives this message and uses the NLTK library to analyze it for a specific string. If the string is recognized, the "Let's Drink! AI" is launched.

[0347] The emotion engine in "Let's Drink! AI" uses the Sentiment Analysis API to analyze emotions from users' messages. Based on the analyzed emotion data, the server prepares to make optimal suggestions to users.

[0348] Next, the server sends a message to the chat screen to notify all users that schedule adjustments have begun. Users reply with the dates they can attend. Example: "I'm free on XX / XX and XX / XX."

[0349] The server aggregates all user inputs, uses the Google Calendar API to identify common dates, and determines the optimal date. The server then notifies all users of the determined date. Example: "The date has been decided! We'll be having a drinking party on XX day at 7 PM."

[0350] Next, "Let's Drink! AI" uses the Yelp API to search for candidate restaurants based on the user's past history, preference data, and the results of the emotion engine analysis. The server then suggests candidate restaurant information to the user. Example: "How about the following restaurants? 1. Izakaya A (relaxing atmosphere) 2. Bar B (with fun events) 3. Restaurant C (a quiet, relaxing place)"

[0351] The user selects one of the suggested restaurants. Example: "Izakaya A is good."

[0352] After the user has made their selection, the server will automatically make an online reservation for the selected restaurant using the OpenTable API. Once the reservation is complete, the server will notify all users of the details. Example: "Your reservation has been completed for Izakaya A on XX date at 7pm. Thank you!"

[0353] Finally, as the date of the drinking party approaches, the server automatically sends a reminder message. Example: "The drinking party will be at Izakaya A tomorrow at 7pm. Don't forget!"

[0354] With this system, users only need to input a specific string of characters, and the system will automatically set up the optimal drinking party based on their emotions and preferences.

[0355] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0356] Step 1:

[0357] User action: Enter message

[0358] User: The user opens a messaging app on their device, types the text "Let's go for a drink!", and presses the send button.

[0359] Input: The message entered by the user: "Let's go for a drink!"

[0360] Output: The message data sent.

[0361] Step 2:

[0362] Message Reception

[0363] Terminal: The terminal sends the message entered by the user to the server.

[0364] Input: User input message

[0365] Output: Message data sent to the server

[0366] Step 3:

[0367] Message Parsing

[0368] Server: Analyzes the received message data.

[0369] What happens: The server uses the NLTK library to tokenize the message and parse it to see if it contains a specific string ("Let's go for a drink!").

[0370] Input: Message data sent to the server

[0371] Output: Result of whether the string contains a specific string

[0372] Step 4:

[0373] Let's go drinking! AI startup

[0374] Server: When a specific string is recognized, launch "Let's go drinking! AI."

[0375] Specific operation: If the conditions for starting the AI ​​are met based on the message analysis results, the AI ​​program is executed.

[0376] Input: Judgment results that contain a specific string

[0377] Output: AI startup status

[0378] Step 5:

[0379] Emotion analysis

[0380] Server: The emotion engine built into "Let's Drink! AI" analyzes the user's emotions from the message.

[0381] Specific operation: The server calls the Sentiment Analysis API to extract and classify sentiment data from the message.

[0382] Input: User's message data

[0383] Output: User's emotion data (e.g., happy, tired, etc.)

[0384] Step 6:

[0385] Start scheduling

[0386] Server: Send a message to the chat screen saying, "Thank you everyone for your hard work! We'll start arranging the date for the drinking party, so please let us know when you can join."

[0387] Specific operation: Send a notification message using the message sending API.

[0388] Input: Sentiment analysis results and AI startup status

[0389] Output: Sending a message to start scheduling

[0390] Step 7:

[0391] User schedule entry

[0392] User: Enter the dates you can attend on the chat screen and send.

[0393] Specific operation: Each user enters the schedule in the reply format and submits it.

[0394] Input: Schedule adjustment start message from the server

[0395] Output: User's available dates

[0396] Step 8:

[0397] Schedule collection and decision

[0398] Server: Aggregates the submitted schedule information and determines a common schedule using the Google Calendar API.

[0399] Specific operation: Run an algorithm to determine common schedules based on the schedule data of all users.

[0400] Input: Schedule data for each user

[0401] Output: The determined common date

[0402] Step 9:

[0403] Schedule notification

[0404] Server: Notify all users of the decided date.

[0405] Specific operation: Use the push notification function to send a schedule confirmation message.

[0406] Input: Common Schedule

[0407] Output: Sending a schedule notification message

[0408] Step 10:

[0409] Store selection

[0410] Server: Using the Yelp API, search for potential stores based on the user's past history, preference data, and the results of the emotion engine analysis, and make suggestions to the user.

[0411] Specific operations: Retrieve history and preference data from the database, submit a search query to the API, and receive the results.

[0412] Example: "How about these places? 1. Izakaya A (relaxing atmosphere) 2. Bar B (fun events) 3. Restaurant C (quiet and relaxing place)"

[0413] Input: User history data, preference data, emotional data

[0414] Output: Sending a proposal message

[0415] Step 11:

[0416] User store selection

[0417] User: Choose one of the suggested stores and reply on the chat screen.

[0418] Specific operation: Reply with the name of the store selected from the list on the chat screen.

[0419] Example: "Izakaya A is good."

[0420] Input: Proposal message

[0421] Output: User selection data

[0422] Step 12:

[0423] Store reservation

[0424] Server: Automatically reserves selected locations using the OpenTable API.

[0425] Specific operation: Based on the selected store information, make an API call to make a reservation.

[0426] Input: User selected data

[0427] Output: Reservation completion notification

[0428] Step 13:

[0429] Reservation completion notification

[0430] Server: Notify all users once the reservation is complete.

[0431] Specific behavior: Use the push notification function to send a message with detailed information.

[0432] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[0433] Input: Reservation completion data

[0434] Output: Send reservation completion notification

[0435] Step 14:

[0436] Send reminder messages

[0437] Server: When the date of the drinking party approaches, the server automatically sends a reminder message.

[0438] What it does: Checks the internal clock system and sends a message the day before the appointment.

[0439] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[0440] Input: Reservation schedule data

[0441] Output: Send reminder message

[0442] The above is the specific flow of program processing for this system.

[0443] (Application example 2)

[0444] 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."

[0445] In modern society, group activities and meetings are frequently coordinated, but these coordination tasks are often performed manually, resulting in labor shortages and inefficiencies. Similar issues also arise when reporting on on-site security situations and setting up countermeasure meetings. Security operations, in particular, require rapid response, necessitating fast and efficient communication methods. Furthermore, appropriate suggestions and responses based on emotions and preferences are often required, and the lack of a way to automate these tasks requires a significant amount of effort.

[0446] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input a message including a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate locations based on the user's preference data, means for reserving the selected location, means for notifying the user that the reservation is complete, means for sending a reminder message the day before the scheduled date, means for recognizing the user's emotions using an emotion engine and taking the most appropriate action, and means for accepting user commands through voice recognition. This enables fast and efficient communication.

[0447] A "communication terminal" is an electronic device that allows a user to input information and communicate.

[0448] A "specific string" is a set of predefined characters that the system recognizes as a trigger.

[0449] A "message" is text information that a user sends through a communication terminal.

[0450] A "trigger" is a condition or signal that causes an event or action to occur.

[0451] "Analysis" is the process of closely examining input data and understanding its meaning and intent.

[0452] "Schedule information" is data relating to a schedule designated by the user.

[0453] A "common date" is a date and time when the date information of multiple users matches.

[0454] "Preference data" is data relating to a user's tastes and preferences.

[0455] "Candidate locations" are multiple selectable locations suggested by the system.

[0456] A "reservation" is the act of reserving a specific location or service for a specified date and time.

[0457] A "remind message" is a message that reminds you of an appointment or important matter in advance.

[0458] The "emotion engine" is a technology that analyzes and recognizes the user's emotions and suggests countermeasures.

[0459] "Speech recognition" is a technology that analyzes voice data and converts it into text information.

[0460] The present invention is a system that uses a communication terminal to recognize specific messages and make optimal suggestions based on the user's emotions and preferences. The system aims to significantly reduce the user's time and effort by automatically adjusting schedules and reserving appropriate facilities.

[0461] First, the user speaks a specific string of characters (for example, "Start a meeting!") into the communication device. Using speech recognition technology, this voice data is converted into text and sent to the server. The server analyzes the received text message and uses natural language processing technology to recognize the specific string of characters as a trigger. This analysis is performed using tools such as the Python speech_recognition library and the Google Cloud Natural Language API.

[0462] Next, the server collects users' schedule information and determines a common schedule. At this stage, users input the dates and times they are available, and the server aggregates the information to determine the optimal date and time, and notifies all users. Notifications are made via messages displayed on their devices or emails.

[0463] The server then suggests potential locations based on the user's preference data and past history. Using an emotion engine, the server takes into account the user's current emotional state. For example, it may suggest places where you can relax, where you can concentrate, or where fun events are taking place. The server uses scikit-learn, a Python machine learning library, and an API for emotion analysis.

[0464] When the user selects one of the candidate locations, the server automatically reserves the location. The server then notifies the user of the reservation completion and sends a reminder message the day before the scheduled date, allowing the user to proceed with the schedule with peace of mind.

[0465] Examples of specific prompts include:

[0466] "Start the meeting!"

[0467] Please schedule the next meeting.

[0468] "Report the security situation."

[0469] This allows users to easily schedule meetings and events, enabling quick responses on-site. The system also enables flexible responses based on users' emotions and preferences.

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

[0471] Step 1:

[0472] The user inputs a specific string of characters into the communication device by voice, and the device converts this voice into text and sends it to the server. At this stage, the input is the user's voice data, and the output is text data. Specifically, the voice data is converted into text using the Python speech_recognition library.

[0473] Step 2:

[0474] The server analyzes the received text message and recognizes specific strings as triggers. The input at this stage is the text data from step 1, and the output is a trigger signal to activate a specific action. Specifically, it uses natural language processing technology to analyze the text and detects trigger keywords (e.g., "Start a meeting!").

[0475] Step 3:

[0476] The server collects users' schedule information and determines a common schedule. At this stage, the input is the available date and time information for each user, and the output is the optimal date and time common to all users. Specifically, the server aggregates replies from users and uses an algorithm to determine the optimal date and time from among them.

[0477] Step 4:

[0478] The server notifies users of the determined date and time. The input at this stage is the common date and time determined in step 3, and the output is a notification message displayed on the user's communication device. Specifically, all users are notified that the date and time have been determined via email or push notification.

[0479] Step 5:

[0480] The server proposes candidate locations based on the user's preference data and past history. The input at this stage is the user's preference data and past history data, and the output is a list of candidate locations. Specifically, the server analyzes the data using Python machine learning libraries (e.g., scikit-learn) and sentiment analysis APIs to generate candidate locations.

[0481] Step 6:

[0482] The user selects one of the candidate locations, and the server automatically reserves that location. The input at this stage is the user's selected location, and the output is reservation confirmation information. Specifically, the API is used to make an online reservation and record the reservation results.

[0483] Step 7:

[0484] The server notifies the user that the reservation is complete and sends a reminder message the day before the scheduled date. The input at this stage is the reservation confirmation information and schedule information, and the output is the reminder message. Specifically, the server monitors the schedule and automatically sends a reminder 24 hours before the scheduled date and time.

[0485] In this way, a series of processes, from user voice command input to natural language processing, sentiment analysis, schedule adjustment and booking, and reminder message sending, are automated, allowing users to easily manage their schedules and respond on-site.

[0486] 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.

[0487] 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.

[0488] 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.

[0489] [Second embodiment]

[0490] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0491] 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.

[0492] 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).

[0493] 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.

[0494] 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.

[0495] 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).

[0496] 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.

[0497] 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.

[0498] 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.

[0499] 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.

[0500] 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.

[0501] 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."

[0502] This invention is a system for easily setting up drinking parties using communication terminals, and is realized mainly through communication between a server, terminals, and users. This system also uses natural language processing technology to analyze messages from users, obtain necessary information, and proceed with the process.

[0503] System Overview

[0504] 1. User Action:

[0505] User: The user uses a messaging app on their communication device to type a specific string of characters (e.g., "Let's go for a drink!") into a chat screen with a friend.

[0506] Device: The device sends the entered message to the LINE server.

[0507] 2. Message reception and analysis:

[0508] Server: The server receives the message sent by the user and analyzes it to see if it contains a specific string. This analysis uses natural language processing technology.

[0509] Server: When the message is recognized as a trigger, it launches "Let's go drinking! AI."

[0510] 3. Scheduling:

[0511] Server: "Let's go drinking! AI" sends a message to the chat screen to inform all users that it is starting to arrange a date.

[0512] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[0513] Users: Each user replies with their available dates.

[0514] Example: "I'm free on XX / XX and XX / XX."

[0515] Server: The server aggregates all users' inputs, determines common availability, determines the optimal schedule, and notifies all users.

[0516] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[0517] 4. Shop selection:

[0518] Server: "Let's Drink! AI" searches for candidate stores based on the user's past history and preference data.

[0519] Server: Suggests candidate stores to the user.

[0520] Example: "How about the following places? 1. Izakaya A 2. Bar B 3. Restaurant C"

[0521] User: The user chooses one of the suggested stores.

[0522] Example: "Izakaya A is good."

[0523] 5. Restaurant reservations:

[0524] Server: After the user has completed their selection, the server automatically makes an online reservation for the selected restaurant.

[0525] Server: Once the reservation is complete, all users will be notified of the details.

[0526] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[0527] 6. Reminder:

[0528] Server: When the date of the drinking party approaches, the server automatically sends a reminder message.

[0529] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[0530] Specific examples

[0531] For example, a user types "Let's go for a drink!" into a chat screen with a friend on LINE. When the device sends this message to the LINE server, the server receives and analyzes the message. As a result of the analysis, the phrase "Let's go for a drink!" is recognized as a specific trigger, activating the "Let's go for a drink! AI." The AI ​​then sends a message to arrange a date, and the user replies with their available dates. The server collects this information, decides on a common date, and notifies the user.

[0532] Next, "Let's Drink! AI" searches for candidate restaurants based on the user's preference data and suggests several options to the user. The user selects Izakaya A from these, and the server automatically makes the reservation. Once the reservation is complete, all users are notified of the details and a reminder message is sent the day before the drinking party.

[0533] This allows users to automatically plan a drinking party simply by entering a specific string of characters.

[0534] The processing flow will be explained below.

[0535] Step 1:

[0536] User: Using a communication device, type "Let's go for a drink!" into the chat screen with a friend.

[0537] Step 2:

[0538] Device: Sends the user's input to the LINE server.

[0539] Step 3:

[0540] Server: The LINE server analyzes the received message and recognizes the phrase "Let's go for a drink!" as a specific trigger.

[0541] Step 4:

[0542] Server: After the trigger is recognized, launch "Let's go drinking! AI" and send a message to the chat screen notifying the start of schedule adjustment.

[0543] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[0544] Step 5:

[0545] User: Each user replies with their available dates.

[0546] Example: "I'm free on XX / XX and XX / XX."

[0547] Step 6:

[0548] Server: Aggregates schedule information sent by all users and determines a common schedule.

[0549] Step 7:

[0550] Server: Determines the optimal schedule and notifies all users of that schedule.

[0551] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[0552] Step 8:

[0553] Server: Searches for store candidates based on the user's past history and preference data.

[0554] Step 9:

[0555] Server: Selects candidate stores and suggests them to the user.

[0556] Example: "How about the following places? 1. Izakaya A 2. Bar B 3. Restaurant C"

[0557] Step 10:

[0558] User: The user chooses one of the suggested stores.

[0559] Example: "Izakaya A is good."

[0560] Step 11:

[0561] Server: Automatically make online reservations at selected restaurants.

[0562] Step 12:

[0563] Server: Once the reservation is complete, the details will be sent to all users.

[0564] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[0565] Step 13:

[0566] Server: Automatically send reminder messages when the date of a drinking party approaches.

[0567] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[0568] Example 1

[0569] 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."

[0570] When planning an event such as a drinking party, it takes time and effort to adjust the date, select a restaurant, and confirm reservations, which can be particularly tedious when there are multiple participants. It is also easy to forget to send reminders to all participants. This system solves these problems and allows users to easily schedule a drinking party.

[0571] 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.

[0572] In this invention, the server includes means for a user to input a message containing a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate restaurants based on the user's preference data, means for reserving the selected restaurant, means for notifying the user that the reservation is complete, means for sending a reminder message the day before the drinking party, means for analyzing the message using a natural language processing engine and detecting the trigger, means for analyzing the optimal available date and time from the aggregated schedule information, and means for automatically making a restaurant reservation online. This allows users to easily adjust schedules, select restaurants, and confirm reservations through the system.

