system

Abstract

Provide a system. The method includes: a means for invoking a generative model to generate text content for expressing brand values; a means for storing the generated content in a database; means for delivering the stored content to the device; means for the terminal to display content to the user; A system including:

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] Brands face challenges in communicating with customers consistently and effectively conveying their values ​​and story. It's also difficult to respond quickly and consistently when customers have questions or feedback about the brand. As a result, customer engagement declines, and brands risk losing credibility. [Means for solving the problem]

[0005] The present invention provides a system that uses a generative model to generate text content to express brand values, stores it in a database, and distributes it to a terminal for display to users. The system also receives questions from users, generates answers using a generative model, and transmits them to the terminal for display, thereby achieving a fast and consistent response. Furthermore, the system uses a generative model to generate consistent brand messages, generates responses based on user feedback, and transmits them to the terminal for display, thereby improving customer engagement while maintaining brand credibility.

[0006] A "generative model" is an algorithm that automatically generates text content or answers based on information about a brand or specific input.

[0007] "Brand value" refers to the unique benefits or advantages a brand offers to customers, or the ideals or culture expressed by a brand.

[0008] "Text content" is a collection of information and messages expressed in written form, intended to convey a brand's value and story to users.

[0009] "Database" means a structured data storage system for organizing and storing generated content, user feedback, and other related data.

[0010] "Terminal" refers to a device (e.g., smartphone, computer, tablet) that is directly used by a user and communicates with a server to display, send, and receive data.

[0011] "User" means an individual or entity that uses a Brand's content or services and provides questions or feedback.

[0012] A "Question" refers to an inquiry or question submitted by a user to a brand seeking information.

[0013] An "answer" is a response to a user's question generated using a generative model, and is information used to resolve the user's doubt.

[0014] "Feedback" refers to the act of a user providing a response to a brand, such as an evaluation, opinion, or suggestion, and the content of that response.

[0015] A "response" is a reply created by the generative model based on user feedback, indicating the brand's reaction to the user's opinion or rating.

[0016] A "message" refers to the information or philosophy that a brand consistently communicates to users, and is used to reinforce the brand's image and value. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention relates to a system for generating text content that expresses brand values ​​using a generative model, enhancing communication with users, and providing a consistent brand image. Specific embodiments of the system are described below.

[0039] Telling your brand story

[0040] 1. Content Generation

[0041] Server: Using a generative model, the server generates text content to express the brand's values. The generated text includes the brand's philosophy, culture, product features, etc.

[0042] Example: A generative model generates a message that reads, "Our brand offers environmentally friendly products for a sustainable future." This message is displayed on the brand's website and social media.

[0043] 2. Content storage and distribution

[0044] Server: Stores the generated content in a database and delivers it to the device, which displays the content to the user.

[0045] Example: A web server stores generated text in a database and displays it on a web page when accessed by a user.

[0046] Improved customer engagement

[0047] 1. Receiving and processing inquiries

[0048] User: Enters a question about the brand through a terminal, for example, can use the application's chatbot.

[0049] Terminal: Sends a question to the server.

[0050] Server: Uses the generative model to generate answers to user questions.

[0051] Example: When a user asks, "How do I use this product?", the server generates an answer such as, "To use this product, press the power button and then select the desired function from the settings menu," and displays it to the user.

[0052] Unifying the brand image

[0053] 1. Message Creation and Storage

[0054] Server: Uses the generative model to generate consistent brand messages and store them in a database.

[0055] Example: A generative model generates the message "Our brand puts quality first and provides peace of mind and satisfaction to all customers," and saves this as the brand's official statement.

[0056] 2. Processing Feedback

[0057] Users: Provide feedback to the brand through their devices.

[0058] Device: Sends feedback to the server.

[0059] Server: Uses a generative model to generate consistent responses based on user feedback.

[0060] Example: When a user submits feedback saying, "I'm not happy with the product recently," the server generates a response saying, "We take your feedback seriously and will use it to improve our products in the future," and displays it to the user.

[0061] Overall processing flow

[0062] Users access a brand's website or application through their device and view generated text content. When they ask a question or provide feedback, the device sends it to the server, which uses the generative model to generate an answer or response, which is then sent back to the device and displayed to the user. This process maintains a consistent brand message and customer engagement.

[0063] Through the above means, the present invention can achieve consistent and effective communication between the brand and its customers, thereby improving the brand image.

[0064] The processing flow will be explained below.

[0065] Telling your brand story

[0066] Step 1:

[0067] Server: Calls the generative model and generates text content to express the brand values.

[0068] How it works: You provide the generative model with your brand's values ​​and philosophy as input, and the model generates a textual brand message.

[0069] Step 2:

[0070] Server: Stores the generated content in a database.

[0071] What it does: Runs a query to store the generated text data into a specific table in a database.

[0072] Step 3:

[0073] Server: Delivers stored content to devices.

[0074] How it works: A web server receives an HTTP request and returns a response containing generated content.

[0075] Step 4:

[0076] Terminal: Displays the content received from the server to the user.

[0077] What it does: Displays the received text content in the specified location on a web page or application.

[0078] Improved customer engagement

[0079] Step 1:

[0080] User: Enters a brand question through the device.

[0081] Action: A user enters a question into a form on a web page or application and clicks the submit button.

[0082] Step 2:

[0083] Terminal: Sends the user's question to the server.

[0084] What it does: Sends the question to the server as a POST request.

[0085] Step 3:

[0086] Server: Uses the generative model to generate answers to user questions.

[0087] How it works: Pass the received question to a generative model to generate an appropriate answer text.

[0088] Step 4:

[0089] Server: Generates and sends the answer back to the device.

[0090] Behavior: A response containing the generated answer text is sent to the terminal via HTTP.

[0091] Step 5:

[0092] Terminal: Display the answer to the user.

[0093] Behavior: Displays the received response text in a designated area on the user interface.

[0094] Unifying the brand image

[0095] Step 1:

[0096] Server: Uses generative models to generate consistent brand messages.

[0097] How it works: Provide the generative model with inputs based on your brand's personality and values ​​to generate a consistent message.

[0098] Step 2:

[0099] Server: Store this message in a database.

[0100] What it does: Executes a query to store the generated messages in a specific table in the database.

[0101] Step 3:

[0102] Users: Provide feedback about the brand through their devices.

[0103] What it does: Use the feedback form or rating feature to provide your thoughts and opinions, then click the submit button.

[0104] Step 4:

[0105] Device: Sends user feedback to the server.

[0106] Behavior: Sends the feedback content to the server as a POST request.

[0107] Step 5:

[0108] Server: Generates a response using a generative model based on the feedback.

[0109] How it works: Passes received feedback data to a generative model to generate appropriate response text.

[0110] Step 6:

[0111] Server: Generates and sends the response to the device.

[0112] Behavior: A response containing the generated response text is sent to the terminal via HTTP.

[0113] Step 7:

[0114] Terminal: Displays the response to the user.

[0115] Behavior: Displays the received response text in a designated area on the user interface.

[0116] Example 1

[0117] 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."

[0118] The lack of a system to provide a consistent message and ensure effective communication between the brand and customers leads to a lack of consistency in the brand image and reduced customer engagement.

[0119] 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.

[0120] In this invention, the server includes means for calling a generative model and generating text to express the brand's values, means for saving the generated text in data storage, means for delivering the saved text to a user device, means for the user device to display the text to the user, means for receiving a query from the user, means for processing the received query using a generative model and generating a response, means for transmitting the generated response to the user device, means for the user device to display the response to the user, means for generating a consistent brand message using the generative model, means for saving the message in data storage, means for receiving opinions from the user, means for generating a response based on the received opinions using the generative model, means for transmitting the generated response to the user device, and means for the user device to display the response to the user, thereby enabling the provision of a consistent brand message and effective communication with customers.

[0121] A "generative model" is a system that uses artificial intelligence techniques to generate natural language text based on input prompts.

[0122] "Brand value" is a general term for the unique philosophy, culture, beliefs, quality, etc. of a company or product, and refers to the added value that it provides to customers.

[0123] "Text" refers to the string of characters output by the generative model, and refers to sentences or messages used to express the brand's values.

[0124] "Data storage" refers to electronic storage devices for storing generated text and other digital data.

[0125] A "user device" is an electronic device that can be directly operated by a user, and includes a personal computer, a smartphone, a tablet, and the like.

[0126] "Inquiry" means a question or request submitted by a User seeking specific information or support.

[0127] A "response" is an answer that a generative model generates to a query, and includes information that is provided to the user.

[0128] "Opinions" refers to feedback and comments provided by users, including satisfaction with products and services and requests.

[0129] A "message" is a coherent text generated by a generative model that expresses the brand's values, philosophy, etc.

[0130] This invention uses a generative AI model to generate text content that expresses brand values, strengthens communication with users, and provides a consistent brand image.

[0131] Hardware and software configuration

[0132] This system mainly uses the following hardware and software:

[0133] Server: Provides the computational resources to run generative AI models (e.g., GPT-4®), and also uses database systems such as MySQL® for data storage.

[0134] Terminal: A device operated by a user (such as a computer, smartphone, or tablet) on which a web browser or application runs.

[0135] Generative AI models: Models that generate text based on prompts, such as OpenAI's APIs (e.g., GPT-4).

[0136] Network: An internet connection for data communication between the server and the device.

[0137] Data processing and calculation

[0138] 1. Server: The server prepares prompts that users input to the generative AI model. An example prompt is, "Generate text that expresses our brand philosophy. Our focus is on providing environmentally friendly products."

[0139] 2. Content generation: The server sends the prompt to the generative model and receives generated text, such as "Our brand offers environmentally friendly products for a sustainable future."

[0140] 3. Text storage: Store the generated text in a data storage, such as a database like MySQL, in the appropriate format (e.g., timestamp or content ID).

[0141] 4. Display: When a user accesses a website or application on their device, the server receives a request to display the stored text and provides the appropriate content, which is then displayed on the user's device.

[0142] Examples and prompts

[0143] 1. Content generation prompt example:

[0144] "Generate text content that expresses your brand's values. The focus is on environmentally friendly product offerings."

[0145] 2. Example prompts for generating answers to user questions:

[0146] "A user asks, 'How do I use this product?' Please generate an answer to this question."

[0147] 3. Example prompts for generating responses to feedback:

[0148] "A user has given us feedback saying, 'I'm not happy with the product lately.' Please generate a response to this feedback."

[0149] With this system, the server uses a generative model to generate various texts, saves the generated texts in data storage, and delivers them to user devices. This allows for the provision of consistent brand messages and improved customer engagement. It also generates and provides quick and consistent answers to questions and feedback entered by users. This series of processes allows brands to achieve effective and consistent communication and improve their brand image.

[0150] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0151] Step 1:

[0152] Content generation prompt preparation

[0153] Server: Prepares prompts to send to the generative AI model based on information about the brand and its philosophy.

[0154] Action: Set a prompt like, "Generate text that expresses our brand philosophy. Our focus is on providing environmentally friendly products."

[0155] Input: Basic information about your brand's values ​​and philosophy.

[0156] Output: The prompt to send to the generative AI model.

[0157] Step 2:

[0158] Text generation using generative AI models

[0159] Server: Sends the prepared prompt sentence to a generative AI model (e.g., GPT-4) and retrieves the generated text.

[0160] Specific operation: Call the GPT-4 API to generate "text that expresses the brand's values" and receive the results.

[0161] Input: The prepared prompt statement.

[0162] Output: Generated text such as "Our brand offers eco-conscious products for a sustainable future."

[0163] Step 3:

[0164] Saving the generated text

[0165] Server: Stores the generated text in a data storage (e.g., MySQL database).

[0166] Specific behavior: Executes an SQL query such as "INSERT INTO contents (content, created_at) VALUES ('generated text', NOW())".

[0167] Input: The generated text.

[0168] Output: Text records saved in data storage.

[0169] Step 4:

[0170] Processing user requests and delivering text

[0171] Device: A user accesses a website or application and sends a request for content to the server.

[0172] Server: Searches and retrieves the generated text from data storage and delivers it to the user device.

[0173] Specific operation: Executes an SQL query such as "SELECT content FROM contents WHERE content_id=1234" and returns the result as an HTTP response.

[0174] Input: The user request.

[0175] Output: Text delivered to the user device.

[0176] Step 5:

[0177] Receiving user questions and generating answers

[0178] User: Enters a brand-related question through the device (e.g., "How do I use this product?").

[0179] Terminal: Sends a question to the server.

[0180] Server: Generates prompts to send questions to the generative model (e.g., "The user asked me, 'How do I use this product?' Please generate an answer to this question."), and calls the generative AI model to obtain the answer.

[0181] What it does: Creates prompts for GPT-4 and calls the API to generate answers.

[0182] Input: A question from the user.

[0183] Output: The generated answer (e.g., "Press the power button, then select the desired function in the settings menu.").

[0184] Step 6:

[0185] Submitting and viewing answers

[0186] Server: Sends the generated answer to the user device.

[0187] Terminal: Receives the answer and displays it to the user.

[0188] Specific operation: The server sends the answer as an HTTP response, and the device displays it.

[0189] Input: The generated answer.

[0190] Output: The answer that is displayed to the user.

[0191] Step 7:

[0192] Receiving feedback and generating responses

[0193] User: Enters feedback about the brand through a device (e.g., "I'm not happy with your recent product").

[0194] Device: Sends feedback to the server.

[0195] Server: Generates prompts to send feedback to the generative model (e.g., "The user has given us feedback saying, 'I'm dissatisfied with our recent product.' Please generate a response to this feedback."), and calls the generative AI model to obtain the response.

[0196] What it does: Creates a prompt for GPT-4 and calls an API to generate a response.

[0197] Input: User feedback.

[0198] Output: The generated response (e.g., "We take your feedback seriously and will use it to improve our products in the future.").

[0199] Step 8:

[0200] Sending and Displaying Responses

[0201] Server: Sends the generated response to the user device.

[0202] Terminal: Receives the response and displays it to the user.

[0203] Specific behavior: The server sends the response as an HTTP response, and the device displays it.

[0204] Input: The generated response.

[0205] Output: The response that is displayed to the user.

[0206] (Application example 1)

[0207] 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."

[0208] Modern brands are required to consistently communicate their values ​​and philosophy to consumers through various channels, including online and virtual. However, there is a lack of an appropriate system for increasing consumer engagement while maintaining a consistent brand message. In particular, virtual stores present a challenge, as consumers have difficulty providing real-time questions and feedback and receiving consistent answers and responses. Therefore, there is a need for a system that can consistently communicate brand values ​​and enable effective communication with consumers.

[0209] 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.

