System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a user's input; means for analyzing the received user's input; means for using a generative AI model to generate appropriate content based on the analysis; means for transmitting the generated content to the user's device; means for collecting the user's access history, behavior history, and purchase history; means for analyzing the collected histories and selecting marketing content optimal for the user; and means for providing the selected marketing content to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Traditional marketing methods rely on third-party cookies, which carry the risk of privacy violations when collecting and using personal information. Furthermore, as major browsers phase out the use of third-party cookies, it is becoming difficult to achieve the same performance with traditional marketing methods that rely on personal information. Against this backdrop, new methods are needed that enable effective marketing while protecting user privacy. [Means for solving the problem]

[0005] The present invention provides a means for receiving and analyzing user input, and a means for using a generative AI model to generate appropriate content based on the analysis results. The system also includes a means for sending the generated content to the user's device and collecting the user's access history, behavioral history, and purchase history. The system further includes a means for analyzing the collected history and selecting and providing optimal marketing content to the user. The generative AI model has the ability to process multimodal data such as text, images, and audio, and can efficiently provide users with the information they truly need by providing news articles, store reservation information, and product recommendations based on user requests. This system enables an effective and user-friendly marketing method that does not rely on traditional third-party cookies.

[0006] "User" refers to an individual or organization that uses the System.

[0007] "Input" refers to the act of a user providing some information or instruction to a system.

[0008] "Terminal" refers to a hardware device that allows a user to access the system, including smartphones, tablets, and PCs.

[0009] "Server" refers to a central processing unit that receives user input and performs the necessary processing.

[0010] "Receiving" refers to the act of the server receiving data or information sent from the terminal.

[0011] "Analysis" refers to the process of analyzing received data or information to understand its meaning and intent.

[0012] A "generative AI model" refers to an algorithm or software that uses artificial intelligence to generate responses or content based on user input.

[0013] "Content" includes information and data provided to users, specifically news articles, reservation information, product recommendations, and the like.

[0014] "Transmission" refers to the act of sending data or content from a server to a terminal.

[0015] "Access history" refers to a record of when a user accesses the system.

[0016] "Behavior history" refers to a record of a series of actions taken by a user using the system.

[0017] "Purchase History" refers to a record of purchases made by a User through the System.

[0018] "Collection" refers to the act of gathering and storing data such as access history, behavioral history, and purchase history.

[0019] "Selection" refers to the process of choosing the marketing content that is most appropriate for the user based on the collected data.

[0020] "Providing" refers to the act of displaying selected marketing content to a user.

[0021] "Multimodal data" refers to data in different formats, such as text, images, and audio.

[0022] A "news article" refers to a piece of writing that reports on current events or social happenings.

[0023] "Store reservation information" refers to information that allows a user to make a reservation at a store.

[0024] "Product recommendation" refers to product information that is recommended for purchase by the user. [Brief explanation of the drawings]

[0025] [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

[0026] 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.

[0027] First, the terms used in the following description will be explained.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] [First embodiment]

[0034] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0035] 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.

[0036] 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).

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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."

[0046] The present invention provides an interactive platform that utilizes generative AI models through interactions between servers, terminals, and users. This platform collects and analyzes first-party data such as user behavioral history, purchase history, and access history, and provides optimal marketing content.

[0047] System Overview

[0048] server

[0049] The server is the main component that receives user requests and generates appropriate content for the requests using generative AI models. The server also collects user access history, behavioral history, and purchase history, and analyzes this data to provide relevant marketing content to users.

[0050] Terminal

[0051] A terminal is a device through which a user accesses the system, and is typically a smartphone, tablet, PC, etc. A user accesses the system through a terminal and inputs a request through an interactive interface.

[0052] User

[0053] A user is an individual who utilizes the system to obtain information or use services. The user uses a terminal to send requests to the server and receive responses from the server.

[0054] Implementation details

[0055] 1. User Access and Authentication

[0056] The user accesses the interactive platform from a terminal and enters authentication information on the login screen. The terminal sends this information to the server, which then verifies the authentication information and authenticates the user. If authentication is successful, the server provides the user with an interactive interface.

[0057] 2. Receiving a User Request

[0058] Through the interactive interface, the user inputs requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight." The terminal sends the user's input to the server.

[0059] 3. Request Parsing and Content Generation

[0060] The server sends the received request to the generative AI model, which analyzes the request and generates appropriate content. For example, in response to the request "Tell me today's news," the model generates the latest news articles and returns them to the server.

[0061] 4. Provision of Content

[0062] The server sends the generated content to the device, which then displays it to the user, who can then check the latest news articles or restaurant information on an interactive interface.

[0063] 5. First-party data collection and analysis

[0064] The server collects the user's access history, behavioral history, and purchase history and stores them in a back-end database, which provides the basis for analyzing the user's interests and behavioral patterns and generating relevant marketing content for the next visit.

[0065] 6. Providing optimal marketing content

[0066] Based on the analyzed data, the server selects the most suitable marketing content for the user. Using the generative AI model again, it generates customized advertisements and promotional information and provides it to the user. For example, if the user is interested in Japanese restaurants, it can provide promotional information for related restaurants.

[0067] Specific examples

[0068] News provision example

[0069] The user types, "Tell me today's news." The device sends this request to the server. The server then sends a request to the generative AI model to generate the latest news articles. The generated news articles are then sent to the device and displayed to the user.

[0070] Restaurant reservation example

[0071] The user types, "I'd like to make a reservation at a Japanese restaurant for tonight." The device sends the request to the server, which then has the generative AI model analyze the request. The generative AI model generates a list of candidate Japanese restaurants and returns it to the server. The server sends this list to the user, and the system makes a reservation at the restaurant selected by the user.

[0072] The present invention makes it possible to achieve a high user experience and effective marketing while protecting user privacy.

[0073] The processing flow will be explained below.

[0074] Processing Steps

[0075] Step 1:

[0076] A user opens an app or web browser to access the system, and a login form appears on the screen.

[0077] Step 2:

[0078] The user enters their user ID and password into the login form.

[0079] Step 3:

[0080] The terminal sends the authentication information entered by the user to the server.

[0081] Step 4:

[0082] The server checks the received authentication information against a backend database and authenticates the user.

[0083] Step 5:

[0084] The server sends the authentication result to the terminal, and if authentication is successful, an interactive interface is displayed to the user. If authentication is unsuccessful, a message is displayed prompting the user to re-enter authentication information.

[0085] Step 6:

[0086] A user inputs requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight" through an interactive interface.

[0087] Step 7:

[0088] The terminal sends the user's input text to the server.

[0089] Step 8:

[0090] The server receives user input and sends it to the generative AI model, performing any preprocessing (such as tokenization and filtering) required for analysis.

[0091] Step 9:

[0092] A generative AI model analyzes the user's input text and generates appropriate content (e.g., a news article or restaurant list).

[0093] Step 10:

[0094] The generative AI model returns the generated content to the server, which contains the information requested by the user.

[0095] Step 11:

[0096] The server formats the generated content and sends it to the terminal for transmission back to the user.

[0097] Step 12:

[0098] The device receives the content from the server and displays it to the user, such as news articles or restaurant information on an interactive interface.

[0099] Step 13:

[0100] The server stores the user's access history, behavioral history, and purchase history in a back-end database, which allows the server to understand the user's interests.

[0101] Step 14:

[0102] The server analyzes the collected historical data to identify the user's interests, for example, which news categories the user frequently visits or the types of restaurants they make reservations at.

[0103] Step 15:

[0104] Based on the analysis results, the server selects the most suitable marketing content for the user (for example, advertisements for related products and promotional information).

[0105] Step 16:

[0106] The server then uses the generative AI model again to generate customized marketing content.

[0107] Step 17:

[0108] The generative AI model generates customized marketing content and returns it to the server.

[0109] Step 18:

[0110] The server transmits the generated marketing content to the user's terminal.

[0111] Step 19:

[0112] The terminal displays the marketing content received from the server to the user.

[0113] Through these steps, users can seamlessly obtain information and enjoy individually customized content and services.

[0114] Example 1

[0115] 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."

[0116] Conventional interactive platforms could only provide simple responses to user requests, making it difficult to effectively provide personalized content or marketing information. They also faced security issues and lacked the technology to properly utilize user behavioral and purchase histories. As a result, the user experience was poor and marketing effectiveness was limited.

[0117] 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.

[0118] In this invention, the server includes means for receiving user input, means for analyzing the received user input, means for utilizing a generative AI model that generates appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, and purchase history, means for analyzing the collected history and selecting optimal marketing content for the user, means for providing the selected marketing content to the user, means for encrypting the user input using SSL / TLS and transmitting it to the server, means for the server to generate a session ID and transmit it to the user's terminal, means for the generative AI model to include a function for performing natural language processing, means for providing an interactive interface to authenticated users, means for transmitting the generated content to the terminal in HTML or JSON format, and means for the server to access a database and verify authentication information. This enables highly personalized content and marketing information to be provided safely and effectively in response to user requests.

[0119] The "means for receiving user input" is a function for receiving data that a user inputs to the interactive platform through a terminal.

[0120] The "means for analyzing received user input" is a function for analyzing received user input data and understanding its contents.

[0121] "Means for utilizing a generative AI model" refers to a function that utilizes an artificial intelligence model to generate appropriate content based on the analysis results.

[0122] "Means for sending generated content to a user's device" refers to a function for sending content created by a generative AI model to a user's device.

[0123] "Means for collecting user access history, behavior history, and purchase history" refers to a function for obtaining and storing records of users' online activities.

[0124] "Means of analyzing collected history and selecting the most suitable marketing content for the user" is a function that analyzes the user's interests and behavioral patterns based on collected data and selects the most suitable marketing content based on that.

[0125] The "means for providing selected marketing content to the user" is a function for presenting selected appropriate marketing content to the user.

[0126] "Means of encrypting user input using SSL / TLS and sending it to the server" is a function that encrypts data using SSL / TLS technology in order to securely send data entered by the user to the server.

[0127] "Means for the server to generate a session ID and send it to the user's terminal" is a function for managing a communication session by passing the session ID generated by the server to the user's terminal.

[0128] "Means for the generative AI model to include the ability to perform natural language processing" refers to the ability of the generative AI model to use natural language processing techniques to analyze and understand text data.

[0129] The "means for providing an interactive interface to an authenticated user" is a function for displaying an interactive user interface to a user who has been successfully authenticated.

[0130] "Means for sending generated content to a terminal in HTML or JSON format" is a function for sending generated content to a user's terminal in HTML or JSON format.

[0131] The "means for the server to access the database and confirm the authentication information" is a function that allows the server to refer to the database and confirm the validity of the user's authentication information.

[0132] The present invention provides an interactive platform that utilizes generative AI models through the interaction of a server, terminals, and users. This platform collects and analyzes data such as user behavioral history, purchase history, and access history to provide optimal marketing content.

[0133] server

[0134] The server is the main component that receives requests from users and generates appropriate content in response to the requests using a generative AI model. The server also collects and analyzes users' access history, behavioral history, and purchase history to provide relevant marketing content to users. Servers are built using programming languages ​​such as Python or Java and utilize machine learning frameworks such as TensorFlow or PyTorch. Furthermore, MySQL or PostgreSQL is often used as the database. Common cloud services include Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[0135] Terminal

[0136] A terminal is a device through which a user accesses a system, and examples include smartphones, tablets, and PCs. The terminal communicates with the server via a web browser or dedicated application, sending the user's requests to the server. The terminal receives responses from the server and displays them to the user. Operating systems such as iOS and Android are commonly used on these terminals.

[0137] User

[0138] A user is an individual who uses the platform to obtain information or use services. The user inputs a request through the interactive interface and sends it to the server via their device. The user receives a response from the server on their device and checks the displayed information.

[0139] Content generation and delivery

[0140] Authentication Process

[0141] A user accesses the interactive platform from a terminal and enters authentication information (email address and password) on the login screen. The entered authentication information is encrypted with SSL / TLS and sent to the server using the HTTPS protocol. The server checks the authentication information by referencing a database, and if authentication is successful, generates a session ID and sends it to the user's terminal. An interactive interface is displayed, allowing the user to continue entering requests.

[0142] Receiving a user request

[0143] The user inputs a request into the interactive interface, such as "Tell me today's news" or "I want to make a restaurant reservation for tonight." The device sends this request in JSON format to the server, and communication is again carried out using the HTTPS protocol.

[0144] Request analysis and content generation using AI models

[0145] The server receives the request and sends it to a generative AI model for analysis. The generative AI model (e.g., GPT-3) analyzes the request and generates appropriate content. For example, in response to the request "Tell me today's news," it generates the latest news articles and returns them to the server.

[0146] Content provision

[0147] The content returned by the generative AI model is converted into HTML or JSON format by the server and sent to the device, which displays it to the user, who can then check the latest news articles or restaurant information through the device.

[0148] Data collection and analysis

[0149] First-party data collection

[0150] When a user uses the interactive platform, the server collects data such as their access history, behavioral history, and purchase history. The collected data is recorded in real time in a log file and stored in a database (for example, Amazon RDS or Google BigQuery).

[0151] Analyzing data and providing marketing content

[0152] The server uses data analysis tools such as Python's pandas library and scikit-learn to analyze the collected data. This identifies the user's interests and behavioral patterns, laying the groundwork for providing appropriate marketing content the next time the user visits. Based on the analysis results, the server selects the most appropriate marketing content for the user and again uses the generative AI model to generate customized advertising and promotional information. This content is sent to the user's device in HTML or JSON format and displayed on an interactive interface.

[0153] Specific examples

[0154] News provision example

[0155] When a user types "Tell me today's news," the device sends this request to the server, which then sends a request to the generative AI model to generate the latest news articles, which are then sent from the server to the device and displayed to the user.

[0156] Example prompt sentence:

[0157] "Tell me the news today"

[0158] Restaurant reservation example

[0159] If a user types, "I'd like to make a reservation at a Japanese restaurant for tonight," the device sends the request to the server. The server analyzes the request using a generative AI model and generates a list of Japanese restaurants. The generated list is sent from the server to the device and displayed to the user. The user then selects the desired restaurant from the list and finally makes the reservation.

[0160] Example prompt sentence:

[0161] "I'd like to make a reservation at a Japanese restaurant for tonight."

[0162] As described above, the embodiment of the present invention realizes a highly personalized user experience and effective marketing while protecting the user's privacy.

[0163] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0164] Step 1: User Access and Authentication

[0165] 1.1 A user accesses the interactive platform from a device (smartphone, tablet, PC), and the device connects to the platform URL using the HTTPS protocol.

[0166] Input: User access request

[0167] Output:Login screen

[0168] 1.2 The user enters their email address and password on the login screen.

[0169] Input: Email address, password

[0170] Output: Credentials

[0171] 1.3 The device encrypts the authentication information using SSL / TLS and sends it to the server.

[0172] Input: Encrypted credentials

[0173] Output: Authentication request to the server

[0174] 1.4 The server checks the authentication information against a database (MySQL, PostgreSQL) to verify the user exists and the password matches.

[0175] Input: Encrypted credentials

[0176] Output: Authentication result

[0177] 1.5 If authentication is successful, the server generates a session ID and sends it to the device, along with HTML and JavaScript code to display the interactive interface.

[0178] Input: Breakdown of successful authentications

[0179] Output: Session ID, interactive interface

[0180] Step 2: Receiving a user request

[0181] 2.1 A user types a request into a conversational interface, such as "What's in the news today?"

[0182] Input: Request text (e.g. "Tell me today's news")

[0183] Output: Request data

[0184] 2.2 The device sends the input request to the server as a JSON format message.

[0185] Input: Request data

[0186] Output: Request message to the server

[0187] Step 3: Parsing the request and generating content

[0188] 3.1 The server sends the received request data to the generative AI model.

[0189] Input: Request message

[0190] Output: Analysis request to the generative AI model

[0191] 3.2 A generative AI model (e.g., GPT-3) analyzes the request and generates appropriate content, e.g., generating the latest news articles in response to the request "What's in the news today?"

[0192] Input: Parsing request (request statement)

[0193] Output: Generated content (e.g., latest news articles)

[0194] 3.3 Return the generated content to the server.

[0195] Input: Generated content (news article)

[0196] Output: Return content to the server

[0197] Step 4: Providing content

[0198] 4.1 The server converts the generated content into HTML or JSON format and sends it to the device.

[0199] Input: Generated content (news article)

[0200] Output: HTML or JSON formatted content

[0201] 4.2 The device receives the content in HTML or JSON format and displays it to the user.

[0202] Input: HTML or JSON formatted content

[0203] Output: The news article displayed to the user

[0204] Step 5: Collect and analyze first-party data

[0205] 5.1 The server collects user access history, behavior history, and purchase history and records them in log files in real time.

[0206] Input: User access history, behavior history, purchase history

[0207] Output: Record data in a log file

[0208] 5.2 Store the collected data in a database (e.g., Amazon RDS, Google BigQuery).

[0209] Input: Collected historical data

[0210] Output: Save data to database

[0211] 5.3 The server analyzes the data and identifies user interests and behavioral patterns using Python's pandas library and scikit-learn.

[0212] Input: Historical data in the database

[0213] Output: Analysis results (user interests and behavioral patterns)

[0214] Step 6: Deliver optimal marketing content

[0215] 6.1 The server selects the most appropriate marketing content for the user based on the analyzed data.

[0216] Input: Analysis results (user interests and behavioral patterns)

[0217] Output: Selected marketing content

[0218] 6.2 The server uses the generated AI model again to generate customized advertising and promotional information.

[0219] Input: Selected Marketing Content

[0220] Output: Generated advertising and promotional information

[0221] 6.3 The server sends the generated marketing content in HTML or JSON format to the terminal, which then provides it to the user.

[0222] Input: Generated advertising and promotion information

[0223] Output: Marketing content that is displayed to the user

[0224] The above is a specific processing flow of the present invention. The present invention makes it possible to provide highly personalized content and effective marketing information while protecting the user's privacy.

[0225] (Application example 1)

[0226] 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."

[0227] In modern e-commerce, providing product recommendations and marketing content that meet the diverse needs of users is important for increasing purchasing motivation. However, conventional systems have had difficulty effectively analyzing users' behavioral and purchase histories to provide optimal product recommendations for each individual. Furthermore, user authentication and content provision were not integrated, which led to problems that impaired the user experience.

[0228] 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.

[0229] In this invention, the server includes means for receiving user input, means for analyzing the received user input, means for using a generative AI model to generate appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, and purchase history, means for analyzing the collected history and selecting optimal marketing content for the user, means for providing the selected marketing content to the user, means for transmitting user authentication information and, if authentication is successful, generating individual product recommendation information, and means for displaying the generated product recommendation information on the user's terminal. This enables individual product recommendations according to the user's needs, improving the user experience and realizing effective marketing.

[0230] "User input" refers to operations or inputs made by a user to send requests or commands to the system.

[0231] A "generative AI model" is an artificial intelligence model that automatically generates appropriate content based on user input or requests.

[0232] A "terminal" is a device that allows a user to access the system and use the interactive interface, including smartphones, tablets, and PCs.

[0233] "Access history" refers to a record of operations a user has performed within the system in the past and pages that the user has visited.

[0234] "Behavioral history" refers to a record of a series of operations and actions performed by a user within the system.

[0235] "Purchase history" refers to a record of products and services a user has purchased in the past.

[0236] "Marketing content" refers to advertising and promotional information provided in response to a user's interests and needs.

[0237] "User authentication information" refers to the authentication information a user uses to log in to a system, and typically includes a username and password.

[0238] "Product recommendation information" refers to information about products and services recommended to a user, which is generated based on the user's access history, behavior history, and purchase history.

[0239] A "smart shopping assistant" is an interactive platform that analyzes a user's behavioral history, purchase history, and access history to provide optimal product recommendations and promotional information.

[0240] This invention aims to build a "smart shopping assistant" system that utilizes a user's behavioral history, purchase history, and access history to provide optimal product recommendations and marketing content. A specific embodiment of the system is described below.

[0241] System Overview

[0242] server

[0243] The server is the central component that receives user requests and generates appropriate content using generative AI models. The server also collects and analyzes users' access history, behavioral history, and purchase history, and provides relevant marketing content to users based on this data.

[0244] Terminal

[0245] A terminal is a device through which a user accesses a system and uses an interactive interface, and generally includes a smartphone, tablet, PC, etc. A user accesses a system through a terminal and performs interactive operations.

[0246] User

[0247] A user is an individual who uses the system to obtain information or use services. The user sends requests to the server through a terminal and receives responses from the server.

[0248] Specific processing flow

[0249] User Access and Authentication

[0250] The user accesses the interactive platform from a terminal and enters login information. The terminal sends the authentication information to the server, which then verifies the authentication information and authenticates the user. If the authentication is successful, the server provides the user with an interactive interface.

[0251] Receiving and analyzing user requests

[0252] Through the conversational interface, users input requests such as "Tell me what products you recommend" or "I'd like to know the latest promotional information." The device sends the user's input to the server, which then uses a generative AI model to analyze the request and generate appropriate content.

[0253] Content generation and delivery

[0254] The generative AI model generates optimal product recommendation information and marketing content based on user input and behavioral, access, and purchase history. The generated content is sent from the server to the device, which then displays it to the user.

[0255] First-party data collection and analysis

[0256] The server continuously collects data such as user access history, behavioral history, and purchase history, and stores it in a back-end database. By analyzing this data, it is possible to create a foundation for providing more accurate content the next time the user accesses the site.