[0573] A "communication terminal" is an electronic device that a user uses to input and send messages, and includes smartphones, personal computers, and the like.

[0574] A "specific string" refers to a group of letters or words that the system is configured to recognize as a trigger.

[0575] A "message" refers to text information that a user inputs via a communication terminal and sends to other users or a server.

[0576] A "natural language processing engine" is a software technology that analyzes messages entered by users and recognizes specific strings of characters that act as triggers.

[0577] "Schedule information" refers to data regarding the dates and times when a user can participate.

[0578] "Preference data" is information about a user's past history and preferences, and is used to suggest candidate stores.

[0579] "Candidate store" refers to a store that the system suggests to the user as a location for a drinking party.

[0580] "Reservation" refers to the act of reserving a candidate store selected by a user and making it available for use on a specific date and time.

[0581] A "reminding message" is a message that notifies the user of the details of the event again when the date of the drinking party approaches.

[0582] "Generative AI models" refer to algorithms and programs that use AI to analyze data and make predictions.

[0583] A "prompt sentence" is a standard sentence to be input into a generative AI model, and contains instructions for the AI ​​to process appropriately.

[0584] The present invention is a system that allows users to easily schedule drinking parties using communication terminals, and is implemented primarily through communications between a server, terminals, and users. This system utilizes a natural language processing engine and a generative AI model to automatically perform message analysis, schedule adjustment, restaurant selection, and reminders. The following describes in detail an embodiment of the present invention.

[0585] System configuration

[0586] 1. Communication terminal

[0587] A communication device is used by users to input and send messages. Typically, this is a smartphone or a PC. Using this communication device, users input specific characters into the chat screen with friends and send them.

[0588] 2. Server

[0589] The server plays a central role in receiving and analyzing messages. It has a built-in natural language processing engine, which analyzes messages sent by users and detects whether they contain specific strings that act as triggers. This allows subsequent processing to proceed automatically.

[0590] 3. Natural Language Processing Engine

[0591] The natural language processing engine analyzes the message entered by the user and recognizes specific triggers. If the engine detects a trigger, the server launches "Let's Drink! AI" and the scheduling process begins.

[0592] 4. Generative AI Models

[0593] A generative AI model refers to an algorithm or program that analyzes data and makes predictions during processes such as scheduling, selecting restaurants, and sending reminders. This model suggests optimal restaurants and dates based on the user's past history and preference data.

[0594] System operation explanation

[0595] When a user operates a communication device and sends a specific string of characters, such as "Let's go for a drink!", the message is sent over the Internet to a server. The server uses a natural language processing engine to analyze the message and detects the specific string that acts as a trigger. This activates the "Let's go for a drink! AI," which then begins the process of arranging a date and selecting a restaurant.

[0596] Specifically, the system operates as follows: The server sends a message to all users to arrange a date and collects the available date information replies from each user. The generative AI model aggregates this date information and analyzes common available dates and times. It then notifies all users of the optimal date. After that, candidate restaurants are suggested based on the user's preference data and past history, and the restaurant selected by the user is automatically reserved online. Once the reservation is complete, the details are notified to all users, and a reminder message is sent as the date of the drinking party approaches.

[0597] Specific examples

[0598] For example, a user types "Let's go for a drink!" into the LINE chat screen and sends it. When the device sends this message to the server, the server analyzes the message. As a result of the analysis, the phrase "Let's go for a drink!" is recognized as a trigger, and the "Let's go for a drink! AI" is activated.

[0599] Next, the server sends a message to the chat screen saying, "We'll start arranging dates, so please let us know when you can join." Each user replies with their available dates, and the server aggregates this information, determines common availability, and decides on the optimal date. Next, "Let's go drinking! AI" searches for candidate restaurants based on the user's preference data and suggests several options to the user. The user selects a restaurant from these, and the server automatically reserves the restaurant and notifies all users of the details. Finally, the server sends a reminder message the day before the drinking party.

[0600] Prompt Sentence Examples

[0601] 1. When the phrase "Let's go for a drink!" is sent via LINE, "Let's go for a drink! AI" will start arranging a date.

[0602] "We will begin arranging the schedule. Please let us know the dates that you can attend."

[0603] 2. After the date of the drinking party is decided, a prompt to suggest a possible restaurant:

[0604] "How about the following establishments? 1. Izakaya 2. Bar 3. Restaurant"

[0605] 3. Once the user has selected a store, a prompt will automatically reserve that store:

[0606] "Reservation completed at 7pm on XX month."

[0607] As described above, the system of the present invention provides an environment in which users can easily set up drinking parties.

[0608] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0609] Step 1:

[0610] The user inputs a specific string into the communication terminal and sends it.

[0611] Input: The user enters the string "Let's go for a drink!" on a communication device such as a smartphone or PC.

[0612] Behavior: The user presses the send button on the chat screen to send this string.

[0613] Output: The communication terminal sends this message to the server.

[0614] Step 2:

[0615] The device sends a message to the server

[0616] Input: A message sent by the user: "Let's go for a drink!"

[0617] How it works: The device sends this message to the server via the internet. This process is carried out through the LINE server.

[0618] Output: The server receives this message.

[0619] Step 3:

[0620] The server parses the message

[0621] Input: The message "Let's go for a drink!" received by the server.

[0622] How it works: The server's natural language processing engine parses the message to see if it contains a specific string. This process uses a natural language processing model.

[0623] Output: When a specific trigger string is detected, "Let's go drinking! AI" will be launched.

[0624] Step 4:

[0625] The server notifies the user that scheduling is about to begin.

[0626] Input: The natural language processing engine detects the trigger "Let's go for a drink!"

[0627] How it works: The server sends a message to all users saying, "We're starting to schedule. Please let us know when you can join."

[0628] Output: A message is displayed to all users informing them that scheduling has begun.

[0629] Step 5:

[0630] The user replies with their available dates

[0631] Input: The rescheduling message sent by the server.

[0632] How it works: Each user replies with the date and time they are available. For example, they might reply, "I'm free on XX / XX and XX / XX."

[0633] Output: The server collects information about the dates that users can attend.

[0634] Step 6:

[0635] The server decides the best date

[0636] Input: Available dates and times provided by each user.

[0637] How it works: The server analyzes the aggregated schedule information and identifies common free dates and times. A generative AI model assists in this analysis.

[0638] Output: The optimal date is determined and the result is notified to all users from the server. For example, a message such as "We will hold a drinking party on XX day at 7 PM" is sent.

[0639] Step 7:

[0640] The server suggests candidate stores

[0641] Input: Best dates and user preference data.

[0642] How it works: The server uses a generative AI model to search for candidate stores based on the user's past history and preference data.

[0643] Output: A list of candidate stores is generated, and the server sends a suggestion message to the user saying, "How about the following stores? 1. Izakaya 2. Bar 3. Restaurant."

[0644] Step 8:

[0645] The user selects a store

[0646] Input: A list of candidate stores sent from the server.

[0647] Operation: The user selects one of the suggested restaurants and replies, for example, "Izakaya is good."

[0648] Output: The selected store information is sent to the server.

[0649] Step 9:

[0650] The server reserves the selected store

[0651] Input: Store information selected by the user.

[0652] How it works: The server automatically reserves the selected store online using an online reservation service.

[0653] Output: The reservation is completed and the details are sent to all users from the server. For example, a message like "Your reservation at the izakaya on the ____ date at 7pm has been completed. Thank you!" is sent.

[0654] Step 10:

[0655] The server sends a reminder message

[0656] Input: Time information and store information after completing reservation.

[0657] How it works: When the date of the drinking party approaches, the server automatically sends a reminder message to the user.

[0658] Output: A reminder message is sent to the user saying "There's a drinking party at the izakaya tomorrow at 7pm. Don't forget!"

[0659] The above specific processing steps realize a system that allows users to easily set up drinking parties.

[0660] (Application example 1)

[0661] 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."

[0662] Traditionally, setting up a drinking party requires a lot of time and effort, including coordinating participants' schedules and reserving restaurants. Furthermore, in order to efficiently complete these tasks, it is necessary to gather the opinions of all participants, which can make the process cumbersome. This reduces the time participants can spend enjoying the party. Furthermore, while there is a demand for easy operation via voice commands, traditional systems do not adequately achieve this.

[0663] 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.

[0664] In this invention, the server includes means for a user to input a message containing a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate restaurants based on the user's preference data, means for making a reservation at the selected restaurant, means for notifying the user that the reservation is complete, means for sending a reminder message the day before the drinking party, means for activating the system by voice command, and means having a natural language processing device for analyzing the voice command. This allows the user to quickly and efficiently set up a drinking party using simple voice commands, improving the convenience of the entire system.

[0665] "Communication terminal" refers to any electronic device that allows a user to input and send messages.

[0666] A "specific character string" refers to a uniquely identifiable phrase or word that a user inputs into a communication terminal.

[0667] A "trigger" is a string or command that causes the system to start a specific process.

[0668] "Date information" refers to data provided by a user regarding available dates and times for participation.

[0669] The "common schedule" refers to a date and time when all users can participate among the available dates and times for participation.

[0670] "Preference data" refers to information based on a user's past behavioral history and preference trends.

[0671] "Candidate stores" refer to multiple restaurants that the user may visit.

[0672] "Reservation" refers to a user reserving one of the candidate stores in advance for a specific date and time.

[0673] A "reminder message" refers to a confirmation notification sent when the date and time of a drinking party approaches.

[0674] "Voice command" refers to a verbal instruction given by a user to a system.

[0675] "Natural language processing device" refers to a technical device that analyzes a user's voice commands or messages and takes appropriate action.

[0676] This invention is a system for easily setting up drinking parties using communication terminals, and is realized through mutual communication between users, terminals, and a server.

[0677] A communication device includes a means for users to input messages containing specific strings of characters and a means for receiving voice commands. Specifically, this is an electronic device such as a smartphone or smart glasses. It may also be a device equipped with a voice recognition function. For example, the speech_recognition library can be used to convert a user's voice commands into text.

[0678] The server is equipped with a means for receiving messages and voice commands sent by users and analyzing them using natural language processing technology, such as the Google Natural Language API. The server also has built-in means for collecting users' schedule information and determining a common schedule.

[0679] Once the date is decided, the server sends a notification to the user and then suggests candidate stores based on the user's preference data. This data includes past behavioral history and user preferences. The server uses map services such as Google Maps API to suggest candidate stores.

[0680] After selecting a restaurant, the server automatically completes the reservation process and notifies the user when it is complete. The reservation system can use a payment system such as the Stripe API. It also has a function to send a reminder message the day before the party.

[0681] As a concrete example, consider the case where a user gives a voice command to a communication device saying, "Schedule a drinking party." The device receives this voice command and converts it to text using the speech_recognition library. The server receives and analyzes this voice command and starts the schedule adjustment process. The server then aggregates the available dates and times for all users and decides on a common date. Next, the Google Maps API is used to suggest candidate restaurants, and the user selects one. Finally, the restaurant is reserved via the Stripe API, and a completion notification and reminder message are sent.

[0682] An example prompt might look like this:

[0683] Please generate a program that will schedule a drinking party, select a restaurant, and make a reservation in response to the following voice commands.

[0684] User: Set up a drinking party

[0685] System: What dates are you available for?

[0686] User: October 15, 2023 is available

[0687] System: How about the following establishments? 1. Izakaya A 2. Bar B 3. Restaurant C

[0688] User: Izakaya A is good

[0689] System: Izakaya A has been reserved for October 15, 2023

[0690] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0691] Step 1:

[0692] The user inputs a voice command into a communication device (smartphone or smart glasses) saying, "Set up a drinking party." The device converts this voice command into text using the speech_recognition library. The input is the user's voice, and the output is a text command. This converted text is sent to the server.

[0693] Step 2:

[0694] The server analyzes the received text data using natural language processing technology such as the Google Natural Language API. The input is a voice command in text format, and the output is the analysis result (the intent of the command). As a result of the analysis, the instruction "Schedule a drinking party" is recognized, and the process proceeds to the next step.

[0695] Step 3:

[0696] After recognizing the intent of the voice command, the server generates a message to collect the user's schedule information and sends it to all participants. The input is the analysis result, and the output is a message to collect schedule information. Specifically, the server sends a message to the user such as "Please tell us your free dates."

[0697] Step 4:

[0698] Users reply with their available dates via their communication terminals. The terminals then collect and send these reply data to the server. The input is the user's schedule information, and the output is the aggregated schedule data sent to the server.

[0699] Step 5:

[0700] The server aggregates the schedule information of all users and determines a common schedule. The input is the aggregated schedule data, and the output is a common schedule that works for everyone. As part of the data processing, the server calculates the schedule information of all users and selects the most appropriate schedule.

[0701] Step 6:

[0702] The server notifies all users of the decided common schedule. The input is the common schedule information, and the output is a notification message. Specifically, it sends a message such as "The date has been decided. A drinking party will be held at 7pm on XX month."

[0703] Step 7:

[0704] The server collects user preference data and suggests candidate restaurants based on past history and preferences. The input is the user's preference data, and the output is a list of candidate restaurants. Data processing uses map services such as Google Maps API to search for suitable restaurants.

[0705] Step 8:

[0706] The user selects one of the proposed candidate stores and sends the result to the server. The input is the candidate store list, and the output is the selected store. The selection result is notified to the server via the communication terminal.

[0707] Step 9:

[0708] The server automatically reserves the selected store online and notifies all users that the reservation is complete. The input is the selected store information, and the output is a reservation confirmation notification message. The reservation procedure is carried out using a payment system such as Stripe API.

[0709] Step 10:

[0710] The server automatically sends a reminder message the day before the drinking party. The input is the reservation date and time and restaurant information, and the output is the reminder message. Specifically, it sends a message such as, "The drinking party will be at Izakaya A tomorrow at 7pm. Don't forget!"

[0711] 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.

[0712] This system allows users to easily schedule drinking parties using communication devices, and is implemented primarily through communication between a server, devices, and users. By combining natural language processing technology with an emotion engine, the system makes optimal suggestions based on the user's emotions and preferences.

[0713] System Overview

[0714] 1. User Action:

[0715] User: The user uses a messaging app on their communication device to type a specific string of characters (e.g., "Let's go for a drink!") into a chat screen with a friend.

[0716] Terminal: The terminal sends the entered message to the server.

[0717] 2. Message reception and analysis:

[0718] Server: The server receives the message sent by the user and analyzes it to see if it contains a specific string. This analysis uses natural language processing technology.

[0719] Server: When the message is recognized as a trigger, it launches "Let's go drinking! AI."

[0720] 3. Emotion recognition:

[0721] Server: "Let's Drink! AI" has an emotion engine built in that recognizes the user's emotions from the words and context contained in the message.

[0722] Server: The emotion engine analyzes the user's emotions (e.g., joy, sadness, stress, etc.) and prepares appropriate suggestions based on the results.

[0723] 4. Scheduling:

[0724] Server: "Let's go drinking! AI" sends a message to the chat screen to inform all users that it is starting to arrange a date.

[0725] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[0726] Users: Each user replies with their available dates.

[0727] Example: "I'm free on XX / XX and XX / XX."

[0728] Server: The server aggregates all user inputs, determines a common schedule, determines the optimal schedule, and notifies all users.

[0729] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[0730] 5. Shop selection:

[0731] Server: "Let's Drink! AI" searches for candidate restaurants based on the user's past history, preference data, and the results of emotion analysis by the emotion engine.

[0732] Server: Suggests candidate stores to the user, including stores that suit the user's current emotional state.

[0733] Example: "How about these places? 1. Izakaya A (relaxing atmosphere) 2. Bar B (fun events) 3. Restaurant C (quiet and relaxing place)"

[0734] User: The user chooses one of the suggested stores.

[0735] Example: "Izakaya A is good."

[0736] 6. Restaurant Reservations:

[0737] Server: After the user has completed their selection, the server automatically makes an online reservation for the selected restaurant.

[0738] Server: Once the reservation is complete, the details will be sent to all users.

[0739] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[0740] 7. Reminder:

[0741] Server: When the date of the drinking party approaches, the server automatically sends a reminder message.

[0742] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[0743] Specific examples

[0744] For example, a user types "Let's go for a drink!" in a chat screen with a friend. When the device sends this message to the server, the server receives and analyzes the message. As a result of the analysis, the phrase "Let's go for a drink!" is recognized as a specific trigger, and the "Let's go for a drink! AI" is activated. Furthermore, the emotion engine recognizes the emotion from the user's message and considers a response based on that emotion.

[0745] Next, the AI ​​sends a message to arrange a date, and the user replies with their available dates. The server collects this information, decides on a common date, and notifies the user. The AI ​​then takes into account the results of sentiment analysis and suggests the most suitable restaurant for the user. The user selects "Izakaya A," and the server automatically makes the reservation. Once the reservation is complete, the details are notified, and a reminder message is sent the day before the party.

[0746] This allows users to simply enter a specific string of characters, and the system will automatically set up the optimal drinking party based on their emotions and preferences.

[0747] The processing flow will be explained below.

[0748] Step 1:

[0749] User: Using a communication device, type "Let's go for a drink!" into the chat screen with a friend.

[0750] Step 2:

[0751] Terminal: Sends messages entered by the user to the communication server.

[0752] Step 3:

[0753] Server: Analyzes the received message and recognizes the phrase "Let's go for a drink!" as a specific trigger. This analysis uses natural language processing technology.

[0754] Step 4:

[0755] Server: Once trigger recognition is complete, launch "Let's go drinking! AI" and send a message to the chat screen notifying the start of schedule adjustments.

[0756] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[0757] Step 5:

[0758] User: Each user replies with their available dates.

[0759] Example: "I'm free on XX / XX and XX / XX."

[0760] Step 6:

[0761] Server: Aggregates schedule information sent by all users and determines a common schedule.

[0762] Step 7:

[0763] Server: Determines the optimal schedule and notifies all users of that schedule.

[0764] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[0765] Step 8:

[0766] Server: At the same time, the emotion engine built into "Let's Drink! AI" recognizes emotions from the user's message and outputs the analysis results.

[0767] Step 9:

[0768] Server: The emotion engine analyzes the user's emotional state (e.g., joy, stress, fatigue, etc.) and searches for store candidates based on the results.

[0769] Step 10:

[0770] Server: Based on the search results, selects candidate restaurants taking into account the user's past history and preference data, and makes suggestions to all users.

[0771] Example: "How about these places? 1. Izakaya A (relaxing place) 2. Bar B (fun atmosphere) 3. Restaurant C (quiet and relaxing place)"

[0772] Step 11:

[0773] User: The user chooses one of the suggested stores.

[0774] Example: "Izakaya A is good."

[0775] Step 12:

[0776] Server: After the user has completed their selection, the server automatically makes an online reservation for the selected restaurant.

[0777] Step 13:

[0778] Server: Once the reservation is complete, the details will be sent to all users.

[0779] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[0780] Step 14:

[0781] Server: Automatically send reminder messages when the date of a drinking party approaches.

[0782] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[0783] This allows users to simply type "Let's go out for drinks!" and the system will automatically arrange dates, select restaurants, make reservations, and even send reminders based on their emotions and preferences.

[0784] Example 2

[0785] 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."

[0786] Few existing drinking party scheduling systems are able to take into account the user's emotions and preferences. Furthermore, it is difficult to automate all stages of scheduling, restaurant selection, and reservations, requiring a lot of manual work from the user. This increases the burden on users and makes it difficult to realize the optimal drinking party. Furthermore, the lack of advance reminders means that some people may miss out on the party.

[0787] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0788] In this invention, the server includes means for a user to input a message containing a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for analyzing the user's emotions and making suggestions based on the results, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate restaurants based on the user's preference data, means for making a reservation at the selected restaurant, means for notifying the user that the reservation is complete, and means for sending a reminder message the day before the drinking party. As a result, the user only needs to input the specific character string to automatically set up the optimal drinking party based on their emotions and preferences, reducing the user's burden and enabling the optimal drinking party to be realized.

[0789] "Communication terminal" is a general term for electronic devices that allow users to input and send messages.

[0790] The "specific string" is a specified phrase that the system recognizes as a trigger to start setting up a drinking party.

[0791] The "means for receiving messages" is a function for receiving messages from users via the Internet.

[0792] The "analysis means" is a technique for analyzing a received message and determining whether it contains a specific character string.

[0793] A "means for recognizing as a trigger" is a mechanism that initiates a specific action based on the results of the analysis.

[0794] "Means for analyzing emotions" refers to technology that extracts and classifies emotions from users' messages.

[0795] The "means for making suggestions" is a function that provides the user with the optimal options based on the analysis results.

[0796] The "means for collecting schedule information" is a function for collecting the user's free schedule.

[0797] The "means for determining a common schedule" is a mechanism for selecting a date on which all users can participate based on the collected schedule information.

[0798] The "means for notifying the schedule" is a function for notifying the user of the determined schedule.

[0799] "Preference data" is a general term for data that indicates a user's past preference history and interests.

[0800] The "means for suggesting candidate restaurants" is a mechanism that provides users with a selection of restaurants that are suitable for them based on their preference data.

[0801] The "means of reserving a store" is the technology for reserving a selected store online.

[0802] The "means for notifying the user that a reservation has been completed" is a function for notifying the user that a reservation for a store has been completed.

[0803] The "means for sending a reminder message" is a mechanism for sending a reminder to the user the day before the drinking party.

[0804] This invention is a system for easily setting up drinking parties via communication terminals. This system is realized mainly through communication between a server, terminals, and users, and combines natural language processing technology and an emotion engine to make optimal suggestions based on the user's emotions and preferences.

[0805] The system uses the following hardware and software: The hardware consists of communication devices such as smartphones and PCs used by users, and a cloud server. The software includes natural language processing technology using the NLTK library, Sentiment Analysis API, Google Calendar API, Yelp API, and OpenTable API.

[0806] Specifically, a user types "Let's go for a drink!" using a messaging app on a communication device. This message is sent from the device to the server. Example: "Let's go for a drink!"

[0807] The server receives this message and uses the NLTK library to analyze it for a specific string. If the string is recognized, the "Let's Drink! AI" is launched.

[0808] The emotion engine in "Let's Drink! AI" uses the Sentiment Analysis API to analyze emotions from users' messages. Based on the analyzed emotion data, the server prepares to make optimal suggestions to users.

[0809] Next, the server sends a message to the chat screen to notify all users that schedule adjustments have begun. Users reply with the dates they can attend. Example: "I'm free on XX / XX and XX / XX."

[0810] The server aggregates all user inputs, uses the Google Calendar API to identify common dates, and determines the optimal date. The server then notifies all users of the determined date. Example: "The date has been decided! We'll be having a drinking party on XX day at 7 PM."

[0811] Next, "Let's Drink! AI" uses the Yelp API to search for candidate restaurants based on the user's past history, preference data, and the results of the emotion engine analysis. The server then suggests candidate restaurant information to the user. Example: "How about the following restaurants? 1. Izakaya A (relaxing atmosphere) 2. Bar B (with fun events) 3. Restaurant C (a quiet, relaxing place)"

[0812] The user selects one of the suggested restaurants. Example: "Izakaya A is good."

[0813] After the user has made their selection, the server will automatically make an online reservation for the selected restaurant using the OpenTable API. Once the reservation is complete, the server will notify all users of the details. Example: "Your reservation has been completed for Izakaya A on XX date at 7pm. Thank you!"

[0814] Finally, as the date of the drinking party approaches, the server automatically sends a reminder message. Example: "The drinking party will be at Izakaya A tomorrow at 7pm. Don't forget!"

[0815] With this system, users only need to input a specific string of characters, and the system will automatically set up the optimal drinking party based on their emotions and preferences.

[0816] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0817] Step 1:

[0818] User action: Enter message

[0819] User: The user opens a messaging app on their device, types the text "Let's go for a drink!", and presses the send button.

[0820] Input: The message entered by the user: "Let's go for a drink!"

[0821] Output: The message data sent.

[0822] Step 2:

[0823] Message Reception

[0824] Terminal: The terminal sends the message entered by the user to the server.

[0825] Input: User input message

[0826] Output: Message data sent to the server

[0827] Step 3:

[0828] Message Parsing

[0829] Server: Analyzes the received message data.

[0830] What happens: The server uses the NLTK library to tokenize the message and parse it to see if it contains a specific string ("Let's go for a drink!").

[0831] Input: Message data sent to the server

[0832] Output: Result of whether the string contains a specific string

[0833] Step 4:

[0834] Let's go drinking! AI startup

[0835] Server: When a specific string is recognized, launch "Let's go drinking! AI."

[0836] Specific operation: If the conditions for starting the AI ​​are met based on the message analysis results, the AI ​​program is executed.

[0837] Input: Judgment results that contain a specific string

[0838] Output: AI startup status

[0839] Step 5:

[0840] Emotion analysis

[0841] Server: The emotion engine built into "Let's Drink! AI" analyzes the user's emotions from the message.

[0842] Specific operation: The server calls the Sentiment Analysis API to extract and classify sentiment data from the message.

[0843] Input: User's message data

[0844] Output: User's emotion data (e.g., happy, tired, etc.)

[0845] Step 6:

[0846] Start scheduling

[0847] Server: Send a message to the chat screen saying, "Thank you everyone for your hard work! We'll start arranging the date for the drinking party, so please let us know when you can join."

[0848] Specific operation: Send a notification message using the message sending API.

[0849] Input: Sentiment analysis results and AI startup status

[0850] Output: Sending a message to start scheduling

[0851] Step 7:

[0852] User schedule entry

[0853] User: Enter the dates you can attend on the chat screen and send.

[0854] Specific operation: Each user enters the schedule in the reply format and submits it.

[0855] Input: Schedule adjustment start message from the server

[0856] Output: User's available dates

[0857] Step 8:

[0858] Schedule collection and decision

[0859] Server: Aggregates the submitted schedule information and determines a common schedule using the Google Calendar API.

[0860] Specific operation: Run an algorithm to determine common schedules based on the schedule data of all users.

[0861] Input: Schedule data for each user

[0862] Output: The determined common date

[0863] Step 9:

[0864] Schedule notification

[0865] Server: Notify all users of the decided date.

[0866] Specific operation: Use the push notification function to send a schedule confirmation message.

[0867] Input: Common Schedule

[0868] Output: Sending a schedule notification message

[0869] Step 10:

[0870] Store selection

[0871] Server: Using the Yelp API, search for potential stores based on the user's past history, preference data, and the results of the emotion engine analysis, and make suggestions to the user.

[0872] Specific operations: Retrieve history and preference data from the database, submit a search query to the API, and receive the results.

[0873] Example: "How about these places? 1. Izakaya A (relaxing atmosphere) 2. Bar B (fun events) 3. Restaurant C (quiet and relaxing place)"

[0874] Input: User history data, preference data, emotional data

[0875] Output: Sending a proposal message

[0876] Step 11:

[0877] User store selection

[0878] User: Choose one of the suggested stores and reply on the chat screen.

[0879] Specific operation: Reply with the name of the store selected from the list on the chat screen.

[0880] Example: "Izakaya A is good."

[0881] Input: Proposal message

[0882] Output: User selection data

[0883] Step 12:

[0884] Store reservation

[0885] Server: Automatically reserves selected locations using the OpenTable API.

[0886] Specific operation: Based on the selected store information, make an API call to make a reservation.

[0887] Input: User selected data

[0888] Output: Reservation completion notification

[0889] Step 13:

[0890] Reservation completion notification

[0891] Server: Notify all users once the reservation is complete.

[0892] Specific behavior: Use the push notification function to send a message with detailed information.

[0893] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[0894] Input: Reservation completion data

[0895] Output: Send reservation completion notification

[0896] Step 14:

[0897] Send reminder messages

[0898] Server: When the date of the drinking party approaches, the server automatically sends a reminder message.

[0899] What it does: Checks the internal clock system and sends a message the day before the appointment.

[0900] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[0901] Input: Reservation schedule data

[0902] Output: Send reminder message

[0903] The above is the specific flow of program processing for this system.

[0904] (Application example 2)

[0905] 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."

[0906] In modern society, group activities and meetings are frequently coordinated, but these coordination tasks are often performed manually, resulting in labor shortages and inefficiencies. Similar issues also arise when reporting on on-site security situations and setting up countermeasure meetings. Security operations, in particular, require rapid response, necessitating fast and efficient communication methods. Furthermore, appropriate suggestions and responses based on emotions and preferences are often required, and the lack of a way to automate these tasks requires a significant amount of effort.

[0907] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input a message including a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate locations based on the user's preference data, means for reserving the selected location, means for notifying the user that the reservation is complete, means for sending a reminder message the day before the scheduled date, means for recognizing the user's emotions using an emotion engine and taking the most appropriate action, and means for accepting user commands through voice recognition. This enables fast and efficient communication.

[0908] A "communication terminal" is an electronic device that allows a user to input information and communicate.

[0909] A "specific string" is a set of predefined characters that the system recognizes as a trigger.

[0910] A "message" is text information that a user sends through a communication terminal.

[0911] A "trigger" is a condition or signal that causes an event or action to occur.

[0912] "Analysis" is the process of closely examining input data and understanding its meaning and intent.

[0913] "Schedule information" is data relating to a schedule designated by the user.

[0914] A "common date" is a date and time when the date information of multiple users matches.

[0915] "Preference data" is data relating to a user's tastes and preferences.

[0916] "Candidate locations" are multiple selectable locations suggested by the system.

[0917] A "reservation" is the act of reserving a specific location or service for a specified date and time.

[0918] A "remind message" is a message that reminds you of an appointment or important matter in advance.

[0919] The "emotion engine" is a technology that analyzes and recognizes the user's emotions and suggests countermeasures.

[0920] "Speech recognition" is a technology that analyzes voice data and converts it into text information.

[0921] The present invention is a system that uses a communication terminal to recognize specific messages and make optimal suggestions based on the user's emotions and preferences. The system aims to significantly reduce the user's time and effort by automatically adjusting schedules and reserving appropriate facilities.

[0922] First, the user speaks a specific string of characters (for example, "Start a meeting!") into the communication device. Using speech recognition technology, this voice data is converted into text and sent to the server. The server analyzes the received text message and uses natural language processing technology to recognize the specific string of characters as a trigger. This analysis is performed using tools such as the Python speech_recognition library and the Google Cloud Natural Language API.

[0923] Next, the server collects users' schedule information and determines a common schedule. At this stage, users input the dates and times they are available, and the server aggregates the information to determine the optimal date and time, and notifies all users. Notifications are made via messages displayed on their devices or emails.

[0924] The server then suggests potential locations based on the user's preference data and past history. Using an emotion engine, the server takes into account the user's current emotional state. For example, it may suggest places where you can relax, where you can concentrate, or where fun events are taking place. The server uses scikit-learn, a Python machine learning library, and an API for emotion analysis.

[0925] When the user selects one of the candidate locations, the server automatically reserves the location. The server then notifies the user of the reservation completion and sends a reminder message the day before the scheduled date, allowing the user to proceed with the schedule with peace of mind.

[0926] Examples of specific prompts include:

[0927] "Start the meeting!"

[0928] Please schedule the next meeting.

[0929] "Report the security situation."

[0930] This allows users to easily schedule meetings and events, enabling quick responses on-site. The system also enables flexible responses based on users' emotions and preferences.

[0931] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0932] Step 1:

[0933] The user inputs a specific string of characters into the communication device by voice, and the device converts this voice into text and sends it to the server. At this stage, the input is the user's voice data, and the output is text data. Specifically, the voice data is converted into text using the Python speech_recognition library.

[0934] Step 2:

[0935] The server analyzes the received text message and recognizes specific strings as triggers. The input at this stage is the text data from step 1, and the output is a trigger signal to activate a specific action. Specifically, it uses natural language processing technology to analyze the text and detects trigger keywords (e.g., "Start a meeting!").

[0936] Step 3:

[0937] The server collects users' schedule information and determines a common schedule. At this stage, the input is the available date and time information for each user, and the output is the optimal date and time common to all users. Specifically, the server aggregates replies from users and uses an algorithm to determine the optimal date and time from among them.

[0938] Step 4:

[0939] The server notifies users of the determined date and time. The input at this stage is the common date and time determined in step 3, and the output is a notification message displayed on the user's communication device. Specifically, all users are notified that the date and time have been determined via email or push notification.

[0940] Step 5:

[0941] The server proposes candidate locations based on the user's preference data and past history. The input at this stage is the user's preference data and past history data, and the output is a list of candidate locations. Specifically, the server analyzes the data using Python machine learning libraries (e.g., scikit-learn) and sentiment analysis APIs to generate candidate locations.

[0942] Step 6:

[0943] The user selects one of the candidate locations, and the server automatically reserves that location. The input at this stage is the user's selected location, and the output is reservation confirmation information. Specifically, the API is used to make an online reservation and record the reservation results.

[0944] Step 7:

[0945] The server notifies the user that the reservation is complete and sends a reminder message the day before the scheduled date. The input at this stage is the reservation confirmation information and schedule information, and the output is the reminder message. Specifically, the server monitors the schedule and automatically sends a reminder 24 hours before the scheduled date and time.

[0946] In this way, a series of processes, from user voice command input to natural language processing, sentiment analysis, schedule adjustment and booking, and reminder message sending, are automated, allowing users to easily manage their schedules and respond on-site.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] [Third embodiment]

[0951] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0952] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0953] 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).