[0210] In this invention, the server includes means for calling a generative model and generating text content for expressing the brand's values, means for saving the generated content in a database, means for delivering the saved content to a terminal, means for receiving a question from a user through the terminal and generating an answer to the question using the generative model, means for receiving feedback from the user and responding to the feedback using the generative model, means for the terminal to display the answer to the question and the feedback response to the user, and means for expressing the brand's values ​​in a virtual store that the user can access using a smartphone or a head-mounted display. This enables effective communication with consumers in real time while maintaining consistency in the brand message.

[0211] A "generative model" is a type of artificial intelligence that generates text content to express a brand's values ​​and philosophy.

[0212] "Brand value" refers to the brand's philosophy, values, culture, and characteristics of its products and services.

[0213] "Content" refers to information materials such as text, images, and videos generated by a generative model to express the brand's values.

[0214] A "database" is a system for systematically storing generated content and for accessing and retrieving it as needed.

[0215] A "terminal" is a device used by a user to view content that expresses the brand's values, and includes a smartphone, a head-mounted display, etc.

[0216] A "virtual store" is a store that is not a physical store but that users can access online or in a virtual reality environment.

[0217] A "question" is a question that a user inputs via a terminal about a brand or its products.

[0218] "Feedback" refers to the opinions and impressions users provide about a brand or its products.

[0219] An "answer" is a response generated by a generative model in response to a user's question.

[0220] A "response" is a reply generated by a generative model based on user feedback.

[0221] A "smartphone" refers to a mobile information terminal that has communication capabilities and can be used by installing a variety of applications.

[0222] A "head-mounted display" is a display device worn on the head, and is used to display virtual reality and augmented reality.

[0223] This invention relates to a system that uses generative AI models to generate text content to express brand values, enhance communication with users, and provide a consistent brand image. This system is particularly useful in virtual stores to effectively communicate brand values.

[0224] System Program

[0225] The system includes the following components:

[0226] A server that invokes the generative model and generates text content to express the brand's values.

[0227] A server that stores generated content in a database

[0228] A server that delivers stored content to devices

[0229] A server that receives questions from users via their devices and generates answers to those questions using a generative model.

[0230] A server that receives feedback from users and responds to the feedback using a generative model

[0231] The device displays the answer to the question and the feedback response to the user.

[0232] The brand's values ​​are expressed in a virtual store, which users can access using a smartphone or head-mounted display.

[0233] System Description

[0234] Hardware and Software

[0235] Server: Use AWS (registered trademark) or Google (registered trademark) Cloud to host the generative AI model (e.g., OpenAI (registered trademark)'s GPT-3 (registered trademark)).

[0236] Device: Smartphone or head-mounted display (HMD).

[0237] Software: Build a web application using Flask and set up API endpoints.

[0238] Data processing and calculation

[0239] The server calls the generative AI model to generate text that expresses the brand's values ​​and stores it in a database. The generated text is then delivered to the device when the user accesses the virtual store, and the device displays the content.

[0240] When a user asks a question through the device, the server receives the question and generates an answer using the generative AI model. The generated answer is sent to the device and displayed to the user. Similarly, when feedback is received from the user, the server uses the generative AI model to generate a response to the feedback, sends it to the device and displays it to the user.

[0241] Specific examples

[0242] If a user asks "How do I use this product?" in a virtual store, the generative model generates an answer such as "First, turn on this product, then set it up using the dedicated app," and displays it to the user.

[0243] If a user sends feedback such as "I'm dissatisfied with our recent product," the generative model generates a response saying, "We take your feedback seriously and will use it to improve our products in the future," and displays it to the user.

[0244] Prompt Sentence Examples

[0245] Generate text that expresses your brand values

[0246] "Our brand is committed to providing a high-quality product. Generate messaging that expresses that philosophy."

[0247] Generating answers to user questions

[0248] "User Question: How do I use this product?\nAnswer:"

[0249] Generate responses to user feedback

[0250] "User feedback: I'm not happy with the product lately.\nResponse:"

[0251] merit

[0252] This system allows for effective real-time communication with consumers while maintaining consistency in brand messaging.

[0253] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0254] Step 1:

[0255] A user accesses the virtual store using a smartphone or a head-mounted display.

[0256] Input: User's access request

[0257] Output: Virtual store interface screen display

[0258] Specific operation: The terminal receives an access request from the user and sends it to the server. The server sends the interface data of the virtual store to the terminal, which then renders and displays it.

[0259] Step 2:

[0260] The server invokes the generative model to generate text content that expresses the brand's values.

[0261] Input: Prompt sentence to express brand values

[0262] Output: The generated text content

[0263] Specific operation: The server sends a prompt to the API of the generative model, which then generates text content. The generated text is returned to the server.

[0264] Step 3:

[0265] Store the generated text content in a database.

[0266] Input: Generated text content

[0267] Output: Text content stored in the database

[0268] Specific operation: The server receives the generated text content and stores it in a database.

[0269] Step 4:

[0270] The stored text content is delivered to the terminal, which displays the content to the user.

[0271] Input: Text content read from the database

[0272] Output: Text content displayed on the terminal

[0273] Specific operation: The server retrieves the necessary text content from the database and sends it to the terminal, which then displays the received text content to the user.

[0274] Step 5:

[0275] A user inputs a question through a terminal, and the terminal transmits the question to a server.

[0276] Input: User question

[0277] Output: The question data sent to the server

[0278] Specific operation: The terminal receives the question entered by the user in the virtual store and sends it to the server.

[0279] Step 6:

[0280] The server uses the generative model to generate answers to questions.

[0281] Input: User question and prompt

[0282] Output: Generated answer text

[0283] Specific operation: Based on the question received from the user, the server sends a request to the API of the generative model along with a prompt text. The generative model generates an answer, and the text is returned to the server.

[0284] Step 7:

[0285] The generated answer is sent to the terminal, which displays the answer to the user.

[0286] Input: Generated answer text

[0287] Output: The answer text that is displayed to the user

[0288] Specific operation: The server receives the generated answer text and sends it to the terminal, which then displays the received answer text to the user.

[0289] Step 8:

[0290] The user inputs feedback through the terminal, and the terminal transmits the feedback to the server.

[0291] Input: User feedback

[0292] Output: Feedback data sent to the server

[0293] Specific operation: The terminal receives the feedback entered by the user in the virtual store and sends it to the server.

[0294] Step 9:

[0295] The server uses the generative model to generate a response to the feedback.

[0296] Input: User feedback and prompts

[0297] Output: The generated response text

[0298] Specific operation: Based on the feedback received from the user, the server sends a request to the API of the generative model along with a prompt text. The generative model generates a response, and the text is sent back to the server.

[0299] Step 10:

[0300] The generated response is sent to the terminal, which displays the response to the user.

[0301] Input: Generated response text

[0302] Output: The response text that is displayed to the user

[0303] Specific operation: The server receives the generated response text and sends it to the terminal, which then displays the received response text to the user.

[0304] 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.

[0305] The present invention relates to a system that combines a generative model and an emotion engine to achieve consistent and effective communication between a brand and its users, thereby enhancing the value of the brand. Specific embodiments of the system are described below.

[0306] Telling your brand story

[0307] 1. Content Generation

[0308] Server: Calls the generative model to generate text content to express the brand's values. The generated text includes the brand's philosophy, culture, product features, etc.

[0309] Example: A generative model generates a message that reads, "Our brand offers environmentally friendly products for a sustainable future." This message is displayed on the brand's website and social media.

[0310] 2. Content storage and distribution

[0311] Server: Stores the generated content in a database and delivers it to the device, which displays the content to the user.

[0312] Example: A web server stores generated text in a database and displays it on a web page when accessed by a user.

[0313] Improved customer engagement

[0314] 1. Receiving and processing inquiries

[0315] User: Enters a question about the brand through a terminal, for example, can use the application's chatbot.

[0316] Terminal: Sends a question to the server.

[0317] Server: An emotion engine is used to recognize the user's emotions, and a generative model generates answers based on those emotions.

[0318] Example: When a user gently asks, "I'd like to know more about that product," the emotion engine recognizes the user's emotion as "interest," and the generative model generates an answer, "This product uses the latest technology and combines ease of use and performance," and displays it to the user.

[0319] Unifying the brand image

[0320] 1. Message Creation and Storage

[0321] Server: Uses the generative model to generate consistent brand messages and store them in a database.

[0322] Example: A generative model generates the message "Our brand puts quality first and provides peace of mind and satisfaction to all customers," and saves this as the brand's official statement.

[0323] 2. Processing Feedback

[0324] Users: Provide feedback to the brand through their devices.

[0325] Device: Sends feedback to the server.

[0326] Server: An emotion engine is used to recognize the user's emotions, and a generative model generates a response based on those emotions.

[0327] Example: If a user expresses anger by saying, "I'm dissatisfied with our latest product," the emotion engine recognizes the user's emotion as "dissatisfied," and the generative model generates a response saying, "We take your feedback seriously and will use it to improve our products in the future," and displays it to the user.

[0328] Overall processing flow

[0329] Users access a brand's website or application through their device and view the generated text content. When they ask a question or provide feedback, the device sends it to the server, which uses an emotion engine to recognize the user's emotion and a generative model to generate an answer or response, which is then sent back to the device and displayed to the user. This process ensures a consistent brand message and customer engagement.

[0330] Through the above means, the present invention can achieve consistent and effective communication between the brand and its customers, thereby improving the brand image.

[0331] The processing flow will be explained below.

[0332] Telling your brand story

[0333] Step 1:

[0334] Server: Calls the generative model and generates text content to express the brand values.

[0335] How it works: You provide the generative model with your brand's values ​​and philosophy as input, and the model generates a textual brand message.

[0336] Step 2:

[0337] Server: Stores the generated content in a database.

[0338] What it does: Runs a query to store the generated text data in a specific table in a database.

[0339] Step 3:

[0340] Server: Delivers stored content to devices.

[0341] How it works: A web server receives an HTTP request and returns a response containing generated content.

[0342] Step 4:

[0343] Terminal: Displays the content received from the server to the user.

[0344] What it does: Displays the received text content in the specified location on a web page or application.

[0345] Improved customer engagement

[0346] Step 1:

[0347] User: Enters a brand question through the device.

[0348] Action: A user enters a question into a form on a web page or application and clicks the submit button.

[0349] Step 2:

[0350] Terminal: Sends the user's question to the server.

[0351] What it does: Sends the question to the server as a POST request.

[0352] Step 3:

[0353] Server: Recognizes user emotions using an emotion engine.

[0354] How it works: Passes the received question data to the emotion engine to recognize the user's emotional state (e.g., joy, interest, anxiety).

[0355] Step 4:

[0356] Server: Uses a generative model to generate answers based on the user's emotions.

[0357] How it works: The generative model generates appropriate answer text, taking into account the perceived emotional state of the user.

[0358] Step 5:

[0359] Server: Generates and sends the answer back to the device.

[0360] Behavior: A response containing the generated answer text is sent to the terminal via HTTP.

[0361] Step 6:

[0362] Terminal: Display the answer to the user.

[0363] Behavior: Displays the received response text in a designated area on the user interface.

[0364] Unifying the brand image

[0365] Step 1:

[0366] Server: Uses generative models to generate consistent brand messages.

[0367] How it works: Provide the generative model with inputs based on your brand's personality and values ​​to generate a consistent message.

[0368] Step 2:

[0369] Server: Store this message in a database.

[0370] What it does: Executes a query to store the generated messages in a specific table in the database.

[0371] Step 3:

[0372] Users: Provide feedback about the brand through their devices.

[0373] What it does: Use the feedback form or rating feature to provide your thoughts and opinions, then click the submit button.

[0374] Step 4:

[0375] Device: Sends user feedback to the server.

[0376] Behavior: Sends the feedback content to the server as a POST request.

[0377] Step 5:

[0378] Server: Recognizes user emotions using an emotion engine.

[0379] How it works: Passes received feedback data to the emotion engine to recognize the user's emotional state (e.g., frustration, joy, excitement).

[0380] Step 6:

[0381] Server: Generates a response using a generative model based on the feedback.

[0382] How it works: The generative model generates an appropriate response text, taking into account the perceived emotional state of the user.

[0383] Step 7:

[0384] Server: Generates and sends the response to the device.

[0385] Behavior: A response containing the generated response text is sent to the terminal via HTTP.

[0386] Step 8:

[0387] Terminal: Displays the response to the user.

[0388] Behavior: Displays the received response text in a designated area on the user interface.

[0389] Example 2

[0390] 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."

[0391] With conventional brand communication systems, it was difficult to consistently convey brand values ​​and messages to users, and it was difficult to properly recognize customer sentiment and respond in real time. This made it difficult to unify brand image and improve customer engagement.

[0392] 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.

[0393] In this invention, the server includes means for calling a generative model to generate text data for expressing the brand's value, means for saving the generated text data in a data storage device, means for distributing the saved text data to an information processing device, means for receiving inquiries from users, means for recognizing user emotions from the received inquiries using a sentiment analysis device and generating a response using the generative model, means for transmitting the generated response to the information processing device, means for generating a consistent brand message using the generative model and saving it in the data storage device, means for receiving evaluations from users, means for recognizing user emotions using the sentiment analysis device based on the received evaluations and generating a response using the generative model, means for transmitting the generated response to the information processing device, and means for the terminal to display the text data, the response, and the response to the user, thereby enabling consistent brand messaging and recognizing customer emotions to generate and display appropriate responses in real time.

[0394] A "generative model" is a model that uses machine learning techniques to generate text or data based on specific prompts.

[0395] "Text data" refers to text content generated by a generative model to express brand values ​​and messages.

[0396] "Data storage device" refers to any storage device for storing generated text data and brand messages.

[0397] The term "information processing device" refers to a terminal in general that displays data and responses delivered from a server to a user.

[0398] An "inquiry" is a question or request sent by a user via a terminal about a brand.

[0399] An "emotion analyzer" is an analytical engine that analyzes the content of user inquiries and feedback and recognizes their emotions.

[0400] An "answer" is a response message generated in response to a user inquiry using a sentiment analyzer and a generative model.

[0401] A "rating" is feedback or opinion provided by a user to a brand via a device.

[0402] A "response" is a reply message from a brand that is generated using a generative model based on a user's evaluation.

[0403] A "brand message" is text content generated using a generative model to consistently express brand values ​​and ideals.

[0404] The present invention is a brand value maximization system that combines a generative model and an emotion engine. This system is implemented with the following specific hardware and software configuration.

[0405] System Configuration

[0406] 1. Server

[0407] Generative models: Includes generative models that use machine learning technology (e.g., GPT-4), which generate text data to express brand values.

[0408] Data storage device: A database for storing generated text data and brand messages.

[0409] Sentiment analyzer: An analytical engine that analyzes the content of user inquiries and feedback and recognizes their emotions (e.g., Azure (registered trademark) Sentiment Analysis).

[0410] 2. Terminal

[0411] Information processing device: A device (e.g., smartphone, PC, tablet) that displays data and responses delivered from the server to the user.

[0412] 3. Users

[0413] Access: Access the system through a brand's website or application to view content, submit inquiries, and provide feedback.

[0414] Operation explanation

[0415] Content Generation and Delivery

[0416] Server: Calls the generative model and generates sentence data that expresses the brand's values. For example, by inputting the prompt "What is your brand's mission?" into the generative model, the generated sentence data is "Our brand values ​​sustainability and provides environmentally friendly products."

[0417] Server: The generated text data is stored in a data storage device, and the stored data can be accessed later.

[0418] Server: The stored text data is delivered to the user's device. For example, this message is displayed when the user visits the brand's website.

[0419] Device: The delivered text data is displayed to the user. For example, a message such as "Our brand values ​​sustainability and offers environmentally friendly products" is displayed on a web page or app.

[0420] User queries and response generation

[0421] User: Enter a brand-related inquiry through the terminal. For example, enter "What are the features of this product?"

[0422] Terminal: Sends the inquiry to the server.

[0423] Server: Recognizes the user's emotion using a sentiment analyzer. For example, recognizes the user's emotion of "interest" from the inquiry.

[0424] Server: Uses a generative model to generate an answer based on the recognized sentiment. For example, it might generate "This product uses the latest technology, combining ease of use with high performance."

[0425] Server: Sends the generated answer to the device.

[0426] Terminal: Displays submitted answers to the user.

[0427] User feedback and response generation

[0428] User: Enters their feedback about the brand through a device. For example, they might enter, "I'm not happy with your recent product."

[0429] Device: Sends feedback to the server.

[0430] Server: Uses a sentiment analyzer to recognize the sentiment of the feedback. For example, recognize the user's sentiment of "dissatisfied" from the feedback.

[0431] Server: Uses a generative model to generate a response based on the recognized sentiment. For example, it might generate "We take your feedback seriously and will use it to improve our products in the future."

[0432] Server: Generates and sends the response to the device.

[0433] Terminal: Displays the sent response to the user.

[0434] The system of the present invention allows brands to achieve high customer engagement by maintaining consistent messaging and responding to user emotions.

[0435] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0436] Step 1:

[0437] Server: Calls the generative model to express the brand values. By providing the prompt sentence as input to the generative model, it generates text data related to the brand’s philosophy and culture.

[0438] What it does: Sends an API request to a generative AI model (e.g., GPT-4) and enters the prompt, "What is your brand's mission statement?"

[0439] Data processing: The generative model generates sentence data based on the prompt sentence.

[0440] Output: The sentence "Our brand values ​​sustainability and offers environmentally friendly products" is generated.

[0441] Step 2:

[0442] Server: Stores the generated text data in a data storage device. Receives the generated text data as input and inserts it into the database.

[0443] Specific operation: Uses an SQL query to save text data to the database table "content".

[0444] Data processing: The generated text data is converted into a database format and saved.

[0445] Output: The sentence data is saved in the database.

[0446] Step 3:

[0447] Server: Delivers the saved text data to the user's device. Receives the user's request as input, retrieves the saved text data, and sends it to the device.

[0448] Specific behavior: Processes HTTP requests and retrieves stored text data from a database.

[0449] Data processing: Select the appropriate text data based on the request content and convert it into a format that can be sent to the terminal.

[0450] Output: The message "Our brand is committed to sustainability and offers environmentally friendly products" is sent to the device.

[0451] Step 4:

[0452] Terminal: Displays the delivered text data to the user. It receives text data received from the server as input for display and renders it in a form that is visible to the user.

[0453] Specific behavior: Renders submitted HTML and text data in web pages and applications.

[0454] Data processing: Display the received data in a format suitable for the user interface.

[0455] Output: A webpage will display the following statement: "Our brand is committed to sustainability and offers environmentally friendly products."

[0456] Step 5:

[0457] User: Enters a brand-related inquiry through a terminal. The user enters the question as input into an interface such as a chatbot and sends it.

[0458] Specific action: Enter "Please tell me the features of this product" into a chatbot or inquiry form and press the send button.

[0459] Data processing: Taking user input and converting it into a format that can be sent to the server.

[0460] Output: The question data "What are the features of this product?" is sent to the server.

[0461] Step 6:

[0462] Server: Recognizes the user's sentiment using a sentiment analyzer. The question data received as input is sent to the sentiment analyzer, and the sentiment is recognized.

[0463] Specific operation: The question content is sent to the sentiment analysis API and emotions such as "interest" are analyzed.

[0464] Data processing: The question data is analyzed by a sentiment analyzer to generate user sentiment data.

[0465] Output: Emotion data recognized as "interest" is generated.

[0466] Step 7:

[0467] Server: Uses a generative model to generate answers based on the recognized emotions. The generative model receives emotion data and question data as input and generates an appropriate answer.

[0468] Specific behavior: The prompt sentence "The user is interested in the features of this product" is input into the generative model, and an answer is generated.

[0469] Data processing: The generative model generates answer data based on the prompt sentence.

[0470] Output: The answer "This product uses the latest technology and is both easy to use and highly functional."

[0471] Step 8:

[0472] Server: Sends the generated answer to the terminal. Receives the generated answer data as input and converts it into a format that can be sent to the terminal.

[0473] Specific operation: Convert the response data into JSON format and send it to the device.

[0474] Data processing: Converting response data into a format for transmission.

[0475] Output: The generated answer data is sent to the device.

[0476] Step 9:

[0477] Terminal: displays submitted answers to the user. It takes the answer data received as input for display and renders it in a form that is visible to the user.

[0478] Specific behavior: Display the message "This product uses the latest technology, combining ease of use and high performance" on the chatbot UI.

[0479] Data processing: Display the received data in a format suitable for the user interface.

[0480] Output: The answer is displayed to the user.

[0481] Step 10:

[0482] User: Enters feedback about the brand through a terminal. The feedback is entered as input into the interface and sent.

[0483] Specific action: Enter "I'm dissatisfied with the recent product" into the feedback form and press the submit button.

[0484] Data processing: Taking user input and converting it into a format that can be sent to the server.

[0485] Output: The feedback data is sent to the server.

[0486] Step 11:

[0487] Server: Recognizes the feedback using a sentiment analyzer. The received feedback data is sent to the sentiment analyzer to recognize the user's emotions.

[0488] What it does: Sends feedback data to a sentiment analysis API and analyzes sentiment, such as "dissatisfied."

[0489] Data processing: Analyze the feedback data and generate user emotion data.

[0490] Output: Emotion data recognized as "dissatisfied" is generated.

[0491] Step 12:

[0492] Server: Uses a generative model to generate responses based on the recognized emotions. The generative model receives emotion data and feedback data as input and generates an appropriate response.

[0493] Specific behavior: The prompt sentence "The user is dissatisfied" is input into the generative model, and a response is generated.

[0494] Data processing: The generative model generates response data based on the prompt sentence.

[0495] Output: The response data generated is "We take your feedback seriously and will use it to improve our products in the future."

[0496] Step 13:

[0497] Server: Sends the generated response to the terminal. Receives the generated response data as input and converts it into a format that can be sent to the terminal.

[0498] Specific operation: Converts response data into JSON format and sends it to the terminal.

[0499] Data processing: Converts response data into a format for transmission.

[0500] Output: The generated response data is sent to the terminal.

[0501] Step 14:

[0502] Terminal: displays the sent response to the user. It takes the response data received as input for display and renders it in a form that is visible to the user.

[0503] Specific behavior: Display a message on the feedback form UI saying, "We take your feedback seriously and will use it to improve our products in the future."

[0504] Data processing: Display the received data in a format suitable for the user interface.

[0505] Output: The response is displayed to the user.

[0506] (Application example 2)

[0507] 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."

[0508] Today's customers expect consistent communication with brands, so it is important to find ways to effectively communicate the brand's values. However, traditional methods have made it difficult to recognize customer emotions in real time and respond appropriately based on them. Furthermore, in physical stores, there is a lack of support for staff to respond quickly and appropriately to individual customers' emotions and requests. This can lead to a decline in brand consistency and customer satisfaction.

[0509] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0510] In this invention, the server includes means for calling the generative model and generating text content for expressing the brand's values, means for saving the generated content in a database, means for delivering the saved content to a terminal, means for the terminal to display the content to the user, means for recognizing customer emotions in real time on a smart device, means for analyzing the recognized emotion data with an emotion engine, means for the generative model to generate an optimal response based on the analysis results, and means for displaying the generated response to store staff. This enables staff in physical stores to respond appropriately to customers' emotions in real time, thereby realizing consistent brand messaging and improved customer satisfaction.

[0511] A "generative model" is an algorithm or system that automatically generates text content and answers to express a brand's values.

[0512] "Brand value" is a concept that expresses a brand's philosophy, culture, product features, etc., and is an important element for appealing to customers.

[0513] "Text content" refers to textual information generated using a generative model, including brand messaging, product descriptions, etc.

[0514] A "database" is a system for storing and managing generated content and customer feedback.

[0515] A "terminal" is a device used by a user to display content and answers, and includes a smartphone, tablet, etc.

[0516] "Smart devices" are wearable devices equipped with cameras and computing power that are used to recognize customer emotions.

[0517] An "emotion engine" is a software system for analyzing recognized emotion data and identifying a customer's emotional state.

[0518] "Analysis results" are the results of customer emotion data analyzed by the emotion engine and used as input for the generative model.

[0519] "Store staff" refers to employees who deal with customers in physical stores and who wear smart devices.

[0520] To implement the present invention, the following system is used.

[0521] The server at the heart of the system first invokes the generative model to generate text content to express the brand's values. The generated text content reflects the brand's philosophy, culture, product features, etc., and functions as an effective message to customers. The generated content is stored in a database and distributed to devices as needed. The devices then display the content to the user.

[0522] Next, smart devices are used in brick-and-mortar stores. Wearable devices such as smart glasses capture images of customers' faces with a camera and recognize their emotions in real time. An emotion engine analyzes the recognized emotion data and identifies the customer's emotional state. Based on the analysis results, the server uses a generative model to generate the optimal response or answer. The generated response is displayed to store staff wearing smart glasses.

[0523] For example, consider the process when a customer asks a question about a product. The customer's question and the emotion recognized in real time are sent to the server. The emotion engine analyzes whether the customer is showing "interest," and based on the analysis results, the generative model generates a detailed product description. The generated description is then displayed to store staff through smart glasses, who then provide the customer with the appropriate information.

[0524] Examples:

[0525] Smart Devices: Smart Glasses

[0526] Software libraries: OpenCV, DeepFace

[0527] Generative model: GPT-4

[0528] Emotion Engine: DeepFace's emotion recognition module

[0529] Example prompt sentence:

[0530] Customer Question: "What's special about this product?"

[0531] Customer sentiment: "Interested"

[0532] Based on this prompt, a generative model generates text that is delivered to the device, resulting in more relevant information for customers in real time, improving consistent brand messaging and customer engagement.

[0533] In this way, the present invention realizes a system that enables consistent and effective communication between brands and customers in physical stores, thereby enhancing brand value.

[0534] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0535] Step 1:

[0536] The server calls the generative model and generates text content to express the brand's values. The input is information such as the brand's philosophy, culture, and product features, and the output is the generated text content. The generative model uses a natural language processing algorithm to generate text based on this input data.

[0537] Step 2:

[0538] The server stores the generated text content in a database. The input is the text content generated in step 1, and the output is the stored database entry. The database system stores the text content in an appropriate format, making it ready for later distribution.

[0539] Step 3:

[0540] The server delivers the stored content to the device. The input is the text content stored in the database, and the output is the text content sent to the device. The data is sent to the required device using the HTTP protocol or a specific API.

[0541] Step 4:

[0542] The terminal displays the content to the user. The input is the text content delivered from the server, and the output is the display on which the user views the content. The user interface displays the text in an appropriate format.

[0543] Step 5:

[0544] The smart device captures a customer's face with a camera and recognizes their emotions in real time. The input is the image data captured by the camera, and the output is the recognized emotion data. OpenCV and DeepFace libraries are used for image analysis and emotion recognition.

[0545] Step 6:

[0546] The emotion engine analyzes the recognized emotion data and identifies the customer's emotional state. The input is the emotion data obtained in step 5, and the output is the analysis result. The analysis result includes the customer's specific emotional state (e.g., "interested" or "anger").

[0547] Step 7:

[0548] The server uses a generative model based on the analysis results to generate the optimal response or answer. The input is the analysis results and the customer's question, and the output is the generated answer or response. A generative model (e.g., GPT-4) is used to generate the optimal text based on the analysis results.

[0549] Step 8:

[0550] The generated answer is sent to the terminal. The input is the text content generated by the server, and the output is the text content sent to the terminal. The server sends the generated text data to the terminal.

[0551] Step 9:

[0552] The terminal displays the generated answer to the store staff. The input is text content sent from the server, and the output is a display that the staff can view. A device such as smart glasses can visually present the generated text to the staff, allowing them to respond immediately.

[0553] 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.

[0554] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0555] 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.

[0556] [Second embodiment]

[0557] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0558] 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.

[0559] 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).

[0560] 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.

[0561] 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.

[0562] 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).

[0563] 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. 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] In the smart glasses 214, 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.

[0568] 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."

[0569] The present invention relates to a system for generating text content that expresses brand values ​​using a generative model, enhancing communication with users, and providing a consistent brand image. Specific embodiments of the system are described below.

[0570] Telling your brand story

[0571] 1. Content Generation

[0572] Server: Using a generative model, the server generates text content to express the brand's values. The generated text includes the brand's philosophy, culture, product features, etc.

[0573] Example: A generative model generates a message that reads, "Our brand offers environmentally friendly products for a sustainable future." This message is displayed on the brand's website and social media.

[0574] 2. Content storage and distribution

[0575] Server: Stores the generated content in a database and delivers it to the device, which displays the content to the user.

[0576] Example: A web server stores generated text in a database and displays it on a web page when accessed by a user.

[0577] Improved customer engagement

[0578] 1. Receiving and processing inquiries

[0579] User: Enters a question about the brand through a terminal, for example, can use the application's chatbot.

[0580] Terminal: Sends a question to the server.

[0581] Server: Uses the generative model to generate answers to user questions.

[0582] Example: When a user asks, "How do I use this product?", the server generates an answer such as, "To use this product, press the power button and then select the desired function from the settings menu," and displays it to the user.

[0583] Unifying the brand image

[0584] 1. Message Creation and Storage

[0585] Server: Uses the generative model to generate consistent brand messages and store them in a database.

[0586] Example: A generative model generates the message "Our brand puts quality first and provides peace of mind and satisfaction to all customers," and saves this as the brand's official statement.

[0587] 2. Processing Feedback

[0588] Users: Provide feedback to the brand through their devices.

[0589] Device: Sends feedback to the server.

[0590] Server: Uses a generative model to generate consistent responses based on user feedback.

[0591] Example: When a user submits feedback saying, "I'm not happy with the product recently," the server generates a response saying, "We take your feedback seriously and will use it to improve our products in the future," and displays it to the user.

[0592] Overall processing flow

[0593] Users access a brand's website or application through their device and view generated text content. When they ask a question or provide feedback, the device sends it to the server, which uses the generative model to generate an answer or response, which is then sent back to the device and displayed to the user. This process maintains a consistent brand message and customer engagement.

[0594] Through the above means, the present invention can achieve consistent and effective communication between the brand and its customers, thereby improving the brand image.

[0595] The processing flow will be explained below.

[0596] Telling your brand story

[0597] Step 1:

[0598] Server: Calls the generative model and generates text content to express the brand values.

[0599] How it works: You provide the generative model with your brand's values ​​and philosophy as input, and the model generates a textual brand message.

[0600] Step 2:

[0601] Server: Stores the generated content in a database.

[0602] What it does: Runs a query to store the generated text data into a specific table in a database.

[0603] Step 3:

[0604] Server: Delivers stored content to devices.

[0605] How it works: A web server receives an HTTP request and returns a response containing generated content.

[0606] Step 4:

[0607] Terminal: Displays the content received from the server to the user.

[0608] What it does: Displays the received text content in the specified location on a web page or application.

[0609] Improved customer engagement

[0610] Step 1:

[0611] User: Enters a brand question through the device.

[0612] Action: A user enters a question into a form on a web page or application and clicks the submit button.

[0613] Step 2:

[0614] Terminal: Sends the user's question to the server.

[0615] What it does: Sends the question to the server as a POST request.

[0616] Step 3:

[0617] Server: Uses the generative model to generate answers to user questions.

[0618] How it works: Pass the received question to a generative model to generate an appropriate answer text.

[0619] Step 4:

[0620] Server: Generates and sends the answer back to the device.

[0621] Behavior: A response containing the generated answer text is sent to the terminal via HTTP.

[0622] Step 5:

[0623] Terminal: Display the answer to the user.

[0624] Behavior: Displays the received response text in a designated area on the user interface.

[0625] Unifying the brand image

[0626] Step 1:

[0627] Server: Uses generative models to generate consistent brand messages.

[0628] How it works: Provide the generative model with inputs based on your brand's personality and values ​​to generate a consistent message.

[0629] Step 2:

[0630] Server: Store this message in a database.

[0631] What it does: Executes a query to store the generated messages in a specific table in the database.

[0632] Step 3:

[0633] Users: Provide feedback about the brand through their devices.

[0634] What it does: Use the feedback form or rating feature to provide your thoughts and opinions, then click the submit button.

[0635] Step 4:

[0636] Device: Sends user feedback to the server.

[0637] Behavior: Sends the feedback content to the server as a POST request.

[0638] Step 5:

[0639] Server: Generates a response using a generative model based on the feedback.

[0640] How it works: Passes received feedback data to a generative model to generate appropriate response text.

[0641] Step 6:

[0642] Server: Generates and sends the response to the device.

[0643] Behavior: A response containing the generated response text is sent to the terminal via HTTP.

[0644] Step 7:

[0645] Terminal: Displays the response to the user.

[0646] Behavior: Displays the received response text in a designated area on the user interface.

[0647] Example 1

[0648] 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."

[0649] The lack of a system to provide a consistent message and ensure effective communication between the brand and customers leads to a lack of consistency in the brand image and reduced customer engagement.

[0650] 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.

[0651] In this invention, the server includes means for calling a generative model and generating text to express the brand's values, means for saving the generated text in data storage, means for delivering the saved text to a user device, means for the user device to display the text to the user, means for receiving a query from the user, means for processing the received query using a generative model and generating a response, means for transmitting the generated response to the user device, means for the user device to display the response to the user, means for generating a consistent brand message using the generative model, means for saving the message in data storage, means for receiving opinions from the user, means for generating a response based on the received opinions using the generative model, means for transmitting the generated response to the user device, and means for the user device to display the response to the user, thereby enabling the provision of a consistent brand message and effective communication with customers.

[0652] A "generative model" is a system that uses artificial intelligence techniques to generate natural language text based on input prompts.

[0653] "Brand value" is a general term for the unique philosophy, culture, beliefs, quality, etc. of a company or product, and refers to the added value that it provides to customers.

[0654] "Text" refers to the string of characters output by the generative model, and refers to sentences or messages used to express the brand's values.

[0655] "Data storage" refers to electronic storage devices for storing generated text and other digital data.

[0656] A "user device" is an electronic device that can be directly operated by a user, and includes a personal computer, a smartphone, a tablet, and the like.

[0657] "Inquiry" means a question or request submitted by a User seeking specific information or support.

[0658] A "response" is an answer that a generative model generates to a query, and includes information that is provided to the user.

[0659] "Opinions" refers to feedback and comments provided by users, including satisfaction with products and services and requests.

[0660] A "message" is a coherent text generated by a generative model that expresses the brand's values, philosophy, etc.

[0661] This invention uses a generative AI model to generate text content that expresses brand values, strengthens communication with users, and provides a consistent brand image.

[0662] Hardware and software configuration

[0663] This system mainly uses the following hardware and software:

[0664] Server: Provides the computational resources to run generative AI models (e.g., GPT-4) and also uses database systems such as MySQL for data storage.

[0665] Terminal: A device operated by a user (such as a computer, smartphone, or tablet) on which a web browser or application runs.

[0666] Generative AI models: Models that generate text based on prompts, such as OpenAI's APIs (e.g., GPT-4).

[0667] Network: An internet connection for data communication between the server and the device.

[0668] Data processing and calculation

[0669] 1. Server: The server prepares prompts that users input to the generative AI model. An example prompt is, "Generate text that expresses our brand philosophy. Our focus is on providing environmentally friendly products."

[0670] 2. Content generation: The server sends the prompt to the generative model and receives generated text, such as "Our brand offers environmentally friendly products for a sustainable future."

[0671] 3. Text storage: Store the generated text in a data storage, such as a database like MySQL, in the appropriate format (e.g., timestamp or content ID).

[0672] 4. Display: When a user accesses a website or application on their device, the server receives a request to display the stored text and provides the appropriate content, which is then displayed on the user's device.

[0673] Examples and prompts

[0674] 1. Content generation prompt example:

[0675] "Generate text content that expresses your brand's values. The focus is on environmentally friendly product offerings."

[0676] 2. Example prompts for generating answers to user questions:

[0677] "A user asks, 'How do I use this product?' Please generate an answer to this question."

[0678] 3. Example prompts for generating responses to feedback:

[0679] "A user has given us feedback saying, 'I'm not happy with the product lately.' Please generate a response to this feedback."

[0680] With this system, the server uses a generative model to generate various texts, saves the generated texts in data storage, and delivers them to user devices. This allows for the provision of consistent brand messages and improved customer engagement. It also generates and provides quick and consistent answers to questions and feedback entered by users. This series of processes allows brands to achieve effective and consistent communication and improve their brand image.

[0681] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0682] Step 1:

[0683] Content generation prompt preparation

[0684] Server: Prepares prompts to send to the generative AI model based on information about the brand and its philosophy.

[0685] Action: Set a prompt like, "Generate text that expresses our brand philosophy. Our focus is on providing environmentally friendly products."

[0686] Input: Basic information about your brand's values ​​and philosophy.

[0687] Output: The prompt to send to the generative AI model.

[0688] Step 2:

[0689] Text generation using generative AI models

[0690] Server: Sends the prepared prompt sentence to a generative AI model (e.g., GPT-4) and retrieves the generated text.

[0691] Specific operation: Call the GPT-4 API to generate "text that expresses the brand's values" and receive the results.

[0692] Input: The prepared prompt statement.

[0693] Output: Generated text such as "Our brand offers eco-conscious products for a sustainable future."

[0694] Step 3:

[0695] Saving the generated text

[0696] Server: Stores the generated text in a data storage (e.g., MySQL database).

[0697] Specific behavior: Executes an SQL query such as "INSERT INTO contents (content, created_at) VALUES ('generated text', NOW())".

[0698] Input: The generated text.

[0699] Output: Text records saved in data storage.

[0700] Step 4:

[0701] Processing user requests and delivering text

[0702] Device: A user accesses a website or application and sends a request for content to the server.

[0703] Server: Searches and retrieves the generated text from data storage and delivers it to the user device.

[0704] Specific operation: Executes an SQL query such as "SELECT content FROM contents WHERE content_id=1234" and returns the result as an HTTP response.

[0705] Input: The user request.

[0706] Output: Text delivered to the user device.

[0707] Step 5:

[0708] Receiving user questions and generating answers

[0709] User: Enters a brand-related question through the device (e.g., "How do I use this product?").

[0710] Terminal: Sends a question to the server.

[0711] Server: Generates prompts to send questions to the generative model (e.g., "The user asked me, 'How do I use this product?' Please generate an answer to this question."), and calls the generative AI model to obtain the answer.

[0712] What it does: Creates prompts for GPT-4 and calls the API to generate answers.

[0713] Input: A question from the user.

[0714] Output: The generated answer (e.g., "Press the power button, then select the desired function in the settings menu.").

[0715] Step 6:

[0716] Submitting and viewing answers

[0717] Server: Sends the generated answer to the user device.

[0718] Terminal: Receives the answer and displays it to the user.

[0719] Specific operation: The server sends the answer as an HTTP response, and the device displays it.

[0720] Input: The generated answer.

[0721] Output: The answer that is displayed to the user.

[0722] Step 7:

[0723] Receiving feedback and generating responses

[0724] User: Enters feedback about the brand through a device (e.g., "I'm not happy with your recent product").

[0725] Device: Sends feedback to the server.

[0726] Server: Generates prompts to send feedback to the generative model (e.g., "The user has given us feedback saying, 'I'm dissatisfied with our recent product.' Please generate a response to this feedback."), and calls the generative AI model to obtain the response.

[0727] What it does: Creates a prompt for GPT-4 and calls an API to generate a response.

[0728] Input: User feedback.

[0729] Output: The generated response (e.g., "We take your feedback seriously and will use it to improve our products in the future.").

[0730] Step 8:

[0731] Sending and Displaying Responses

[0732] Server: Sends the generated response to the user device.

[0733] Terminal: Receives the response and displays it to the user.

[0734] Specific behavior: The server sends the response as an HTTP response, and the device displays it.

[0735] Input: The generated response.

[0736] Output: The response that is displayed to the user.

[0737] (Application example 1)

[0738] 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."

[0739] Modern brands are required to consistently communicate their values ​​and philosophy to consumers through various channels, including online and virtual. However, there is a lack of an appropriate system for increasing consumer engagement while maintaining a consistent brand message. In particular, virtual stores present a challenge, as consumers have difficulty providing real-time questions and feedback and receiving consistent answers and responses. Therefore, there is a need for a system that can consistently communicate brand values ​​and enable effective communication with consumers.

[0740] 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.

[0741] In this invention, the server includes means for calling a generative model and generating text content for expressing the brand's values, means for saving the generated content in a database, means for delivering the saved content to a terminal, means for receiving a question from a user through the terminal and generating an answer to the question using the generative model, means for receiving feedback from the user and responding to the feedback using the generative model, means for the terminal to display the answer to the question and the feedback response to the user, and means for expressing the brand's values ​​in a virtual store that the user can access using a smartphone or a head-mounted display. This enables effective communication with consumers in real time while maintaining consistency in the brand message.

[0742] A "generative model" is a type of artificial intelligence that generates text content to express a brand's values ​​and philosophy.

[0743] "Brand value" refers to the brand's philosophy, values, culture, and characteristics of its products and services.

[0744] "Content" refers to information materials such as text, images, and videos generated by a generative model to express the brand's values.

[0745] A "database" is a system for systematically storing generated content and for accessing and retrieving it as needed.

[0746] A "terminal" is a device used by a user to view content that expresses the brand's values, and includes a smartphone, a head-mounted display, etc.

[0747] A "virtual store" is a store that is not a physical store but that users can access online or in a virtual reality environment.

[0748] A "question" is a question that a user inputs via a terminal about a brand or its products.

[0749] "Feedback" refers to the opinions and impressions users provide about a brand or its products.

[0750] An "answer" is a response generated by a generative model in response to a user's question.

[0751] A "response" is a reply generated by a generative model based on user feedback.

[0752] A "smartphone" refers to a mobile information terminal that has communication capabilities and can be used by installing a variety of applications.

[0753] A "head-mounted display" is a display device worn on the head, and is used to display virtual reality and augmented reality.

[0754] This invention relates to a system that uses generative AI models to generate text content to express brand values, enhance communication with users, and provide a consistent brand image. This system is particularly useful in virtual stores to effectively communicate brand values.

[0755] System Program

[0756] The system includes the following components:

[0757] A server that invokes the generative model and generates text content to express the brand's values.

[0758] A server that stores generated content in a database

[0759] A server that delivers stored content to devices

[0760] A server that receives questions from users via their devices and generates answers to those questions using a generative model.

[0761] A server that receives feedback from users and responds to the feedback using a generative model

[0762] The device displays the answer to the question and the feedback response to the user.

[0763] The brand's values ​​are expressed in a virtual store, which users can access using a smartphone or head-mounted display.

[0764] System Description

[0765] Hardware and Software

[0766] Server: Use AWS or Google Cloud to host the generative AI model (e.g., OpenAI's GPT-3).

[0767] Device: Smartphone or head-mounted display (HMD).

[0768] Software: Build a web application using Flask and set up API endpoints.

[0769] Data processing and calculation

[0770] The server calls the generative AI model to generate text that expresses the brand's values ​​and stores it in a database. The generated text is then delivered to the device when the user accesses the virtual store, and the device displays the content.

[0771] When a user asks a question through the device, the server receives the question and generates an answer using the generative AI model. The generated answer is sent to the device and displayed to the user. Similarly, when feedback is received from the user, the server uses the generative AI model to generate a response to the feedback, sends it to the device and displays it to the user.

[0772] Specific examples

[0773] If a user asks "How do I use this product?" in a virtual store, the generative model generates an answer such as "First, turn on this product, then set it up using the dedicated app," and displays it to the user.

[0774] If a user sends feedback such as "I'm dissatisfied with our recent product," the generative model generates a response saying, "We take your feedback seriously and will use it to improve our products in the future," and displays it to the user.

[0775] Prompt Sentence Examples

[0776] Generate text that expresses your brand values

[0777] "Our brand is committed to providing a high-quality product. Generate messaging that expresses that philosophy."

[0778] Generating answers to user questions

[0779] "User Question: How do I use this product?\nAnswer:"

[0780] Generate responses to user feedback

[0781] "User feedback: I'm not happy with the product lately.\nResponse:"

[0782] merit

[0783] This system allows for effective real-time communication with consumers while maintaining consistency in brand messaging.

[0784] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0785] Step 1:

[0786] A user accesses the virtual store using a smartphone or a head-mounted display.

[0787] Input: User's access request

[0788] Output: Virtual store interface screen display

[0789] Specific operation: The terminal receives an access request from the user and sends it to the server. The server sends the interface data of the virtual store to the terminal, which then renders and displays it.

[0790] Step 2:

[0791] The server invokes the generative model to generate text content that expresses the brand's values.

[0792] Input: Prompt sentence to express brand values

[0793] Output: The generated text content

[0794] Specific operation: The server sends a prompt to the API of the generative model, which then generates text content. The generated text is returned to the server.

[0795] Step 3:

[0796] Store the generated text content in a database.

[0797] Input: Generated text content

[0798] Output: Text content stored in the database

[0799] Specific operation: The server receives the generated text content and stores it in a database.

[0800] Step 4:

[0801] The stored text content is delivered to the terminal, which displays the content to the user.

[0802] Input: Text content read from the database

[0803] Output: Text content displayed on the terminal

[0804] Specific operation: The server retrieves the necessary text content from the database and sends it to the terminal, which then displays the received text content to the user.

[0805] Step 5:

[0806] A user inputs a question through a terminal, and the terminal transmits the question to a server.

[0807] Input: User question

[0808] Output: The question data sent to the server

[0809] Specific operation: The terminal receives the question entered by the user in the virtual store and sends it to the server.

[0810] Step 6:

[0811] The server uses the generative model to generate answers to questions.

[0812] Input: User question and prompt

[0813] Output: Generated answer text

[0814] Specific operation: Based on the question received from the user, the server sends a request to the API of the generative model along with a prompt text. The generative model generates an answer, and the text is returned to the server.

[0815] Step 7:

[0816] The generated answer is sent to the terminal, which displays the answer to the user.

[0817] Input: Generated answer text

[0818] Output: The answer text that is displayed to the user

[0819] Specific operation: The server receives the generated answer text and sends it to the terminal, which then displays the received answer text to the user.

[0820] Step 8:

[0821] The user inputs feedback through the terminal, and the terminal transmits the feedback to the server.

[0822] Input: User feedback

[0823] Output: Feedback data sent to the server

[0824] Specific operation: The terminal receives the feedback entered by the user in the virtual store and sends it to the server.

[0825] Step 9:

[0826] The server uses the generative model to generate a response to the feedback.

[0827] Input: User feedback and prompts

[0828] Output: The generated response text

[0829] Specific operation: Based on the feedback received from the user, the server sends a request to the API of the generative model along with a prompt text. The generative model generates a response, and the text is sent back to the server.

[0830] Step 10:

[0831] The generated response is sent to the terminal, which displays the response to the user.

[0832] Input: Generated response text

[0833] Output: The response text that is displayed to the user

[0834] Specific operation: The server receives the generated response text and sends it to the terminal, which then displays the received response text to the user.

[0835] 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.

[0836] The present invention relates to a system that combines a generative model and an emotion engine to achieve consistent and effective communication between a brand and its users, thereby enhancing the value of the brand. Specific embodiments of the system are described below.

[0837] Telling your brand story

[0838] 1. Content Generation

[0839] Server: Calls the generative model to generate text content to express the brand's values. The generated text includes the brand's philosophy, culture, product features, etc.

[0840] Example: A generative model generates a message that reads, "Our brand offers environmentally friendly products for a sustainable future." This message is displayed on the brand's website and social media.

[0841] 2. Content storage and distribution

[0842] Server: Stores the generated content in a database and delivers it to the device, which displays the content to the user.

[0843] Example: A web server stores generated text in a database and displays it on a web page when accessed by a user.

[0844] Improved customer engagement

[0845] 1. Receiving and processing inquiries

[0846] User: Enters a question about the brand through a terminal, for example, can use the application's chatbot.

[0847] Terminal: Sends a question to the server.

[0848] Server: An emotion engine is used to recognize the user's emotions, and a generative model generates answers based on those emotions.

[0849] Example: When a user gently asks, "I'd like to know more about that product," the emotion engine recognizes the user's emotion as "interest," and the generative model generates an answer, "This product uses the latest technology and combines ease of use and performance," and displays it to the user.

[0850] Unifying the brand image

[0851] 1. Message Creation and Storage

[0852] Server: Uses the generative model to generate consistent brand messages and store them in a database.

[0853] Example: A generative model generates the message "Our brand puts quality first and provides peace of mind and satisfaction to all customers," and saves this as the brand's official statement.

[0854] 2. Processing Feedback

[0855] Users: Provide feedback to the brand through their devices.

[0856] Device: Sends feedback to the server.

[0857] Server: An emotion engine is used to recognize the user's emotions, and a generative model generates a response based on those emotions.

[0858] Example: If a user expresses anger by saying, "I'm dissatisfied with our latest product," the emotion engine recognizes the user's emotion as "dissatisfied," and the generative model generates a response saying, "We take your feedback seriously and will use it to improve our products in the future," and displays it to the user.

[0859] Overall processing flow

[0860] Users access a brand's website or application through their device and view the generated text content. When they ask a question or provide feedback, the device sends it to the server, which uses an emotion engine to recognize the user's emotion and a generative model to generate an answer or response, which is then sent back to the device and displayed to the user. This process ensures a consistent brand message and customer engagement.

[0861] Through the above means, the present invention can achieve consistent and effective communication between the brand and its customers, thereby improving the brand image.

[0862] The processing flow will be explained below.

[0863] Telling your brand story

[0864] Step 1:

[0865] Server: Calls the generative model and generates text content to express the brand values.

[0866] How it works: You provide the generative model with your brand's values ​​and philosophy as input, and the model generates a textual brand message.

[0867] Step 2:

[0868] Server: Stores the generated content in a database.

[0869] What it does: Runs a query to store the generated text data in a specific table in a database.

[0870] Step 3:

[0871] Server: Delivers stored content to devices.

[0872] How it works: A web server receives an HTTP request and returns a response containing generated content.

[0873] Step 4:

[0874] Terminal: Displays the content received from the server to the user.

[0875] What it does: Displays the received text content in the specified location on a web page or application.

[0876] Improved customer engagement

[0877] Step 1:

[0878] User: Enters a brand question through the device.

[0879] Action: A user enters a question into a form on a web page or application and clicks the submit button.

[0880] Step 2:

[0881] Terminal: Sends the user's question to the server.

[0882] What it does: Sends the question to the server as a POST request.

[0883] Step 3:

[0884] Server: Recognizes user emotions using an emotion engine.

[0885] How it works: Passes the received question data to the emotion engine to recognize the user's emotional state (e.g., joy, interest, anxiety).

[0886] Step 4:

[0887] Server: Uses a generative model to generate answers based on the user's emotions.

[0888] How it works: The generative model generates appropriate answer text, taking into account the perceived emotional state of the user.

[0889] Step 5:

[0890] Server: Generates and sends the answer back to the device.

[0891] Behavior: A response containing the generated answer text is sent to the terminal via HTTP.

[0892] Step 6:

[0893] Terminal: Display the answer to the user.

[0894] Behavior: Displays the received response text in a designated area on the user interface.

[0895] Unifying the brand image

[0896] Step 1:

[0897] Server: Uses generative models to generate consistent brand messages.

[0898] How it works: Provide the generative model with inputs based on your brand's personality and values ​​to generate a consistent message.

[0899] Step 2:

[0900] Server: Store this message in a database.

[0901] What it does: Executes a query to store the generated messages in a specific table in the database.

[0902] Step 3:

[0903] Users: Provide feedback about the brand through their devices.

[0904] What it does: Use the feedback form or rating feature to provide your thoughts and opinions, then click the submit button.

[0905] Step 4:

[0906] Device: Sends user feedback to the server.

[0907] Behavior: Sends the feedback content to the server as a POST request.

[0908] Step 5:

[0909] Server: Recognizes user emotions using an emotion engine.

[0910] How it works: Passes received feedback data to the emotion engine to recognize the user's emotional state (e.g., frustration, joy, excitement).

[0911] Step 6:

[0912] Server: Generates a response using a generative model based on the feedback.

[0913] How it works: The generative model generates an appropriate response text, taking into account the perceived emotional state of the user.

[0914] Step 7:

[0915] Server: Generates and sends the response to the device.

[0916] Behavior: A response containing the generated response text is sent to the terminal via HTTP.

[0917] Step 8:

[0918] Terminal: Displays the response to the user.

[0919] Behavior: Displays the received response text in a designated area on the user interface.

[0920] Example 2

[0921] 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."

[0922] With conventional brand communication systems, it was difficult to consistently convey brand values ​​and messages to users, and it was difficult to properly recognize customer sentiment and respond in real time. This made it difficult to unify brand image and improve customer engagement.

[0923] 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.

[0924] In this invention, the server includes means for calling a generative model to generate text data for expressing the brand's value, means for saving the generated text data in a data storage device, means for distributing the saved text data to an information processing device, means for receiving inquiries from users, means for recognizing user emotions from the received inquiries using a sentiment analysis device and generating a response using the generative model, means for transmitting the generated response to the information processing device, means for generating a consistent brand message using the generative model and saving it in the data storage device, means for receiving evaluations from users, means for recognizing user emotions using the sentiment analysis device based on the received evaluations and generating a response using the generative model, means for transmitting the generated response to the information processing device, and means for the terminal to display the text data, the response, and the response to the user, thereby enabling consistent brand messaging and recognizing customer emotions to generate and display appropriate responses in real time.

[0925] A "generative model" is a model that uses machine learning techniques to generate text or data based on specific prompts.

[0926] "Text data" refers to text content generated by a generative model to express brand values ​​and messages.

[0927] "Data storage device" refers to any storage device for storing generated text data and brand messages.

[0928] The term "information processing device" refers to a terminal in general that displays data and responses delivered from a server to a user.

[0929] An "inquiry" is a question or request sent by a user via a terminal about a brand.

[0930] An "emotion analyzer" is an analytical engine that analyzes the content of user inquiries and feedback and recognizes their emotions.

[0931] An "answer" is a response message generated in response to a user inquiry using a sentiment analyzer and a generative model.

[0932] A "rating" is feedback or opinion provided by a user to a brand via a device.

[0933] A "response" is a reply message from a brand that is generated using a generative model based on a user's evaluation.

[0934] A "brand message" is text content generated using a generative model to consistently express brand values ​​and ideals.

[0935] The present invention is a brand value maximization system that combines a generative model and an emotion engine. This system is implemented with the following specific hardware and software configuration.

[0936] System Configuration

[0937] 1. Server

[0938] Generative models: Includes generative models that use machine learning technology (e.g., GPT-4), which generate text data to express brand values.

[0939] Data storage device: A database for storing generated text data and brand messages.

[0940] Sentiment analyzer: An analytical engine that analyzes the content of user inquiries and feedback and recognizes their emotions (e.g., Azure Sentiment Analysis).

[0941] 2. Terminal

[0942] Information processing device: A device (e.g., smartphone, PC, tablet) that displays data and responses delivered from the server to the user.

[0943] 3. Users

[0944] Access: Access the system through a brand's website or application to view content, submit inquiries, and provide feedback.

[0945] Operation explanation

[0946] Content Generation and Delivery

[0947] Server: Calls the generative model and generates sentence data that expresses the brand's values. For example, by inputting the prompt "What is your brand's mission?" into the generative model, the generated sentence data is "Our brand values ​​sustainability and provides environmentally friendly products."

[0948] Server: The generated text data is stored in a data storage device, and the stored data can be accessed later.

[0949] Server: The stored text data is delivered to the user's device. For example, this message is displayed when the user visits the brand's website.

[0950] Device: The delivered text data is displayed to the user. For example, a message such as "Our brand values ​​sustainability and offers environmentally friendly products" is displayed on a web page or app.

[0951] User queries and response generation

[0952] User: Enter a brand-related inquiry through the terminal. For example, enter "What are the features of this product?"

[0953] Terminal: Sends the inquiry to the server.

[0954] Server: Recognizes the user's emotion using a sentiment analyzer. For example, recognizes the user's emotion of "interest" from the inquiry.

[0955] Server: Uses a generative model to generate an answer based on the recognized sentiment. For example, it might generate "This product uses the latest technology, combining ease of use with high performance."

[0956] Server: Sends the generated answer to the device.

[0957] Terminal: Displays submitted answers to the user.

[0958] User feedback and response generation

[0959] User: Enters their feedback about the brand through a device. For example, they might enter, "I'm not happy with your recent product."

[0960] Device: Sends feedback to the server.

[0961] Server: Uses a sentiment analyzer to recognize the sentiment of the feedback. For example, recognize the user's sentiment of "dissatisfied" from the feedback.

[0962] Server: Uses a generative model to generate a response based on the recognized sentiment. For example, it might generate "We take your feedback seriously and will use it to improve our products in the future."

[0963] Server: Generates and sends the response to the device.

[0964] Terminal: Displays the sent response to the user.

[0965] The system of the present invention allows brands to achieve high customer engagement by maintaining consistent messaging and responding to user emotions.

[0966] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0967] Step 1:

[0968] Server: Calls the generative model to express the brand values. By providing the prompt sentence as input to the generative model, it generates text data related to the brand’s philosophy and culture.

[0969] What it does: Sends an API request to a generative AI model (e.g., GPT-4) and enters the prompt, "What is your brand's mission statement?"

[0970] Data processing: The generative model generates sentence data based on the prompt sentence.

[0971] Output: The sentence "Our brand values ​​sustainability and offers environmentally friendly products" is generated.

[0972] Step 2:

[0973] Server: Stores the generated text data in a data storage device. Receives the generated text data as input and inserts it into the database.

[0974] Specific operation: Uses an SQL query to save text data to the database table "content".

[0975] Data processing: The generated text data is converted into a database format and saved.

[0976] Output: The sentence data is saved in the database.

[0977] Step 3:

[0978] Server: Delivers the saved text data to the user's device. Receives the user's request as input, retrieves the saved text data, and sends it to the device.

[0979] Specific behavior: Processes HTTP requests and retrieves stored text data from a database.

[0980] Data processing: Select the appropriate text data based on the request content and convert it into a format that can be sent to the terminal.

[0981] Output: The message "Our brand is committed to sustainability and offers environmentally friendly products" is sent to the device.

[0982] Step 4:

[0983] Terminal: Displays the delivered text data to the user. It receives text data received from the server as input for display and renders it in a form that is visible to the user.

[0984] Specific behavior: Renders submitted HTML and text data in web pages and applications.

[0985] Data processing: Display the received data in a format suitable for the user interface.

[0986] Output: A webpage will display the following statement: "Our brand is committed to sustainability and offers environmentally friendly products."

[0987] Step 5:

[0988] User: Enters a brand-related inquiry through a terminal. The user enters the question as input into an interface such as a chatbot and sends it.

[0989] Specific action: Enter "Please tell me the features of this product" into a chatbot or inquiry form and press the send button.

[0990] Data processing: Taking user input and converting it into a format that can be sent to the server.

[0991] Output: The question data "What are the features of this product?" is sent to the server.

[0992] Step 6:

[0993] Server: Recognizes the user's sentiment using a sentiment analyzer. The question data received as input is sent to the sentiment analyzer, and the sentiment is recognized.

[0994] Specific operation: The question content is sent to the sentiment analysis API and emotions such as "interest" are analyzed.

[0995] Data processing: The question data is analyzed by a sentiment analyzer to generate user sentiment data.

[0996] Output: Emotion data recognized as "interest" is generated.

[0997] Step 7:

[0998] Server: Uses a generative model to generate answers based on the recognized emotions. The generative model receives emotion data and question data as input and generates an appropriate answer.

[0999] Specific behavior: The prompt sentence "The user is interested in the features of this product" is input into the generative model, and an answer is generated.

[1000] Data processing: The generative model generates answer data based on the prompt sentence.

[1001] Output: The answer "This product uses the latest technology and is both easy to use and highly functional."

[1002] Step 8:

[1003] Server: Sends the generated answer to the terminal. Receives the generated answer data as input and converts it into a format that can be sent to the terminal.

[1004] Specific operation: Convert the response data into JSON format and send it to the device.

[1005] Data processing: Converting response data into a format for transmission.

[1006] Output: The generated answer data is sent to the device.

[1007] Step 9:

[1008] Terminal: displays submitted answers to the user. It takes the answer data received as input for display and renders it in a form that is visible to the user.

[1009] Specific behavior: Display the message "This product uses the latest technology, combining ease of use and high performance" on the chatbot UI.

[1010] Data processing: Display the received data in a format suitable for the user interface.

[1011] Output: The answer is displayed to the user.

[1012] Step 10:

[1013] User: Enters feedback about the brand through a terminal. The feedback is entered as input into the interface and sent.

[1014] Specific action: Enter "I'm dissatisfied with the recent product" into the feedback form and press the submit button.

[1015] Data processing: Taking user input and converting it into a format that can be sent to the server.

[1016] Output: The feedback data is sent to the server.

[1017] Step 11:

[1018] Server: Recognizes the feedback using a sentiment analyzer. The received feedback data is sent to the sentiment analyzer to recognize the user's emotions.

[1019] What it does: Sends feedback data to a sentiment analysis API and analyzes sentiment, such as "dissatisfied."

[1020] Data processing: Analyze the feedback data and generate user emotion data.

[1021] Output: Emotion data recognized as "dissatisfied" is generated.

[1022] Step 12:

[1023] Server: Uses a generative model to generate responses based on the recognized emotions. The generative model receives emotion data and feedback data as input and generates an appropriate response.

[1024] Specific behavior: The prompt sentence "The user is dissatisfied" is input into the generative model, and a response is generated.

[1025] Data processing: The generative model generates response data based on the prompt sentence.

[1026] Output: The response data generated is "We take your feedback seriously and will use it to improve our products in the future."

[1027] Step 13:

[1028] Server: Sends the generated response to the terminal. Receives the generated response data as input and converts it into a format that can be sent to the terminal.

[1029] Specific operation: Converts response data into JSON format and sends it to the terminal.

[1030] Data processing: Converts response data into a format for transmission.

[1031] Output: The generated response data is sent to the terminal.

[1032] Step 14:

[1033] Terminal: displays the sent response to the user. It takes the response data received as input for display and renders it in a form that is visible to the user.

[1034] Specific behavior: Display a message on the feedback form UI saying, "We take your feedback seriously and will use it to improve our products in the future."

[1035] Data processing: Display the received data in a format suitable for the user interface.

[1036] Output: The response is displayed to the user.

[1037] (Application example 2)

[1038] 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."

[1039] Today's customers expect consistent communication with brands, so it is important to find ways to effectively communicate the brand's values. However, traditional methods have made it difficult to recognize customer emotions in real time and respond appropriately based on them. Furthermore, in physical stores, there is a lack of support for staff to respond quickly and appropriately to individual customers' emotions and requests. This can lead to a decline in brand consistency and customer satisfaction.

[1040] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1041] In this invention, the server includes means for calling the generative model and generating text content for expressing the brand's values, means for saving the generated content in a database, means for delivering the saved content to a terminal, means for the terminal to display the content to the user, means for recognizing customer emotions in real time on a smart device, means for analyzing the recognized emotion data with an emotion engine, means for the generative model to generate an optimal response based on the analysis results, and means for displaying the generated response to store staff. This enables staff in physical stores to respond appropriately to customers' emotions in real time, thereby realizing consistent brand messaging and improved customer satisfaction.

[1042] A "generative model" is an algorithm or system that automatically generates text content and answers to express a brand's values.

[1043] "Brand value" is a concept that expresses a brand's philosophy, culture, product features, etc., and is an important element for appealing to customers.

[1044] "Text content" refers to textual information generated using a generative model, including brand messaging, product descriptions, etc.

[1045] A "database" is a system for storing and managing generated content and customer feedback.

[1046] A "terminal" is a device used by a user to display content and answers, and includes a smartphone, tablet, etc.

[1047] "Smart devices" are wearable devices equipped with cameras and computing power that are used to recognize customer emotions.

[1048] An "emotion engine" is a software system for analyzing recognized emotion data and identifying a customer's emotional state.

[1049] "Analysis results" are the results of customer emotion data analyzed by the emotion engine and used as input for the generative model.

[1050] "Store staff" refers to employees who deal with customers in physical stores and who wear smart devices.

[1051] To implement the present invention, the following system is used.

[1052] The server at the heart of the system first invokes the generative model to generate text content to express the brand's values. The generated text content reflects the brand's philosophy, culture, product features, etc., and functions as an effective message to customers. The generated content is stored in a database and distributed to devices as needed. The devices then display the content to the user.

[1053] Next, smart devices are used in brick-and-mortar stores. Wearable devices such as smart glasses capture images of customers' faces with a camera and recognize their emotions in real time. An emotion engine analyzes the recognized emotion data and identifies the customer's emotional state. Based on the analysis results, the server uses a generative model to generate the optimal response or answer. The generated response is displayed to store staff wearing smart glasses.

[1054] For example, consider the process when a customer asks a question about a product. The customer's question and the emotion recognized in real time are sent to the server. The emotion engine analyzes whether the customer is showing "interest," and based on the analysis results, the generative model generates a detailed product description. The generated description is then displayed to store staff through smart glasses, who then provide the customer with the appropriate information.

[1055] Examples:

[1056] Smart Devices: Smart Glasses

[1057] Software libraries: OpenCV, DeepFace

[1058] Generative model: GPT-4

[1059] Emotion Engine: DeepFace's emotion recognition module

[1060] Example prompt sentence:

[1061] Customer Question: "What's special about this product?"

[1062] Customer sentiment: "Interested"

[1063] Based on this prompt, a generative model generates text that is delivered to the device, resulting in more relevant information for customers in real time, improving consistent brand messaging and customer engagement.

[1064] In this way, the present invention realizes a system that enables consistent and effective communication between brands and customers in physical stores, thereby enhancing brand value.

[1065] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1066] Step 1:

[1067] The server calls the generative model and generates text content to express the brand's values. The input is information such as the brand's philosophy, culture, and product features, and the output is the generated text content. The generative model uses a natural language processing algorithm to generate text based on this input data.

[1068] Step 2:

[1069] The server stores the generated text content in a database. The input is the text content generated in step 1, and the output is the stored database entry. The database system stores the text content in an appropriate format, making it ready for later distribution.

[1070] Step 3:

[1071] The server delivers the stored content to the device. The input is the text content stored in the database, and the output is the text content sent to the device. The data is sent to the required device using the HTTP protocol or a specific API.

[1072] Step 4:

[1073] The terminal displays the content to the user. The input is the text content delivered from the server, and the output is the display on which the user views the content. The user interface displays the text in an appropriate format.

[1074] Step 5:

[1075] The smart device captures a customer's face with a camera and recognizes their emotions in real time. The input is the image data captured by the camera, and the output is the recognized emotion data. OpenCV and DeepFace libraries are used for image analysis and emotion recognition.

[1076] Step 6:

[1077] The emotion engine analyzes the recognized emotion data and identifies the customer's emotional state. The input is the emotion data obtained in step 5, and the output is the analysis result. The analysis result includes the customer's specific emotional state (e.g., "interested" or "anger").

[1078] Step 7:

[1079] The server uses a generative model based on the analysis results to generate the optimal response or answer. The input is the analysis results and the customer's question, and the output is the generated answer or response. A generative model (e.g., GPT-4) is used to generate the optimal text based on the analysis results.

[1080] Step 8:

[1081] The generated answer is sent to the terminal. The input is the text content generated by the server, and the output is the text content sent to the terminal. The server sends the generated text data to the terminal.

[1082] Step 9:

[1083] The terminal displays the generated answer to the store staff. The input is text content sent from the server, and the output is a display that the staff can view. A device such as smart glasses can visually present the generated text to the staff, allowing them to respond immediately.

[1084] 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.

[1085] 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.

[1086] 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.

[1087] [Third embodiment]

[1088] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1089] 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.

[1090] 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).

[1091] 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.

[1092] 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.

[1093] 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).

[1094] 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. 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.

[1095] 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.

[1096] 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.

[1097] 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.

[1098] 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.

[1099] 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."

[1100] The present invention relates to a system for generating text content that expresses brand values ​​using a generative model, enhancing communication with users, and providing a consistent brand image. Specific embodiments of the system are described below.

[1101] Telling your brand story

[1102] 1. Content Generation

[1103] Server: Using a generative model, the server generates text content to express the brand's values. The generated text includes the brand's philosophy, culture, product features, etc.

[1104] Example: A generative model generates a message that reads, "Our brand offers environmentally friendly products for a sustainable future." This message is displayed on the brand's website and social media.

[1105] 2. Content storage and distribution

[1106] Server: Stores the generated content in a database and delivers it to the device, which displays the content to the user.

[1107] Example: A web server stores generated text in a database and displays it on a web page when accessed by a user.

[1108] Improved customer engagement

[1109] 1. Receiving and processing inquiries

[1110] User: Enters a question about the brand through a terminal, for example, can use the application's chatbot.

[1111] Terminal: Sends a question to the server.

[1112] Server: Uses the generative model to generate answers to user questions.

[1113] Example: When a user asks, "How do I use this product?", the server generates an answer such as, "To use this product, press the power button and then select the desired function from the settings menu," and displays it to the user.

[1114] Unifying the brand image

[1115] 1. Message Creation and Storage

[1116] Server: Uses the generative model to generate consistent brand messages and store them in a database.

[1117] Example: A generative model generates the message "Our brand puts quality first and provides peace of mind and satisfaction to all customers," and saves this as the brand's official statement.

[1118] 2. Processing Feedback

[1119] Users: Provide feedback to the brand through their devices.

[1120] Device: Sends feedback to the server.

[1121] Server: Uses a generative model to generate consistent responses based on user feedback.

[1122] Example: When a user submits feedback saying, "I'm not happy with the product recently," the server generates a response saying, "We take your feedback seriously and will use it to improve our products in the future," and displays it to the user.

[1123] Overall processing flow

[1124] Users access a brand's website or application through their device and view generated text content. When they ask a question or provide feedback, the device sends it to the server, which uses the generative model to generate an answer or response, which is then sent back to the device and displayed to the user. This process maintains a consistent brand message and customer engagement.

[1125] Through the above means, the present invention can achieve consistent and effective communication between the brand and its customers, thereby improving the brand image.

[1126] The processing flow will be explained below.

[1127] Telling your brand story

[1128] Step 1:

[1129] Server: Calls the generative model and generates text content to express the brand values.

[1130] How it works: You provide the generative model with your brand's values ​​and philosophy as input, and the model generates a textual brand message.

[1131] Step 2:

[1132] Server: Stores the generated content in a database.

[1133] What it does: Runs a query to store the generated text data into a specific table in a database.

[1134] Step 3:

[1135] Server: Delivers stored content to devices.

[1136] How it works: A web server receives an HTTP request and returns a response containing generated content.

[1137] Step 4:

[1138] Terminal: Displays the content received from the server to the user.

[1139] What it does: Displays the received text content in the specified location on a web page or application.

[1140] Improved customer engagement

[1141] Step 1:

[1142] User: Enters a brand question through the device.

[1143] Action: A user enters a question into a form on a web page or application and clicks the submit button.

[1144] Step 2:

[1145] Terminal: Sends the user's question to the server.

[1146] What it does: Sends the question to the server as a POST request.

[1147] Step 3:

[1148] Server: Uses the generative model to generate answers to user questions.

[1149] How it works: Pass the received question to a generative model to generate an appropriate answer text.

[1150] Step 4:

[1151] Server: Generates and sends the answer back to the device.

[1152] Behavior: A response containing the generated answer text is sent to the terminal via HTTP.

[1153] Step 5:

[1154] Terminal: Display the answer to the user.

[1155] Behavior: Displays the received response text in a designated area on the user interface.

[1156] Unifying the brand image

[1157] Step 1:

[1158] Server: Uses generative models to generate consistent brand messages.

[1159] How it works: Provide the generative model with inputs based on your brand's personality and values ​​to generate a consistent message.

[1160] Step 2:

[1161] Server: Store this message in a database.

[1162] What it does: Executes a query to store the generated messages in a specific table in the database.

[1163] Step 3:

[1164] Users: Provide feedback about the brand through their devices.

[1165] What it does: Use the feedback form or rating feature to provide your thoughts and opinions, then click the submit button.

[1166] Step 4:

[1167] Device: Sends user feedback to the server.

[1168] Behavior: Sends the feedback content to the server as a POST request.

[1169] Step 5:

[1170] Server: Generates a response using a generative model based on the feedback.

[1171] How it works: Passes received feedback data to a generative model to generate appropriate response text.

[1172] Step 6:

[1173] Server: Generates and sends the response to the device.

[1174] Behavior: A response containing the generated response text is sent to the terminal via HTTP.

[1175] Step 7:

[1176] Terminal: Displays the response to the user.

[1177] Behavior: Displays the received response text in a designated area on the user interface.

[1178] Example 1

[1179] 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."

[1180] The lack of a system to provide a consistent message and ensure effective communication between the brand and customers leads to a lack of consistency in the brand image and reduced customer engagement.

[1181] 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.

[1182] In this invention, the server includes means for calling a generative model and generating text to express the brand's values, means for saving the generated text in data storage, means for delivering the saved text to a user device, means for the user device to display the text to the user, means for receiving a query from the user, means for processing the received query using a generative model and generating a response, means for transmitting the generated response to the user device, means for the user device to display the response to the user, means for generating a consistent brand message using the generative model, means for saving the message in data storage, means for receiving opinions from the user, means for generating a response based on the received opinions using the generative model, means for transmitting the generated response to the user device, and means for the user device to display the response to the user, thereby enabling the provision of a consistent brand message and effective communication with customers.

[1183] A "generative model" is a system that uses artificial intelligence techniques to generate natural language text based on input prompts.

[1184] "Brand value" is a general term for the unique philosophy, culture, beliefs, quality, etc. of a company or product, and refers to the added value that it provides to customers.

[1185] "Text" refers to the string of characters output by the generative model, and refers to sentences or messages used to express the brand's values.

[1186] "Data storage" refers to electronic storage devices for storing generated text and other digital data.

[1187] A "user device" is an electronic device that can be directly operated by a user, and includes a personal computer, a smartphone, a tablet, and the like.

[1188] "Inquiry" means a question or request submitted by a User seeking specific information or support.

[1189] A "response" is an answer that a generative model generates to a query, and includes information that is provided to the user.

[1190] "Opinions" refers to feedback and comments provided by users, including satisfaction with products and services and requests.

[1191] A "message" is a coherent text generated by a generative model that expresses the brand's values, philosophy, etc.

[1192] This invention uses a generative AI model to generate text content that expresses brand values, strengthens communication with users, and provides a consistent brand image.

[1193] Hardware and software configuration

[1194] This system mainly uses the following hardware and software:

[1195] Server: Provides the computational resources to run generative AI models (e.g., GPT-4) and also uses database systems such as MySQL for data storage.

[1196] Terminal: A device operated by a user (such as a computer, smartphone, or tablet) on which a web browser or application runs.

[1197] Generative AI models: Models that generate text based on prompts, such as OpenAI's APIs (e.g., GPT-4).

[1198] Network: An internet connection for data communication between the server and the device.

[1199] Data processing and calculation

[1200] 1. Server: The server prepares prompts that users input to the generative AI model. An example prompt is, "Generate text that expresses our brand philosophy. Our focus is on providing environmentally friendly products."

[1201] 2. Content generation: The server sends the prompt to the generative model and receives generated text, such as "Our brand offers environmentally friendly products for a sustainable future."

[1202] 3. Text storage: Store the generated text in a data storage, such as a database like MySQL, in the appropriate format (e.g., timestamp or content ID).

[1203] 4. Display: When a user accesses a website or application on their device, the server receives a request to display the stored text and provides the appropriate content, which is then displayed on the user's device.

[1204] Examples and prompts

[1205] 1. Content generation prompt example:

[1206] "Generate text content that expresses your brand's values. The focus is on environmentally friendly product offerings."

[1207] 2. Example prompts for generating answers to user questions:

[1208] "A user asks, 'How do I use this product?' Please generate an answer to this question."

[1209] 3. Example prompts for generating responses to feedback:

[1210] "A user has given us feedback saying, 'I'm not happy with the product lately.' Please generate a response to this feedback."

[1211] With this system, the server uses a generative model to generate various texts, saves the generated texts in data storage, and delivers them to user devices. This allows for the provision of consistent brand messages and improved customer engagement. It also generates and provides quick and consistent answers to questions and feedback entered by users. This series of processes allows brands to achieve effective and consistent communication and improve their brand image.

[1212] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1213] Step 1:

[1214] Content generation prompt preparation

[1215] Server: Prepares prompts to send to the generative AI model based on information about the brand and its philosophy.

[1216] Action: Set a prompt like, "Generate text that expresses our brand philosophy. Our focus is on providing environmentally friendly products."

[1217] Input: Basic information about your brand's values ​​and philosophy.

[1218] Output: The prompt to send to the generative AI model.

[1219] Step 2:

[1220] Text generation using generative AI models

[1221] Server: Sends the prepared prompt sentence to a generative AI model (e.g., GPT-4) and retrieves the generated text.

[1222] Specific operation: Call the GPT-4 API to generate "text that expresses the brand's values" and receive the results.

[1223] Input: The prepared prompt statement.

[1224] Output: Generated text such as "Our brand offers eco-conscious products for a sustainable future."

[1225] Step 3:

[1226] Saving the generated text

[1227] Server: Stores the generated text in a data storage (e.g., MySQL database).

[1228] Specific behavior: Executes an SQL query such as "INSERT INTO contents (content, created_at) VALUES ('generated text', NOW())".

[1229] Input: The generated text.

[1230] Output: Text records saved in data storage.

[1231] Step 4:

[1232] Processing user requests and delivering text

[1233] Device: A user accesses a website or application and sends a request for content to the server.

[1234] Server: Searches and retrieves the generated text from data storage and delivers it to the user device.

[1235] Specific operation: Executes an SQL query such as "SELECT content FROM contents WHERE content_id=1234" and returns the result as an HTTP response.

[1236] Input: The user request.

[1237] Output: Text delivered to the user device.

[1238] Step 5:

[1239] Receiving user questions and generating answers

[1240] User: Enters a brand-related question through the device (e.g., "How do I use this product?").

[1241] Terminal: Sends a question to the server.

[1242] Server: Generates prompts to send questions to the generative model (e.g., "The user asked me, 'How do I use this product?' Please generate an answer to this question."), and calls the generative AI model to obtain the answer.

[1243] What it does: Creates prompts for GPT-4 and calls the API to generate answers.

[1244] Input: A question from the user.

[1245] Output: The generated answer (e.g., "Press the power button, then select the desired function in the settings menu.").

[1246] Step 6:

[1247] Submitting and viewing answers

[1248] Server: Sends the generated answer to the user device.

[1249] Terminal: Receives the answer and displays it to the user.

[1250] Specific operation: The server sends the answer as an HTTP response, and the device displays it.

[1251] Input: The generated answer.

[1252] Output: The answer that is displayed to the user.

[1253] Step 7:

[1254] Receiving feedback and generating responses

[1255] User: Enters feedback about the brand through a device (e.g., "I'm not happy with your recent product").

[1256] Device: Sends feedback to the server.

[1257] Server: Generates prompts to send feedback to the generative model (e.g., "The user has given us feedback saying, 'I'm dissatisfied with our recent product.' Please generate a response to this feedback."), and calls the generative AI model to obtain the response.

[1258] What it does: Creates a prompt for GPT-4 and calls an API to generate a response.

[1259] Input: User feedback.

[1260] Output: The generated response (e.g., "We take your feedback seriously and will use it to improve our products in the future.").

[1261] Step 8:

[1262] Sending and Displaying Responses

[1263] Server: Sends the generated response to the user device.

[1264] Terminal: Receives the response and displays it to the user.

[1265] Specific behavior: The server sends the response as an HTTP response, and the device displays it.

[1266] Input: The generated response.

[1267] Output: The response that is displayed to the user.

[1268] (Application example 1)

[1269] 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."

[1270] Modern brands are required to consistently communicate their values ​​and philosophy to consumers through various channels, including online and virtual. However, there is a lack of an appropriate system for increasing consumer engagement while maintaining a consistent brand message. In particular, virtual stores present a challenge, as consumers have difficulty providing real-time questions and feedback and receiving consistent answers and responses. Therefore, there is a need for a system that can consistently communicate brand values ​​and enable effective communication with consumers.

[1271] 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.

[1272] In this invention, the server includes means for calling a generative model and generating text content for expressing the brand's values, means for saving the generated content in a database, means for delivering the saved content to a terminal, means for receiving a question from a user through the terminal and generating an answer to the question using the generative model, means for receiving feedback from the user and responding to the feedback using the generative model, means for the terminal to display the answer to the question and the feedback response to the user, and means for expressing the brand's values ​​in a virtual store that the user can access using a smartphone or a head-mounted display. This enables effective communication with consumers in real time while maintaining consistency in the brand message.

[1273] A "generative model" is a type of artificial intelligence that generates text content to express a brand's values ​​and philosophy.

[1274] "Brand value" refers to the brand's philosophy, values, culture, and characteristics of its products and services.

[1275] "Content" refers to information materials such as text, images, and videos generated by a generative model to express the brand's values.

[1276] A "database" is a system for systematically storing generated content and for accessing and retrieving it as needed.

[1277] A "terminal" is a device used by a user to view content that expresses the brand's values, and includes a smartphone, a head-mounted display, etc.

[1278] A "virtual store" is a store that is not a physical store but that users can access online or in a virtual reality environment.

[1279] A "question" is a question that a user inputs via a terminal about a brand or its products.

[1280] "Feedback" refers to the opinions and impressions users provide about a brand or its products.

[1281] An "answer" is a response generated by a generative model in response to a user's question.

[1282] A "response" is a reply generated by a generative model based on user feedback.

[1283] A "smartphone" refers to a mobile information terminal that has communication capabilities and can be used by installing a variety of applications.

[1284] A "head-mounted display" is a display device worn on the head, and is used to display virtual reality and augmented reality.

[1285] This invention relates to a system that uses generative AI models to generate text content to express brand values, enhance communication with users, and provide a consistent brand image. This system is particularly useful in virtual stores to effectively communicate brand values.

[1286] System Program

[1287] The system includes the following components:

[1288] A server that invokes the generative model and generates text content to express the brand's values.

[1289] A server that stores generated content in a database

[1290] A server that delivers stored content to devices

[1291] A server that receives questions from users via their devices and generates answers to those questions using a generative model.

[1292] A server that receives feedback from users and responds to the feedback using a generative model

[1293] The device displays the answer to the question and the feedback response to the user.

[1294] The brand's values ​​are expressed in a virtual store, which users can access using a smartphone or head-mounted display.

[1295] System Description

[1296] Hardware and Software

[1297] Server: Use AWS or Google Cloud to host the generative AI model (e.g., OpenAI's GPT-3).

[1298] Device: Smartphone or head-mounted display (HMD).

[1299] Software: Build a web application using Flask and set up API endpoints.

[1300] Data processing and calculation

[1301] The server calls the generative AI model to generate text that expresses the brand's values ​​and stores it in a database. The generated text is then delivered to the device when the user accesses the virtual store, and the device displays the content.

[1302] When a user asks a question through the device, the server receives the question and generates an answer using the generative AI model. The generated answer is sent to the device and displayed to the user. Similarly, when feedback is received from the user, the server uses the generative AI model to generate a response to the feedback, sends it to the device and displays it to the user.

[1303] Specific examples

[1304] If a user asks "How do I use this product?" in a virtual store, the generative model generates an answer such as "First, turn on this product, then set it up using the dedicated app," and displays it to the user.

[1305] If a user sends feedback such as "I'm dissatisfied with our recent product," the generative model generates a response saying, "We take your feedback seriously and will use it to improve our products in the future," and displays it to the user.

[1306] Prompt Sentence Examples

[1307] Generate text that expresses your brand values

[1308] "Our brand is committed to providing a high-quality product. Generate messaging that expresses that philosophy."

[1309] Generating answers to user questions

[1310] "User Question: How do I use this product?\nAnswer:"

[1311] Generate responses to user feedback

[1312] "User feedback: I'm not happy with the product lately.\nResponse:"

[1313] merit

[1314] This system allows for effective real-time communication with consumers while maintaining consistency in brand messaging.

[1315] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1316] Step 1:

[1317] A user accesses the virtual store using a smartphone or a head-mounted display.

[1318] Input: User's access request

[1319] Output: Virtual store interface screen display

[1320] Specific operation: The terminal receives an access request from the user and sends it to the server. The server sends the interface data of the virtual store to the terminal, which then renders and displays it.

[1321] Step 2:

[1322] The server invokes the generative model to generate text content that expresses the brand's values.

[1323] Input: Prompt sentence to express brand values

[1324] Output: The generated text content

[1325] Specific operation: The server sends a prompt to the API of the generative model, which then generates text content. The generated text is returned to the server.

[1326] Step 3:

[1327] Store the generated text content in a database.

[1328] Input: Generated text content

[1329] Output: Text content stored in the database

[1330] Specific operation: The server receives the generated text content and stores it in a database.

[1331] Step 4:

[1332] The stored text content is delivered to the terminal, which displays the content to the user.

[1333] Input: Text content read from the database

[1334] Output: Text content displayed on the terminal

[1335] Specific operation: The server retrieves the necessary text content from the database and sends it to the terminal, which then displays the received text content to the user.

[1336] Step 5:

[1337] A user inputs a question through a terminal, and the terminal transmits the question to a server.

[1338] Input: User question

[1339] Output: The question data sent to the server

[1340] Specific operation: The terminal receives the question entered by the user in the virtual store and sends it to the server.

[1341] Step 6:

[1342] The server uses the generative model to generate answers to questions.

[1343] Input: User question and prompt

[1344] Output: Generated answer text

[1345] Specific operation: Based on the question received from the user, the server sends a request to the API of the generative model along with a prompt text. The generative model generates an answer, and the text is returned to the server.

[1346] Step 7:

[1347] The generated answer is sent to the terminal, which displays the answer to the user.

[1348] Input: Generated answer text

[1349] Output: The answer text that is displayed to the user

[1350] Specific operation: The server receives the generated answer text and sends it to the terminal, which then displays the received answer text to the user.

[1351] Step 8:

[1352] The user inputs feedback through the terminal, and the terminal transmits the feedback to the server.

[1353] Input: User feedback

[1354] Output: Feedback data sent to the server

[1355] Specific operation: The terminal receives the feedback entered by the user in the virtual store and sends it to the server.

[1356] Step 9:

[1357] The server uses the generative model to generate a response to the feedback.

[1358] Input: User feedback and prompts

[1359] Output: The generated response text

[1360] Specific operation: Based on the feedback received from the user, the server sends a request to the API of the generative model along with a prompt text. The generative model generates a response, and the text is sent back to the server.

[1361] Step 10:

[1362] The generated response is sent to the terminal, which displays the response to the user.

[1363] Input: Generated response text

[1364] Output: The response text that is displayed to the user

[1365] Specific operation: The server receives the generated response text and sends it to the terminal, which then displays the received response text to the user.

[1366] 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.

[1367] The present invention relates to a system that combines a generative model and an emotion engine to achieve consistent and effective communication between a brand and its users, thereby enhancing the value of the brand. Specific embodiments of the system are described below.

[1368] Telling your brand story

[1369] 1. Content Generation

[1370] Server: Calls the generative model to generate text content to express the brand's values. The generated text includes the brand's philosophy, culture, product features, etc.

[1371] Example: A generative model generates a message that reads, "Our brand offers environmentally friendly products for a sustainable future." This message is displayed on the brand's website and social media.

[1372] 2. Content storage and distribution

[1373] Server: Stores the generated content in a database and delivers it to the device, which displays the content to the user.

[1374] Example: A web server stores generated text in a database and displays it on a web page when accessed by a user.

[1375] Improved customer engagement

[1376] 1. Receiving and processing inquiries

[1377] User: Enters a question about the brand through a terminal, for example, can use the application's chatbot.

[1378] Terminal: Sends a question to the server.

[1379] Server: An emotion engine is used to recognize the user's emotions, and a generative model generates answers based on those emotions.

[1380] Example: When a user gently asks, "I'd like to know more about that product," the emotion engine recognizes the user's emotion as "interest," and the generative model generates an answer, "This product uses the latest technology and combines ease of use and performance," and displays it to the user.

[1381] Unifying the brand image

[1382] 1. Message Creation and Storage

[1383] Server: Uses the generative model to generate consistent brand messages and store them in a database.

[1384] Example: A generative model generates the message "Our brand puts quality first and provides peace of mind and satisfaction to all customers," and saves this as the brand's official statement.

[1385] 2. Processing Feedback

[1386] Users: Provide feedback to the brand through their devices.

[1387] Device: Sends feedback to the server.

[1388] Server: An emotion engine is used to recognize the user's emotions, and a generative model generates a response based on those emotions.

[1389] Example: If a user expresses anger by saying, "I'm dissatisfied with our latest product," the emotion engine recognizes the user's emotion as "dissatisfied," and the generative model generates a response saying, "We take your feedback seriously and will use it to improve our products in the future," and displays it to the user.

[1390] Overall processing flow

[1391] Users access a brand's website or application through their device and view the generated text content. When they ask a question or provide feedback, the device sends it to the server, which uses an emotion engine to recognize the user's emotion and a generative model to generate an answer or response, which is then sent back to the device and displayed to the user. This process ensures a consistent brand message and customer engagement.

[1392] Through the above means, the present invention can achieve consistent and effective communication between the brand and its customers, thereby improving the brand image.

[1393] The processing flow will be explained below.

[1394] Telling your brand story

[1395] Step 1:

[1396] Server: Calls the generative model and generates text content to express the brand values.

[1397] How it works: You provide the generative model with your brand's values ​​and philosophy as input, and the model generates a textual brand message.

[1398] Step 2:

[1399] Server: Stores the generated content in a database.

[1400] What it does: Runs a query to store the generated text data in a specific table in a database.

[1401] Step 3:

[1402] Server: Delivers stored content to devices.

[1403] How it works: A web server receives an HTTP request and returns a response containing generated content.

[1404] Step 4:

[1405] Terminal: Displays the content received from the server to the user.

[1406] What it does: Displays the received text content in the specified location on a web page or application.

[1407] Improved customer engagement

[1408] Step 1:

[1409] User: Enters a brand question through the device.

[1410] Action: A user enters a question into a form on a web page or application and clicks the submit button.

[1411] Step 2:

[1412] Terminal: Sends the user's question to the server.

[1413] What it does: Sends the question to the server as a POST request.

[1414] Step 3:

[1415] Server: Recognizes user emotions using an emotion engine.

[1416] How it works: Passes the received question data to the emotion engine to recognize the user's emotional state (e.g., joy, interest, anxiety).

[1417] Step 4:

[1418] Server: Uses a generative model to generate answers based on the user's emotions.

[1419] How it works: The generative model generates appropriate answer text, taking into account the perceived emotional state of the user.

[1420] Step 5:

[1421] Server: Generates and sends the answer back to the device.

[1422] Behavior: A response containing the generated answer text is sent to the terminal via HTTP.

[1423] Step 6:

[1424] Terminal: Display the answer to the user.

[1425] Behavior: Displays the received response text in a designated area on the user interface.

[1426] Unifying the brand image

[1427] Step 1:

[1428] Server: Uses generative models to generate consistent brand messages.

[1429] How it works: Provide the generative model with inputs based on your brand's personality and values ​​to generate a consistent message.

[1430] Step 2:

[1431] Server: Store this message in a database.

[1432] What it does: Executes a query to store the generated messages in a specific table in the database.

[1433] Step 3:

[1434] Users: Provide feedback about the brand through their devices.

[1435] What it does: Use the feedback form or rating feature to provide your thoughts and opinions, then click the submit button.

[1436] Step 4:

[1437] Device: Sends user feedback to the server.

[1438] Behavior: Sends the feedback content to the server as a POST request.

[1439] Step 5:

[1440] Server: Recognizes user emotions using an emotion engine.

[1441] How it works: Passes received feedback data to the emotion engine to recognize the user's emotional state (e.g., frustration, joy, excitement).

[1442] Step 6:

[1443] Server: Generates a response using a generative model based on the feedback.

[1444] How it works: The generative model generates an appropriate response text, taking into account the perceived emotional state of the user.

[1445] Step 7:

[1446] Server: Generates and sends the response to the device.

[1447] Behavior: A response containing the generated response text is sent to the terminal via HTTP.

[1448] Step 8:

[1449] Terminal: Displays the response to the user.

[1450] Behavior: Displays the received response text in a designated area on the user interface.

[1451] Example 2

[1452] 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."

[1453] With conventional brand communication systems, it was difficult to consistently convey brand values ​​and messages to users, and it was difficult to properly recognize customer sentiment and respond in real time. This made it difficult to unify brand image and improve customer engagement.

[1454] 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.

[1455] In this invention, the server includes means for calling a generative model to generate text data for expressing the brand's value, means for saving the generated text data in a data storage device, means for distributing the saved text data to an information processing device, means for receiving inquiries from users, means for recognizing user emotions from the received inquiries using a sentiment analysis device and generating a response using the generative model, means for transmitting the generated response to the information processing device, means for generating a consistent brand message using the generative model and saving it in the data storage device, means for receiving evaluations from users, means for recognizing user emotions using the sentiment analysis device based on the received evaluations and generating a response using the generative model, means for transmitting the generated response to the information processing device, and means for the terminal to display the text data, the response, and the response to the user, thereby enabling consistent brand messaging and recognizing customer emotions to generate and display appropriate responses in real time.

[1456] A "generative model" is a model that uses machine learning techniques to generate text or data based on specific prompts.

[1457] "Text data" refers to text content generated by a generative model to express brand values ​​and messages.

[1458] "Data storage device" refers to any storage device for storing generated text data and brand messages.

[1459] The term "information processing device" refers to a terminal in general that displays data and responses delivered from a server to a user.

[1460] An "inquiry" is a question or request sent by a user via a terminal about a brand.

[1461] An "emotion analyzer" is an analytical engine that analyzes the content of user inquiries and feedback and recognizes their emotions.

[1462] An "answer" is a response message generated in response to a user inquiry using a sentiment analyzer and a generative model.

[1463] A "rating" is feedback or opinion provided by a user to a brand via a device.

[1464] A "response" is a reply message from a brand that is generated using a generative model based on a user's evaluation.

[1465] A "brand message" is text content generated using a generative model to consistently express brand values ​​and ideals.

[1466] The present invention is a brand value maximization system that combines a generative model and an emotion engine. This system is implemented with the following specific hardware and software configuration.

[1467] System Configuration

[1468] 1. Server

[1469] Generative models: Includes generative models that use machine learning technology (e.g., GPT-4), which generate text data to express brand values.

[1470] Data storage device: A database for storing generated text data and brand messages.

[1471] Sentiment analyzer: An analytical engine that analyzes the content of user inquiries and feedback and recognizes their emotions (e.g., Azure Sentiment Analysis).

[1472] 2. Terminal

[1473] Information processing device: A device (e.g., smartphone, PC, tablet) that displays data and responses delivered from the server to the user.

[1474] 3. Users

[1475] Access: Access the system through a brand's website or application to view content, submit inquiries, and provide feedback.

[1476] Operation explanation

[1477] Content Generation and Delivery

[1478] Server: Calls the generative model and generates sentence data that expresses the brand's values. For example, by inputting the prompt "What is your brand's mission?" into the generative model, the generated sentence data is "Our brand values ​​sustainability and provides environmentally friendly products."

[1479] Server: The generated text data is stored in a data storage device, and the stored data can be accessed later.

[1480] Server: The stored text data is delivered to the user's device. For example, this message is displayed when the user visits the brand's website.

[1481] Device: The delivered text data is displayed to the user. For example, a message such as "Our brand values ​​sustainability and offers environmentally friendly products" is displayed on a web page or app.

[1482] User queries and response generation

[1483] User: Enter a brand-related inquiry through the terminal. For example, enter "What are the features of this product?"

[1484] Terminal: Sends the inquiry to the server.

[1485] Server: Recognizes the user's emotion using a sentiment analyzer. For example, recognizes the user's emotion of "interest" from the inquiry.

[1486] Server: Uses a generative model to generate an answer based on the recognized sentiment. For example, it might generate "This product uses the latest technology, combining ease of use with high performance."

[1487] Server: Sends the generated answer to the device.

[1488] Terminal: Displays submitted answers to the user.

[1489] User feedback and response generation

[1490] User: Enters their feedback about the brand through a device. For example, they might enter, "I'm not happy with your recent product."

[1491] Device: Sends feedback to the server.

[1492] Server: Uses a sentiment analyzer to recognize the sentiment of the feedback. For example, recognize the user's sentiment of "dissatisfied" from the feedback.

[1493] Server: Uses a generative model to generate a response based on the recognized sentiment. For example, it might generate "We take your feedback seriously and will use it to improve our products in the future."

[1494] Server: Generates and sends the response to the device.

[1495] Terminal: Displays the sent response to the user.

[1496] The system of the present invention allows brands to achieve high customer engagement by maintaining consistent messaging and responding to user emotions.

[1497] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1498] Step 1:

[1499] Server: Calls the generative model to express the brand values. By providing the prompt sentence as input to the generative model, it generates text data related to the brand’s philosophy and culture.

[1500] What it does: Sends an API request to a generative AI model (e.g., GPT-4) and enters the prompt, "What is your brand's mission statement?"

[1501] Data processing: The generative model generates sentence data based on the prompt sentence.

[1502] Output: The sentence "Our brand values ​​sustainability and offers environmentally friendly products" is generated.

[1503] Step 2:

[1504] Server: Stores the generated text data in a data storage device. Receives the generated text data as input and inserts it into the database.

[1505] Specific operation: Uses an SQL query to save text data to the database table "content".

[1506] Data processing: The generated text data is converted into a database format and saved.

[1507] Output: The sentence data is saved in the database.

[1508] Step 3:

[1509] Server: Delivers the saved text data to the user's device. Receives the user's request as input, retrieves the saved text data, and sends it to the device.

[1510] Specific behavior: Processes HTTP requests and retrieves stored text data from a database.

[1511] Data processing: Select the appropriate text data based on the request content and convert it into a format that can be sent to the terminal.

[1512] Output: The message "Our brand is committed to sustainability and offers environmentally friendly products" is sent to the device.

[1513] Step 4:

[1514] Terminal: Displays the delivered text data to the user. It receives text data received from the server as input for display and renders it in a form that is visible to the user.

[1515] Specific behavior: Renders submitted HTML and text data in web pages and applications.

[1516] Data processing: Display the received data in a format suitable for the user interface.

[1517] Output: A webpage will display the following statement: "Our brand is committed to sustainability and offers environmentally friendly products."

[1518] Step 5:

[1519] User: Enters a brand-related inquiry through a terminal. The user enters the question as input into an interface such as a chatbot and sends it.

[1520] Specific action: Enter "Please tell me the features of this product" into a chatbot or inquiry form and press the send button.

[1521] Data processing: Taking user input and converting it into a format that can be sent to the server.

[1522] Output: The question data "What are the features of this product?" is sent to the server.

[1523] Step 6:

[1524] Server: Recognizes the user's sentiment using a sentiment analyzer. The question data received as input is sent to the sentiment analyzer, and the sentiment is recognized.

[1525] Specific operation: The question content is sent to the sentiment analysis API and emotions such as "interest" are analyzed.

[1526] Data processing: The question data is analyzed by a sentiment analyzer to generate user sentiment data.

[1527] Output: Emotion data recognized as "interest" is generated.

[1528] Step 7:

[1529] Server: Uses a generative model to generate answers based on the recognized emotions. The generative model receives emotion data and question data as input and generates an appropriate answer.

[1530] Specific behavior: The prompt sentence "The user is interested in the features of this product" is input into the generative model, and an answer is generated.

[1531] Data processing: The generative model generates answer data based on the prompt sentence.

[1532] Output: The answer "This product uses the latest technology and is both easy to use and highly functional."

[1533] Step 8:

[1534] Server: Sends the generated answer to the terminal. Receives the generated answer data as input and converts it into a format that can be sent to the terminal.

[1535] Specific operation: Convert the response data into JSON format and send it to the device.

[1536] Data processing: Converting response data into a format for transmission.

[1537] Output: The generated answer data is sent to the device.

[1538] Step 9:

[1539] Terminal: displays submitted answers to the user. It takes the answer data received as input for display and renders it in a form that is visible to the user.

[1540] Specific behavior: Display the message "This product uses the latest technology, combining ease of use and high performance" on the chatbot UI.

[1541] Data processing: Display the received data in a format suitable for the user interface.

[1542] Output: The answer is displayed to the user.

[1543] Step 10:

[1544] User: Enters feedback about the brand through a terminal. The feedback is entered as input into the interface and sent.

[1545] Specific action: Enter "I'm dissatisfied with the recent product" into the feedback form and press the submit button.

[1546] Data processing: Taking user input and converting it into a format that can be sent to the server.

[1547] Output: The feedback data is sent to the server.

[1548] Step 11:

[1549] Server: Recognizes the feedback using a sentiment analyzer. The received feedback data is sent to the sentiment analyzer to recognize the user's emotions.

[1550] What it does: Sends feedback data to a sentiment analysis API and analyzes sentiment, such as "dissatisfied."

[1551] Data processing: Analyze the feedback data and generate user emotion data.

[1552] Output: Emotion data recognized as "dissatisfied" is generated.

[1553] Step 12:

[1554] Server: Uses a generative model to generate responses based on the recognized emotions. The generative model receives emotion data and feedback data as input and generates an appropriate response.

[1555] Specific behavior: The prompt sentence "The user is dissatisfied" is input into the generative model, and a response is generated.

[1556] Data processing: The generative model generates response data based on the prompt sentence.

[1557] