System

Abstract

A system is provided.SOLUTION: A system, comprising: means for receiving preference information of a user; means for storing the preference information; means for generating a recommendation suitable for the user based on the preference information; means for providing the recommendation to the user; and means for recording a communication with the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Traditional hotel services are standardized, making it difficult to meet the unique tastes and needs of individual guests. This leads to lower guest satisfaction and makes it difficult to secure repeat customers. Providing personalized service is essential to improving customer satisfaction, especially in luxury and boutique hotels. [Means for solving the problem]

[0005] The present invention provides a system including means for receiving user preference information, means for storing the preference information, means for generating recommendations suited to the user based on the preference information, means for providing the recommendations to the user, and means for recording communications with the user. This enables automated provision of personalized services based on the specific preferences and needs of individual guests, providing guests with a unique and memorable stay. In particular, highly accurate customization is achieved by generating recommendations using a generative AI model.

[0006] "User preference information" refers to information about the individual preferences and requests of hotel guests, including room type, floor location, preferences for specific facilities and services, and the like.

[0007] The "receiving means" refers to a device or program for receiving user preference information, and corresponds to an input reception function of a terminal or server.

[0008] The "storing means" refers to a device or program for storing the received user preference information in a storage device or database.

[0009] A "means for generating recommendations" is a program or algorithm that creates suggestions or advice suited to the user based on the stored preference information.

[0010] The "means for providing" refers to a device or program for presenting the generated recommendations to the user, and includes a user interface and a notification system.

[0011] "Means for recording communication" refers to a device or program for saving interactions and dialogues with users, and has the function of saving them in a log file or database.

[0012] A "generative AI model" is a mathematical model or program that uses artificial intelligence technology to generate and analyze data, analyzing user preferences and providing highly customized recommendations. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] The present invention is a system for providing personalized service based on guest preferences at a hotel or other lodging facility, including receiving and storing user preference information, generating recommendations based on the preference information, providing the recommendations, and recording communications with the user.

[0035] Receiving and storing user preference information

[0036] First, the device receives preference information directly from the user. This includes the user inputting their preferred room type and requests for specific services through the device. For example, the device receives information that the user prefers a "deluxe room" and a "high floor with an ocean view." This information is then sent from the device to the server.

[0037] The server stores the received preference information in a database. For example, for a guest ID of "guest123," "deluxe room" and "high floor with ocean view" are stored as preference information. This storage process allows each guest's preference information to be managed individually.

[0038] Generate recommendations

[0039] The server generates recommendations based on the stored preference information. Using a generative AI model, the received preference information can be analyzed with high precision to provide appropriate recommendations. For example, the recommended room for "guest 123" is automatically generated as "101 - Deluxe Room with Ocean View."

[0040] Providing recommendations

[0041] Once a recommendation is generated, the server sends this information to the device, which then displays it to the user. For example, the device might display "Recommended room: 101 - Deluxe Room with Ocean View," allowing the user to instantly confirm the recommendation.

[0042] Record of communication

[0043] In addition, all communication with the user is recorded on the server. For example, when a user makes a check-in inquiry, the content and timestamp of the inquiry are saved as a log. The server stores this log for future reference and analysis.

[0044] For example, if "guest456" inputs his preference for a "standard room" and "non-smoking," this information is sent to the server. The server generates a recommendation for "202 - Standard Room with City View" based on "guest456's" preferences and displays it on the terminal. At the same time, the content of the user's inquiry is recorded and used to improve services in the future.

[0045] As described above, the system of the present invention can provide personalized services based on the user's preferences and improve the accommodation experience.

[0046] The processing flow will be explained below.

[0047] Step 1: The device receives user preference information

[0048] The terminal provides an interface for accepting input from the user. For example, the user inputs information such as "room type: deluxe" and "special request: high floor, ocean view room" on the screen.

[0049] The terminal temporarily stores the input information in its memory.

[0050] Step 2: The device sends preference information to the server

[0051] The device collects the received user preference information into packets and sends them to the server, specifically using HTTP requests or API calls.

[0052] The terminal waits for a response from the server to confirm that the transmission was successful.

[0053] Step 3: The server receives and stores the preference information

[0054] The server receives the packets sent from the terminal and extracts the preference information therefrom.

[0055] The server stores the extracted preference information in a database managed for each guest. For example, for a guest ID of "guest123," information such as "room type: deluxe" and "special request: high floor, room with ocean view" is stored.

[0056] Step 4: The server generates recommendations

[0057] The server invokes a generative AI model based on the stored preference information to generate recommendations.

[0058] The generative AI model analyzes user preferences and recommends the most suitable room and services. For example, it might recommend "Room 101 - Deluxe Room with Ocean View" to "guest 123."

[0059] Step 5: The server sends the recommendations to the device

[0060] The server sends the generated recommendations to the device, again using HTTP responses or APIs.

[0061] The server records the completion of the process for later review.

[0062] Step 6: The device displays the recommendations to the user

[0063] The terminal analyzes the recommendations received from the server and displays them on the user interface.

[0064] For example, a message such as "Recommended room: 101 - Deluxe Room with Ocean View" may be displayed on the user's screen.

[0065] Step 7: User reviews recommendations and selects action

[0066] The user checks the recommendations displayed on the terminal and selects the next action, such as making a reservation or making an inquiry.

[0067] For example, a user clicks on the "Book room 101" button.

[0068] Step 8: The server records the communication

[0069] The server logs communications, including any follow-up requests or inquiries from the user.

[0070] The recorded logs are stored with a timestamp and are used to improve the service and for future reference.

[0071] Example 1

[0072] 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."

[0073] Conventional service provision systems for accommodation facilities have difficulty effectively utilizing user preference information to provide personalized services. They also have issues with not being able to fully utilize communication history with users, which can lead to a decline in service quality. Furthermore, the accuracy and applicability of recommendations provided to users are insufficient, limiting the improvement of user experience.

[0074] 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.

[0075] In this invention, the server includes means for receiving user preference information, means for storing the preference information, and means for generating recommendations suited to the user based on the preference information. This enables the provision of highly accurate personalized services. The server further includes means for providing the recommendations to the user, means for recording communication with the user, communication means for sending and receiving the preference information and recommendations, and means for generating recommendations based on prompt sentences using a generative AI model. This enables the integration of the user's preference information and communication history to provide more accurate and comprehensive services.

[0076] "User preference information" is information about the room type and services that the user prefers at accommodation facilities.

[0077] "Server" means a computer system that receives and stores user preference information and generates and provides recommendations appropriate to the user.

[0078] The term "means" refers to a combination of hardware and software for realizing various functions in the present invention.

[0079] The "receiving means" is an interface for receiving preference information from a user and transmitting it to a server.

[0080] The "storing means" is a data storage device that records the received preference information as data and allows it to be referenced as needed.

[0081] The "means for generating recommendations" refers to algorithms and generative AI models for presenting services and room types that are suitable for the user based on the user's preference information.

[0082] A "means for providing recommendations" is an interface for notifying and displaying generated recommendations to the user.

[0083] "Means for recording communication with users" is a function for saving inquiries and expressions of intent from users as a log.

[0084] A "communication means" is a network interface for sending and receiving preference information and recommendations.

[0085] A "generative AI model" is an artificial intelligence model that analyzes user preference information based on prompt sentences and generates appropriate recommendations.

[0086] A "prompt" is an instruction given to a generative AI model that provides information for the model to generate recommendations.

[0087] The present invention provides a system for providing personalized services based on user preferences at accommodation facilities such as hotels. The system includes functions for receiving and storing user preference information, generating recommendations based on the preference information, providing the recommendations, and recording communications with the user.

[0088] Receiving and storing user preference information

[0089] The terminal first receives preference information from the user. The user uses the terminal to input their preferred room type and requests for specific services. For example, a user who prefers a "deluxe room" and a "high floor with an ocean view" inputs this information. The input information is sent from the terminal to the server. The server saves the received preference information in a database. For example, preference information such as "deluxe room" and "high floor with an ocean view" is saved for a guest ID of "guest123." This saving process uses a database management system such as MySQL.

[0090] Generate recommendations

[0091] The server generates recommendations based on the stored preference information. Using a generative AI model, it is possible to analyze the received preference information with high accuracy and provide appropriate recommendations. For example, a recommended room, "101 - Deluxe Room with Ocean View," is automatically generated for "guest123." The generative AI model operates by inputting a prompt statement using a Python script. For example, the generated recommendation can be obtained by inputting the prompt statement "User ID: guest123 Preference information: deluxe room, high floor with ocean view Recommendation to be generated: room."

[0092] Providing recommendations

[0093] Once the recommendation is generated, the server sends this information to the terminal, and the terminal displays it to the user. Specifically, the generated recommendation is sent to the terminal in JSON format, and the terminal displays it in the user interface. For example, the user can confirm "Recommended room: 101 - Deluxe Room with Ocean View" through the terminal.

[0094] Record of communication

[0095] Furthermore, all communication with users is logged on the server. When a user makes a check-in inquiry, the question and timestamp are saved as a log. This operation is performed using a log management system such as the ELK stack. For example, if "guest123" asks "What time is check-in?", the question and timestamp are recorded.

[0096] As a concrete example, consider the case where user "guest456" inputs preference information of "standard room" and "non-smoking" into the terminal. This information is sent to the server, which generates a recommendation of "202 - Standard Room with City View" based on the preferences of "guest456." This recommendation is displayed on the terminal, while the content of the inquiry with the user is also recorded. An example of a prompt sentence input to the generative AI model is as follows:

[0097] User ID: guest456

[0098] Preferences: Standard room, Non-smoking

[0099] Generate recommendations for: Rooms

[0100] As a result, the accommodation experience can be improved by providing personalized services based on the user's preferences. This system also contributes to increasing user satisfaction and the number of repeat visitors.

[0101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0102] System program processing flow

[0103] Step 1: Enter and submit user preference information

[0104] The user inputs his / her preference information using the terminal.

[0105] Input: The user enters information such as "room type: deluxe" and "view: high floor with ocean view" on the device screen.

[0106] Operation: The device converts the input preference information into JSON format and sends a POST request to the server.

[0107] Output: The JSON data that is sent to the server.

[0108] json

[0109] {

[0110] "userID": "guest123",

[0111] "preferences": {

[0112] "roomType": "deluxe",

[0113] "view": "ocean high floor"

[0114] }

[0115] }

[0116] Step 2: Saving your preferences

[0117] The server analyzes the received preference information and stores it in a database.

[0118] Input: JSON data sent from the terminal.

[0119] Operation: The server parses the received JSON data, extracts the user ID and preference information, and stores them in a database.

[0120] Output: User preference information stored in a database.

[0121] sql

[0122] INSERT INTO user_preferences (user_id, room_type, view)

[0123] VALUES ("guest123", "deluxe", "ocean high floor");

[0124] Step 3: Generate recommendations

[0125] The server generates recommendations using a generative AI model based on the stored preference information.

[0126] Input: User preference information stored in the database ("guest123", "deluxe room", "high floor with sea view").

[0127] How it works: The server runs a Python script and inputs prompt statements to the generative AI model, which then generates recommendations based on the prompt statements.

[0128] Output: The generated recommendation (e.g., "101 - Deluxe Room with Ocean View").

[0129] python

[0130] prompt = "User ID: guest123 Preferences: deluxe room, high floor with ocean view Recommendation to generate: room"

[0131] generated_recommendation = "101 - Deluxe Room with Ocean View"

[0132] Step 4: Providing recommendations

[0133] The server sends the generated recommendations to the terminal, which displays them to the user.

[0134] Input: Generated recommendation ("101 - Deluxe Room with Ocean View").

[0135] Operation: The server converts the recommendations into JSON format and sends them to the device, which displays them in the user interface.

[0136] Output: Recommendations displayed on the user's terminal.

[0137] json

[0138] {

[0139] "recommendedRoom": "101 - Deluxe Room with Ocean View"

[0140] }

[0141] Step 5: Record your communications

[0142] The server logs all communication with the user.

[0143] Input: User queries and their responses.

[0144] How it works: The server stores the user query and the corresponding timestamp in a logging system, for example, using the ELK stack to store the data.

[0145] Output: The data that is logged.

[0146] json

[0147] {

[0148] "userID": "guest123",

[0149] "question": "What time is check-in?",

[0150] "timestamp": "2023-10-01 10:15:00"

[0151] }

[0152] This completes the explanation of the specific processing flow of the system and the operation of each step.

[0153] (Application example 1)

[0154] 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."

[0155] Conventional accommodation facilities and brick-and-mortar stores have systems that provide personalized recommendations based on the preferences and requests of each individual user, but these systems have difficulty effectively analyzing user preference information and providing accurate and immediate optimal recommendations.In addition, they have an issue in that they do not adequately record communication with users, making it difficult to use the information to improve services.

[0156] 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.

[0157] In this invention, the server includes means for receiving user preference information, means for storing the preference information, means for generating recommendations suited to the user based on the preference information, means for analyzing the user's preferences and generating the recommendations using a generative AI model in order to provide recommendations for specific products and services, and means for providing the recommendations to the user on a smartphone application. This makes it possible to instantly provide highly accurate recommendations based on the user's preferences and also record communications.

[0158] "Means for receiving user preference information" refers to a device or interface through which a user inputs information about their preferences and needs and the system receives that information.

[0159] "Means for storing preference information" refers to a device or mechanism that holds received user preference information and stores it in a database so that it can be referenced when necessary.

[0160] "Means for generating recommendations" refers to mechanisms or algorithms that automatically generate product or service suggestions appropriate to the user based on stored preference information.

[0161] "Means for generating recommendations using a generative AI model" refers to a mechanism that uses an artificial intelligence model to analyze user preferences and automatically generate optimal product and service recommendations.

[0162] "Means for providing recommendations to users on a smartphone application" refers to a system for displaying and notifying users of the generated recommendations via a smartphone application.

[0163] "Means for recording communication with users" refers to devices or mechanisms that store interactions with users and inquiries as logs so that they can be referenced or analyzed later.

[0164] A "database" refers to a group of data that manages the preference information of a large number of users individually, stores it efficiently, and organizes it in a searchable manner.

[0165] This invention is a system that provides personalized services based on user preference information at accommodation facilities and brick-and-mortar stores. Specifically, it uses various devices and software to receive user input information, generate recommendations based on that information, and provide them to the user.

[0166] Receiving user preference information

[0167] First, a user inputs their preferences using a smartphone application, including the types of products they prefer and specific requests (e.g., size, color, brand, etc.), which are then sent to the server via the app.

[0168] Saving your preferences

[0169] The server receives the preference information and stores it in a database. This database is used to individually manage preference information from multiple users, and for example, it stores information such as "sportswear, black, size L" for user ID "user123."

[0170] Generate recommendations

[0171] The server uses a generative AI model to generate appropriate recommendations based on the stored preference information. This AI model analyzes the input information and recommends the most suitable products and services. For example, it recommends a "black sports jacket in size L" to "user123."

[0172] Providing recommendations

[0173] The generated recommendations are sent from the server to a smartphone application, which then displays this information to the user immediately, allowing the user to review the recommended products and services. For example, a notification might appear in the app saying, "Today's recommended product: Black sports jacket, size L."

[0174] Record of communication

[0175] Furthermore, all inquiries and requests made by users through the app are recorded on the server. These records are saved as logs and used for future reference and analysis. For example, interactions such as "checking product inventory" and "reserving a fitting room" are recorded.

[0176] Hardware and software used

[0177] The system uses a smartphone, a cloud server, a database (e.g., SQL database), a generative AI model (e.g., GPT-3.5), etc. Data is sent from the device via the internet to the server, where it is analyzed and stored.

[0178] Examples of specific examples and prompts

[0179] For example, if a user is looking for sportswear, they might enter the following preferences:

[0180] "I want some sportswear. Nike is a good brand. Black, size L."

[0181] An example of a prompt sentence to input to the generative AI model is:

[0182] "I'm looking for Nike brand sportswear in size medium in black. Please generate the best product recommendations for this user."

[0183] Examples include:

[0184] The above is a specific embodiment for carrying out the present invention, and this system allows users to have a highly accurate personalized experience based on their individual preferences.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] The user launches a smartphone application and inputs their own preference information. This preference information includes the type, brand, color, size, etc. of the product the user prefers. The input information is sent to the server by the application. The input data is in text format, for example, "I want sportswear. Nike is a good brand, the color is black, and the size is large." The preference information is sent to the server as output.

[0188] Step 2:

[0189] The server stores the received preference information in a database. The server converts the sent preference information into structured data (e.g., JSON format) and stores it in the database along with the user ID. For example, for user ID "user123," data such as "category: sportswear, brand: Nike, color: black, size: L" is stored. The input is the user's preference information, and the output is structured data stored in the database.

[0190] Step 3:

[0191] The server generates recommendations based on the stored preference information. To do this, it uses a generative AI model. The server inputs the stored preference information into the AI ​​model and analyzes it. For example, the input to the model is a prompt statement such as, "I am looking for black Nike sportswear in size L. Please generate the best product recommendations for this user." As an output, the model returns a recommendation of "black Nike sports jacket in size L."

[0192] Step 4:

[0193] The server sends the generated recommendations to a smartphone application. The application receives the recommendations and displays them on the user's screen. For example, a notification such as "Today's recommended item: Nike sports jacket in black, size large" is displayed on the user's smartphone. The input is the generated recommendations, and the output is the notification displayed in the smartphone application.

[0194] Step 5:

[0195] When a user makes an inquiry or request through the application, the server records this interaction. For example, the contents of a request such as "checking product availability" or "reserving a fitting room" are saved as a log. The server stores this in a database for future reference and analysis. The input is the user's inquiry, and the output is the recorded log data.

[0196] The above are the specific processing steps of the program in this system, and details of the input and output at each step.

[0197] 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.

[0198] The present invention is a system for providing personalized services based on the preferences and emotions of guests at hotels and other accommodation facilities. The system recognizes and stores user preference information and emotions, generates recommendations based on the preferences and emotions, provides the recommendations, and records communications with the user.

[0199] Receiving and storing user preferences and emotions

[0200] First, the device receives preference information and emotion information from the user. This includes the user inputting their preferred room type and requests for specific services through the device. The emotion engine recognizes the user's emotions through voice analysis and facial expression analysis. For example, the device receives information that the user prefers a "deluxe room" and a "high floor with an ocean view," as well as the emotion of "joy." This information is then sent from the device to the server.

[0201] The server stores the received preference information and emotion information in a database. For example, for a guest ID of "guest123," the following information is stored: "deluxe room," "high floor with ocean view," and "joy" as the emotion. This storage process allows each guest's preference information and emotion information to be managed individually.

[0202] Generate recommendations

[0203] The server generates recommendations based on the stored preference and emotion information. Using a generative AI model, the received preference and emotion information can be analyzed with high accuracy to provide appropriate recommendations. For example, taking into account the emotion of "joy" for "guest 123," the recommended room "101 - Deluxe Room with Ocean View" is automatically generated.

[0204] Providing recommendations

[0205] Once a recommendation is generated, the server sends this information to the device, which then displays it to the user. For example, the device might display "Recommended room: 101 - Deluxe Room with Ocean View," allowing the user to instantly confirm the recommendation.

[0206] Record of communication

[0207] Furthermore, all communication with the user is recorded on the server. For example, when a user makes a check-in inquiry, the content of the inquiry and emotional information are also saved as logs. The server stores this log for future reference and analysis.

[0208] Specific examples

[0209] If "guest456" inputs his preference information of "standard room" and "non-smoking," the emotion engine recognizes the emotion of "relief." This information is sent to the server, which generates a recommendation of "202 - Standard Room with City View" based on "guest456's" preference and emotion information and displays it on the device. At the same time, the user's inquiry and emotion are recorded and used to improve services in the future.

[0210] Use of emotional information

[0211] The server dynamically adjusts recommendations based on the recognized emotional information. For example, if a user is feeling stressed, more personalized services will be provided, such as recommending a quiet room or relaxation services. Furthermore, by including emotional information in communication records, it is possible to improve the quality of services and respond quickly to problems.

[0212] As described above, the system of the present invention not only provides personalized services based on the user's preferences, but also realizes even more personalized services by adding emotional information, thereby significantly improving the accommodation experience.

[0213] The processing flow will be explained below.

[0214] Step 1: The device receives the user's preference information and emotion information.

[0215] The terminal provides an interface for accepting input from the user, who inputs information such as "room type: deluxe" and "special request: high floor, ocean view room" on the screen.

[0216] The emotion engine analyzes the user's voice and recognizes their emotions. For example, if the user expresses the emotion "joy," that information is recorded on the device.

[0217] Step 2: The device sends preference information and emotion information to the server.

[0218] The device collects the received user preference information and emotion information into packets and sends them to the server, specifically using HTTP requests or API calls.

[0219] The terminal waits for a response from the server to confirm whether the transmission was successful.

[0220] Step 3: The server receives and stores preference and emotion information.

[0221] The server receives the packets sent from the terminal and extracts the preference information and emotion information therefrom.

[0222] The server stores the extracted information in a database managed for each guest. For example, for a guest ID of "guest123," information such as "room type: deluxe," "special request: high floor, ocean view room," and "emotion: joy" is stored.

[0223] Step 4: The server generates recommendations

[0224] The server invokes a generative AI model based on the stored preference and emotion information to generate recommendations.

[0225] The generative AI model analyzes user preferences and emotions to recommend the most suitable room and service. For example, for guest 123, it would recommend "101 - Deluxe Room with Ocean View" based on the emotion "joy."

[0226] Step 5: The server sends the recommendations to the device

[0227] The server sends the generated recommendations to the device, again using HTTP responses or APIs.

[0228] The server records the completion of the process for later review.

[0229] Step 6: The device displays the recommendations to the user

[0230] The terminal analyzes the recommendations received from the server and displays them on the user interface.

[0231] For example, a message such as "Recommended room: 101 - Deluxe Room with Ocean View" may be displayed on the user's screen.

[0232] Step 7: User reviews recommendations and selects action

[0233] The user checks the recommendations displayed on the terminal and selects the next action, such as making a reservation or making an inquiry.

[0234] For example, a user clicks on the "Book room 101" button.

[0235] Step 8: The server records the communication

[0236] The server logs the communication content, including any additional requests or inquiries from the user, along with emotional information.

[0237] The recorded logs are stored with a timestamp and are used to improve the service and for future reference.

[0238] Step 9: The server uses the sentiment information to adjust the recommendations

[0239] The server dynamically adjusts recommendations based on the stored emotional information: for example, if the user is perceived as feeling "stressed," the server may recommend a quiet room or relaxation services.

[0240] This allows for personalized services tailored to the user's emotional state.

[0241] Example 2

[0242] 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."

[0243] Conventional service provision systems in accommodation facilities mainly provide services based on user preferences, but do not provide personalized services that take into account the user's emotional information. This makes it difficult to provide detailed responses to maximize user satisfaction. In addition, there is no adequate system in place to record communication with users and use it to improve services in the future.

[0244] 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.

[0245] In this invention, the server includes means for receiving user preference information, means for saving the preference information, means for recognizing and saving emotion information, means for generating recommendations suited to the user based on the preference information and emotion information, means for providing the recommendations to the user, and means for recording communication with the user, thereby enabling the provision of more personalized services based on individual preference information and emotion information.

[0246] "User" means any person or group of people using the services of the Accommodation Facility.

[0247] "Preference information" is information that specifically indicates the user's preferences and wishes, and includes the type of room and the conditions for using the facility.

[0248] "Emotion information" is information that indicates the user's emotional state, and is obtained by voice analysis, facial expression analysis, etc.

[0249] "Means for receiving" refers to the functions and devices for obtaining the necessary information from the user through the terminal.

[0250] "Means for storing" refers to the functions and devices for storing received information in a database or the like.

[0251] "Recommendations" refers to suggestions for providing appropriate services and rooms based on the user's preference information and emotional information.

[0252] "Generating means" refers to the functionality or devices used to create recommendations based on stored information, including generative AI models.

[0253] "Means for providing" refers to the functions and devices for communicating generated recommendations to users.

[0254] "Means for recording" refers to the functions and devices for saving the content of communication with the user.

[0255] A "generative AI model" refers to an algorithm that uses machine learning and natural language processing to analyze user preference and emotional information and generate appropriate recommendations.

[0256] The present invention provides a system for providing personalized services based on the preferences and emotions of guests at accommodation facilities such as hotels. The system recognizes and stores user preference information and emotions, generates recommendations based on the user preference information and emotions, and provides the recommendations and records communication with the user. The following describes in detail the embodiments of the present invention.

[0257] Receiving and storing user preferences and emotions

[0258] First, the terminal receives preference and emotion information from the user. This process involves the user inputting their preferred room type and requests for specific services through the terminal. The emotion engine then recognizes the user's emotions through voice and facial expression analysis. The terminal then sends this information to the server. The server stores the received preference and emotion information in a database and manages each guest's information individually.

[0259] For example, if a user inputs their preference for a "deluxe room" and a "high floor with an ocean view" into the device, the emotion engine will recognize the emotion of "joy." This information is sent from the device to the server and stored on the server.

[0260] Generate recommendations

[0261] The server generates recommendations based on the stored preference and emotion information. A generative AI model is used in this process. The generative AI model analyzes the user's preference and emotion information with high accuracy and provides appropriate recommendations. As a specific example, the server considers the emotion of "happiness" for "guest123" and generates the recommended room, "101 - Deluxe Room with Ocean View."

[0262] Examples of sentences that can be used as prompts are:

[0263] User ID: guest123

[0264] Preference: Deluxe room, high floor with sea view

[0265] Emotion: Joy

[0266] Generate recommendations.

[0267] Providing recommendations

[0268] The generated recommendation is sent from the server to the device, and the device displays it to the user. For example, the server sends a recommendation of "101 - Deluxe Room with Ocean View" to the device, and the device displays "Recommended room: 101 - Deluxe Room with Ocean View".

[0269] Record of communication

[0270] The server also records all communication with the user. For example, when a user makes a check-in inquiry, the content of the inquiry and emotional information are recorded. This record is saved in a log file or database for later reference and analysis.

[0271] Use of emotional information

[0272] The server dynamically adjusts recommendations based on the recognized emotional information. For example, if a user is feeling stressed, more personalized services can be provided, such as recommending a quiet room or relaxation services. Furthermore, by including emotional information in communication records, it is possible to improve the quality of services and respond quickly to problems.

[0273] As described above, the system of the present invention not only provides personalized services based on the user's preferences, but also realizes even more personalized services by adding emotional information, thereby significantly improving the accommodation experience.

[0274] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0275] Step 1:

[0276] Receiving user preference and emotion information

[0277] The user operates the terminal and inputs preference information such as the preferred room type and desired facilities.

[0278] Input: Preference information entered by the user into the device (e.g., deluxe room, high floor with ocean view).

[0279] Specific operation: The user selects preference information on the device screen by tapping, clicking, or entering text.

[0280] At the same time, the device uses its built-in emotion engine to capture the user's face with a camera and perform voice and facial expression analysis to collect emotional information.

[0281] Input: Video and audio of the user's face.

[0282] How it works: The device's camera and microphone capture the user's face and voice, and the built-in emotion analysis engine determines emotions such as "joy" or "relief."

[0283] Output: Preference information and emotion information are temporarily stored inside the device.

[0284] Step 2:

[0285] Transmission and storage of received information

[0286] The terminal transmits the collected preference information and emotion information to the server.

[0287] Input: Preference and emotion information stored on the device.

[0288] Specific operation: The device sends data to the server using an HTTP request or other communication protocol.

[0289] The server stores the received information in a database.

[0290] Input: Preference and emotion information received by the server.

[0291] What happens: The server inserts information into the database using an SQL query.

[0292] Output: The information is stored in a database, and individual preference and emotion information for each user is managed.

[0293] Step 3:

[0294] Generate recommendations

[0295] The server generates recommendations using a generative AI model based on the stored preference and emotion information.

[0296] Input: Preference and emotion information stored in a database.

[0297] Specific operation: The server sends a prompt to the generative AI model to generate recommendations.

[0298] Example prompt sentence:

[0299] User ID: guest123

[0300] Preference: Deluxe room, high floor with sea view

[0301] Emotion: Joy

[0302] Generate recommendations.

[0303] Output: Recommended room and service information (e.g. 101 - Deluxe Room with Ocean View).

[0304] Step 4:

[0305] Providing recommendations

[0306] The server sends the generated recommendations to the device.

[0307] Input: Server-generated recommendations.

[0308] Specific operation: The server sends the recommendation information to the device using an HTTP response or other communication protocol.

[0309] The device displays the received recommendations to the user.

[0310] Input: The recommendations received from the server.

[0311] Specific behavior: The device displays "Recommended room: 101 - Deluxe Room with Ocean View" on the display screen.

[0312] Output: Recommendations visually communicated to the user.

[0313] Step 5:

[0314] Record of communication

[0315] The server records all communication with the user.

[0316] Input: User inquiry, dialogue log, and emotional information.

[0317] Specific operation: The server stores the received information in a log file or database.

[0318] Output: Logged communications with users.

[0319] Through the above process, highly personalized services based on the user's preference information and emotional information are provided.

[0320] (Application example 2)

[0321] 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."

[0322] Traditional services provided in brick-and-mortar stores often provided uniform services without considering information about users' preferences or emotions. This meant that users took a long time to find products or services that suited them, resulting in an unsatisfactory experience. Furthermore, it was difficult to respond to real-time changes in users' emotions using only user purchase history and behavioral data, so further improvements in services were required.

[0323] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user preference information, means for saving preference information and emotion information, means for generating recommendations suitable for the user based on the preference information, means for receiving and analyzing user emotion information, means for dynamically adjusting the recommendations based on the emotion information, means for recording communication with the user, and means for generating prompt sentences using a generative AI model when generating recommendations and generating recommendations based on the analysis results. This makes it possible to provide personalized recommendations in real time based on the user's preferences and emotions.

[0324] The "means for receiving user preference information" refers to equipment or software for acquiring preference information regarding specific products or services from a user.

[0325] The "means for storing preference information and emotional information" refers to storage facilities such as databases and servers that continuously store and manage user preference information and emotional information.

[0326] The "means for generating recommendations suited to the user based on preference information" refers to an algorithm or program that analyzes the stored preference information and selects the most suitable products and services for the user.

[0327] "Means for receiving and analyzing user emotional information" refers to devices and software for recognizing and analyzing a user's emotions from their voice and facial expressions.

[0328] A "means for dynamically adjusting recommendations based on emotional information" is a system or algorithm that changes recommendations in real time based on analyzed emotional information.

[0329] "Means for recording communication with users" refers to devices or software that record and save conversations with users and inquiries.

[0330] "Means for generating prompt sentences using a generative AI model when generating recommendations, and generating recommendations based on the analysis results" refers to algorithms or programs that create prompt sentences using a generative AI model based on user preference information and emotional information, and then use the analysis results to generate appropriate recommendations.

[0331] A specific embodiment for carrying out the present invention will be described.

[0332] Users input preference information such as their preferred product categories, specific brands, and price ranges through a smartphone application. In addition, the smartphone's camera and microphone are used to capture facial expressions and voices, and the user's emotional information is analyzed.

[0333] The server collects user preference information and emotion information and stores this data in a database. The stored data includes identifiers for multiple users and is managed individually.

[0334] The server uses a generative AI model to generate prompts and recommendations based on the collected and stored data. Specifically, it generates prompts such as the following:

[0335] User prefers: fashion, and their current emotion is: joy. What should we recommend?

[0336] The server inputs the generated prompt sentences into the AI ​​model and obtains recommendations as an analysis result, which are displayed in real time in the application on the user's smartphone.

[0337] As a specific example, if a user likes "fashion" and the camera recognizes an expression of "joy," the server stores that information and recommends "the latest fashion trend items" to the user.

[0338] It also has the ability to dynamically adjust recommendations based on the user's emotional state: for example, if the user is feeling stressed, products with a relaxation effect (e.g., aroma candles or relaxing music) will be recommended.

[0339] Furthermore, all interactions and inquiries with users are recorded and used to improve services in the future, as data analysis will enable more precise service provision.

[0340] This system allows users to receive the most appropriate products and services based on their emotions and preferences at the time, greatly improving the shopping experience in physical stores.

[0341] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0342] Step 1:

[0343] A user launches a smartphone application and inputs preference information such as their preferred product categories, specific brands, and price ranges. The input data is temporarily stored in the device's memory. When the user accesses the application via the camera and microphone, the device captures the user's facial expressions and voice and analyzes their emotional information. Input: User's preference information and emotional information. Output: Analysis results of preference information and emotional information.

[0344] Step 2:

[0345] The device sends the analyzed preference information and emotion information to the server. The server stores the received data in a database, where each user's identifier (ID) is also recorded. Input: preference information and emotion information. Output: user information stored in the database.

[0346] Step 3:

[0347] The server retrieves the user's preference and emotion information stored in the database and generates a prompt using a generative AI model. The prompt reflects the user's preference and emotion. For example, the generated sentence is "User prefers: fashion, and their current emotion is: joy. What should we recommend?" Input: Preference information and emotion information. Output: Generated prompt.

[0348] Step 4:

[0349] The server inputs the generated prompt into the generative AI model and obtains recommendations as the analysis result. At this time, the AI ​​model recommends the most suitable products and services to the user based on the prompt. Input: Prompt. Output: Recommendations.

[0350] Step 5:

[0351] The server sends the obtained recommendations to the device, which then displays them on the application screen. The user can check the recommended products and services in real time. Input: Recommendations. Output: Recommendations displayed in the application.

[0352] Step 6:

[0353] The products and services selected by the user through the application, or the content of inquiries made, are sent to the server, which records these communications. This information will be used to improve future services. Input: User selections and inquiries. Output: Communication content recorded on the server.

[0354] Step 7:

[0355] The server analyzes changes in the user's emotions in real time and dynamically adjusts recommendations as needed. For example, if the server determines that the user is feeling stressed, it will re-recommend products with a relaxation effect. Input: Real-time emotional information. Output: Adjusted recommendations.

[0356] 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.

[0357] 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.

[0358] 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.

[0359] [Second embodiment]

[0360] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0361] 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.

[0362] 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).

[0363] 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.

[0364] 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.

[0365] 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).

[0366] 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.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] 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.

[0371] 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."

[0372] The present invention is a system for providing personalized service based on guest preferences at a hotel or other lodging facility, including receiving and storing user preference information, generating recommendations based on the preference information, providing the recommendations, and recording communications with the user.

[0373] Receiving and storing user preference information

[0374] First, the device receives preference information directly from the user. This includes the user inputting their preferred room type and requests for specific services through the device. For example, the device receives information that the user prefers a "deluxe room" and a "high floor with an ocean view." This information is then sent from the device to the server.

[0375] The server stores the received preference information in a database. For example, for a guest ID of "guest123," "deluxe room" and "high floor with ocean view" are stored as preference information. This storage process allows each guest's preference information to be managed individually.

[0376] Generate recommendations

[0377] The server generates recommendations based on the stored preference information. Using a generative AI model, the received preference information can be analyzed with high precision to provide appropriate recommendations. For example, the recommended room for "guest 123" is automatically generated as "101 - Deluxe Room with Ocean View."

[0378] Providing recommendations

[0379] Once a recommendation is generated, the server sends this information to the device, which then displays it to the user. For example, the device might display "Recommended room: 101 - Deluxe Room with Ocean View," allowing the user to instantly confirm the recommendation.

[0380] Record of communication

[0381] In addition, all communication with the user is recorded on the server. For example, when a user makes a check-in inquiry, the content and timestamp of the inquiry are saved as a log. The server stores this log for future reference and analysis.

[0382] For example, if "guest456" inputs his preference for a "standard room" and "non-smoking," this information is sent to the server. The server generates a recommendation for "202 - Standard Room with City View" based on "guest456's" preferences and displays it on the terminal. At the same time, the content of the user's inquiry is recorded and used to improve services in the future.

[0383] As described above, the system of the present invention can provide personalized services based on the user's preferences and improve the accommodation experience.

[0384] The processing flow will be explained below.

[0385] Step 1: The device receives user preference information

[0386] The terminal provides an interface for accepting input from the user. For example, the user inputs information such as "room type: deluxe" and "special request: high floor, ocean view room" on the screen.

[0387] The terminal temporarily stores the input information in its memory.

[0388] Step 2: The device sends preference information to the server

[0389] The device collects the received user preference information into packets and sends them to the server, specifically using HTTP requests or API calls.

[0390] The terminal waits for a response from the server to confirm that the transmission was successful.

[0391] Step 3: The server receives and stores the preference information

[0392] The server receives the packets sent from the terminal and extracts the preference information therefrom.

[0393] The server stores the extracted preference information in a database managed for each guest. For example, for a guest ID of "guest123," information such as "room type: deluxe" and "special request: high floor, room with ocean view" is stored.

[0394] Step 4: The server generates recommendations

[0395] The server invokes a generative AI model based on the stored preference information to generate recommendations.

[0396] The generative AI model analyzes user preferences and recommends the most suitable room and services. For example, it might recommend "Room 101 - Deluxe Room with Ocean View" to "guest 123."

[0397] Step 5: The server sends the recommendations to the device

[0398] The server sends the generated recommendations to the device, again using HTTP responses or APIs.

[0399] The server records the completion of the process for later review.

[0400] Step 6: The device displays the recommendations to the user

[0401] The terminal analyzes the recommendations received from the server and displays them on the user interface.

[0402] For example, a message such as "Recommended room: 101 - Deluxe Room with Ocean View" may be displayed on the user's screen.

[0403] Step 7: User reviews recommendations and selects action

[0404] The user checks the recommendations displayed on the terminal and selects the next action, such as making a reservation or making an inquiry.

[0405] For example, a user clicks on the "Book room 101" button.

[0406] Step 8: The server records the communication

[0407] The server logs communications, including any follow-up requests or inquiries from the user.

[0408] The recorded logs are stored with a timestamp and are used to improve the service and for future reference.

[0409] Example 1

[0410] 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."

[0411] Conventional service provision systems for accommodation facilities have difficulty effectively utilizing user preference information to provide personalized services. They also have issues with not being able to fully utilize communication history with users, which can lead to a decline in service quality. Furthermore, the accuracy and applicability of recommendations provided to users are insufficient, limiting the improvement of user experience.

[0412] 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.

[0413] In this invention, the server includes means for receiving user preference information, means for storing the preference information, and means for generating recommendations suited to the user based on the preference information. This enables the provision of highly accurate personalized services. The server further includes means for providing the recommendations to the user, means for recording communication with the user, communication means for sending and receiving the preference information and recommendations, and means for generating recommendations based on prompt sentences using a generative AI model. This enables the integration of the user's preference information and communication history to provide more accurate and comprehensive services.

[0414] "User preference information" is information about the room type and services that the user prefers at accommodation facilities.

[0415] "Server" means a computer system that receives and stores user preference information and generates and provides recommendations appropriate to the user.

[0416] The term "means" refers to a combination of hardware and software for realizing various functions in the present invention.

[0417] The "receiving means" is an interface for receiving preference information from a user and transmitting it to a server.

[0418] The "storing means" is a data storage device that records the received preference information as data and allows it to be referenced as needed.

[0419] The "means for generating recommendations" refers to algorithms and generative AI models for presenting services and room types that are suitable for the user based on the user's preference information.

[0420] A "means for providing recommendations" is an interface for notifying and displaying generated recommendations to the user.

[0421] "Means for recording communication with users" is a function for saving inquiries and expressions of intent from users as a log.

[0422] A "communication means" is a network interface for sending and receiving preference information and recommendations.

[0423] A "generative AI model" is an artificial intelligence model that analyzes user preference information based on prompt sentences and generates appropriate recommendations.

[0424] A "prompt" is an instruction given to a generative AI model that provides information for the model to generate recommendations.

[0425] The present invention provides a system for providing personalized services based on user preferences at accommodation facilities such as hotels. The system includes functions for receiving and storing user preference information, generating recommendations based on the preference information, providing the recommendations, and recording communications with the user.

[0426] Receiving and storing user preference information

[0427] The terminal first receives preference information from the user. The user uses the terminal to input their preferred room type and requests for specific services. For example, a user who prefers a "deluxe room" and a "high floor with an ocean view" inputs this information. The input information is sent from the terminal to the server. The server saves the received preference information in a database. For example, preference information such as "deluxe room" and "high floor with an ocean view" is saved for a guest ID of "guest123." This saving process uses a database management system such as MySQL.

[0428] Generate recommendations

[0429] The server generates recommendations based on the stored preference information. Using a generative AI model, it is possible to analyze the received preference information with high accuracy and provide appropriate recommendations. For example, a recommended room, "101 - Deluxe Room with Ocean View," is automatically generated for "guest123." The generative AI model operates by inputting a prompt statement using a Python script. For example, the generated recommendation can be obtained by inputting the prompt statement "User ID: guest123 Preference information: deluxe room, high floor with ocean view Recommendation to be generated: room."

[0430] Providing recommendations

[0431] Once the recommendation is generated, the server sends this information to the terminal, and the terminal displays it to the user. Specifically, the generated recommendation is sent to the terminal in JSON format, and the terminal displays it in the user interface. For example, the user can confirm "Recommended room: 101 - Deluxe Room with Ocean View" through the terminal.

[0432] Record of communication

[0433] Furthermore, all communication with users is logged on the server. When a user makes a check-in inquiry, the question and timestamp are saved as a log. This operation is performed using a log management system such as the ELK stack. For example, if "guest123" asks "What time is check-in?", the question and timestamp are recorded.

[0434] As a concrete example, consider the case where user "guest456" inputs preference information of "standard room" and "non-smoking" into the terminal. This information is sent to the server, which generates a recommendation of "202 - Standard Room with City View" based on the preferences of "guest456." This recommendation is displayed on the terminal, while the content of the inquiry with the user is also recorded. An example of a prompt sentence input to the generative AI model is as follows:

[0435] User ID: guest456

[0436] Preferences: Standard room, Non-smoking

[0437] Generate recommendations for: Rooms

[0438] As a result, the accommodation experience can be improved by providing personalized services based on the user's preferences. This system also contributes to increasing user satisfaction and the number of repeat visitors.

[0439] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0440] System program processing flow

[0441] Step 1: Enter and submit user preference information

[0442] The user inputs his / her preference information using the terminal.

[0443] Input: The user enters information such as "room type: deluxe" and "view: high floor with ocean view" on the device screen.

[0444] Operation: The device converts the input preference information into JSON format and sends a POST request to the server.

[0445] Output: The JSON data that is sent to the server.

[0446] json

[0447] {

[0448] "userID": "guest123",

[0449] "preferences": {

[0450] "roomType": "deluxe",

[0451] "view": "ocean high floor"

[0452] }

[0453] }

[0454] Step 2: Saving your preferences

[0455] The server analyzes the received preference information and stores it in a database.

[0456] Input: JSON data sent from the terminal.

[0457] Operation: The server parses the received JSON data, extracts the user ID and preference information, and stores them in a database.

[0458] Output: User preference information stored in a database.

[0459] sql

[0460] INSERT INTO user_preferences (user_id, room_type, view)

[0461] VALUES ("guest123", "deluxe", "ocean high floor");

[0462] Step 3: Generate recommendations

[0463] The server generates recommendations using a generative AI model based on the stored preference information.

[0464] Input: User preference information stored in the database ("guest123", "deluxe room", "high floor with sea view").

[0465] How it works: The server runs a Python script and inputs prompt statements to the generative AI model, which then generates recommendations based on the prompt statements.

[0466] Output: The generated recommendation (e.g., "101 - Deluxe Room with Ocean View").

[0467] python

[0468] prompt = "User ID: guest123 Preferences: deluxe room, high floor with ocean view Recommendation to generate: room"

[0469] generated_recommendation = "101 - Deluxe Room with Ocean View"

[0470] Step 4: Providing recommendations

[0471] The server sends the generated recommendations to the terminal, which displays them to the user.

[0472] Input: Generated recommendation ("101 - Deluxe Room with Ocean View").

[0473] Operation: The server converts the recommendations into JSON format and sends them to the device, which displays them in the user interface.

[0474] Output: Recommendations displayed on the user's terminal.

[0475] json

[0476] {

[0477] "recommendedRoom": "101 - Deluxe Room with Ocean View"

[0478] }

[0479] Step 5: Record your communications

[0480] The server logs all communication with the user.

[0481] Input: User queries and their responses.

[0482] How it works: The server stores the user query and the corresponding timestamp in a logging system, for example, using the ELK stack to store the data.

[0483] Output: The data that is logged.

[0484] json

[0485] {

[0486] "userID": "guest123",

[0487] "question": "What time is check-in?",

[0488] "timestamp": "2023-10-01 10:15:00"

[0489] }

[0490] This completes the explanation of the specific processing flow of the system and the operation of each step.

[0491] (Application example 1)

[0492] 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."

[0493] Conventional accommodation facilities and brick-and-mortar stores have systems that provide personalized recommendations based on the preferences and requests of each individual user, but these systems have difficulty effectively analyzing user preference information and providing accurate and immediate optimal recommendations.In addition, they have an issue in that they do not adequately record communication with users, making it difficult to use the information to improve services.

[0494] 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.

[0495] In this invention, the server includes means for receiving user preference information, means for storing the preference information, means for generating recommendations suited to the user based on the preference information, means for analyzing the user's preferences and generating the recommendations using a generative AI model in order to provide recommendations for specific products and services, and means for providing the recommendations to the user on a smartphone application. This makes it possible to instantly provide highly accurate recommendations based on the user's preferences and also record communications.

[0496] "Means for receiving user preference information" refers to a device or interface through which a user inputs information about their preferences and needs and the system receives that information.

[0497] "Means for storing preference information" refers to a device or mechanism that holds received user preference information and stores it in a database so that it can be referenced when necessary.

[0498] "Means for generating recommendations" refers to mechanisms or algorithms that automatically generate product or service suggestions appropriate to the user based on stored preference information.

[0499] "Means for generating recommendations using a generative AI model" refers to a mechanism that uses an artificial intelligence model to analyze user preferences and automatically generate optimal product and service recommendations.

[0500] "Means for providing recommendations to users on a smartphone application" refers to a system for displaying and notifying users of the generated recommendations via a smartphone application.

[0501] "Means for recording communication with users" refers to devices or mechanisms that store interactions with users and inquiries as logs so that they can be referenced or analyzed later.

[0502] A "database" refers to a group of data that manages the preference information of a large number of users individually, stores it efficiently, and organizes it in a searchable manner.

[0503] This invention is a system that provides personalized services based on user preference information at accommodation facilities and brick-and-mortar stores. Specifically, it uses various devices and software to receive user input information, generate recommendations based on that information, and provide them to the user.

[0504] Receiving user preference information

[0505] First, a user inputs their preferences using a smartphone application, including the types of products they prefer and specific requests (e.g., size, color, brand, etc.), which are then sent to the server via the app.

[0506] Saving your preferences

[0507] The server receives the preference information and stores it in a database. This database is used to individually manage preference information from multiple users, and for example, it stores information such as "sportswear, black, size L" for user ID "user123."

[0508] Generate recommendations

[0509] The server uses a generative AI model to generate appropriate recommendations based on the stored preference information. This AI model analyzes the input information and recommends the most suitable products and services. For example, it recommends a "black sports jacket in size L" to "user123."

[0510] Providing recommendations

[0511] The generated recommendations are sent from the server to a smartphone application, which then displays this information to the user immediately, allowing the user to review the recommended products and services. For example, a notification might appear in the app saying, "Today's recommended product: Black sports jacket, size L."

[0512] Record of communication

[0513] Furthermore, all inquiries and requests made by users through the app are recorded on the server. These records are saved as logs and used for future reference and analysis. For example, interactions such as "checking product inventory" and "reserving a fitting room" are recorded.

[0514] Hardware and software used

[0515] The system uses a smartphone, a cloud server, a database (e.g., SQL database), a generative AI model (e.g., GPT-3.5), etc. Data is sent from the device via the internet to the server, where it is analyzed and stored.

[0516] Examples of specific examples and prompts

[0517] For example, if a user is looking for sportswear, they might enter the following preferences:

[0518] "I want some sportswear. Nike is a good brand. Black, size L."

[0519] An example of a prompt sentence to input to the generative AI model is:

[0520] "I'm looking for Nike brand sportswear in size medium in black. Please generate the best product recommendations for this user."

[0521] Examples include:

[0522] The above is a specific embodiment for carrying out the present invention, and this system allows users to have a highly accurate personalized experience based on their individual preferences.

[0523] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0524] Step 1:

[0525] The user launches a smartphone application and inputs their own preference information. This preference information includes the type, brand, color, size, etc. of the product the user prefers. The input information is sent to the server by the application. The input data is in text format, for example, "I want sportswear. Nike is a good brand, the color is black, and the size is large." The preference information is sent to the server as output.

[0526] Step 2:

[0527] The server stores the received preference information in a database. The server converts the sent preference information into structured data (e.g., JSON format) and stores it in the database along with the user ID. For example, for user ID "user123," data such as "category: sportswear, brand: Nike, color: black, size: L" is stored. The input is the user's preference information, and the output is structured data stored in the database.

[0528] Step 3:

[0529] The server generates recommendations based on the stored preference information. To do this, it uses a generative AI model. The server inputs the stored preference information into the AI ​​model and analyzes it. For example, the input to the model is a prompt statement such as, "I am looking for black Nike sportswear in size L. Please generate the best product recommendations for this user." As an output, the model returns a recommendation of "black Nike sports jacket in size L."

[0530] Step 4:

[0531] The server sends the generated recommendations to a smartphone application. The application receives the recommendations and displays them on the user's screen. For example, a notification such as "Today's recommended item: Nike sports jacket in black, size large" is displayed on the user's smartphone. The input is the generated recommendations, and the output is the notification displayed in the smartphone application.

[0532] Step 5:

[0533] When a user makes an inquiry or request through the application, the server records this interaction. For example, the contents of a request such as "checking product availability" or "reserving a fitting room" are saved as a log. The server stores this in a database for future reference and analysis. The input is the user's inquiry, and the output is the recorded log data.

[0534] The above are the specific processing steps of the program in this system, and details of the input and output at each step.

[0535] 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.

[0536] The present invention is a system for providing personalized services based on the preferences and emotions of guests at hotels and other accommodation facilities. The system recognizes and stores user preference information and emotions, generates recommendations based on the preferences and emotions, provides the recommendations, and records communications with the user.

[0537] Receiving and storing user preferences and emotions

[0538] First, the device receives preference information and emotion information from the user. This includes the user inputting their preferred room type and requests for specific services through the device. The emotion engine recognizes the user's emotions through voice analysis and facial expression analysis. For example, the device receives information that the user prefers a "deluxe room" and a "high floor with an ocean view," as well as the emotion of "joy." This information is then sent from the device to the server.

[0539] The server stores the received preference information and emotion information in a database. For example, for a guest ID of "guest123," the following information is stored: "deluxe room," "high floor with ocean view," and "joy" as the emotion. This storage process allows each guest's preference information and emotion information to be managed individually.

[0540] Generate recommendations

[0541] The server generates recommendations based on the stored preference and emotion information. Using a generative AI model, the received preference and emotion information can be analyzed with high accuracy to provide appropriate recommendations. For example, taking into account the emotion of "joy" for "guest 123," the recommended room "101 - Deluxe Room with Ocean View" is automatically generated.

[0542] Providing recommendations

[0543] Once a recommendation is generated, the server sends this information to the device, which then displays it to the user. For example, the device might display "Recommended room: 101 - Deluxe Room with Ocean View," allowing the user to instantly confirm the recommendation.

[0544] Record of communication

[0545] Furthermore, all communication with the user is recorded on the server. For example, when a user makes a check-in inquiry, the content of the inquiry and emotional information are also saved as logs. The server stores this log for future reference and analysis.

[0546] Specific examples

[0547] If "guest456" inputs his preference information of "standard room" and "non-smoking," the emotion engine recognizes the emotion of "relief." This information is sent to the server, which generates a recommendation of "202 - Standard Room with City View" based on "guest456's" preference and emotion information and displays it on the device. At the same time, the user's inquiry and emotion are recorded and used to improve services in the future.

[0548] Use of emotional information

[0549] The server dynamically adjusts recommendations based on the recognized emotional information. For example, if a user is feeling stressed, more personalized services will be provided, such as recommending a quiet room or relaxation services. Furthermore, by including emotional information in communication records, it is possible to improve the quality of services and respond quickly to problems.

[0550] As described above, the system of the present invention not only provides personalized services based on the user's preferences, but also realizes even more personalized services by adding emotional information, thereby significantly improving the accommodation experience.

[0551] The processing flow will be explained below.

[0552] Step 1: The device receives the user's preference information and emotion information.

[0553] The terminal provides an interface for accepting input from the user, who inputs information such as "room type: deluxe" and "special request: high floor, ocean view room" on the screen.

[0554] The emotion engine analyzes the user's voice and recognizes their emotions. For example, if the user expresses the emotion "joy," that information is recorded on the device.

[0555] Step 2: The device sends preference information and emotion information to the server.

[0556] The device collects the received user preference information and emotion information into packets and sends them to the server, specifically using HTTP requests or API calls.

[0557] The terminal waits for a response from the server to confirm whether the transmission was successful.

[0558] Step 3: The server receives and stores preference and emotion information.

[0559] The server receives the packets sent from the terminal and extracts the preference information and emotion information therefrom.

[0560] The server stores the extracted information in a database managed for each guest. For example, for a guest ID of "guest123," information such as "room type: deluxe," "special request: high floor, ocean view room," and "emotion: joy" is stored.

[0561] Step 4: The server generates recommendations

[0562] The server invokes a generative AI model based on the stored preference and emotion information to generate recommendations.

[0563] The generative AI model analyzes user preferences and emotions to recommend the most suitable room and service. For example, for guest 123, it would recommend "101 - Deluxe Room with Ocean View" based on the emotion "joy."

[0564] Step 5: The server sends the recommendations to the device

[0565] The server sends the generated recommendations to the device, again using HTTP responses or APIs.

[0566] The server records the completion of the process for later review.

[0567] Step 6: The device displays the recommendations to the user

[0568] The terminal analyzes the recommendations received from the server and displays them on the user interface.

[0569] For example, a message such as "Recommended room: 101 - Deluxe Room with Ocean View" may be displayed on the user's screen.

[0570] Step 7: User reviews recommendations and selects action

[0571] The user checks the recommendations displayed on the terminal and selects the next action, such as making a reservation or making an inquiry.

[0572] For example, a user clicks on the "Book room 101" button.

[0573] Step 8: The server records the communication

[0574] The server logs the communication content, including any additional requests or inquiries from the user, along with emotional information.

[0575] The recorded logs are stored with a timestamp and are used to improve the service and for future reference.

[0576] Step 9: The server uses the sentiment information to adjust the recommendations

[0577] The server dynamically adjusts recommendations based on the stored emotional information: for example, if the user is perceived as feeling "stressed," the server may recommend a quiet room or relaxation services.

[0578] This allows for personalized services tailored to the user's emotional state.

[0579] Example 2

[0580] 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."

[0581] Conventional service provision systems in accommodation facilities mainly provide services based on user preferences, but do not provide personalized services that take into account the user's emotional information. This makes it difficult to provide detailed responses to maximize user satisfaction. In addition, there is no adequate system in place to record communication with users and use it to improve services in the future.

[0582] 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.

[0583] In this invention, the server includes means for receiving user preference information, means for saving the preference information, means for recognizing and saving emotion information, means for generating recommendations suited to the user based on the preference information and emotion information, means for providing the recommendations to the user, and means for recording communication with the user, thereby enabling the provision of more personalized services based on individual preference information and emotion information.

[0584] "User" means any person or group of people using the services of the Accommodation Facility.

[0585] "Preference information" is information that specifically indicates the user's preferences and wishes, and includes the type of room and the conditions for using the facility.

[0586] "Emotion information" is information that indicates the user's emotional state, and is obtained by voice analysis, facial expression analysis, etc.

[0587] "Means for receiving" refers to the functions and devices for obtaining the necessary information from the user through the terminal.

[0588] "Means for storing" refers to the functions and devices for storing received information in a database or the like.

[0589] "Recommendations" refers to suggestions for providing appropriate services and rooms based on the user's preference information and emotional information.

[0590] "Generating means" refers to the functionality or devices used to create recommendations based on stored information, including generative AI models.

[0591] "Means for providing" refers to the functions and devices for communicating generated recommendations to users.

[0592] "Means for recording" refers to the functions and devices for saving the content of communication with the user.

[0593] A "generative AI model" refers to an algorithm that uses machine learning and natural language processing to analyze user preference and emotional information and generate appropriate recommendations.

[0594] The present invention provides a system for providing personalized services based on the preferences and emotions of guests at accommodation facilities such as hotels. The system recognizes and stores user preference information and emotions, generates recommendations based on the user preference information and emotions, and provides the recommendations and records communication with the user. The following describes in detail the embodiments of the present invention.

[0595] Receiving and storing user preferences and emotions

[0596] First, the terminal receives preference and emotion information from the user. This process involves the user inputting their preferred room type and requests for specific services through the terminal. The emotion engine then recognizes the user's emotions through voice and facial expression analysis. The terminal then sends this information to the server. The server stores the received preference and emotion information in a database and manages each guest's information individually.

[0597] For example, if a user inputs their preference for a "deluxe room" and a "high floor with an ocean view" into the device, the emotion engine will recognize the emotion of "joy." This information is sent from the device to the server and stored on the server.

[0598] Generate recommendations

[0599] The server generates recommendations based on the stored preference and emotion information. A generative AI model is used in this process. The generative AI model analyzes the user's preference and emotion information with high accuracy and provides appropriate recommendations. As a specific example, the server considers the emotion of "happiness" for "guest123" and generates the recommended room, "101 - Deluxe Room with Ocean View."

[0600] Examples of sentences that can be used as prompts are:

[0601] User ID: guest123

[0602] Preference: Deluxe room, high floor with sea view

[0603] Emotion: Joy

[0604] Generate recommendations.

[0605] Providing recommendations

[0606] The generated recommendation is sent from the server to the device, and the device displays it to the user. For example, the server sends a recommendation of "101 - Deluxe Room with Ocean View" to the device, and the device displays "Recommended room: 101 - Deluxe Room with Ocean View".

[0607] Record of communication

[0608] The server also records all communication with the user. For example, when a user makes a check-in inquiry, the content of the inquiry and emotional information are recorded. This record is saved in a log file or database for later reference and analysis.

[0609] Use of emotional information

[0610] The server dynamically adjusts recommendations based on the recognized emotional information. For example, if a user is feeling stressed, more personalized services can be provided, such as recommending a quiet room or relaxation services. Furthermore, by including emotional information in communication records, it is possible to improve the quality of services and respond quickly to problems.

[0611] As described above, the system of the present invention not only provides personalized services based on the user's preferences, but also realizes even more personalized services by adding emotional information, thereby significantly improving the accommodation experience.

[0612] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0613] Step 1:

[0614] Receiving user preference and emotion information

[0615] The user operates the terminal and inputs preference information such as the preferred room type and desired facilities.

[0616] Input: Preference information entered by the user into the device (e.g., deluxe room, high floor with ocean view).

[0617] Specific operation: The user selects preference information on the device screen by tapping, clicking, or entering text.

[0618] At the same time, the device uses its built-in emotion engine to capture the user's face with a camera and perform voice and facial expression analysis to collect emotional information.

[0619] Input: Video and audio of the user's face.

[0620] How it works: The device's camera and microphone capture the user's face and voice, and the built-in emotion analysis engine determines emotions such as "joy" or "relief."

[0621] Output: Preference information and emotion information are temporarily stored inside the device.

[0622] Step 2:

[0623] Transmission and storage of received information

[0624] The terminal transmits the collected preference information and emotion information to the server.

[0625] Input: Preference and emotion information stored on the device.

[0626] Specific operation: The device sends data to the server using an HTTP request or other communication protocol.

[0627] The server stores the received information in a database.

[0628] Input: Preference and emotion information received by the server.

[0629] What happens: The server inserts information into the database using an SQL query.

[0630] Output: The information is stored in a database, and individual preference and emotion information for each user is managed.

[0631] Step 3:

[0632] Generate recommendations

[0633] The server generates recommendations using a generative AI model based on the stored preference and emotion information.

[0634] Input: Preference and emotion information stored in a database.

[0635] Specific operation: The server sends a prompt to the generative AI model to generate recommendations.

[0636] Example prompt sentence:

[0637] User ID: guest123

[0638] Preference: Deluxe room, high floor with sea view

[0639] Emotion: Joy

[0640] Generate recommendations.

[0641] Output: Recommended room and service information (e.g. 101 - Deluxe Room with Ocean View).

[0642] Step 4:

[0643] Providing recommendations

[0644] The server sends the generated recommendations to the device.

[0645] Input: Server-generated recommendations.

[0646] Specific operation: The server sends the recommendation information to the device using an HTTP response or other communication protocol.

[0647] The device displays the received recommendations to the user.

[0648] Input: The recommendations received from the server.

[0649] Specific behavior: The device displays "Recommended room: 101 - Deluxe Room with Ocean View" on the display screen.

[0650] Output: Recommendations visually communicated to the user.

[0651] Step 5:

[0652] Record of communication

[0653] The server records all communication with the user.

[0654] Input: User inquiry, dialogue log, and emotional information.

[0655] Specific operation: The server stores the received information in a log file or database.

[0656] Output: Logged communications with users.

[0657] Through the above process, highly personalized services based on the user's preference information and emotional information are provided.

[0658] (Application example 2)

[0659] 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."

[0660] Traditional services provided in brick-and-mortar stores often provided uniform services without considering information about users' preferences or emotions. This meant that users took a long time to find products or services that suited them, resulting in an unsatisfactory experience. Furthermore, it was difficult to respond to real-time changes in users' emotions using only user purchase history and behavioral data, so further improvements in services were required.

[0661] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user preference information, means for saving preference information and emotion information, means for generating recommendations suitable for the user based on the preference information, means for receiving and analyzing user emotion information, means for dynamically adjusting the recommendations based on the emotion information, means for recording communication with the user, and means for generating prompt sentences using a generative AI model when generating recommendations and generating recommendations based on the analysis results. This makes it possible to provide personalized recommendations in real time based on the user's preferences and emotions.

[0662] The "means for receiving user preference information" refers to equipment or software for acquiring preference information regarding specific products or services from a user.

[0663] The "means for storing preference information and emotional information" refers to storage facilities such as databases and servers that continuously store and manage user preference information and emotional information.

[0664] The "means for generating recommendations suited to the user based on preference information" refers to an algorithm or program that analyzes the stored preference information and selects the most suitable products and services for the user.

[0665] "Means for receiving and analyzing user emotional information" refers to devices and software for recognizing and analyzing a user's emotions from their voice and facial expressions.

[0666] A "means for dynamically adjusting recommendations based on emotional information" is a system or algorithm that changes recommendations in real time based on analyzed emotional information.

[0667] "Means for recording communication with users" refers to devices or software that record and save conversations with users and inquiries.

[0668] "Means for generating prompt sentences using a generative AI model when generating recommendations, and generating recommendations based on the analysis results" refers to algorithms or programs that create prompt sentences using a generative AI model based on user preference information and emotional information, and then use the analysis results to generate appropriate recommendations.

[0669] A specific embodiment for carrying out the present invention will be described.

[0670] Users input preference information such as their preferred product categories, specific brands, and price ranges through a smartphone application. In addition, the smartphone's camera and microphone are used to capture facial expressions and voices, and the user's emotional information is analyzed.

[0671] The server collects user preference information and emotion information and stores this data in a database. The stored data includes identifiers for multiple users and is managed individually.

[0672] The server uses a generative AI model to generate prompts and recommendations based on the collected and stored data. Specifically, it generates prompts such as the following:

[0673] User prefers: fashion, and their current emotion is: joy. What should we recommend?

[0674] The server inputs the generated prompt sentences into the AI ​​model and obtains recommendations as an analysis result, which are displayed in real time in the application on the user's smartphone.

[0675] As a specific example, if a user likes "fashion" and the camera recognizes an expression of "joy," the server stores that information and recommends "the latest fashion trend items" to the user.

[0676] It also has the ability to dynamically adjust recommendations based on the user's emotional state: for example, if the user is feeling stressed, products with a relaxation effect (e.g., aroma candles or relaxing music) will be recommended.

[0677] Furthermore, all interactions and inquiries with users are recorded and used to improve services in the future, as data analysis will enable more precise service provision.

[0678] This system allows users to receive the most appropriate products and services based on their emotions and preferences at the time, greatly improving the shopping experience in physical stores.

[0679] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0680] Step 1:

[0681] A user launches a smartphone application and inputs preference information such as their preferred product categories, specific brands, and price ranges. The input data is temporarily stored in the device's memory. When the user accesses the application via the camera and microphone, the device captures the user's facial expressions and voice and analyzes their emotional information. Input: User's preference information and emotional information. Output: Analysis results of preference information and emotional information.

[0682] Step 2:

[0683] The device sends the analyzed preference information and emotion information to the server. The server stores the received data in a database, where each user's identifier (ID) is also recorded. Input: preference information and emotion information. Output: user information stored in the database.

[0684] Step 3:

[0685] The server retrieves the user's preference and emotion information stored in the database and generates a prompt using a generative AI model. The prompt reflects the user's preference and emotion. For example, the generated sentence is "User prefers: fashion, and their current emotion is: joy. What should we recommend?" Input: Preference information and emotion information. Output: Generated prompt.

[0686] Step 4:

[0687] The server inputs the generated prompt into the generative AI model and obtains recommendations as the analysis result. At this time, the AI ​​model recommends the most suitable products and services to the user based on the prompt. Input: Prompt. Output: Recommendations.

[0688] Step 5:

[0689] The server sends the obtained recommendations to the device, which then displays them on the application screen. The user can check the recommended products and services in real time. Input: Recommendations. Output: Recommendations displayed in the application.

[0690] Step 6:

[0691] The products and services selected by the user through the application, or the content of inquiries made, are sent to the server, which records these communications. This information will be used to improve future services. Input: User selections and inquiries. Output: Communication content recorded on the server.

[0692] Step 7:

[0693] The server analyzes changes in the user's emotions in real time and dynamically adjusts recommendations as needed. For example, if the server determines that the user is feeling stressed, it will re-recommend products with a relaxation effect. Input: Real-time emotional information. Output: Adjusted recommendations.

[0694] 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.

[0695] 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.

[0696] 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.

[0697] [Third embodiment]

[0698] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0699] 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.

[0700] 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).

[0701] 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.

[0702] 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.

[0703] 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).

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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."

[0710] The present invention is a system for providing personalized service based on guest preferences at a hotel or other lodging facility, including receiving and storing user preference information, generating recommendations based on the preference information, providing the recommendations, and recording communications with the user.

[0711] Receiving and storing user preference information

[0712] First, the device receives preference information directly from the user. This includes the user inputting their preferred room type and requests for specific services through the device. For example, the device receives information that the user prefers a "deluxe room" and a "high floor with an ocean view." This information is then sent from the device to the server.

[0713] The server stores the received preference information in a database. For example, for a guest ID of "guest123," "deluxe room" and "high floor with ocean view" are stored as preference information. This storage process allows each guest's preference information to be managed individually.

[0714] Generate recommendations

[0715] The server generates recommendations based on the stored preference information. Using a generative AI model, the received preference information can be analyzed with high precision to provide appropriate recommendations. For example, the recommended room for "guest 123" is automatically generated as "101 - Deluxe Room with Ocean View."

[0716] Providing recommendations

[0717] Once a recommendation is generated, the server sends this information to the device, which then displays it to the user. For example, the device might display "Recommended room: 101 - Deluxe Room with Ocean View," allowing the user to instantly confirm the recommendation.

[0718] Record of communication

[0719] In addition, all communication with the user is recorded on the server. For example, when a user makes a check-in inquiry, the content and timestamp of the inquiry are saved as a log. The server stores this log for future reference and analysis.

[0720] For example, if "guest456" inputs his preference for a "standard room" and "non-smoking," this information is sent to the server. The server generates a recommendation for "202 - Standard Room with City View" based on "guest456's" preferences and displays it on the terminal. At the same time, the content of the user's inquiry is recorded and used to improve services in the future.

[0721] As described above, the system of the present invention can provide personalized services based on the user's preferences and improve the accommodation experience.

[0722] The processing flow will be explained below.

[0723] Step 1: The device receives user preference information

[0724] The terminal provides an interface for accepting input from the user. For example, the user inputs information such as "room type: deluxe" and "special request: high floor, ocean view room" on the screen.

[0725] The terminal temporarily stores the input information in its memory.

[0726] Step 2: The device sends preference information to the server

[0727] The device collects the received user preference information into packets and sends them to the server, specifically using HTTP requests or API calls.

[0728] The terminal waits for a response from the server to confirm that the transmission was successful.

[0729] Step 3: The server receives and stores the preference information

[0730] The server receives the packets sent from the terminal and extracts the preference information therefrom.

[0731] The server stores the extracted preference information in a database managed for each guest. For example, for a guest ID of "guest123," information such as "room type: deluxe" and "special request: high floor, room with ocean view" is stored.

[0732] Step 4: The server generates recommendations

[0733] The server invokes a generative AI model based on the stored preference information to generate recommendations.

[0734] The generative AI model analyzes user preferences and recommends the most suitable room and services. For example, it might recommend "Room 101 - Deluxe Room with Ocean View" to "guest 123."

[0735] Step 5: The server sends the recommendations to the device

[0736] The server sends the generated recommendations to the device, again using HTTP responses or APIs.

[0737] The server records the completion of the process for later review.

[0738] Step 6: The device displays the recommendations to the user

[0739] The terminal analyzes the recommendations received from the server and displays them on the user interface.

[0740] For example, a message such as "Recommended room: 101 - Deluxe Room with Ocean View" may be displayed on the user's screen.

[0741] Step 7: User reviews recommendations and selects action

[0742] The user checks the recommendations displayed on the terminal and selects the next action, such as making a reservation or making an inquiry.

[0743] For example, a user clicks on the "Book room 101" button.

[0744] Step 8: The server records the communication

[0745] The server logs communications, including any follow-up requests or inquiries from the user.

[0746] The recorded logs are stored with a timestamp and are used to improve the service and for future reference.

[0747] Example 1

[0748] 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."

[0749] Conventional service provision systems for accommodation facilities have difficulty effectively utilizing user preference information to provide personalized services. They also have issues with not being able to fully utilize communication history with users, which can lead to a decline in service quality. Furthermore, the accuracy and applicability of recommendations provided to users are insufficient, limiting the improvement of user experience.

[0750] 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.

[0751] In this invention, the server includes means for receiving user preference information, means for storing the preference information, and means for generating recommendations suited to the user based on the preference information. This enables the provision of highly accurate personalized services. The server further includes means for providing the recommendations to the user, means for recording communication with the user, communication means for sending and receiving the preference information and recommendations, and means for generating recommendations based on prompt sentences using a generative AI model. This enables the integration of the user's preference information and communication history to provide more accurate and comprehensive services.

[0752] "User preference information" is information about the room type and services that the user prefers at accommodation facilities.

[0753] "Server" means a computer system that receives and stores user preference information and generates and provides recommendations appropriate to the user.

[0754] The term "means" refers to a combination of hardware and software for realizing various functions in the present invention.

[0755] The "receiving means" is an interface for receiving preference information from a user and transmitting it to a server.

[0756] The "storing means" is a data storage device that records the received preference information as data and allows it to be referenced as needed.

[0757] The "means for generating recommendations" refers to algorithms and generative AI models for presenting services and room types that are suitable for the user based on the user's preference information.

[0758] A "means for providing recommendations" is an interface for notifying and displaying generated recommendations to the user.

[0759] "Means for recording communication with users" is a function for saving inquiries and expressions of intent from users as a log.

[0760] A "communication means" is a network interface for sending and receiving preference information and recommendations.

[0761] A "generative AI model" is an artificial intelligence model that analyzes user preference information based on prompt sentences and generates appropriate recommendations.

[0762] A "prompt" is an instruction given to a generative AI model that provides information for the model to generate recommendations.

[0763] The present invention provides a system for providing personalized services based on user preferences at accommodation facilities such as hotels. The system includes functions for receiving and storing user preference information, generating recommendations based on the preference information, providing the recommendations, and recording communications with the user.

[0764] Receiving and storing user preference information

[0765] The terminal first receives preference information from the user. The user uses the terminal to input their preferred room type and requests for specific services. For example, a user who prefers a "deluxe room" and a "high floor with an ocean view" inputs this information. The input information is sent from the terminal to the server. The server saves the received preference information in a database. For example, preference information such as "deluxe room" and "high floor with an ocean view" is saved for a guest ID of "guest123." This saving process uses a database management system such as MySQL.

[0766] Generate recommendations

[0767] The server generates recommendations based on the stored preference information. Using a generative AI model, it is possible to analyze the received preference information with high accuracy and provide appropriate recommendations. For example, a recommended room, "101 - Deluxe Room with Ocean View," is automatically generated for "guest123." The generative AI model operates by inputting a prompt statement using a Python script. For example, the generated recommendation can be obtained by inputting the prompt statement "User ID: guest123 Preference information: deluxe room, high floor with ocean view Recommendation to be generated: room."

[0768] Providing recommendations

[0769] Once the recommendation is generated, the server sends this information to the terminal, and the terminal displays it to the user. Specifically, the generated recommendation is sent to the terminal in JSON format, and the terminal displays it in the user interface. For example, the user can confirm "Recommended room: 101 - Deluxe Room with Ocean View" through the terminal.

[0770] Record of communication

[0771] Furthermore, all communication with users is logged on the server. When a user makes a check-in inquiry, the question and timestamp are saved as a log. This operation is performed using a log management system such as the ELK stack. For example, if "guest123" asks "What time is check-in?", the question and timestamp are recorded.

[0772] As a concrete example, consider the case where user "guest456" inputs preference information of "standard room" and "non-smoking" into the terminal. This information is sent to the server, which generates a recommendation of "202 - Standard Room with City View" based on the preferences of "guest456." This recommendation is displayed on the terminal, while the content of the inquiry with the user is also recorded. An example of a prompt sentence input to the generative AI model is as follows:

[0773] User ID: guest456

[0774] Preferences: Standard room, Non-smoking

[0775] Generate recommendations for: Rooms

[0776] As a result, the accommodation experience can be improved by providing personalized services based on the user's preferences. This system also contributes to increasing user satisfaction and the number of repeat visitors.

[0777] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0778] System program processing flow

[0779] Step 1: Enter and submit user preference information

[0780] The user inputs his / her preference information using the terminal.

[0781] Input: The user enters information such as "room type: deluxe" and "view: high floor with ocean view" on the device screen.

[0782] Operation: The device converts the input preference information into JSON format and sends a POST request to the server.

[0783] Output: The JSON data that is sent to the server.

[0784] json

[0785] {

[0786] "userID": "guest123",

[0787] "preferences": {

[0788] "roomType": "deluxe",

[0789] "view": "ocean high floor"

[0790] }

[0791] }

[0792] Step 2: Saving your preferences

[0793] The server analyzes the received preference information and stores it in a database.

[0794] Input: JSON data sent from the terminal.

[0795] Operation: The server parses the received JSON data, extracts the user ID and preference information, and stores them in a database.

[0796] Output: User preference information stored in a database.

[0797] sql

[0798] INSERT INTO user_preferences (user_id, room_type, view)

[0799] VALUES ("guest123", "deluxe", "ocean high floor");

[0800] Step 3: Generate recommendations

[0801] The server generates recommendations using a generative AI model based on the stored preference information.

[0802] Input: User preference information stored in the database ("guest123", "deluxe room", "high floor with sea view").

[0803] How it works: The server runs a Python script and inputs prompt statements to the generative AI model, which then generates recommendations based on the prompt statements.

[0804] Output: The generated recommendation (e.g., "101 - Deluxe Room with Ocean View").

[0805] python

[0806] prompt = "User ID: guest123 Preferences: deluxe room, high floor with ocean view Recommendation to generate: room"

[0807] generated_recommendation = "101 - Deluxe Room with Ocean View"

[0808] Step 4: Providing recommendations

[0809] The server sends the generated recommendations to the terminal, which displays them to the user.

[0810] Input: Generated recommendation ("101 - Deluxe Room with Ocean View").

[0811] Operation: The server converts the recommendations into JSON format and sends them to the device, which displays them in the user interface.

[0812] Output: Recommendations displayed on the user's terminal.

[0813] json

[0814] {

[0815] "recommendedRoom": "101 - Deluxe Room with Ocean View"

[0816] }

[0817] Step 5: Record your communications

[0818] The server logs all communication with the user.

[0819] Input: User queries and their responses.

[0820] How it works: The server stores the user query and the corresponding timestamp in a logging system, for example, using the ELK stack to store the data.

[0821] Output: The data that is logged.

[0822] json

[0823] {

[0824] "userID": "guest123",

[0825] "question": "What time is check-in?",

[0826] "timestamp": "2023-10-01 10:15:00"

[0827] }

[0828] This completes the explanation of the specific processing flow of the system and the operation of each step.

[0829] (Application example 1)

[0830] 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."

[0831] Conventional accommodation facilities and brick-and-mortar stores have systems that provide personalized recommendations based on the preferences and requests of each individual user, but these systems have difficulty effectively analyzing user preference information and providing accurate and immediate optimal recommendations.In addition, they have an issue in that they do not adequately record communication with users, making it difficult to use the information to improve services.

[0832] 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.

[0833] In this invention, the server includes means for receiving user preference information, means for storing the preference information, means for generating recommendations suited to the user based on the preference information, means for analyzing the user's preferences and generating the recommendations using a generative AI model in order to provide recommendations for specific products and services, and means for providing the recommendations to the user on a smartphone application. This makes it possible to instantly provide highly accurate recommendations based on the user's preferences and also record communications.

[0834] "Means for receiving user preference information" refers to a device or interface through which a user inputs information about their preferences and needs and the system receives that information.

[0835] "Means for storing preference information" refers to a device or mechanism that holds received user preference information and stores it in a database so that it can be referenced when necessary.

[0836] "Means for generating recommendations" refers to mechanisms or algorithms that automatically generate product or service suggestions appropriate to the user based on stored preference information.

[0837] "Means for generating recommendations using a generative AI model" refers to a mechanism that uses an artificial intelligence model to analyze user preferences and automatically generate optimal product and service recommendations.

[0838] "Means for providing recommendations to users on a smartphone application" refers to a system for displaying and notifying users of the generated recommendations via a smartphone application.

[0839] "Means for recording communication with users" refers to devices or mechanisms that store interactions with users and inquiries as logs so that they can be referenced or analyzed later.

[0840] A "database" refers to a group of data that manages the preference information of a large number of users individually, stores it efficiently, and organizes it in a searchable manner.

[0841] This invention is a system that provides personalized services based on user preference information at accommodation facilities and brick-and-mortar stores. Specifically, it uses various devices and software to receive user input information, generate recommendations based on that information, and provide them to the user.

[0842] Receiving user preference information

[0843] First, a user inputs their preferences using a smartphone application, including the types of products they prefer and specific requests (e.g., size, color, brand, etc.), which are then sent to the server via the app.

[0844] Saving your preferences

[0845] The server receives the preference information and stores it in a database. This database is used to individually manage preference information from multiple users, and for example, it stores information such as "sportswear, black, size L" for user ID "user123."

[0846] Generate recommendations

[0847] The server uses a generative AI model to generate appropriate recommendations based on the stored preference information. This AI model analyzes the input information and recommends the most suitable products and services. For example, it recommends a "black sports jacket in size L" to "user123."

[0848] Providing recommendations

[0849] The generated recommendations are sent from the server to a smartphone application, which then displays this information to the user immediately, allowing the user to review the recommended products and services. For example, a notification might appear in the app saying, "Today's recommended product: Black sports jacket, size L."

[0850] Record of communication

[0851] Furthermore, all inquiries and requests made by users through the app are recorded on the server. These records are saved as logs and used for future reference and analysis. For example, interactions such as "checking product inventory" and "reserving a fitting room" are recorded.

[0852] Hardware and software used

[0853] The system uses a smartphone, a cloud server, a database (e.g., SQL database), a generative AI model (e.g., GPT-3.5), etc. Data is sent from the device via the internet to the server, where it is analyzed and stored.

[0854] Examples of specific examples and prompts

[0855] For example, if a user is looking for sportswear, they might enter the following preferences:

[0856] "I want some sportswear. Nike is a good brand. Black, size L."

[0857] An example of a prompt sentence to input to the generative AI model is:

[0858] "I'm looking for Nike brand sportswear in size medium in black. Please generate the best product recommendations for this user."

[0859] Examples include:

[0860] The above is a specific embodiment for carrying out the present invention, and this system allows users to have a highly accurate personalized experience based on their individual preferences.

[0861] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0862] Step 1:

[0863] The user launches a smartphone application and inputs their own preference information. This preference information includes the type, brand, color, size, etc. of the product the user prefers. The input information is sent to the server by the application. The input data is in text format, for example, "I want sportswear. Nike is a good brand, the color is black, and the size is large." The preference information is sent to the server as output.

[0864] Step 2:

[0865] The server stores the received preference information in a database. The server converts the sent preference information into structured data (e.g., JSON format) and stores it in the database along with the user ID. For example, for user ID "user123," data such as "category: sportswear, brand: Nike, color: black, size: L" is stored. The input is the user's preference information, and the output is structured data stored in the database.

[0866] Step 3:

[0867] The server generates recommendations based on the stored preference information. To do this, it uses a generative AI model. The server inputs the stored preference information into the AI ​​model and analyzes it. For example, the input to the model is a prompt statement such as, "I am looking for black Nike sportswear in size L. Please generate the best product recommendations for this user." As an output, the model returns a recommendation of "black Nike sports jacket in size L."

[0868] Step 4:

[0869] The server sends the generated recommendations to a smartphone application. The application receives the recommendations and displays them on the user's screen. For example, a notification such as "Today's recommended item: Nike sports jacket in black, size large" is displayed on the user's smartphone. The input is the generated recommendations, and the output is the notification displayed in the smartphone application.

[0870] Step 5:

[0871] When a user makes an inquiry or request through the application, the server records this interaction. For example, the contents of a request such as "checking product availability" or "reserving a fitting room" are saved as a log. The server stores this in a database for future reference and analysis. The input is the user's inquiry, and the output is the recorded log data.

[0872] The above are the specific processing steps of the program in this system, and details of the input and output at each step.

[0873] 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.

[0874] The present invention is a system for providing personalized services based on the preferences and emotions of guests at hotels and other accommodation facilities. The system recognizes and stores user preference information and emotions, generates recommendations based on the preferences and emotions, provides the recommendations, and records communications with the user.

[0875] Receiving and storing user preferences and emotions

[0876] First, the device receives preference information and emotion information from the user. This includes the user inputting their preferred room type and requests for specific services through the device. The emotion engine recognizes the user's emotions through voice analysis and facial expression analysis. For example, the device receives information that the user prefers a "deluxe room" and a "high floor with an ocean view," as well as the emotion of "joy." This information is then sent from the device to the server.

[0877] The server stores the received preference information and emotion information in a database. For example, for a guest ID of "guest123," the following information is stored: "deluxe room," "high floor with ocean view," and "joy" as the emotion. This storage process allows each guest's preference information and emotion information to be managed individually.

[0878] Generate recommendations

[0879] The server generates recommendations based on the stored preference and emotion information. Using a generative AI model, the received preference and emotion information can be analyzed with high accuracy to provide appropriate recommendations. For example, taking into account the emotion of "joy" for "guest 123," the recommended room "101 - Deluxe Room with Ocean View" is automatically generated.

[0880] Providing recommendations

[0881] Once a recommendation is generated, the server sends this information to the device, which then displays it to the user. For example, the device might display "Recommended room: 101 - Deluxe Room with Ocean View," allowing the user to instantly confirm the recommendation.

[0882] Record of communication

[0883] Furthermore, all communication with the user is recorded on the server. For example, when a user makes a check-in inquiry, the content of the inquiry and emotional information are also saved as logs. The server stores this log for future reference and analysis.

[0884] Specific examples

[0885] If "guest456" inputs his preference information of "standard room" and "non-smoking," the emotion engine recognizes the emotion of "relief." This information is sent to the server, which generates a recommendation of "202 - Standard Room with City View" based on "guest456's" preference and emotion information and displays it on the device. At the same time, the user's inquiry and emotion are recorded and used to improve services in the future.

[0886] Use of emotional information

[0887] The server dynamically adjusts recommendations based on the recognized emotional information. For example, if a user is feeling stressed, more personalized services will be provided, such as recommending a quiet room or relaxation services. Furthermore, by including emotional information in communication records, it is possible to improve the quality of services and respond quickly to problems.

[0888] As described above, the system of the present invention not only provides personalized services based on the user's preferences, but also realizes even more personalized services by adding emotional information, thereby significantly improving the accommodation experience.

[0889] The processing flow will be explained below.

[0890] Step 1: The device receives the user's preference information and emotion information.

[0891] The terminal provides an interface for accepting input from the user, who inputs information such as "room type: deluxe" and "special request: high floor, ocean view room" on the screen.

[0892] The emotion engine analyzes the user's voice and recognizes their emotions. For example, if the user expresses the emotion "joy," that information is recorded on the device.

[0893] Step 2: The device sends preference information and emotion information to the server.

[0894] The device collects the received user preference information and emotion information into packets and sends them to the server, specifically using HTTP requests or API calls.

[0895] The terminal waits for a response from the server to confirm whether the transmission was successful.

[0896] Step 3: The server receives and stores preference and emotion information.

[0897] The server receives the packets sent from the terminal and extracts the preference information and emotion information therefrom.

[0898] The server stores the extracted information in a database managed for each guest. For example, for a guest ID of "guest123," information such as "room type: deluxe," "special request: high floor, ocean view room," and "emotion: joy" is stored.

[0899] Step 4: The server generates recommendations

[0900] The server invokes a generative AI model based on the stored preference and emotion information to generate recommendations.

[0901] The generative AI model analyzes user preferences and emotions to recommend the most suitable room and service. For example, for guest 123, it would recommend "101 - Deluxe Room with Ocean View" based on the emotion "joy."

[0902] Step 5: The server sends the recommendations to the device

[0903] The server sends the generated recommendations to the device, again using HTTP responses or APIs.

[0904] The server records the completion of the process for later review.

[0905] Step 6: The device displays the recommendations to the user

[0906] The terminal analyzes the recommendations received from the server and displays them on the user interface.

[0907] For example, a message such as "Recommended room: 101 - Deluxe Room with Ocean View" may be displayed on the user's screen.

[0908] Step 7: User reviews recommendations and selects action

[0909] The user checks the recommendations displayed on the terminal and selects the next action, such as making a reservation or making an inquiry.

[0910] For example, a user clicks on the "Book room 101" button.

[0911] Step 8: The server records the communication

[0912] The server logs the communication content, including any additional requests or inquiries from the user, along with emotional information.

[0913] The recorded logs are stored with a timestamp and are used to improve the service and for future reference.

[0914] Step 9: The server uses the sentiment information to adjust the recommendations

[0915] The server dynamically adjusts recommendations based on the stored emotional information: for example, if the user is perceived as feeling "stressed," the server may recommend a quiet room or relaxation services.

[0916] This allows for personalized services tailored to the user's emotional state.

[0917] Example 2

[0918] 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."

[0919] Conventional service provision systems in accommodation facilities mainly provide services based on user preferences, but do not provide personalized services that take into account the user's emotional information. This makes it difficult to provide detailed responses to maximize user satisfaction. In addition, there is no adequate system in place to record communication with users and use it to improve services in the future.

[0920] 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.

[0921] In this invention, the server includes means for receiving user preference information, means for saving the preference information, means for recognizing and saving emotion information, means for generating recommendations suited to the user based on the preference information and emotion information, means for providing the recommendations to the user, and means for recording communication with the user, thereby enabling the provision of more personalized services based on individual preference information and emotion information.

[0922] "User" means any person or group of people using the services of the Accommodation Facility.

[0923] "Preference information" is information that specifically indicates the user's preferences and wishes, and includes the type of room and the conditions for using the facility.

[0924] "Emotion information" is information that indicates the user's emotional state, and is obtained by voice analysis, facial expression analysis, etc.

[0925] "Means for receiving" refers to the functions and devices for obtaining the necessary information from the user through the terminal.

[0926] "Means for storing" refers to the functions and devices for storing received information in a database or the like.

[0927] "Recommendations" refers to suggestions for providing appropriate services and rooms based on the user's preference information and emotional information.

[0928] "Generating means" refers to the functionality or devices used to create recommendations based on stored information, including generative AI models.

[0929] "Means for providing" refers to the functions and devices for communicating generated recommendations to users.

[0930] "Means for recording" refers to the functions and devices for saving the content of communication with the user.

[0931] A "generative AI model" refers to an algorithm that uses machine learning and natural language processing to analyze user preference and emotional information and generate appropriate recommendations.

[0932] The present invention provides a system for providing personalized services based on the preferences and emotions of guests at accommodation facilities such as hotels. The system recognizes and stores user preference information and emotions, generates recommendations based on the user preference information and emotions, and provides the recommendations and records communication with the user. The following describes in detail the embodiments of the present invention.

[0933] Receiving and storing user preferences and emotions

[0934] First, the terminal receives preference and emotion information from the user. This process involves the user inputting their preferred room type and requests for specific services through the terminal. The emotion engine then recognizes the user's emotions through voice and facial expression analysis. The terminal then sends this information to the server. The server stores the received preference and emotion information in a database and manages each guest's information individually.

[0935] For example, if a user inputs their preference for a "deluxe room" and a "high floor with an ocean view" into the device, the emotion engine will recognize the emotion of "joy." This information is sent from the device to the server and stored on the server.

[0936] Generate recommendations

[0937] The server generates recommendations based on the stored preference and emotion information. A generative AI model is used in this process. The generative AI model analyzes the user's preference and emotion information with high accuracy and provides appropriate recommendations. As a specific example, the server considers the emotion of "happiness" for "guest123" and generates the recommended room, "101 - Deluxe Room with Ocean View."

[0938] Examples of sentences that can be used as prompts are:

[0939] User ID: guest123

[0940] Preference: Deluxe room, high floor with sea view

[0941] Emotion: Joy

[0942] Generate recommendations.

[0943] Providing recommendations

[0944] The generated recommendation is sent from the server to the device, and the device displays it to the user. For example, the server sends a recommendation of "101 - Deluxe Room with Ocean View" to the device, and the device displays "Recommended room: 101 - Deluxe Room with Ocean View".

[0945] Record of communication

[0946] The server also records all communication with the user. For example, when a user makes a check-in inquiry, the content of the inquiry and emotional information are recorded. This record is saved in a log file or database for later reference and analysis.

[0947] Use of emotional information

[0948] The server dynamically adjusts recommendations based on the recognized emotional information. For example, if a user is feeling stressed, more personalized services can be provided, such as recommending a quiet room or relaxation services. Furthermore, by including emotional information in communication records, it is possible to improve the quality of services and respond quickly to problems.

[0949] As described above, the system of the present invention not only provides personalized services based on the user's preferences, but also realizes even more personalized services by adding emotional information, thereby significantly improving the accommodation experience.

[0950] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0951] Step 1:

[0952] Receiving user preference and emotion information

[0953] The user operates the terminal and inputs preference information such as the preferred room type and desired facilities.

[0954] Input: Preference information entered by the user into the device (e.g., deluxe room, high floor with ocean view).

[0955] Specific operation: The user selects preference information on the device screen by tapping, clicking, or entering text.

[0956] At the same time, the device uses its built-in emotion engine to capture the user's face with a camera and perform voice and facial expression analysis to collect emotional information.

[0957] Input: Video and audio of the user's face.

[0958] How it works: The device's camera and microphone capture the user's face and voice, and the built-in emotion analysis engine determines emotions such as "joy" or "relief."

[0959] Output: Preference information and emotion information are temporarily stored inside the device.

[0960] Step 2:

[0961] Transmission and storage of received information

[0962] The terminal transmits the collected preference information and emotion information to the server.

[0963] Input: Preference and emotion information stored on the device.

[0964] Specific operation: The device sends data to the server using an HTTP request or other communication protocol.

[0965] The server stores the received information in a database.

[0966] Input: Preference and emotion information received by the server.

[0967] What happens: The server inserts information into the database using an SQL query.

[0968] Output: The information is stored in a database, and individual preference and emotion information for each user is managed.

[0969] Step 3:

[0970] Generate recommendations

[0971] The server generates recommendations using a generative AI model based on the stored preference and emotion information.

[0972] Input: Preference and emotion information stored in a database.

[0973] Specific operation: The server sends a prompt to the generative AI model to generate recommendations.

[0974] Example prompt sentence:

[0975] User ID: guest123

[0976] Preference: Deluxe room, high floor with sea view

[0977] Emotion: Joy

[0978] Generate recommendations.

[0979] Output: Recommended room and service information (e.g. 101 - Deluxe Room with Ocean View).

[0980] Step 4:

[0981] Providing recommendations

[0982] The server sends the generated recommendations to the device.

[0983] Input: Server-generated recommendations.

[0984] Specific operation: The server sends the recommendation information to the device using an HTTP response or other communication protocol.

[0985] The device displays the received recommendations to the user.

[0986] Input: The recommendations received from the server.

[0987] Specific behavior: The device displays "Recommended room: 101 - Deluxe Room with Ocean View" on the display screen.

[0988] Output: Recommendations visually communicated to the user.

[0989] Step 5:

[0990] Record of communication

[0991] The server records all communication with the user.

[0992] Input: User inquiry, dialogue log, and emotional information.

[0993] Specific operation: The server stores the received information in a log file or database.

[0994] Output: Logged communications with users.

[0995] Through the above process, highly personalized services based on the user's preference information and emotional information are provided.

[0996] (Application example 2)

[0997] 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."

[0998] Traditional services provided in brick-and-mortar stores often provided uniform services without considering information about users' preferences or emotions. This meant that users took a long time to find products or services that suited them, resulting in an unsatisfactory experience. Furthermore, it was difficult to respond to real-time changes in users' emotions using only user purchase history and behavioral data, so further improvements in services were required.

[0999] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user preference information, means for saving preference information and emotion information, means for generating recommendations suitable for the user based on the preference information, means for receiving and analyzing user emotion information, means for dynamically adjusting the recommendations based on the emotion information, means for recording communication with the user, and means for generating prompt sentences using a generative AI model when generating recommendations and generating recommendations based on the analysis results. This makes it possible to provide personalized recommendations in real time based on the user's preferences and emotions.

[1000] The "means for receiving user preference information" refers to equipment or software for acquiring preference information regarding specific products or services from a user.

[1001] The "means for storing preference information and emotional information" refers to storage facilities such as databases and servers that continuously store and manage user preference information and emotional information.

[1002] The "means for generating recommendations suited to the user based on preference information" refers to an algorithm or program that analyzes the stored preference information and selects the most suitable products and services for the user.

[1003] "Means for receiving and analyzing user emotional information" refers to devices and software for recognizing and analyzing a user's emotions from their voice and facial expressions.

[1004] A "means for dynamically adjusting recommendations based on emotional information" is a system or algorithm that changes recommendations in real time based on analyzed emotional information.

[1005] "Means for recording communication with users" refers to devices or software that record and save conversations with users and inquiries.

[1006] "Means for generating prompt sentences using a generative AI model when generating recommendations, and generating recommendations based on the analysis results" refers to algorithms or programs that create prompt sentences using a generative AI model based on user preference information and emotional information, and then use the analysis results to generate appropriate recommendations.

[1007] A specific embodiment for carrying out the present invention will be described.

[1008] Users input preference information such as their preferred product categories, specific brands, and price ranges through a smartphone application. In addition, the smartphone's camera and microphone are used to capture facial expressions and voices, and the user's emotional information is analyzed.

[1009] The server collects user preference information and emotion information and stores this data in a database. The stored data includes identifiers for multiple users and is managed individually.

[1010] The server uses a generative AI model to generate prompts and recommendations based on the collected and stored data. Specifically, it generates prompts such as the following:

[1011] User prefers: fashion, and their current emotion is: joy. What should we recommend?

[1012] The server inputs the generated prompt sentences into the AI ​​model and obtains recommendations as an analysis result, which are displayed in real time in the application on the user's smartphone.

[1013] As a specific example, if a user likes "fashion" and the camera recognizes an expression of "joy," the server stores that information and recommends "the latest fashion trend items" to the user.

[1014] It also has the ability to dynamically adjust recommendations based on the user's emotional state: for example, if the user is feeling stressed, products with a relaxation effect (e.g., aroma candles or relaxing music) will be recommended.

[1015] Furthermore, all interactions and inquiries with users are recorded and used to improve services in the future, as data analysis will enable more precise service provision.

[1016] This system allows users to receive the most appropriate products and services based on their emotions and preferences at the time, greatly improving the shopping experience in physical stores.

[1017] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1018] Step 1:

[1019] A user launches a smartphone application and inputs preference information such as their preferred product categories, specific brands, and price ranges. The input data is temporarily stored in the device's memory. When the user accesses the application via the camera and microphone, the device captures the user's facial expressions and voice and analyzes their emotional information. Input: User's preference information and emotional information. Output: Analysis results of preference information and emotional information.

[1020] Step 2:

[1021] The device sends the analyzed preference information and emotion information to the server. The server stores the received data in a database, where each user's identifier (ID) is also recorded. Input: preference information and emotion information. Output: user information stored in the database.

[1022] Step 3:

[1023] The server retrieves the user's preference and emotion information stored in the database and generates a prompt using a generative AI model. The prompt reflects the user's preference and emotion. For example, the generated sentence is "User prefers: fashion, and their current emotion is: joy. What should we recommend?" Input: Preference information and emotion information. Output: Generated prompt.

[1024] Step 4:

[1025] The server inputs the generated prompt into the generative AI model and obtains recommendations as the analysis result. At this time, the AI ​​model recommends the most suitable products and services to the user based on the prompt. Input: Prompt. Output: Recommendations.

[1026] Step 5:

[1027] The server sends the obtained recommendations to the device, which then displays them on the application screen. The user can check the recommended products and services in real time. Input: Recommendations. Output: Recommendations displayed in the application.

[1028] Step 6:

[1029] The products and services selected by the user through the application, or the content of inquiries made, are sent to the server, which records these communications. This information will be used to improve future services. Input: User selections and inquiries. Output: Communication content recorded on the server.

[1030] Step 7:

[1031] The server analyzes changes in the user's emotions in real time and dynamically adjusts recommendations as needed. For example, if the server determines that the user is feeling stressed, it will re-recommend products with a relaxation effect. Input: Real-time emotional information. Output: Adjusted recommendations.

[1032] 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.

[1033] 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.

[1034] 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.

[1035] [Fourth embodiment]

[1036] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1037] 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.

[1038] 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).

[1039] 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.

[1040] 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.

[1041] 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).

[1042] 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.

[1043] 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.

[1044] 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.

[1045] 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.

[1046] 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.

[1047] 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.

[1048] 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."

[1049] The present invention is a system for providing personalized service based on guest preferences at a hotel or other lodging facility, including receiving and storing user preference information, generating recommendations based on the preference information, providing the recommendations, and recording communications with the user.

[1050] Receiving and storing user preference information

[1051] First, the device receives preference information directly from the user. This includes the user inputting their preferred room type and requests for specific services through the device. For example, the device receives information that the user prefers a "deluxe room" and a "high floor with an ocean view." This information is then sent from the device to the server.

[1052] The server stores the received preference information in a database. For example, for a guest ID of "guest123," "deluxe room" and "high floor with ocean view" are stored as preference information. This storage process allows each guest's preference information to be managed individually.

[1053] Generate recommendations

[1054] The server generates recommendations based on the stored preference information. Using a generative AI model, the received preference information can be analyzed with high precision to provide appropriate recommendations. For example, the recommended room for "guest 123" is automatically generated as "101 - Deluxe Room with Ocean View."

[1055] Providing recommendations

[1056] Once a recommendation is generated, the server sends this information to the device, which then displays it to the user. For example, the device might display "Recommended room: 101 - Deluxe Room with Ocean View," allowing the user to instantly confirm the recommendation.

[1057] Record of communication

[1058] In addition, all communication with the user is recorded on the server. For example, when a user makes a check-in inquiry, the content and timestamp of the inquiry are saved as a log. The server stores this log for future reference and analysis.

[1059] For example, if "guest456" inputs his preference for a "standard room" and "non-smoking," this information is sent to the server. The server generates a recommendation for "202 - Standard Room with City View" based on "guest456's" preferences and displays it on the terminal. At the same time, the content of the user's inquiry is recorded and used to improve services in the future.

[1060] As described above, the system of the present invention can provide personalized services based on the user's preferences and improve the accommodation experience.

[1061] The processing flow will be explained below.

[1062] Step 1: The device receives user preference information

[1063] The terminal provides an interface for accepting input from the user. For example, the user inputs information such as "room type: deluxe" and "special request: high floor, ocean view room" on the screen.

[1064] The terminal temporarily stores the input information in its memory.

[1065] Step 2: The device sends preference information to the server

[1066] The device collects the received user preference information into packets and sends them to the server, specifically using HTTP requests or API calls.

[1067] The terminal waits for a response from the server to confirm that the transmission was successful.

[1068] Step 3: The server receives and stores the preference information

[1069] The server receives the packets sent from the terminal and extracts the preference information therefrom.

[1070] The server stores the extracted preference information in a database managed for each guest. For example, for a guest ID of "guest123," information such as "room type: deluxe" and "special request: high floor, room with ocean view" is stored.

[1071] Step 4: The server generates recommendations

[1072] The server invokes a generative AI model based on the stored preference information to generate recommendations.

[1073] The generative AI model analyzes user preferences and recommends the most suitable room and services. For example, it might recommend "Room 101 - Deluxe Room with Ocean View" to "guest 123."

[1074] Step 5: The server sends the recommendations to the device

[1075] The server sends the generated recommendations to the device, again using HTTP responses or APIs.

[1076] The server records the completion of the process for later review.

[1077] Step 6: The device displays the recommendations to the user

[1078] The terminal analyzes the recommendations received from the server and displays them on the user interface.

[1079] For example, a message such as "Recommended room: 101 - Deluxe Room with Ocean View" may be displayed on the user's screen.

[1080] Step 7: User reviews recommendations and selects action

[1081] The user checks the recommendations displayed on the terminal and selects the next action, such as making a reservation or making an inquiry.

[1082] For example, a user clicks on the "Book room 101" button.

[1083] Step 8: The server records the communication

[1084] The server logs communications, including any follow-up requests or inquiries from the user.

[1085] The recorded logs are stored with a timestamp and are used to improve the service and for future reference.

[1086] Example 1

[1087] 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."

[1088] Conventional service provision systems for accommodation facilities have difficulty effectively utilizing user preference information to provide personalized services. They also have issues with not being able to fully utilize communication history with users, which can lead to a decline in service quality. Furthermore, the accuracy and applicability of recommendations provided to users are insufficient, limiting the improvement of user experience.

[1089] 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.

[1090] In this invention, the server includes means for receiving user preference information, means for storing the preference information, and means for generating recommendations suited to the user based on the preference information. This enables the provision of highly accurate personalized services. The server further includes means for providing the recommendations to the user, means for recording communication with the user, communication means for sending and receiving the preference information and recommendations, and means for generating recommendations based on prompt sentences using a generative AI model. This enables the integration of the user's preference information and communication history to provide more accurate and comprehensive services.

[1091] "User preference information" is information about the room type and services that the user prefers at accommodation facilities.

[1092] "Server" means a computer system that receives and stores user preference information and generates and provides recommendations appropriate to the user.

[1093] The term "means" refers to a combination of hardware and software for realizing various functions in the present invention.

[1094] The "receiving means" is an interface for receiving preference information from a user and transmitting it to a server.

[1095] The "storing means" is a data storage device that records the received preference information as data and allows it to be referenced as needed.

[1096] The "means for generating recommendations" refers to algorithms and generative AI models for presenting services and room types that are suitable for the user based on the user's preference information.

[1097] A "means for providing recommendations" is an interface for notifying and displaying generated recommendations to the user.

[1098] "Means for recording communication with users" is a function for saving inquiries and expressions of intent from users as a log.

[1099] A "communication means" is a network interface for sending and receiving preference information and recommendations.

[1100] A "generative AI model" is an artificial intelligence model that analyzes user preference information based on prompt sentences and generates appropriate recommendations.

[1101] A "prompt" is an instruction given to a generative AI model that provides information for the model to generate recommendations.

[1102] The present invention provides a system for providing personalized services based on user preferences at accommodation facilities such as hotels. The system includes functions for receiving and storing user preference information, generating recommendations based on the preference information, providing the recommendations, and recording communications with the user.

[1103] Receiving and storing user preference information

[1104] The terminal first receives preference information from the user. The user uses the terminal to input their preferred room type and requests for specific services. For example, a user who prefers a "deluxe room" and a "high floor with an ocean view" inputs this information. The input information is sent from the terminal to the server. The server saves the received preference information in a database. For example, preference information such as "deluxe room" and "high floor with an ocean view" is saved for a guest ID of "guest123." This saving process uses a database management system such as MySQL.

[1105] Generate recommendations

[1106] The server generates recommendations based on the stored preference information. Using a generative AI model, it is possible to analyze the received preference information with high accuracy and provide appropriate recommendations. For example, a recommended room, "101 - Deluxe Room with Ocean View," is automatically generated for "guest123." The generative AI model operates by inputting a prompt statement using a Python script. For example, the generated recommendation can be obtained by inputting the prompt statement "User ID: guest123 Preference information: deluxe room, high floor with ocean view Recommendation to be generated: room."

[1107] Providing recommendations

[1108] Once the recommendation is generated, the server sends this information to the terminal, and the terminal displays it to the user. Specifically, the generated recommendation is sent to the terminal in JSON format, and the terminal displays it in the user interface. For example, the user can confirm "Recommended room: 101 - Deluxe Room with Ocean View" through the terminal.

[1109] Record of communication

[1110] Furthermore, all communication with users is logged on the server. When a user makes a check-in inquiry, the question and timestamp are saved as a log. This operation is performed using a log management system such as the ELK stack. For example, if "guest123" asks "What time is check-in?", the question and timestamp are recorded.

[1111] As a concrete example, consider the case where user "guest456" inputs preference information of "standard room" and "non-smoking" into the terminal. This information is sent to the server, which generates a recommendation of "202 - Standard Room with City View" based on the preferences of "guest456." This recommendation is displayed on the terminal, while the content of the inquiry with the user is also recorded. An example of a prompt sentence input to the generative AI model is as follows:

[1112] User ID: guest456

[1113] Preferences: Standard room, Non-smoking

[1114] Generate recommendations for: Rooms

[1115] As a result, the accommodation experience can be improved by providing personalized services based on the user's preferences. This system also contributes to increasing user satisfaction and the number of repeat visitors.

[1116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1117] System program processing flow

[1118] Step 1: Enter and submit user preference information

[1119] The user inputs his / her preference information using the terminal.

[1120] Input: The user enters information such as "room type: deluxe" and "view: high floor with ocean view" on the device screen.

[1121] Operation: The device converts the input preference information into JSON format and sends a POST request to the server.

[1122] Output: The JSON data that is sent to the server.

[1123] json

[1124] {

[1125] "userID": "guest123",

[1126] "preferences": {

[1127] "roomType": "deluxe",

[1128] "view": "ocean high floor"

[1129] }

[1130] }

[1131] Step 2: Saving your preferences

[1132] The server analyzes the received preference information and stores it in a database.

[1133] Input: JSON data sent from the terminal.

[1134] Operation: The server parses the received JSON data, extracts the user ID and preference information, and stores them in a database.

[1135] Output: User preference information stored in a database.

[1136] sql

[1137] INSERT INTO user_preferences (user_id, room_type, view)

[1138] VALUES ("guest123", "deluxe", "ocean high floor");

[1139] Step 3: Generate recommendations

[1140] The server generates recommendations using a generative AI model based on the stored preference information.

[1141] Input: User preference information stored in the database ("guest123", "deluxe room", "high floor with sea view").

[1142] How it works: The server runs a Python script and inputs prompt statements to the generative AI model, which then generates recommendations based on the prompt statements.

[1143] Output: The generated recommendation (e.g., "101 - Deluxe Room with Ocean View").

[1144] python

[1145] prompt = "User ID: guest123 Preferences: deluxe room, high floor with ocean view Recommendation to generate: room"

[1146] generated_recommendation = "101 - Deluxe Room with Ocean View"

[1147] Step 4: Providing recommendations

[1148] The server sends the generated recommendations to the terminal, which displays them to the user.

[1149] Input: Generated recommendation ("101 - Deluxe Room with Ocean View").

[1150] Operation: The server converts the recommendations into JSON format and sends them to the device, which displays them in the user interface.

[1151] Output: Recommendations displayed on the user's terminal.

[1152] json

[1153] {

[1154] "recommendedRoom": "101 - Deluxe Room with Ocean View"

[1155] }

[1156] Step 5: Record your communications

[1157] The server logs all communication with the user.

[1158] Input: User queries and their responses.

[1159] How it works: The server stores the user query and the corresponding timestamp in a logging system, for example, using the ELK stack to store the data.

[1160] Output: The data that is logged.

[1161] json

[1162] {

[1163] "userID": "guest123",

[1164] "question": "What time is check-in?",

[1165] "timestamp": "2023-10-01 10:15:00"

[1166] }

[1167] This completes the explanation of the specific processing flow of the system and the operation of each step.

[1168] (Application example 1)

[1169] 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."

[1170] Conventional accommodation facilities and brick-and-mortar stores have systems that provide personalized recommendations based on the preferences and requests of each individual user, but these systems have difficulty effectively analyzing user preference information and providing accurate and immediate optimal recommendations.In addition, they have an issue in that they do not adequately record communication with users, making it difficult to use the information to improve services.

[1171] 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.

[1172] In this invention, the server includes means for receiving user preference information, means for storing the preference information, means for generating recommendations suited to the user based on the preference information, means for analyzing the user's preferences and generating the recommendations using a generative AI model in order to provide recommendations for specific products and services, and means for providing the recommendations to the user on a smartphone application. This makes it possible to instantly provide highly accurate recommendations based on the user's preferences and also record communications.

[1173] "Means for receiving user preference information" refers to a device or interface through which a user inputs information about their preferences and needs and the system receives that information.

[1174] "Means for storing preference information" refers to a device or mechanism that holds received user preference information and stores it in a database so that it can be referenced when necessary.

[1175] "Means for generating recommendations" refers to mechanisms or algorithms that automatically generate product or service suggestions appropriate to the user based on stored preference information.

[1176] "Means for generating recommendations using a generative AI model" refers to a mechanism that uses an artificial intelligence model to analyze user preferences and automatically generate optimal product and service recommendations.

[1177] "Means for providing recommendations to users on a smartphone application" refers to a system for displaying and notifying users of the generated recommendations via a smartphone application.

[1178] "Means for recording communication with users" refers to devices or mechanisms that store interactions with users and inquiries as logs so that they can be referenced or analyzed later.

[1179] A "database" refers to a group of data that manages the preference information of a large number of users individually, stores it efficiently, and organizes it in a searchable manner.

[1180] This invention is a system that provides personalized services based on user preference information at accommodation facilities and brick-and-mortar stores. Specifically, it uses various devices and software to receive user input information, generate recommendations based on that information, and provide them to the user.

[1181] Receiving user preference information

[1182] First, a user inputs their preferences using a smartphone application, including the types of products they prefer and specific requests (e.g., size, color, brand, etc.), which are then sent to the server via the app.

[1183] Saving your preferences

[1184] The server receives the preference information and stores it in a database. This database is used to individually manage preference information from multiple users, and for example, it stores information such as "sportswear, black, size L" for user ID "user123."

[1185] Generate recommendations

[1186] The server uses a generative AI model to generate appropriate recommendations based on the stored preference information. This AI model analyzes the input information and recommends the most suitable products and services. For example, it recommends a "black sports jacket in size L" to "user123."

[1187] Providing recommendations

[1188] The generated recommendations are sent from the server to a smartphone application, which then displays this information to the user immediately, allowing the user to review the recommended products and services. For example, a notification might appear in the app saying, "Today's recommended product: Black sports jacket, size L."

[1189] Record of communication

[1190] Furthermore, all inquiries and requests made by users through the app are recorded on the server. These records are saved as logs and used for future reference and analysis. For example, interactions such as "checking product inventory" and "reserving a fitting room" are recorded.

[1191] Hardware and software used

[1192] The system uses a smartphone, a cloud server, a database (e.g., SQL database), a generative AI model (e.g., GPT-3.5), etc. Data is sent from the device via the internet to the server, where it is analyzed and stored.

[1193] Examples of specific examples and prompts

[1194] For example, if a user is looking for sportswear, they might enter the following preferences:

[1195] "I want some sportswear. Nike is a good brand. Black, size L."

[1196] An example of a prompt sentence to input to the generative AI model is:

[1197] "I'm looking for Nike brand sportswear in size medium in black. Please generate the best product recommendations for this user."

[1198] Examples include:

[1199] The above is a specific embodiment for carrying out the present invention, and this system allows users to have a highly accurate personalized experience based on their individual preferences.

[1200] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1201] Step 1:

[1202] The user launches a smartphone application and inputs their own preference information. This preference information includes the type, brand, color, size, etc. of the product the user prefers. The input information is sent to the server by the application. The input data is in text format, for example, "I want sportswear. Nike is a good brand, the color is black, and the size is large." The preference information is sent to the server as output.

[1203] Step 2:

[1204] The server stores the received preference information in a database. The server converts the sent preference information into structured data (e.g., JSON format) and stores it in the database along with the user ID. For example, for user ID "user123," data such as "category: sportswear, brand: Nike, color: black, size: L" is stored. The input is the user's preference information, and the output is structured data stored in the database.

[1205] Step 3:

[1206] The server generates recommendations based on the stored preference information. To do this, it uses a generative AI model. The server inputs the stored preference information into the AI ​​model and analyzes it. For example, the input to the model is a prompt statement such as, "I am looking for black Nike sportswear in size L. Please generate the best product recommendations for this user." As an output, the model returns a recommendation of "black Nike sports jacket in size L."

[1207] Step 4:

[1208] The server sends the generated recommendations to a smartphone application. The application receives the recommendations and displays them on the user's screen. For example, a notification such as "Today's recommended item: Nike sports jacket in black, size large" is displayed on the user's smartphone. The input is the generated recommendations, and the output is the notification displayed in the smartphone application.

[1209] Step 5:

[1210] When a user makes an inquiry or request through the application, the server records this interaction. For example, the contents of a request such as "checking product availability" or "reserving a fitting room" are saved as a log. The server stores this in a database for future reference and analysis. The input is the user's inquiry, and the output is the recorded log data.

[1211] The above are the specific processing steps of the program in this system, and details of the input and output at each step.

[1212] 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.

[1213] The present invention is a system for providing personalized services based on the preferences and emotions of guests at hotels and other accommodation facilities. The system recognizes and stores user preference information and emotions, generates recommendations based on the preferences and emotions, provides the recommendations, and records communications with the user.

[1214] Receiving and storing user preferences and emotions

[1215] First, the device receives preference information and emotion information from the user. This includes the user inputting their preferred room type and requests for specific services through the device. The emotion engine recognizes the user's emotions through voice analysis and facial expression analysis. For example, the device receives information that the user prefers a "deluxe room" and a "high floor with an ocean view," as well as the emotion of "joy." This information is then sent from the device to the server.

[1216] The server stores the received preference information and emotion information in a database. For example, for a guest ID of "guest123," the following information is stored: "deluxe room," "high floor with ocean view," and "joy" as the emotion. This storage process allows each guest's preference information and emotion information to be managed individually.

[1217] Generate recommendations

[1218] The server generates recommendations based on the stored preference and emotion information. Using a generative AI model, the received preference and emotion information can be analyzed with high accuracy to provide appropriate recommendations. For example, taking into account the emotion of "joy" for "guest 123," the recommended room "101 - Deluxe Room with Ocean View" is automatically generated.

[1219] Providing recommendations

[1220] Once a recommendation is generated, the server sends this information to the device, which then displays it to the user. For example, the device might display "Recommended room: 101 - Deluxe Room with Ocean View," allowing the user to instantly confirm the recommendation.

[1221] Record of communication

[1222] Furthermore, all communication with the user is recorded on the server. For example, when a user makes a check-in inquiry, the content of the inquiry and emotional information are also saved as logs. The server stores this log for future reference and analysis.

[1223] Specific examples

[1224] If "guest456" inputs his preference information of "standard room" and "non-smoking," the emotion engine recognizes the emotion of "relief." This information is sent to the server, which generates a recommendation of "202 - Standard Room with City View" based on "guest456's" preference and emotion information and displays it on the device. At the same time, the user's inquiry and emotion are recorded and used to improve services in the future.

[1225] Use of emotional information

[1226] The server dynamically adjusts recommendations based on the recognized emotional information. For example, if a user is feeling stressed, more personalized services will be provided, such as recommending a quiet room or relaxation services. Furthermore, by including emotional information in communication records, it is possible to improve the quality of services and respond quickly to problems.

[1227] As described above, the system of the present invention not only provides personalized services based on the user's preferences, but also realizes even more personalized services by adding emotional information, thereby significantly improving the accommodation experience.

[1228] The processing flow will be explained below.

[1229] Step 1: The device receives the user's preference information and emotion information.

[1230] The terminal provides an interface for accepting input from the user, who inputs information such as "room type: deluxe" and "special request: high floor, ocean view room" on the screen.

[1231] The emotion engine analyzes the user's voice and recognizes their emotions. For example, if the user expresses the emotion "joy," that information is recorded on the device.

[1232] Step 2: The device sends preference information and emotion information to the server.

[1233] The device collects the received user preference information and emotion information into packets and sends them to the server, specifically using HTTP requests or API calls.

[1234] The terminal waits for a response from the server to confirm whether the transmission was successful.

[1235] Step 3: The server receives and stores preference and emotion information.

[1236] The server receives the packets sent from the terminal and extracts the preference information and emotion information therefrom.

[1237] The server stores the extracted information in a database managed for each guest. For example, for a guest ID of "guest123," information such as "room type: deluxe," "special request: high floor, ocean view room," and "emotion: joy" is stored.

[1238] Step 4: The server generates recommendations

[1239] The server invokes a generative AI model based on the stored preference and emotion information to generate recommendations.

[1240] The generative AI model analyzes user preferences and emotions to recommend the most suitable room and service. For example, for guest 123, it would recommend "101 - Deluxe Room with Ocean View" based on the emotion "joy."

[1241] Step 5: The server sends the recommendations to the device

[1242] The server sends the generated recommendations to the device, again using HTTP responses or APIs.

[1243] The server records the completion of the process for later review.

[1244] Step 6: The device displays the recommendations to the user

[1245] The terminal analyzes the recommendations received from the server and displays them on the user interface.

[1246] For example, a message such as "Recommended room: 101 - Deluxe Room with Ocean View" may be displayed on the user's screen.

[1247] Step 7: User reviews recommendations and selects action

[1248] The user checks the recommendations displayed on the terminal and selects the next action, such as making a reservation or making an inquiry.

[1249] For example, a user clicks on the "Book room 101" button.

[1250] Step 8: The server records the communication

[1251] The server logs the communication content, including any additional requests or inquiries from the user, along with emotional information.

[1252] The recorded logs are stored with a timestamp and are used to improve the service and for future reference.

[1253] Step 9: The server uses the sentiment information to adjust the recommendations

[1254] The server dynamically adjusts recommendations based on the stored emotional information: for example, if the user is perceived as feeling "stressed," the server may recommend a quiet room or relaxation services.

[1255] This allows for personalized services tailored to the user's emotional state.

[1256] Example 2

[1257] 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."

[1258] Conventional service provision systems in accommodation facilities mainly provide services based on user preferences, but do not provide personalized services that take into account the user's emotional information. This makes it difficult to provide detailed responses to maximize user satisfaction. In addition, there is no adequate system in place to record communication with users and use it to improve services in the future.

[1259] 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.

[1260] In this invention, the server includes means for receiving user preference information, means for saving the preference information, means for recognizing and saving emotion information, means for generating recommendations suited to the user based on the preference information and emotion information, means for providing the recommendations to the user, and means for recording communication with the user, thereby enabling the provision of more personalized services based on individual preference information and emotion information.

[1261] "User" means any person or group of people using the services of the Accommodation Facility.

[1262] "Preference information" is information that specifically indicates the user's preferences and wishes, and includes the type of room and the conditions for using the facility.

[1263] "Emotion information" is information that indicates the user's emotional state, and is obtained by voice analysis, facial expression analysis, etc.

[1264] "Means for receiving" refers to the functions and devices for obtaining the necessary information from the user through the terminal.

[1265] "Means for storing" refers to the functions and devices for storing received information in a database or the like.

[1266] "Recommendations" refers to suggestions for providing appropriate services and rooms based on the user's preference information and emotional information.

[1267] "Generating means" refers to the functionality or devices used to create recommendations based on stored information, including generative AI models.

[1268] "Means for providing" refers to the functions and devices for communicating generated recommendations to users.

[1269] "Means for recording" refers to the functions and devices for saving the content of communication with the user.

[1270] A "generative AI model" refers to an algorithm that uses machine learning and natural language processing to analyze user preference and emotional information and generate appropriate recommendations.

[1271] The present invention provides a system for providing personalized services based on the preferences and emotions of guests at accommodation facilities such as hotels. The system recognizes and stores user preference information and emotions, generates recommendations based on the user preference information and emotions, and provides the recommendations and records communication with the user. The following describes in detail the embodiments of the present invention.

[1272] Receiving and storing user preferences and emotions

[1273] First, the terminal receives preference and emotion information from the user. This process involves the user inputting their preferred room type and requests for specific services through the terminal. The emotion engine then recognizes the user's emotions through voice and facial expression analysis. The terminal then sends this information to the server. The server stores the received preference and emotion information in a database and manages each guest's information individually.

[1274] For example, if a user inputs their preference for a "deluxe room" and a "high floor with an ocean view" into the device, the emotion engine will recognize the emotion of "joy." This information is sent from the device to the server and stored on the server.

[1275] Generate recommendations

[1276] The server generates recommendations based on the stored preference and emotion information. A generative AI model is used in this process. The generative AI model analyzes the user's preference and emotion information with high accuracy and provides appropriate recommendations. As a specific example, the server considers the emotion of "happiness" for "guest123" and generates the recommended room, "101 - Deluxe Room with Ocean View."

[1277] Examples of sentences that can be used as prompts are:

[1278] User ID: guest123

[1279] Preference: Deluxe room, high floor with sea view

[1280] Emotion: Joy

[1281] Generate recommendations.

[1282] Providing recommendations

[1283] The generated recommendation is sent from the server to the device, and the device displays it to the user. For example, the server sends a recommendation of "101 - Deluxe Room with Ocean View" to the device, and the device displays "Recommended room: 101 - Deluxe Room with Ocean View".

[1284] Record of communication

[1285] The server also records all communication with the user. For example, when a user makes a check-in inquiry, the content of the inquiry and emotional information are recorded. This record is saved in a log file or database for later reference and analysis.

[1286] Use of emotional information

[1287] The server dynamically adjusts recommendations based on the recognized emotional information. For example, if a user is feeling stressed, more personalized services can be provided, such as recommending a quiet room or relaxation services. Furthermore, by including emotional information in communication records, it is possible to improve the quality of services and respond quickly to problems.

[1288] As described above, the system of the present invention not only provides personalized services based on the user's preferences, but also realizes even more personalized services by adding emotional information, thereby significantly improving the accommodation experience.

[1289] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1290] Step 1:

[1291] Receiving user preference and emotion information

[1292] The user operates the terminal and inputs preference information such as the preferred room type and desired facilities.

[1293] Input: Preference information entered by the user into the device (e.g., deluxe room, high floor with ocean view).

[1294] Specific operation: The user selects preference information on the device screen by tapping, clicking, or entering text.

[1295] At the same time, the device uses its built-in emotion engine to capture the user's face with a camera and perform voice and facial expression analysis to collect emotional information.

[1296] Input: Video and audio of the user's face.

[1297] How it works: The device's camera and microphone capture the user's face and voice, and the built-in emotion analysis engine determines emotions such as "joy" or "relief."

[1298] Output: Preference information and emotion information are temporarily stored inside the device.

[1299] Step 2:

[1300] Transmission and storage of received information

[1301] The terminal transmits the collected preference information and emotion information to the server.

[1302] Input: Preference and emotion information stored on the device.

[1303] Specific operation: The device sends data to the server using an HTTP request or other communication protocol.

[1304] The server stores the received information in a database.

[1305] Input: Preference and emotion information received by the server.

[1306] What happens: The server inserts information into the database using an SQL query.

[1307] Output: The information is stored in a database, and individual preference and emotion information for each user is managed.

[1308] Step 3:

[1309] Generate recommendations

[1310] The server generates recommendations using a generative AI model based on the stored preference and emotion information.

[1311] Input: Preference and emotion information stored in a database.

[1312] Specific operation: The server sends a prompt to the generative AI model to generate recommendations.

[1313] Example prompt sentence:

[1314] User ID: guest123

[1315] Preference: Deluxe room, high floor with sea view

[1316] Emotion: Joy

[1317] Generate recommendations.

[1318] Output: Recommended room and service information (e.g. 101 - Deluxe Room with Ocean View).

[1319] Step 4:

[1320] Providing recommendations

[1321] The server sends the generated recommendations to the device.

[1322] Input: Server-generated recommendations.

[1323] Specific operation: The server sends the recommendation information to the device using an HTTP response or other communication protocol.

[1324] The device displays the received recommendations to the user.

[1325] Input: The recommendations received from the server.

[1326] Specific behavior: The device displays "Recommended room: 101 - Deluxe Room with Ocean View" on the display screen.

[1327] Output: Recommendations visually communicated to the user.

[1328] Step 5:

[1329] Record of communication

[1330] The server records all communication with the user.

[1331] Input: User inquiry, dialogue log, and emotional information.

[1332] Specific operation: The server stores the received information in a log file or database.

[1333] Output: Logged communications with users.

[1334] Through the above process, highly personalized services based on the user's preference information and emotional information are provided.

[1335] (Application example 2)

[1336] 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."

[1337] Traditional services provided in brick-and-mortar stores often provided uniform services without considering information about users' preferences or emotions. This meant that users took a long time to find products or services that suited them, resulting in an unsatisfactory experience. Furthermore, it was difficult to respond to real-time changes in users' emotions using only user purchase history and behavioral data, so further improvements in services were required.

[1338] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user preference information, means for saving preference information and emotion information, means for generating recommendations suitable for the user based on the preference information, means for receiving and analyzing user emotion information, means for dynamically adjusting the recommendations based on the emotion information, means for recording communication with the user, and means for generating prompt sentences using a generative AI model when generating recommendations and generating recommendations based on the analysis results. This makes it possible to provide personalized recommendations in real time based on the user's preferences and emotions.

[1339] The "means for receiving user preference information" refers to equipment or software for acquiring preference information regarding specific products or services from a user.

[1340] The "means for storing preference information and emotional information" refers to storage facilities such as databases and servers that continuously store and manage user preference information and emotional information.

[1341] The "means for generating recommendations suited to the user based on preference information" refers to an algorithm or program that analyzes the stored preference information and selects the most suitable products and services for the user.

[1342] "Means for receiving and analyzing user emotional information" refers to devices and software for recognizing and analyzing a user's emotions from their voice and facial expressions.

[1343] A "means for dynamically adjusting recommendations based on emotional information" is a system or algorithm that changes recommendations in real time based on analyzed emotional information.

[1344] "Means for recording communication with users" refers to devices or software that record and save conversations with users and inquiries.

[1345] "Means for generating prompt sentences using a generative AI model when generating recommendations, and generating recommendations based on the analysis results" refers to algorithms or programs that create prompt sentences using a generative AI model based on user preference information and emotional information, and then use the analysis results to generate appropriate recommendations.

[1346] A specific embodiment for carrying out the present invention will be described.

[1347] Users input preference information such as their preferred product categories, specific brands, and price ranges through a smartphone application. In addition, the smartphone's camera and microphone are used to capture facial expressions and voices, and the user's emotional information is analyzed.

[1348] The server collects user preference information and emotion information and stores this data in a database. The stored data includes identifiers for multiple users and is managed individually.

[1349] The server uses a generative AI model to generate prompts and recommendations based on the collected and stored data. Specifically, it generates prompts such as the following:

[1350] User prefers: fashion, and their current emotion is: joy. What should we recommend?

[1351] The server inputs the generated prompt sentences into the AI ​​model and obtains recommendations as an analysis result, which are displayed in real time in the application on the user's smartphone.

[1352] As a specific example, if a user likes "fashion" and the camera recognizes an expression of "joy," the server stores that information and recommends "the latest fashion trend items" to the user.

[1353] It also has the ability to dynamically adjust recommendations based on the user's emotional state: for example, if the user is feeling stressed, products with a relaxation effect (e.g., aroma candles or relaxing music) will be recommended.

[1354] Furthermore, all interactions and inquiries with users are recorded and used to improve services in the future, as data analysis will enable more precise service provision.

[1355] This system allows users to receive the most appropriate products and services based on their emotions and preferences at the time, greatly improving the shopping experience in physical stores.

[1356] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1357] Step 1:

[1358] A user launches a smartphone application and inputs preference information such as their preferred product categories, specific brands, and price ranges. The input data is temporarily stored in the device's memory. When the user accesses the application via the camera and microphone, the device captures the user's facial expressions and voice and analyzes their emotional information. Input: User's preference information and emotional information. Output: Analysis results of preference information and emotional information.

[1359] Step 2:

[1360] The device sends the analyzed preference information and emotion information to the server. The server stores the received data in a database, where each user's identifier (ID) is also recorded. Input: preference information and emotion information. Output: user information stored in the database.

[1361] Step 3:

[1362] The server retrieves the user's preference and emotion information stored in the database and generates a prompt using a generative AI model. The prompt reflects the user's preference and emotion. For example, the generated sentence is "User prefers: fashion, and their current emotion is: joy. What should we recommend?" Input: Preference information and emotion information. Output: Generated prompt.

[1363] Step 4:

[1364] The server inputs the generated prompt into the generative AI model and obtains recommendations as the analysis result. At this time, the AI ​​model recommends the most suitable products and services to the user based on the prompt. Input: Prompt. Output: Recommendations.

[1365] Step 5:

[1366] The server sends the obtained recommendations to the device, which then displays them on the application screen. The user can check the recommended products and services in real time. Input: Recommendations. Output: Recommendations displayed in the application.

[1367] Step 6:

[1368] The products and services selected by the user through the application, or the content of inquiries made, are sent to the server, which records these communications. This information will be used to improve future services. Input: User selections and inquiries. Output: Communication content recorded on the server.

[1369] Step 7:

[1370] The server analyzes changes in the user's emotions in real time and dynamically adjusts recommendations as needed. For example, if the server determines that the user is feeling stressed, it will re-recommend products with a relaxation effect. Input: Real-time emotional information. Output: Adjusted recommendations.

[1371] 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.

[1372] 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.

[1373] 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.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] 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).

[1378] 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.

[1379] 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."

[1380] 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.

[1381] 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).

[1382] 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.

[1383] 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.

[1384] 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.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] 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.

[1389] 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.

[1390] 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.

[1391] 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.

[1392] The following is further disclosed regarding the above embodiment.

[1393] (Claim 1)

[1394] means for receiving user preference information;

[1395] means for storing the preference information;

[1396] means for generating recommendations suited to the user based on the preference information;

[1397] means for providing said recommendations to a user;

[1398] a means for recording communications with the user;

[1399] A system including:

[1400] (Claim 2)

[1401] 2. The system according to claim 1, wherein the means for storing the preference information is a database that stores preference information of a plurality of users individually.

[1402] (Claim 3)

[1403] 2. The system of claim 1, wherein the means for generating recommendations includes an algorithm that uses a generative AI model to analyze user preferences and generate appropriate recommendations.

[1404] "Example 1"

[1405] (Claim 1)

[1406] means for receiving user preference information;

[1407] means for storing the preference information;

[1408] means for generating recommendations suited to the user based on the preference information;

[1409] means for providing said recommendations to a user;

[1410] a means for recording communications with the user;

[1411] communication means for sending and receiving preference information and recommendations;

[1412] a means for generating recommendations based on the prompt using a generative AI model;

[1413] A system including:

[1414] (Claim 2)

[1415] 2. The system according to claim 1, wherein the means for storing the preference information is a data storage device that stores preference information of a plurality of users individually.

[1416] (Claim 3)

[1417] 2. The system of claim 1, wherein the means for generating recommendations includes an algorithm that uses a generative AI model to analyze user preferences and generate appropriate recommendations.

[1418] "Application Example 1"

[1419] (Claim 1)

[1420] means for receiving user preference information;

[1421] means for storing the preference information;

[1422] means for generating recommendations suited to the user based on the preference information;

[1423] means for providing said recommendations to a user;

[1424] a means for recording communications with the user;

[1425] a means for analyzing user preferences to provide recommendations for specific products or services and generating recommendations using a generative AI model;

[1426] means for providing the recommendations to a user on a smartphone application;

[1427] A system including:

[1428] (Claim 2)

[1429] 2. The system according to claim 1, wherein the means for storing the preference information is a database that stores preference information of a plurality of users individually.

[1430] (Claim 3)

[1431] 2. The system of claim 1, wherein the means for generating recommendations includes an algorithm that uses a generative AI model to analyze user preferences and generate appropriate recommendations.

[1432] "Example 2: Combining Emotion Engines"

[1433] (Claim 1)

[1434] means for receiving user preference information;

[1435] means for storing the preference information;

[1436] a means of recognizing and storing emotional information;

[1437] means for generating recommendations suited to the user based on the preference information and emotion information;

[1438] means for providing said recommendations to a user;

[1439] a means for recording communications with the user;

[1440] A system including:

[1441] (Claim 2)

[1442] 2. The system according to claim 1, wherein the means for storing the preference information and emotion information is a database that stores preference information and emotion information of a plurality of users individually.

[1443] (Claim 3)

[1444] 10. The system of claim 1, wherein the means for generating recommendations includes an algorithm that uses a generative AI model to analyze user preferences and emotions and generate appropriate recommendations.

[1445] "Application example 2 when combining emotion engines"

[1446] (Claim 1)

[1447] means for receiving user preference information;

[1448] means for storing the preference information;

[1449] means for generating recommendations suited to the user based on the preference information;

[1450] means for providing said recommendations to a user;

[1451] means for receiving and analyzing user emotion information;

[1452] means for dynamically adjusting recommendations based on the sentiment information;

[1453] a means for recording communications with the user;

[1454] A means for generating a prompt sentence using a generative AI model when generating the recommendation, and generating a recommendation based on the analysis result;

[1455] A system including:

[1456] (Claim 2)

[1457] 2. The system according to claim 1, wherein the system is a database that stores preference information and emotion information of a plurality of users individually.

[1458] (Claim 3)

[1459] 2. The system of claim 1, wherein the means for generating recommendations includes an algorithm that uses a generative AI model to analyze user preference information and emotional information and generate appropriate recommendations. [Explanation of symbols]

[1460] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving user preference information; means for storing the preference information; means for generating recommendations suited to the user based on the preference information; means for providing said recommendations to a user; a means for recording communications with the user; A system including:

2. 2. The system according to claim 1, wherein the means for storing the preference information is a database for individually storing preference information of a plurality of users.

3. 2. The system of claim 1, wherein the means for generating recommendations includes an algorithm that uses a generative AI model to analyze user preferences and generate appropriate recommendations.

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