[0257] Hardware and Software Use

[0258] Hardware

[0259] Smartphones, tablets, PCs, smart glasses, head-mounted displays

[0260] software

[0261] Flask (server-side framework)

[0262] Python (server and client side programs)

[0263] API request library (requests)

[0264] Specific examples and prompts for the generative AI model

[0265] Specific examples

[0266] User: "What products do you recommend?"

[0267] The system analyzes a user's past purchase history and access history to generate a list of products relevant to the user. For example, if a user has purchased a lot of sports equipment in the past, it will recommend new sportswear and equipment.

[0268] Prompt Sentence Examples

[0269] "Based on the user's purchase history, please recommend the best products for the following product categories:

[0270] Category: Sporting Goods

[0271] Past purchases: running shoes, fitness tracker, sports t-shirts

[0272] Keywords: latest, trending, recommended"

[0273] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0274] Step 1:

[0275] A user accesses the interactive platform from a terminal and enters login information. The entered information is a username and password. The terminal sends this input information to the server. The server receives it and authenticates the user. If authentication is successful, the server provides the user with an interactive interface. The input is a username and password, and the output is the authentication result.

[0276] Step 2:

[0277] The user inputs a request (for example, "What products do you recommend?") through an interactive interface. The device sends the user's input as is to the server. The server receives it and prepares it for analysis. The input is the user's request, and the output is the unanalyzed request data.

[0278] Step 3:

[0279] The server sends the received request to the generative AI model, which analyzes the request. The generative AI model understands the request and processes and calculates the data to generate appropriate content. For example, if the request is "Tell me what products you recommend," the model uses the user's access history and purchase history to generate relevant product information. The input is unanalyzed request data, and the output is the content data to be generated.

[0280] Step 4:

[0281] The generated content data is sent back to the server. Based on this data, the server generates optimal product recommendation information for the user. At this point, the server accesses the product database and extracts specific product information based on the recommendation results obtained from the generative AI model. The input is the generated content data, and the output is specific product recommendation information.

[0282] Step 5:

[0283] The server sends the generated product recommendation information to the terminal. The terminal receives this information and displays it to the user. For example, a list of recommended products based on the user's purchase history is displayed on the interface. The input is specific product recommendation information, and the output is user interface content for display.

[0284] Step 6:

[0285] The user selects from the recommended products and clicks or taps on the detailed information. The device sends this selection information to the server. The server generates detailed information about the selected product and sends it back to the device. The input is the user's selection information, and the output is detailed product information.

[0286] Step 7:

[0287] When a user wishes to make a purchase, a purchase request is sent from the terminal to the server. The server receives this and initiates the payment process. An electronic payment service API may be used here. The input is the purchase request, and the output is a purchase confirmation and payment information.

[0288] Step 8:

[0289] If the payment is successful, the server sends the purchase confirmation information to the terminal and displays it to the user. The new purchase information is also added to the purchase history database. The input is the payment information, and the output is the purchase confirmation information and the updated purchase history.

[0290] Keywords: generative AI model, prompt sentence

[0291] 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.

[0292] The present invention relates to a system that combines a generative AI model and an emotion engine in an interactive platform for users to provide more personalized content and marketing strategies. Specific embodiments are described below.

[0293] System Overview

[0294] server

[0295] The server receives and analyzes user requests and generates appropriate content using a generative AI model and emotion engine. It also has the ability to collect and analyze user access history, behavioral history, purchase history, and emotion data.

[0296] Terminal

[0297] A terminal is a device through which a user accesses the system, such as a smartphone, tablet, or PC. The user sends requests to the server through the terminal and receives responses from the server.

[0298] User

[0299] A user is an individual who uses the system to obtain information or access services. The user uses a device to send requests to the server and receive generated content and marketing information.

[0300] Implementation details

[0301] 1. User Access and Authentication

[0302] A user accesses the system from a terminal and enters authentication information on the login screen. The terminal sends the authentication information to the server, which then performs the authentication process. If authentication is successful, an interactive interface is presented to the user.

[0303] 2. Receiving and analyzing user requests

[0304] Through the conversational interface, users input requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight." The device sends this input to the server. The server receives the speech and sends it to the generative AI model. The generative AI model analyzes the input and generates appropriate content.

[0305] 3. Emotion Recognition by Emotion Engine

[0306] The server sends the user's input text, voice, facial expressions, etc. to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The recognized emotion data is used by the generative AI model to generate content and select marketing content.

[0307] 4. Content Generation and Delivery

[0308] The generative AI model generates content (e.g., news articles, restaurant listings, etc.) based on user requests and emotion data. The server sends the generated content to the device, which then displays it to the user.

[0309] 5. First-party data collection and analysis

[0310] The server stores the user's access history, behavioral history, purchase history, and emotional data in a back-end database. The server analyzes this data to understand the user's interests.

[0311] 6. Selection and provision of marketing content

[0312] Based on the analysis results, the server selects the most suitable marketing content for the user, taking into account the emotional data recognized by the emotion engine. The generative AI model is then used again to generate customized marketing content (e.g., advertisements and promotional information). The generated marketing content is then sent from the server to the device and provided to the user.

[0313] Specific examples

[0314] News provision example

[0315] The user inputs a request such as "Tell me today's news." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates the latest news articles and returns them to the server. The server sends the generated news articles to the device, which displays them to the user. The emotion engine recognizes the user's emotions and can provide relevant news and advertisements based on the emotions the next time the device is accessed.

[0316] Restaurant reservation example

[0317] The user inputs a request such as "I would like to make a reservation at a Japanese restaurant for tonight." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates a list of candidate Japanese restaurants and returns it to the server. The server provides the candidate list to the user and makes a reservation at the restaurant selected by the user. The emotion engine provides restaurant information that best suits the user's wishes and emotions, thereby achieving a more personalized service.

[0318] In this way, the present invention makes it possible to provide highly personalized content while taking into account the emotional state of the user, thereby improving the user experience and realizing effective marketing.

[0319] The processing flow will be explained below.

[0320] Processing Steps

[0321] Step 1:

[0322] A user opens an app or web browser to access the system and is presented with a login screen.

[0323] Step 2:

[0324] The user enters their user ID and password into the login form.

[0325] Step 3:

[0326] The device sends the authentication information entered by the user to the server.

[0327] Step 4:

[0328] The server receives the authentication information, checks it against a backend database, and authenticates the user.

[0329] Step 5:

[0330] The server sends the authentication result to the terminal, and if authentication is successful, an interactive interface is displayed; if authentication is unsuccessful, the user is prompted to enter authentication information again.

[0331] Step 6:

[0332] Users input requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight" through an interactive interface.

[0333] Step 7:

[0334] The device sends the user's input text to the server.

[0335] Step 8:

[0336] The server receives user input and sends it to the generative AI model and emotion engine.

[0337] Step 9:

[0338] A generative AI model analyzes the user's input text and generates appropriate content (e.g., a news article or restaurant list).

[0339] Step 10:

[0340] The emotion engine recognizes emotions from the user's text, tone of voice, facial expressions, etc., and provides the emotion data to the generative AI model.

[0341] Step 11:

[0342] The generative AI model customizes the content based on the emotional data and generates the final content.

[0343] Step 12:

[0344] The server sends the generated content to the device, which displays it to the user.

[0345] Step 13:

[0346] Users can view news articles and restaurant information displayed on an interactive interface.

[0347] Step 14:

[0348] The server stores user access history, behavioral history, purchase history and emotional data in a back-end database.

[0349] Step 15:

[0350] The server analyzes the stored data to identify users' interests, concerns, and emotional patterns.

[0351] Step 16:

[0352] Based on the analysis results, the server selects the most appropriate marketing content for the user.

[0353] Step 17:

[0354] A generative AI model generates selected marketing content and customizes it taking into account sentiment data.

[0355] Step 18:

[0356] The server sends the generated marketing content to the device, which then provides it to the user.

[0357] Through these steps, users can seamlessly obtain information and enjoy individually customized content and services.

[0358] Example 2

[0359] 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."

[0360] Conventional content delivery systems often provide general information without considering the user's emotional state, which limits their ability to improve user experience and maximize marketing effectiveness. Furthermore, selecting marketing content based solely on a user's access history, behavioral history, and purchase history makes it difficult to deliver content tailored to the user's current emotional state and interests.

[0361] 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.

[0362] In this invention, the server includes means for receiving user input, means for analyzing the received user input, means for utilizing a generative AI model that generates appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, purchase history, and emotional data, means for analyzing the collected history and utilizing an emotional engine that recognizes the user's emotional state, means for selecting optimal marketing content for the user based on the analysis results and the emotional data, and means for providing the selected marketing content to the user. This makes it possible to provide highly personalized content and marketing measures based on the user's emotional state.

[0363] The "means for receiving user input" refers to the means by which the server receives text, voice, and other data input by the user through the terminal.

[0364] "Means for analyzing received user input" refers to the means by which the server decodes the received user input data and properly understands and classifies it.

[0365] "Means for utilizing generative AI models" refers to means for utilizing artificial intelligence models that automatically generate content and information based on analysis results.

[0366] "Means for transmitting generated content to a user's device" refers to means for transmitting content generated by a generative AI model to a device used by a user.

[0367] The "means for collecting user access history, behavior history, purchase history, and emotional data" refers to a means for accumulating user activity records, purchase history, and emotional data inferred from input data.

[0368] The "means for analyzing the collected history and utilizing an emotion engine to recognize the user's emotional state" refers to a means for analyzing the collected data and utilizing an emotion recognition device to identify the user's current emotional state.

[0369] "Means for selecting the most suitable marketing content for a user based on analysis results and emotional data" refers to means for selecting the most suitable advertisements and promotional information for a user based on the results of data analysis and emotional recognition information.

[0370] The "means for providing selected marketing content to a user" refers to a means for displaying and providing selected marketing information on a terminal used by a user.

[0371] The present invention relates to a system that combines a generative AI model and an emotion engine in an interactive platform with users to provide personalized content and marketing strategies. This system includes a series of processes that receive user input, analyze it, generate appropriate content, and provide it to the user.

[0372] server

[0373] The server is configured using the following software and hardware. It receives and analyzes user requests using a generative AI model and emotion engine, and generates appropriate content. It also has the ability to collect and analyze user access history, behavioral history, purchase history, and emotion data.

[0374] 1. A parsing module for parsing received user input.

[0375] 2. Generative AI model that generates content

[0376] 3. Emotion engine for recognizing the user's emotional state

[0377] 4. Back-end database for data management and analysis

[0378] Specific software examples include natural language processing libraries (e.g., NLTK), machine learning frameworks (e.g., TensorFlow), and database management systems (e.g., MySQL).

[0379] Terminal

[0380] A terminal is a device that a user uses to access the system and can take various forms (smartphone, tablet, PC, etc.). The user sends requests to the server through the terminal and receives responses from the server.

[0381] 1. An input interface for receiving user input

[0382] 2. A user interface that displays the content sent from the server

[0383] Specific hardware examples include Apple's iPhone and Samsung's Galaxy series. Software uses web browsers (e.g., Google Chrome, Safari) and dedicated applications (e.g., iOS and Android apps).

[0384] User

[0385] A user is an individual who uses the system to obtain information or use services. A user uses the system in the following steps:

[0386] 1. Access the system using a terminal and enter your authentication information on the login screen.

[0387] 2. After successful authentication, you enter your request through the conversational interface (e.g., "Tell me today's news" or "I'd like to make a restaurant reservation for tonight").

[0388] 3. View the content sent from the server.

[0389] Specific Examples

[0390] News provision example

[0391] The user inputs a request such as "Tell me today's news." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates the latest news articles and returns them to the server. The server sends the generated news articles to the device, which displays them to the user. The emotion engine recognizes the user's emotions and can provide relevant news and advertisements based on the emotions the next time the device is accessed.

[0392] Restaurant reservation example

[0393] The user inputs a request such as "I would like to make a reservation at a Japanese restaurant for tonight." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates a list of candidate Japanese restaurants and returns it to the server. The server provides the candidate list to the user and makes a reservation at the restaurant selected by the user. The emotion engine provides restaurant information that best suits the user's wishes and emotions, thereby achieving a more personalized service.

[0394] In this way, the present invention makes it possible to provide highly personalized content while taking into account the emotional state of the user, thereby improving the user experience and realizing effective marketing.

[0395] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0396] Step 1:

[0397] User Access and Authentication

[0398] A user accesses the system from a terminal and enters their ID and password on the login screen. The terminal sends the entered authentication information to the server. The server receives the authentication information and checks it against a back-end database. If authentication is successful, the server returns an interactive interface to the user.

[0399] Input: User ID and password

[0400] Output: An interactive interface is displayed, and a message confirming successful user authentication is displayed.

[0401] Specifically, the user operates their smartphone to enter their ID and password into the login form and tap the submit button. The server then accesses the database and checks whether the ID and password match.

[0402] Step 2:

[0403] Receiving and analyzing user requests

[0404] The user inputs a prompt (e.g., "Tell me today's news") through a conversational interface. The device sends this prompt to the server. The server then forwards the request to the generative AI model, which analyzes the text data. The generative AI model understands the request and generates an analysis result to be passed on to the next step.

[0405] Input: User prompt text

[0406] Output: Analysis results from the generative AI model

[0407] Specifically, when a user types "I'd like to make a restaurant reservation for tonight" into the tablet interface and presses the send button, the device transfers the text to the server, which then passes it to the generative AI model to begin analyzing it.

[0408] Step 3:

[0409] Emotion recognition by emotion engine

[0410] The server sends the user's input text and voice data to the emotion engine, which analyzes this data and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The server then sends the recognized emotion data to the generative AI model, which uses it as a reference for content generation.

[0411] Input: User-entered text and voice data

[0412] Output: Emotion data from the emotion engine

[0413] As a specific example of operation, a user inputs a voice message such as "I've been feeling down lately" into the terminal, and the terminal sends the voice message to the server. The server passes the voice data to the emotion engine and recognizes the user's emotional state.

[0414] Step 4:

[0415] Content generation and delivery

[0416] The generative AI model generates content (e.g., news articles or restaurant listings) based on user requests and emotion data. The server sends the generated content to the device, which then displays it to the user.

[0417] Input: User request and emotion data

[0418] Output: Generated content (news articles, restaurant listings, etc.)

[0419] Specifically, the server generates a news article based on the prompt "Tell me today's news" and the emotional state "I'm depressed," and transfers the generated article to the terminal, which then displays the news article on the user's screen.

[0420] Step 5:

[0421] First-party data collection and analysis

[0422] The server stores the user's access history, behavioral history, purchase history, and emotional data in a backend database. The server periodically analyzes this data to understand the user's interests.

[0423] Input: User access history, behavioral history, purchase history, emotional data

[0424] Output: Analysis results that reflect the user's interests

[0425] As a specific example of operation, the news categories that users frequently browse and the product purchase history are recorded in a database, and the user's preferences are identified by periodically analyzing this data.

[0426] Step 6:

[0427] Selection and provision of marketing content

[0428] Based on the analysis results, the server selects the most suitable marketing content for the user, taking into account the emotional data recognized by the emotion engine. The generative AI model is used again to generate customized marketing content. The server then sends the generated marketing content to the device, which then provides it to the user.

[0429] Input: Analysis results and emotion data

[0430] Output: Marketing content delivered to the user

[0431] Specifically, the server generates an advertisement for a healthy food product that will cheer you up based on the user's purchasing history and emotional state (e.g., "I feel low"), and sends it to the device to display. The user sees the advertisement and is encouraged to purchase the product.

[0432] (Application example 2)

[0433] 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."

[0434] While conventional content delivery systems can provide information in response to user requests, they lack personalized content and marketing strategies that reflect the user's emotional state, which results in poor user experience and in ineffective marketing. Therefore, there is a need for a system that can provide highly personalized content and marketing strategies that take the user's emotional state into account.

[0435] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user input, means for analyzing the received user input, means for utilizing a generative AI model that generates appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, and purchase history, means for analyzing the collected history and selecting optimal marketing content for the user, means for providing the selected marketing content to the user, means for utilizing an emotion engine that analyzes the user's emotional state, and means for customizing content using the generative AI model based on the user's emotional state analyzed using the emotion engine. This enables the provision of highly personalized content that reflects the user's emotional state and effective marketing.

[0436] A "means for receiving user input" is a device or software that receives text or voice requests, commands, etc., made by a user to the system.

[0437] "Means for parsing received user input" is software or algorithms that understand the input data received from the user and take appropriate action based on it.

[0438] "Means for utilizing a generative AI model to generate appropriate content" refers to devices or software that utilize a generative AI model to generate content such as text, images, and videos to be provided to users based on the analysis results.

[0439] The "means for transmitting the generated content to the user's terminal" refers to a communication device or software for transferring the generated content to a terminal such as a smartphone or a personal computer used by the user.

[0440] "Means for collecting user access history, behavioral history, and purchase history" refers to devices or software that collect data such as a user's past website browsing history, action logs, and purchase records.

[0441] "Means for analyzing collected history and selecting optimal marketing content for the user" refers to devices or software that analyze collected history data and select optimal advertising and promotional information for the user based on that data.

[0442] The "means for providing the selected marketing content to the user" refers to a communication device or software for displaying the selected marketing content on the user's terminal.

[0443] "Means for utilizing an emotion engine to analyze the user's emotional state" refers to a device or software that utilizes an emotion engine to analyze the user's facial expressions, voice, text, etc., and determine their emotional state.

[0444] "Means for customizing content using a generative AI model based on the emotional state of a user analyzed using an emotion engine" refers to devices or software that customize content so that the generative AI model generates optimal content for the user based on the analyzed emotional state.

[0445] This invention is a system that realizes highly personalized content provision and marketing measures that take into account the emotional state of the user. This system consists of three main components: a server, a terminal, and a user.

[0446] server

[0447] The server has the following functions:

[0448] 1. A means of receiving user input

[0449] The server receives user requests (e.g., text or voice requests) from the device, allowing the server to understand the information or action the user is seeking.

[0450] 2. A means of parsing received user input

[0451] The server uses natural language processing (NLP) algorithms to analyze the user input it receives, allowing it to clearly understand the user's intent and request. For example, when analyzing the request "Looking for a new dress," it understands that the user wants to buy a dress.

[0452] 3. Using generative AI models to generate relevant content

[0453] The server then uses a generative AI model based on the analysis results to generate appropriate content. Specific generative AI models used here include OpenAI GPT-3. For example, if a user is looking for a new dress, the server generates the latest fashion trends and a list of recommended products.

[0454] 4. Means for sending generated content to the user's device

[0455] The server then sends the generated content to users' devices such as smartphones, tablets, and personal computers, allowing users to obtain information in real time.

[0456] 5. Means of collecting user access history, behavior history, and purchase history

[0457] The server collects and centrally manages data such as the user's past website browsing history, action logs, and purchase records.

[0458] 6. A means of analyzing collected history and selecting the most appropriate marketing content for the user

[0459] The server analyzes the collected data and selects marketing content (e.g., advertisements and promotional information) that matches the user's interests.

[0460] 7. How we deliver selected marketing content to you

[0461] The server transmits the selected marketing content to the user's terminal and presents it to the user.

[0462] 8. Using an emotion engine to analyze the user's emotional state

[0463] The server uses an emotion engine (specifically, the Affectiva SDK) to analyze the user's emotional state from facial expressions, voice, text, etc.

[0464] 9. A means of customizing content using generative AI models based on the user's emotional state analyzed using an emotion engine.

[0465] The server customizes the generative AI model to generate optimal content for the user based on the emotional state analyzed by the emotion engine. This provides content that matches the user's emotional state, providing a more personalized experience. Specifically, if the user is in a relaxed state, the server will suggest products suitable for relaxation.

[0466] Terminal

[0467] A terminal is a device that users use to access the system, and includes smartphones, tablets, PCs, etc. Terminals have the following functions:

[0468] Sending a request: The user uses the terminal to send a request to the server.

[0469] Display Content: Displaying content and marketing information sent from the server to the user.

[0470] User

[0471] A user is an individual who uses the system to obtain information or access services. The user uses a terminal to send requests to the server and receive generated content and marketing information.

[0472] Examples of prompt statements

[0473] text

[0474] User Request: Looking for a new dress

[0475] User's emotional state: joy

[0476] Generate the right content.

[0477] By implementing this invention, it is possible to provide highly personalized content that reflects the user's emotional state and to implement effective marketing. This system can improve user satisfaction and improve the effectiveness of corporate marketing.

[0478] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0479] Step 1:

[0480] User Authentication

[0481] The terminal receives login information (username and password) as input from the user. The terminal sends this input data to the server, which then authenticates the user. The server checks the user information against a database, and if authentication is successful, it generates an authentication token and returns it to the terminal. This allows the user to access the system.

[0482] Input: Username, Password

[0483] Output: Authentication token

[0484] Step 2:

[0485] Receiving and analyzing user requests

[0486] A user uses a device to input a request, for example, text such as "I'm looking for a new dress." The device sends this request to a server, which receives the input. The server uses natural language processing algorithms to analyze the request and understand the user's intent.

[0487] Input: User request (text)

[0488] Output: Analysis result (user intent)

[0489] Step 3:

[0490] User emotional state analysis

[0491] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends them to the emotion engine in real time. The server receives this input data and uses the emotion engine to analyze the user's emotional state (happiness, surprise, sadness, etc.). The analyzed emotion data is saved along with the analysis results of the user's request.

[0492] Input: User's facial expression and voice data

[0493] Output: Emotional state (user's emotion)

[0494] Step 4:

[0495] Content generation using generative AI models

[0496] The server sends the user's request analysis results and emotional state to the generative AI model. The prompt is formatted as follows: "User request: Looking for a new dress\nUser's emotional state: Joy\nPlease generate appropriate content." The generative AI model analyzes the input prompt and generates appropriate content (e.g., the latest fashion information or recommended dresses). The generated content is returned to the server.

[0497] Input: Request analysis results, emotional state

[0498] Output: Generated content (text, images)

[0499] Step 5:

[0500] Content provision

[0501] The server transmits the generated content to the terminal, which then displays it to the user, allowing the user to receive the required information in real time.

[0502] Input: Generated content

[0503] Output: Content displayed on the user's device

[0504] Step 6:

[0505] Historical Data Collection and Analysis

[0506] Every time a user views, clicks, or purchases content, that data is sent from the device to the server. The server stores this access history, behavioral history, and purchase history in a database and periodically analyzes it. Based on the analysis results, the user's interests can be identified.

[0507] Input: access history, behavior history, purchase history

[0508] Output: Analysis results (user interests)

[0509] Step 7:

[0510] Personalized Marketing

[0511] The server uses the analysis results and emotional data to generate optimal marketing content for the user. It also uses the generative AI model to create customized advertisements and promotional information based on the user's interests and emotional state. The generated marketing content is then sent to the device and provided to the user.

[0512] Input: Analysis results, emotion data

[0513] Output: Marketing content (advertising, promotional information)

[0514] This allows users to receive content and marketing information that reflects their emotional state, enabling companies to achieve highly personalized marketing.

[0515] 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.

[0516] 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.

[0517] 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.

[0518] [Second embodiment]

[0519] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0520] 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.

[0521] 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).

[0522] 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.

[0523] 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.

[0524] 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).

[0525] 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.

[0526] 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.

[0527] 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.

[0528] 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.

[0529] 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.

[0530] 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."

[0531] The present invention provides an interactive platform that utilizes generative AI models through interactions between servers, terminals, and users. This platform collects and analyzes first-party data such as user behavioral history, purchase history, and access history, and provides optimal marketing content.

[0532] System Overview

[0533] server

[0534] The server is the main component that receives user requests and generates appropriate content for the requests using generative AI models. The server also collects user access history, behavioral history, and purchase history, and analyzes this data to provide relevant marketing content to users.

[0535] Terminal

[0536] A terminal is a device through which a user accesses the system, and is typically a smartphone, tablet, PC, etc. A user accesses the system through a terminal and inputs a request through an interactive interface.

[0537] User

[0538] A user is an individual who utilizes the system to obtain information or use services. The user uses a terminal to send requests to the server and receive responses from the server.

[0539] Implementation details

[0540] 1. User Access and Authentication

[0541] The user accesses the interactive platform from a terminal and enters authentication information on the login screen. The terminal sends this information to the server, which then verifies the authentication information and authenticates the user. If authentication is successful, the server provides the user with an interactive interface.

[0542] 2. Receiving a User Request

[0543] Through the interactive interface, the user inputs requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight." The terminal sends the user's input to the server.

[0544] 3. Request Parsing and Content Generation

[0545] The server sends the received request to the generative AI model, which analyzes the request and generates appropriate content. For example, in response to the request "Tell me today's news," the model generates the latest news articles and returns them to the server.

[0546] 4. Provision of Content

[0547] The server sends the generated content to the device, which then displays it to the user, who can then check the latest news articles or restaurant information on an interactive interface.

[0548] 5. First-party data collection and analysis

[0549] The server collects the user's access history, behavioral history, and purchase history and stores them in a back-end database, which provides the basis for analyzing the user's interests and behavioral patterns and generating relevant marketing content for the next visit.

[0550] 6. Providing optimal marketing content

[0551] Based on the analyzed data, the server selects the most suitable marketing content for the user. Using the generative AI model again, it generates customized advertisements and promotional information and provides it to the user. For example, if the user is interested in Japanese restaurants, it can provide promotional information for related restaurants.

[0552] Specific examples

[0553] News provision example

[0554] The user types, "Tell me today's news." The device sends this request to the server. The server then sends a request to the generative AI model to generate the latest news articles. The generated news articles are then sent to the device and displayed to the user.

[0555] Restaurant reservation example

[0556] The user types, "I'd like to make a reservation at a Japanese restaurant for tonight." The device sends the request to the server, which then has the generative AI model analyze the request. The generative AI model generates a list of candidate Japanese restaurants and returns it to the server. The server sends this list to the user, and the system makes a reservation at the restaurant selected by the user.

[0557] The present invention makes it possible to achieve a high user experience and effective marketing while protecting user privacy.

[0558] The processing flow will be explained below.

[0559] Processing Steps

[0560] Step 1:

[0561] A user opens an app or web browser to access the system, and a login form appears on the screen.

[0562] Step 2:

[0563] The user enters their user ID and password into the login form.

[0564] Step 3:

[0565] The terminal sends the authentication information entered by the user to the server.

[0566] Step 4:

[0567] The server checks the received authentication information against a backend database and authenticates the user.

[0568] Step 5:

[0569] The server sends the authentication result to the terminal, and if authentication is successful, an interactive interface is displayed to the user. If authentication is unsuccessful, a message is displayed prompting the user to re-enter authentication information.

[0570] Step 6:

[0571] A user inputs requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight" through an interactive interface.

[0572] Step 7:

[0573] The terminal sends the user's input text to the server.

[0574] Step 8:

[0575] The server receives user input and sends it to the generative AI model, performing any preprocessing (such as tokenization and filtering) required for analysis.

[0576] Step 9:

[0577] A generative AI model analyzes the user's input text and generates appropriate content (e.g., a news article or restaurant list).

[0578] Step 10:

[0579] The generative AI model returns the generated content to the server, which contains the information requested by the user.

[0580] Step 11:

[0581] The server formats the generated content and sends it to the terminal for transmission back to the user.

[0582] Step 12:

[0583] The device receives the content from the server and displays it to the user, such as news articles or restaurant information on an interactive interface.

[0584] Step 13:

[0585] The server stores the user's access history, behavioral history, and purchase history in a back-end database, which allows the server to understand the user's interests.

[0586] Step 14:

[0587] The server analyzes the collected historical data to identify the user's interests, for example, which news categories the user frequently visits or the types of restaurants they make reservations at.

[0588] Step 15:

[0589] Based on the analysis results, the server selects the most suitable marketing content for the user (for example, advertisements for related products and promotional information).

[0590] Step 16:

[0591] The server then uses the generative AI model again to generate customized marketing content.

[0592] Step 17:

[0593] The generative AI model generates customized marketing content and returns it to the server.

[0594] Step 18:

[0595] The server transmits the generated marketing content to the user's terminal.

[0596] Step 19:

[0597] The terminal displays the marketing content received from the server to the user.

[0598] Through these steps, users can seamlessly obtain information and enjoy individually customized content and services.

[0599] Example 1

[0600] 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."

[0601] Conventional interactive platforms could only provide simple responses to user requests, making it difficult to effectively provide personalized content or marketing information. They also faced security issues and lacked the technology to properly utilize user behavioral and purchase histories. As a result, the user experience was poor and marketing effectiveness was limited.

[0602] 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.

[0603] In this invention, the server includes means for receiving user input, means for analyzing the received user input, means for utilizing a generative AI model that generates appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, and purchase history, means for analyzing the collected history and selecting optimal marketing content for the user, means for providing the selected marketing content to the user, means for encrypting the user input using SSL / TLS and transmitting it to the server, means for the server to generate a session ID and transmit it to the user's terminal, means for the generative AI model to include a function for performing natural language processing, means for providing an interactive interface to authenticated users, means for transmitting the generated content to the terminal in HTML or JSON format, and means for the server to access a database and verify authentication information. This enables highly personalized content and marketing information to be provided safely and effectively in response to user requests.

[0604] The "means for receiving user input" is a function for receiving data that a user inputs to the interactive platform through a terminal.

[0605] The "means for analyzing received user input" is a function for analyzing received user input data and understanding its contents.

[0606] "Means for utilizing a generative AI model" refers to a function that utilizes an artificial intelligence model to generate appropriate content based on the analysis results.

[0607] "Means for sending generated content to a user's device" refers to a function for sending content created by a generative AI model to a user's device.

[0608] "Means for collecting user access history, behavior history, and purchase history" refers to a function for obtaining and storing records of users' online activities.

[0609] "Means of analyzing collected history and selecting the most suitable marketing content for the user" is a function that analyzes the user's interests and behavioral patterns based on collected data and selects the most suitable marketing content based on that.

[0610] The "means for providing selected marketing content to the user" is a function for presenting selected appropriate marketing content to the user.

[0611] "Means of encrypting user input using SSL / TLS and sending it to the server" is a function that encrypts data using SSL / TLS technology in order to securely send data entered by the user to the server.

[0612] "Means for the server to generate a session ID and send it to the user's terminal" is a function for managing a communication session by passing the session ID generated by the server to the user's terminal.

[0613] "Means for the generative AI model to include the ability to perform natural language processing" refers to the ability of the generative AI model to use natural language processing techniques to analyze and understand text data.

[0614] The "means for providing an interactive interface to an authenticated user" is a function for displaying an interactive user interface to a user who has been successfully authenticated.

[0615] "Means for sending generated content to a terminal in HTML or JSON format" is a function for sending generated content to a user's terminal in HTML or JSON format.

[0616] The "means for the server to access the database and confirm the authentication information" is a function that allows the server to refer to the database and confirm the validity of the user's authentication information.

[0617] The present invention provides an interactive platform that utilizes generative AI models through the interaction of a server, terminals, and users. This platform collects and analyzes data such as user behavioral history, purchase history, and access history to provide optimal marketing content.

[0618] server

[0619] The server is the main component that receives requests from users and generates appropriate content in response to the requests using a generative AI model. The server also collects and analyzes users' access history, behavioral history, and purchase history to provide relevant marketing content to users. Servers are built using programming languages ​​such as Python or Java and utilize machine learning frameworks such as TensorFlow or PyTorch. Furthermore, MySQL or PostgreSQL is often used as the database. Common cloud services include Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[0620] Terminal

[0621] A terminal is a device through which a user accesses a system, and examples include smartphones, tablets, and PCs. The terminal communicates with the server via a web browser or dedicated application, sending the user's requests to the server. The terminal receives responses from the server and displays them to the user. Operating systems such as iOS and Android are commonly used on these terminals.

[0622] User

[0623] A user is an individual who uses the platform to obtain information or use services. The user inputs a request through the interactive interface and sends it to the server via their device. The user receives a response from the server on their device and checks the displayed information.

[0624] Content generation and delivery

[0625] Authentication Process

[0626] A user accesses the interactive platform from a terminal and enters authentication information (email address and password) on the login screen. The entered authentication information is encrypted with SSL / TLS and sent to the server using the HTTPS protocol. The server checks the authentication information by referencing a database, and if authentication is successful, generates a session ID and sends it to the user's terminal. An interactive interface is displayed, allowing the user to continue entering requests.

[0627] Receiving a user request

[0628] The user inputs a request into the interactive interface, such as "Tell me today's news" or "I want to make a restaurant reservation for tonight." The device sends this request in JSON format to the server, and communication is again carried out using the HTTPS protocol.

[0629] Request analysis and content generation using AI models

[0630] The server receives the request and sends it to a generative AI model for analysis. The generative AI model (e.g., GPT-3) analyzes the request and generates appropriate content. For example, in response to the request "Tell me today's news," it generates the latest news articles and returns them to the server.

[0631] Content provision

[0632] The content returned by the generative AI model is converted into HTML or JSON format by the server and sent to the device, which displays it to the user, who can then check the latest news articles or restaurant information through the device.

[0633] Data collection and analysis

[0634] First-party data collection

[0635] When a user uses the interactive platform, the server collects data such as their access history, behavioral history, and purchase history. The collected data is recorded in real time in a log file and stored in a database (for example, Amazon RDS or Google BigQuery).

[0636] Analyzing data and providing marketing content

[0637] The server uses data analysis tools such as Python's pandas library and scikit-learn to analyze the collected data. This identifies the user's interests and behavioral patterns, laying the groundwork for providing appropriate marketing content the next time the user visits. Based on the analysis results, the server selects the most appropriate marketing content for the user and again uses the generative AI model to generate customized advertising and promotional information. This content is sent to the user's device in HTML or JSON format and displayed on an interactive interface.

[0638] Specific examples

[0639] News provision example

[0640] When a user types "Tell me today's news," the device sends this request to the server, which then sends a request to the generative AI model to generate the latest news articles, which are then sent from the server to the device and displayed to the user.

[0641] Example prompt sentence:

[0642] "Tell me the news today"

[0643] Restaurant reservation example

[0644] If a user types, "I'd like to make a reservation at a Japanese restaurant for tonight," the device sends the request to the server. The server analyzes the request using a generative AI model and generates a list of Japanese restaurants. The generated list is sent from the server to the device and displayed to the user. The user then selects the desired restaurant from the list and finally makes the reservation.

[0645] Example prompt sentence:

[0646] "I'd like to make a reservation at a Japanese restaurant for tonight."

[0647] As described above, the embodiment of the present invention realizes a highly personalized user experience and effective marketing while protecting the user's privacy.

[0648] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0649] Step 1: User Access and Authentication

[0650] 1.1 A user accesses the interactive platform from a device (smartphone, tablet, PC), and the device connects to the platform URL using the HTTPS protocol.

[0651] Input: User access request

[0652] Output:Login screen

[0653] 1.2 The user enters their email address and password on the login screen.

[0654] Input: Email address, password

[0655] Output: Credentials

[0656] 1.3 The device encrypts the authentication information using SSL / TLS and sends it to the server.

[0657] Input: Encrypted credentials

[0658] Output: Authentication request to the server

[0659] 1.4 The server checks the authentication information against a database (MySQL, PostgreSQL) to verify the user exists and the password matches.

[0660] Input: Encrypted credentials

[0661] Output: Authentication result

[0662] 1.5 If authentication is successful, the server generates a session ID and sends it to the device, along with HTML and JavaScript code to display the interactive interface.

[0663] Input: Breakdown of successful authentications

[0664] Output: Session ID, interactive interface

[0665] Step 2: Receiving a user request

[0666] 2.1 A user types a request into a conversational interface, such as "What's in the news today?"

[0667] Input: Request text (e.g. "Tell me today's news")

[0668] Output: Request data

[0669] 2.2 The device sends the input request to the server as a JSON format message.

[0670] Input: Request data

[0671] Output: Request message to the server

[0672] Step 3: Parsing the request and generating content

[0673] 3.1 The server sends the received request data to the generative AI model.

[0674] Input: Request message

[0675] Output: Analysis request to the generative AI model

[0676] 3.2 A generative AI model (e.g., GPT-3) analyzes the request and generates appropriate content, e.g., generating the latest news articles in response to the request "What's in the news today?"

[0677] Input: Parsing request (request statement)

[0678] Output: Generated content (e.g., latest news articles)

[0679] 3.3 Return the generated content to the server.

[0680] Input: Generated content (news article)

[0681] Output: Return content to the server

[0682] Step 4: Providing content

[0683] 4.1 The server converts the generated content into HTML or JSON format and sends it to the device.

[0684] Input: Generated content (news article)

[0685] Output: HTML or JSON formatted content

[0686] 4.2 The device receives the content in HTML or JSON format and displays it to the user.

[0687] Input: HTML or JSON formatted content

[0688] Output: The news article displayed to the user

[0689] Step 5: Collect and analyze first-party data

[0690] 5.1 The server collects user access history, behavior history, and purchase history and records them in log files in real time.

[0691] Input: User access history, behavior history, purchase history

[0692] Output: Record data in a log file

[0693] 5.2 Store the collected data in a database (e.g., Amazon RDS, Google BigQuery).

[0694] Input: Collected historical data

[0695] Output: Save data to database

[0696] 5.3 The server analyzes the data and identifies user interests and behavioral patterns using Python's pandas library and scikit-learn.

[0697] Input: Historical data in the database

[0698] Output: Analysis results (user interests and behavioral patterns)

[0699] Step 6: Deliver optimal marketing content

[0700] 6.1 The server selects the most appropriate marketing content for the user based on the analyzed data.

[0701] Input: Analysis results (user interests and behavioral patterns)

[0702] Output: Selected marketing content

[0703] 6.2 The server uses the generated AI model again to generate customized advertising and promotional information.

[0704] Input: Selected Marketing Content

[0705] Output: Generated advertising and promotional information

[0706] 6.3 The server sends the generated marketing content in HTML or JSON format to the terminal, which then provides it to the user.

[0707] Input: Generated advertising and promotion information

[0708] Output: Marketing content that is displayed to the user

[0709] The above is a specific processing flow of the present invention. The present invention makes it possible to provide highly personalized content and effective marketing information while protecting the user's privacy.

[0710] (Application example 1)

[0711] 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."

[0712] In modern e-commerce, providing product recommendations and marketing content that meet the diverse needs of users is important for increasing purchasing motivation. However, conventional systems have had difficulty effectively analyzing users' behavioral and purchase histories to provide optimal product recommendations for each individual. Furthermore, user authentication and content provision were not integrated, which led to problems that impaired the user experience.

[0713] 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.

[0714] In this invention, the server includes means for receiving user input, means for analyzing the received user input, means for using a generative AI model to generate appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, and purchase history, means for analyzing the collected history and selecting optimal marketing content for the user, means for providing the selected marketing content to the user, means for transmitting user authentication information and, if authentication is successful, generating individual product recommendation information, and means for displaying the generated product recommendation information on the user's terminal. This enables individual product recommendations according to the user's needs, improving the user experience and realizing effective marketing.

[0715] "User input" refers to operations or inputs made by a user to send requests or commands to the system.

[0716] A "generative AI model" is an artificial intelligence model that automatically generates appropriate content based on user input or requests.

[0717] A "terminal" is a device that allows a user to access the system and use the interactive interface, including smartphones, tablets, and PCs.

[0718] "Access history" refers to a record of operations a user has performed within the system in the past and pages that the user has visited.

[0719] "Behavioral history" refers to a record of a series of operations and actions performed by a user within the system.

[0720] "Purchase history" refers to a record of products and services a user has purchased in the past.

[0721] "Marketing content" refers to advertising and promotional information provided in response to a user's interests and needs.

[0722] "User authentication information" refers to the authentication information a user uses to log in to a system, and typically includes a username and password.

[0723] "Product recommendation information" refers to information about products and services recommended to a user, which is generated based on the user's access history, behavior history, and purchase history.

[0724] A "smart shopping assistant" is an interactive platform that analyzes a user's behavioral history, purchase history, and access history to provide optimal product recommendations and promotional information.

[0725] This invention aims to build a "smart shopping assistant" system that utilizes a user's behavioral history, purchase history, and access history to provide optimal product recommendations and marketing content. A specific embodiment of the system is described below.

[0726] System Overview

[0727] server

[0728] The server is the central component that receives user requests and generates appropriate content using generative AI models. The server also collects and analyzes users' access history, behavioral history, and purchase history, and provides relevant marketing content to users based on this data.

[0729] Terminal

[0730] A terminal is a device through which a user accesses a system and uses an interactive interface, and generally includes a smartphone, tablet, PC, etc. A user accesses a system through a terminal and performs interactive operations.

[0731] User

[0732] A user is an individual who uses the system to obtain information or use services. The user sends requests to the server through a terminal and receives responses from the server.

[0733] Specific processing flow

[0734] User Access and Authentication

[0735] The user accesses the interactive platform from a terminal and enters login information. The terminal sends the authentication information to the server, which then verifies the authentication information and authenticates the user. If the authentication is successful, the server provides the user with an interactive interface.

[0736] Receiving and analyzing user requests

[0737] Through the conversational interface, users input requests such as "Tell me what products you recommend" or "I'd like to know the latest promotional information." The device sends the user's input to the server, which then uses a generative AI model to analyze the request and generate appropriate content.

[0738] Content generation and delivery

[0739] The generative AI model generates optimal product recommendation information and marketing content based on user input and behavioral, access, and purchase history. The generated content is sent from the server to the device, which then displays it to the user.

[0740] First-party data collection and analysis

[0741] The server continuously collects data such as user access history, behavioral history, and purchase history, and stores it in a back-end database. By analyzing this data, it is possible to create a foundation for providing more accurate content the next time the user accesses the site.

[0742] Hardware and Software Use

[0743] Hardware

[0744] Smartphones, tablets, PCs, smart glasses, head-mounted displays

[0745] software

[0746] Flask (server-side framework)

[0747] Python (server and client side programs)

[0748] API request library (requests)

[0749] Specific examples and prompts for the generative AI model

[0750] Specific examples

[0751] User: "What products do you recommend?"

[0752] The system analyzes a user's past purchase history and access history to generate a list of products relevant to the user. For example, if a user has purchased a lot of sports equipment in the past, it will recommend new sportswear and equipment.

[0753] Prompt Sentence Examples

[0754] "Based on the user's purchase history, please recommend the best products for the following product categories:

[0755] Category: Sporting Goods

[0756] Past purchases: running shoes, fitness tracker, sports t-shirts

[0757] Keywords: latest, trending, recommended"

[0758] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0759] Step 1:

[0760] A user accesses the interactive platform from a terminal and enters login information. The entered information is a username and password. The terminal sends this input information to the server. The server receives it and authenticates the user. If authentication is successful, the server provides the user with an interactive interface. The input is a username and password, and the output is the authentication result.

[0761] Step 2:

[0762] The user inputs a request (for example, "What products do you recommend?") through an interactive interface. The device sends the user's input as is to the server. The server receives it and prepares it for analysis. The input is the user's request, and the output is the unanalyzed request data.

[0763] Step 3:

[0764] The server sends the received request to the generative AI model, which analyzes the request. The generative AI model understands the request and processes and calculates the data to generate appropriate content. For example, if the request is "Tell me what products you recommend," the model uses the user's access history and purchase history to generate relevant product information. The input is unanalyzed request data, and the output is the content data to be generated.

[0765] Step 4:

[0766] The generated content data is sent back to the server. Based on this data, the server generates optimal product recommendation information for the user. At this point, the server accesses the product database and extracts specific product information based on the recommendation results obtained from the generative AI model. The input is the generated content data, and the output is specific product recommendation information.

[0767] Step 5:

[0768] The server sends the generated product recommendation information to the terminal. The terminal receives this information and displays it to the user. For example, a list of recommended products based on the user's purchase history is displayed on the interface. The input is specific product recommendation information, and the output is user interface content for display.

[0769] Step 6:

[0770] The user selects from the recommended products and clicks or taps on the detailed information. The device sends this selection information to the server. The server generates detailed information about the selected product and sends it back to the device. The input is the user's selection information, and the output is detailed product information.

[0771] Step 7:

[0772] When a user wishes to make a purchase, a purchase request is sent from the terminal to the server. The server receives this and initiates the payment process. An electronic payment service API may be used here. The input is the purchase request, and the output is a purchase confirmation and payment information.

[0773] Step 8:

[0774] If the payment is successful, the server sends the purchase confirmation information to the terminal and displays it to the user. The new purchase information is also added to the purchase history database. The input is the payment information, and the output is the purchase confirmation information and the updated purchase history.

[0775] Keywords: generative AI model, prompt sentence

[0776] 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.

[0777] The present invention relates to a system that combines a generative AI model and an emotion engine in an interactive platform for users to provide more personalized content and marketing strategies. Specific embodiments are described below.

[0778] System Overview

[0779] server

[0780] The server receives and analyzes user requests and generates appropriate content using a generative AI model and emotion engine. It also has the ability to collect and analyze user access history, behavioral history, purchase history, and emotion data.

[0781] Terminal

[0782] A terminal is a device through which a user accesses the system, such as a smartphone, tablet, or PC. The user sends requests to the server through the terminal and receives responses from the server.

[0783] User

[0784] A user is an individual who uses the system to obtain information or access services. The user uses a device to send requests to the server and receive generated content and marketing information.

[0785] Implementation details

[0786] 1. User Access and Authentication

[0787] A user accesses the system from a terminal and enters authentication information on the login screen. The terminal sends the authentication information to the server, which then performs the authentication process. If authentication is successful, an interactive interface is presented to the user.

[0788] 2. Receiving and analyzing user requests

[0789] Through the conversational interface, users input requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight." The device sends this input to the server. The server receives the speech and sends it to the generative AI model. The generative AI model analyzes the input and generates appropriate content.

[0790] 3. Emotion Recognition by Emotion Engine

[0791] The server sends the user's input text, voice, facial expressions, etc. to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The recognized emotion data is used by the generative AI model to generate content and select marketing content.

[0792] 4. Content Generation and Delivery

[0793] The generative AI model generates content (e.g., news articles, restaurant listings, etc.) based on user requests and emotion data. The server sends the generated content to the device, which then displays it to the user.

[0794] 5. First-party data collection and analysis

[0795] The server stores the user's access history, behavioral history, purchase history, and emotional data in a back-end database. The server analyzes this data to understand the user's interests.

[0796] 6. Selection and provision of marketing content

[0797] Based on the analysis results, the server selects the most suitable marketing content for the user, taking into account the emotional data recognized by the emotion engine. The generative AI model is then used again to generate customized marketing content (e.g., advertisements and promotional information). The generated marketing content is then sent from the server to the device and provided to the user.

[0798] Specific examples

[0799] News provision example

[0800] The user inputs a request such as "Tell me today's news." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates the latest news articles and returns them to the server. The server sends the generated news articles to the device, which displays them to the user. The emotion engine recognizes the user's emotions and can provide relevant news and advertisements based on the emotions the next time the device is accessed.

[0801] Restaurant reservation example

[0802] The user inputs a request such as "I would like to make a reservation at a Japanese restaurant for tonight." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates a list of candidate Japanese restaurants and returns it to the server. The server provides the candidate list to the user and makes a reservation at the restaurant selected by the user. The emotion engine provides restaurant information that best suits the user's wishes and emotions, thereby achieving a more personalized service.

[0803] In this way, the present invention makes it possible to provide highly personalized content while taking into account the emotional state of the user, thereby improving the user experience and realizing effective marketing.

[0804] The processing flow will be explained below.

[0805] Processing Steps

[0806] Step 1:

[0807] A user opens an app or web browser to access the system and is presented with a login screen.

[0808] Step 2:

[0809] The user enters their user ID and password into the login form.

[0810] Step 3:

[0811] The device sends the authentication information entered by the user to the server.

[0812] Step 4:

[0813] The server receives the authentication information, checks it against a backend database, and authenticates the user.

[0814] Step 5:

[0815] The server sends the authentication result to the terminal, and if authentication is successful, an interactive interface is displayed; if authentication is unsuccessful, the user is prompted to enter authentication information again.

[0816] Step 6:

[0817] Users input requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight" through an interactive interface.

[0818] Step 7:

[0819] The device sends the user's input text to the server.

[0820] Step 8:

[0821] The server receives user input and sends it to the generative AI model and emotion engine.

[0822] Step 9:

[0823] A generative AI model analyzes the user's input text and generates appropriate content (e.g., a news article or restaurant list).

[0824] Step 10:

[0825] The emotion engine recognizes emotions from the user's text, tone of voice, facial expressions, etc., and provides the emotion data to the generative AI model.

[0826] Step 11:

[0827] The generative AI model customizes the content based on the emotional data and generates the final content.

[0828] Step 12:

[0829] The server sends the generated content to the device, which displays it to the user.

[0830] Step 13:

[0831] Users can view news articles and restaurant information displayed on an interactive interface.

[0832] Step 14:

[0833] The server stores user access history, behavioral history, purchase history and emotional data in a back-end database.

[0834] Step 15:

[0835] The server analyzes the stored data to identify users' interests, concerns, and emotional patterns.

[0836] Step 16:

[0837] Based on the analysis results, the server selects the most appropriate marketing content for the user.

[0838] Step 17:

[0839] A generative AI model generates selected marketing content and customizes it taking into account sentiment data.

[0840] Step 18:

[0841] The server sends the generated marketing content to the device, which then provides it to the user.

[0842] Through these steps, users can seamlessly obtain information and enjoy individually customized content and services.

[0843] Example 2

[0844] 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."

[0845] Conventional content delivery systems often provide general information without considering the user's emotional state, which limits their ability to improve user experience and maximize marketing effectiveness. Furthermore, selecting marketing content based solely on a user's access history, behavioral history, and purchase history makes it difficult to deliver content tailored to the user's current emotional state and interests.

[0846] 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.

[0847] In this invention, the server includes means for receiving user input, means for analyzing the received user input, means for utilizing a generative AI model that generates appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, purchase history, and emotional data, means for analyzing the collected history and utilizing an emotional engine that recognizes the user's emotional state, means for selecting optimal marketing content for the user based on the analysis results and the emotional data, and means for providing the selected marketing content to the user. This makes it possible to provide highly personalized content and marketing measures based on the user's emotional state.

[0848] The "means for receiving user input" refers to the means by which the server receives text, voice, and other data input by the user through the terminal.

[0849] "Means for analyzing received user input" refers to the means by which the server decodes the received user input data and properly understands and classifies it.

[0850] "Means for utilizing generative AI models" refers to means for utilizing artificial intelligence models that automatically generate content and information based on analysis results.

[0851] "Means for transmitting generated content to a user's device" refers to means for transmitting content generated by a generative AI model to a device used by a user.

[0852] The "means for collecting user access history, behavior history, purchase history, and emotional data" refers to a means for accumulating user activity records, purchase history, and emotional data inferred from input data.

[0853] The "means for analyzing the collected history and utilizing an emotion engine to recognize the user's emotional state" refers to a means for analyzing the collected data and utilizing an emotion recognition device to identify the user's current emotional state.

[0854] "Means for selecting the most suitable marketing content for a user based on analysis results and emotional data" refers to means for selecting the most suitable advertisements and promotional information for a user based on the results of data analysis and emotional recognition information.

[0855] The "means for providing selected marketing content to a user" refers to a means for displaying and providing selected marketing information on a terminal used by a user.

[0856] The present invention relates to a system that combines a generative AI model and an emotion engine in an interactive platform with users to provide personalized content and marketing strategies. This system includes a series of processes that receive user input, analyze it, generate appropriate content, and provide it to the user.

[0857] server

[0858] The server is configured using the following software and hardware. It receives and analyzes user requests using a generative AI model and emotion engine, and generates appropriate content. It also has the ability to collect and analyze user access history, behavioral history, purchase history, and emotion data.

[0859] 1. A parsing module for parsing received user input.

[0860] 2. Generative AI model that generates content

[0861] 3. Emotion engine for recognizing the user's emotional state

[0862] 4. Back-end database for data management and analysis

[0863] Specific software examples include natural language processing libraries (e.g., NLTK), machine learning frameworks (e.g., TensorFlow), and database management systems (e.g., MySQL).

[0864] Terminal

[0865] A terminal is a device that a user uses to access the system and can take various forms (smartphone, tablet, PC, etc.). The user sends requests to the server through the terminal and receives responses from the server.

[0866] 1. An input interface for receiving user input

[0867] 2. A user interface that displays the content sent from the server

[0868] Specific hardware examples include Apple's iPhone and Samsung's Galaxy series. Software uses web browsers (e.g., Google Chrome, Safari) and dedicated applications (e.g., iOS and Android apps).

[0869] User

[0870] A user is an individual who uses the system to obtain information or use services. A user uses the system in the following steps:

[0871] 1. Access the system using a terminal and enter your authentication information on the login screen.

[0872] 2. After successful authentication, you enter your request through the conversational interface (e.g., "Tell me today's news" or "I'd like to make a restaurant reservation for tonight").

[0873] 3. View the content sent from the server.

[0874] Specific Examples

[0875] News provision example

[0876] The user inputs a request such as "Tell me today's news." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates the latest news articles and returns them to the server. The server sends the generated news articles to the device, which displays them to the user. The emotion engine recognizes the user's emotions and can provide relevant news and advertisements based on the emotions the next time the device is accessed.

[0877] Restaurant reservation example

[0878] The user inputs a request such as "I would like to make a reservation at a Japanese restaurant for tonight." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates a list of candidate Japanese restaurants and returns it to the server. The server provides the candidate list to the user and makes a reservation at the restaurant selected by the user. The emotion engine provides restaurant information that best suits the user's wishes and emotions, thereby achieving a more personalized service.

[0879] In this way, the present invention makes it possible to provide highly personalized content while taking into account the emotional state of the user, thereby improving the user experience and realizing effective marketing.

[0880] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0881] Step 1:

[0882] User Access and Authentication

[0883] A user accesses the system from a terminal and enters their ID and password on the login screen. The terminal sends the entered authentication information to the server. The server receives the authentication information and checks it against a back-end database. If authentication is successful, the server returns an interactive interface to the user.

[0884] Input: User ID and password

[0885] Output: An interactive interface is displayed, and a message confirming successful user authentication is displayed.

[0886] Specifically, the user operates their smartphone to enter their ID and password into the login form and tap the submit button. The server then accesses the database and checks whether the ID and password match.

[0887] Step 2:

[0888] Receiving and analyzing user requests

[0889] The user inputs a prompt (e.g., "Tell me today's news") through a conversational interface. The device sends this prompt to the server. The server then forwards the request to the generative AI model, which analyzes the text data. The generative AI model understands the request and generates an analysis result to be passed on to the next step.

[0890] Input: User prompt text

[0891] Output: Analysis results from the generative AI model

[0892] Specifically, when a user types "I'd like to make a restaurant reservation for tonight" into the tablet interface and presses the send button, the device transfers the text to the server, which then passes it to the generative AI model to begin analyzing it.

[0893] Step 3:

[0894] Emotion recognition by emotion engine

[0895] The server sends the user's input text and voice data to the emotion engine, which analyzes this data and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The server then sends the recognized emotion data to the generative AI model, which uses it as a reference for content generation.

[0896] Input: User-entered text and voice data

[0897] Output: Emotion data from the emotion engine

[0898] As a specific example of operation, a user inputs a voice message such as "I've been feeling down lately" into the terminal, and the terminal sends the voice message to the server. The server passes the voice data to the emotion engine and recognizes the user's emotional state.

[0899] Step 4:

[0900] Content generation and delivery

[0901] The generative AI model generates content (e.g., news articles or restaurant listings) based on user requests and emotion data. The server sends the generated content to the device, which then displays it to the user.

[0902] Input: User request and emotion data

[0903] Output: Generated content (news articles, restaurant listings, etc.)

[0904] Specifically, the server generates a news article based on the prompt "Tell me today's news" and the emotional state "I'm depressed," and transfers the generated article to the terminal, which then displays the news article on the user's screen.

[0905] Step 5:

[0906] First-party data collection and analysis

[0907] The server stores the user's access history, behavioral history, purchase history, and emotional data in a backend database. The server periodically analyzes this data to understand the user's interests.

[0908] Input: User access history, behavioral history, purchase history, emotional data

[0909] Output: Analysis results that reflect the user's interests

[0910] As a specific example of operation, the news categories that users frequently browse and the product purchase history are recorded in a database, and the user's preferences are identified by periodically analyzing this data.

[0911] Step 6:

[0912] Selection and provision of marketing content

[0913] Based on the analysis results, the server selects the most suitable marketing content for the user, taking into account the emotional data recognized by the emotion engine. The generative AI model is used again to generate customized marketing content. The server then sends the generated marketing content to the device, which then provides it to the user.

[0914] Input: Analysis results and emotion data

[0915] Output: Marketing content delivered to the user

[0916] Specifically, the server generates an advertisement for a healthy food product that will cheer you up based on the user's purchasing history and emotional state (e.g., "I feel low"), and sends it to the device to display. The user sees the advertisement and is encouraged to purchase the product.

[0917] (Application example 2)

[0918] 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."

[0919] While conventional content delivery systems can provide information in response to user requests, they lack personalized content and marketing strategies that reflect the user's emotional state, which results in poor user experience and in ineffective marketing. Therefore, there is a need for a system that can provide highly personalized content and marketing strategies that take the user's emotional state into account.

[0920] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user input, means for analyzing the received user input, means for utilizing a generative AI model that generates appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, and purchase history, means for analyzing the collected history and selecting optimal marketing content for the user, means for providing the selected marketing content to the user, means for utilizing an emotion engine that analyzes the user's emotional state, and means for customizing content using the generative AI model based on the user's emotional state analyzed using the emotion engine. This enables the provision of highly personalized content that reflects the user's emotional state and effective marketing.

[0921] A "means for receiving user input" is a device or software that receives text or voice requests, commands, etc., made by a user to the system.

[0922] "Means for parsing received user input" is software or algorithms that understand the input data received from the user and take appropriate action based on it.

[0923] "Means for utilizing a generative AI model to generate appropriate content" refers to devices or software that utilize a generative AI model to generate content such as text, images, and videos to be provided to users based on the analysis results.

[0924] The "means for transmitting the generated content to the user's terminal" refers to a communication device or software for transferring the generated content to a terminal such as a smartphone or a personal computer used by the user.

[0925] "Means for collecting user access history, behavioral history, and purchase history" refers to devices or software that collect data such as a user's past website browsing history, action logs, and purchase records.

[0926] "Means for analyzing collected history and selecting optimal marketing content for the user" refers to devices or software that analyze collected history data and select optimal advertising and promotional information for the user based on that data.

[0927] The "means for providing the selected marketing content to the user" refers to a communication device or software for displaying the selected marketing content on the user's terminal.

[0928] "Means for utilizing an emotion engine to analyze the user's emotional state" refers to a device or software that utilizes an emotion engine to analyze the user's facial expressions, voice, text, etc., and determine their emotional state.

[0929] "Means for customizing content using a generative AI model based on the emotional state of a user analyzed using an emotion engine" refers to devices or software that customize content so that the generative AI model generates optimal content for the user based on the analyzed emotional state.

[0930] This invention is a system that realizes highly personalized content provision and marketing measures that take into account the emotional state of the user. This system consists of three main components: a server, a terminal, and a user.

[0931] server

[0932] The server has the following functions:

[0933] 1. A means of receiving user input

[0934] The server receives user requests (e.g., text or voice requests) from the device, allowing the server to understand the information or action the user is seeking.

[0935] 2. A means of parsing received user input

[0936] The server uses natural language processing (NLP) algorithms to analyze the user input it receives, allowing it to clearly understand the user's intent and request. For example, when analyzing the request "Looking for a new dress," it understands that the user wants to buy a dress.

[0937] 3. Using generative AI models to generate relevant content

[0938] The server then uses a generative AI model based on the analysis results to generate appropriate content. Specific generative AI models used here include OpenAI GPT-3. For example, if a user is looking for a new dress, the server generates the latest fashion trends and a list of recommended products.

[0939] 4. Means for sending generated content to the user's device

[0940] The server then sends the generated content to users' devices such as smartphones, tablets, and personal computers, allowing users to obtain information in real time.

[0941] 5. Means of collecting user access history, behavior history, and purchase history

[0942] The server collects and centrally manages data such as the user's past website browsing history, action logs, and purchase records.

[0943] 6. A means of analyzing collected history and selecting the most appropriate marketing content for the user

[0944] The server analyzes the collected data and selects marketing content (e.g., advertisements and promotional information) that matches the user's interests.

[0945] 7. How we deliver selected marketing content to you

[0946] The server transmits the selected marketing content to the user's terminal and presents it to the user.

[0947] 8. Using an emotion engine to analyze the user's emotional state

[0948] The server uses an emotion engine (specifically, the Affectiva SDK) to analyze the user's emotional state from facial expressions, voice, text, etc.

[0949] 9. A means of customizing content using generative AI models based on the user's emotional state analyzed using an emotion engine.

[0950] The server customizes the generative AI model to generate optimal content for the user based on the emotional state analyzed by the emotion engine. This provides content that matches the user's emotional state, providing a more personalized experience. Specifically, if the user is in a relaxed state, the server will suggest products suitable for relaxation.

[0951] Terminal

[0952] A terminal is a device that users use to access the system, and includes smartphones, tablets, PCs, etc. Terminals have the following functions:

[0953] Sending a request: The user uses the terminal to send a request to the server.

[0954] Display Content: Displaying content and marketing information sent from the server to the user.

[0955] User

[0956] A user is an individual who uses the system to obtain information or access services. The user uses a terminal to send requests to the server and receive generated content and marketing information.

[0957] Examples of prompt statements

[0958] text

[0959] User Request: Looking for a new dress

[0960] User's emotional state: joy

[0961] Generate the right content.

[0962] By implementing this invention, it is possible to provide highly personalized content that reflects the user's emotional state and to implement effective marketing. This system can improve user satisfaction and improve the effectiveness of corporate marketing.

[0963] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0964] Step 1:

[0965] User Authentication

[0966] The terminal receives login information (username and password) as input from the user. The terminal sends this input data to the server, which then authenticates the user. The server checks the user information against a database, and if authentication is successful, it generates an authentication token and returns it to the terminal. This allows the user to access the system.

[0967] Input: Username, Password

[0968] Output: Authentication token

[0969] Step 2:

[0970] Receiving and analyzing user requests

[0971] A user uses a device to input a request, for example, text such as "I'm looking for a new dress." The device sends this request to a server, which receives the input. The server uses natural language processing algorithms to analyze the request and understand the user's intent.

[0972] Input: User request (text)

[0973] Output: Analysis result (user intent)

[0974] Step 3:

[0975] User emotional state analysis

[0976] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends them to the emotion engine in real time. The server receives this input data and uses the emotion engine to analyze the user's emotional state (happiness, surprise, sadness, etc.). The analyzed emotion data is saved along with the analysis results of the user's request.

[0977] Input: User's facial expression and voice data

[0978] Output: Emotional state (user's emotion)

[0979] Step 4:

[0980] Content generation using generative AI models

[0981] The server sends the user's request analysis results and emotional state to the generative AI model. The prompt is formatted as follows: "User request: Looking for a new dress\nUser's emotional state: Joy\nPlease generate appropriate content." The generative AI model analyzes the input prompt and generates appropriate content (e.g., the latest fashion information or recommended dresses). The generated content is returned to the server.

[0982] Input: Request analysis results, emotional state

[0983] Output: Generated content (text, images)

[0984] Step 5:

[0985] Content provision

[0986] The server transmits the generated content to the terminal, which then displays it to the user, allowing the user to receive the required information in real time.

[0987] Input: Generated content

[0988] Output: Content displayed on the user's device

[0989] Step 6:

[0990] Historical Data Collection and Analysis

[0991] Every time a user views, clicks, or purchases content, that data is sent from the device to the server. The server stores this access history, behavioral history, and purchase history in a database and periodically analyzes it. Based on the analysis results, the user's interests can be identified.

[0992] Input: access history, behavior history, purchase history

[0993] Output: Analysis results (user interests)

[0994] Step 7:

[0995] Personalized Marketing

[0996] The server uses the analysis results and emotional data to generate optimal marketing content for the user. It also uses the generative AI model to create customized advertisements and promotional information based on the user's interests and emotional state. The generated marketing content is then sent to the device and provided to the user.

[0997] Input: Analysis results, emotion data

[0998] Output: Marketing content (advertising, promotional information)

[0999] This allows users to receive content and marketing information that reflects their emotional state, enabling companies to achieve highly personalized marketing.

[1000] 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.

[1001] 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.

[1002] 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.

[1003] [Third embodiment]

[1004] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1005] 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.

[1006] 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).

[1007] 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.

[1008] 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.

[1009] 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).

[1010] 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.

[1011] 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.

[1012] 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.

[1013] 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.

[1014] 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.

[1015] 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."

[1016] The present invention provides an interactive platform that utilizes generative AI models through interactions between servers, terminals, and users. This platform collects and analyzes first-party data such as user behavioral history, purchase history, and access history, and provides optimal marketing content.

[1017] System Overview

[1018] server

[1019] The server is the main component that receives user requests and generates appropriate content for the requests using generative AI models. The server also collects user access history, behavioral history, and purchase history, and analyzes this data to provide relevant marketing content to users.

[1020] Terminal

[1021] A terminal is a device through which a user accesses the system, and is typically a smartphone, tablet, PC, etc. A user accesses the system through a terminal and inputs a request through an interactive interface.

[1022] User

[1023] A user is an individual who utilizes the system to obtain information or use services. The user uses a terminal to send requests to the server and receive responses from the server.

[1024] Implementation details

[1025] 1. User Access and Authentication

[1026] The user accesses the interactive platform from a terminal and enters authentication information on the login screen. The terminal sends this information to the server, which then verifies the authentication information and authenticates the user. If authentication is successful, the server provides the user with an interactive interface.

[1027] 2. Receiving a User Request

[1028] Through the interactive interface, the user inputs requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight." The terminal sends the user's input to the server.

[1029] 3. Request Parsing and Content Generation

[1030] The server sends the received request to the generative AI model, which analyzes the request and generates appropriate content. For example, in response to the request "Tell me today's news," the model generates the latest news articles and returns them to the server.

[1031] 4. Provision of Content

[1032] The server sends the generated content to the device, which then displays it to the user, who can then check the latest news articles or restaurant information on an interactive interface.

[1033] 5. First-party data collection and analysis

[1034] The server collects the user's access history, behavioral history, and purchase history and stores them in a back-end database, which provides the basis for analyzing the user's interests and behavioral patterns and generating relevant marketing content for the next visit.

[1035] 6. Providing optimal marketing content

[1036] Based on the analyzed data, the server selects the most suitable marketing content for the user. Using the generative AI model again, it generates customized advertisements and promotional information and provides it to the user. For example, if the user is interested in Japanese restaurants, it can provide promotional information for related restaurants.

[1037] Specific examples

[1038] News provision example

[1039] The user types, "Tell me today's news." The device sends this request to the server. The server then sends a request to the generative AI model to generate the latest news articles. The generated news articles are then sent to the device and displayed to the user.

[1040] Restaurant reservation example

[1041] The user types, "I'd like to make a reservation at a Japanese restaurant for tonight." The device sends the request to the server, which then has the generative AI model analyze the request. The generative AI model generates a list of candidate Japanese restaurants and returns it to the server. The server sends this list to the user, and the system makes a reservation at the restaurant selected by the user.

[1042] The present invention makes it possible to achieve a high user experience and effective marketing while protecting user privacy.

[1043] The processing flow will be explained below.

[1044] Processing Steps

[1045] Step 1:

[1046] A user opens an app or web browser to access the system, and a login form appears on the screen.

[1047] Step 2:

[1048] The user enters their user ID and password into the login form.

[1049] Step 3:

[1050] The terminal sends the authentication information entered by the user to the server.

[1051] Step 4:

[1052] The server checks the received authentication information against a backend database and authenticates the user.

[1053] Step 5:

[1054] The server sends the authentication result to the terminal, and if authentication is successful, an interactive interface is displayed to the user. If authentication is unsuccessful, a message is displayed prompting the user to re-enter authentication information.

[1055] Step 6:

[1056] A user inputs requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight" through an interactive interface.

[1057] Step 7:

[1058] The terminal sends the user's input text to the server.

[1059] Step 8:

[1060] The server receives user input and sends it to the generative AI model, performing any preprocessing (such as tokenization and filtering) required for analysis.

[1061] Step 9:

[1062] A generative AI model analyzes the user's input text and generates appropriate content (e.g., a news article or restaurant list).

[1063] Step 10:

[1064] The generative AI model returns the generated content to the server, which contains the information requested by the user.

[1065] Step 11:

[1066] The server formats the generated content and sends it to the terminal for transmission back to the user.

[1067] Step 12:

[1068] The device receives the content from the server and displays it to the user, such as news articles or restaurant information on an interactive interface.

[1069] Step 13:

[1070] The server stores the user's access history, behavioral history, and purchase history in a back-end database, which allows the server to understand the user's interests.

[1071] Step 14:

[1072] The server analyzes the collected historical data to identify the user's interests, for example, which news categories the user frequently visits or the types of restaurants they make reservations at.

[1073] Step 15:

[1074] Based on the analysis results, the server selects the most suitable marketing content for the user (for example, advertisements for related products and promotional information).

[1075] Step 16:

[1076] The server then uses the generative AI model again to generate customized marketing content.

[1077] Step 17:

[1078] The generative AI model generates customized marketing content and returns it to the server.

[1079] Step 18:

[1080] The server transmits the generated marketing content to the user's terminal.

[1081] Step 19:

[1082] The terminal displays the marketing content received from the server to the user.

[1083] Through these steps, users can seamlessly obtain information and enjoy individually customized content and services.

[1084] Example 1

[1085] 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."

[1086] Conventional interactive platforms could only provide simple responses to user requests, making it difficult to effectively provide personalized content or marketing information. They also faced security issues and lacked the technology to properly utilize user behavioral and purchase histories. As a result, the user experience was poor and marketing effectiveness was limited.

[1087] 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.

[1088] In this invention, the server includes means for receiving user input, means for analyzing the received user input, means for utilizing a generative AI model that generates appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, and purchase history, means for analyzing the collected history and selecting optimal marketing content for the user, means for providing the selected marketing content to the user, means for encrypting the user input using SSL / TLS and transmitting it to the server, means for the server to generate a session ID and transmit it to the user's terminal, means for the generative AI model to include a function for performing natural language processing, means for providing an interactive interface to authenticated users, means for transmitting the generated content to the terminal in HTML or JSON format, and means for the server to access a database and verify authentication information. This enables highly personalized content and marketing information to be provided safely and effectively in response to user requests.

[1089] The "means for receiving user input" is a function for receiving data that a user inputs to the interactive platform through a terminal.

[1090] The "means for analyzing received user input" is a function for analyzing received user input data and understanding its contents.

[1091] "Means for utilizing a generative AI model" refers to a function that utilizes an artificial intelligence model to generate appropriate content based on the analysis results.

[1092] "Means for sending generated content to a user's device" refers to a function for sending content created by a generative AI model to a user's device.

[1093] "Means for collecting user access history, behavior history, and purchase history" refers to a function for obtaining and storing records of users' online activities.

[1094] "Means of analyzing collected history and selecting the most suitable marketing content for the user" is a function that analyzes the user's interests and behavioral patterns based on collected data and selects the most suitable marketing content based on that.

[1095] The "means for providing selected marketing content to the user" is a function for presenting selected appropriate marketing content to the user.

[1096] "Means of encrypting user input using SSL / TLS and sending it to the server" is a function that encrypts data using SSL / TLS technology in order to securely send data entered by the user to the server.

[1097] "Means for the server to generate a session ID and send it to the user's terminal" is a function for managing a communication session by passing the session ID generated by the server to the user's terminal.

[1098] "Means for the generative AI model to include the ability to perform natural language processing" refers to the ability of the generative AI model to use natural language processing techniques to analyze and understand text data.

[1099] The "means for providing an interactive interface to an authenticated user" is a function for displaying an interactive user interface to a user who has been successfully authenticated.

[1100] "Means for sending generated content to a terminal in HTML or JSON format" is a function for sending generated content to a user's terminal in HTML or JSON format.

[1101] The "means for the server to access the database and confirm the authentication information" is a function that allows the server to refer to the database and confirm the validity of the user's authentication information.

[1102] The present invention provides an interactive platform that utilizes generative AI models through the interaction of a server, terminals, and users. This platform collects and analyzes data such as user behavioral history, purchase history, and access history to provide optimal marketing content.

[1103] server

[1104] The server is the main component that receives requests from users and generates appropriate content in response to the requests using a generative AI model. The server also collects and analyzes users' access history, behavioral history, and purchase history to provide relevant marketing content to users. Servers are built using programming languages ​​such as Python or Java and utilize machine learning frameworks such as TensorFlow or PyTorch. Furthermore, MySQL or PostgreSQL is often used as the database. Common cloud services include Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[1105] Terminal

[1106] A terminal is a device through which a user accesses a system, and examples include smartphones, tablets, and PCs. The terminal communicates with the server via a web browser or dedicated application, sending the user's requests to the server. The terminal receives responses from the server and displays them to the user. Operating systems such as iOS and Android are commonly used on these terminals.

[1107] User

[1108] A user is an individual who uses the platform to obtain information or use services. The user inputs a request through the interactive interface and sends it to the server via their device. The user receives a response from the server on their device and checks the displayed information.

[1109] Content generation and delivery

[1110] Authentication Process

[1111] A user accesses the interactive platform from a terminal and enters authentication information (email address and password) on the login screen. The entered authentication information is encrypted with SSL / TLS and sent to the server using the HTTPS protocol. The server checks the authentication information by referencing a database, and if authentication is successful, generates a session ID and sends it to the user's terminal. An interactive interface is displayed, allowing the user to continue entering requests.

[1112] Receiving a user request

[1113] The user inputs a request into the interactive interface, such as "Tell me today's news" or "I want to make a restaurant reservation for tonight." The device sends this request in JSON format to the server, and communication is again carried out using the HTTPS protocol.

[1114] Request analysis and content generation using AI models

[1115] The server receives the request and sends it to a generative AI model for analysis. The generative AI model (e.g., GPT-3) analyzes the request and generates appropriate content. For example, in response to the request "Tell me today's news," it generates the latest news articles and returns them to the server.

[1116] Content provision

[1117] The content returned by the generative AI model is converted into HTML or JSON format by the server and sent to the device, which displays it to the user, who can then check the latest news articles or restaurant information through the device.

[1118] Data collection and analysis

[1119] First-party data collection

[1120] When a user uses the interactive platform, the server collects data such as their access history, behavioral history, and purchase history. The collected data is recorded in real time in a log file and stored in a database (for example, Amazon RDS or Google BigQuery).

[1121] Analyzing data and providing marketing content

[1122] The server uses data analysis tools such as Python's pandas library and scikit-learn to analyze the collected data. This identifies the user's interests and behavioral patterns, laying the groundwork for providing appropriate marketing content the next time the user visits. Based on the analysis results, the server selects the most appropriate marketing content for the user and again uses the generative AI model to generate customized advertising and promotional information. This content is sent to the user's device in HTML or JSON format and displayed on an interactive interface.

[1123] Specific examples

[1124] News provision example

[1125] When a user types "Tell me today's news," the device sends this request to the server, which then sends a request to the generative AI model to generate the latest news articles, which are then sent from the server to the device and displayed to the user.

[1126] Example prompt sentence:

[1127] "Tell me the news today"

[1128] Restaurant reservation example

[1129] If a user types, "I'd like to make a reservation at a Japanese restaurant for tonight," the device sends the request to the server. The server analyzes the request using a generative AI model and generates a list of Japanese restaurants. The generated list is sent from the server to the device and displayed to the user. The user then selects the desired restaurant from the list and finally makes the reservation.

[1130] Example prompt sentence:

[1131] "I'd like to make a reservation at a Japanese restaurant for tonight."

[1132] As described above, the embodiment of the present invention realizes a highly personalized user experience and effective marketing while protecting the user's privacy.

[1133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1134] Step 1: User Access and Authentication

[1135] 1.1 A user accesses the interactive platform from a device (smartphone, tablet, PC), and the device connects to the platform URL using the HTTPS protocol.

[1136] Input: User access request

[1137] Output:Login screen

[1138] 1.2 The user enters their email address and password on the login screen.

[1139] Input: Email address, password

[1140] Output: Credentials

[1141] 1.3 The device encrypts the authentication information using SSL / TLS and sends it to the server.

[1142] Input: Encrypted credentials

[1143] Output: Authentication request to the server

[1144] 1.4 The server checks the authentication information against a database (MySQL, PostgreSQL) to verify the user exists and the password matches.

[1145] Input: Encrypted credentials

[1146] Output: Authentication result

[1147] 1.5 If authentication is successful, the server generates a session ID and sends it to the device, along with HTML and JavaScript code to display the interactive interface.

[1148] Input: Breakdown of successful authentications

[1149] Output: Session ID, interactive interface

[1150] Step 2: Receiving a user request

[1151] 2.1 A user types a request into a conversational interface, such as "What's in the news today?"

[1152] Input: Request text (e.g. "Tell me today's news")

[1153] Output: Request data

[1154] 2.2 The device sends the input request to the server as a JSON format message.

[1155] Input: Request data

[1156] Output: Request message to the server

[1157] Step 3: Parsing the request and generating content

[1158] 3.1 The server sends the received request data to the generative AI model.

[1159] Input: Request message

[1160] Output: Analysis request to the generative AI model

[1161] 3.2 A generative AI model (e.g., GPT-3) analyzes the request and generates appropriate content, e.g., generating the latest news articles in response to the request "What's in the news today?"

[1162] Input: Parsing request (request statement)

[1163] Output: Generated content (e.g., latest news articles)

[1164] 3.3 Return the generated content to the server.

[1165] Input: Generated content (news article)

[1166] Output: Return content to the server

[1167] Step 4: Providing content

[1168] 4.1 The server converts the generated content into HTML or JSON format and sends it to the device.

[1169] Input: Generated content (news article)

[1170] Output: HTML or JSON formatted content

[1171] 4.2 The device receives the content in HTML or JSON format and displays it to the user.

[1172] Input: HTML or JSON formatted content

[1173] Output: The news article displayed to the user

[1174] Step 5: Collect and analyze first-party data

[1175] 5.1 The server collects user access history, behavior history, and purchase history and records them in log files in real time.

[1176] Input: User access history, behavior history, purchase history

[1177] Output: Record data in a log file

[1178] 5.2 Store the collected data in a database (e.g., Amazon RDS, Google BigQuery).

[1179] Input: Collected historical data

[1180] Output: Save data to database

[1181] 5.3 The server analyzes the data and identifies user interests and behavioral patterns using Python's pandas library and scikit-learn.

[1182] Input: Historical data in the database

[1183] Output: Analysis results (user interests and behavioral patterns)

[1184] Step 6: Deliver optimal marketing content

[1185] 6.1 The server selects the most appropriate marketing content for the user based on the analyzed data.

[1186] Input: Analysis results (user interests and behavioral patterns)

[1187] Output: Selected marketing content

[1188] 6.2 The server uses the generated AI model again to generate customized advertising and promotional information.

[1189] Input: Selected Marketing Content

[1190] Output: Generated advertising and promotional information

[1191] 6.3 The server sends the generated marketing content in HTML or JSON format to the terminal, which then provides it to the user.

[1192] Input: Generated advertising and promotion information

[1193] Output: Marketing content that is displayed to the user

[1194] The above is a specific processing flow of the present invention. The present invention makes it possible to provide highly personalized content and effective marketing information while protecting the user's privacy.

[1195] (Application example 1)

[1196] 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."

[1197] In modern e-commerce, providing product recommendations and marketing content that meet the diverse needs of users is important for increasing purchasing motivation. However, conventional systems have had difficulty effectively analyzing users' behavioral and purchase histories to provide optimal product recommendations for each individual. Furthermore, user authentication and content provision were not integrated, which led to problems that impaired the user experience.

[1198] 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.

[1199] In this invention, the server includes means for receiving user input, means for analyzing the received user input, means for using a generative AI model to generate appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, and purchase history, means for analyzing the collected history and selecting optimal marketing content for the user, means for providing the selected marketing content to the user, means for transmitting user authentication information and, if authentication is successful, generating individual product recommendation information, and means for displaying the generated product recommendation information on the user's terminal. This enables individual product recommendations according to the user's needs, improving the user experience and realizing effective marketing.

[1200] "User input" refers to operations or inputs made by a user to send requests or commands to the system.

[1201] A "generative AI model" is an artificial intelligence model that automatically generates appropriate content based on user input or requests.

[1202] A "terminal" is a device that allows a user to access the system and use the interactive interface, including smartphones, tablets, and PCs.

[1203] "Access history" refers to a record of operations a user has performed within the system in the past and pages that the user has visited.

[1204] "Behavioral history" refers to a record of a series of operations and actions performed by a user within the system.

[1205] "Purchase history" refers to a record of products and services a user has purchased in the past.

[1206] "Marketing content" refers to advertising and promotional information provided in response to a user's interests and needs.

[1207] "User authentication information" refers to the authentication information a user uses to log in to a system, and typically includes a username and password.

[1208] "Product recommendation information" refers to information about products and services recommended to a user, which is generated based on the user's access history, behavior history, and purchase history.

[1209] A "smart shopping assistant" is an interactive platform that analyzes a user's behavioral history, purchase history, and access history to provide optimal product recommendations and promotional information.

[1210] This invention aims to build a "smart shopping assistant" system that utilizes a user's behavioral history, purchase history, and access history to provide optimal product recommendations and marketing content. A specific embodiment of the system is described below.

[1211] System Overview

[1212] server

[1213] The server is the central component that receives user requests and generates appropriate content using generative AI models. The server also collects and analyzes users' access history, behavioral history, and purchase history, and provides relevant marketing content to users based on this data.

[1214] Terminal

[1215] A terminal is a device through which a user accesses a system and uses an interactive interface, and generally includes a smartphone, tablet, PC, etc. A user accesses a system through a terminal and performs interactive operations.

[1216] User

[1217] A user is an individual who uses the system to obtain information or use services. The user sends requests to the server through a terminal and receives responses from the server.

[1218] Specific processing flow

[1219] User Access and Authentication

[1220] The user accesses the interactive platform from a terminal and enters login information. The terminal sends the authentication information to the server, which then verifies the authentication information and authenticates the user. If the authentication is successful, the server provides the user with an interactive interface.

[1221] Receiving and analyzing user requests

[1222] Through the conversational interface, users input requests such as "Tell me what products you recommend" or "I'd like to know the latest promotional information." The device sends the user's input to the server, which then uses a generative AI model to analyze the request and generate appropriate content.

[1223] Content generation and delivery

[1224] The generative AI model generates optimal product recommendation information and marketing content based on user input and behavioral, access, and purchase history. The generated content is sent from the server to the device, which then displays it to the user.

[1225] First-party data collection and analysis

[1226] The server continuously collects data such as user access history, behavioral history, and purchase history, and stores it in a back-end database. By analyzing this data, it is possible to create a foundation for providing more accurate content the next time the user accesses the site.

[1227] Hardware and Software Use

[1228] Hardware

[1229] Smartphones, tablets, PCs, smart glasses, head-mounted displays

[1230] software

[1231] Flask (server-side framework)

[1232] Python (server and client side programs)

[1233] API request library (requests)

[1234] Specific examples and prompts for the generative AI model

[1235] Specific examples

[1236] User: "What products do you recommend?"

[1237] The system analyzes a user's past purchase history and access history to generate a list of products relevant to the user. For example, if a user has purchased a lot of sports equipment in the past, it will recommend new sportswear and equipment.

[1238] Prompt Sentence Examples

[1239] "Based on the user's purchase history, please recommend the best products for the following product categories:

[1240] Category: Sporting Goods

[1241] Past purchases: running shoes, fitness tracker, sports t-shirts

[1242] Keywords: latest, trending, recommended"

[1243] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1244] Step 1:

[1245] A user accesses the interactive platform from a terminal and enters login information. The entered information is a username and password. The terminal sends this input information to the server. The server receives it and authenticates the user. If authentication is successful, the server provides the user with an interactive interface. The input is a username and password, and the output is the authentication result.

[1246] Step 2:

[1247] The user inputs a request (for example, "What products do you recommend?") through an interactive interface. The device sends the user's input as is to the server. The server receives it and prepares it for analysis. The input is the user's request, and the output is the unanalyzed request data.

[1248] Step 3:

[1249] The server sends the received request to the generative AI model, which analyzes the request. The generative AI model understands the request and processes and calculates the data to generate appropriate content. For example, if the request is "Tell me what products you recommend," the model uses the user's access history and purchase history to generate relevant product information. The input is unanalyzed request data, and the output is the content data to be generated.

[1250] Step 4:

[1251] The generated content data is sent back to the server. Based on this data, the server generates optimal product recommendation information for the user. At this point, the server accesses the product database and extracts specific product information based on the recommendation results obtained from the generative AI model. The input is the generated content data, and the output is specific product recommendation information.

