System

Abstract

A system is provided.SOLUTION: The system includes a means for converting preference data for managing the preference data of a user into a dedicated data format and storing the preference data, a means for uploading the preference data, a means for downloading the preference data, and a means for evaluating the preference data.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] Conventional generative AI models require individual users to spend a great deal of time and effort training them to obtain responses based on their preferences. This situation posed a significant barrier, especially for users using generative AI models for the first time or for users who are not good at devising ways to ask questions. Furthermore, there was no efficient way to share each user's preference data. Therefore, there was a need for a system that would allow users to easily manage their individual preference data and efficiently install it into generative AI models. [Means for solving the problem]

[0005] The present invention is a system for managing user preference data. It includes means for converting preference data into a dedicated data format and saving it, means for uploading preference data, means for downloading preference data, and means for evaluating preference data. It also includes means for providing preference data for a fee and means for filtering preference data based on evaluation information, allowing users to easily obtain high-quality preference data and install it into a generative AI model. In this way, a system is provided that allows users to efficiently manage and use preference data.

[0006] "Preference Data" is data that reflects a user's personal preferences and interests.

[0007] A "proprietary data format" is a specific format designed for specific data processing or management purposes.

[0008] "Uploading" is the act of sending data from a user's device to a server.

[0009] "Downloading" is the act of obtaining data from a server to a user's device.

[0010] "Evaluation" refers to the act of a user judging the quality and usefulness of the provided preference data and assigning a score or review.

[0011] "Means of providing data for a fee" refers to a method in which a user pays a fee for specific data and obtains the data in proportion to that fee.

[0012] "Filtering" is the process of selecting appropriate data from a large amount of data based on specific conditions. [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 following describes a specific embodiment of the system of the present invention. This system manages user preference data and efficiently installs it into a generative AI model.

[0035] This system includes a server for converting and saving user preference data into a dedicated data format, a user terminal for uploading the preference data, a user terminal for downloading the preference data, and a user terminal for evaluating the preference data.

[0036] Specific processing of the program

[0037] The program of this system performs the following processing.

[0038] 1. Uploading preference data

[0039] The terminal displays an interface for the user to select preference data.

[0040] The user selects a preference data file and presses the upload button.

[0041] The terminal transmits the selected preference data to the server.

[0042] The server converts the received preference data into a dedicated data format and stores it in a database.

[0043] The server returns a notification of upload completion to the user's device.

[0044] The device will display a message to the user that the upload is complete.

[0045] 2. Downloading preference data

[0046] The terminal provides an interface for displaying a list of preference data.

[0047] The user selects the preference data they wish to download.

[0048] The terminal transmits a download request for the selected preference data to the server.

[0049] The server retrieves the relevant preference data from the database and sends it to the user's device.

[0050] The device installs the received preference data into the generated AI model.

[0051] 3. Evaluation of preference data

[0052] The terminal displays a rating interface including a rating button and a review input field.

[0053] The user enters rating information and reviews and presses the submit button.

[0054] The terminal transmits the evaluation information to the server.

[0055] The server stores the evaluation information in a database and returns a notification of evaluation completion to the terminal.

[0056] The terminal displays a message to the user that the evaluation is complete.

[0057] Specific examples

[0058] For example, when a user uploads new preference data, the specific flow is as follows.

[0059] 1. The terminal displays a preference data upload form to the user.

[0060] 2. The user selects their preference data and clicks the "Upload" button.

[0061] 3. The terminal transmits the selected data to the server.

[0062] 4. The server receives the data, converts it into a proprietary data format, and stores it in a database.

[0063] 5. The server returns an upload completion notification to the device, and the device displays that information to the user.

[0064] The flow when a user purchases preference data created by another user is as follows.

[0065] 1. The terminal displays a list of preference data that is available for a fee.

[0066] 2. The user selects the data they want to purchase, enters payment information, and presses the "Purchase" button.

[0067] 3. The terminal sends the entered payment information to the server.

[0068] 4. The server processes the payment, and if the payment is successful, retrieves the relevant data from the database and sends it to the user's device.

[0069] 5. The device receives the data and installs it into the generative AI model.

[0070] Through the above process, the system can effectively manage user preference data and apply it to the generative AI model, allowing users to quickly obtain responses that match their preferences and significantly improving the utilization efficiency of the generative AI model.

[0071] The processing flow will be explained below.

[0072] Uploading preference data

[0073] Step 1:

[0074] The terminal displays the preference data upload interface.

[0075] Step 2:

[0076] The user selects a preference data file and clicks the upload button.

[0077] Step 3:

[0078] The device sends the selected file to the server as an HTTP POST request.

[0079] Step 4:

[0080] The server receives the request and temporarily stores the file.

[0081] Step 5:

[0082] The server converts the received file into a dedicated data format.

[0083] Step 6:

[0084] The server stores the converted data in a database.

[0085] Step 7:

[0086] The server will return a notification of upload completion and the ID of the saved data to the device.

[0087] Step 8:

[0088] The device displays a message to the user that the upload is complete.

[0089] Download preference data

[0090] Step 1:

[0091] The terminal displays an interface listing the preference data.

[0092] Step 2:

[0093] The user selects the preference data that he or she wishes to download.

[0094] Step 3:

[0095] The device sends an HTTP GET request including the ID of the selected data to the server.

[0096] Step 4:

[0097] The server receives the request and retrieves the corresponding preference data from the database.

[0098] Step 5:

[0099] The server returns the acquired data to the user's terminal.

[0100] Step 6:

[0101] The device receives the returned data and installs it into the generative AI model.

[0102] Step 7:

[0103] The device displays a download completion message to the user.

[0104] Evaluating preference data

[0105] Step 1:

[0106] The terminal displays the evaluation interface.

[0107] Step 2:

[0108] The user enters the rating score and review comments and clicks the submit button.

[0109] Step 3:

[0110] The device sends the rating information to the server as an HTTP POST request.

[0111] Step 4:

[0112] The server receives the request and stores the rating information in a database.

[0113] Step 5:

[0114] The server returns a notification of evaluation completion to the terminal.

[0115] Step 6:

[0116] The terminal displays a message to the user that the evaluation is complete.

[0117] Purchase of preference data

[0118] Step 1:

[0119] The terminal displays an interface listing paid preference data.

[0120] Step 2:

[0121] The user selects the preference data he or she wishes to purchase and clicks the purchase button.

[0122] Step 3:

[0123] The terminal displays a form for entering payment information.

[0124] Step 4:

[0125] The user enters payment information and clicks the submit button.

[0126] Step 5:

[0127] The terminal sends an HTTP POST request containing payment information to the server.

[0128] Step 6:

[0129] The server receives the request and communicates with the payment provider to process the payment.

[0130] Step 7:

[0131] The server confirms the success of the payment and retrieves the relevant preference data from the database.

[0132] Step 8:

[0133] The server transmits the acquired data to the user's terminal.

[0134] Step 9:

[0135] The device receives the data and installs it into the generative AI model.

[0136] Step 10:

[0137] The terminal displays a purchase completion message to the user.

[0138] The above is a detailed description of the specific operations at each processing step. By following these steps, the system can effectively manage user preference data and apply it to the generative AI model.

[0139] Example 1

[0140] 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."

[0141] There is a need to efficiently manage user preference data and apply it to generative AI models to provide fast and accurate responses that match individual user preferences. There is also a growing need for a smooth interface for uploading and downloading preference data and the rating process, as well as functions such as paid data provision and data filtering based on rating information.

[0142] 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.

[0143] In this invention, the server includes a means for transmitting preference data selected from the user's terminal to the server, a means for storing the preference data received by the server in a database, and a means for displaying a notification to the user upon completion of uploading the preference data. This allows users to easily manage their own preference data and apply it to generative AI models. In addition, accurate data storage and notification of upload completion enhances feedback to the user, improving the utilization efficiency of the entire system.

[0144] "User" refers to any individual or corporation that uses this system.

[0145] "Preference Data" means data containing information about your preferences and tastes.

[0146] "Private data format" refers to a specific data format in which data converted from another format is stored.

[0147] "Uploading means" refers to a method or device that allows a user to transmit preference data to the system.

[0148] "Device" refers to an electronic device operated by a user, such as a computer, smartphone, or tablet.

[0149] "Server" refers to a computer system that receives, stores, transforms, and transmits data.

[0150] "Database" refers to a data management system for efficiently storing and retrieving large amounts of data.

[0151] "Means for converting into a dedicated data format and saving" refers to a method or device for converting received preference data into a specific format and saving it in that format.

[0152] "Means for downloading" refers to a method or device for transmitting preference data from a server to a user terminal.

[0153] "Generative AI model" refers to an artificial intelligence model that generates output based on user preference data.

[0154] The term "rating means" refers to a method or device for a user to input an evaluation of preference data.

[0155] "Means for providing for a fee" refers to a method or device for providing preference data in exchange for a certain fee.

[0156] The term "means for filtering based on rating information" refers to a method or device for selecting preference data based on rating information input by a user.

[0157] "Notification means" refers to a method or device for notifying the user of the system status or processing results.

[0158] By using the above definitions, the components and functions of the system of the present invention can be clearly understood and explained.

[0159] MODE FOR CARRYING OUT THE INVENTION

[0160] The following describes an example of a specific embodiment of the system of the present invention. This system effectively manages user preference data and applies it to a generative AI model.

[0161] The system mainly consists of the following components:

[0162] 1. Devices that upload user preference data

[0163] 2. Server that stores and converts received preference data

[0164] 3. A server that provides preference data in response to requests from user devices

[0165] 4. Generative AI models that install user preference data

[0166] 5. A device that provides an interface for users to evaluate preference data

[0167] Hardware and software used

[0168] Device:

[0169] A device is an electronic device operated by a user, such as a computer, smartphone, or tablet. A web browser interface or dedicated application is installed on the device, providing an interface for the user to select, upload, and rate preference data. The device communicates with the server using the HTTP protocol.

[0170] Examples: laptops, smartphones

[0171] server:

[0172] A server is a computer system that receives, stores, converts, and transmits data. The server has the functionality to convert preference data into a dedicated data format and store it in a database. The server also provides selected data and receives rating information.

[0173] Examples: AWS EC2, IBM Cloud

[0174] Database:

[0175] A database is a data management system for efficiently storing and retrieving large amounts of data. Databases store user preference data and ratings.

[0176] Examples: MySQL, MongoDB

[0177] Generative AI models:

[0178] A generative AI model is an artificial intelligence model that generates output based on user preference data. This model uses the preference data installed from the device to generate responses that are customized for each user.

[0179] Examples: GPT-3, BERT

[0180] Specific examples

[0181] Upload your preference data:

[0182] When a user uploads new preference data, a form including a file selection button and an upload button is displayed on the device. When the user selects "Music Preference Data.csv" and clicks the upload button, the data is sent from the device to the server. The server receives the data, converts it into a dedicated data format (e.g., JSON format), and stores it in a database. The server then returns a notification that the upload is complete to the device, and the device displays a message to the user saying "Upload completed."

[0183] Install an example prompt into your generative AI model:

[0184] When a user uses a prompt such as "Recommend me a list of new movies based on my preferences," the prompt is sent to the generative AI model via the device. The generative AI model generates an appropriate movie list based on the user's installed preference data and returns it to the device. This process allows users to quickly obtain a recommendation list that suits their preferences.

[0185] From the above description, it is believed that a specific embodiment of the system according to the present invention can be understood, which makes the system a useful tool for efficiently managing user preference data and applying it to generative AI models.

[0186] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0187] Step 1:

[0188] Interface Display

[0189] The terminal displays a preference data upload form to the user. The inputs include a "file selection button" and an "upload button." This action is displayed using a GUI library, allowing the user to select preference data.

[0190] Step 2:

[0191] Data Selection

[0192] The user selects the preference data file they want to upload from their device. The input includes the file path (e.g., "music preference data.csv"). The user's operation saves the path of the selected file in a variable on the device.

[0193] Step 3:

[0194] Data transmission

[0195] After selecting a preference data file, the user clicks the upload button. The device sends the selected preference data file to the server. The transmission uses an HTTP POST request, with the selected preference data file as input and the status of the file transmission to the server as output. This process transfers the file from the device to the server.

[0196] Step 4:

[0197] Data reception

[0198] The server temporarily stores the received preference data in a specific directory. The input is the file data in the HTTP request body, and the output is the temporary storage path in the server. In this step, the file data is stored in the server.

[0199] Step 5:

[0200] Data Conversion

[0201] The server converts the temporarily stored preference data into a dedicated data format (e.g., JSON format). The conversion uses the Python pandas library, and includes the temporarily stored file path as input and the converted data object as output. In this step, the CSV format data is converted into JSON format.

[0202] Step 6:

[0203] Data storage

[0204] The server saves the transformed data to a database, using MySQL or MongoDB, with the transformed data object as input and the status of the data being saved in the database as output. This process writes the data to the database.

[0205] Step 7:

[0206] Upload completion notification

[0207] After the server confirms that the data has been saved correctly, it returns an upload completion notification to the user's device. The input is the database save status, and the output is a completion notification message in the HTTP response body. This causes the device to display "Upload completed" to the user.

[0208] Step 8:

[0209] Data list display

[0210] The terminal provides an interface that displays a list of available preference data. The input includes the list of preference data retrieved from the server, and the output includes the displayed list of data. The terminal then presents the user with preference data options.

[0211] Step 9:

[0212] Data selection and download request submission

[0213] The user selects the preference data they wish to download and clicks the download button. The terminal sends a download request for the selected preference data to the server. The input is the user-selected data, and the output is the server transmission status. The download request is sent from the terminal to the server.

[0214] Step 10:

[0215] Data Acquisition and Transmission

[0216] The server retrieves the relevant preference data from the database and sends it to the user's device. The input is a download request, and the output is the downloaded data included in the HTTP response. The device then receives the necessary data.

[0217] Step 11:

[0218] Data installation

[0219] The terminal installs the received preference data into the generative AI model. The input is the downloaded data, and the output is the status of data application to the generative AI model. This process applies the preference data to the generative AI model.

[0220] Step 12:

[0221] Evaluation interface display

[0222] The terminal displays a rating interface that includes rating buttons and review input fields, a rating form object as input, and user input fields as output, allowing users to rate preference data.

[0223] Step 13:

[0224] Enter and submit your rating

[0225] The user inputs rating information and reviews and presses the send button. The terminal sends the rating information to the server. The input is the user's rating information, and the output is the server transmission status. The rating information is sent to the server.

[0226] Step 14:

[0227] Saving rating information

[0228] The server saves the rating information to the database. The input is a rating information object, and the output is a database save status. This saves the rating information to the database.

[0229] Step 15:

[0230] Evaluation completion notification

[0231] After the server confirms that the evaluation has been saved correctly, it returns an evaluation completion notification to the terminal. The input is the database saving status, and the output is an evaluation completion message in the HTTP response body. The terminal displays the evaluation completion message to the user.

[0232] (Application example 1)

[0233] 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."

[0234] Conventional systems for managing preference data did not efficiently suggest products based on individual users' preferences, resulting in low user satisfaction. It was also difficult to use other users' preference data to receive product suggestions that matched one's hobbies and interests. Furthermore, the lack of a preference data evaluation or filtering function meant that inappropriate data could be mixed in.

[0235] 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.

[0236] In this invention, the server includes means for converting preference data into a dedicated data format and saving it, means for uploading preference data, means for downloading preference data, means for evaluating the preference data, and means for providing personalized product suggestions using the preference data. This allows users to receive optimal product suggestions using their own and other users' preference data. Furthermore, filtering the preference data based on the evaluation information allows more appropriate and reliable suggestions to be provided.

[0237] 1. "Preference Data" means information relating to your interests and concerns.

[0238] 2. "Private Data Format" means a data structure specifically designed for efficiently storing and processing Preference Data.

[0239] 3. "Uploading means" means a function that allows a user to send preference data from their device to the server.

[0240] 4. "Downloading means" means a function that allows a user to obtain preference data from a server to their own device.

[0241] 5. "Rating means" refers to the functionality that allows users to rate and review preference data and suggestions based on it.

[0242] 6. "Personalized product suggestions" means a function that suggests optimal products and services based on a user's preference data.

[0243] 7. "Paid provision means" means a function for providing preference data for a certain fee.

[0244] 8. "Filtering means" refers to a function that selects preference data based on evaluation information.

[0245] The system of the present invention is designed to effectively manage user preference data and apply it to a generative AI model. The system mainly consists of three elements: a server, a user's device, and a generative AI model. The role of each element and the specific processing content are explained below.

[0246] server

[0247] The server plays a central role in efficiently storing, transforming, and serving preference data.

[0248] Storage and conversion of preference data: The server converts the preference data received from the user into a dedicated data format and stores it in a database. This data format is designed for efficient processing of preference data.

[0249] Data upload and download: The server receives requests to upload preference data from the user device and stores the data in the database. It also retrieves the relevant data from the database in response to download requests and sends it to the user device.

[0250] Processing rating information: The server receives the user's rating information and stores it in a database, which can be used as a reference for other users to select highly rated preference data.

[0251] User's device

[0252] The user's terminal provides an interface for uploading, downloading, and rating preference data.

[0253] Data upload: The user can select preference data using the device interface and send it to the server by pressing the upload button.

[0254] Data download: Using the device interface, users can select, purchase, and download preference data provided by other users for a fee. This data is then installed into the generative AI model and used to make personalized product recommendations.

[0255] Data rating: Provides an interface for users to rate and review proposed products and services. The rating information is sent to the server and stored in a database.

[0256] Generative AI Models

[0257] The generative AI model is responsible for making personalized product suggestions based on user preference data.

[0258] Personalized product recommendations: User preference data is used to recommend the best products and services for each user, based on the preference data installed in the generative AI model.

[0259] Specific examples

[0260] For example, if a user wants to upload new preference data, he or she can perform the following operations:

[0261] 1. The user selects preference data that reflects their interests and preferences in the device's upload form.

[0262] 2. When you press the upload button, the data is sent to the server, converted into a dedicated data format, and saved.

[0263] A specific example of purchasing highly rated preference data created by other users is as follows.

[0264] 1. The user displays a list of preference data that is available on the device for a fee.

[0265] 2. Select the data you want to purchase, enter your payment information, and press the purchase button.

[0266] 3. Once the payment is successful, the relevant data is downloaded from the server to the device and installed into the generative AI model.

[0267] Examples of prompt statements

[0268] "My hobbies are cooking and gardening, so I'd like product suggestions that suit my tastes."

[0269] "I've recently become interested in yoga, so I'd like to receive product suggestions related to it."

[0270] In this way, the system enables the recommendation of products and services optimized to the user's preferences, improving the user experience.

[0271] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0272] Step 1:

[0273] Uploading preference data

[0274] Input: The user selects their preference data on the device and presses the upload button.

[0275] process:

[0276] The terminal reads the preference data file selected by the user.

[0277] A request to be sent to the server is created to convert the read data into a dedicated data format.

[0278] Data processing: Preference data is sent to the server in a standard format such as JSON and converted into a dedicated data format on the server side.

[0279] Output: The server saves the preference data in the database and sends an upload completion notification to the device.

[0280] Step 2:

[0281] Download preference data

[0282] Input: The user selects the preference data they want to download on their device and presses the download button.

[0283] process:

[0284] The terminal transmits a download request for the selected preference data to the server.

[0285] The server retrieves the relevant preference data from the database.

[0286] Data calculation: The server analyzes the user's request and extracts the corresponding preference data.

[0287] Output: The extracted preference data is sent to the user's device and installed into the generative AI model.

[0288] Step 3:

[0289] Evaluating preference data

[0290] Input: The user enters rating information or a review in the rating interface and presses the submit button.

[0291] process:

[0292] The terminal reads the user's input.

[0293] The read evaluation information is sent to the server.

[0294] Data processing: The server stores the received evaluation information in a database in an appropriate format.

[0295] Output: The server sends a notification of the completion of the evaluation to the terminal, and the terminal displays a message to the user that the evaluation is complete.

[0296] Step 4:

[0297] Personalized product recommendations

[0298] Input: User preference data and a prompt from the user (e.g., "My hobbies are cooking and gardening, so please suggest products that suit my preferences.") are input into the generative AI model.

[0299] process:

[0300] The generative AI model analyzes the input preference data and prompt text.

[0301] Select the most suitable products and services based on the data and generate a list of proposals.

[0302] Data calculation: The generative AI model runs an algorithm that matches input data with an internal database to make the most suitable product recommendations for the user.

[0303] Output: A personalized product suggestion list is displayed on the user's device.

[0304] In this way, each step works in conjunction with each other to make optimal product suggestions to users and improve the user experience.

[0305] 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.