[0954] 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.

[0955] 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.

[0956] 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).

[0957] 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.

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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."

[0963] This invention is a system for easily setting up drinking parties using communication terminals, and is realized mainly through communication between a server, terminals, and users. This system also uses natural language processing technology to analyze messages from users, obtain necessary information, and proceed with the process.

[0964] System Overview

[0965] 1. User Action:

[0966] User: The user uses a messaging app on their communication device to type a specific string of characters (e.g., "Let's go for a drink!") into a chat screen with a friend.

[0967] Device: The device sends the entered message to the LINE server.

[0968] 2. Message reception and analysis:

[0969] Server: The server receives the message sent by the user and analyzes it to see if it contains a specific string. This analysis uses natural language processing technology.

[0970] Server: When the message is recognized as a trigger, it launches "Let's go drinking! AI."

[0971] 3. Scheduling:

[0972] Server: "Let's go drinking! AI" sends a message to the chat screen to inform all users that it is starting to arrange a date.

[0973] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[0974] Users: Each user replies with their available dates.

[0975] Example: "I'm free on XX / XX and XX / XX."

[0976] Server: The server aggregates all users' inputs, determines common availability, determines the optimal schedule, and notifies all users.

[0977] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[0978] 4. Shop selection:

[0979] Server: "Let's Drink! AI" searches for candidate stores based on the user's past history and preference data.

[0980] Server: Suggests candidate stores to the user.

[0981] Example: "How about the following places? 1. Izakaya A 2. Bar B 3. Restaurant C"

[0982] User: The user chooses one of the suggested stores.

[0983] Example: "Izakaya A is good."

[0984] 5. Restaurant reservations:

[0985] Server: After the user has completed their selection, the server automatically makes an online reservation for the selected restaurant.

[0986] Server: Once the reservation is complete, all users will be notified of the details.

[0987] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[0988] 6. Reminder:

[0989] Server: When the date of the drinking party approaches, the server automatically sends a reminder message.

[0990] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[0991] Specific examples

[0992] For example, a user types "Let's go for a drink!" into a chat screen with a friend on LINE. When the device sends this message to the LINE server, the server receives and analyzes the message. As a result of the analysis, the phrase "Let's go for a drink!" is recognized as a specific trigger, activating the "Let's go for a drink! AI." The AI ​​then sends a message to arrange a date, and the user replies with their available dates. The server collects this information, decides on a common date, and notifies the user.

[0993] Next, "Let's Drink! AI" searches for candidate restaurants based on the user's preference data and suggests several options to the user. The user selects Izakaya A from these, and the server automatically makes the reservation. Once the reservation is complete, all users are notified of the details and a reminder message is sent the day before the drinking party.

[0994] This allows users to automatically plan a drinking party simply by entering a specific string of characters.

[0995] The processing flow will be explained below.

[0996] Step 1:

[0997] User: Using a communication device, type "Let's go for a drink!" into the chat screen with a friend.

[0998] Step 2:

[0999] Device: Sends the user's input to the LINE server.

[1000] Step 3:

[1001] Server: The LINE server analyzes the received message and recognizes the phrase "Let's go for a drink!" as a specific trigger.

[1002] Step 4:

[1003] Server: After the trigger is recognized, launch "Let's go drinking! AI" and send a message to the chat screen notifying the start of schedule adjustment.

[1004] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[1005] Step 5:

[1006] User: Each user replies with their available dates.

[1007] Example: "I'm free on XX / XX and XX / XX."

[1008] Step 6:

[1009] Server: Aggregates schedule information sent by all users and determines a common schedule.

[1010] Step 7:

[1011] Server: Determines the optimal schedule and notifies all users of that schedule.

[1012] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[1013] Step 8:

[1014] Server: Searches for store candidates based on the user's past history and preference data.

[1015] Step 9:

[1016] Server: Selects candidate stores and suggests them to the user.

[1017] Example: "How about the following places? 1. Izakaya A 2. Bar B 3. Restaurant C"

[1018] Step 10:

[1019] User: The user chooses one of the suggested stores.

[1020] Example: "Izakaya A is good."

[1021] Step 11:

[1022] Server: Automatically make online reservations at selected restaurants.

[1023] Step 12:

[1024] Server: Once the reservation is complete, the details will be sent to all users.

[1025] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[1026] Step 13:

[1027] Server: Automatically send reminder messages when the date of a drinking party approaches.

[1028] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[1029] Example 1

[1030] 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."

[1031] When planning an event such as a drinking party, it takes time and effort to adjust the date, select a restaurant, and confirm reservations, which can be particularly tedious when there are multiple participants. It is also easy to forget to send reminders to all participants. This system solves these problems and allows users to easily schedule a drinking party.

[1032] 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.

[1033] In this invention, the server includes means for a user to input a message containing a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate restaurants based on the user's preference data, means for reserving the selected restaurant, means for notifying the user that the reservation is complete, means for sending a reminder message the day before the drinking party, means for analyzing the message using a natural language processing engine and detecting the trigger, means for analyzing the optimal available date and time from the aggregated schedule information, and means for automatically making a restaurant reservation online. This allows users to easily adjust schedules, select restaurants, and confirm reservations through the system.

[1034] A "communication terminal" is an electronic device that a user uses to input and send messages, and includes smartphones, personal computers, and the like.

[1035] A "specific string" refers to a group of letters or words that the system is configured to recognize as a trigger.

[1036] A "message" refers to text information that a user inputs via a communication terminal and sends to other users or a server.

[1037] A "natural language processing engine" is a software technology that analyzes messages entered by users and recognizes specific strings of characters that act as triggers.

[1038] "Schedule information" refers to data regarding the dates and times when a user can participate.

[1039] "Preference data" is information about a user's past history and preferences, and is used to suggest candidate stores.

[1040] "Candidate store" refers to a store that the system suggests to the user as a location for a drinking party.

[1041] "Reservation" refers to the act of reserving a candidate store selected by a user and making it available for use on a specific date and time.

[1042] A "reminding message" is a message that notifies the user of the details of the event again when the date of the drinking party approaches.

[1043] "Generative AI models" refer to algorithms and programs that use AI to analyze data and make predictions.

[1044] A "prompt sentence" is a standard sentence to be input into a generative AI model, and contains instructions for the AI ​​to process appropriately.

[1045] The present invention is a system that allows users to easily schedule drinking parties using communication terminals, and is implemented primarily through communications between a server, terminals, and users. This system utilizes a natural language processing engine and a generative AI model to automatically perform message analysis, schedule adjustment, restaurant selection, and reminders. The following describes in detail an embodiment of the present invention.

[1046] System configuration

[1047] 1. Communication terminal

[1048] A communication device is used by users to input and send messages. Typically, this is a smartphone or a PC. Using this communication device, users input specific characters into the chat screen with friends and send them.

[1049] 2. Server

[1050] The server plays a central role in receiving and analyzing messages. It has a built-in natural language processing engine, which analyzes messages sent by users and detects whether they contain specific strings that act as triggers. This allows subsequent processing to proceed automatically.

[1051] 3. Natural Language Processing Engine

[1052] The natural language processing engine analyzes the message entered by the user and recognizes specific triggers. If the engine detects a trigger, the server launches "Let's Drink! AI" and the scheduling process begins.

[1053] 4. Generative AI Models

[1054] A generative AI model refers to an algorithm or program that analyzes data and makes predictions during processes such as scheduling, selecting restaurants, and sending reminders. This model suggests optimal restaurants and dates based on the user's past history and preference data.

[1055] System operation explanation

[1056] When a user operates a communication device and sends a specific string of characters, such as "Let's go for a drink!", the message is sent over the Internet to a server. The server uses a natural language processing engine to analyze the message and detects the specific string that acts as a trigger. This activates the "Let's go for a drink! AI," which then begins the process of arranging a date and selecting a restaurant.

[1057] Specifically, the system operates as follows: The server sends a message to all users to arrange a date and collects the available date information replies from each user. The generative AI model aggregates this date information and analyzes common available dates and times. It then notifies all users of the optimal date. After that, candidate restaurants are suggested based on the user's preference data and past history, and the restaurant selected by the user is automatically reserved online. Once the reservation is complete, the details are notified to all users, and a reminder message is sent as the date of the drinking party approaches.

[1058] Specific examples

[1059] For example, a user types "Let's go for a drink!" into the LINE chat screen and sends it. When the device sends this message to the server, the server analyzes the message. As a result of the analysis, the phrase "Let's go for a drink!" is recognized as a trigger, and the "Let's go for a drink! AI" is activated.

[1060] Next, the server sends a message to the chat screen saying, "We'll start arranging dates, so please let us know when you can join." Each user replies with their available dates, and the server aggregates this information, determines common availability, and decides on the optimal date. Next, "Let's go drinking! AI" searches for candidate restaurants based on the user's preference data and suggests several options to the user. The user selects a restaurant from these, and the server automatically reserves the restaurant and notifies all users of the details. Finally, the server sends a reminder message the day before the drinking party.

[1061] Prompt Sentence Examples

[1062] 1. When the phrase "Let's go for a drink!" is sent via LINE, "Let's go for a drink! AI" will start arranging a date.

[1063] "We will begin arranging the schedule. Please let us know the dates that you can attend."

[1064] 2. After the date of the drinking party is decided, a prompt to suggest a possible restaurant:

[1065] "How about the following establishments? 1. Izakaya 2. Bar 3. Restaurant"

[1066] 3. Once the user has selected a store, a prompt will automatically reserve that store:

[1067] "Reservation completed at 7pm on XX month."

[1068] As described above, the system of the present invention provides an environment in which users can easily set up drinking parties.

[1069] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1070] Step 1:

[1071] The user inputs a specific string into the communication terminal and sends it.

[1072] Input: The user enters the string "Let's go for a drink!" on a communication device such as a smartphone or PC.

[1073] Behavior: The user presses the send button on the chat screen to send this string.

[1074] Output: The communication terminal sends this message to the server.

[1075] Step 2:

[1076] The device sends a message to the server

[1077] Input: A message sent by the user: "Let's go for a drink!"

[1078] How it works: The device sends this message to the server via the internet. This process is carried out through the LINE server.

[1079] Output: The server receives this message.

[1080] Step 3:

[1081] The server parses the message

[1082] Input: The message "Let's go for a drink!" received by the server.

[1083] How it works: The server's natural language processing engine parses the message to see if it contains a specific string. This process uses a natural language processing model.

[1084] Output: When a specific trigger string is detected, "Let's go drinking! AI" will be launched.

[1085] Step 4:

[1086] The server notifies the user that scheduling is about to begin.

[1087] Input: The natural language processing engine detects the trigger "Let's go for a drink!"

[1088] How it works: The server sends a message to all users saying, "We're starting to schedule. Please let us know when you can join."

[1089] Output: A message is displayed to all users informing them that scheduling has begun.

[1090] Step 5:

[1091] The user replies with their available dates

[1092] Input: The rescheduling message sent by the server.

[1093] How it works: Each user replies with the date and time they are available. For example, they might reply, "I'm free on XX / XX and XX / XX."

[1094] Output: The server collects information about the dates that users can attend.

[1095] Step 6:

[1096] The server decides the best date

[1097] Input: Available dates and times provided by each user.

[1098] How it works: The server analyzes the aggregated schedule information and identifies common free dates and times. A generative AI model assists in this analysis.

[1099] Output: The optimal date is determined and the result is notified to all users from the server. For example, a message such as "We will hold a drinking party on XX day at 7 PM" is sent.

[1100] Step 7:

[1101] The server suggests candidate stores

[1102] Input: Best dates and user preference data.

[1103] How it works: The server uses a generative AI model to search for candidate stores based on the user's past history and preference data.

[1104] Output: A list of candidate stores is generated, and the server sends a suggestion message to the user saying, "How about the following stores? 1. Izakaya 2. Bar 3. Restaurant."

[1105] Step 8:

[1106] The user selects a store

[1107] Input: A list of candidate stores sent from the server.

[1108] Operation: The user selects one of the suggested restaurants and replies, for example, "Izakaya is good."

[1109] Output: The selected store information is sent to the server.

[1110] Step 9:

[1111] The server reserves the selected store

[1112] Input: Store information selected by the user.

[1113] How it works: The server automatically reserves the selected store online using an online reservation service.

[1114] Output: The reservation is completed and the details are sent to all users from the server. For example, a message like "Your reservation at the izakaya on the ____ date at 7pm has been completed. Thank you!" is sent.

[1115] Step 10:

[1116] The server sends a reminder message

[1117] Input: Time information and store information after completing reservation.

[1118] How it works: When the date of the drinking party approaches, the server automatically sends a reminder message to the user.

[1119] Output: A reminder message is sent to the user saying "There's a drinking party at the izakaya tomorrow at 7pm. Don't forget!"

[1120] The above specific processing steps realize a system that allows users to easily set up drinking parties.

[1121] (Application example 1)

[1122] 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."

[1123] Traditionally, setting up a drinking party requires a lot of time and effort, including coordinating participants' schedules and reserving restaurants. Furthermore, in order to efficiently complete these tasks, it is necessary to gather the opinions of all participants, which can make the process cumbersome. This reduces the time participants can spend enjoying the party. Furthermore, while there is a demand for easy operation via voice commands, traditional systems do not adequately achieve this.

[1124] 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.

[1125] In this invention, the server includes means for a user to input a message containing a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate restaurants based on the user's preference data, means for making a reservation at the selected restaurant, means for notifying the user that the reservation is complete, means for sending a reminder message the day before the drinking party, means for activating the system by voice command, and means having a natural language processing device for analyzing the voice command. This allows the user to quickly and efficiently set up a drinking party using simple voice commands, improving the convenience of the entire system.

[1126] "Communication terminal" refers to any electronic device that allows a user to input and send messages.

[1127] A "specific character string" refers to a uniquely identifiable phrase or word that a user inputs into a communication terminal.

[1128] A "trigger" is a string or command that causes the system to start a specific process.

[1129] "Date information" refers to data provided by a user regarding available dates and times for participation.

[1130] The "common schedule" refers to a date and time when all users can participate among the available dates and times for participation.

[1131] "Preference data" refers to information based on a user's past behavioral history and preference trends.

[1132] "Candidate stores" refer to multiple restaurants that the user may visit.

[1133] "Reservation" refers to a user reserving one of the candidate stores in advance for a specific date and time.

[1134] A "reminder message" refers to a confirmation notification sent when the date and time of a drinking party approaches.

[1135] "Voice command" refers to a verbal instruction given by a user to a system.

[1136] "Natural language processing device" refers to a technical device that analyzes a user's voice commands or messages and takes appropriate action.

[1137] This invention is a system for easily setting up drinking parties using communication terminals, and is realized through mutual communication between users, terminals, and a server.

[1138] A communication device includes a means for users to input messages containing specific strings of characters and a means for receiving voice commands. Specifically, this is an electronic device such as a smartphone or smart glasses. It may also be a device equipped with a voice recognition function. For example, the speech_recognition library can be used to convert a user's voice commands into text.

[1139] The server is equipped with a means for receiving messages and voice commands sent by users and analyzing them using natural language processing technology, such as the Google Natural Language API. The server also has built-in means for collecting users' schedule information and determining a common schedule.

[1140] Once the date is decided, the server sends a notification to the user and then suggests candidate stores based on the user's preference data. This data includes past behavioral history and user preferences. The server uses map services such as Google Maps API to suggest candidate stores.

[1141] After selecting a restaurant, the server automatically completes the reservation process and notifies the user when it is complete. The reservation system can use a payment system such as the Stripe API. It also has a function to send a reminder message the day before the party.

[1142] As a concrete example, consider the case where a user gives a voice command to a communication device saying, "Schedule a drinking party." The device receives this voice command and converts it to text using the speech_recognition library. The server receives and analyzes this voice command and starts the schedule adjustment process. The server then aggregates the available dates and times for all users and decides on a common date. Next, the Google Maps API is used to suggest candidate restaurants, and the user selects one. Finally, the restaurant is reserved via the Stripe API, and a completion notification and reminder message are sent.

[1143] An example prompt might look like this:

[1144] Please generate a program that will schedule a drinking party, select a restaurant, and make a reservation in response to the following voice commands.

[1145] User: Set up a drinking party

[1146] System: What dates are you available for?

[1147] User: October 15, 2023 is available

[1148] System: How about the following establishments? 1. Izakaya A 2. Bar B 3. Restaurant C

[1149] User: Izakaya A is good

[1150] System: Izakaya A has been reserved for October 15, 2023

[1151] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1152] Step 1:

[1153] The user inputs a voice command into a communication device (smartphone or smart glasses) saying, "Set up a drinking party." The device converts this voice command into text using the speech_recognition library. The input is the user's voice, and the output is a text command. This converted text is sent to the server.

[1154] Step 2:

[1155] The server analyzes the received text data using natural language processing technology such as the Google Natural Language API. The input is a voice command in text format, and the output is the analysis result (the intent of the command). As a result of the analysis, the instruction "Schedule a drinking party" is recognized, and the process proceeds to the next step.

[1156] Step 3:

[1157] After recognizing the intent of the voice command, the server generates a message to collect the user's schedule information and sends it to all participants. The input is the analysis result, and the output is a message to collect schedule information. Specifically, the server sends a message to the user such as "Please tell us your free dates."

[1158] Step 4:

[1159] Users reply with their available dates via their communication terminals. The terminals then collect and send these reply data to the server. The input is the user's schedule information, and the output is the aggregated schedule data sent to the server.

[1160] Step 5:

[1161] The server aggregates the schedule information of all users and determines a common schedule. The input is the aggregated schedule data, and the output is a common schedule that works for everyone. As part of the data processing, the server calculates the schedule information of all users and selects the most appropriate schedule.

[1162] Step 6:

[1163] The server notifies all users of the decided common schedule. The input is the common schedule information, and the output is a notification message. Specifically, it sends a message such as "The date has been decided. A drinking party will be held at 7pm on XX month."

[1164] Step 7:

[1165] The server collects user preference data and suggests candidate restaurants based on past history and preferences. The input is the user's preference data, and the output is a list of candidate restaurants. Data processing uses map services such as Google Maps API to search for suitable restaurants.

[1166] Step 8:

[1167] The user selects one of the proposed candidate stores and sends the result to the server. The input is the candidate store list, and the output is the selected store. The selection result is notified to the server via the communication terminal.

[1168] Step 9:

[1169] The server automatically reserves the selected store online and notifies all users that the reservation is complete. The input is the selected store information, and the output is a reservation confirmation notification message. The reservation procedure is carried out using a payment system such as Stripe API.

[1170] Step 10:

[1171] The server automatically sends a reminder message the day before the drinking party. The input is the reservation date and time and restaurant information, and the output is the reminder message. Specifically, it sends a message such as, "The drinking party will be at Izakaya A tomorrow at 7pm. Don't forget!"

[1172] 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.

[1173] This system allows users to easily schedule drinking parties using communication devices, and is implemented primarily through communication between a server, devices, and users. By combining natural language processing technology with an emotion engine, the system makes optimal suggestions based on the user's emotions and preferences.

[1174] System Overview

[1175] 1. User Action:

[1176] User: The user uses a messaging app on their communication device to type a specific string of characters (e.g., "Let's go for a drink!") into a chat screen with a friend.

[1177] Terminal: The terminal sends the entered message to the server.

[1178] 2. Message reception and analysis:

[1179] Server: The server receives the message sent by the user and analyzes it to see if it contains a specific string. This analysis uses natural language processing technology.

[1180] Server: When the message is recognized as a trigger, it launches "Let's go drinking! AI."

[1181] 3. Emotion recognition:

[1182] Server: "Let's Drink! AI" has an emotion engine built in that recognizes the user's emotions from the words and context contained in the message.

[1183] Server: The emotion engine analyzes the user's emotions (e.g., joy, sadness, stress, etc.) and prepares appropriate suggestions based on the results.

[1184] 4. Scheduling:

[1185] Server: "Let's go drinking! AI" sends a message to the chat screen to inform all users that it is starting to arrange a date.

[1186] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[1187] Users: Each user replies with their available dates.

[1188] Example: "I'm free on XX / XX and XX / XX."

[1189] Server: The server aggregates all user inputs, determines a common schedule, determines the optimal schedule, and notifies all users.

[1190] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[1191] 5. Shop selection:

[1192] Server: "Let's Drink! AI" searches for candidate restaurants based on the user's past history, preference data, and the results of emotion analysis by the emotion engine.

[1193] Server: Suggests candidate stores to the user, including stores that suit the user's current emotional state.

[1194] Example: "How about these places? 1. Izakaya A (relaxing atmosphere) 2. Bar B (fun events) 3. Restaurant C (quiet and relaxing place)"

[1195] User: The user chooses one of the suggested stores.

[1196] Example: "Izakaya A is good."

[1197] 6. Restaurant Reservations:

[1198] Server: After the user has completed their selection, the server automatically makes an online reservation for the selected restaurant.

[1199] Server: Once the reservation is complete, the details will be sent to all users.

[1200] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[1201] 7. Reminder:

[1202] Server: When the date of the drinking party approaches, the server automatically sends a reminder message.

[1203] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[1204] Specific examples

[1205] For example, a user types "Let's go for a drink!" in a chat screen with a friend. When the device sends this message to the server, the server receives and analyzes the message. As a result of the analysis, the phrase "Let's go for a drink!" is recognized as a specific trigger, and the "Let's go for a drink! AI" is activated. Furthermore, the emotion engine recognizes the emotion from the user's message and considers a response based on that emotion.

[1206] Next, the AI ​​sends a message to arrange a date, and the user replies with their available dates. The server collects this information, decides on a common date, and notifies the user. The AI ​​then takes into account the results of sentiment analysis and suggests the most suitable restaurant for the user. The user selects "Izakaya A," and the server automatically makes the reservation. Once the reservation is complete, the details are notified, and a reminder message is sent the day before the party.

[1207] This allows users to simply enter a specific string of characters, and the system will automatically set up the optimal drinking party based on their emotions and preferences.

[1208] The processing flow will be explained below.

[1209] Step 1:

[1210] User: Using a communication device, type "Let's go for a drink!" into the chat screen with a friend.

[1211] Step 2:

[1212] Terminal: Sends messages entered by the user to the communication server.

[1213] Step 3:

[1214] Server: Analyzes the received message and recognizes the phrase "Let's go for a drink!" as a specific trigger. This analysis uses natural language processing technology.

[1215] Step 4:

[1216] Server: Once trigger recognition is complete, launch "Let's go drinking! AI" and send a message to the chat screen notifying the start of schedule adjustments.

[1217] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[1218] Step 5:

[1219] User: Each user replies with their available dates.

[1220] Example: "I'm free on XX / XX and XX / XX."

[1221] Step 6:

[1222] Server: Aggregates schedule information sent by all users and determines a common schedule.

[1223] Step 7:

[1224] Server: Determines the optimal schedule and notifies all users of that schedule.

[1225] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[1226] Step 8:

[1227] Server: At the same time, the emotion engine built into "Let's Drink! AI" recognizes emotions from the user's message and outputs the analysis results.

[1228] Step 9:

[1229] Server: The emotion engine analyzes the user's emotional state (e.g., joy, stress, fatigue, etc.) and searches for store candidates based on the results.

[1230] Step 10:

[1231] Server: Based on the search results, selects candidate restaurants taking into account the user's past history and preference data, and makes suggestions to all users.

[1232] Example: "How about these places? 1. Izakaya A (relaxing place) 2. Bar B (fun atmosphere) 3. Restaurant C (quiet and relaxing place)"

[1233] Step 11:

[1234] User: The user chooses one of the suggested stores.

[1235] Example: "Izakaya A is good."

[1236] Step 12:

[1237] Server: After the user has completed their selection, the server automatically makes an online reservation for the selected restaurant.

[1238] Step 13:

[1239] Server: Once the reservation is complete, the details will be sent to all users.

[1240] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[1241] Step 14:

[1242] Server: Automatically send reminder messages when the date of a drinking party approaches.

[1243] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[1244] This allows users to simply type "Let's go out for drinks!" and the system will automatically arrange dates, select restaurants, make reservations, and even send reminders based on their emotions and preferences.

[1245] Example 2

[1246] 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."

[1247] Few existing drinking party scheduling systems are able to take into account the user's emotions and preferences. Furthermore, it is difficult to automate all stages of scheduling, restaurant selection, and reservations, requiring a lot of manual work from the user. This increases the burden on users and makes it difficult to realize the optimal drinking party. Furthermore, the lack of advance reminders means that some people may miss out on the party.

[1248] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1249] In this invention, the server includes means for a user to input a message containing a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for analyzing the user's emotions and making suggestions based on the results, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate restaurants based on the user's preference data, means for making a reservation at the selected restaurant, means for notifying the user that the reservation is complete, and means for sending a reminder message the day before the drinking party. As a result, the user only needs to input the specific character string to automatically set up the optimal drinking party based on their emotions and preferences, reducing the user's burden and enabling the optimal drinking party to be realized.

[1250] "Communication terminal" is a general term for electronic devices that allow users to input and send messages.

[1251] The "specific string" is a specified phrase that the system recognizes as a trigger to start setting up a drinking party.

[1252] The "means for receiving messages" is a function for receiving messages from users via the Internet.

[1253] The "analysis means" is a technique for analyzing a received message and determining whether it contains a specific character string.

[1254] A "means for recognizing as a trigger" is a mechanism that initiates a specific action based on the results of the analysis.

[1255] "Means for analyzing emotions" refers to technology that extracts and classifies emotions from users' messages.

[1256] The "means for making suggestions" is a function that provides the user with the optimal options based on the analysis results.

[1257] The "means for collecting schedule information" is a function for collecting the user's free schedule.

[1258] The "means for determining a common schedule" is a mechanism for selecting a date on which all users can participate based on the collected schedule information.

[1259] The "means for notifying the schedule" is a function for notifying the user of the determined schedule.

[1260] "Preference data" is a general term for data that indicates a user's past preference history and interests.

[1261] The "means for suggesting candidate restaurants" is a mechanism that provides users with a selection of restaurants that are suitable for them based on their preference data.

[1262] The "means of reserving a store" is the technology for reserving a selected store online.

[1263] The "means for notifying the user that a reservation has been completed" is a function for notifying the user that a reservation for a store has been completed.

[1264] The "means for sending a reminder message" is a mechanism for sending a reminder to the user the day before the drinking party.

[1265] This invention is a system for easily setting up drinking parties via communication terminals. This system is realized mainly through communication between a server, terminals, and users, and combines natural language processing technology and an emotion engine to make optimal suggestions based on the user's emotions and preferences.

[1266] The system uses the following hardware and software: The hardware consists of communication devices such as smartphones and PCs used by users, and a cloud server. The software includes natural language processing technology using the NLTK library, Sentiment Analysis API, Google Calendar API, Yelp API, and OpenTable API.

[1267] Specifically, a user types "Let's go for a drink!" using a messaging app on a communication device. This message is sent from the device to the server. Example: "Let's go for a drink!"

[1268] The server receives this message and uses the NLTK library to analyze it for a specific string. If the string is recognized, the "Let's Drink! AI" is launched.

[1269] The emotion engine in "Let's Drink! AI" uses the Sentiment Analysis API to analyze emotions from users' messages. Based on the analyzed emotion data, the server prepares to make optimal suggestions to users.

[1270] Next, the server sends a message to the chat screen to notify all users that schedule adjustments have begun. Users reply with the dates they can attend. Example: "I'm free on XX / XX and XX / XX."

[1271] The server aggregates all user inputs, uses the Google Calendar API to identify common dates, and determines the optimal date. The server then notifies all users of the determined date. Example: "The date has been decided! We'll be having a drinking party on XX day at 7 PM."

[1272] Next, "Let's Drink! AI" uses the Yelp API to search for candidate restaurants based on the user's past history, preference data, and the results of the emotion engine analysis. The server then suggests candidate restaurant information to the user. Example: "How about the following restaurants? 1. Izakaya A (relaxing atmosphere) 2. Bar B (with fun events) 3. Restaurant C (a quiet, relaxing place)"

[1273] The user selects one of the suggested restaurants. Example: "Izakaya A is good."

[1274] After the user has made their selection, the server will automatically make an online reservation for the selected restaurant using the OpenTable API. Once the reservation is complete, the server will notify all users of the details. Example: "Your reservation has been completed for Izakaya A on XX date at 7pm. Thank you!"

[1275] Finally, as the date of the drinking party approaches, the server automatically sends a reminder message. Example: "The drinking party will be at Izakaya A tomorrow at 7pm. Don't forget!"

[1276] With this system, users only need to input a specific string of characters, and the system will automatically set up the optimal drinking party based on their emotions and preferences.

[1277] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1278] Step 1:

[1279] User action: Enter message

[1280] User: The user opens a messaging app on their device, types the text "Let's go for a drink!", and presses the send button.

[1281] Input: The message entered by the user: "Let's go for a drink!"

[1282] Output: The message data sent.

[1283] Step 2:

[1284] Message Reception

[1285] Terminal: The terminal sends the message entered by the user to the server.

[1286] Input: User input message

[1287] Output: Message data sent to the server

[1288] Step 3:

[1289] Message Parsing

[1290] Server: Analyzes the received message data.

[1291] What happens: The server uses the NLTK library to tokenize the message and parse it to see if it contains a specific string ("Let's go for a drink!").

[1292] Input: Message data sent to the server

[1293] Output: Result of whether the string contains a specific string

[1294] Step 4:

[1295] Let's go drinking! AI startup

[1296] Server: When a specific string is recognized, launch "Let's go drinking! AI."

[1297] Specific operation: If the conditions for starting the AI ​​are met based on the message analysis results, the AI ​​program is executed.

[1298] Input: Judgment results that contain a specific string

[1299] Output: AI startup status

[1300] Step 5:

[1301] Emotion analysis

[1302] Server: The emotion engine built into "Let's Drink! AI" analyzes the user's emotions from the message.

[1303] Specific operation: The server calls the Sentiment Analysis API to extract and classify sentiment data from the message.

[1304] Input: User's message data

[1305] Output: User's emotion data (e.g., happy, tired, etc.)

[1306] Step 6:

[1307] Start scheduling

[1308] Server: Send a message to the chat screen saying, "Thank you everyone for your hard work! We'll start arranging the date for the drinking party, so please let us know when you can join."

[1309] Specific operation: Send a notification message using the message sending API.

[1310] Input: Sentiment analysis results and AI startup status

[1311] Output: Sending a message to start scheduling

[1312] Step 7:

[1313] User schedule entry

[1314] User: Enter the dates you can attend on the chat screen and send.

[1315] Specific operation: Each user enters the schedule in the reply format and submits it.

[1316] Input: Schedule adjustment start message from the server

[1317] Output: User's available dates

[1318] Step 8:

[1319] Schedule collection and decision

[1320] Server: Aggregates the submitted schedule information and determines a common schedule using the Google Calendar API.

[1321] Specific operation: Run an algorithm to determine common schedules based on the schedule data of all users.

[1322] Input: Schedule data for each user

[1323] Output: The determined common date

[1324] Step 9:

[1325] Schedule notification

[1326] Server: Notify all users of the decided date.

[1327] Specific operation: Use the push notification function to send a schedule confirmation message.

[1328] Input: Common Schedule

[1329] Output: Sending a schedule notification message

[1330] Step 10:

[1331] Store selection

[1332] Server: Using the Yelp API, search for potential stores based on the user's past history, preference data, and the results of the emotion engine analysis, and make suggestions to the user.

[1333] Specific operations: Retrieve history and preference data from the database, submit a search query to the API, and receive the results.

[1334] Example: "How about these places? 1. Izakaya A (relaxing atmosphere) 2. Bar B (fun events) 3. Restaurant C (quiet and relaxing place)"

[1335] Input: User history data, preference data, emotional data

[1336] Output: Sending a proposal message

[1337] Step 11:

[1338] User store selection

[1339] User: Choose one of the suggested stores and reply on the chat screen.

[1340] Specific operation: Reply with the name of the store selected from the list on the chat screen.

[1341] Example: "Izakaya A is good."

[1342] Input: Proposal message

[1343] Output: User selection data

[1344] Step 12:

[1345] Store reservation

[1346] Server: Automatically reserves selected locations using the OpenTable API.

[1347] Specific operation: Based on the selected store information, make an API call to make a reservation.

[1348] Input: User selected data

[1349] Output: Reservation completion notification

[1350] Step 13:

[1351] Reservation completion notification

[1352] Server: Notify all users once the reservation is complete.

[1353] Specific behavior: Use the push notification function to send a message with detailed information.

[1354] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[1355] Input: Reservation completion data

[1356] Output: Send reservation completion notification

[1357] Step 14:

[1358] Send reminder messages

[1359] Server: When the date of the drinking party approaches, the server automatically sends a reminder message.

[1360] What it does: Checks the internal clock system and sends a message the day before the appointment.

[1361] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[1362] Input: Reservation schedule data

[1363] Output: Send reminder message

[1364] The above is the specific flow of program processing for this system.

[1365] (Application example 2)

[1366] 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."