[1252] Step 5:

[1253] The server sends the generated product recommendation information to the terminal. The terminal receives this information and displays it to the user. For example, a list of recommended products based on the user's purchase history is displayed on the interface. The input is specific product recommendation information, and the output is user interface content for display.

[1254] Step 6:

[1255] The user selects from the recommended products and clicks or taps on the detailed information. The device sends this selection information to the server. The server generates detailed information about the selected product and sends it back to the device. The input is the user's selection information, and the output is detailed product information.

[1256] Step 7:

[1257] When a user wishes to make a purchase, a purchase request is sent from the terminal to the server. The server receives this and initiates the payment process. An electronic payment service API may be used here. The input is the purchase request, and the output is a purchase confirmation and payment information.

[1258] Step 8:

[1259] If the payment is successful, the server sends the purchase confirmation information to the terminal and displays it to the user. The new purchase information is also added to the purchase history database. The input is the payment information, and the output is the purchase confirmation information and the updated purchase history.

[1260] Keywords: generative AI model, prompt sentence

[1261] 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.

[1262] The present invention relates to a system that combines a generative AI model and an emotion engine in an interactive platform for users to provide more personalized content and marketing strategies. Specific embodiments are described below.

[1263] System Overview

[1264] server

[1265] The server receives and analyzes user requests and generates appropriate content using a generative AI model and emotion engine. It also has the ability to collect and analyze user access history, behavioral history, purchase history, and emotion data.

[1266] Terminal

[1267] A terminal is a device through which a user accesses the system, such as a smartphone, tablet, or PC. The user sends requests to the server through the terminal and receives responses from the server.

[1268] User

[1269] A user is an individual who uses the system to obtain information or access services. The user uses a device to send requests to the server and receive generated content and marketing information.

[1270] Implementation details

[1271] 1. User Access and Authentication

[1272] A user accesses the system from a terminal and enters authentication information on the login screen. The terminal sends the authentication information to the server, which then performs the authentication process. If authentication is successful, an interactive interface is presented to the user.

[1273] 2. Receiving and analyzing user requests

[1274] Through the conversational interface, users input requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight." The device sends this input to the server. The server receives the speech and sends it to the generative AI model. The generative AI model analyzes the input and generates appropriate content.

[1275] 3. Emotion Recognition by Emotion Engine

[1276] The server sends the user's input text, voice, facial expressions, etc. to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The recognized emotion data is used by the generative AI model to generate content and select marketing content.

[1277] 4. Content Generation and Delivery

[1278] The generative AI model generates content (e.g., news articles, restaurant listings, etc.) based on user requests and emotion data. The server sends the generated content to the device, which then displays it to the user.

[1279] 5. First-party data collection and analysis

[1280] The server stores the user's access history, behavioral history, purchase history, and emotional data in a back-end database. The server analyzes this data to understand the user's interests.

[1281] 6. Selection and provision of marketing content

[1282] Based on the analysis results, the server selects the most suitable marketing content for the user, taking into account the emotional data recognized by the emotion engine. The generative AI model is then used again to generate customized marketing content (e.g., advertisements and promotional information). The generated marketing content is then sent from the server to the device and provided to the user.

[1283] Specific examples

[1284] News provision example

[1285] The user inputs a request such as "Tell me today's news." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates the latest news articles and returns them to the server. The server sends the generated news articles to the device, which displays them to the user. The emotion engine recognizes the user's emotions and can provide relevant news and advertisements based on the emotions the next time the device is accessed.

[1286] Restaurant reservation example

[1287] The user inputs a request such as "I would like to make a reservation at a Japanese restaurant for tonight." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates a list of candidate Japanese restaurants and returns it to the server. The server provides the candidate list to the user and makes a reservation at the restaurant selected by the user. The emotion engine provides restaurant information that best suits the user's wishes and emotions, thereby achieving a more personalized service.

[1288] In this way, the present invention makes it possible to provide highly personalized content while taking into account the emotional state of the user, thereby improving the user experience and realizing effective marketing.

[1289] The processing flow will be explained below.

[1290] Processing Steps

[1291] Step 1:

[1292] A user opens an app or web browser to access the system and is presented with a login screen.

[1293] Step 2:

[1294] The user enters their user ID and password into the login form.

[1295] Step 3:

[1296] The device sends the authentication information entered by the user to the server.

[1297] Step 4:

[1298] The server receives the authentication information, checks it against a backend database, and authenticates the user.

[1299] Step 5:

[1300] The server sends the authentication result to the terminal, and if authentication is successful, an interactive interface is displayed; if authentication is unsuccessful, the user is prompted to enter authentication information again.

[1301] Step 6:

[1302] Users input requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight" through an interactive interface.

[1303] Step 7:

[1304] The device sends the user's input text to the server.

[1305] Step 8:

[1306] The server receives user input and sends it to the generative AI model and emotion engine.

[1307] Step 9:

[1308] A generative AI model analyzes the user's input text and generates appropriate content (e.g., a news article or restaurant list).

[1309] Step 10:

[1310] The emotion engine recognizes emotions from the user's text, tone of voice, facial expressions, etc., and provides the emotion data to the generative AI model.

[1311] Step 11:

[1312] The generative AI model customizes the content based on the emotional data and generates the final content.

[1313] Step 12:

[1314] The server sends the generated content to the device, which displays it to the user.

[1315] Step 13:

[1316] Users can view news articles and restaurant information displayed on an interactive interface.

[1317] Step 14:

[1318] The server stores user access history, behavioral history, purchase history and emotional data in a back-end database.

[1319] Step 15:

[1320] The server analyzes the stored data to identify users' interests, concerns, and emotional patterns.

[1321] Step 16:

[1322] Based on the analysis results, the server selects the most appropriate marketing content for the user.

[1323] Step 17:

[1324] A generative AI model generates selected marketing content and customizes it taking into account sentiment data.

[1325] Step 18:

[1326] The server sends the generated marketing content to the device, which then provides it to the user.

[1327] Through these steps, users can seamlessly obtain information and enjoy individually customized content and services.

[1328] Example 2

[1329] 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."

[1330] Conventional content delivery systems often provide general information without considering the user's emotional state, which limits their ability to improve user experience and maximize marketing effectiveness. Furthermore, selecting marketing content based solely on a user's access history, behavioral history, and purchase history makes it difficult to deliver content tailored to the user's current emotional state and interests.

[1331] 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.

[1332] In this invention, the server includes means for receiving user input, means for analyzing the received user input, means for utilizing a generative AI model that generates appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, purchase history, and emotional data, means for analyzing the collected history and utilizing an emotional engine that recognizes the user's emotional state, means for selecting optimal marketing content for the user based on the analysis results and the emotional data, and means for providing the selected marketing content to the user. This makes it possible to provide highly personalized content and marketing measures based on the user's emotional state.

[1333] The "means for receiving user input" refers to the means by which the server receives text, voice, and other data input by the user through the terminal.

[1334] "Means for analyzing received user input" refers to the means by which the server decodes the received user input data and properly understands and classifies it.

[1335] "Means for utilizing generative AI models" refers to means for utilizing artificial intelligence models that automatically generate content and information based on analysis results.

[1336] "Means for transmitting generated content to a user's device" refers to means for transmitting content generated by a generative AI model to a device used by a user.

[1337] The "means for collecting user access history, behavior history, purchase history, and emotional data" refers to a means for accumulating user activity records, purchase history, and emotional data inferred from input data.

[1338] The "means for analyzing the collected history and utilizing an emotion engine to recognize the user's emotional state" refers to a means for analyzing the collected data and utilizing an emotion recognition device to identify the user's current emotional state.

[1339] "Means for selecting the most suitable marketing content for a user based on analysis results and emotional data" refers to means for selecting the most suitable advertisements and promotional information for a user based on the results of data analysis and emotional recognition information.

[1340] The "means for providing selected marketing content to a user" refers to a means for displaying and providing selected marketing information on a terminal used by a user.

[1341] The present invention relates to a system that combines a generative AI model and an emotion engine in an interactive platform with users to provide personalized content and marketing strategies. This system includes a series of processes that receive user input, analyze it, generate appropriate content, and provide it to the user.

[1342] server

[1343] The server is configured using the following software and hardware. It receives and analyzes user requests using a generative AI model and emotion engine, and generates appropriate content. It also has the ability to collect and analyze user access history, behavioral history, purchase history, and emotion data.

[1344] 1. A parsing module for parsing received user input.

[1345] 2. Generative AI model that generates content

[1346] 3. Emotion engine for recognizing the user's emotional state

[1347] 4. Back-end database for data management and analysis

[1348] Specific software examples include natural language processing libraries (e.g., NLTK), machine learning frameworks (e.g., TensorFlow), and database management systems (e.g., MySQL).

[1349] Terminal

[1350] A terminal is a device that a user uses to access the system and can take various forms (smartphone, tablet, PC, etc.). The user sends requests to the server through the terminal and receives responses from the server.

[1351] 1. An input interface for receiving user input

[1352] 2. A user interface that displays the content sent from the server

[1353] Specific hardware examples include Apple's iPhone and Samsung's Galaxy series. Software uses web browsers (e.g., Google Chrome, Safari) and dedicated applications (e.g., iOS and Android apps).

[1354] User

[1355] A user is an individual who uses the system to obtain information or use services. A user uses the system in the following steps:

[1356] 1. Access the system using a terminal and enter your authentication information on the login screen.

[1357] 2. After successful authentication, you enter your request through the conversational interface (e.g., "Tell me today's news" or "I'd like to make a restaurant reservation for tonight").

[1358] 3. View the content sent from the server.

[1359] Specific Examples

[1360] News provision example

[1361] The user inputs a request such as "Tell me today's news." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates the latest news articles and returns them to the server. The server sends the generated news articles to the device, which displays them to the user. The emotion engine recognizes the user's emotions and can provide relevant news and advertisements based on the emotions the next time the device is accessed.

[1362] Restaurant reservation example

[1363] The user inputs a request such as "I would like to make a reservation at a Japanese restaurant for tonight." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates a list of candidate Japanese restaurants and returns it to the server. The server provides the candidate list to the user and makes a reservation at the restaurant selected by the user. The emotion engine provides restaurant information that best suits the user's wishes and emotions, thereby achieving a more personalized service.

[1364] In this way, the present invention makes it possible to provide highly personalized content while taking into account the emotional state of the user, thereby improving the user experience and realizing effective marketing.

[1365] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1366] Step 1:

[1367] User Access and Authentication

[1368] A user accesses the system from a terminal and enters their ID and password on the login screen. The terminal sends the entered authentication information to the server. The server receives the authentication information and checks it against a back-end database. If authentication is successful, the server returns an interactive interface to the user.

[1369] Input: User ID and password

[1370] Output: An interactive interface is displayed, and a message confirming successful user authentication is displayed.

[1371] Specifically, the user operates their smartphone to enter their ID and password into the login form and tap the submit button. The server then accesses the database and checks whether the ID and password match.

[1372] Step 2:

[1373] Receiving and analyzing user requests

[1374] The user inputs a prompt (e.g., "Tell me today's news") through a conversational interface. The device sends this prompt to the server. The server then forwards the request to the generative AI model, which analyzes the text data. The generative AI model understands the request and generates an analysis result to be passed on to the next step.

[1375] Input: User prompt text

[1376] Output: Analysis results from the generative AI model

[1377] Specifically, when a user types "I'd like to make a restaurant reservation for tonight" into the tablet interface and presses the send button, the device transfers the text to the server, which then passes it to the generative AI model to begin analyzing it.

[1378] Step 3:

[1379] Emotion recognition by emotion engine

[1380] The server sends the user's input text and voice data to the emotion engine, which analyzes this data and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The server then sends the recognized emotion data to the generative AI model, which uses it as a reference for content generation.

[1381] Input: User-entered text and voice data

[1382] Output: Emotion data from the emotion engine

[1383] As a specific example of operation, a user inputs a voice message such as "I've been feeling down lately" into the terminal, and the terminal sends the voice message to the server. The server passes the voice data to the emotion engine and recognizes the user's emotional state.

[1384] Step 4:

[1385] Content generation and delivery

[1386] The generative AI model generates content (e.g., news articles or restaurant listings) based on user requests and emotion data. The server sends the generated content to the device, which then displays it to the user.

[1387] Input: User request and emotion data

[1388] Output: Generated content (news articles, restaurant listings, etc.)

[1389] Specifically, the server generates a news article based on the prompt "Tell me today's news" and the emotional state "I'm depressed," and transfers the generated article to the terminal, which then displays the news article on the user's screen.

[1390] Step 5:

[1391] First-party data collection and analysis

[1392] The server stores the user's access history, behavioral history, purchase history, and emotional data in a backend database. The server periodically analyzes this data to understand the user's interests.

[1393] Input: User access history, behavioral history, purchase history, emotional data

[1394] Output: Analysis results that reflect the user's interests

[1395] As a specific example of operation, the news categories that users frequently browse and the product purchase history are recorded in a database, and the user's preferences are identified by periodically analyzing this data.

[1396] Step 6:

[1397] Selection and provision of marketing content

[1398] Based on the analysis results, the server selects the most suitable marketing content for the user, taking into account the emotional data recognized by the emotion engine. The generative AI model is used again to generate customized marketing content. The server then sends the generated marketing content to the device, which then provides it to the user.

[1399] Input: Analysis results and emotion data

[1400] Output: Marketing content delivered to the user

[1401] Specifically, the server generates an advertisement for a healthy food product that will cheer you up based on the user's purchasing history and emotional state (e.g., "I feel low"), and sends it to the device to display. The user sees the advertisement and is encouraged to purchase the product.

[1402] (Application example 2)

[1403] 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."

[1404] While conventional content delivery systems can provide information in response to user requests, they lack personalized content and marketing strategies that reflect the user's emotional state, which results in poor user experience and in ineffective marketing. Therefore, there is a need for a system that can provide highly personalized content and marketing strategies that take the user's emotional state into account.

[1405] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user input, means for analyzing the received user input, means for utilizing a generative AI model that generates appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, and purchase history, means for analyzing the collected history and selecting optimal marketing content for the user, means for providing the selected marketing content to the user, means for utilizing an emotion engine that analyzes the user's emotional state, and means for customizing content using the generative AI model based on the user's emotional state analyzed using the emotion engine. This enables the provision of highly personalized content that reflects the user's emotional state and effective marketing.

[1406] A "means for receiving user input" is a device or software that receives text or voice requests, commands, etc., made by a user to the system.

[1407] "Means for parsing received user input" is software or algorithms that understand the input data received from the user and take appropriate action based on it.

[1408] "Means for utilizing a generative AI model to generate appropriate content" refers to devices or software that utilize a generative AI model to generate content such as text, images, and videos to be provided to users based on the analysis results.

[1409] The "means for transmitting the generated content to the user's terminal" refers to a communication device or software for transferring the generated content to a terminal such as a smartphone or a personal computer used by the user.

[1410] "Means for collecting user access history, behavioral history, and purchase history" refers to devices or software that collect data such as a user's past website browsing history, action logs, and purchase records.

[1411] "Means for analyzing collected history and selecting optimal marketing content for the user" refers to devices or software that analyze collected history data and select optimal advertising and promotional information for the user based on that data.

[1412] The "means for providing the selected marketing content to the user" refers to a communication device or software for displaying the selected marketing content on the user's terminal.

[1413] "Means for utilizing an emotion engine to analyze the user's emotional state" refers to a device or software that utilizes an emotion engine to analyze the user's facial expressions, voice, text, etc., and determine their emotional state.

[1414] "Means for customizing content using a generative AI model based on the emotional state of a user analyzed using an emotion engine" refers to devices or software that customize content so that the generative AI model generates optimal content for the user based on the analyzed emotional state.

[1415] This invention is a system that realizes highly personalized content provision and marketing measures that take into account the emotional state of the user. This system consists of three main components: a server, a terminal, and a user.

[1416] server

[1417] The server has the following functions:

[1418] 1. A means of receiving user input

[1419] The server receives user requests (e.g., text or voice requests) from the device, allowing the server to understand the information or action the user is seeking.

[1420] 2. A means of parsing received user input

[1421] The server uses natural language processing (NLP) algorithms to analyze the user input it receives, allowing it to clearly understand the user's intent and request. For example, when analyzing the request "Looking for a new dress," it understands that the user wants to buy a dress.

[1422] 3. Using generative AI models to generate relevant content

[1423] The server then uses a generative AI model based on the analysis results to generate appropriate content. Specific generative AI models used here include OpenAI GPT-3. For example, if a user is looking for a new dress, the server generates the latest fashion trends and a list of recommended products.

[1424] 4. Means for sending generated content to the user's device

[1425] The server then sends the generated content to users' devices such as smartphones, tablets, and personal computers, allowing users to obtain information in real time.

[1426] 5. Means of collecting user access history, behavior history, and purchase history

[1427] The server collects and centrally manages data such as the user's past website browsing history, action logs, and purchase records.

[1428] 6. A means of analyzing collected history and selecting the most appropriate marketing content for the user

[1429] The server analyzes the collected data and selects marketing content (e.g., advertisements and promotional information) that matches the user's interests.

[1430] 7. How we deliver selected marketing content to you

[1431] The server transmits the selected marketing content to the user's terminal and presents it to the user.

[1432] 8. Using an emotion engine to analyze the user's emotional state

[1433] The server uses an emotion engine (specifically, the Affectiva SDK) to analyze the user's emotional state from facial expressions, voice, text, etc.

[1434] 9. A means of customizing content using generative AI models based on the user's emotional state analyzed using an emotion engine.

[1435] The server customizes the generative AI model to generate optimal content for the user based on the emotional state analyzed by the emotion engine. This provides content that matches the user's emotional state, providing a more personalized experience. Specifically, if the user is in a relaxed state, the server will suggest products suitable for relaxation.

[1436] Terminal

[1437] A terminal is a device that users use to access the system, and includes smartphones, tablets, PCs, etc. Terminals have the following functions:

[1438] Sending a request: The user uses the terminal to send a request to the server.

[1439] Display Content: Displaying content and marketing information sent from the server to the user.

[1440] User

[1441] A user is an individual who uses the system to obtain information or access services. The user uses a terminal to send requests to the server and receive generated content and marketing information.

[1442] Examples of prompt statements

[1443] text

[1444] User Request: Looking for a new dress

[1445] User's emotional state: joy

[1446] Generate the right content.

[1447] By implementing this invention, it is possible to provide highly personalized content that reflects the user's emotional state and to implement effective marketing. This system can improve user satisfaction and improve the effectiveness of corporate marketing.

[1448] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1449] Step 1:

[1450] User Authentication

[1451] The terminal receives login information (username and password) as input from the user. The terminal sends this input data to the server, which then authenticates the user. The server checks the user information against a database, and if authentication is successful, it generates an authentication token and returns it to the terminal. This allows the user to access the system.

[1452] Input: Username, Password

[1453] Output: Authentication token

[1454] Step 2:

[1455] Receiving and analyzing user requests

[1456] A user uses a device to input a request, for example, text such as "I'm looking for a new dress." The device sends this request to a server, which receives the input. The server uses natural language processing algorithms to analyze the request and understand the user's intent.

[1457] Input: User request (text)

[1458] Output: Analysis result (user intent)

[1459] Step 3:

[1460] User emotional state analysis

[1461] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends them to the emotion engine in real time. The server receives this input data and uses the emotion engine to analyze the user's emotional state (happiness, surprise, sadness, etc.). The analyzed emotion data is saved along with the analysis results of the user's request.

[1462] Input: User's facial expression and voice data

[1463] Output: Emotional state (user's emotion)

[1464] Step 4:

[1465] Content generation using generative AI models

[1466] The server sends the user's request analysis results and emotional state to the generative AI model. The prompt is formatted as follows: "User request: Looking for a new dress\nUser's emotional state: Joy\nPlease generate appropriate content." The generative AI model analyzes the input prompt and generates appropriate content (e.g., the latest fashion information or recommended dresses). The generated content is returned to the server.

[1467] Input: Request analysis results, emotional state

[1468] Output: Generated content (text, images)

[1469] Step 5:

[1470] Content provision

[1471] The server transmits the generated content to the terminal, which then displays it to the user, allowing the user to receive the required information in real time.

[1472] Input: Generated content

[1473] Output: Content displayed on the user's device

[1474] Step 6:

[1475] Historical Data Collection and Analysis

[1476] Every time a user views, clicks, or purchases content, that data is sent from the device to the server. The server stores this access history, behavioral history, and purchase history in a database and periodically analyzes it. Based on the analysis results, the user's interests can be identified.

[1477] Input: access history, behavior history, purchase history

[1478] Output: Analysis results (user interests)

[1479] Step 7:

[1480] Personalized Marketing

[1481] The server uses the analysis results and emotional data to generate optimal marketing content for the user. It also uses the generative AI model to create customized advertisements and promotional information based on the user's interests and emotional state. The generated marketing content is then sent to the device and provided to the user.

[1482] Input: Analysis results, emotion data

[1483] Output: Marketing content (advertising, promotional information)

[1484] This allows users to receive content and marketing information that reflects their emotional state, enabling companies to achieve highly personalized marketing.

[1485] 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.

[1486] 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.

[1487] 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.

[1488] [Fourth embodiment]

[1489] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1490] 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.

[1491] 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).

[1492] 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.

[1493] 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.

[1494] 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).

[1495] 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.

[1496] 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.

[1497] 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.

[1498] 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.

[1499] 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.

[1500] 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.

[1501] 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."

[1502] The present invention provides an interactive platform that utilizes generative AI models through interactions between servers, terminals, and users. This platform collects and analyzes first-party data such as user behavioral history, purchase history, and access history, and provides optimal marketing content.

[1503] System Overview

[1504] server

[1505] The server is the main component that receives user requests and generates appropriate content for the requests using generative AI models. The server also collects user access history, behavioral history, and purchase history, and analyzes this data to provide relevant marketing content to users.

[1506] Terminal

[1507] A terminal is a device through which a user accesses the system, and is typically a smartphone, tablet, PC, etc. A user accesses the system through a terminal and inputs a request through an interactive interface.

[1508] User

[1509] A user is an individual who utilizes the system to obtain information or use services. The user uses a terminal to send requests to the server and receive responses from the server.

[1510] Implementation details

[1511] 1. User Access and Authentication

[1512] The user accesses the interactive platform from a terminal and enters authentication information on the login screen. The terminal sends this information to the server, which then verifies the authentication information and authenticates the user. If authentication is successful, the server provides the user with an interactive interface.

[1513] 2. Receiving a User Request

[1514] Through the interactive interface, the user inputs requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight." The terminal sends the user's input to the server.

[1515] 3. Request Parsing and Content Generation

[1516] The server sends the received request to the generative AI model, which analyzes the request and generates appropriate content. For example, in response to the request "Tell me today's news," the model generates the latest news articles and returns them to the server.

[1517] 4. Provision of Content

[1518] The server sends the generated content to the device, which then displays it to the user, who can then check the latest news articles or restaurant information on an interactive interface.

[1519] 5. First-party data collection and analysis

[1520] The server collects the user's access history, behavioral history, and purchase history and stores them in a back-end database, which provides the basis for analyzing the user's interests and behavioral patterns and generating relevant marketing content for the next visit.

[1521] 6. Providing optimal marketing content

[1522] Based on the analyzed data, the server selects the most suitable marketing content for the user. Using the generative AI model again, it generates customized advertisements and promotional information and provides it to the user. For example, if the user is interested in Japanese restaurants, it can provide promotional information for related restaurants.

[1523] Specific examples

[1524] News provision example

[1525] The user types, "Tell me today's news." The device sends this request to the server. The server then sends a request to the generative AI model to generate the latest news articles. The generated news articles are then sent to the device and displayed to the user.

[1526] Restaurant reservation example

[1527] The user types, "I'd like to make a reservation at a Japanese restaurant for tonight." The device sends the request to the server, which then has the generative AI model analyze the request. The generative AI model generates a list of candidate Japanese restaurants and returns it to the server. The server sends this list to the user, and the system makes a reservation at the restaurant selected by the user.

[1528] The present invention makes it possible to achieve a high user experience and effective marketing while protecting user privacy.

[1529] The processing flow will be explained below.

[1530] Processing Steps

[1531] Step 1:

[1532] A user opens an app or web browser to access the system, and a login form appears on the screen.

[1533] Step 2:

[1534] The user enters their user ID and password into the login form.

[1535] Step 3:

[1536] The terminal sends the authentication information entered by the user to the server.

[1537] Step 4:

[1538] The server checks the received authentication information against a backend database and authenticates the user.

[1539] Step 5:

[1540] The server sends the authentication result to the terminal, and if authentication is successful, an interactive interface is displayed to the user. If authentication is unsuccessful, a message is displayed prompting the user to re-enter authentication information.

[1541] Step 6:

[1542] A user inputs requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight" through an interactive interface.

[1543] Step 7:

[1544] The terminal sends the user's input text to the server.

[1545] Step 8:

[1546] The server receives user input and sends it to the generative AI model, performing any preprocessing (such as tokenization and filtering) required for analysis.

[1547] Step 9:

[1548] A generative AI model analyzes the user's input text and generates appropriate content (e.g., a news article or restaurant list).

[1549] Step 10:

[1550] The generative AI model returns the generated content to the server, which contains the information requested by the user.

[1551] Step 11:

[1552] The server formats the generated content and sends it to the terminal for transmission back to the user.

[1553] Step 12:

[1554] The device receives the content from the server and displays it to the user, such as news articles or restaurant information on an interactive interface.

[1555] Step 13:

[1556] The server stores the user's access history, behavioral history, and purchase history in a back-end database, which allows the server to understand the user's interests.

[1557] Step 14:

[1558] The server analyzes the collected historical data to identify the user's interests, for example, which news categories the user frequently visits or the types of restaurants they make reservations at.

[1559] Step 15:

[1560] Based on the analysis results, the server selects the most suitable marketing content for the user (for example, advertisements for related products and promotional information).

[1561] Step 16:

[1562] The server then uses the generative AI model again to generate customized marketing content.

[1563] Step 17:

[1564] The generative AI model generates customized marketing content and returns it to the server.

[1565] Step 18:

[1566] The server transmits the generated marketing content to the user's terminal.

[1567] Step 19:

[1568] The terminal displays the marketing content received from the server to the user.

[1569] Through these steps, users can seamlessly obtain information and enjoy individually customized content and services.

[1570] Example 1

[1571] 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."

[1572] Conventional interactive platforms could only provide simple responses to user requests, making it difficult to effectively provide personalized content or marketing information. They also faced security issues and lacked the technology to properly utilize user behavioral and purchase histories. As a result, the user experience was poor and marketing effectiveness was limited.

[1573] 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.

[1574] In this invention, the server includes means for receiving user input, means for analyzing the received user input, means for utilizing a generative AI model that generates appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, and purchase history, means for analyzing the collected history and selecting optimal marketing content for the user, means for providing the selected marketing content to the user, means for encrypting the user input using SSL / TLS and transmitting it to the server, means for the server to generate a session ID and transmit it to the user's terminal, means for the generative AI model to include a function for performing natural language processing, means for providing an interactive interface to authenticated users, means for transmitting the generated content to the terminal in HTML or JSON format, and means for the server to access a database and verify authentication information. This enables highly personalized content and marketing information to be provided safely and effectively in response to user requests.

[1575] The "means for receiving user input" is a function for receiving data that a user inputs to the interactive platform through a terminal.

[1576] The "means for analyzing received user input" is a function for analyzing received user input data and understanding its contents.

[1577] "Means for utilizing a generative AI model" refers to a function that utilizes an artificial intelligence model to generate appropriate content based on the analysis results.

[1578] "Means for sending generated content to a user's device" refers to a function for sending content created by a generative AI model to a user's device.

[1579] "Means for collecting user access history, behavior history, and purchase history" refers to a function for obtaining and storing records of users' online activities.

[1580] "Means of analyzing collected history and selecting the most suitable marketing content for the user" is a function that analyzes the user's interests and behavioral patterns based on collected data and selects the most suitable marketing content based on that.

[1581] The "means for providing selected marketing content to the user" is a function for presenting selected appropriate marketing content to the user.

[1582] "Means of encrypting user input using SSL / TLS and sending it to the server" is a function that encrypts data using SSL / TLS technology in order to securely send data entered by the user to the server.

[1583] "Means for the server to generate a session ID and send it to the user's terminal" is a function for managing a communication session by passing the session ID generated by the server to the user's terminal.

[1584] "Means for the generative AI model to include the ability to perform natural language processing" refers to the ability of the generative AI model to use natural language processing techniques to analyze and understand text data.

[1585] The "means for providing an interactive interface to an authenticated user" is a function for displaying an interactive user interface to a user who has been successfully authenticated.

[1586] "Means for sending generated content to a terminal in HTML or JSON format" is a function for sending generated content to a user's terminal in HTML or JSON format.

[1587] The "means for the server to access the database and confirm the authentication information" is a function that allows the server to refer to the database and confirm the validity of the user's authentication information.

[1588] The present invention provides an interactive platform that utilizes generative AI models through the interaction of a server, terminals, and users. This platform collects and analyzes data such as user behavioral history, purchase history, and access history to provide optimal marketing content.

[1589] server

[1590] The server is the main component that receives requests from users and generates appropriate content in response to the requests using a generative AI model. The server also collects and analyzes users' access history, behavioral history, and purchase history to provide relevant marketing content to users. Servers are built using programming languages ​​such as Python or Java and utilize machine learning frameworks such as TensorFlow or PyTorch. Furthermore, MySQL or PostgreSQL is often used as the database. Common cloud services include Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[1591] Terminal

[1592] A terminal is a device through which a user accesses a system, and examples include smartphones, tablets, and PCs. The terminal communicates with the server via a web browser or dedicated application, sending the user's requests to the server. The terminal receives responses from the server and displays them to the user. Operating systems such as iOS and Android are commonly used on these terminals.

[1593] User

[1594] A user is an individual who uses the platform to obtain information or use services. The user inputs a request through the interactive interface and sends it to the server via their device. The user receives a response from the server on their device and checks the displayed information.

[1595] Content generation and delivery

[1596] Authentication Process

[1597] A user accesses the interactive platform from a terminal and enters authentication information (email address and password) on the login screen. The entered authentication information is encrypted with SSL / TLS and sent to the server using the HTTPS protocol. The server checks the authentication information by referencing a database, and if authentication is successful, generates a session ID and sends it to the user's terminal. An interactive interface is displayed, allowing the user to continue entering requests.

[1598] Receiving a user request

[1599] The user inputs a request into the interactive interface, such as "Tell me today's news" or "I want to make a restaurant reservation for tonight." The device sends this request in JSON format to the server, and communication is again carried out using the HTTPS protocol.

[1600] Request analysis and content generation using AI models

[1601] The server receives the request and sends it to a generative AI model for analysis. The generative AI model (e.g., GPT-3) analyzes the request and generates appropriate content. For example, in response to the request "Tell me today's news," it generates the latest news articles and returns them to the server.

[1602] Content provision

[1603] The content returned by the generative AI model is converted into HTML or JSON format by the server and sent to the device, which displays it to the user, who can then check the latest news articles or restaurant information through the device.

[1604] Data collection and analysis

[1605] First-party data collection

[1606] When a user uses the interactive platform, the server collects data such as their access history, behavioral history, and purchase history. The collected data is recorded in real time in a log file and stored in a database (for example, Amazon RDS or Google BigQuery).

[1607] Analyzing data and providing marketing content

[1608] The server uses data analysis tools such as Python's pandas library and scikit-learn to analyze the collected data. This identifies the user's interests and behavioral patterns, laying the groundwork for providing appropriate marketing content the next time the user visits. Based on the analysis results, the server selects the most appropriate marketing content for the user and again uses the generative AI model to generate customized advertising and promotional information. This content is sent to the user's device in HTML or JSON format and displayed on an interactive interface.

[1609] Specific examples

[1610] News provision example

[1611] When a user types "Tell me today's news," the device sends this request to the server, which then sends a request to the generative AI model to generate the latest news articles, which are then sent from the server to the device and displayed to the user.

[1612] Example prompt sentence:

[1613] "Tell me the news today"

[1614] Restaurant reservation example

[1615] If a user types, "I'd like to make a reservation at a Japanese restaurant for tonight," the device sends the request to the server. The server analyzes the request using a generative AI model and generates a list of Japanese restaurants. The generated list is sent from the server to the device and displayed to the user. The user then selects the desired restaurant from the list and finally makes the reservation.

[1616] Example prompt sentence:

[1617] "I'd like to make a reservation at a Japanese restaurant for tonight."

[1618] As described above, the embodiment of the present invention realizes a highly personalized user experience and effective marketing while protecting the user's privacy.

[1619] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1620] Step 1: User Access and Authentication

[1621] 1.1 A user accesses the interactive platform from a device (smartphone, tablet, PC), and the device connects to the platform URL using the HTTPS protocol.

[1622] Input: User access request

[1623] Output:Login screen

[1624] 1.2 The user enters their email address and password on the login screen.

[1625] Input: Email address, password

[1626] Output: Credentials

[1627] 1.3 The device encrypts the authentication information using SSL / TLS and sends it to the server.

[1628] Input: Encrypted credentials

[1629] Output: Authentication request to the server

[1630] 1.4 The server checks the authentication information against a database (MySQL, PostgreSQL) to verify the user exists and the password matches.

[1631] Input: Encrypted credentials

[1632] Output: Authentication result

[1633] 1.5 If authentication is successful, the server generates a session ID and sends it to the device, along with HTML and JavaScript code to display the interactive interface.

[1634] Input: Breakdown of successful authentications

[1635] Output: Session ID, interactive interface

[1636] Step 2: Receiving a user request

[1637] 2.1 A user types a request into a conversational interface, such as "What's in the news today?"

[1638] Input: Request text (e.g. "Tell me today's news")

[1639] Output: Request data

[1640] 2.2 The device sends the input request to the server as a JSON format message.

[1641] Input: Request data

[1642] Output: Request message to the server

[1643] Step 3: Parsing the request and generating content

[1644] 3.1 The server sends the received request data to the generative AI model.

[1645] Input: Request message

[1646] Output: Analysis request to the generative AI model

[1647] 3.2 A generative AI model (e.g., GPT-3) analyzes the request and generates appropriate content, e.g., generating the latest news articles in response to the request "What's in the news today?"

[1648] Input: Parsing request (request statement)

[1649] Output: Generated content (e.g., latest news articles)

[1650] 3.3 Return the generated content to the server.

[1651] Input: Generated content (news article)

[1652] Output: Return content to the server

[1653] Step 4: Providing content

[1654] 4.1 The server converts the generated content into HTML or JSON format and sends it to the device.

[1655] Input: Generated content (news article)

[1656] Output: HTML or JSON formatted content

[1657] 4.2 The device receives the content in HTML or JSON format and displays it to the user.

[1658] Input: HTML or JSON formatted content

[1659] Output: The news article displayed to the user

[1660] Step 5: Collect and analyze first-party data

[1661] 5.1 The server collects user access history, behavior history, and purchase history and records them in log files in real time.

[1662] Input: User access history, behavior history, purchase history

[1663] Output: Record data in a log file

[1664] 5.2 Store the collected data in a database (e.g., Amazon RDS, Google BigQuery).

[1665] Input: Collected historical data

[1666] Output: Save data to database

[1667] 5.3 The server analyzes the data and identifies user interests and behavioral patterns using Python's pandas library and scikit-learn.

[1668] Input: Historical data in the database

[1669] Output: Analysis results (user interests and behavioral patterns)

[1670] Step 6: Deliver optimal marketing content

[1671] 6.1 The server selects the most appropriate marketing content for the user based on the analyzed data.

[1672] Input: Analysis results (user interests and behavioral patterns)

[1673] Output: Selected marketing content

[1674] 6.2 The server uses the generated AI model again to generate customized advertising and promotional information.

[1675] Input: Selected Marketing Content

[1676] Output: Generated advertising and promotional information

[1677] 6.3 The server sends the generated marketing content in HTML or JSON format to the terminal, which then provides it to the user.

[1678] Input: Generated advertising and promotion information

[1679] Output: Marketing content that is displayed to the user

[1680] The above is a specific processing flow of the present invention. The present invention makes it possible to provide highly personalized content and effective marketing information while protecting the user's privacy.

[1681] (Application example 1)

[1682] 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."

[1683] In modern e-commerce, providing product recommendations and marketing content that meet the diverse needs of users is important for increasing purchasing motivation. However, conventional systems have had difficulty effectively analyzing users' behavioral and purchase histories to provide optimal product recommendations for each individual. Furthermore, user authentication and content provision were not integrated, which led to problems that impaired the user experience.

[1684] 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.

[1685] In this invention, the server includes means for receiving user input, means for analyzing the received user input, means for using a generative AI model to generate appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, and purchase history, means for analyzing the collected history and selecting optimal marketing content for the user, means for providing the selected marketing content to the user, means for transmitting user authentication information and, if authentication is successful, generating individual product recommendation information, and means for displaying the generated product recommendation information on the user's terminal. This enables individual product recommendations according to the user's needs, improving the user experience and realizing effective marketing.

[1686] "User input" refers to operations or inputs made by a user to send requests or commands to the system.

[1687] A "generative AI model" is an artificial intelligence model that automatically generates appropriate content based on user input or requests.

[1688] A "terminal" is a device that allows a user to access the system and use the interactive interface, including smartphones, tablets, and PCs.

[1689] "Access history" refers to a record of operations a user has performed within the system in the past and pages that the user has visited.

[1690] "Behavioral history" refers to a record of a series of operations and actions performed by a user within the system.

[1691] "Purchase history" refers to a record of products and services a user has purchased in the past.

[1692] "Marketing content" refers to advertising and promotional information provided in response to a user's interests and needs.

[1693] "User authentication information" refers to the authentication information a user uses to log in to a system, and typically includes a username and password.

[1694] "Product recommendation information" refers to information about products and services recommended to a user, which is generated based on the user's access history, behavior history, and purchase history.

[1695] A "smart shopping assistant" is an interactive platform that analyzes a user's behavioral history, purchase history, and access history to provide optimal product recommendations and promotional information.

[1696] This invention aims to build a "smart shopping assistant" system that utilizes a user's behavioral history, purchase history, and access history to provide optimal product recommendations and marketing content. A specific embodiment of the system is described below.

[1697] System Overview

[1698] server

[1699] The server is the central component that receives user requests and generates appropriate content using generative AI models. The server also collects and analyzes users' access history, behavioral history, and purchase history, and provides relevant marketing content to users based on this data.

[1700] Terminal

[1701] A terminal is a device through which a user accesses a system and uses an interactive interface, and generally includes a smartphone, tablet, PC, etc. A user accesses a system through a terminal and performs interactive operations.

[1702] User

[1703] A user is an individual who uses the system to obtain information or use services. The user sends requests to the server through a terminal and receives responses from the server.

[1704] Specific processing flow

[1705] User Access and Authentication

[1706] The user accesses the interactive platform from a terminal and enters login information. The terminal sends the authentication information to the server, which then verifies the authentication information and authenticates the user. If the authentication is successful, the server provides the user with an interactive interface.

[1707] Receiving and analyzing user requests

[1708] Through the conversational interface, users input requests such as "Tell me what products you recommend" or "I'd like to know the latest promotional information." The device sends the user's input to the server, which then uses a generative AI model to analyze the request and generate appropriate content.

[1709] Content generation and delivery

[1710] The generative AI model generates optimal product recommendation information and marketing content based on user input and behavioral, access, and purchase history. The generated content is sent from the server to the device, which then displays it to the user.

[1711] First-party data collection and analysis

[1712] The server continuously collects data such as user access history, behavioral history, and purchase history, and stores it in a back-end database. By analyzing this data, it is possible to create a foundation for providing more accurate content the next time the user accesses the site.

[1713] Hardware and Software Use

[1714] Hardware

[1715] Smartphones, tablets, PCs, smart glasses, head-mounted displays

[1716] software

[1717] Flask (server-side framework)

[1718] Python (server and client side programs)

[1719] API request library (requests)

[1720] Specific examples and prompts for the generative AI model

[1721] Specific examples

[1722] User: "What products do you recommend?"

[1723] The system analyzes a user's past purchase history and access history to generate a list of products relevant to the user. For example, if a user has purchased a lot of sports equipment in the past, it will recommend new sportswear and equipment.

[1724] Prompt Sentence Examples

[1725] "Based on the user's purchase history, please recommend the best products for the following product categories:

[1726] Category: Sporting Goods

[1727] Past purchases: running shoes, fitness tracker, sports t-shirts

[1728] Keywords: latest, trending, recommended"

[1729] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1730] Step 1:

[1731] A user accesses the interactive platform from a terminal and enters login information. The entered information is a username and password. The terminal sends this input information to the server. The server receives it and authenticates the user. If authentication is successful, the server provides the user with an interactive interface. The input is a username and password, and the output is the authentication result.

[1732] Step 2:

[1733] The user inputs a request (for example, "What products do you recommend?") through an interactive interface. The device sends the user's input as is to the server. The server receives it and prepares it for analysis. The input is the user's request, and the output is the unanalyzed request data.

[1734] Step 3:

[1735] The server sends the received request to the generative AI model, which analyzes the request. The generative AI model understands the request and processes and calculates the data to generate appropriate content. For example, if the request is "Tell me what products you recommend," the model uses the user's access history and purchase history to generate relevant product information. The input is unanalyzed request data, and the output is the content data to be generated.

[1736] Step 4:

[1737] The generated content data is sent back to the server. Based on this data, the server generates optimal product recommendation information for the user. At this point, the server accesses the product database and extracts specific product information based on the recommendation results obtained from the generative AI model. The input is the generated content data, and the output is specific product recommendation information.

[1738] Step 5:

[1739] The server sends the generated product recommendation information to the terminal. The terminal receives this information and displays it to the user. For example, a list of recommended products based on the user's purchase history is displayed on the interface. The input is specific product recommendation information, and the output is user interface content for display.

[1740] Step 6:

[1741] The user selects from the recommended products and clicks or taps on the detailed information. The device sends this selection information to the server. The server generates detailed information about the selected product and sends it back to the device. The input is the user's selection information, and the output is detailed product information.

[1742] Step 7:

[1743] When a user wishes to make a purchase, a purchase request is sent from the terminal to the server. The server receives this and initiates the payment process. An electronic payment service API may be used here. The input is the purchase request, and the output is a purchase confirmation and payment information.

[1744] Step 8:

[1745] If the payment is successful, the server sends the purchase confirmation information to the terminal and displays it to the user. The new purchase information is also added to the purchase history database. The input is the payment information, and the output is the purchase confirmation information and the updated purchase history.

[1746] Keywords: generative AI model, prompt sentence

[1747] 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.

[1748] The present invention relates to a system that combines a generative AI model and an emotion engine in an interactive platform for users to provide more personalized content and marketing strategies. Specific embodiments are described below.

[1749] System Overview

[1750] server

[1751] The server receives and analyzes user requests and generates appropriate content using a generative AI model and emotion engine. It also has the ability to collect and analyze user access history, behavioral history, purchase history, and emotion data.

[1752] Terminal

[1753] A terminal is a device through which a user accesses the system, such as a smartphone, tablet, or PC. The user sends requests to the server through the terminal and receives responses from the server.

[1754] User

[1755] A user is an individual who uses the system to obtain information or access services. The user uses a device to send requests to the server and receive generated content and marketing information.

[1756] Implementation details

[1757] 1. User Access and Authentication

[1758] A user accesses the system from a terminal and enters authentication information on the login screen. The terminal sends the authentication information to the server, which then performs the authentication process. If authentication is successful, an interactive interface is presented to the user.

[1759] 2. Receiving and analyzing user requests

[1760] Through the conversational interface, users input requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight." The device sends this input to the server. The server receives the speech and sends it to the generative AI model. The generative AI model analyzes the input and generates appropriate content.

[1761] 3. Emotion Recognition by Emotion Engine

[1762] The server sends the user's input text, voice, facial expressions, etc. to the emotion engine. The emotion engine analyzes this data and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The recognized emotion data is used by the generative AI model to generate content and select marketing content.

[1763] 4. Content Generation and Delivery

[1764] The generative AI model generates content (e.g., news articles, restaurant listings, etc.) based on user requests and emotion data. The server sends the generated content to the device, which then displays it to the user.

[1765] 5. First-party data collection and analysis

[1766] The server stores the user's access history, behavioral history, purchase history, and emotional data in a back-end database. The server analyzes this data to understand the user's interests.

[1767] 6. Selection and provision of marketing content

[1768] Based on the analysis results, the server selects the most suitable marketing content for the user, taking into account the emotional data recognized by the emotion engine. The generative AI model is then used again to generate customized marketing content (e.g., advertisements and promotional information). The generated marketing content is then sent from the server to the device and provided to the user.

[1769] Specific examples

[1770] News provision example

[1771] The user inputs a request such as "Tell me today's news." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates the latest news articles and returns them to the server. The server sends the generated news articles to the device, which displays them to the user. The emotion engine recognizes the user's emotions and can provide relevant news and advertisements based on the emotions the next time the device is accessed.

[1772] Restaurant reservation example

[1773] The user inputs a request such as "I would like to make a reservation at a Japanese restaurant for tonight." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates a list of candidate Japanese restaurants and returns it to the server. The server provides the candidate list to the user and makes a reservation at the restaurant selected by the user. The emotion engine provides restaurant information that best suits the user's wishes and emotions, thereby achieving a more personalized service.

[1774] In this way, the present invention makes it possible to provide highly personalized content while taking into account the emotional state of the user, thereby improving the user experience and realizing effective marketing.

[1775] The processing flow will be explained below.

[1776] Processing Steps

[1777] Step 1:

[1778] A user opens an app or web browser to access the system and is presented with a login screen.

[1779] Step 2:

[1780] The user enters their user ID and password into the login form.

[1781] Step 3:

[1782] The device sends the authentication information entered by the user to the server.

[1783] Step 4:

[1784] The server receives the authentication information, checks it against a backend database, and authenticates the user.

[1785] Step 5:

[1786] The server sends the authentication result to the terminal, and if authentication is successful, an interactive interface is displayed; if authentication is unsuccessful, the user is prompted to enter authentication information again.

[1787] Step 6:

[1788] Users input requests such as "Tell me today's news" or "I'd like to make a restaurant reservation for tonight" through an interactive interface.

[1789] Step 7:

[1790] The device sends the user's input text to the server.

[1791] Step 8:

[1792] The server receives user input and sends it to the generative AI model and emotion engine.

[1793] Step 9:

[1794] A generative AI model analyzes the user's input text and generates appropriate content (e.g., a news article or restaurant list).

[1795] Step 10:

[1796] The emotion engine recognizes emotions from the user's text, tone of voice, facial expressions, etc., and provides the emotion data to the generative AI model.

[1797] Step 11:

[1798] The generative AI model customizes the content based on the emotional data and generates the final content.

[1799] Step 12:

[1800] The server sends the generated content to the device, which displays it to the user.

[1801] Step 13:

[1802] Users can view news articles and restaurant information displayed on an interactive interface.

[1803] Step 14:

[1804] The server stores user access history, behavioral history, purchase history and emotional data in a back-end database.

[1805] Step 15:

[1806] The server analyzes the stored data to identify users' interests, concerns, and emotional patterns.

[1807] Step 16:

[1808] Based on the analysis results, the server selects the most appropriate marketing content for the user.

[1809] Step 17:

[1810] A generative AI model generates selected marketing content and customizes it taking into account sentiment data.

[1811] Step 18:

[1812] The server sends the generated marketing content to the device, which then provides it to the user.

[1813] Through these steps, users can seamlessly obtain information and enjoy individually customized content and services.

[1814] Example 2

[1815] 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."

[1816] Conventional content delivery systems often provide general information without considering the user's emotional state, which limits their ability to improve user experience and maximize marketing effectiveness. Furthermore, selecting marketing content based solely on a user's access history, behavioral history, and purchase history makes it difficult to deliver content tailored to the user's current emotional state and interests.

[1817] 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.

[1818] In this invention, the server includes means for receiving user input, means for analyzing the received user input, means for utilizing a generative AI model that generates appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, purchase history, and emotional data, means for analyzing the collected history and utilizing an emotional engine that recognizes the user's emotional state, means for selecting optimal marketing content for the user based on the analysis results and the emotional data, and means for providing the selected marketing content to the user. This makes it possible to provide highly personalized content and marketing measures based on the user's emotional state.

[1819] The "means for receiving user input" refers to the means by which the server receives text, voice, and other data input by the user through the terminal.

[1820] "Means for analyzing received user input" refers to the means by which the server decodes the received user input data and properly understands and classifies it.

[1821] "Means for utilizing generative AI models" refers to means for utilizing artificial intelligence models that automatically generate content and information based on analysis results.

[1822] "Means for transmitting generated content to a user's device" refers to means for transmitting content generated by a generative AI model to a device used by a user.

[1823] The "means for collecting user access history, behavior history, purchase history, and emotional data" refers to a means for accumulating user activity records, purchase history, and emotional data inferred from input data.

[1824] The "means for analyzing the collected history and utilizing an emotion engine to recognize the user's emotional state" refers to a means for analyzing the collected data and utilizing an emotion recognition device to identify the user's current emotional state.

[1825] "Means for selecting the most suitable marketing content for a user based on analysis results and emotional data" refers to means for selecting the most suitable advertisements and promotional information for a user based on the results of data analysis and emotional recognition information.

[1826] The "means for providing selected marketing content to a user" refers to a means for displaying and providing selected marketing information on a terminal used by a user.

[1827] The present invention relates to a system that combines a generative AI model and an emotion engine in an interactive platform with users to provide personalized content and marketing strategies. This system includes a series of processes that receive user input, analyze it, generate appropriate content, and provide it to the user.

[1828] server

[1829] The server is configured using the following software and hardware. It receives and analyzes user requests using a generative AI model and emotion engine, and generates appropriate content. It also has the ability to collect and analyze user access history, behavioral history, purchase history, and emotion data.

[1830] 1. A parsing module for parsing received user input.

[1831] 2. Generative AI model that generates content

[1832] 3. Emotion engine for recognizing the user's emotional state

[1833] 4. Back-end database for data management and analysis

[1834] Specific software examples include natural language processing libraries (e.g., NLTK), machine learning frameworks (e.g., TensorFlow), and database management systems (e.g., MySQL).

[1835] Terminal

[1836] A terminal is a device that a user uses to access the system and can take various forms (smartphone, tablet, PC, etc.). The user sends requests to the server through the terminal and receives responses from the server.

[1837] 1. An input interface for receiving user input

[1838] 2. A user interface that displays the content sent from the server

[1839] Specific hardware examples include Apple's iPhone and Samsung's Galaxy series. Software uses web browsers (e.g., Google Chrome, Safari) and dedicated applications (e.g., iOS and Android apps).

[1840] User

[1841] A user is an individual who uses the system to obtain information or use services. A user uses the system in the following steps:

[1842] 1. Access the system using a terminal and enter your authentication information on the login screen.

[1843] 2. After successful authentication, you enter your request through the conversational interface (e.g., "Tell me today's news" or "I'd like to make a restaurant reservation for tonight").

[1844] 3. View the content sent from the server.

[1845] Specific Examples

[1846] News provision example

[1847] The user inputs a request such as "Tell me today's news." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates the latest news articles and returns them to the server. The server sends the generated news articles to the device, which displays them to the user. The emotion engine recognizes the user's emotions and can provide relevant news and advertisements based on the emotions the next time the device is accessed.

[1848] Restaurant reservation example

[1849] The user inputs a request such as "I would like to make a reservation at a Japanese restaurant for tonight." The device sends this request to the server. The server sends the request and the user's emotion data to the generative AI model and emotion engine. The generative AI model generates a list of candidate Japanese restaurants and returns it to the server. The server provides the candidate list to the user and makes a reservation at the restaurant selected by the user. The emotion engine provides restaurant information that best suits the user's wishes and emotions, thereby achieving a more personalized service.

[1850] In this way, the present invention makes it possible to provide highly personalized content while taking into account the emotional state of the user, thereby improving the user experience and realizing effective marketing.

[1851] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1852] Step 1:

[1853] User Access and Authentication

[1854] A user accesses the system from a terminal and enters their ID and password on the login screen. The terminal sends the entered authentication information to the server. The server receives the authentication information and checks it against a back-end database. If authentication is successful, the server returns an interactive interface to the user.

[1855] Input: User ID and password

[1856] Output: An interactive interface is displayed, and a message confirming successful user authentication is displayed.

[1857] Specifically, the user operates their smartphone to enter their ID and password into the login form and tap the submit button. The server then accesses the database and checks whether the ID and password match.

[1858] Step 2:

[1859] Receiving and analyzing user requests

[1860] The user inputs a prompt (e.g., "Tell me today's news") through a conversational interface. The device sends this prompt to the server. The server then forwards the request to the generative AI model, which analyzes the text data. The generative AI model understands the request and generates an analysis result to be passed on to the next step.

[1861] Input: User prompt text

[1862] Output: Analysis results from the generative AI model

[1863] Specifically, when a user types "I'd like to make a restaurant reservation for tonight" into the tablet interface and presses the send button, the device transfers the text to the server, which then passes it to the generative AI model to begin analyzing it.

[1864] Step 3:

[1865] Emotion recognition by emotion engine

[1866] The server sends the user's input text and voice data to the emotion engine, which analyzes this data and recognizes the user's emotional state (e.g., joy, sadness, anger, etc.). The server then sends the recognized emotion data to the generative AI model, which uses it as a reference for content generation.

[1867] Input: User-entered text and voice data

[1868] Output: Emotion data from the emotion engine

[1869] As a specific example of operation, a user inputs a voice message such as "I've been feeling down lately" into the terminal, and the terminal sends the voice message to the server. The server passes the voice data to the emotion engine and recognizes the user's emotional state.

[1870] Step 4:

[1871] Content generation and delivery

[1872] The generative AI model generates content (e.g., news articles or restaurant listings) based on user requests and emotion data. The server sends the generated content to the device, which then displays it to the user.

[1873] Input: User request and emotion data

[1874] Output: Generated content (news articles, restaurant listings, etc.)

[1875] Specifically, the server generates a news article based on the prompt "Tell me today's news" and the emotional state "I'm depressed," and transfers the generated article to the terminal, which then displays the news article on the user's screen.

[1876] Step 5:

[1877] First-party data collection and analysis

[1878] The server stores the user's access history, behavioral history, purchase history, and emotional data in a backend database. The server periodically analyzes this data to understand the user's interests.

[1879] Input: User access history, behavioral history, purchase history, emotional data

[1880] Output: Analysis results that reflect the user's interests

[1881] As a specific example of operation, the news categories that users frequently browse and the product purchase history are recorded in a database, and the user's preferences are identified by periodically analyzing this data.

[1882] Step 6:

[1883] Selection and provision of marketing content

[1884] Based on the analysis results, the server selects the most suitable marketing content for the user, taking into account the emotional data recognized by the emotion engine. The generative AI model is used again to generate customized marketing content. The server then sends the generated marketing content to the device, which then provides it to the user.

[1885] Input: Analysis results and emotion data

[1886] Output: Marketing content delivered to the user

[1887] Specifically, the server generates an advertisement for a healthy food product that will cheer you up based on the user's purchasing history and emotional state (e.g., "I feel low"), and sends it to the device to display. The user sees the advertisement and is encouraged to purchase the product.

[1888] (Application example 2)

[1889] 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."

[1890] While conventional content delivery systems can provide information in response to user requests, they lack personalized content and marketing strategies that reflect the user's emotional state, which results in poor user experience and in ineffective marketing. Therefore, there is a need for a system that can provide highly personalized content and marketing strategies that take the user's emotional state into account.

[1891] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user input, means for analyzing the received user input, means for utilizing a generative AI model that generates appropriate content based on the analysis results, means for transmitting the generated content to the user's terminal, means for collecting the user's access history, behavioral history, and purchase history, means for analyzing the collected history and selecting optimal marketing content for the user, means for providing the selected marketing content to the user, means for utilizing an emotion engine that analyzes the user's emotional state, and means for customizing content using the generative AI model based on the user's emotional state analyzed using the emotion engine. This enables the provision of highly personalized content that reflects the user's emotional state and effective marketing.

[1892] A "means for receiving user input" is a device or software that receives text or voice requests, commands, etc., made by a user to the system.

[1893] "Means for parsing received user input" is software or algorithms that understand the input data received from the user and take appropriate action based on it.

[1894] "Means for utilizing a generative AI model to generate appropriate content" refers to devices or software that utilize a generative AI model to generate content such as text, images, and videos to be provided to users based on the analysis results.

[1895] The "means for transmitting the generated content to the user's terminal" refers to a communication device or software for transferring the generated content to a terminal such as a smartphone or a personal computer used by the user.

[1896] "Means for collecting user access history, behavioral history, and purchase history" refers to devices or software that collect data such as a user's past website browsing history, action logs, and purchase records.

[1897] "Means for analyzing collected history and selecting optimal marketing content for the user" refers to devices or software that analyze collected history data and select optimal advertising and promotional information for the user based on that data.

[1898] The "means for providing the selected marketing content to the user" refers to a communication device or software for displaying the selected marketing content on the user's terminal.

[1899] "Means for utilizing an emotion engine to analyze the user's emotional state" refers to a device or software that utilizes an emotion engine to analyze the user's facial expressions, voice, text, etc., and determine their emotional state.

[1900] "Means for customizing content using a generative AI model based on the emotional state of a user analyzed using an emotion engine" refers to devices or software that customize content so that the generative AI model generates optimal content for the user based on the analyzed emotional state.

[1901] This invention is a system that realizes highly personalized content provision and marketing measures that take into account the emotional state of the user. This system consists of three main components: a server, a terminal, and a user.

[1902] server

[1903] The server has the following functions:

[1904] 1. A means of receiving user input

[1905] The server receives user requests (e.g., text or voice requests) from the device, allowing the server to understand the information or action the user is seeking.

[1906] 2. A means of parsing received user input

[1907] The server uses natural language processing (NLP) algorithms to analyze the user input it receives, allowing it to clearly understand the user's intent and request. For example, when analyzing the request "Looking for a new dress," it understands that the user wants to buy a dress.

[1908] 3. Using generative AI models to generate relevant content

[1909] The server then uses a generative AI model based on the analysis results to generate appropriate content. Specific generative AI models used here include OpenAI GPT-3. For example, if a user is looking for a new dress, the server generates the latest fashion trends and a list of recommended products.

[1910] 4. Means for sending generated content to the user's device

[1911] The server then sends the generated content to users' devices such as smartphones, tablets, and personal computers, allowing users to obtain information in real time.

[1912] 5. Means of collecting user access history, behavior history, and purchase history

[1913] The server collects and centrally manages data such as the user's past website browsing history, action logs, and purchase records.

[1914] 6. A means of analyzing collected history and selecting the most appropriate marketing content for the user

[1915] The server analyzes the collected data and selects marketing content (e.g., advertisements and promotional information) that matches the user's interests.

[1916] 7. How we deliver selected marketing content to you

[1917] The server transmits the selected marketing content to the user's terminal and presents it to the user.

[1918] 8. Using an emotion engine to analyze the user's emotional state

[1919] The server uses an emotion engine (specifically, the Affectiva SDK) to analyze the user's emotional state from facial expressions, voice, text, etc.

[1920] 9. A means of customizing content using generative AI models based on the user's emotional state analyzed using an emotion engine.

[1921] The server customizes the generative AI model to generate optimal content for the user based on the emotional state analyzed by the emotion engine. This provides content that matches the user's emotional state, providing a more personalized experience. Specifically, if the user is in a relaxed state, the server will suggest products suitable for relaxation.

[1922] Terminal

[1923] A terminal is a device that users use to access the system, and includes smartphones, tablets, PCs, etc. Terminals have the following functions:

[1924] Sending a request: The user uses the terminal to send a request to the server.

[1925] Display Content: Displaying content and marketing information sent from the server to the user.

[1926] User

[1927] A user is an individual who uses the system to obtain information or access services. The user uses a terminal to send requests to the server and receive generated content and marketing information.

[1928] Examples of prompt statements

[1929] text

[1930] User Request: Looking for a new dress

[1931] User's emotional state: joy

[1932] Generate the right content.

[1933] By implementing this invention, it is possible to provide highly personalized content that reflects the user's emotional state and to implement effective marketing. This system can improve user satisfaction and improve the effectiveness of corporate marketing.

[1934] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1935] Step 1:

[1936] User Authentication

[1937] The terminal receives login information (username and password) as input from the user. The terminal sends this input data to the server, which then authenticates the user. The server checks the user information against a database, and if authentication is successful, it generates an authentication token and returns it to the terminal. This allows the user to access the system.

[1938] Input: Username, Password

[1939] Output: Authentication token

[1940] Step 2:

[1941] Receiving and analyzing user requests

[1942] A user uses a device to input a request, for example, text such as "I'm looking for a new dress." The device sends this request to a server, which receives the input. The server uses natural language processing algorithms to analyze the request and understand the user's intent.

[1943] Input: User request (text)

[1944] Output: Analysis result (user intent)

[1945] Step 3:

[1946] User emotional state analysis

[1947] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends them to the emotion engine in real time. The server receives this input data and uses the emotion engine to analyze the user's emotional state (happiness, surprise, sadness, etc.). The analyzed emotion data is saved along with the analysis results of the user's request.

[1948] Input: User's facial expression and voice data

[1949] Output: Emotional state (user's emotion)

[1950] Step 4:

[1951] Content generation using generative AI models

[1952] The server sends the user's request analysis results and emotional state to the generative AI model. The prompt is formatted as follows: "User request: Looking for a new dress\nUser's emotional state: Joy\nPlease generate appropriate content." The generative AI model analyzes the input prompt and generates appropriate content (e.g., the latest fashion information or recommended dresses). The generated content is returned to the server.

[1953] Input: Request analysis results, emotional state

[1954] Output: Generated content (text, images)

[1955] Step 5:

[1956] Content provision

[1957] The server transmits the generated content to the terminal, which then displays it to the user, allowing the user to receive the required information in real time.

[1958] Input: Generated content

[1959] Output: Content displayed on the user's device

[1960] Step 6:

[1961] Historical Data Collection and Analysis

[1962] Every time a user views, clicks, or purchases content, that data is sent from the device to the server. The server stores this access history, behavioral history, and purchase history in a database and periodically analyzes it. Based on the analysis results, the user's interests can be identified.

[1963] Input: access history, behavior history, purchase history

[1964] Output: Analysis results (user interests)

[1965] Step 7:

[1966] Personalized Marketing

[1967] The server uses the analysis results and emotional data to generate optimal marketing content for the user. It also uses the generative AI model to create customized advertisements and promotional information based on the user's interests and emotional state. The generated marketing content is then sent to the device and provided to the user.

[1968] Input: Analysis results, emotion data

[1969] Output: Marketing content (advertising, promotional information)

[1970] This allows users to receive content and marketing information that reflects their emotional state, enabling companies to achieve highly personalized marketing.

[1971] 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.

[1972] 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.

[1973] 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.

[1974] 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.

[1975] 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.

[1976] 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.

[1977] 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).

[1978] 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.

[1979] 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."

[1980] 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.

[1981] 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).

[1982] 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.

[1983] In the above embodiment, an example in which the specific processing program 56 is stor...

Claims

1. means for receiving user input; means for parsing received user input; A means of using generative AI models to generate appropriate content based on the analysis results; and means for transmitting the generated content to a user's terminal; A means for collecting user access history, behavior history, and purchase history; A means of analyzing the collected history and selecting the most suitable marketing content for the user; A system including means for providing selected marketing content to a user.

2. The system of claim 1, wherein the generative AI model has the ability to process multimodal data such as text, images, and audio.

3. 2. The system according to claim 1, further comprising a function of providing news articles, store reservation information, and product recommendations based on user requests.

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