[0306] The following describes a specific embodiment of the system of the present invention. This system not only manages user preference data and efficiently installs it into a generative AI model, but also combines it with an emotion engine to enable responses that correspond to the user's emotions.

[0307] The system includes a server for converting user preference data into a dedicated data format and storing it, a user terminal for uploading and downloading the preference data, a user terminal for evaluating and filtering the preference data, and an emotion engine for recognizing user emotions.

[0308] Specific processing of the program

[0309] The program of this system performs the following processing.

[0310] 1. Uploading preference data

[0311] The terminal displays an interface for the user to select preference data.

[0312] The user selects a preference data file and presses the upload button.

[0313] The terminal transmits the selected preference data to the server.

[0314] The server converts the received preference data into a dedicated data format and stores it in a database.

[0315] The server returns a notification of upload completion to the user's device.

[0316] The device will display a message to the user that the upload is complete.

[0317] 2. Downloading preference data

[0318] The terminal provides an interface for displaying a list of preference data.

[0319] The user selects the preference data they wish to download.

[0320] The terminal sends an HTTP GET request including the ID of the selected data to the server.

[0321] The server receives the request and retrieves the corresponding preference data from the database.

[0322] The server returns the acquired data to the user's terminal.

[0323] The device installs the received preference data into the generated AI model.

[0324] The device will display a download complete message to the user.

[0325] 3. Evaluating and filtering preference data

[0326] The terminal displays a rating interface including a rating button and a review input field.

[0327] The user enters the rating score and review comments and presses the submit button.

[0328] The terminal transmits the evaluation information to the server.

[0329] The server receives the request and stores the rating information in a database.

[0330] The server returns a notification of evaluation completion to the terminal.

[0331] The terminal displays a message to the user that the evaluation is complete.

[0332] The server filters the preference data based on the rating information.

[0333] 4. Reflecting the Emotion Engine in Generative AI Models

[0334] The device activates an emotion engine and analyzes the user's emotions.

[0335] The emotion engine analyzes data such as the user's voice and facial expressions to recognize emotions.

[0336] The server receives the emotion data from the emotion engine and stores it in a database.

[0337] The generative AI model adjusts the response sentence based on the emotional data and replies to the user.

[0338] Specific examples

[0339] For example, when a user uploads new preference data, the specific flow is as follows.

[0340] 1. The device presents the user with a preference data upload form.

[0341] 2. The user selects their preference data and clicks the "Upload" button.

[0342] 3. The terminal transmits the selected data to the server.

[0343] 4. The server receives the data, converts it into a proprietary data format, and stores it in a database.

[0344] 5. The server returns an upload completion notification to the device, and the device displays that information to the user.

[0345] Also, if a user recognizes emotions and adjusts the generative AI model's response based on them, the process is as follows:

[0346] 1. The device activates the emotion engine and analyzes the user's words and actions.

[0347] 2. The emotion engine analyzes the user's tone of voice and facial expressions to recognize their emotions.

[0348] 3. The server receives the analysis results and stores them in a database.

[0349] 4. The generative AI model adjusts responses based on emotional data.

[0350] 5. The device provides the adjusted response to the user.

[0351] Through the above process, the system can effectively manage user preference and emotion data and apply it to the generative AI model, allowing users to quickly obtain responses that match their preferences and emotions, greatly improving the utilization efficiency of the generative AI model.

[0352] The processing flow will be explained below.

[0353] Uploading preference data

[0354] Step 1:

[0355] The terminal displays the preference data upload interface.

[0356] Step 2:

[0357] The user selects a preference data file and clicks the upload button.

[0358] Step 3:

[0359] The device sends the selected file to the server as an HTTP POST request.

[0360] Step 4:

[0361] The server receives the request and temporarily stores the file.

[0362] Step 5:

[0363] The server converts the received file into a dedicated data format.

[0364] Step 6:

[0365] The server stores the converted data in a database.

[0366] Step 7:

[0367] The server will return a notification of upload completion and the ID of the saved data to the device.

[0368] Step 8:

[0369] The device displays a message to the user that the upload is complete.

[0370] Download preference data

[0371] Step 1:

[0372] The terminal displays an interface listing the preference data.

[0373] Step 2:

[0374] The user selects the preference data that he or she wishes to download.

[0375] Step 3:

[0376] The device sends an HTTP GET request including the ID of the selected data to the server.

[0377] Step 4:

[0378] The server receives the request and retrieves the corresponding preference data from the database.

[0379] Step 5:

[0380] The server returns the acquired data to the user's terminal.

[0381] Step 6:

[0382] The device receives the returned data and installs it into the generative AI model.

[0383] Step 7:

[0384] The device displays a download completion message to the user.

[0385] Evaluating and filtering preference data

[0386] Step 1:

[0387] The terminal displays an interface for evaluating the preference data.

[0388] Step 2:

[0389] The user enters the rating score and review comments and clicks the submit button.

[0390] Step 3:

[0391] The device sends the rating information to the server as an HTTP POST request.

[0392] Step 4:

[0393] The server receives the request and stores the rating information in a database.

[0394] Step 5:

[0395] The server returns a notification of evaluation completion to the terminal.

[0396] Step 6:

[0397] The terminal displays a message to the user that the evaluation is complete.

[0398] Step 7:

[0399] The server filters the preference data based on the rating information.

[0400] Reflecting the emotion engine in the generative AI model

[0401] Step 1:

[0402] The device activates the emotion engine and displays an interface that analyzes the user's emotions.

[0403] Step 2:

[0404] The emotion engine analyzes the user's tone of voice and facial expression data to recognize emotions.

[0405] Step 3:

[0406] The device transmits the emotion data to the server.

[0407] Step 4:

[0408] The server stores the emotion data in a database.

[0409] Step 5:

[0410] A generative AI model adjusts responses based on emotional data.

[0411] Step 6:

[0412] The server returns the adjusted response to the terminal.

[0413] Step 7:

[0414] The terminal displays the adjusted response sentence to the user.

[0415] Specific examples

[0416] Example of uploading preference data

[0417] Step 1:

[0418] The terminal displays a preference data upload form and provides the user with file selection options.

[0419] Step 2:

[0420] The user selects his / her preference data and clicks the "Upload" button.

[0421] Step 3:

[0422] The device collects the selected files and sends them to the server via an HTTP POST request.

[0423] Step 4:

[0424] The server receives the request and temporarily stores the file.

[0425] Step 5:

[0426] The server converts the received file into a dedicated data format and checks the conversion results.

[0427] Step 6:

[0428] The server saves the converted data to the database and confirms the success of the save.

[0429] Step 7:

[0430] The server will return a notification of upload completion and the ID of the saved data to the device.

[0431] Step 8:

[0432] The device displays a message to the user that the upload is complete.

[0433] Example of response adjustment when using the emotion engine

[0434] Step 1:

[0435] The device activates an emotion engine and displays an interface that analyzes the user's facial expressions and voice in real time.

[0436] Step 2:

[0437] The emotion engine detects the user's tone of voice and facial expressions in real time and recognizes their emotions.

[0438] Step 3:

[0439] The device collects the recognized emotion data and sends it to the server via an HTTP POST request.

[0440] Step 4:

[0441] The server receives the emotion data and stores it in a database.

[0442] Step 5:

[0443] A generative AI model adjusts response strategies based on stored emotional data.

[0444] Step 6:

[0445] The server returns the adjusted response to the terminal.

[0446] Step 7:

[0447] The terminal displays the adjusted response sentence to the user.

[0448] The above is a detailed description of the specific operations at each processing step. By following these steps, the system can effectively manage user preference data and emotion data and apply them to the generative AI model.

[0449] Example 2

[0450] 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."

[0451] Conventional generative AI models have difficulty managing user preference and emotional data and providing personalized responses based on this data. Furthermore, there was no centralized system for managing data uploads, downloads, ratings, and emotional analysis, making it difficult to improve the user experience.

[0452] 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.

[0453] In this invention, the server includes means for converting user preference data into a dedicated data format and saving it, means for uploading the preference data, means for downloading the preference data, means for evaluating the preference data, means for activating an emotion engine that analyzes user emotions, and means for adjusting the response of the generative AI model based on the analyzed emotion data, thereby enabling effective management of the user's preference data and emotion data and providing personalized responses through the generative AI model.

[0454] "Preference Data" refers to information about your personal preferences, interests, and concerns.

[0455] "Private Data Format" refers to a data format or structure that is specialized for storing and processing preference data.

[0456] "Upload" refers to the operation of sending data from a user's device to a server.

[0457] "Download" refers to the operation of obtaining data from a server to a user's device.

[0458] "Evaluation" refers to the operation in which a user assigns a score or review comment to preference data.

[0459] An "emotion engine" refers to software or hardware that analyzes data such as the user's voice and facial expressions and recognizes emotions.

[0460] "Analysis" refers to the process of analyzing given data and finding meaning and patterns within it.

[0461] A "generative AI model" refers to an artificial intelligence model that generates responses based on input from a user.

[0462] DETAILED DESCRIPTION OF THE INVENTION The present invention is a system for effectively managing user preference data and providing personalized responses through a generative AI model.

[0463] The system consists of the following components:

[0464] 1. Server

[0465] 2. Terminal

[0466] 3. Users

[0467] 4. Emotion Engine

[0468] 5. Generative AI Models

[0469] Uploading and storing preference data

[0470] The device displays an interface for the user to upload preference data. The interface is implemented using HTML and JavaScript, and provides a form including a file selection button and an upload button. The user selects their preference data file and clicks the upload button. The device sends the selected file to the server, and the server converts the received data into a dedicated data format and stores it in a database.

[0471] For example, if a user uploads movie preference data:

[0472] Prompt: "Please select your movie preferences and press the upload button."

[0473] Download preference data

[0474] The device provides an interface that displays a list of preference data. The user selects the data they wish to download from the list. The device then sends an HTTP GET request including the selected data ID to the server, and the server retrieves the corresponding preference data from the database and sends it to the device. The device then installs the received preference data into the generative AI model.

[0475] For example, if a user downloads music preference data:

[0476] Prompt: "Select the music preferences you would like to download."

[0477] Evaluating and filtering preference data

[0478] The device displays an interface for the user to input ratings and reviews of the preference data. The user enters a rating score and review comments and clicks the submit button. The device sends the entered rating information to the server, which stores the information in a database. The server filters the preference data based on the rating information.

[0479] For example, if a user rates their preference data for a cooking recipe:

[0480] Prompt: "Please rate the preference data for this recipe."

[0481] Using an emotion engine and incorporating it into generative AI models

[0482] The device activates the emotion engine to analyze the user's emotions. The emotion engine analyzes the user's voice and facial expressions to recognize emotions. The server receives the emotion data and stores it in a database. The generative AI model adjusts the response based on the emotion data, and the device provides the response to the user.

[0483] For example, to tailor your response based on user sentiment:

[0484] Prompt: "I am tailoring my response based on how you are currently feeling."

[0485] The system effectively manages user preference data and emotion data and provides users with personalized responses through generative AI models, improving the user experience and significantly improving the utilization efficiency of generative AI models.

[0486] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0487] Step 1:

[0488] The terminal displays an interface for the user to upload preference data. The interface provides an HTML form with a file selection button and an "Upload" button. The user selects a preference data file and clicks the upload button (input: preference data file, output: data from the selected file).

[0489] Step 2:

[0490] The device reads the user-selected preference data file using a JavaScript File object and sends it to the server as an HTTP POST request (input: data from the selected file, output: preference data sent to the server).

[0491] Step 3:

[0492] The server calls a function to convert the received preference data into a dedicated data format and stores the converted data in a database (input: received preference data, output: data converted into dedicated data format).

[0493] Step 4:

[0494] The server sends an HTTP response to the terminal indicating that the data has been successfully saved (input: data conversion and saving results, output: notification that upload has been completed).

[0495] Step 5:

[0496] The device receives the response from the server and displays a message to the user saying "Upload completed" (Input: Notification of upload completion, Output: Display message to user).

[0497] Step 6:

[0498] The terminal requests current preference data from the database to provide the user with an interface that displays a list of preference data (input: user request, output: list of preference data).

[0499] Step 7:

[0500] The user selects the preference data they wish to download from the list and clicks the "Download" button (input: list of preference data, output: data ID to be downloaded).

[0501] Step 8:

[0502] The terminal sends an HTTP GET request including the selected data ID to the server, and the server retrieves the corresponding preference data from the database and sends it to the terminal (input: data ID, output: corresponding preference data).

[0503] Step 9:

[0504] The device installs the received preference data into the generative AI model (input: received preference data, output: data installed into the generative AI model).

[0505] Step 10:

[0506] The device displays a message to the user that the download is complete. (Input: Download and installation complete information; Output: Message displayed to the user.)

[0507] Step 11:

[0508] The terminal displays a rating interface including rating buttons and a review input field. The user enters a rating score and review comments and clicks the submit button (input: preference data, output: rating score and review comments).

[0509] Step 12:

[0510] The terminal transmits data including the rating score and review comments to the server (input: rating score and review comments, output: rating information transmitted to the server).

[0511] Step 13:

[0512] The server stores the received rating information in a database (input: received rating information, output: rating information stored in the database).

[0513] Step 14:

[0514] The server sends an HTTP response to the terminal indicating that the rating was successfully saved (input: rating saved result, output: notification of rating completion).

[0515] Step 15:

[0516] The terminal receives a notification from the server that the evaluation is complete and displays a message to the user saying "Evaluation is complete" (Input: Notification of evaluation completion, Output: Display a message to the user).

[0517] Step 16:

[0518] The server filters the preference data based on the rating information, for example, by excluding data below a certain rating score (input: rating information, output: filtered preference data).

[0519] Step 17:

[0520] The device starts the emotion engine and captures the user's facial expressions and voice using the camera and microphone (input: user's facial expressions and voice, output: captured data).

[0521] Step 18:

[0522] The emotion engine analyzes the captured data and recognizes the user's emotions (input: captured data, output: recognized emotion data).

[0523] Step 19:

[0524] The server receives the emotion data sent from the emotion engine and stores it in a database (input: recognized emotion data, output: emotion data stored in the database).

[0525] Step 20:

[0526] The generative AI model generates responses based on emotion data stored in a database, tailoring the response to specific emotions (input: emotion data, output: tailored response).

[0527] Step 21:

[0528] The device receives the response from the generative AI model and displays it to the user (input: adjusted response, output: response presented to the user).

[0529] This allows us to utilize user preference and emotion data to optimize the generative AI model and provide personalized services to users.

[0530] (Application example 2)

[0531] 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."

[0532] In today's brick-and-mortar stores, improving the customer experience requires accurately understanding customer preferences and emotions and providing personalized services based on those. However, current technology lacks the means to analyze and respond to customer preferences and emotions in real time. As a result, it is difficult to improve customer satisfaction or stimulate purchasing motivation. Therefore, to improve the customer experience, a system is needed that can manage and analyze customer preference and emotion data in real time and provide responses based on that data.

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

[0534] In this invention, the server includes means for converting preference data into a dedicated data format and saving it, means for uploading the preference data, means for downloading the preference data, means for evaluating the preference data, means for analyzing the user's emotions using an emotion engine, means for reflecting the preference data and emotion data in a generative AI model, and means for providing a response based on the user's preferences and emotions through an application installed on the smart device. This makes it possible to provide personalized services in physical stores according to the customer's preferences and emotions.

[0535] "Preference Data" is information about your preferences and interests.

[0536] A "specialized data format" is a data format designed to efficiently store and manage specific data.

[0537] A "means for uploading preference data" is a method or device that allows a user to transmit their preference data to the system.

[0538] A "means for downloading preference data" is a method or device for obtaining preference data from the system.

[0539] The "means for evaluating preference data" refers to a method or device for evaluating the quality and usefulness of data based on a user's preference data.

[0540] An "emotion engine" is software or hardware that analyzes a user's emotions and generates the results as information.

[0541] A "generative AI model" is an artificial intelligence model that responds or generates appropriate responses based on given data and conditions.

[0542] A "smart device" is an electronic device that has advanced computing power and built-in communication functions and sensors.

[0543] "Means for reflecting preference data and emotional data" refers to a method or device for incorporating preference data and emotional data obtained from users into a system and using the data.

[0544] A "personalized service" is a service that is customized to suit the preferences and feelings of each individual user.

[0545] A specific embodiment of the system of the present invention will be described below, which aims to improve customer experience, particularly in brick-and-mortar stores.

[0546] Users collect their own preference data and upload it to the system via their smart devices (e.g., smart glasses). This preference data is sent to a server, converted into a dedicated data format, and stored in a database. When the user visits a store, the smart device downloads the latest preference data from the server and presents it to store staff.

[0547] The server is equipped with means for storing, uploading, downloading, and evaluating preference data. This allows user preference data to be accumulated and evaluated or filtered as needed. For example, if preference data is stored in the form of a number, it can be filtered using SQL queries, etc.

[0548] The emotion engine is used to analyze users' real-time emotions. This emotion engine uses the smart device's camera and microphone to collect the user's facial expressions and tone of voice and convert them into emotion data. This emotion data is sent to the server and reflected in the generative AI model. The emotion engine uses customer signal analysis software.

[0549] The generative AI model generates responses and suggestions for users based on preference and emotion data. The generative AI model has advanced computing capabilities and takes both preference and emotion data into account to derive optimal responses. These responses are provided to staff via smart devices, enabling personalized service for customers.

[0550] As a concrete example, when User A visits a store, their smart device downloads their preference data from the server. The emotion engine analyzes User A's facial expressions and tone of voice in real time to detect the emotion of "happiness." Based on this data, the generative AI model generates a response such as, "We'd like to introduce you to some new products we currently recommend. This coffee in particular has been very popular recently." A staff member checks this information via their smart device and makes a suggestion to User A.

[0551] Another example of a prompt is, "Please suggest the most suitable related products based on the product selected by the customer. Also, please analyze the customer's emotions from their facial expressions and speech and provide advice on how to provide appropriate customer service." This prompt allows the generative AI model to comprehensively determine the user's preferences and emotions and make optimal suggestions and responses.

[0552] This system significantly improves the customer experience in physical stores and makes it possible to provide services that highly satisfy customers.

[0553] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0554] Step 1:

[0555] Uploading preference data

[0556] Input: The user selects their preference data.

[0557] Specific operation: The terminal (smart device) displays an interface for the user to select preference data. When the user selects a preference data file and presses the "Upload" button, the terminal sends the data to the server.

[0558] Data processing / calculation: The server converts the received data into a dedicated data format and stores it in a database.

[0559] Output: A notification that the upload is complete is sent back to the device, which displays it to the user.

[0560] Step 2:

[0561] Download preference data

[0562] Input: When a user enters a physical store, the device sends a data request to the server.

[0563] Specific operation: The device periodically sends an HTTP GET request to the server to obtain the latest preference data. The server receives the request and retrieves the corresponding preference data from the database.

[0564] Data processing / calculation: The server sends the acquired data to the terminal, which receives and displays it.

[0565] Output: The latest preference data is downloaded and presented to the store staff.

[0566] Step 3:

[0567] Evaluating preference data

[0568] Input: Users and staff input their ratings for preference data.

[0569] Specific operation: The terminal displays the evaluation interface to the user and staff. When the user and staff input the evaluation score and review comments and press the send button, the evaluation information is sent to the server.

[0570] Data processing / calculation: The server stores the rating information in a database and filters the preference data based on the rating information.

[0571] Output: The evaluation results are sent back to the terminal, which displays them to the user and staff.

[0572] Step 4:

[0573] Emotion engine activation and analysis

[0574] Input: The user's facial expressions and tone of voice are collected in the store.

[0575] How it works: The device captures the user's facial expressions and tone of voice in real time through its built-in camera and microphone, and this data is sent to the emotion engine for analysis.

[0576] Data processing / calculation: The emotion engine analyzes the collected data and recognizes the user's emotional state. The analysis results are sent to the server as emotion data.

[0577] Output: Emotion data is generated and stored on the server.

[0578] Step 5:

[0579] Generative AI model generates responses

[0580] Input: User preference and emotion data is fed into the generative AI model.

[0581] Specific operation: The server provides the stored preference data and emotion data to the generative AI model, which then generates an appropriate response based on the prompt sentence.

[0582] Data processing / calculation: Generative AI models perform calculations based on preference data and emotion data to generate optimal responses.

[0583] Output: The generated response is sent to the terminal and presented to the store staff.

[0584] An example prompt is, "Please suggest the most suitable related products based on the product selected by the customer. Also, please analyze the customer's emotions from their facial expressions and speech and display advice on how to provide appropriate customer service."

[0585] Through these processing steps, this system can significantly improve the customer experience in physical stores.

[0586] 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.

[0587] 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.

[0588] 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.

[0589] [Second embodiment]

[0590] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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."

[0602] The following describes a specific embodiment of the system of the present invention. This system manages user preference data and efficiently installs it into a generative AI model.

[0603] This system includes a server for converting and saving user preference data into a dedicated data format, a user terminal for uploading the preference data, a user terminal for downloading the preference data, and a user terminal for evaluating the preference data.

[0604] Specific processing of the program

[0605] The program of this system performs the following processing.

[0606] 1. Uploading preference data

[0607] The terminal displays an interface for the user to select preference data.

[0608] The user selects a preference data file and presses the upload button.

[0609] The terminal transmits the selected preference data to the server.

[0610] The server converts the received preference data into a dedicated data format and stores it in a database.

[0611] The server returns a notification of upload completion to the user's device.

[0612] The device will display a message to the user that the upload is complete.

[0613] 2. Downloading preference data

[0614] The terminal provides an interface for displaying a list of preference data.

[0615] The user selects the preference data they wish to download.

[0616] The terminal transmits a download request for the selected preference data to the server.

[0617] The server retrieves the relevant preference data from the database and sends it to the user's device.

[0618] The device installs the received preference data into the generated AI model.

[0619] 3. Evaluation of preference data

[0620] The terminal displays a rating interface including a rating button and a review input field.

[0621] The user enters rating information and reviews and presses the submit button.

[0622] The terminal transmits the evaluation information to the server.

[0623] The server stores the evaluation information in a database and returns a notification of evaluation completion to the terminal.

[0624] The terminal displays a message to the user that the evaluation is complete.

[0625] Specific examples

[0626] For example, when a user uploads new preference data, the specific flow is as follows.

[0627] 1. The terminal displays a preference data upload form to the user.

[0628] 2. The user selects their preference data and clicks the "Upload" button.

[0629] 3. The terminal transmits the selected data to the server.

[0630] 4. The server receives the data, converts it into a proprietary data format, and stores it in a database.

[0631] 5. The server returns an upload completion notification to the device, and the device displays that information to the user.

[0632] The flow when a user purchases preference data created by another user is as follows.

[0633] 1. The terminal displays a list of preference data that is available for a fee.

[0634] 2. The user selects the data they want to purchase, enters payment information, and presses the "Purchase" button.

[0635] 3. The terminal sends the entered payment information to the server.

[0636] 4. The server processes the payment, and if the payment is successful, retrieves the relevant data from the database and sends it to the user's device.

[0637] 5. The device receives the data and installs it into the generative AI model.

[0638] Through the above process, the system can effectively manage user preference data and apply it to the generative AI model, allowing users to quickly obtain responses that match their preferences and significantly improving the utilization efficiency of the generative AI model.

[0639] The processing flow will be explained below.

[0640] Uploading preference data

[0641] Step 1:

[0642] The terminal displays the preference data upload interface.

[0643] Step 2:

[0644] The user selects a preference data file and clicks the upload button.

[0645] Step 3:

[0646] The device sends the selected file to the server as an HTTP POST request.

[0647] Step 4:

[0648] The server receives the request and temporarily stores the file.

[0649] Step 5:

[0650] The server converts the received file into a dedicated data format.

[0651] Step 6:

[0652] The server stores the converted data in a database.

[0653] Step 7:

[0654] The server will return a notification of upload completion and the ID of the saved data to the device.

[0655] Step 8:

[0656] The device displays a message to the user that the upload is complete.

[0657] Download preference data

[0658] Step 1:

[0659] The terminal displays an interface listing the preference data.

[0660] Step 2:

[0661] The user selects the preference data that he or she wishes to download.

[0662] Step 3:

[0663] The device sends an HTTP GET request including the ID of the selected data to the server.

[0664] Step 4:

[0665] The server receives the request and retrieves the corresponding preference data from the database.

[0666] Step 5:

[0667] The server returns the acquired data to the user's terminal.

[0668] Step 6:

[0669] The device receives the returned data and installs it into the generative AI model.

[0670] Step 7:

[0671] The device displays a download completion message to the user.

[0672] Evaluating preference data

[0673] Step 1:

[0674] The terminal displays the evaluation interface.

[0675] Step 2:

[0676] The user enters the rating score and review comments and clicks the submit button.

[0677] Step 3:

[0678] The device sends the rating information to the server as an HTTP POST request.

[0679] Step 4:

[0680] The server receives the request and stores the rating information in a database.

[0681] Step 5:

[0682] The server returns a notification of evaluation completion to the terminal.

[0683] Step 6:

[0684] The terminal displays a message to the user that the evaluation is complete.

[0685] Purchase of preference data

[0686] Step 1:

[0687] The terminal displays an interface listing paid preference data.

[0688] Step 2:

[0689] The user selects the preference data he or she wishes to purchase and clicks the purchase button.

[0690] Step 3:

[0691] The terminal displays a form for entering payment information.

[0692] Step 4:

[0693] The user enters payment information and clicks the submit button.

[0694] Step 5:

[0695] The terminal sends an HTTP POST request containing payment information to the server.

[0696] Step 6:

[0697] The server receives the request and communicates with the payment provider to process the payment.

[0698] Step 7:

[0699] The server confirms the success of the payment and retrieves the relevant preference data from the database.

[0700] Step 8:

[0701] The server transmits the acquired data to the user's terminal.

[0702] Step 9:

[0703] The device receives the data and installs it into the generative AI model.

[0704] Step 10:

[0705] The terminal displays a purchase completion message to the user.

[0706] The above is a detailed description of the specific operations at each processing step. By following these steps, the system can effectively manage user preference data and apply it to the generative AI model.

[0707] Example 1

[0708] 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."

[0709] There is a need to efficiently manage user preference data and apply it to generative AI models to provide fast and accurate responses that match individual user preferences. There is also a growing need for a smooth interface for uploading and downloading preference data and the rating process, as well as functions such as paid data provision and data filtering based on rating information.

[0710] 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.

[0711] In this invention, the server includes a means for transmitting preference data selected from the user's terminal to the server, a means for storing the preference data received by the server in a database, and a means for displaying a notification to the user upon completion of uploading the preference data. This allows users to easily manage their own preference data and apply it to generative AI models. In addition, accurate data storage and notification of upload completion enhances feedback to the user, improving the utilization efficiency of the entire system.

[0712] "User" refers to any individual or corporation that uses this system.

[0713] "Preference Data" means data containing information about your preferences and tastes.

[0714] "Private data format" refers to a specific data format in which data converted from another format is stored.

[0715] "Uploading means" refers to a method or device that allows a user to transmit preference data to the system.

[0716] "Device" refers to an electronic device operated by a user, such as a computer, smartphone, or tablet.

[0717] "Server" refers to a computer system that receives, stores, transforms, and transmits data.

[0718] "Database" refers to a data management system for efficiently storing and retrieving large amounts of data.

[0719] "Means for converting into a dedicated data format and saving" refers to a method or device for converting received preference data into a specific format and saving it in that format.

[0720] "Means for downloading" refers to a method or device for transmitting preference data from a server to a user terminal.

[0721] "Generative AI model" refers to an artificial intelligence model that generates output based on user preference data.

[0722] The term "rating means" refers to a method or device for a user to input an evaluation of preference data.

[0723] "Means for providing for a fee" refers to a method or device for providing preference data in exchange for a certain fee.

[0724] The term "means for filtering based on rating information" refers to a method or device for selecting preference data based on rating information input by a user.

[0725] "Notification means" refers to a method or device for notifying the user of the system status or processing results.

[0726] By using the above definitions, the components and functions of the system of the present invention can be clearly understood and explained.

[0727] MODE FOR CARRYING OUT THE INVENTION

[0728] The following describes an example of a specific embodiment of the system of the present invention. This system effectively manages user preference data and applies it to a generative AI model.

[0729] The system mainly consists of the following components:

[0730] 1. Devices that upload user preference data

[0731] 2. Server that stores and converts received preference data

[0732] 3. A server that provides preference data in response to requests from user devices

[0733] 4. Generative AI models that install user preference data

[0734] 5. A device that provides an interface for users to evaluate preference data

[0735] Hardware and software used

[0736] Device:

[0737] A device is an electronic device operated by a user, such as a computer, smartphone, or tablet. A web browser interface or dedicated application is installed on the device, providing an interface for the user to select, upload, and rate preference data. The device communicates with the server using the HTTP protocol.

[0738] Examples: laptops, smartphones

[0739] server:

[0740] A server is a computer system that receives, stores, converts, and transmits data. The server has the functionality to convert preference data into a dedicated data format and store it in a database. The server also provides selected data and receives rating information.

[0741] Examples: AWS EC2, IBM Cloud

[0742] Database:

[0743] A database is a data management system for efficiently storing and retrieving large amounts of data. Databases store user preference data and ratings.

[0744] Examples: MySQL, MongoDB

[0745] Generative AI models:

[0746] A generative AI model is an artificial intelligence model that generates output based on user preference data. This model uses the preference data installed from the device to generate responses that are customized for each user.

[0747] Examples: GPT-3, BERT

[0748] Specific examples

[0749] Upload your preference data:

[0750] When a user uploads new preference data, a form including a file selection button and an upload button is displayed on the device. When the user selects "Music Preference Data.csv" and clicks the upload button, the data is sent from the device to the server. The server receives the data, converts it into a dedicated data format (e.g., JSON format), and stores it in a database. The server then returns a notification that the upload is complete to the device, and the device displays a message to the user saying "Upload completed."

[0751] Install an example prompt into your generative AI model:

[0752] When a user uses a prompt such as "Recommend me a list of new movies based on my preferences," the prompt is sent to the generative AI model via the device. The generative AI model generates an appropriate movie list based on the user's installed preference data and returns it to the device. This process allows users to quickly obtain a recommendation list that suits their preferences.

[0753] From the above description, it is believed that a specific embodiment of the system according to the present invention can be understood, which makes the system a useful tool for efficiently managing user preference data and applying it to generative AI models.

[0754] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0755] Step 1:

[0756] Interface Display

[0757] The terminal displays a preference data upload form to the user. The inputs include a "file selection button" and an "upload button." This action is displayed using a GUI library, allowing the user to select preference data.

[0758] Step 2:

[0759] Data Selection

[0760] The user selects the preference data file they want to upload from their device. The input includes the file path (e.g., "music preference data.csv"). The user's operation saves the path of the selected file in a variable on the device.

[0761] Step 3:

[0762] Data transmission

[0763] After selecting a preference data file, the user clicks the upload button. The device sends the selected preference data file to the server. The transmission uses an HTTP POST request, with the selected preference data file as input and the status of the file transmission to the server as output. This process transfers the file from the device to the server.

[0764] Step 4:

[0765] Data reception

[0766] The server temporarily stores the received preference data in a specific directory. The input is the file data in the HTTP request body, and the output is the temporary storage path in the server. In this step, the file data is stored in the server.

[0767] Step 5:

[0768] Data Conversion

[0769] The server converts the temporarily stored preference data into a dedicated data format (e.g., JSON format). The conversion uses the Python pandas library, and includes the temporarily stored file path as input and the converted data object as output. In this step, the CSV format data is converted into JSON format.

[0770] Step 6:

[0771] Data storage

[0772] The server saves the transformed data to a database, using MySQL or MongoDB, with the transformed data object as input and the status of the data being saved in the database as output. This process writes the data to the database.

[0773] Step 7:

[0774] Upload completion notification

[0775] After the server confirms that the data has been saved correctly, it returns an upload completion notification to the user's device. The input is the database save status, and the output is a completion notification message in the HTTP response body. This causes the device to display "Upload completed" to the user.

[0776] Step 8:

[0777] Data list display

[0778] The terminal provides an interface that displays a list of available preference data. The input includes the list of preference data retrieved from the server, and the output includes the displayed list of data. The terminal then presents the user with preference data options.

[0779] Step 9:

[0780] Data selection and download request submission

[0781] The user selects the preference data they wish to download and clicks the download button. The terminal sends a download request for the selected preference data to the server. The input is the user-selected data, and the output is the server transmission status. The download request is sent from the terminal to the server.

[0782] Step 10:

[0783] Data Acquisition and Transmission

[0784] The server retrieves the relevant preference data from the database and sends it to the user's device. The input is a download request, and the output is the downloaded data included in the HTTP response. The device then receives the necessary data.

[0785] Step 11:

[0786] Data installation

[0787] The terminal installs the received preference data into the generative AI model. The input is the downloaded data, and the output is the status of data application to the generative AI model. This process applies the preference data to the generative AI model.

[0788] Step 12:

[0789] Evaluation interface display

[0790] The terminal displays a rating interface that includes rating buttons and review input fields, a rating form object as input, and user input fields as output, allowing users to rate preference data.

[0791] Step 13:

[0792] Enter and submit your rating

[0793] The user inputs rating information and reviews and presses the send button. The terminal sends the rating information to the server. The input is the user's rating information, and the output is the server transmission status. The rating information is sent to the server.

[0794] Step 14:

[0795] Saving rating information

[0796] The server saves the rating information to the database. The input is a rating information object, and the output is a database save status. This saves the rating information to the database.

[0797] Step 15:

[0798] Evaluation completion notification

[0799] After the server confirms that the evaluation has been saved correctly, it returns an evaluation completion notification to the terminal. The input is the database saving status, and the output is an evaluation completion message in the HTTP response body. The terminal displays the evaluation completion message to the user.

[0800] (Application example 1)

[0801] 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."

[0802] Conventional systems for managing preference data did not efficiently suggest products based on individual users' preferences, resulting in low user satisfaction. It was also difficult to use other users' preference data to receive product suggestions that matched one's hobbies and interests. Furthermore, the lack of a preference data evaluation or filtering function meant that inappropriate data could be mixed in.

[0803] 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.

[0804] In this invention, the server includes means for converting preference data into a dedicated data format and saving it, means for uploading preference data, means for downloading preference data, means for evaluating the preference data, and means for providing personalized product suggestions using the preference data. This allows users to receive optimal product suggestions using their own and other users' preference data. Furthermore, filtering the preference data based on the evaluation information allows more appropriate and reliable suggestions to be provided.

[0805] 1. "Preference Data" means information relating to your interests and concerns.

[0806] 2. "Private Data Format" means a data structure specifically designed for efficiently storing and processing Preference Data.

[0807] 3. "Uploading means" means a function that allows a user to send preference data from their device to the server.

[0808] 4. "Downloading means" means a function that allows a user to obtain preference data from a server to their own device.

[0809] 5. "Rating means" refers to the functionality that allows users to rate and review preference data and suggestions based on it.

[0810] 6. "Personalized product suggestions" means a function that suggests optimal products and services based on a user's preference data.

[0811] 7. "Paid provision means" means a function for providing preference data for a certain fee.

[0812] 8. "Filtering means" refers to a function that selects preference data based on evaluation information.

[0813] The system of the present invention is designed to effectively manage user preference data and apply it to a generative AI model. The system mainly consists of three elements: a server, a user's device, and a generative AI model. The role of each element and the specific processing content are explained below.

[0814] server

[0815] The server plays a central role in efficiently storing, transforming, and serving preference data.

[0816] Storage and conversion of preference data: The server converts the preference data received from the user into a dedicated data format and stores it in a database. This data format is designed for efficient processing of preference data.

[0817] Data upload and download: The server receives requests to upload preference data from the user device and stores the data in the database. It also retrieves the relevant data from the database in response to download requests and sends it to the user device.

[0818] Processing rating information: The server receives the user's rating information and stores it in a database, which can be used as a reference for other users to select highly rated preference data.

[0819] User's device

[0820] The user's terminal provides an interface for uploading, downloading, and rating preference data.

[0821] Data upload: The user can select preference data using the device interface and send it to the server by pressing the upload button.

[0822] Data download: Using the device interface, users can select, purchase, and download preference data provided by other users for a fee. This data is then installed into the generative AI model and used to make personalized product recommendations.

[0823] Data rating: Provides an interface for users to rate and review proposed products and services. The rating information is sent to the server and stored in a database.

[0824] Generative AI Models

[0825] The generative AI model is responsible for making personalized product suggestions based on user preference data.

[0826] Personalized product recommendations: User preference data is used to recommend the best products and services for each user, based on the preference data installed in the generative AI model.

[0827] Specific examples

[0828] For example, if a user wants to upload new preference data, he or she can perform the following operations:

[0829] 1. The user selects preference data that reflects their interests and preferences in the device's upload form.

[0830] 2. When you press the upload button, the data is sent to the server, converted into a dedicated data format, and saved.

[0831] A specific example of purchasing highly rated preference data created by other users is as follows.

[0832] 1. The user displays a list of preference data that is available on the device for a fee.

[0833] 2. Select the data you want to purchase, enter your payment information, and press the purchase button.

[0834] 3. Once the payment is successful, the relevant data is downloaded from the server to the device and installed into the generative AI model.

[0835] Examples of prompt statements

[0836] "My hobbies are cooking and gardening, so I'd like product suggestions that suit my tastes."

[0837] "I've recently become interested in yoga, so I'd like to receive product suggestions related to it."

[0838] In this way, the system enables the recommendation of products and services optimized to the user's preferences, improving the user experience.

[0839] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0840] Step 1:

[0841] Uploading preference data

[0842] Input: The user selects their preference data on the device and presses the upload button.

[0843] process:

[0844] The terminal reads the preference data file selected by the user.

[0845] A request to be sent to the server is created to convert the read data into a dedicated data format.

[0846] Data processing: Preference data is sent to the server in a standard format such as JSON and converted into a dedicated data format on the server side.

[0847] Output: The server saves the preference data in the database and sends an upload completion notification to the device.

[0848] Step 2:

[0849] Download preference data

[0850] Input: The user selects the preference data they want to download on their device and presses the download button.

[0851] process:

[0852] The terminal transmits a download request for the selected preference data to the server.

[0853] The server retrieves the relevant preference data from the database.

[0854] Data calculation: The server analyzes the user's request and extracts the corresponding preference data.

[0855] Output: The extracted preference data is sent to the user's device and installed into the generative AI model.

[0856] Step 3:

[0857] Evaluating preference data

[0858] Input: The user enters rating information or a review in the rating interface and presses the submit button.

[0859] process:

[0860] The terminal reads the user's input.

[0861] The read evaluation information is sent to the server.

[0862] Data processing: The server stores the received evaluation information in a database in an appropriate format.

[0863] Output: The server sends a notification of the completion of the evaluation to the terminal, and the terminal displays a message to the user that the evaluation is complete.

[0864] Step 4:

[0865] Personalized product recommendations

[0866] Input: User preference data and a prompt from the user (e.g., "My hobbies are cooking and gardening, so please suggest products that suit my preferences.") are input into the generative AI model.

[0867] process:

[0868] The generative AI model analyzes the input preference data and prompt text.

[0869] Select the most suitable products and services based on the data and generate a list of proposals.

[0870] Data calculation: The generative AI model runs an algorithm that matches input data with an internal database to make the most suitable product recommendations for the user.

[0871] Output: A personalized product suggestion list is displayed on the user's device.

[0872] In this way, each step works in conjunction with each other to make optimal product suggestions to users and improve the user experience.

[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 following describes a specific embodiment of the system of the present invention. This system not only manages user preference data and efficiently installs it into a generative AI model, but also combines it with an emotion engine to enable responses that correspond to the user's emotions.

[0875] The system includes a server for converting user preference data into a dedicated data format and storing it, a user terminal for uploading and downloading the preference data, a user terminal for evaluating and filtering the preference data, and an emotion engine for recognizing user emotions.

[0876] Specific processing of the program

[0877] The program of this system performs the following processing.

[0878] 1. Uploading preference data

[0879] The terminal displays an interface for the user to select preference data.

[0880] The user selects a preference data file and presses the upload button.

[0881] The terminal transmits the selected preference data to the server.

[0882] The server converts the received preference data into a dedicated data format and stores it in a database.

[0883] The server returns a notification of upload completion to the user's device.

[0884] The device will display a message to the user that the upload is complete.

[0885] 2. Downloading preference data

[0886] The terminal provides an interface for displaying a list of preference data.

[0887] The user selects the preference data they wish to download.

[0888] The terminal sends an HTTP GET request including the ID of the selected data to the server.

[0889] The server receives the request and retrieves the corresponding preference data from the database.

[0890] The server returns the acquired data to the user's terminal.

[0891] The device installs the received preference data into the generated AI model.

[0892] The device will display a download complete message to the user.

[0893] 3. Evaluating and filtering preference data

[0894] The terminal displays a rating interface including a rating button and a review input field.

[0895] The user enters the rating score and review comments and presses the submit button.

[0896] The terminal transmits the evaluation information to the server.

[0897] The server receives the request and stores the rating information in a database.

[0898] The server returns a notification of evaluation completion to the terminal.

[0899] The terminal displays a message to the user that the evaluation is complete.

[0900] The server filters the preference data based on the rating information.

[0901] 4. Reflecting the Emotion Engine in Generative AI Models

[0902] The device activates an emotion engine and analyzes the user's emotions.

[0903] The emotion engine analyzes data such as the user's voice and facial expressions to recognize emotions.

[0904] The server receives the emotion data from the emotion engine and stores it in a database.

[0905] The generative AI model adjusts the response sentence based on the emotional data and replies to the user.

[0906] Specific examples

[0907] For example, when a user uploads new preference data, the specific flow is as follows.

[0908] 1. The device presents the user with a preference data upload form.

[0909] 2. The user selects their preference data and clicks the "Upload" button.

[0910] 3. The terminal transmits the selected data to the server.

[0911] 4. The server receives the data, converts it into a proprietary data format, and stores it in a database.

[0912] 5. The server returns an upload completion notification to the device, and the device displays that information to the user.

[0913] Also, if a user recognizes emotions and adjusts the generative AI model's response based on them, the process is as follows:

[0914] 1. The device activates the emotion engine and analyzes the user's words and actions.

[0915] 2. The emotion engine analyzes the user's tone of voice and facial expressions to recognize their emotions.

[0916] 3. The server receives the analysis results and stores them in a database.

[0917] 4. The generative AI model adjusts responses based on emotional data.

[0918] 5. The device provides the adjusted response to the user.

[0919] Through the above process, the system can effectively manage user preference and emotion data and apply it to the generative AI model, allowing users to quickly obtain responses that match their preferences and emotions, greatly improving the utilization efficiency of the generative AI model.

[0920] The processing flow will be explained below.

[0921] Uploading preference data

[0922] Step 1:

[0923] The terminal displays the preference data upload interface.

[0924] Step 2:

[0925] The user selects a preference data file and clicks the upload button.

[0926] Step 3:

[0927] The device sends the selected file to the server as an HTTP POST request.

[0928] Step 4:

[0929] The server receives the request and temporarily stores the file.

[0930] Step 5:

[0931] The server converts the received file into a dedicated data format.

[0932] Step 6:

[0933] The server stores the converted data in a database.

[0934] Step 7:

[0935] The server will return a notification of upload completion and the ID of the saved data to the device.

[0936] Step 8:

[0937] The device displays a message to the user that the upload is complete.

[0938] Download preference data

[0939] Step 1:

[0940] The terminal displays an interface listing the preference data.

[0941] Step 2:

[0942] The user selects the preference data that he or she wishes to download.

[0943] Step 3:

[0944] The device sends an HTTP GET request including the ID of the selected data to the server.

[0945] Step 4:

[0946] The server receives the request and retrieves the corresponding preference data from the database.

[0947] Step 5:

[0948] The server returns the acquired data to the user's terminal.

[0949] Step 6:

[0950] The device receives the returned data and installs it into the generative AI model.

[0951] Step 7:

[0952] The device displays a download completion message to the user.

[0953] Evaluating and filtering preference data

[0954] Step 1:

[0955] The terminal displays an interface for evaluating the preference data.

[0956] Step 2:

[0957] The user enters the rating score and review comments and clicks the submit button.

[0958] Step 3:

[0959] The device sends the rating information to the server as an HTTP POST request.

[0960] Step 4:

[0961] The server receives the request and stores the rating information in a database.

[0962] Step 5:

[0963] The server returns a notification of evaluation completion to the terminal.

[0964] Step 6:

[0965] The terminal displays a message to the user that the evaluation is complete.

[0966] Step 7:

[0967] The server filters the preference data based on the rating information.

[0968] Reflecting the emotion engine in the generative AI model

[0969] Step 1:

[0970] The device activates the emotion engine and displays an interface that analyzes the user's emotions.

[0971] Step 2:

[0972] The emotion engine analyzes the user's tone of voice and facial expression data to recognize emotions.

[0973] Step 3:

[0974] The device transmits the emotion data to the server.

[0975] Step 4:

[0976] The server stores the emotion data in a database.

[0977] Step 5:

[0978] A generative AI model adjusts responses based on emotional data.

[0979] Step 6:

[0980] The server returns the adjusted response to the terminal.

[0981] Step 7:

[0982] The terminal displays the adjusted response sentence to the user.

[0983] Specific examples

[0984] Example of uploading preference data

[0985] Step 1:

[0986] The terminal displays a preference data upload form and provides the user with file selection options.

[0987] Step 2:

[0988] The user selects his / her preference data and clicks the "Upload" button.

[0989] Step 3:

[0990] The device collects the selected files and sends them to the server via an HTTP POST request.

[0991] Step 4:

[0992] The server receives the request and temporarily stores the file.

[0993] Step 5:

[0994] The server converts the received file into a dedicated data format and checks the conversion results.

[0995] Step 6:

[0996] The server saves the converted data to the database and confirms the success of the save.

[0997] Step 7:

[0998] The server will return a notification of upload completion and the ID of the saved data to the device.

[0999] Step 8:

[1000] The device displays a message to the user that the upload is complete.

[1001] Example of response adjustment when using the emotion engine

[1002] Step 1:

[1003] The device activates an emotion engine and displays an interface that analyzes the user's facial expressions and voice in real time.

[1004] Step 2:

[1005] The emotion engine detects the user's tone of voice and facial expressions in real time and recognizes their emotions.

[1006] Step 3:

[1007] The device collects the recognized emotion data and sends it to the server via an HTTP POST request.

[1008] Step 4:

[1009] The server receives the emotion data and stores it in a database.

[1010] Step 5:

[1011] A generative AI model adjusts response strategies based on stored emotional data.

[1012] Step 6:

[1013] The server returns the adjusted response to the terminal.

[1014] Step 7:

[1015] The terminal displays the adjusted response sentence to the user.

[1016] The above is a detailed description of the specific operations at each processing step. By following these steps, the system can effectively manage user preference data and emotion data and apply them to the generative AI model.

[1017] Example 2

[1018] 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."

[1019] Conventional generative AI models have difficulty managing user preference and emotional data and providing personalized responses based on this data. Furthermore, there was no centralized system for managing data uploads, downloads, ratings, and emotional analysis, making it difficult to improve the user experience.

[1020] 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.

[1021] In this invention, the server includes means for converting user preference data into a dedicated data format and saving it, means for uploading the preference data, means for downloading the preference data, means for evaluating the preference data, means for activating an emotion engine that analyzes user emotions, and means for adjusting the response of the generative AI model based on the analyzed emotion data, thereby enabling effective management of the user's preference data and emotion data and providing personalized responses through the generative AI model.

[1022] "Preference Data" refers to information about your personal preferences, interests, and concerns.

[1023] "Private Data Format" refers to a data format or structure that is specialized for storing and processing preference data.

[1024] "Upload" refers to the operation of sending data from a user's device to a server.

[1025] "Download" refers to the operation of obtaining data from a server to a user's device.

[1026] "Evaluation" refers to the operation in which a user assigns a score or review comment to preference data.

[1027] An "emotion engine" refers to software or hardware that analyzes data such as the user's voice and facial expressions and recognizes emotions.

[1028] "Analysis" refers to the process of analyzing given data and finding meaning and patterns within it.

[1029] A "generative AI model" refers to an artificial intelligence model that generates responses based on input from a user.

[1030] DETAILED DESCRIPTION OF THE INVENTION The present invention is a system for effectively managing user preference data and providing personalized responses through a generative AI model.

[1031] The system consists of the following components:

[1032] 1. Server

[1033] 2. Terminal

[1034] 3. Users

[1035] 4. Emotion Engine

[1036] 5. Generative AI Models

[1037] Uploading and storing preference data

[1038] The device displays an interface for the user to upload preference data. The interface is implemented using HTML and JavaScript, and provides a form including a file selection button and an upload button. The user selects their preference data file and clicks the upload button. The device sends the selected file to the server, and the server converts the received data into a dedicated data format and stores it in a database.

[1039] For example, if a user uploads movie preference data:

[1040] Prompt: "Please select your movie preferences and press the upload button."

[1041] Download preference data

[1042] The device provides an interface that displays a list of preference data. The user selects the data they wish to download from the list. The device then sends an HTTP GET request including the selected data ID to the server, and the server retrieves the corresponding preference data from the database and sends it to the device. The device then installs the received preference data into the generative AI model.

[1043] For example, if a user downloads music preference data:

[1044] Prompt: "Select the music preferences you would like to download."

[1045] Evaluating and filtering preference data

[1046] The device displays an interface for the user to input ratings and reviews of the preference data. The user enters a rating score and review comments and clicks the submit button. The device sends the entered rating information to the server, which stores the information in a database. The server filters the preference data based on the rating information.

[1047] For example, if a user rates their preference data for a cooking recipe:

[1048] Prompt: "Please rate the preference data for this recipe."

[1049] Using an emotion engine and incorporating it into generative AI models

[1050] The device activates the emotion engine to analyze the user's emotions. The emotion engine analyzes the user's voice and facial expressions to recognize emotions. The server receives the emotion data and stores it in a database. The generative AI model adjusts the response based on the emotion data, and the device provides the response to the user.

[1051] For example, to tailor your response based on user sentiment:

[1052] Prompt: "I am tailoring my response based on how you are currently feeling."

[1053] The system effectively manages user preference data and emotion data and provides users with personalized responses through generative AI models, improving the user experience and significantly improving the utilization efficiency of generative AI models.

[1054] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1055] Step 1:

[1056] The terminal displays an interface for the user to upload preference data. The interface provides an HTML form with a file selection button and an "Upload" button. The user selects a preference data file and clicks the upload button (input: preference data file, output: data from the selected file).

[1057] Step 2:

[1058] The device reads the user-selected preference data file using a JavaScript File object and sends it to the server as an HTTP POST request (input: data from the selected file, output: preference data sent to the server).

[1059] Step 3:

[1060] The server calls a function to convert the received preference data into a dedicated data format and stores the converted data in a database (input: received preference data, output: data converted into dedicated data format).

[1061] Step 4:

[1062] The server sends an HTTP response to the terminal indicating that the data has been successfully saved (input: data conversion and saving results, output: notification that upload has been completed).

[1063] Step 5:

[1064] The device receives the response from the server and displays a message to the user saying "Upload completed" (Input: Notification of upload completion, Output: Display message to user).

[1065] Step 6:

[1066] The terminal requests current preference data from the database to provide the user with an interface that displays a list of preference data (input: user request, output: list of preference data).

[1067] Step 7:

[1068] The user selects the preference data they wish to download from the list and clicks the "Download" button (input: list of preference data, output: data ID to be downloaded).

[1069] Step 8:

[1070] The terminal sends an HTTP GET request including the selected data ID to the server, and the server retrieves the corresponding preference data from the database and sends it to the terminal (input: data ID, output: corresponding preference data).

[1071] Step 9:

[1072] The device installs the received preference data into the generative AI model (input: received preference data, output: data installed into the generative AI model).

[1073] Step 10:

[1074] The device displays a message to the user that the download is complete. (Input: Download and installation complete information; Output: Message displayed to the user.)

[1075] Step 11:

[1076] The terminal displays a rating interface including rating buttons and a review input field. The user enters a rating score and review comments and clicks the submit button (input: preference data, output: rating score and review comments).

[1077] Step 12:

[1078] The terminal transmits data including the rating score and review comments to the server (input: rating score and review comments, output: rating information transmitted to the server).

[1079] Step 13:

[1080] The server stores the received rating information in a database (input: received rating information, output: rating information stored in the database).

[1081] Step 14:

[1082] The server sends an HTTP response to the terminal indicating that the rating was successfully saved (input: rating saved result, output: notification of rating completion).

[1083] Step 15:

[1084] The terminal receives a notification from the server that the evaluation is complete and displays a message to the user saying "Evaluation is complete" (Input: Notification of evaluation completion, Output: Display a message to the user).

[1085] Step 16:

[1086] The server filters the preference data based on the rating information, for example, by excluding data below a certain rating score (input: rating information, output: filtered preference data).

[1087] Step 17:

[1088] The device starts the emotion engine and captures the user's facial expressions and voice using the camera and microphone (input: user's facial expressions and voice, output: captured data).

[1089] Step 18:

[1090] The emotion engine analyzes the captured data and recognizes the user's emotions (input: captured data, output: recognized emotion data).

[1091] Step 19:

[1092] The server receives the emotion data sent from the emotion engine and stores it in a database (input: recognized emotion data, output: emotion data stored in the database).

[1093] Step 20:

[1094] The generative AI model generates responses based on emotion data stored in a database, tailoring the response to specific emotions (input: emotion data, output: tailored response).

[1095] Step 21:

[1096] The device receives the response from the generative AI model and displays it to the user (input: adjusted response, output: response presented to the user).

[1097] This allows us to utilize user preference and emotion data to optimize the generative AI model and provide personalized services to users.

[1098] (Application example 2)

[1099] 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."

[1100] In today's brick-and-mortar stores, improving the customer experience requires accurately understanding customer preferences and emotions and providing personalized services based on those. However, current technology lacks the means to analyze and respond to customer preferences and emotions in real time. As a result, it is difficult to improve customer satisfaction or stimulate purchasing motivation. Therefore, to improve the customer experience, a system is needed that can manage and analyze customer preference and emotion data in real time and provide responses based on that data.

[1101] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1102] In this invention, the server includes means for converting preference data into a dedicated data format and saving it, means for uploading the preference data, means for downloading the preference data, means for evaluating the preference data, means for analyzing the user's emotions using an emotion engine, means for reflecting the preference data and emotion data in a generative AI model, and means for providing a response based on the user's preferences and emotions through an application installed on the smart device. This makes it possible to provide personalized services in physical stores according to the customer's preferences and emotions.

[1103] "Preference Data" is information about your preferences and interests.

[1104] A "specialized data format" is a data format designed to efficiently store and manage specific data.

[1105] A "means for uploading preference data" is a method or device that allows a user to transmit their preference data to the system.

[1106] A "means for downloading preference data" is a method or device for obtaining preference data from the system.

[1107] The "means for evaluating preference data" refers to a method or device for evaluating the quality and usefulness of data based on a user's preference data.

[1108] An "emotion engine" is software or hardware that analyzes a user's emotions and generates the results as information.

[1109] A "generative AI model" is an artificial intelligence model that responds or generates appropriate responses based on given data and conditions.

[1110] A "smart device" is an electronic device that has advanced computing power and built-in communication functions and sensors.

[1111] "Means for reflecting preference data and emotional data" refers to a method or device for incorporating preference data and emotional data obtained from users into a system and using the data.

[1112] A "personalized service" is a service that is customized to suit the preferences and feelings of each individual user.

[1113] A specific embodiment of the system of the present invention will be described below, which aims to improve customer experience, particularly in brick-and-mortar stores.

[1114] Users collect their own preference data and upload it to the system via their smart devices (e.g., smart glasses). This preference data is sent to a server, converted into a dedicated data format, and stored in a database. When the user visits a store, the smart device downloads the latest preference data from the server and presents it to store staff.

[1115] The server is equipped with means for storing, uploading, downloading, and evaluating preference data. This allows user preference data to be accumulated and evaluated or filtered as needed. For example, if preference data is stored in the form of a number, it can be filtered using SQL queries, etc.

[1116] The emotion engine is used to analyze users' real-time emotions. This emotion engine uses the smart device's camera and microphone to collect the user's facial expressions and tone of voice and convert them into emotion data. This emotion data is sent to the server and reflected in the generative AI model. The emotion engine uses customer signal analysis software.

[1117] The generative AI model generates responses and suggestions for users based on preference and emotion data. The generative AI model has advanced computing capabilities and takes both preference and emotion data into account to derive optimal responses. These responses are provided to staff via smart devices, enabling personalized service for customers.

[1118] As a concrete example, when User A visits a store, their smart device downloads their preference data from the server. The emotion engine analyzes User A's facial expressions and tone of voice in real time to detect the emotion of "happiness." Based on this data, the generative AI model generates a response such as, "We'd like to introduce you to some new products we currently recommend. This coffee in particular has been very popular recently." A staff member checks this information via their smart device and makes a suggestion to User A.

[1119] Another example of a prompt is, "Please suggest the most suitable related products based on the product selected by the customer. Also, please analyze the customer's emotions from their facial expressions and speech and provide advice on how to provide appropriate customer service." This prompt allows the generative AI model to comprehensively determine the user's preferences and emotions and make optimal suggestions and responses.

[1120] This system significantly improves the customer experience in physical stores and makes it possible to provide services that highly satisfy customers.

[1121] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1122] Step 1:

[1123] Uploading preference data

[1124] Input: The user selects their preference data.

[1125] Specific operation: The terminal (smart device) displays an interface for the user to select preference data. When the user selects a preference data file and presses the "Upload" button, the terminal sends the data to the server.

[1126] Data processing / calculation: The server converts the received data into a dedicated data format and stores it in a database.

[1127] Output: A notification that the upload is complete is sent back to the device, which displays it to the user.

[1128] Step 2:

[1129] Download preference data

[1130] Input: When a user enters a physical store, the device sends a data request to the server.

[1131] Specific operation: The device periodically sends an HTTP GET request to the server to obtain the latest preference data. The server receives the request and retrieves the corresponding preference data from the database.

[1132] Data processing / calculation: The server sends the acquired data to the terminal, which receives and displays it.

[1133] Output: The latest preference data is downloaded and presented to the store staff.

[1134] Step 3:

[1135] Evaluating preference data

[1136] Input: Users and staff input their ratings for preference data.

[1137] Specific operation: The terminal displays the evaluation interface to the user and staff. When the user and staff input the evaluation score and review comments and press the send button, the evaluation information is sent to the server.

[1138] Data processing / calculation: The server stores the rating information in a database and filters the preference data based on the rating information.

[1139] Output: The evaluation results are sent back to the terminal, which displays them to the user and staff.

[1140] Step 4:

[1141] Emotion engine activation and analysis

[1142] Input: The user's facial expressions and tone of voice are collected in the store.

[1143] How it works: The device captures the user's facial expressions and tone of voice in real time through its built-in camera and microphone, and this data is sent to the emotion engine for analysis.

[1144] Data processing / calculation: The emotion engine analyzes the collected data and recognizes the user's emotional state. The analysis results are sent to the server as emotion data.

[1145] Output: Emotion data is generated and stored on the server.

[1146] Step 5:

[1147] Generative AI model generates responses

[1148] Input: User preference and emotion data is fed into the generative AI model.

[1149] Specific operation: The server provides the stored preference data and emotion data to the generative AI model, which then generates an appropriate response based on the prompt sentence.

[1150] Data processing / calculation: Generative AI models perform calculations based on preference data and emotion data to generate optimal responses.

[1151] Output: The generated response is sent to the terminal and presented to the store staff.

[1152] An example prompt is, "Please suggest the most suitable related products based on the product selected by the customer. Also, please analyze the customer's emotions from their facial expressions and speech and display advice on how to provide appropriate customer service."

[1153] Through these processing steps, this system can significantly improve the customer experience in physical stores.

[1154] 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.

[1155] 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.

[1156] 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.

[1157] [Third embodiment]

[1158] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1159] 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.

[1160] 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).

[1161] 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.

[1162] 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.

[1163] 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).

[1164] 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.

[1165] 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.

[1166] 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.

[1167] 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.

[1168] 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.

[1169] 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."

[1170] The following describes a specific embodiment of the system of the present invention. This system manages user preference data and efficiently installs it into a generative AI model.

[1171] This system includes a server for converting and saving user preference data into a dedicated data format, a user terminal for uploading the preference data, a user terminal for downloading the preference data, and a user terminal for evaluating the preference data.

[1172] Specific processing of the program

[1173] The program of this system performs the following processing.

[1174] 1. Uploading your preference data

[1175] The terminal displays an interface for the user to select preference data.

[1176] The user selects a preference data file and presses the upload button.

[1177] The terminal transmits the selected preference data to the server.

[1178] The server converts the received preference data into a dedicated data format and stores it in a database.

[1179] The server returns a notification of upload completion to the user's device.

[1180] The device will display a message to the user that the upload is complete.

[1181] 2. Downloading preference data

[1182] The terminal provides an interface for displaying a list of preference data.

[1183] The user selects the preference data they wish to download.

[1184] The terminal transmits a download request for the selected preference data to the server.

[1185] The server retrieves the relevant preference data from the database and sends it to the user's device.

[1186] The device installs the received preference data into the generated AI model.

[1187] 3. Evaluation of preference data

[1188] The terminal displays a rating interface including a rating button and a review input field.

[1189] The user enters rating information and reviews and presses the submit button.

[1190] The terminal transmits the evaluation information to the server.

[1191] The server stores the evaluation information in a database and returns a notification of evaluation completion to the terminal.

[1192] The terminal displays a message to the user that the evaluation is complete.

[1193] Specific examples

[1194] For example, when a user uploads new preference data, the specific flow is as follows.

[1195] 1. The terminal displays a preference data upload form to the user.

[1196] 2. The user selects their preference data and clicks the "Upload" button.

[1197] 3. The terminal transmits the selected data to the server.

[1198] 4. The server receives the data, converts it into a proprietary data format, and stores it in a database.

[1199] 5. The server returns an upload completion notification to the device, and the device displays that information to the user.

[1200] The flow when a user purchases preference data created by another user is as follows.

[1201] 1. The terminal displays a list of preference data that is available for a fee.

[1202] 2. The user selects the data they want to purchase, enters payment information, and presses the "Purchase" button.

[1203] 3. The terminal sends the entered payment information to the server.

[1204] 4. The server processes the payment, and if the payment is successful, retrieves the relevant data from the database and sends it to the user's device.

[1205] 5. The device receives the data and installs it into the generative AI model.

[1206] Through the above process, the system can effectively manage user preference data and apply it to the generative AI model, allowing users to quickly obtain responses that match their preferences and significantly improving the utilization efficiency of the generative AI model.

[1207] The processing flow will be explained below.

[1208] Uploading preference data

[1209] Step 1:

[1210] The terminal displays the preference data upload interface.

[1211] Step 2:

[1212] The user selects a preference data file and clicks the upload button.

[1213] Step 3:

[1214] The device sends the selected file to the server as an HTTP POST request.

[1215] Step 4:

[1216] The server receives the request and temporarily stores the file.

[1217] Step 5:

[1218] The server converts the received file into a dedicated data format.

[1219] Step 6:

[1220] The server stores the converted data in a database.

[1221] Step 7:

[1222] The server will return a notification of upload completion and the ID of the saved data to the device.

[1223] Step 8:

[1224] The device displays a message to the user that the upload is complete.

[1225] Download preference data

[1226] Step 1:

[1227] The terminal displays an interface listing the preference data.

[1228] Step 2:

[1229] The user selects the preference data that he or she wishes to download.

[1230] Step 3:

[1231] The device sends an HTTP GET request including the ID of the selected data to the server.

[1232] Step 4:

[1233] The server receives the request and retrieves the corresponding preference data from the database.

[1234] Step 5:

[1235] The server returns the acquired data to the user's terminal.

[1236] Step 6:

[1237] The device receives the returned data and installs it into the generative AI model.

[1238] Step 7:

[1239] The device displays a download completion message to the user.

[1240] Evaluating preference data

[1241] Step 1:

[1242] The terminal displays the evaluation interface.

[1243] Step 2:

[1244] The user enters the rating score and review comments and clicks the submit button.

[1245] Step 3:

[1246] The device sends the rating information to the server as an HTTP POST request.

[1247] Step 4:

[1248] The server receives the request and stores the rating information in a database.

[1249] Step 5:

[1250] The server returns a notification of evaluation completion to the terminal.

[1251] Step 6:

[1252] The terminal displays a message to the user that the evaluation is complete.

[1253] Purchase of preference data

[1254] Step 1:

[1255] The terminal displays an interface listing paid preference data.

[1256] Step 2:

[1257] The user selects the preference data he or she wishes to purchase and clicks the purchase button.

[1258] Step 3:

[1259] The terminal displays a form for entering payment information.

[1260] Step 4:

[1261] The user enters payment information and clicks the submit button.

[1262] Step 5:

[1263] The terminal sends an HTTP POST request containing payment information to the server.

[1264] Step 6:

[1265] The server receives the request and communicates with the payment provider to process the payment.

[1266] Step 7:

[1267] The server confirms the success of the payment and retrieves the relevant preference data from the database.

[1268] Step 8:

[1269] The server transmits the acquired data to the user's terminal.

[1270] Step 9:

[1271] The device receives the data and installs it into the generative AI model.

[1272] Step 10:

[1273] The terminal displays a purchase completion message to the user.

[1274] The above is a detailed description of the specific operations at each processing step. By following these steps, the system can effectively manage user preference data and apply it to the generative AI model.

[1275] Example 1

[1276] 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."

[1277] There is a need to efficiently manage user preference data and apply it to generative AI models to provide fast and accurate responses that match individual user preferences. There is also a growing need for a smooth interface for uploading and downloading preference data and the rating process, as well as functions such as paid data provision and data filtering based on rating information.

[1278] 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.

[1279] In this invention, the server includes a means for transmitting preference data selected from the user's terminal to the server, a means for storing the preference data received by the server in a database, and a means for displaying a notification to the user upon completion of uploading the preference data. This allows users to easily manage their own preference data and apply it to generative AI models. In addition, accurate data storage and notification of upload completion enhances feedback to the user, improving the utilization efficiency of the entire system.

[1280] "User" refers to any individual or corporation that uses this system.

[1281] "Preference Data" means data containing information about your preferences and tastes.

[1282] "Private data format" refers to a specific data format in which data converted from another format is stored.

[1283] "Uploading means" refers to a method or device that allows a user to transmit preference data to the system.

[1284] "Device" refers to an electronic device operated by a user, such as a computer, smartphone, or tablet.

[1285] "Server" refers to a computer system that receives, stores, transforms, and transmits data.

[1286] "Database" refers to a data management system for efficiently storing and retrieving large amounts of data.

[1287] "Means for converting into a dedicated data format and saving" refers to a method or device for converting received preference data into a specific format and saving it in that format.

[1288] "Means for downloading" refers to a method or device for transmitting preference data from a server to a user terminal.

[1289] "Generative AI model" refers to an artificial intelligence model that generates output based on user preference data.

[1290] The term "rating means" refers to a method or device for a user to input an evaluation of preference data.

[1291] "Means for providing for a fee" refers to a method or device for providing preference data in exchange for a certain fee.

[1292] The term "means for filtering based on rating information" refers to a method or device for selecting preference data based on rating information input by a user.

[1293] "Notification means" refers to a method or device for notifying the user of the system status or processing results.

[1294] By using the above definitions, the components and functions of the system of the present invention can be clearly understood and explained.

[1295] MODE FOR CARRYING OUT THE INVENTION

[1296] The following describes an example of a specific embodiment of the system of the present invention. This system effectively manages user preference data and applies it to a generative AI model.

[1297] The system mainly consists of the following components:

[1298] 1. Devices that upload user preference data

[1299] 2. Server that stores and converts received preference data

[1300] 3. A server that provides preference data in response to requests from user devices

[1301] 4. Generative AI models that install user preference data

[1302] 5. A device that provides an interface for users to evaluate preference data

[1303] Hardware and software used

[1304] Device:

[1305] A device is an electronic device operated by a user, such as a computer, smartphone, or tablet. A web browser interface or dedicated application is installed on the device, providing an interface for the user to select, upload, and rate preference data. The device communicates with the server using the HTTP protocol.

[1306] Examples: laptops, smartphones

[1307] server:

[1308] A server is a computer system that receives, stores, converts, and transmits data. The server has the functionality to convert preference data into a dedicated data format and store it in a database. The server also provides selected data and receives rating information.

[1309] Examples: AWS EC2, IBM Cloud

[1310] Database:

[1311] A database is a data management system for efficiently storing and retrieving large amounts of data. Databases store user preference data and ratings.

[1312] Examples: MySQL, MongoDB

[1313] Generative AI models:

[1314] A generative AI model is an artificial intelligence model that generates output based on user preference data. This model uses the preference data installed from the device to generate responses that are customized for each user.

[1315] Examples: GPT-3, BERT

[1316] Specific examples

[1317] Upload your preference data:

[1318] When a user uploads new preference data, a form including a file selection button and an upload button is displayed on the device. When the user selects "Music Preference Data.csv" and clicks the upload button, the data is sent from the device to the server. The server receives the data, converts it into a dedicated data format (e.g., JSON format), and stores it in a database. The server then returns a notification that the upload is complete to the device, and the device displays a message to the user saying "Upload completed."

[1319] Install an example prompt into your generative AI model:

[1320] When a user uses a prompt such as "Recommend me a list of new movies based on my preferences," the prompt is sent to the generative AI model via the device. The generative AI model generates an appropriate movie list based on the user's installed preference data and returns it to the device. This process allows users to quickly obtain a recommendation list that suits their preferences.

[1321] From the above description, it is believed that a specific embodiment of the system according to the present invention can be understood, which makes the system a useful tool for efficiently managing user preference data and applying it to generative AI models.

[1322] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1323] Step 1:

[1324] Interface Display

[1325] The terminal displays a preference data upload form to the user. The inputs include a "file selection button" and an "upload button." This action is displayed using a GUI library, allowing the user to select preference data.

[1326] Step 2:

[1327] Data Selection

[1328] The user selects the preference data file they want to upload from their device. The input includes the file path (e.g., "music preference data.csv"). The user's operation saves the path of the selected file in a variable on the device.

[1329] Step 3:

[1330] Data transmission

[1331] After selecting a preference data file, the user clicks the upload button. The device sends the selected preference data file to the server. The transmission uses an HTTP POST request, with the selected preference data file as input and the status of the file transmission to the server as output. This process transfers the file from the device to the server.

[1332] Step 4:

[1333] Data reception

[1334] The server temporarily stores the received preference data in a specific directory. The input is the file data in the HTTP request body, and the output is the temporary storage path in the server. In this step, the file data is stored in the server.

[1335] Step 5:

[1336] Data Conversion

[1337] The server converts the temporarily stored preference data into a dedicated data format (e.g., JSON format). The conversion uses the Python pandas library, and includes the temporarily stored file path as input and the converted data object as output. In this step, the CSV format data is converted into JSON format.

[1338] Step 6:

[1339] Data storage

[1340] The server saves the transformed data to a database, using MySQL or MongoDB, with the transformed data object as input and the status of the data being saved in the database as output. This process writes the data to the database.

[1341] Step 7:

[1342] Upload completion notification

[1343] After the server confirms that the data has been saved correctly, it returns an upload completion notification to the user's device. The input is the database save status, and the output is a completion notification message in the HTTP response body. This causes the device to display "Upload completed" to the user.

[1344] Step 8:

[1345] Data list display

[1346] The terminal provides an interface that displays a list of available preference data. The input includes the list of preference data retrieved from the server, and the output includes the displayed list of data. The terminal then presents the user with preference data options.

[1347] Step 9:

[1348] Data selection and download request submission

[1349] The user selects the preference data they wish to download and clicks the download button. The terminal sends a download request for the selected preference data to the server. The input is the user-selected data, and the output is the server transmission status. The download request is sent from the terminal to the server.

[1350] Step 10:

[1351] Data Acquisition and Transmission

[1352] The server retrieves the relevant preference data from the database and sends it to the user's device. The input is a download request, and the output is the downloaded data included in the HTTP response. The device then receives the necessary data.

[1353] Step 11:

[1354] Data installation

[1355] The terminal installs the received preference data into the generative AI model. The input is the downloaded data, and the output is the status of data application to the generative AI model. This process applies the preference data to the generative AI model.

[1356] Step 12:

[1357] Evaluation interface display

[1358] The terminal displays a rating interface that includes rating buttons and review input fields, a rating form object as input, and user input fields as output, allowing users to rate preference data.

[1359] Step 13:

[1360] Enter and submit your rating

[1361] The user inputs rating information and reviews and presses the send button. The terminal sends the rating information to the server. The input is the user's rating information, and the output is the server transmission status. The rating information is sent to the server.

[1362] Step 14:

[1363] Saving rating information

[1364] The server saves the rating information to the database. The input is a rating information object, and the output is a database save status. This saves the rating information to the database.

[1365] Step 15:

[1366] Evaluation completion notification

[1367] After the server confirms that the evaluation has been saved correctly, it returns an evaluation completion notification to the terminal. The input is the database saving status, and the output is an evaluation completion message in the HTTP response body. The terminal displays the evaluation completion message to the user.

[1368] (Application example 1)

[1369] 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."

[1370] Conventional systems for managing preference data did not efficiently suggest products based on individual users' preferences, resulting in low user satisfaction. It was also difficult to use other users' preference data to receive product suggestions that matched one's hobbies and interests. Furthermore, the lack of a preference data evaluation or filtering function meant that inappropriate data could be mixed in.

[1371] 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.

[1372] In this invention, the server includes means for converting preference data into a dedicated data format and saving it, means for uploading preference data, means for downloading preference data, means for evaluating the preference data, and means for providing personalized product suggestions using the preference data. This allows users to receive optimal product suggestions using their own and other users' preference data. Furthermore, filtering the preference data based on the evaluation information allows more appropriate and reliable suggestions to be provided.

[1373] 1. "Preference Data" means information relating to your interests and concerns.

[1374] 2. "Private Data Format" means a data structure specifically designed for efficiently storing and processing Preference Data.

[1375] 3. "Uploading means" means a function that allows a user to send preference data from their device to the server.

[1376] 4. "Downloading means" means a function that allows a user to obtain preference data from a server to their own device.

[1377] 5. "Rating means" refers to the functionality that allows users to rate and review preference data and suggestions based on it.

[1378] 6. "Personalized product suggestions" means a function that suggests optimal products and services based on a user's preference data.

[1379] 7. "Paid provision means" means a function for providing preference data for a certain fee.

[1380] 8. "Filtering means" refers to a function that selects preference data based on evaluation information.

[1381] The system of the present invention is configured to effectively manage user preference data and apply it to a generative AI model. The system mainly consists of three elements: a server, a user's device, and a generative AI model. The role of each element and the specific processing content are explained below.

[1382] server

[1383] The server plays a central role in efficiently storing, transforming, and serving preference data.

[1384] Storage and conversion of preference data: The server converts the preference data received from the user into a dedicated data format and stores it in a database. This data format is designed for efficient processing of preference data.

[1385] Data upload and download: The server receives requests to upload preference data from the user's device and stores the data in the database. It also retrieves the relevant data from the database in response to download requests and sends it to the user's device.

[1386] Processing rating information: The server receives the user's rating information and stores it in a database, which can be used as a reference for other users to select highly rated preference data.

[1387] User's device

[1388] The user's terminal provides an interface for uploading, downloading, and rating preference data.

[1389] Data upload: The user can select preference data using the device interface and send it to the server by pressing the upload button.

[1390] Data download: Using the device interface, users can select, purchase, and download preference data provided by other users for a fee. This data is then installed into the generative AI model and used to make personalized product recommendations.

[1391] Data rating: Provides an interface for users to rate and review proposed products and services. The rating information is sent to the server and stored in a database.

[1392] Generative AI Models

[1393] The generative AI model is responsible for making personalized product suggestions based on user preference data.

[1394] Personalized product recommendations: User preference data is used to recommend the best products and services for each user, based on the preference data installed in the generative AI model.

[1395] Specific examples

[1396] For example, if a user wants to upload new preference data, he or she can perform the following operations:

[1397] 1. The user selects preference data that reflects their interests and preferences in the device's upload form.

[1398] 2. When you press the upload button, the data is sent to the server, converted into a dedicated data format, and saved.

[1399] A specific example of purchasing highly rated preference data created by other users is as follows.

[1400] 1. The user displays a list of preference data that is available on the device for a fee.

[1401] 2. Select the data you want to purchase, enter your payment information, and press the purchase button.

[1402] 3. Once the payment is successful, the relevant data is downloaded from the server to the device and installed into the generative AI model.

[1403] Examples of prompt statements

[1404] "My hobbies are cooking and gardening, so I'd like product suggestions that suit my tastes."

[1405] "I've recently become interested in yoga, so I'd like to receive product suggestions related to it."

[1406] In this way, the system enables the recommendation of products and services optimized to the user's preferences, improving the user experience.

[1407] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1408] Step 1:

[1409] Uploading preference data

[1410] Input: The user selects their preference data on the device and presses the upload button.

[1411] process:

[1412] The terminal reads the preference data file selected by the user.

[1413] A request to be sent to the server is created to convert the read data into a dedicated data format.

[1414] Data processing: Preference data is sent to the server in a standard format such as JSON and converted into a dedicated data format on the server side.

[1415] Output: The server saves the preference data in the database and sends an upload completion notification to the device.

[1416] Step 2:

[1417] Download preference data

[1418] Input: The user selects the preference data they want to download on their device and presses the download button.

[1419] process:

[1420] The terminal transmits a download request for the selected preference data to the server.

[1421] The server retrieves the relevant preference data from the database.

[1422] Data calculation: The server analyzes the user's request and extracts the corresponding preference data.

[1423] Output: The extracted preference data is sent to the user's device and installed into the generative AI model.

[1424] Step 3:

[1425] Evaluating preference data

[1426] Input: The user enters rating information or a review in the rating interface and presses the submit button.

[1427] process:

[1428] The terminal reads the user's input.

[1429] The read evaluation information is sent to the server.

[1430] Data processing: The server stores the received evaluation information in a database in an appropriate format.

[1431] Output: The server sends a notification of the completion of the evaluation to the terminal, and the terminal displays a message to the user that the evaluation is complete.

[1432] Step 4:

[1433] Personalized product recommendations

[1434] Input: User preference data and a prompt from the user (e.g., "My hobbies are cooking and gardening, so please suggest products that suit my preferences.") are input into the generative AI model.

[1435] process:

[1436] The generative AI model analyzes the input preference data and prompt text.

[1437] Select the most suitable products and services based on the data and generate a list of proposals.

[1438] Data calculation: The generative AI model runs an algorithm that matches input data with an internal database to make the most suitable product recommendations for the user.

[1439] Output: A personalized product suggestion list is displayed on the user's device.

[1440] In this way, each step works in conjunction with each other to make optimal product suggestions to users and improve the user experience.

[1441] 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.

[1442] The following describes a specific embodiment of the system of the present invention. This system not only manages user preference data and efficiently installs it into a generative AI model, but also combines it with an emotion engine to enable responses that correspond to the user's emotions.

[1443] The system includes a server for converting user preference data into a dedicated data format and storing it, a user terminal for uploading and downloading the preference data, a user terminal for evaluating and filtering the preference data, and an emotion engine for recognizing user emotions.

[1444] Specific processing of the program

[1445] The program of this system performs the following processing.

[1446] 1. Uploading preference data

[1447] The terminal displays an interface for the user to select preference data.

[1448] The user selects a preference data file and presses the upload button.

[1449] The terminal transmits the selected preference data to the server.

[1450] The server converts the received preference data into a dedicated data format and stores it in a database.

[1451] The server returns a notification of upload completion to the user's device.

[1452] The device will display a message to the user that the upload is complete.

[1453] 2. Downloading preference data

[1454] The terminal provides an interface for displaying a list of preference data.

[1455] The user selects the preference data they wish to download.

[1456] The terminal sends an HTTP GET request including the ID of the selected data to the server.

[1457] The server receives the request and retrieves the corresponding preference data from the database.

[1458] The server returns the acquired data to the user's terminal.

[1459] The device installs the received preference data into the generated AI model.

[1460] The device will display a download complete message to the user.

[1461] 3. Evaluating and filtering preference data

[1462] The terminal displays a rating interface including a rating button and a review input field.

[1463] The user enters the rating score and review comments and presses the submit button.

[1464] The terminal transmits the evaluation information to the server.

[1465] The server receives the request and stores the rating information in a database.

[1466] The server returns a notification of evaluation completion to the terminal.

[1467] The terminal displays a message to the user that the evaluation is complete.

[1468] The server filters the preference data based on the rating information.

[1469] 4. Reflecting the Emotion Engine in Generative AI Models

[1470] The device activates an emotion engine and analyzes the user's emotions.

[1471] The emotion engine analyzes data such as the user's voice and facial expressions to recognize emotions.

[1472] The server receives the emotion data from the emotion engine and stores it in a database.

[1473] The generative AI model adjusts the response sentence based on the emotional data and replies to the user.

[1474] Specific examples

[1475] For example, when a user uploads new preference data, the specific flow is as follows.

[1476] 1. The device presents the user with a preference data upload form.

[1477] 2. The user selects their preference data and clicks the "Upload" button.

[1478] 3. The terminal transmits the selected data to the server.

[1479] 4. The server receives the data, converts it into a proprietary data format, and stores it in a database.

[1480] 5. The server returns an upload completion notification to the device, and the device displays that information to the user.

[1481] Also, if a user recognizes emotions and adjusts the generative AI model's response based on them, the process is as follows:

[1482] 1. The device activates the emotion engine and analyzes the user's words and actions.

[1483] 2. The emotion engine analyzes the user's tone of voice and facial expressions to recognize their emotions.

[1484] 3. The server receives the analysis results and stores them in a database.

[1485] 4. The generative AI model adjusts responses based on emotional data.

[1486] 5. The device provides the adjusted response to the user.

[1487] Through the above process, the system can effectively manage user preference and emotion data and apply it to the generative AI model, allowing users to quickly obtain responses that match their preferences and emotions, greatly improving the utilization efficiency of the generative AI model.

[1488] The processing flow will be explained below.

[1489] Uploading preference data

[1490] Step 1:

[1491] The terminal displays the preference data upload interface.

[1492] Step 2:

[1493] The user selects a preference data file and clicks the upload button.

[1494] Step 3:

[1495] The device sends the selected file to the server as an HTTP POST request.

[1496] Step 4:

[1497] The server receives the request and temporarily stores the file.

[1498] Step 5:

[1499] The server converts the received file into a dedicated data format.

[1500] Step 6:

[1501] The server stores the converted data in a database.

[1502] Step 7:

[1503] The server will return a notification of upload completion and the ID of the saved data to the device.

[1504] Step 8:

[1505] The device displays a message to the user that the upload is complete.

[1506] Download preference data

[1507] Step 1:

[1508] The terminal displays an interface listing the preference data.

[1509] Step 2:

[1510] The user selects the preference data that he or she wishes to download.

[1511] Step 3:

[1512] The device sends an HTTP GET request including the ID of the selected data to the server.

[1513] Step 4:

[1514] The server receives the request and retrieves the corresponding preference data from the database.

[1515] Step 5:

[1516] The server returns the acquired data to the user's terminal.

[1517] Step 6:

[1518] The device receives the returned data and installs it into the generative AI model.

[1519] Step 7:

[1520] The device displays a download completion message to the user.

[1521] Evaluating and filtering preference data

[1522] Step 1:

[1523] The terminal displays an interface for evaluating the preference data.

[1524] Step 2:

[1525] The user enters the rating score and review comments and clicks the submit button.

[1526] Step 3:

[1527] The device sends the rating information to the server as an HTTP POST request.

[1528] Step 4:

[1529] The server receives the request and stores the rating information in a database.

[1530] Step 5:

[1531] The server returns a notification of evaluation completion to the terminal.

[1532] Step 6:

[1533] The terminal displays a message to the user that the evaluation is complete.

[1534] Step 7:

[1535] The server filters the preference data based on the rating information.

[1536] Reflecting the emotion engine in the generative AI model

[1537] Step 1:

[1538] The device activates the emotion engine and displays an interface that analyzes the user's emotions.

[1539] Step 2:

[1540] The emotion engine analyzes the user's tone of voice and facial expression data to recognize emotions.

[1541] Step 3:

[1542] The device transmits the emotion data to the server.

[1543] Step 4:

[1544] The server stores the emotion data in a database.

[1545] Step 5:

[1546] A generative AI model adjusts responses based on emotional data.

[1547] Step 6:

[1548] The server returns the adjusted response to the terminal.

[1549] Step 7:

[1550] The terminal displays the adjusted response sentence to the user.

[1551] Specific examples

[1552] Example of uploading preference data

[1553] Step 1:

[1554] The terminal displays a preference data upload form and provides the user with file selection options.

[1555] Step 2:

[1556] The user selects his / her preference data and clicks the "Upload" button.

[1557] Step 3:

[1558] The device collects the selected files and sends them to the server via an HTTP POST request.

[1559] Step 4:

[1560] The server receives the request and temporarily stores the file.

[1561] Step 5:

[1562] The server converts the received file into a dedicated data format and checks the conversion results.

[1563] Step 6:

[1564] The server saves the converted data to the database and confirms the success of the save.

[1565] Step 7:

[1566] The server will return a notification of upload completion and the ID of the saved data to the device.

[1567] Step 8:

[1568] The device displays a message to the user that the upload is complete.

[1569] Example of response adjustment when using the emotion engine

[1570] Step 1:

[1571] The device activates an emotion engine and displays an interface that analyzes the user's facial expressions and voice in real time.

[1572] Step 2:

[1573] The emotion engine detects the user's tone of voice and facial expressions in real time and recognizes their emotions.

[1574] Step 3:

[1575] The device collects the recognized emotion data and sends it to the server via an HTTP POST request.

[1576] Step 4:

[1577] The server receives the emotion data and stores it in a database.

[1578] Step 5:

[1579] A generative AI model adjusts response strategies based on stored emotional data.

[1580] Step 6:

[1581] The server returns the adjusted response to the terminal.

[1582] Step 7:

[1583] The terminal displays the adjusted response sentence to the user.

[1584] The above is a detailed description of the specific operations at each processing step. By following these steps, the system can effectively manage user preference data and emotion data and apply them to the generative AI model.

[1585] Example 2

[1586] 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."

[1587] Conventional generative AI models have difficulty managing user preference and emotional data and providing personalized responses based on this data. Furthermore, there was no centralized system for managing data uploads, downloads, ratings, and emotional analysis, making it difficult to improve the user experience.

[1588] 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.

[1589] In this invention, the server includes means for converting user preference data into a dedicated data format and saving it, means for uploading the preference data, means for downloading the preference data, means for evaluating the preference data, means for activating an emotion engine that analyzes user emotions, and means for adjusting the response of the generative AI model based on the analyzed emotion data, thereby enabling effective management of the user's preference data and emotion data and providing personalized responses through the generative AI model.

[1590] "Preference Data" refers to information about your personal preferences, interests, and concerns.

[1591] "Private Data Format" refers to a data format or structure that is specialized for storing and processing preference data.

[1592] "Upload" refers to the operation of sending data from a user's device to a server.

[1593] "Download" refers to the operation of obtaining data from a server to a user's device.

[1594] "Evaluation" refers to the operation in which a user assigns a score or review comment to preference data.

[1595] An "emotion engine" refers to software or hardware that analyzes data such as the user's voice and facial expressions and recognizes emotions.

[1596] "Analysis" refers to the process of analyzing given data and finding meaning and patterns within it.

[1597] A "generative AI model" refers to an artificial intelligence model that generates responses based on input from a user.

[1598] DETAILED DESCRIPTION OF THE INVENTION The present invention is a system for effectively managing user preference data and providing personalized responses through a generative AI model.

[1599] The system consists of the following components:

[1600] 1. Server

[1601] 2. Terminal

[1602] 3. Users

[1603] 4. Emotion Engine

[1604] 5. Generative AI Models

[1605] Uploading and storing preference data

[1606] The device displays an interface for the user to upload preference data. The interface is implemented using HTML and JavaScript, and provides a form including a file selection button and an upload button. The user selects their preference data file and clicks the upload button. The device sends the selected file to the server, and the server converts the received data into a dedicated data format and stores it in a database.

[1607] For example, if a user uploads movie preference data:

[1608] Prompt: "Please select your movie preferences and press the upload button."

[1609] Download preference data

[1610] The device provides an interface that displays a list of preference data. The user selects the data they wish to download from the list. The device then sends an HTTP GET request including the selected data ID to the server, and the server retrieves the corresponding preference data from the database and sends it to the device. The device then installs the received preference data into the generative AI model.

[1611] For example, if a user downloads music preference data:

[1612] Prompt: "Select the music preferences you would like to download."

[1613] Evaluating and filtering preference data

[1614] The device displays an interface for the user to input ratings and reviews of the preference data. The user enters a rating score and review comments and clicks the submit button. The device sends the entered rating information to the server, which stores the information in a database. The server filters the preference data based on the rating information.

[1615] For example, if a user rates their preference data for a cooking recipe:

[1616] Prompt: "Please rate the preference data for this recipe."

[1617] Using an emotion engine and incorporating it into generative AI models

[1618] The device activates the emotion engine to analyze the user's emotions. The emotion engine analyzes the user's voice and facial expressions to recognize emotions. The server receives the emotion data and stores it in a database. The generative AI model adjusts the response based on the emotion data, and the device provides the response to the user.

[1619] For example, to tailor your response based on user sentiment:

[1620] Prompt: "I am tailoring my response based on how you are currently feeling."

[1621] The system effectively manages user preference data and emotion data and provides users with personalized responses through generative AI models, improving the user experience and significantly improving the utilization efficiency of generative AI models.

[1622] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1623] Step 1:

[1624] The terminal displays an interface for the user to upload preference data. The interface provides an HTML form with a file selection button and an "Upload" button. The user selects a preference data file and clicks the upload button (input: preference data file, output: data from the selected file).

[1625] Step 2:

[1626] The device reads the user-selected preference data file using a JavaScript File object and sends it to the server as an HTTP POST request (input: data from the selected file, output: preference data sent to the server).

[1627] Step 3:

[1628] The server calls a function to convert the received preference data into a dedicated data format and stores the converted data in a database (input: received preference data, output: data converted into dedicated data format).

[1629] Step 4:

[1630] The server sends an HTTP response to the terminal indicating that the data has been successfully saved (input: data conversion and saving results, output: notification that upload has been completed).

[1631] Step 5:

[1632] The device receives the response from the server and displays a message to the user saying "Upload completed" (Input: Notification of upload completion, Output: Display message to user).

[1633] Step 6:

[1634] The terminal requests current preference data from the database to provide the user with an interface that displays a list of preference data (input: user request, output: list of preference data).

[1635] Step 7:

[1636] The user selects the preference data they wish to download from the list and clicks the "Download" button (input: list of preference data, output: data ID to be downloaded).

[1637] Step 8:

[1638] The terminal sends an HTTP GET request including the selected data ID to the server, and the server retrieves the corresponding preference data from the database and sends it to the terminal (input: data ID, output: corresponding preference data).

[1639] Step 9:

[1640] The device installs the received preference data into the generative AI model (input: received preference data, output: data installed into the generative AI model).

[1641] Step 10:

[1642] The device displays a message to the user that the download is complete. (Input: Download and installation complete information; Output: Message displayed to the user.)

[1643] Step 11:

[1644] The terminal displays a rating interface including rating buttons and a review input field. The user enters a rating score and review comments and clicks the submit button (input: preference data, output: rating score and review comments).

[1645] Step 12:

[1646] The terminal transmits data including the rating score and review comments to the server (input: rating score and review comments, output: rating information transmitted to the server).

[1647] Step 13:

[1648] The server stores the received rating information in a database (input: received rating information, output: rating information stored in the database).

[1649] Step 14:

[1650] The server sends an HTTP response to the terminal indicating that the rating was successfully saved (input: rating saved result, output: notification of rating completion).

[1651] Step 15:

[1652] The terminal receives a notification from the server that the evaluation is complete and displays a message to the user saying "Evaluation is complete" (Input: Notification of evaluation completion, Output: Display a message to the user).

[1653] Step 16:

[1654] The server filters the preference data based on the rating information, for example, by excluding data below a certain rating score (input: rating information, output: filtered preference data).

[1655] Step 17:

[1656] The device starts the emotion engine and captures the user's facial expressions and voice using the camera and microphone (input: user's facial expressions and voice, output: captured data).

[1657] Step 18:

[1658] The emotion engine analyzes the captured data and recognizes the user's emotions (input: captured data, output: recognized emotion data).

[1659] Step 19:

[1660] The server receives the emotion data sent from the emotion engine and stores it in a database (input: recognized emotion data, output: emotion data stored in the database).

[1661] Step 20:

[1662] The generative AI model generates responses based on emotion data stored in a database, tailoring the response to specific emotions (input: emotion data, output: tailored response).

[1663] Step 21:

[1664] The device receives the response from the generative AI model and displays it to the user (input: adjusted response, output: response presented to the user).

[1665] This allows us to utilize user preference and emotion data to optimize the generative AI model and provide personalized services to users.

[1666] (Application example 2)

[1667] 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."

[1668] In today's brick-and-mortar stores, improving the customer experience requires accurately understanding customer preferences and emotions and providing personalized services based on those. However, current technology lacks the means to analyze and respond to customer preferences and emotions in real time. As a result, it is difficult to improve customer satisfaction or stimulate purchasing motivation. Therefore, to improve the customer experience, a system is needed that can manage and analyze customer preference and emotion data in real time and provide responses based on that data.

[1669] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1670] In this invention, the server includes means for converting preference data into a dedicated data format and saving it, means for uploading the preference data, means for downloading the preference data, means for evaluating the preference data, means for analyzing the user's emotions using an emotion engine, means for reflecting the preference data and emotion data in a generative AI model, and means for providing a response based on the user's preferences and emotions through an application installed on the smart device. This makes it possible to provide personalized services in physical stores according to the customer's preferences and emotions.

[1671] "Preference Data" is information about your preferences and interests.

[1672] A "specialized data format" is a data format designed to efficiently store and manage specific data.

[1673] A "means for uploading preference data" is a method or device that allows a user to transmit their preference data to the system.

[1674] A "means for downloading preference data" is a method or device for obtaining preference data from the system.

[1675] The "means for evaluating preference data" refers to a method or device for evaluating the quality and usefulness of data based on a user's preference data.

[1676] An "emotion engine" is software or hardware that analyzes a user's emotions and generates the results as information.

[1677] A "generative AI model" is an artificial intelligence model that responds or generates appropriate responses based on given data and conditions.

[1678] A "smart device" is an electronic device that has advanced computing power and built-in communication functions and sensors.

[1679] "Means for reflecting preference data and emotional data" refers to a method or device for incorporating preference data and emotional data obtained from users into a system and using the data.

[1680] A "personalized service" is a service that is customized to suit the preferences and feelings of each individual user.

[1681] A specific embodiment of the system of the present invention will be described below, which aims to improve customer experience, particularly in brick-and-mortar stores.

[1682] Users collect their own preference data and upload it to the system via their smart devices (e.g., smart glasses). This preference data is sent to a server, converted into a dedicated data format, and stored in a database. When the user visits a store, the smart device downloads the latest preference data from the server and presents it to store staff.

[1683] The server is equipped with means for storing, uploading, downloading, and evaluating preference data. This allows user preference data to be accumulated and evaluated or filtered as needed. For example, if preference data is stored in the form of a number, it can be filtered using SQL queries, etc.

[1684] The emotion engine is used to analyze users' real-time emotions. This emotion engine uses the smart device's camera and microphone to collect the user's facial expressions and tone of voice and convert them into emotion data. This emotion data is sent to the server and reflected in the generative AI model. The emotion engine uses customer signal analysis software.

[1685] The generative AI model generates responses and suggestions for users based on preference and emotion data. The generative AI model has advanced computing capabilities and takes both preference and emotion data into account to derive optimal responses. These responses are provided to staff via smart devices, enabling personalized service for customers.

[1686] As a concrete example, when User A visits a store, their smart device downloads their preference data from the server. The emotion engine analyzes User A's facial expressions and tone of voice in real time to detect the emotion of "happiness." Based on this data, the generative AI model generates a response such as, "We'd like to introduce you to some new products we currently recommend. This coffee in particular has been very popular recently." A staff member checks this information via their smart device and makes a suggestion to User A.

[1687] Another example of a prompt is, "Please suggest the most suitable related products based on the product selected by the customer. Also, please analyze the customer's emotions from their facial expressions and speech and provide advice on how to provide appropriate customer service." This prompt allows the generative AI model to comprehensively determine the user's preferences and emotions and make optimal suggestions and responses.

[1688] This system significantly improves the customer experience in physical stores and makes it possible to provide services that highly satisfy customers.

[1689] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1690] Step 1:

[1691] Uploading preference data

[1692] Input: The user selects their preference data.

[1693] Specific operation: The terminal (smart device) displays an interface for the user to select preference data. When the user selects a preference data file and presses the "Upload" button, the terminal sends the data to the server.

[1694] Data processing / calculation: The server converts the received data into a dedicated data format and stores it in a database.

[1695] Output: A notification that the upload is complete is sent back to the device, which displays it to the user.

[1696] Step 2:

[1697] Download preference data

[1698] Input: When a user enters a physical store, the device sends a data request to the server.

[1699] Specific operation: The device periodically sends an HTTP GET request to the server to obtain the latest preference data. The server receives the request and retrieves the corresponding preference data from the database.

[1700] Data processing / calculation: The server sends the acquired data to the terminal, which receives and displays it.

[1701] Output: The latest preference data is downloaded and presented to the store staff.

[1702] Step 3:

[1703] Evaluating preference data

[1704] Input: Users and staff input their ratings for preference data.

[1705] Specific operation: The terminal displays the evaluation interface to the user and staff. When the user and staff input the evaluation score and review comments and press the send button, the evaluation information is sent to the server.

[1706] Data processing / calculation: The server stores the rating information in a database and filters the preference data based on the rating information.

[1707] Output: The evaluation results are sent back to the terminal, which displays them to the user and staff.

[1708] Step 4:

[1709] Emotion engine activation and analysis

[1710] Input: The user's facial expressions and tone of voice are collected in the store.

[1711] How it works: The device captures the user's facial expressions and tone of voice in real time through its built-in camera and microphone, and this data is sent to the emotion engine for analysis.

[1712] Data processing / calculation: The emotion engine analyzes the collected data and recognizes the user's emotional state. The analysis results are sent to the server as emotion data.

[1713] Output: Emotion data is generated and stored on the server.

[1714] Step 5:

[1715] Generative AI model generates responses

[1716] Input: User preference and emotion data is fed into the generative AI model.

[1717] Specific operation: The server provides the stored preference data and emotion data to the generative AI model, which then generates an appropriate response based on the prompt sentence.

[1718] Data processing / calculation: Generative AI models perform calculations based on preference data and emotion data to generate optimal responses.

[1719] Output: The generated response is sent to the terminal and presented to the store staff.

[1720] An example prompt is, "Please suggest the most suitable related products based on the product selected by the customer. Also, please analyze the customer's emotions from their facial expressions and speech and display advice on how to provide appropriate customer service."

[1721] Through these processing steps, this system can significantly improve the customer experience in physical stores.

[1722] 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.

[1723] 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.

[1724] 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.

[1725] [Fourth embodiment]

[1726] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1727] 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.

[1728] 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).

[1729] 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.

[1730] 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.

[1731] 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).

[1732] 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.

[1733] 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.

[1734] 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.

[1735] 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.

[1736] 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.

[1737] 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.

[1738] 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."

[1739] The following describes a specific embodiment of the system of the present invention. This system manages user preference data and efficiently installs it into a generative AI model.

[1740] This system includes a server for converting and saving user preference data into a dedicated data format, a user terminal for uploading the preference data, a user terminal for downloading the preference data, and a user terminal for evaluating the preference data.

[1741] Specific processing of the program

[1742] The program of this system performs the following processing.

[1743] 1. Uploading your preference data

[1744] The terminal displays an interface for the user to select preference data.

[1745] The user selects a preference data file and presses the upload button.

[1746] The terminal transmits the selected preference data to the server.

[1747] The server converts the received preference data into a dedicated data format and stores it in a database.

[1748] The server returns a notification of upload completion to the user's device.

[1749] The device will display a message to the user that the upload is complete.

[1750] 2. Downloading preference data

[1751] The terminal provides an interface for displaying a list of preference data.

[1752] The user selects the preference data they wish to download.

[1753] The terminal transmits a download request for the selected preference data to the server.

[1754] The server retrieves the relevant preference data from the database and sends it to the user's device.

[1755] The device installs the received preference data into the generated AI model.

[1756] 3. Evaluation of preference data

[1757] The terminal displays a rating interface including a rating button and a review input field.

[1758] The user enters rating information and reviews and presses the submit button.

[1759] The terminal transmits the evaluation information to the server.

[1760] The server stores the evaluation information in a database and returns a notification of evaluation completion to the terminal.

[1761] The terminal displays a message to the user that the evaluation is complete.

[1762] Specific examples

[1763] For example, when a user uploads new preference data, the specific flow is as follows.

[1764] 1. The terminal displays a preference data upload form to the user.

[1765] 2. The user selects their preference data and clicks the "Upload" button.

[1766] 3. The terminal transmits the selected data to the server.

[1767] 4. The server receives the data, converts it into a proprietary data format, and stores it in a database.

[1768] 5. The server returns an upload completion notification to the device, and the device displays that information to the user.

[1769] The flow when a user purchases preference data created by another user is as follows.

[1770] 1. The terminal displays a list of preference data that is available for a fee.

[1771] 2. The user selects the data they want to purchase, enters payment information, and presses the "Purchase" button.

[1772] 3. The terminal sends the entered payment information to the server.

[1773] 4. The server processes the payment, and if the payment is successful, retrieves the relevant data from the database and sends it to the user's device.

[1774] 5. The device receives the data and installs it into the generative AI model.

[1775] Through the above process, the system can effectively manage user preference data and apply it to the generative AI model, allowing users to quickly obtain responses that match their preferences and significantly improving the utilization efficiency of the generative AI model.

[1776] The processing flow will be explained below.

[1777] Uploading preference data

[1778] Step 1:

[1779] The terminal displays the preference data upload interface.

[1780] Step 2:

[1781] The user selects a preference data file and clicks the upload button.

[1782] Step 3:

[1783] The device sends the selected file to the server as an HTTP POST request.

[1784] Step 4:

[1785] The server receives the request and temporarily stores the file.

[1786] Step 5:

[1787] The server converts the received file into a dedicated data format.

[1788] Step 6:

[1789] The server stores the converted data in a database.

[1790] Step 7:

[1791] The server will return a notification of upload completion and the ID of the saved data to the device.

[1792] Step 8:

[1793] The device displays a message to the user that the upload is complete.

[1794] Download preference data

[1795] Step 1:

[1796] The terminal displays an interface listing the preference data.

[1797] Step 2:

[1798] The user selects the preference data that he or she wishes to download.

[1799] Step 3:

[1800] The device sends an HTTP GET request including the ID of the selected data to the server.

[1801] Step 4:

[1802] The server receives the request and retrieves the corresponding preference data from the database.

[1803] Step 5:

[1804] The server returns the acquired data to the user's terminal.

[1805] Step 6:

[1806] The device receives the returned data and installs it into the generative AI model.

[1807] Step 7:

[1808] The device displays a download completion message to the user.

[1809] Evaluating preference data

[1810] Step 1:

[1811] The terminal displays the evaluation interface.

[1812] Step 2:

[1813] The user enters the rating score and review comments and clicks the submit button.

[1814] Step 3:

[1815] The device sends the rating information to the server as an HTTP POST request.

[1816] Step 4:

[1817] The server receives the request and stores the rating information in a database.

[1818] Step 5:

[1819] The server returns a notification of evaluation completion to the terminal.

[1820] Step 6:

[1821] The terminal displays a message to the user that the evaluation is complete.

[1822] Purchase of preference data

[1823] Step 1:

[1824] The terminal displays an interface listing paid preference data.

[1825] Step 2:

[1826] The user selects the preference data he or she wishes to purchase and clicks the purchase button.

[1827] Step 3:

[1828] The terminal displays a form for entering payment information.

[1829] Step 4:

[1830] The user enters payment information and clicks the submit button.

[1831] Step 5:

[1832] The terminal sends an HTTP POST request containing payment information to the server.

[1833] Step 6:

[1834] The server receives the request and communicates with the payment provider to process the payment.

[1835] Step 7:

[1836] The server confirms the success of the payment and retrieves the relevant preference data from the database.

[1837] Step 8:

[1838] The server transmits the acquired data to the user's terminal.

[1839] Step 9:

[1840] The device receives the data and installs it into the generative AI model.

[1841] Step 10:

[1842] The terminal displays a purchase completion message to the user.

[1843] The above is a detailed description of the specific operations at each processing step. By following these steps, the system can effectively manage user preference data and apply it to the generative AI model.

[1844] Example 1

[1845] 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."

[1846] There is a need to efficiently manage user preference data and apply it to generative AI models to provide fast and accurate responses that match individual user preferences. There is also a growing need for a smooth interface for uploading and downloading preference data and the rating process, as well as functions such as paid data provision and data filtering based on rating information.

[1847] 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.

[1848] In this invention, the server includes a means for transmitting preference data selected from the user's terminal to the server, a means for storing the preference data received by the server in a database, and a means for displaying a notification to the user upon completion of uploading the preference data. This allows users to easily manage their own preference data and apply it to generative AI models. In addition, accurate data storage and notification of upload completion enhances feedback to the user, improving the utilization efficiency of the entire system.

[1849] "User" refers to any individual or corporation that uses this system.

[1850] "Preference Data" means data containing information about your preferences and tastes.

[1851] "Private data format" refers to a specific data format in which data converted from another format is stored.

[1852] "Uploading means" refers to a method or device that allows a user to transmit preference data to the system.

[1853] "Device" refers to an electronic device operated by a user, such as a computer, smartphone, or tablet.

[1854] "Server" refers to a computer system that receives, stores, transforms, and transmits data.

[1855] "Database" refers to a data management system for efficiently storing and retrieving large amounts of data.

[1856] "Means for converting into a dedicated data format and saving" refers to a method or device for converting received preference data into a specific format and saving it in that format.

[1857] "Means for downloading" refers to a method or device for transmitting preference data from a server to a user terminal.

[1858] "Generative AI model" refers to an artificial intelligence model that generates output based on user preference data.

[1859] The term "rating means" refers to a method or device for a user to input an evaluation of preference data.

[1860] "Means for providing for a fee" refers to a method or device for providing preference data in exchange for a certain fee.

[1861] The term "means for filtering based on rating information" refers to a method or device for selecting preference data based on rating information input by a user.

[1862] "Notification means" refers to a method or device for notifying the user of the system status or processing results.

[1863] By using the above definitions, the components and functions of the system of the present invention can be clearly understood and explained.

[1864] MODE FOR CARRYING OUT THE INVENTION

[1865] The following describes an example of a specific embodiment of the system of the present invention. This system effectively manages user preference data and applies it to a generative AI model.

[1866] The system mainly consists of the following components:

[1867] 1. Devices that upload user preference data

[1868] 2. Server that stores and converts received preference data

[1869] 3. A server that provides preference data in response to requests from user devices

[1870] 4. Generative AI models that install user preference data

[1871] 5. A device that provides an interface for users to evaluate preference data

[1872] Hardware and software used

[1873] Device:

[1874] A device is an electronic device operated by a user, such as a computer, smartphone, or tablet. A web browser interface or dedicated application is installed on the device, providing an interface for the user to select, upload, and rate preference data. The device communicates with the server using the HTTP protocol.

[1875] Examples: laptops, smartphones

[1876] server:

[1877] A server is a computer system that receives, stores, converts, and transmits data. The server has the functionality to convert preference data into a dedicated data format and store it in a database. The server also provides selected data and receives rating information.

[1878] Examples: AWS EC2, IBM Cloud

[1879] Database:

[1880] A database is a data management system for efficiently storing and retrieving large amounts of data. Databases store user preference data and ratings.

[1881] Examples: MySQL, MongoDB

[1882] Generative AI models:

[1883] A generative AI model is an artificial intelligence model that generates output based on user preference data. This model uses the preference data installed from the device to generate responses that are customized for each user.

[1884] Examples: GPT-3, BERT

[1885] Specific examples

[1886] Upload your preference data:

[1887] When a user uploads new preference data, a form including a file selection button and an upload button is displayed on the device. When the user selects "Music Preference Data.csv" and clicks the upload button, the data is sent from the device to the server. The server receives the data, converts it into a dedicated data format (e.g., JSON format), and stores it in a database. The server then returns a notification that the upload is complete to the device, and the device displays a message to the user saying "Upload completed."

[1888] Install an example prompt into your generative AI model:

[1889] When a user uses a prompt such as "Recommend me a list of new movies based on my preferences," the prompt is sent to the generative AI model via the device. The generative AI model generates an appropriate movie list based on the user's installed preference data and returns it to the device. This process allows users to quickly obtain a recommendation list that suits their preferences.

[1890] From the above description, it is believed that a specific embodiment of the system according to the present invention can be understood, which makes the system a useful tool for efficiently managing user preference data and applying it to generative AI models.

[1891] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1892] Step 1:

[1893] Interface Display

[1894] The terminal displays a preference data upload form to the user. The inputs include a "file selection button" and an "upload button." This action is displayed using a GUI library, allowing the user to select preference data.

[1895] Step 2:

[1896] Data Selection

[1897] The user selects the preference data file they want to upload from their device. The input includes the file path (e.g., "music preference data.csv"). The user's operation saves the path of the selected file in a variable on the device.

[1898] Step 3:

[1899] Data transmission

[1900] After selecting a preference data file, the user clicks the upload button. The device sends the selected preference data file to the server. The transmission uses an HTTP POST request, with the selected preference data file as input and the status of the file transmission to the server as output. This process transfers the file from the device to the server.

[1901] Step 4:

[1902] Data reception

[1903] The server temporarily stores the received preference data in a specific directory. The input is the file data in the HTTP request body, and the output is the temporary storage path in the server. In this step, the file data is stored in the server.

[1904] Step 5:

[1905] Data Conversion

[1906] The server converts the temporarily stored preference data into a dedicated data format (e.g., JSON format). The conversion uses the Python pandas library, and includes the temporarily stored file path as input and the converted data object as output. In this step, the CSV format data is converted into JSON format.

[1907] Step 6:

[1908] Data storage

[1909] The server saves the transformed data to a database, using MySQL or MongoDB, with the transformed data object as input and the status of the data being saved in the database as output. This process writes the data to the database.

[1910] Step 7:

[1911] Upload completion notification

[1912] After the server confirms that the data has been saved correctly, it returns an upload completion notification to the user's device. The input is the database save status, and the output is a completion notification message in the HTTP response body. This causes the device to display "Upload completed" to the user.

[1913] Step 8:

[1914] Data list display

[1915] The terminal provides an interface that displays a list of available preference data. The input includes the list of preference data retrieved from the server, and the output includes the displayed list of data. The terminal then presents the user with preference data options.

[1916] Step 9:

[1917] Data selection and download request submission

[1918] The user selects the preference data they wish to download and clicks the download button. The terminal sends a download request for the selected preference data to the server. The input is the user-selected data, and the output is the server transmission status. The download request is sent from the terminal to the server.

[1919] Step 10:

[1920] Data Acquisition and Transmission

[1921] The server retrieves the relevant preference data from the database and sends it to the user's device. The input is a download request, and the output is the downloaded data included in the HTTP response. The device then receives the necessary data.

[1922] Step 11:

[1923] Data installation

[1924] The terminal installs the received preference data into the generative AI model. The input is the downloaded data, and the output is the status of data application to the generative AI model. This process applies the preference data to the generative AI model.

[1925] Step 12:

[1926] Evaluation interface display

[1927] The terminal displays a rating interface that includes rating buttons and review input fields, a rating form object as input, and user input fields as output, allowing users to rate preference data.

[1928] Step 13:

[1929] Enter and submit your rating

[1930] The user inputs rating information and reviews and presses the send button. The terminal sends the rating information to the server. The input is the user's rating information, and the output is the server transmission status. The rating information is sent to the server.

[1931] Step 14:

[1932] Saving rating information

[1933] The server saves the rating information to the database. The input is a rating information object, and the output is a database save status. This saves the rating information to the database.

[1934] Step 15:

[1935] Evaluation completion notification

[1936] After the server confirms that the evaluation has been saved correctly, it returns an evaluation completion notification to the terminal. The input is the database saving status, and the output is an evaluation completion message in the HTTP response body. The terminal displays the evaluation completion message to the user.

[1937] (Application example 1)

[1938] 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."

[1939] Conventional systems for managing preference data did not efficiently suggest products based on individual users' preferences, resulting in low user satisfaction. It was also difficult to use other users' preference data to receive product suggestions that matched one's hobbies and interests. Furthermore, the lack of a preference data evaluation or filtering function meant that inappropriate data could be mixed in.

[1940] 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.

[1941] In this invention, the server includes means for converting preference data into a dedicated data format and saving it, means for uploading preference data, means for downloading preference data, means for evaluating the preference data, and means for providing personalized product suggestions using the preference data. This allows users to receive optimal product suggestions using their own and other users' preference data. Furthermore, filtering the preference data based on the evaluation information allows more appropriate and reliable suggestions to be provided.

[1942] 1. "Preference Data" means information relating to your interests and concerns.

[1943] 2. "Private Data Format" means a data structure specifically designed for efficiently storing and processing Preference Data.

[1944] 3. "Uploading means" means a function that allows a user to send preference data from their device to the server.

[1945] 4. "Downloading means" means a function that allows a user to obtain preference data from a server to their own device.

[1946] 5. "Rating means" refers to the functionality that allows users to rate and review preference data and suggestions based on it.

[1947] 6. "Personalized product suggestions" means a function that suggests optimal products and services based on a user's preference data.

[1948] 7. "Paid provision means" means a function for providing preference data for a certain fee.

[1949] 8. "Filtering means" refers to a function that selects preference data based on evaluation information.

[1950] The system of the present invention is configured to effectively manage user preference data and apply it to a generative AI model. The system mainly consists of three elements: a server, a user's device, and a generative AI model. The role of each element and the specific processing content are explained below.

[1951] server

[1952] The server plays a central role in efficiently storing, transforming, and serving preference data.

[1953] Storage and conversion of preference data: The server converts the preference data received from the user into a dedicated data format and stores it in a database. This data format is designed for efficient processing of preference data.

[1954] Data upload and download: The server receives requests to upload preference data from the user's device and stores the data in the database. It also retrieves the relevant data from the database in response to download requests and sends it to the user's device.

[1955] Processing rating information: The server receives the user's rating information and stores it in a database, which can be used as a reference for other users to select highly rated preference data.

[1956] User's device

[1957] The user's terminal provides an interface for uploading, downloading, and rating preference data.

[1958] Data upload: The user can select preference data using the device interface and send it to the server by pressing the upload button.

[1959] Data download: Using the device interface, users can select, purchase, and download preference data provided by other users for a fee. This data is then installed into the generative AI model and used to make personalized product recommendations.

[1960] Data rating: Provides an interface for users to rate and review proposed products and services. The rating information is sent to the server and stored in a database.

[1961] Generative AI Models

[1962] The generative AI model is responsible for making personalized product suggestions based on user preference data.

[1963] Personalized product recommendations: User preference data is used to recommend the best products and services for each user, based on the preference data installed in the generative AI model.

[1964] Specific examples

[1965] For example, if a user wants to upload new preference data, he or she can perform the following operations:

[1966] 1. The user selects preference data that reflects their interests and preferences in the device's upload form.

[1967] 2. When you press the upload button, the data is sent to the server, converted into a dedicated data format, and saved.

[1968] A specific example of purchasing highly rated preference data created by other users is as follows.

[1969] 1. The user displays a list of preference data that is available on the device for a fee.

[1970] 2. Select the data you want to purchase, enter your payment information, and press the purchase button.

[1971] 3. Once the payment is successful, the relevant data is downloaded from the server to the device and installed into the generative AI model.

[1972] Examples of prompt statements

[1973] "My hobbies are cooking and gardening, so I'd like product suggestions that suit my tastes."

[1974] "I've recently become interested in yoga, so I'd like to receive product suggestions related to it."

[1975] In this way, the system enables the recommendation of products and services optimized to the user's preferences, improving the user experience.

[1976] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1977] Step 1:

[1978] Uploading preference data

[1979] Input: The user selects their preference data on the device and presses the upload button.

[1980] process:

[1981] The terminal reads the preference data file selected by the user.

[1982] A request to be sent to the server is created to convert the read data into a dedicated data format.

[1983] Data processing: Preference data is sent to the server in a standard format such as JSON and converted into a dedicated data format on the server side.

[1984] Output: The server saves the preference data in the database and sends an upload completion notification to the device.

[1985] Step 2:

[1986] Download preference data

[1987] Input: The user selects the preference data they want to download on their device and presses the download button.

[1988] process:

[1989] The terminal transmits a download request for the selected preference data to the server.

[1990] The server retrieves the relevant preference data from the database.

[1991] Data calculation: The server analyzes the user's request and extracts the corresponding preference data.

[1992] Output: The extracted preference data is sent to the user's device and installed into the generative AI model.

[1993] Step 3:

[1994] Evaluating preference data

[1995] Input: The user enters rating information or a review in the rating interface and presses the submit button.

[1996] process:

[1997] The terminal reads the user's input.

[1998] The read evaluation information is sent to the server.

[1999] Data processing: The server stores the received evaluation information in a database in an appropriate format.

[2000] Output: The server sends a notification of the completion of the evaluation to the terminal, and the terminal displays a message to the user that the evaluation is complete.

[2001] Step 4:

[2002] Personalized product recommendations

[2003] Input: User preference data and a prompt from the user (e.g., "My hobbies are cooking and gardening, so please suggest products that suit my preferences.") are input into the generative AI model.

[2004] process:

[2005] The generative AI model analyzes the input preference data and prompt text.

[2006] Select the most suitable products and services based on the data and generate a list of proposals.

[2007] Data calculation: The generative AI model runs an algorithm that matches input data with an internal database to make the most suitable product recommendations for the user.

[2008] Output: A personalized product suggestion list is displayed on the user's device.

[2009] In this way, each step works in conjunction with each other to make optimal product suggestions to users and improve the user experience.

[2010] 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.

[2011] The following describes a specific embodiment of the system of the present invention. This system not only manages user preference data and efficiently installs it into a generative AI model, but also combines it with an emotion engine to enable responses that correspond to the user's emotions.

[2012] The system includes a server for converting user preference data into a dedicated data format and storing it, a user terminal for uploading and downloading the preference data, a user terminal for evaluating and filtering the preference data, and an emotion engine for recognizing user emotions.

[2013] Specific processing of the program

[2014] The program of this system performs the following processing.

[2015] 1. Uploading your preference data

[2016] The terminal displays an interface for the user to select preference data.

[2017] The user selects a preference data file and presses the upload button.

[2018] The terminal transmits the selected preference data to the server.

[2019] The server converts the received preference data into a dedicated data format and stores it in a database.

[2020] The server returns a notification of upload completion to the user's device.

[2021] The device will display a message to the user that the upload is complete.

[2022] 2. Downloading preference data

[2023] The terminal provides an interface for displaying a list of preference data.

[2024] The user selects the preference data they wish to download.

[2025] The terminal sends an HTTP GET request including the ID of the selected data to the server.

[2026] The server receives the request and retrieves the corresponding preference data from the database.

[2027] The server returns the acquired data to the user's terminal.

[2028] The device installs the received preference data into the generated AI model.

[2029] The device will display a download complete message to the user.

[2030] 3. Evaluating and filtering preference data

[2031] The terminal displays a rating interface including a rating button and a review input field.

[2032] The user enters the rating score and review comments and presses the submit button.

[2033] The terminal transmits the evaluation information to the server.

[2034] The server receives the request and stores the rating information in a database.

[2035] The server returns a notification of evaluation completion to the terminal.

[2036] The terminal displays a message to the user that the evaluation is complete.

[2037] The server filters the preference data based on the rating information.

[2038] 4. Reflecting the Emotion Engine in Generative AI Models

[2039] The device activates an emotion engine and analyzes the user's emotions.

[2040] The emotion engine analyzes data such as the user's voice and facial expressions to recognize emotions.

[2041] The server receives the emotion data from the emotion engine and stores it in a database.

[2042] The generative AI model adjusts the response sentence based on the emotional data and replies to the user.

[2043] Specific examples

[2044] For example, when a user uploads new preference data, the specific flow is as follows.

[2045] 1. The device presents the user with a preference data upload form.

[2046] 2. The user selects their preference data and clicks the "Upload" button.

[2047] 3. The terminal transmits the selected data to the server.

[2048] 4. The server receives the data, converts it into a proprietary data format, and stores it in a database.

[2049] 5. The server returns an upload completion notification to the device, and the device displays that information to the user.

[2050] Also, if a user recognizes emotions and adjusts the generative AI model's response based on them, the process is as follows:

[2051] 1. The device activates the emotion engine and analyzes the user's words and actions.

[2052] 2. The emotion engine analyzes the user's tone of voice and facial expressions to recognize their emotions.

[2053] 3. The server receives the analysis results and stores them in a database.

[2054] 4. The generative AI model adjusts responses based on emotional data.

[2055] 5. The device provides the adjusted response to the user.

[2056] Through the above process, the system can effectively manage user preference and emotion data and apply it to the generative AI model, allowing users to quickly obtain responses that match their preferences and emotions, greatly improving the utilization efficiency of the generative AI model.

[2057] The processing flow will be explained below.

[2058] Uploading preference data

[2059] Step 1:

[2060] The terminal displays the preference data upload interface.

[2061] Step 2:

[2062] The user selects a preference data file and clicks the upload button.

[2063] Step 3:

[2064] The device sends the selected file to the server as an HTTP POST request.

[2065] Step 4:

[2066] The server receives the request and temporarily stores the file.

[2067] Step 5:

[2068] The server converts the received file into a dedicated data format.

[2069] Step 6:

[2070] The server stores the converted data in a database.

[2071] Step 7:

[2072] The server will return a notification of upload completion and the ID of the saved data to the device.

[2073] Step 8:

[2074] The device displays a message to the user that the upload is complete.

[2075] Download preference data

[2076] Step 1:

[2077] The terminal displays an interface listing the preference data.

[2078] Step 2:

[2079] The user selects the preference data that he or she wishes to download.

[2080] Step 3:

[2081] The device sends an HTTP GET request including the ID of the selected data to the server.

[2082] Step 4:

[2083] The server receives the request and retrieves the corresponding preference data from the database.

[2084] Step 5:

[2085] The server returns the acquired data to the user's terminal.

[2086] Step 6:

[2087] The device receives the returned data and installs it into the generative AI model.

[2088] Step 7:

[2089] The device displays a download completion message to the user.

[2090] Evaluating and filtering preference data

[2091] Step 1:

[2092] The terminal displays an interface for evaluating the preference data.

[2093] Step 2:

[2094] The user enters the rating score and review comments and clicks the submit button.

[2095] Step 3:

[2096] The device sends the rating information to the server as an HTTP POST request.

[2097] Step 4:

[2098] The server receives the request and stores the rating information in a database.

[2099] Step 5:

[2100] The server returns a notification of evaluation completion to the terminal.

[2101] Step 6:

[2102] The terminal displays a message to the user that the evaluation is complete.

[2103] Step 7:

[2104] The server filters the preference data based on the rating information.

[2105] Reflecting the emotion engine in the generative AI model

[2106] Step 1:

[2107] The device activates the emotion engine and displays an interface that analyzes the user's emotions.

[2108] Step 2:

[2109] The emotion engine analyzes the user's tone of voice and facial expression data to recognize emotions.

[2110] Step 3:

[2111] The device transmits the emotion data to the server.

[2112] Step 4:

[2113] The server stores the emotion data in a database.

[2114] Step 5:

[2115] A generative AI model adjusts responses based on emotional data.

[2116] Step 6:

[2117] The server returns the adjusted response to the terminal.

[2118] Step 7:

[2119] The terminal displays the adjusted response sentence to the user.

[2120] Specific examples

[2121] Example of uploading preference data

[2122] Step 1:

[2123] The terminal displays a preference data upload form and provides the user with file selection options.

[2124] Step 2:

[2125] The user selects his / her preference data and clicks the "Upload" button.

[2126] Step 3:

[2127] The device collects the selected files and sends them to the server via an HTTP POST request.

[2128] Step 4:

[2129] The server receives the request and temporarily stores the file.

[2130] Step 5:

[2131] The server converts the received file into a dedicated data format and checks the conversion results.

[2132] Step 6:

[2133] The server saves the converted data to the database and confirms the success of the save.

[2134] Step 7:

[2135] The server will return a notification of upload completion and the ID of the saved data to the device.

[2136] Step 8:

[2137] The device displays a message to the user that the upload is complete.

[2138] Example of response adjustment when using the emotion engine

[2139] Step 1:

[2140] The device activates an emotion engine and displays an interface that analyzes the user's facial expressions and voice in real time.

[2141] Step 2:

[2142] The emotion engine detects the user's tone of voice and facial expressions in real time and recognizes their emotions.

[2143] Step 3:

[2144] The device collects the recognized emotion data and sends it to the server via an HTTP POST request.

[2145] Step 4:

[2146] The server receives the emotion data and stores it in a database.

[2147] Step 5:

[2148] A generative AI model adjusts response strategies based on stored emotional data.

[2149] Step 6:

[2150] The server returns the adjusted response to the terminal.

[2151] Step 7:

[2152] The terminal displays the adjusted response sentence to the user.

[2153] The above is a detailed description of the specific operations at each processing step. By following these steps, the system can effectively manage user preference data and emotion data and apply them to the generative AI model.

[2154] Example 2

[2155] 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."

[2156] Conventional generative AI models have difficulty managing user preference and emotional data and providing personalized responses based on this data. Furthermore, there was no centralized system for managing data uploads, downloads, ratings, and emotional analysis, making it difficult to improve the user experience.

[2157] 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.

[2158] In this invention, the server includes means for converting user preference data into a dedicated data format and saving it, means for uploading the preference data, means for downloading the preference data, means for evaluating the preference data, means for activating an emotion engine that analyzes user emotions, and means for adjusting the response of the generative AI model based on the analyzed emotion data, thereby enabling effective management of the user's preference data and emotion data and providing personalized responses through the generative AI model.

[2159] "Preference Data" refers to information about your personal preferences, interests, and concerns.

[2160] "Private Data Format" refers to a data format or structure that is specialized for storing and processing preference data.

[2161] "Upload" refers to the operation of sending data from a user's device to a server.

[2162] "Download" refers to the operation of obtaining data from a server to a user's device.

[2163] "Evaluation" refers to the operation in which a user assigns a score or review comment to preference data.

[2164] An "emotion engine" refers to software or hardware that analyzes data such as the user's voice and facial expressions and recognizes emotions.

[2165] "Analysis" refers to the process of analyzing given data and finding meaning and patterns within it.

[2166] A "generative AI model" refers to an artificial intelligence model that generates responses based on input from a user.

[2167] DETAILED DESCRIPTION OF THE INVENTION The present invention is a system for effectively managing user preference data and providing personalized responses through a generative AI model.

[2168] The system consists of the following components:

[2169] 1. Server

[2170] 2. Terminal

[2171] 3. Users

[2172] 4. Emotion Engine

[2173] 5. Generative AI Models

[2174] Uploading and storing preference data

[2175] The device displays an interface for the user to upload preference data. The interface is implemented using HTML and JavaScript, and provides a form including a file selection button and an upload button. The user selects their preference data file and clicks the upload button. The device sends the selected file to the server, and the server converts the received data into a dedicated data format and stores it in a database.

[2176] For example, if a user uploads movie preference data:

[2177] Prompt: "Please select your movie preferences and press the upload button."

[2178] Download preference data

[2179] The device provides an interface that displays a list of preference data. The user selects the data they wish to download from the list. The device then sends an HTTP GET request including the selected data ID to the server, and the server retrieves the corresponding preference data from the database and sends it to the device. The device then installs the received preference data into the generative AI model.

[2180] For example, if a user downloads music preference data:

[2181] Prompt: "Select the music preferences you would like to download."

[2182] Evaluating and filtering preference data

[2183] The device displays an interface for the user to input ratings and reviews of the preference data. The user enters a rating score and review comments and clicks the submit button. The device sends the entered rating information to the server, which stores the information in a database. The server filters the preference data based on the rating information.

[2184] For example, if a user rates their preference data for a cooking recipe:

[2185] Prompt: "Please rate the preference data for this recipe."

[2186] Using an emotion engine and incorporating it into generative AI models

[2187] The device activates the emotion engine to analyze the user's emotions. The emotion engine analyzes the user's voice and facial expressions to recognize emotions. The server receives the emotion data and stores it in a database. The generative AI model adjusts the response based on the emotion data, and the device provides the response to the user.

[2188] For example, to tailor your response based on user sentiment:

[2189] Prompt: "I am tailoring my response based on how you are currently feeling."

[2190] The system effectively manages user preference data and emotion data and provides users with personalized responses through generative AI models, improving the user experience and significantly improving the utilization efficiency of generative AI models.

[2191] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2192] Step 1:

[2193] The terminal displays an interface for the user to upload preference data. The interface provides an HTML form with a file selection button and an "Upload" button. The user selects a preference data file and clicks the upload button (input: preference data file, output: data from the selected file).

[2194] Step 2:

[2195] The device reads the user-selected preference data file using a JavaScript File object and sends it to the server as an HTTP POST request (input: data from the selected file, output: preference data sent to the server).

[2196] Step 3:

[2197] The server calls a function to convert the received preference data into a dedicated data format and stores the converted data in a database (input: received preference data, output: data converted into dedicated data format).

[2198] Step 4:

[2199] The server sends an HTTP response to the terminal indicating that the data has been successfully saved (input: data conversion and saving results, output: notification that upload has been completed).

[2200] Step 5:

[2201] The device receives the response from the server and displays a message to the user saying "Upload completed" (Input: Notification of upload completion, Output: Display message to user).

[2202] Step 6:

[2203] The terminal requests current preference data from the database to provide the user with an interface that displays a list of preference data (input: user request, output: list of preference data).

[2204] Step 7:

[2205] The user selects the preference data they wish to download from the list and clicks the "Download" button (input: list of preference data, output: data ID to be downloaded).

[2206] Step 8:

[2207] The terminal sends an HTTP GET request including the selected data ID to the server, and the server retrieves the corresponding preference data from the database and sends it to the terminal (input: data ID, output: corresponding preference data).

[2208] Step 9:

[2209] The device installs the received preference data into the generative AI model (input: received preference data, output: data installed into the generative AI model).

[2210] Step 10:

[2211] The device displays a message to the user that the download is complete. (Input: Download and installation complete information; Output: Message displayed to the user.)

[2212] Step 11:

[2213] The terminal displays a rating interface including rating buttons and a review input field. The user enters a rating score and review comments and clicks the submit button (input: preference data, output: rating score and review comments).

[2214] Step 12:

[2215] The terminal transmits data including the rating score and review comments to the server (input: rating score and review comments, output: rating information transmitted to the server).

[2216] Step 13:

[2217] The server stores the received rating information in a database (input: received rating information, output: rating information stored in the database).

[2218] Step 14:

[2219] The server sends an HTTP response to the terminal indicating that the rating was successfully saved (input: rating saved result, output: notification of rating completion).

[2220] Step 15:

[2221] The terminal receives a notification from the server that the evaluation is complete and displays a message to the user saying "Evaluation completed" (Input: Notification of evaluation completion, Output: Displaying a message to the user).

[2222] Step 16:

[2223] The server filters the preference data based on the rating information, for example, by excluding data below a certain rating score (input: rating information, output: filtered preference data).

[2224] Step 17:

[2225] The device starts the emotion engine and captures the user's facial expressions and voice using the camera and microphone (input: user's facial expressions and voice, output: captured data).

[2226] Step 18:

[2227] The emotion engine analyzes the captured data and recognizes the user's emotions (input: captured data, output: recognized emotion data).

[2228] Step 19:

[2229] The server receives the emotion data sent from the emotion engine and stores it in a database (input: recognized emotion data, output: emotion data stored in the database).

[2230] Step 20:

[2231] The generative AI model generates responses based on emotion data stored in a database, tailoring the response to specific emotions (input: emotion data, output: tailored response).

[2232] Step 21:

[2233] The device receives the response from the generative AI model and displays it to the user (input: adjusted response, output: response presented to the user).

[2234] This allows us to utilize user preference and emotion data to optimize the generative AI model and provide personalized services to users.

[2235] (Application example 2)

[2236] 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."

[2237] In today's brick-and-mortar stores, improving the customer experience requires accurately understanding customer preferences and emotions and providing personalized services based on those. However, current technology lacks the means to analyze and respond to customer preferences and emotions in real time. As a result, it is difficult to improve customer satisfaction or stimulate purchasing motivation. Therefore, to improve the customer experience, a system is needed that can manage and analyze customer preference and emotion data in real time and provide responses based on that data.

[2238] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2239] In this invention, the server includes means for converting preference data into a dedicated data format and saving it, means for uploading the preference data, means for downloading the preference data, means for evaluating the preference data, means for analyzing the user's emotions using an emotion engine, means for reflecting the preference data and emotion data in a generative AI model, and means for providing a response based on the user's preferences and emotions through an application installed on the smart device. This makes it possible to provide personalized services in physical stores according to the customer's preferences and emotions.

[2240] "Preference Data" is information about your preferences and interests.

[2241] A "specialized data format" is a data format designed to efficiently store and manage specific data.

[2242] A "means for uploading preference data" is a method or device that allows a user to transmit their preference data to the system.

[2243] A "means for downloading preference data" is a method or device for obtaining preference data from the system.

[2244] The "means for evaluating preference data" refers to a method or device for evaluating the quality and usefulness of data based on a user's preference data.

[2245] An "emotion engine" is software or hardware that analyzes a user's emotions and generates the results as information.

[2246] A "generative AI model" is an artificial intelligence model that responds or generates appropriate responses based on given data and conditions.

[2247] A "smart device" is an electronic device that has advanced computing power and built-in communication functions and sensors.

[2248] "Means for reflecting preference data and emotional data" refers to a method or device for incorporating preference data and emotional data obtained from users into a system and using the data.

[2249] A "personalized service" is a service that is customized to suit the preferences and feelings of each individual user.

[2250] A specific embodiment of the system of the present invention will be described below, which aims to improve customer experience, particularly in brick-and-mortar stores.

[2251] Users collect their own preference data and upload it to the system via their smart devices (e.g., smart glasses). This preference data is sent to a server, converted into a dedicated data format, and stored in a database. When the user visits a store, the smart device downloads the latest preference data from the server and presents it to store staff.

[2252] The server is equipped with means for storing, uploading, downloading, and evaluating preference data. This allows user preference data to be accumulated and evaluated or filtered as needed. For example, if preference data is stored in the form of a number, it can be filtered using SQL queries, etc.

[2253] The emotion engine is used to analyze users' real-time emotions. This emotion engine uses the smart device's camera and microphone to collect the user's facial expressions and tone of voice and convert them into emotion data. This emotion data is sent to the server and reflected in the generative AI model. The emotion engine uses customer signal analysis software.

[2254] The generative AI model generates responses and suggestions for users based on preference and emotion data. The generative AI model has advanced computing capabilities and takes both preference and emotion data into account to derive optimal responses. These responses are provided to staff via smart devices, enabling personalized service for customers.

[2255] As a concrete example, when User A visits a store, their smart device downloads their preference data from the server. The emotion engine analyzes User A's facial expressions and tone of voice in real time to detect the emotion of "happiness." Based on this data, the generative AI model generates a response such as, "We'd like to introduce you to some new products we currently recommend. This coffee in particular has been very popular recently." A staff member checks this information via their smart device and makes a suggestion to User A.

[2256] Another example of a prompt is, "Please suggest the most suitable related products based on the product selected by the customer. Also, please analyze the customer's emotions from their facial expressions and speech and provide advice on how to provide appropriate customer service." This prompt allows the generative AI model to comprehensively determine the user's preferences and emotions and make optimal suggestions and responses.

[2257] This system significantly improves the customer experience in physical stores and makes it possible to provide services that highly satisfy customers.

[2258] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2259] Step 1:

[2260] Uploading preference data

[2261] Input: The user selects their preference data.

[2262] Specific operation: The terminal (smart device) displays an interface for the user to select preference data. When the user selects a preference data file and presses the "Upload" button, the terminal sends the data to the server.

[2263] Data processing / calculation: The server converts the received data into a dedicated data format and stores it in a database.

[2264] Output: A notification that the upload is complete is sent back to the device, which displays it to the user.

[2265] Step 2:

[2266] Download preference data

[2267] Input: When a user enters a physical store, the device sends a data request to the server.

[2268] Specific operation: The device periodically sends an HTTP GET request to the server to obtain the latest preference data. The server receives the request and retrieves the corresponding preference data from the database.

[2269] Data processing / calculation: The server sends the acquired data to the terminal, which receives and displays it.

[2270] Output: The latest preference data is downloaded and presented to the store staff.

[2271] Step 3:

[2272] Evaluating preference data

[2273] Input: Users and staff input their ratings for preference data.

[2274] Specific operation: The terminal displays the evaluation interface to the user and staff. When the user and staff input the evaluation score and review comments and press the send button, the evaluation information is sent to the server.

[2275] Data processing / calculation: The server stores the rating information in a database and filters the preference data based on the rating information.

[2276] Output: The evaluation results are sent back to the terminal, which displays them to the user and staff.

[2277] Step 4:

[2278] Emotion engine activation and analysis

[2279] Input: The user's facial expressions and tone of voice are collected in the store.

[2280] How it works: The device captures the user's facial expressions and tone of voice in real time through its built-in camera and microphone, and this data is sent to the emotion engine for analysis.

[2281] Data processing / calculation: The emotion engine analyzes the collected data and recognizes the user's emotional state. The analysis results are sent to the server as emotion data.

[2282] Output: Emotion data is generated and stored on the server.

[2283] Step 5:

[2284] Generative AI model generates responses

[2285] Input: User preference and emotion data is fed into the generative AI model.

[2286] Specific operation: The server provides the stored preference data and emotion data to the generative AI model, which then generates an appropriate response based on the prompt sentence.

[2287] Data processing / calculation: Generative AI models perform calculations based on preference data and emotion data to generate optimal responses.

[2288] Output: The generated response is sent to the terminal and presented to the store staff.

[2289] An example prompt is, "Please suggest the most suitable related products based on the product selected by the customer. Also, please analyze the customer's emotions from their facial expressions and speech and display advice on how to provide appropriate customer service."

[2290] Through these processing steps, this system can significantly improve the customer experience in physical stores.

[2291] 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.

[2292] 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.

[2293] 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.

[2294] 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.

[2295] 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.

[2296] 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.

[2297] 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).

[2298] 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.

[2299] 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."

[2300] 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.

[2301] 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).

[2302] 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.

[2303] 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.

[2304] 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.

[2305] 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.

[2306] 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.

[2307] 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.

[2308] 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.

[2309] 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.

[2310] 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.

[2311] 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.

[2312] The following is further disclosed regarding the above embodiment.

[2313] (Claim 1)

[2314] To manage user preference data

[2315] A means for converting the preference data into a dedicated data format and storing the converted data;

[2316] a means for uploading preference data;

[2317] means for downloading preference data;

[2318] means for evaluating preference data;

[2319] A system including:

[2320] (Claim 2)

[2321] 10. The system of claim 1, further comprising means for providing preference data for a fee.

[2322] (Claim 3)

[2323] 10. The system of claim 1, further comprising means for filtering the preference data based on the rating information.

[2324] "Example 1"

[2325] (Claim 1)

[2326] To manage user preference data

[2327] A means for converting the preference data into a dedicated data format and storing the converted data;

[2328] a means for uploading preference data;

[2329] means for transmitting selected preference data from a user's terminal to a server;

[2330] means for storing the received preference data in a database by the server;

[2331] a means for displaying a notification to the user that the preference data has been uploaded;

[2332] means for downloading preference data;

[2333] A means of installing the preference data into the generative AI model;

[2334] means for evaluating preference data;

[2335] A system including:

[2336] (Claim 2)

[2337] 10. The system of claim 1, further comprising means for providing preference data for a fee.

[2338] (Claim 3)

[2339] 10. The system of claim 1, further comprising means for filtering the preference data based on the rating information.

[2340] "Application Example 1"

[2341] (Claim 1)

[2342] A means for converting the preference data into a dedicated data format and storing the converted data;

[2343] a means for uploading preference data;

[2344] means for downloading preference data;

[2345] means for evaluating preference data;

[2346] A means for making personalized product suggestions using preference data;

[2347] A system including:

[2348] (Claim 2)

[2349] 10. The system of claim 1, further comprising means for providing preference data for a fee.

[2350] (Claim 3)

[2351] 10. The system of claim 1, further comprising means for filtering the preference data based on the rating information.

[2352] "Example 2: Combining Emotion Engines"

[2353] (Claim 1)

[2354] To manage user preference data

[2355] A means for converting the preference data into a dedicated data format and storing the converted data;

[2356] a means for uploading preference data;

[2357] means for downloading preference data;

[2358] means for evaluating preference data;

[2359] a means for activating an emotion engine that analyzes the emotions of a user;

[2360] a means for adjusting the response of the generative AI model based on the analyzed emotion data; and

[2361] A system including:

[2362] (Claim 2)

[2363] 10. The system of claim 1, further comprising means for providing preference data for a fee.

[2364] (Claim 3)

[2365] 10. The system of claim 1, further comprising means for filtering the preference data based on the rating information.

[2366] "Application example 2 when combining emotion engines"

[2367] (Claim 1)

[2368] To manage user preference data

[2369] A means for converting the preference data into a dedicated data format and storing the converted data;

[2370] a means for uploading preference data;

[2371] means for downloading preference data;

[2372] means for evaluating preference data;

[2373] a means for analyzing user sentiment using an emotion engine;

[2374] A means for reflecting preference data and emotion data in the generative AI model;

[2375] A means for providing responses based on user preferences and emotions through an application installed on the smart device;

[2376] A system including:

[2377] (Claim 2)

[2378] 10. The system of claim 1, further comprising means for providing preference data for a fee.

[2379] (Claim 3)

[2380] 10. The system of claim 1, further comprising means for filtering the preference data based on the rating information. [Explanation of symbols]

[2381] 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. To manage user preference data A means for converting the preference data into a dedicated data format and storing the converted data; a means for uploading preference data; means for downloading preference data; means for evaluating preference data; A system including:

2. 10. The system of claim 1, further comprising means for providing preference data for a fee.

3. The system of claim 1 further comprising means for filtering the preference data based on the rating information.

Images