[1367] In modern society, group activities and meetings are frequently coordinated, but these coordination tasks are often performed manually, resulting in labor shortages and inefficiencies. Similar issues also arise when reporting on on-site security situations and setting up countermeasure meetings. Security operations, in particular, require rapid response, necessitating fast and efficient communication methods. Furthermore, appropriate suggestions and responses based on emotions and preferences are often required, and the lack of a way to automate these tasks requires a significant amount of effort.

[1368] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input a message including a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate locations based on the user's preference data, means for reserving the selected location, means for notifying the user that the reservation is complete, means for sending a reminder message the day before the scheduled date, means for recognizing the user's emotions using an emotion engine and taking the most appropriate action, and means for accepting user commands through voice recognition. This enables fast and efficient communication.

[1369] A "communication terminal" is an electronic device that allows a user to input information and communicate.

[1370] A "specific string" is a set of predefined characters that the system recognizes as a trigger.

[1371] A "message" is text information that a user sends through a communication terminal.

[1372] A "trigger" is a condition or signal that causes an event or action to occur.

[1373] "Analysis" is the process of closely examining input data and understanding its meaning and intent.

[1374] "Schedule information" is data relating to a schedule designated by the user.

[1375] A "common date" is a date and time when the date information of multiple users matches.

[1376] "Preference data" is data relating to a user's tastes and preferences.

[1377] "Candidate locations" are multiple selectable locations suggested by the system.

[1378] A "reservation" is the act of reserving a specific location or service for a specified date and time.

[1379] A "remind message" is a message that reminds you of an appointment or important matter in advance.

[1380] The "emotion engine" is a technology that analyzes and recognizes the user's emotions and suggests countermeasures.

[1381] "Speech recognition" is a technology that analyzes voice data and converts it into text information.

[1382] The present invention is a system that uses a communication terminal to recognize specific messages and make optimal suggestions based on the user's emotions and preferences. The system aims to significantly reduce the user's time and effort by automatically adjusting schedules and reserving appropriate facilities.

[1383] First, the user speaks a specific string of characters (for example, "Start a meeting!") into the communication device. Using speech recognition technology, this voice data is converted into text and sent to the server. The server analyzes the received text message and uses natural language processing technology to recognize the specific string of characters as a trigger. This analysis is performed using tools such as the Python speech_recognition library and the Google Cloud Natural Language API.

[1384] Next, the server collects users' schedule information and determines a common schedule. At this stage, users input the dates and times they are available, and the server aggregates the information to determine the optimal date and time, and notifies all users. Notifications are made via messages displayed on their devices or emails.

[1385] The server then suggests potential locations based on the user's preference data and past history. Using an emotion engine, the server takes into account the user's current emotional state. For example, it may suggest places where you can relax, where you can concentrate, or where fun events are taking place. The server uses scikit-learn, a Python machine learning library, and an API for emotion analysis.

[1386] When the user selects one of the candidate locations, the server automatically reserves the location. The server then notifies the user of the reservation completion and sends a reminder message the day before the scheduled date, allowing the user to proceed with the schedule with peace of mind.

[1387] Examples of specific prompts include:

[1388] "Start the meeting!"

[1389] Please schedule the next meeting.

[1390] "Report the security situation."

[1391] This allows users to easily schedule meetings and events, enabling quick responses on-site. The system also enables flexible responses based on users' emotions and preferences.

[1392] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1393] Step 1:

[1394] The user inputs a specific string of characters into the communication device by voice, and the device converts this voice into text and sends it to the server. At this stage, the input is the user's voice data, and the output is text data. Specifically, the voice data is converted into text using the Python speech_recognition library.

[1395] Step 2:

[1396] The server analyzes the received text message and recognizes specific strings as triggers. The input at this stage is the text data from step 1, and the output is a trigger signal to activate a specific action. Specifically, it uses natural language processing technology to analyze the text and detects trigger keywords (e.g., "Start a meeting!").

[1397] Step 3:

[1398] The server collects users' schedule information and determines a common schedule. At this stage, the input is the available date and time information for each user, and the output is the optimal date and time common to all users. Specifically, the server aggregates replies from users and uses an algorithm to determine the optimal date and time from among them.

[1399] Step 4:

[1400] The server notifies users of the determined date and time. The input at this stage is the common date and time determined in step 3, and the output is a notification message displayed on the user's communication device. Specifically, all users are notified that the date and time have been determined via email or push notification.

[1401] Step 5:

[1402] The server proposes candidate locations based on the user's preference data and past history. The input at this stage is the user's preference data and past history data, and the output is a list of candidate locations. Specifically, the server analyzes the data using Python machine learning libraries (e.g., scikit-learn) and sentiment analysis APIs to generate candidate locations.

[1403] Step 6:

[1404] The user selects one of the candidate locations, and the server automatically reserves that location. The input at this stage is the user's selected location, and the output is reservation confirmation information. Specifically, the API is used to make an online reservation and record the reservation results.

[1405] Step 7:

[1406] The server notifies the user that the reservation is complete and sends a reminder message the day before the scheduled date. The input at this stage is the reservation confirmation information and schedule information, and the output is the reminder message. Specifically, the server monitors the schedule and automatically sends a reminder 24 hours before the scheduled date and time.

[1407] In this way, a series of processes, from user voice command input to natural language processing, sentiment analysis, schedule adjustment and booking, and reminder message sending, are automated, allowing users to easily manage their schedules and respond on-site.

[1408] 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.

[1409] 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.

[1410] 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.

[1411] [Fourth embodiment]

[1412] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1413] 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.

[1414] 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).

[1415] 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.

[1416] 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.

[1417] 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).

[1418] 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.

[1419] 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.

[1420] 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.

[1421] 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.

[1422] 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.

[1423] 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.

[1424] 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."

[1425] This invention is a system for easily setting up drinking parties using communication terminals, and is realized mainly through communication between a server, terminals, and users. This system also uses natural language processing technology to analyze messages from users, obtain necessary information, and proceed with the process.

[1426] System Overview

[1427] 1. User Action:

[1428] User: The user uses a messaging app on their communication device to type a specific string of characters (e.g., "Let's go for a drink!") into a chat screen with a friend.

[1429] Device: The device sends the entered message to the LINE server.

[1430] 2. Message reception and analysis:

[1431] Server: The server receives the message sent by the user and analyzes it to see if it contains a specific string. This analysis uses natural language processing technology.

[1432] Server: When the message is recognized as a trigger, it launches "Let's go drinking! AI."

[1433] 3. Scheduling:

[1434] Server: "Let's go drinking! AI" sends a message to the chat screen to inform all users that it is starting to arrange a date.

[1435] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[1436] Users: Each user replies with their available dates.

[1437] Example: "I'm free on XX / XX and XX / XX."

[1438] Server: The server aggregates all users' inputs, determines common availability, determines the optimal schedule, and notifies all users.

[1439] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[1440] 4. Shop selection:

[1441] Server: "Let's Drink! AI" searches for candidate stores based on the user's past history and preference data.

[1442] Server: Suggests candidate stores to the user.

[1443] Example: "How about the following places? 1. Izakaya A 2. Bar B 3. Restaurant C"

[1444] User: The user chooses one of the suggested stores.

[1445] Example: "Izakaya A is good."

[1446] 5. Restaurant reservations:

[1447] Server: After the user has completed their selection, the server automatically makes an online reservation for the selected restaurant.

[1448] Server: Once the reservation is complete, all users will be notified of the details.

[1449] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[1450] 6. Reminder:

[1451] Server: When the date of the drinking party approaches, the server automatically sends a reminder message.

[1452] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[1453] Specific examples

[1454] For example, a user types "Let's go for a drink!" into a chat screen with a friend on LINE. When the device sends this message to the LINE server, the server receives and analyzes the message. As a result of the analysis, the phrase "Let's go for a drink!" is recognized as a specific trigger, activating the "Let's go for a drink! AI." The AI ​​then sends a message to arrange a date, and the user replies with their available dates. The server collects this information, decides on a common date, and notifies the user.

[1455] Next, "Let's Drink! AI" searches for candidate restaurants based on the user's preference data and suggests several options to the user. The user selects Izakaya A from these, and the server automatically makes the reservation. Once the reservation is complete, all users are notified of the details and a reminder message is sent the day before the drinking party.

[1456] This allows users to automatically plan a drinking party simply by entering a specific string of characters.

[1457] The processing flow will be explained below.

[1458] Step 1:

[1459] User: Using a communication device, type "Let's go for a drink!" into the chat screen with a friend.

[1460] Step 2:

[1461] Device: Sends the user's input to the LINE server.

[1462] Step 3:

[1463] Server: The LINE server analyzes the received message and recognizes the phrase "Let's go for a drink!" as a specific trigger.

[1464] Step 4:

[1465] Server: After the trigger is recognized, launch "Let's go drinking! AI" and send a message to the chat screen notifying the start of schedule adjustment.

[1466] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[1467] Step 5:

[1468] User: Each user replies with their available dates.

[1469] Example: "I'm free on XX / XX and XX / XX."

[1470] Step 6:

[1471] Server: Aggregates schedule information sent by all users and determines a common schedule.

[1472] Step 7:

[1473] Server: Determines the optimal schedule and notifies all users of that schedule.

[1474] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[1475] Step 8:

[1476] Server: Searches for store candidates based on the user's past history and preference data.

[1477] Step 9:

[1478] Server: Selects candidate stores and suggests them to the user.

[1479] Example: "How about the following places? 1. Izakaya A 2. Bar B 3. Restaurant C"

[1480] Step 10:

[1481] User: The user chooses one of the suggested stores.

[1482] Example: "Izakaya A is good."

[1483] Step 11:

[1484] Server: Automatically make online reservations at selected restaurants.

[1485] Step 12:

[1486] Server: Once the reservation is complete, the details will be sent to all users.

[1487] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[1488] Step 13:

[1489] Server: Automatically send reminder messages when the date of a drinking party approaches.

[1490] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[1491] Example 1

[1492] 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."

[1493] When planning an event such as a drinking party, it takes time and effort to adjust the date, select a restaurant, and confirm reservations, which can be particularly tedious when there are multiple participants. It is also easy to forget to send reminders to all participants. This system solves these problems and allows users to easily schedule a drinking party.

[1494] 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.

[1495] In this invention, the server includes means for a user to input a message containing a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate restaurants based on the user's preference data, means for reserving the selected restaurant, means for notifying the user that the reservation is complete, means for sending a reminder message the day before the drinking party, means for analyzing the message using a natural language processing engine and detecting the trigger, means for analyzing the optimal available date and time from the aggregated schedule information, and means for automatically making a restaurant reservation online. This allows users to easily adjust schedules, select restaurants, and confirm reservations through the system.

[1496] A "communication terminal" is an electronic device that a user uses to input and send messages, and includes smartphones, personal computers, and the like.

[1497] A "specific string" refers to a group of letters or words that the system is configured to recognize as a trigger.

[1498] A "message" refers to text information that a user inputs via a communication terminal and sends to other users or a server.

[1499] A "natural language processing engine" is a software technology that analyzes messages entered by users and recognizes specific strings of characters that act as triggers.

[1500] "Schedule information" refers to data regarding the dates and times when a user can participate.

[1501] "Preference data" is information about a user's past history and preferences, and is used to suggest candidate stores.

[1502] "Candidate store" refers to a store that the system suggests to the user as a location for a drinking party.

[1503] "Reservation" refers to the act of reserving a candidate store selected by a user and making it available for use on a specific date and time.

[1504] A "reminding message" is a message that notifies the user of the details of the event again when the date of the drinking party approaches.

[1505] "Generative AI models" refer to algorithms and programs that use AI to analyze data and make predictions.

[1506] A "prompt sentence" is a standard sentence to be input into a generative AI model, and contains instructions for the AI ​​to process appropriately.

[1507] The present invention is a system that allows users to easily schedule drinking parties using communication terminals, and is implemented primarily through communications between a server, terminals, and users. This system utilizes a natural language processing engine and a generative AI model to automatically perform message analysis, schedule adjustment, restaurant selection, and reminders. The following describes in detail an embodiment of the present invention.

[1508] System configuration

[1509] 1. Communication terminal

[1510] A communication device is used by users to input and send messages. Typically, this is a smartphone or a PC. Using this communication device, users input specific characters into the chat screen with friends and send them.

[1511] 2. Server

[1512] The server plays a central role in receiving and analyzing messages. It has a built-in natural language processing engine, which analyzes messages sent by users and detects whether they contain specific strings that act as triggers. This allows subsequent processing to proceed automatically.

[1513] 3. Natural Language Processing Engine

[1514] The natural language processing engine analyzes the message entered by the user and recognizes specific triggers. If the engine detects a trigger, the server launches "Let's Drink! AI" and the scheduling process begins.

[1515] 4. Generative AI Models

[1516] A generative AI model refers to an algorithm or program that analyzes data and makes predictions during processes such as scheduling, selecting restaurants, and sending reminders. This model suggests optimal restaurants and dates based on the user's past history and preference data.

[1517] System operation explanation

[1518] When a user operates a communication device and sends a specific string of characters, such as "Let's go for a drink!", the message is sent over the Internet to a server. The server uses a natural language processing engine to analyze the message and detects the specific string that acts as a trigger. This activates the "Let's go for a drink! AI," which then begins the process of arranging a date and selecting a restaurant.

[1519] Specifically, the system operates as follows: The server sends a message to all users to arrange a date and collects the available date information replies from each user. The generative AI model aggregates this date information and analyzes common available dates and times. It then notifies all users of the optimal date. After that, candidate restaurants are suggested based on the user's preference data and past history, and the restaurant selected by the user is automatically reserved online. Once the reservation is complete, the details are notified to all users, and a reminder message is sent as the date of the drinking party approaches.

[1520] Specific examples

[1521] For example, a user types "Let's go for a drink!" into the LINE chat screen and sends it. When the device sends this message to the server, the server analyzes the message. As a result of the analysis, the phrase "Let's go for a drink!" is recognized as a trigger, and the "Let's go for a drink! AI" is activated.

[1522] Next, the server sends a message to the chat screen saying, "We'll start arranging dates, so please let us know when you can join." Each user replies with their available dates, and the server aggregates this information, determines common availability, and decides on the optimal date. Next, "Let's go drinking! AI" searches for candidate restaurants based on the user's preference data and suggests several options to the user. The user selects a restaurant from these, and the server automatically reserves the restaurant and notifies all users of the details. Finally, the server sends a reminder message the day before the drinking party.

[1523] Prompt Sentence Examples

[1524] 1. When the phrase "Let's go for a drink!" is sent via LINE, "Let's go for a drink! AI" will start arranging a date.

[1525] "We will begin arranging the schedule. Please let us know the dates that you can attend."

[1526] 2. After the date of the drinking party is decided, a prompt to suggest a possible restaurant:

[1527] "How about the following establishments? 1. Izakaya 2. Bar 3. Restaurant"

[1528] 3. Once the user has selected a store, a prompt will automatically reserve that store:

[1529] "Reservation completed at 7pm on XX month."

[1530] As described above, the system of the present invention provides an environment in which users can easily set up drinking parties.

[1531] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1532] Step 1:

[1533] The user inputs a specific string into the communication terminal and sends it.

[1534] Input: The user enters the string "Let's go for a drink!" on a communication device such as a smartphone or PC.

[1535] Behavior: The user presses the send button on the chat screen to send this string.

[1536] Output: The communication terminal sends this message to the server.

[1537] Step 2:

[1538] The device sends a message to the server

[1539] Input: A message sent by the user: "Let's go for a drink!"

[1540] How it works: The device sends this message to the server via the internet. This process is carried out through the LINE server.

[1541] Output: The server receives this message.

[1542] Step 3:

[1543] The server parses the message

[1544] Input: The message "Let's go for a drink!" received by the server.

[1545] How it works: The server's natural language processing engine parses the message to see if it contains a specific string. This process uses a natural language processing model.

[1546] Output: When a specific trigger string is detected, "Let's go drinking! AI" will be launched.

[1547] Step 4:

[1548] The server notifies the user that scheduling is about to begin.

[1549] Input: The natural language processing engine detects the trigger "Let's go for a drink!"

[1550] How it works: The server sends a message to all users saying, "We're starting to schedule. Please let us know when you can join."

[1551] Output: A message is displayed to all users informing them that scheduling has begun.

[1552] Step 5:

[1553] The user replies with their available dates

[1554] Input: The rescheduling message sent by the server.

[1555] How it works: Each user replies with the date and time they are available. For example, they might reply, "I'm free on XX / XX and XX / XX."

[1556] Output: The server collects information about the dates that users can attend.

[1557] Step 6:

[1558] The server decides the best date

[1559] Input: Available dates and times provided by each user.

[1560] How it works: The server analyzes the aggregated schedule information and identifies common free dates and times. A generative AI model assists in this analysis.

[1561] Output: The optimal date is determined and the result is notified to all users from the server. For example, a message such as "We will hold a drinking party on XX day at 7 PM" is sent.

[1562] Step 7:

[1563] The server suggests candidate stores

[1564] Input: Best dates and user preference data.

[1565] How it works: The server uses a generative AI model to search for candidate stores based on the user's past history and preference data.

[1566] Output: A list of candidate stores is generated, and the server sends a suggestion message to the user saying, "How about the following stores? 1. Izakaya 2. Bar 3. Restaurant."

[1567] Step 8:

[1568] The user selects a store

[1569] Input: A list of candidate stores sent from the server.

[1570] Operation: The user selects one of the suggested restaurants and replies, for example, "Izakaya is good."

[1571] Output: The selected store information is sent to the server.

[1572] Step 9:

[1573] The server reserves the selected store

[1574] Input: Store information selected by the user.

[1575] How it works: The server automatically reserves the selected store online using an online reservation service.

[1576] Output: The reservation is completed and the details are sent to all users from the server. For example, a message like "Your reservation at the izakaya on the ____ date at 7pm has been completed. Thank you!" is sent.

[1577] Step 10:

[1578] The server sends a reminder message

[1579] Input: Time information and store information after completing reservation.

[1580] How it works: When the date of the drinking party approaches, the server automatically sends a reminder message to the user.

[1581] Output: A reminder message is sent to the user saying "There's a drinking party at the izakaya tomorrow at 7pm. Don't forget!"

[1582] The above specific processing steps realize a system that allows users to easily set up drinking parties.

[1583] (Application example 1)

[1584] 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."

[1585] Traditionally, setting up a drinking party requires a lot of time and effort, including coordinating participants' schedules and reserving restaurants. Furthermore, in order to efficiently complete these tasks, it is necessary to gather the opinions of all participants, which can make the process cumbersome. This reduces the time participants can spend enjoying the party. Furthermore, while there is a demand for easy operation via voice commands, traditional systems do not adequately achieve this.

[1586] 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.

[1587] In this invention, the server includes means for a user to input a message containing a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate restaurants based on the user's preference data, means for making a reservation at the selected restaurant, means for notifying the user that the reservation is complete, means for sending a reminder message the day before the drinking party, means for activating the system by voice command, and means having a natural language processing device for analyzing the voice command. This allows the user to quickly and efficiently set up a drinking party using simple voice commands, improving the convenience of the entire system.

[1588] "Communication terminal" refers to any electronic device that allows a user to input and send messages.

[1589] A "specific character string" refers to a uniquely identifiable phrase or word that a user inputs into a communication terminal.

[1590] A "trigger" is a string or command that causes the system to start a specific process.

[1591] "Date information" refers to data provided by a user regarding available dates and times for participation.

[1592] The "common schedule" refers to a date and time when all users can participate among the available dates and times for participation.

[1593] "Preference data" refers to information based on a user's past behavioral history and preference trends.

[1594] "Candidate stores" refer to multiple restaurants that the user may visit.

[1595] "Reservation" refers to a user reserving one of the candidate stores in advance for a specific date and time.

[1596] A "reminder message" refers to a confirmation notification sent when the date and time of a drinking party approaches.

[1597] "Voice command" refers to a verbal instruction given by a user to a system.

[1598] "Natural language processing device" refers to a technical device that analyzes a user's voice commands or messages and takes appropriate action.

[1599] This invention is a system for easily setting up drinking parties using communication terminals, and is realized through mutual communication between users, terminals, and a server.

[1600] A communication device includes a means for users to input messages containing specific strings of characters and a means for receiving voice commands. Specifically, this is an electronic device such as a smartphone or smart glasses. It may also be a device equipped with a voice recognition function. For example, the speech_recognition library can be used to convert a user's voice commands into text.

[1601] The server is equipped with a means for receiving messages and voice commands sent by users and analyzing them using natural language processing technology, such as the Google Natural Language API. The server also has built-in means for collecting users' schedule information and determining a common schedule.

[1602] Once the date is decided, the server sends a notification to the user and then suggests candidate stores based on the user's preference data. This data includes past behavioral history and user preferences. The server uses map services such as Google Maps API to suggest candidate stores.

[1603] After selecting a restaurant, the server automatically completes the reservation process and notifies the user when it is complete. The reservation system can use a payment system such as the Stripe API. It also has a function to send a reminder message the day before the party.

[1604] As a concrete example, consider the case where a user gives a voice command to a communication device saying, "Schedule a drinking party." The device receives this voice command and converts it to text using the speech_recognition library. The server receives and analyzes this voice command and starts the schedule adjustment process. The server then aggregates the available dates and times for all users and decides on a common date. Next, the Google Maps API is used to suggest candidate restaurants, and the user selects one. Finally, the restaurant is reserved via the Stripe API, and a completion notification and reminder message are sent.

[1605] An example prompt might look like this:

[1606] Please generate a program that will schedule a drinking party, select a restaurant, and make a reservation in response to the following voice commands.

[1607] User: Set up a drinking party

[1608] System: What dates are you available for?

[1609] User: October 15, 2023 is available

[1610] System: How about the following establishments? 1. Izakaya A 2. Bar B 3. Restaurant C

[1611] User: Izakaya A is good

[1612] System: Izakaya A has been reserved for October 15, 2023

[1613] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1614] Step 1:

[1615] The user inputs a voice command into a communication device (smartphone or smart glasses) saying, "Set up a drinking party." The device converts this voice command into text using the speech_recognition library. The input is the user's voice, and the output is a text command. This converted text is sent to the server.

[1616] Step 2:

[1617] The server analyzes the received text data using natural language processing technology such as the Google Natural Language API. The input is a voice command in text format, and the output is the analysis result (the intent of the command). As a result of the analysis, the instruction "Schedule a drinking party" is recognized, and the process proceeds to the next step.

[1618] Step 3:

[1619] After recognizing the intent of the voice command, the server generates a message to collect the user's schedule information and sends it to all participants. The input is the analysis result, and the output is a message to collect schedule information. Specifically, the server sends a message to the user such as "Please tell us your free dates."

[1620] Step 4:

[1621] Users reply with their available dates via their communication terminals. The terminals then collect and send these reply data to the server. The input is the user's schedule information, and the output is the aggregated schedule data sent to the server.

[1622] Step 5:

[1623] The server aggregates the schedule information of all users and determines a common schedule. The input is the aggregated schedule data, and the output is a common schedule that works for everyone. As part of the data processing, the server calculates the schedule information of all users and selects the most appropriate schedule.

[1624] Step 6:

[1625] The server notifies all users of the decided common schedule. The input is the common schedule information, and the output is a notification message. Specifically, it sends a message such as "The date has been decided. A drinking party will be held at 7pm on XX month."

[1626] Step 7:

[1627] The server collects user preference data and suggests candidate restaurants based on past history and preferences. The input is the user's preference data, and the output is a list of candidate restaurants. Data processing uses map services such as Google Maps API to search for suitable restaurants.

[1628] Step 8:

[1629] The user selects one of the proposed candidate stores and sends the result to the server. The input is the candidate store list, and the output is the selected store. The selection result is notified to the server via the communication terminal.

[1630] Step 9:

[1631] The server automatically reserves the selected store online and notifies all users that the reservation is complete. The input is the selected store information, and the output is a reservation confirmation notification message. The reservation procedure is carried out using a payment system such as Stripe API.

[1632] Step 10:

[1633] The server automatically sends a reminder message the day before the drinking party. The input is the reservation date and time and restaurant information, and the output is the reminder message. Specifically, it sends a message such as, "The drinking party will be at Izakaya A tomorrow at 7pm. Don't forget!"

[1634] 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.

[1635] This system allows users to easily schedule drinking parties using communication devices, and is implemented primarily through communication between a server, devices, and users. By combining natural language processing technology with an emotion engine, the system makes optimal suggestions based on the user's emotions and preferences.

[1636] System Overview

[1637] 1. User Action:

[1638] User: The user uses a messaging app on their communication device to type a specific string of characters (e.g., "Let's go for a drink!") into a chat screen with a friend.

[1639] Terminal: The terminal sends the entered message to the server.

[1640] 2. Message reception and analysis:

[1641] Server: The server receives the message sent by the user and analyzes it to see if it contains a specific string. This analysis uses natural language processing technology.

[1642] Server: When the message is recognized as a trigger, it launches "Let's go drinking! AI."

[1643] 3. Emotion recognition:

[1644] Server: "Let's Drink! AI" has an emotion engine built in that recognizes the user's emotions from the words and context contained in the message.

[1645] Server: The emotion engine analyzes the user's emotions (e.g., joy, sadness, stress, etc.) and prepares appropriate suggestions based on the results.

[1646] 4. Scheduling:

[1647] Server: "Let's go drinking! AI" sends a message to the chat screen to inform all users that it is starting to arrange a date.

[1648] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[1649] Users: Each user replies with their available dates.

[1650] Example: "I'm free on XX / XX and XX / XX."

[1651] Server: The server aggregates all user inputs, determines a common schedule, determines the optimal schedule, and notifies all users.

[1652] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[1653] 5. Shop selection:

[1654] Server: "Let's Drink! AI" searches for candidate restaurants based on the user's past history, preference data, and the results of emotion analysis by the emotion engine.

[1655] Server: Suggests candidate stores to the user, including stores that suit the user's current emotional state.

[1656] Example: "How about these places? 1. Izakaya A (relaxing atmosphere) 2. Bar B (fun events) 3. Restaurant C (quiet and relaxing place)"

[1657] User: The user chooses one of the suggested stores.

[1658] Example: "Izakaya A is good."

[1659] 6. Restaurant Reservations:

[1660] Server: After the user has completed their selection, the server automatically makes an online reservation for the selected restaurant.

[1661] Server: Once the reservation is complete, the details will be sent to all users.

[1662] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[1663] 7. Reminder:

[1664] Server: When the date of the drinking party approaches, the server automatically sends a reminder message.

[1665] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[1666] Specific examples

[1667] For example, a user types "Let's go for a drink!" in a chat screen with a friend. When the device sends this message to the server, the server receives and analyzes the message. As a result of the analysis, the phrase "Let's go for a drink!" is recognized as a specific trigger, and the "Let's go for a drink! AI" is activated. Furthermore, the emotion engine recognizes the emotion from the user's message and considers a response based on that emotion.

[1668] Next, the AI ​​sends a message to arrange a date, and the user replies with their available dates. The server collects this information, decides on a common date, and notifies the user. The AI ​​then takes into account the results of sentiment analysis and suggests the most suitable restaurant for the user. The user selects "Izakaya A," and the server automatically makes the reservation. Once the reservation is complete, the details are notified, and a reminder message is sent the day before the party.

[1669] This allows users to simply enter a specific string of characters, and the system will automatically set up the optimal drinking party based on their emotions and preferences.

[1670] The processing flow will be explained below.

[1671] Step 1:

[1672] User: Using a communication device, type "Let's go for a drink!" into the chat screen with a friend.

[1673] Step 2:

[1674] Terminal: Sends messages entered by the user to the communication server.

[1675] Step 3:

[1676] Server: Analyzes the received message and recognizes the phrase "Let's go for a drink!" as a specific trigger. This analysis uses natural language processing technology.

[1677] Step 4:

[1678] Server: Once trigger recognition is complete, launch "Let's go drinking! AI" and send a message to the chat screen notifying the start of schedule adjustments.

[1679] Example: "Thank you everyone! I'm starting to schedule a drinking party, so please let me know when you can join."

[1680] Step 5:

[1681] User: Each user replies with their available dates.

[1682] Example: "I'm free on XX / XX and XX / XX."

[1683] Step 6:

[1684] Server: Aggregates schedule information sent by all users and determines a common schedule.

[1685] Step 7:

[1686] Server: Determines the optimal schedule and notifies all users of that schedule.

[1687] Example: "The date has been decided! We'll be having a drinking party on XX day at 7pm."

[1688] Step 8:

[1689] Server: At the same time, the emotion engine built into "Let's Drink! AI" recognizes emotions from the user's message and outputs the analysis results.

[1690] Step 9:

[1691] Server: The emotion engine analyzes the user's emotional state (e.g., joy, stress, fatigue, etc.) and searches for store candidates based on the results.

[1692] Step 10:

[1693] Server: Based on the search results, selects candidate restaurants taking into account the user's past history and preference data, and makes suggestions to all users.

[1694] Example: "How about these places? 1. Izakaya A (relaxing place) 2. Bar B (fun atmosphere) 3. Restaurant C (quiet and relaxing place)"

[1695] Step 11:

[1696] User: The user chooses one of the suggested stores.

[1697] Example: "Izakaya A is good."

[1698] Step 12:

[1699] Server: After the user has completed their selection, the server automatically makes an online reservation for the selected restaurant.

[1700] Step 13:

[1701] Server: Once the reservation is complete, the details will be sent to all users.

[1702] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[1703] Step 14:

[1704] Server: Automatically send reminder messages when the date of a drinking party approaches.

[1705] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[1706] This allows users to simply type "Let's go out for drinks!" and the system will automatically arrange dates, select restaurants, make reservations, and even send reminders based on their emotions and preferences.

[1707] Example 2

[1708] 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."

[1709] Few existing drinking party scheduling systems are able to take into account the user's emotions and preferences. Furthermore, it is difficult to automate all stages of scheduling, restaurant selection, and reservations, requiring a lot of manual work from the user. This increases the burden on users and makes it difficult to realize the optimal drinking party. Furthermore, the lack of advance reminders means that some people may miss out on the party.

[1710] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1711] In this invention, the server includes means for a user to input a message containing a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for analyzing the user's emotions and making suggestions based on the results, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate restaurants based on the user's preference data, means for making a reservation at the selected restaurant, means for notifying the user that the reservation is complete, and means for sending a reminder message the day before the drinking party. As a result, the user only needs to input the specific character string to automatically set up the optimal drinking party based on their emotions and preferences, reducing the user's burden and enabling the optimal drinking party to be realized.

[1712] "Communication terminal" is a general term for electronic devices that allow users to input and send messages.

[1713] The "specific string" is a specified phrase that the system recognizes as a trigger to start setting up a drinking party.

[1714] The "means for receiving messages" is a function for receiving messages from users via the Internet.

[1715] The "analysis means" is a technique for analyzing a received message and determining whether it contains a specific character string.

[1716] A "means for recognizing as a trigger" is a mechanism that initiates a specific action based on the results of the analysis.

[1717] "Means for analyzing emotions" refers to technology that extracts and classifies emotions from users' messages.

[1718] The "means for making suggestions" is a function that provides the user with the optimal options based on the analysis results.

[1719] The "means for collecting schedule information" is a function for collecting the user's free schedule.

[1720] The "means for determining a common schedule" is a mechanism for selecting a date on which all users can participate based on the collected schedule information.

[1721] The "means for notifying the schedule" is a function for notifying the user of the determined schedule.

[1722] "Preference data" is a general term for data that indicates a user's past preference history and interests.

[1723] The "means for suggesting candidate restaurants" is a mechanism that provides users with a selection of restaurants that are suitable for them based on their preference data.

[1724] The "means of reserving a store" is the technology for reserving a selected store online.

[1725] The "means for notifying the user that a reservation has been completed" is a function for notifying the user that a reservation for a store has been completed.

[1726] The "means for sending a reminder message" is a mechanism for sending a reminder to the user the day before the drinking party.

[1727] This invention is a system for easily setting up drinking parties via communication terminals. This system is realized mainly through communication between a server, terminals, and users, and combines natural language processing technology and an emotion engine to make optimal suggestions based on the user's emotions and preferences.

[1728] The system uses the following hardware and software: The hardware consists of communication devices such as smartphones and PCs used by users, and a cloud server. The software includes natural language processing technology using the NLTK library, Sentiment Analysis API, Google Calendar API, Yelp API, and OpenTable API.

[1729] Specifically, a user types "Let's go for a drink!" using a messaging app on a communication device. This message is sent from the device to the server. Example: "Let's go for a drink!"

[1730] The server receives this message and uses the NLTK library to analyze it for a specific string. If the string is recognized, the "Let's Drink! AI" is launched.

[1731] The emotion engine in "Let's Drink! AI" uses the Sentiment Analysis API to analyze emotions from users' messages. Based on the analyzed emotion data, the server prepares to make optimal suggestions to users.

[1732] Next, the server sends a message to the chat screen to notify all users that schedule adjustments have begun. Users reply with the dates they can attend. Example: "I'm free on XX / XX and XX / XX."

[1733] The server aggregates all user inputs, uses the Google Calendar API to identify common dates, and determines the optimal date. The server then notifies all users of the determined date. Example: "The date has been decided! We'll be having a drinking party on XX day at 7 PM."

[1734] Next, "Let's Drink! AI" uses the Yelp API to search for candidate restaurants based on the user's past history, preference data, and the results of the emotion engine analysis. The server then suggests candidate restaurant information to the user. Example: "How about the following restaurants? 1. Izakaya A (relaxing atmosphere) 2. Bar B (with fun events) 3. Restaurant C (a quiet, relaxing place)"

[1735] The user selects one of the suggested restaurants. Example: "Izakaya A is good."

[1736] After the user has made their selection, the server will automatically make an online reservation for the selected restaurant using the OpenTable API. Once the reservation is complete, the server will notify all users of the details. Example: "Your reservation has been completed for Izakaya A on XX date at 7pm. Thank you!"

[1737] Finally, as the date of the drinking party approaches, the server automatically sends a reminder message. Example: "The drinking party will be at Izakaya A tomorrow at 7pm. Don't forget!"

[1738] With this system, users only need to input a specific string of characters, and the system will automatically set up the optimal drinking party based on their emotions and preferences.

[1739] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1740] Step 1:

[1741] User action: Enter message

[1742] User: The user opens a messaging app on their device, types the text "Let's go for a drink!", and presses the send button.

[1743] Input: The message entered by the user: "Let's go for a drink!"

[1744] Output: The message data sent.

[1745] Step 2:

[1746] Message Reception

[1747] Terminal: The terminal sends the message entered by the user to the server.

[1748] Input: User input message

[1749] Output: Message data sent to the server

[1750] Step 3:

[1751] Message Parsing

[1752] Server: Analyzes the received message data.

[1753] What happens: The server uses the NLTK library to tokenize the message and parse it to see if it contains a specific string ("Let's go for a drink!").

[1754] Input: Message data sent to the server

[1755] Output: Result of whether the string contains a specific string

[1756] Step 4:

[1757] Let's go drinking! AI startup

[1758] Server: When a specific string is recognized, launch "Let's go drinking! AI."

[1759] Specific operation: If the conditions for starting the AI ​​are met based on the message analysis results, the AI ​​program is executed.

[1760] Input: Judgment results that contain a specific string

[1761] Output: AI startup status

[1762] Step 5:

[1763] Emotion analysis

[1764] Server: The emotion engine built into "Let's Drink! AI" analyzes the user's emotions from the message.

[1765] Specific operation: The server calls the Sentiment Analysis API to extract and classify sentiment data from the message.

[1766] Input: User's message data

[1767] Output: User's emotion data (e.g., happy, tired, etc.)

[1768] Step 6:

[1769] Start scheduling

[1770] Server: Send a message to the chat screen saying, "Thank you everyone for your hard work! We'll start arranging the date for the drinking party, so please let us know when you can join."

[1771] Specific operation: Send a notification message using the message sending API.

[1772] Input: Sentiment analysis results and AI startup status

[1773] Output: Sending a message to start scheduling

[1774] Step 7:

[1775] User schedule entry

[1776] User: Enter the dates you can attend on the chat screen and send.

[1777] Specific operation: Each user enters the schedule in the reply format and submits it.

[1778] Input: Schedule adjustment start message from the server

[1779] Output: User's available dates

[1780] Step 8:

[1781] Schedule collection and decision

[1782] Server: Aggregates the submitted schedule information and determines a common schedule using the Google Calendar API.

[1783] Specific operation: Run an algorithm to determine common schedules based on the schedule data of all users.

[1784] Input: Schedule data for each user

[1785] Output: The determined common date

[1786] Step 9:

[1787] Schedule notification

[1788] Server: Notify all users of the decided date.

[1789] Specific operation: Use the push notification function to send a schedule confirmation message.

[1790] Input: Common Schedule

[1791] Output: Sending a schedule notification message

[1792] Step 10:

[1793] Store selection

[1794] Server: Using the Yelp API, search for potential stores based on the user's past history, preference data, and the results of the emotion engine analysis, and make suggestions to the user.

[1795] Specific operations: Retrieve history and preference data from the database, submit a search query to the API, and receive the results.

[1796] Example: "How about these places? 1. Izakaya A (relaxing atmosphere) 2. Bar B (fun events) 3. Restaurant C (quiet and relaxing place)"

[1797] Input: User history data, preference data, emotional data

[1798] Output: Sending a proposal message

[1799] Step 11:

[1800] User store selection

[1801] User: Choose one of the suggested stores and reply on the chat screen.

[1802] Specific operation: Reply with the name of the store selected from the list on the chat screen.

[1803] Example: "Izakaya A is good."

[1804] Input: Proposal message

[1805] Output: User selection data

[1806] Step 12:

[1807] Store reservation

[1808] Server: Automatically reserves selected locations using the OpenTable API.

[1809] Specific operation: Based on the selected store information, make an API call to make a reservation.

[1810] Input: User selected data

[1811] Output: Reservation completion notification

[1812] Step 13:

[1813] Reservation completion notification

[1814] Server: Notify all users once the reservation is complete.

[1815] Specific behavior: Use the push notification function to send a message with detailed information.

[1816] Example: "Your reservation has been completed at Izakaya A on XX / XX at 7 PM. Thank you!"

[1817] Input: Reservation completion data

[1818] Output: Send reservation completion notification

[1819] Step 14:

[1820] Send reminder messages

[1821] Server: When the date of the drinking party approaches, the server automatically sends a reminder message.

[1822] What it does: Checks the internal clock system and sends a message the day before the appointment.

[1823] Example: "We're having a drinking party at Izakaya A tomorrow at 7pm. Don't forget!"

[1824] Input: Reservation schedule data

[1825] Output: Send reminder message

[1826] The above is the specific flow of program processing for this system.

[1827] (Application example 2)

[1828] 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."

[1829] In modern society, group activities and meetings are frequently coordinated, but these coordination tasks are often performed manually, resulting in labor shortages and inefficiencies. Similar issues also arise when reporting on on-site security situations and setting up countermeasure meetings. Security operations, in particular, require rapid response, necessitating fast and efficient communication methods. Furthermore, appropriate suggestions and responses based on emotions and preferences are often required, and the lack of a way to automate these tasks requires a significant amount of effort.

[1830] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input a message including a specific character string on a communication terminal, means for receiving and analyzing the message and recognizing the specific character string as a trigger, means for collecting the user's schedule information and determining a common schedule, means for notifying the user of the determined schedule, means for suggesting candidate locations based on the user's preference data, means for reserving the selected location, means for notifying the user that the reservation is complete, means for sending a reminder message the day before the scheduled date, means for recognizing the user's emotions using an emotion engine and taking the most appropriate action, and means for accepting user commands through voice recognition. This enables fast and efficient communication.

[1831] A "communication terminal" is an electronic device that allows a user to input information and communicate.

[1832] A "specific string" is a set of predefined characters that the system recognizes as a trigger.

[1833] A "message" is text information that a user sends through a communication terminal.

[1834] A "trigger" is a condition or signal that causes an event or action to occur.

[1835] "Analysis" is the process of closely examining input data and understanding its meaning and intent.

[1836] "Schedule information" is data relating to a schedule designated by the user.

[1837] A "common date" is a date and time when the date information of multiple users matches.

[1838] "Preference data" is data relating to a user's tastes and preferences.

[1839] "Candidate locations" are multiple selectable locations suggested by the system.

[1840] A "reservation" is the act of reserving a specific location or service for a specified date and time.

[1841] A "remind message" is a message that reminds you of an appointment or important matter in advance.

[1842] The "emotion engine" is a technology that analyzes and recognizes the user's emotions and suggests countermeasures.

[1843] "Speech recognition" is a technology that analyzes voice data and converts it into text information.

[1844] The present invention is a system that uses a communication terminal to recognize specific messages and make optimal suggestions based on the user's emotions and preferences. The system aims to significantly reduce the user's time and effort by automatically adjusting schedules and reserving appropriate facilities.

[1845] First, the user speaks a specific string of characters (for example, "Start a meeting!") into the communication device. Using speech recognition technology, this voice data is converted into text and sent to the server. The server analyzes the received text message and uses natural language processing technology to recognize the specific string of characters as a trigger. This analysis is performed using tools such as the Python speech_recognition library and the Google Cloud Natural Language API.

[1846] Next, the server collects users' schedule information and determines a common schedule. At this stage, users input the dates and times they are available, and the server aggregates the information to determine the optimal date and time, and notifies all users. Notifications are made via messages displayed on their devices or emails.

[1847] The server then suggests potential locations based on the user's preference data and past history. Using an emotion engine, the server takes into account the user's current emotional state. For example, it may suggest places where you can relax, where you can concentrate, or where fun events are taking place. The server uses scikit-learn, a Python machine learning library, and an API for emotion analysis.

[1848] When the user selects one of the candidate locations, the server automatically reserves the location. The server then notifies the user of the reservation completion and sends a reminder message the day before the scheduled date, allowing the user to proceed with the schedule with peace of mind.

[1849] Examples of specific prompts include:

[1850] "Start the meeting!"

[1851] Please schedule the next meeting.

[1852] "Report the security situation."

[1853] This allows users to easily schedule meetings and events, enabling quick responses on-site. The system also enables flexible responses based on users' emotions and preferences.

[1854] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1855] Step 1:

[1856] The user inputs a specific string of characters into the communication device by voice, and the device converts this voice into text and sends it to the server. At this stage, the input is the user's voice data, and the output is text data. Specifically, the voice data is converted into text using the Python speech_recognition library.

[1857] Step 2:

[1858] The server analyzes the received text message and recognizes specific strings as triggers. The input at this stage is the text data from step 1, and the output is a trigger signal to activate a specific action. Specifically, it uses natural language processing technology to analyze the text and detects trigger keywords (e.g., "Start a meeting!").

[1859] Step 3:

[1860] The server collects users' schedule information and determines a common schedule. At this stage, the input is the available date and time information for each user, and the output is the optimal date and time common to all users. Specifically, the server aggregates replies from users and uses an algorithm to determine the optimal date and time from among them.

[1861] Step 4:

[1862] The server notifies users of the determined date and time. The input at this stage is the common date and time determined in step 3, and the output is a notification message displayed on the user's communication device. Specifically, all users are notified that the date and time have been determined via email or push notification.

[1863] Step 5:

[1864] The server proposes candidate locations based on the user's preference data and past history. The input at this stage is the user's preference data and past history data, and the output is a list of candidate locations. Specifically, the server analyzes the data using Python machine learning libraries (e.g., scikit-learn) and sentiment analysis APIs to generate candidate locations.

[1865] Step 6:

[1866] The user selects one of the candidate locations, and the server automatically reserves that location. The input at this stage is the user's selected location, and the output is reservation confirmation information. Specifically, the API is used to make an online reservation and record the reservation results.

[1867] Step 7:

[1868] The server notifies the user that the reservation is complete and sends a reminder message the day before the scheduled date. The input at this stage is the reservation confirmation information and schedule information, and the output is the reminder message. Specifically, the server monitors the schedule and automatically sends a reminder 24 hours before the scheduled date and time.

[1869] In this way, a series of processes, from user voice command input to natural language processing, sentiment analysis, schedule adjustment and booking, and reminder message sending, are automated, allowing users to easily manage their schedules and respond on-site.

[1870] 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.

[1871] 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.

[1872] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1873] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1874] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1875] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1876] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1877] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1878] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1879] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1880] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1881] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1882] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1883] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1884] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1885] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1886] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1887] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1888] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1889] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1890] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1891] The following is further disclosed regarding the above embodiment.

[1892] (Claim 1)

[1893] a means for a user to input a message including a specific character string on a communication terminal;

[1894] A means of receiving and analyzing messages and recognizing specific strings as triggers;

[1895] means for collecting schedule information of users and determining a common schedule;

[1896] a means for notifying the user of the determined schedule;

[1897] A means for suggesting candidate stores based on user preference data;

[1898] A means to reserve the selected store;

[1899] A means of notifying the completion of the reservation;

[1900] A way to send a reminder message the day before the drinking party,

[1901] A system including:

[1902] (Claim 2)

[1903] 2. The system according to claim 1, further comprising means for searching for and suggesting candidate shops based on the user's past history and preference data.

[1904] (Claim 3)

[1905] 2. The system according to claim 1, further comprising means for using natural language processing techniques when analyzing a message and recognizing a specific character string as a trigger.

[1906] "Example 1"

[1907] (Claim 1)

[1908] a means for a user to input a message including a specific character string on a communication terminal;

[1909] A means of receiving and analyzing messages and recognizing specific strings as triggers;

[1910] means for collecting schedule information of users and determining a common schedule;

[1911] a means for notifying the user of the determined schedule;

[1912] A means for suggesting candidate stores based on user preference data;

[1913] A means to reserve the selected store;

[1914] A means of notifying the completion of the reservation;

[1915] A way to send a reminder message the day before the drinking party,

[1916] a means of analyzing messages and detecting triggers using a natural language processing engine;

[1917] A means to analyze the best available dates and times from the aggregated schedule information,

[1918] A way to automatically make store reservations online,

[1919] A system including:

[1920] (Claim 2)

[1921] 2. The system according to claim 1, further comprising means for searching for and suggesting candidate shops based on the user's past history and preference data.

[1922] (Claim 3)

[1923] The system of claim 1, further comprising means for using the generative AI model to select stores and manage schedules.

[1924] "Application Example 1"

[1925] (Claim 1)

[1926] a means for a user to input a message including a specific character string on a communication terminal;

[1927] A means of receiving and analyzing messages and recognizing specific strings as triggers;

[1928] means for collecting schedule information of users and determining a common schedule;

[1929] a means for notifying the user of the determined schedule;

[1930] A means for suggesting candidate stores based on user preference data;

[1931] A means to reserve the selected store;

[1932] A means of notifying the completion of the reservation;

[1933] A way to send a reminder message the day before the drinking party,

[1934] means for activating the system by voice command;

[1935] means for analyzing voice commands, the means comprising a natural language processing device;

[1936] A system including:

[1937] (Claim 2)

[1938] 2. The system according to claim 1, further comprising means for searching for and suggesting candidate shops based on the user's past history and preference data.

[1939] (Claim 3)

[1940] 2. The system according to claim 1, further comprising means for using natural language processing techniques when analyzing a message and recognizing a specific character string as a trigger.

[1941] "Example 2: Combining Emotion Engines"

[1942] (Claim 1)

[1943] a means for a user to input a message including a specific character string on a communication terminal;

[1944] A means of receiving and analyzing messages and recognizing specific strings as triggers;

[1945] means for analyzing user emotions and making suggestions based on the results of the analysis;

[1946] means for collecting schedule information of users and determining a common schedule;

[1947] a means for notifying the user of the determined schedule;

[1948] A means for suggesting candidate stores based on user preference data;

[1949] A means to reserve the selected store;

[1950] A means of notifying the completion of the reservation;

[1951] A way to send a reminder message the day before the drinking party,

[1952] A system including:

[1953] (Claim 2)

[1954] 2. The system according to claim 1, further comprising means for searching for and suggesting candidate shops based on the user's past history and preference data.

[1955] (Claim 3)

[1956] 2. The system according to claim 1, further comprising means for using natural language processing techniques when analyzing a message and recognizing a specific character string as a trigger.

[1957] "Application example 2 when combining emotion engines"

[1958] Claims based on the content of a new invention

[1959] (Claim 1)

[1960] a means for a user to input a message including a specific character string on a communication terminal;

[1961] A means of receiving and analyzing messages and recognizing specific strings as triggers;

[1962] means for collecting schedule information of users and determining a common schedule;

[1963] a means for notifying the user of the determined schedule;

[1964] means for suggesting candidate locations based on user preference data;

[1965] a means for reserving the selected location;

[1966] A means of notifying the completion of the reservation;

[1967] A method to send a reminder message the day before the schedule,

[1968] A means for recognizing a user's emotions using an emotion engine and taking an optimal response;

[1969] means for accepting user commands by voice recognition;

[1970] A system including:

[1971] (Claim 2)

[1972] 2. The system according to claim 1, further comprising means for searching for and suggesting location candidates based on the user's past history and preference data.

[1973] (Claim 3)

[1974] 2. The system according to claim 1, further comprising means for using natural language processing techniques when analyzing a message and recognizing a specific character string as a trigger. [Explanation of symbols]

[1975] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input a message including a specific character string on a communication terminal; A means of receiving and analyzing messages and recognizing specific strings as triggers; means for collecting schedule information of users and determining a common schedule; a means for notifying the user of the determined schedule; A means for suggesting candidate stores based on user preference data; A means to reserve the selected store; A means of notifying the completion of the reservation; A way to send a reminder message the day before the drinking party, A system including:

2. 2. The system according to claim 1, further comprising means for searching for and suggesting store candidates based on the user's past history and preference data.

3. 2. The system according to claim 1, further comprising means for using natural language processing techniques when analyzing a message and recognizing a specific character string as a trigger.

Images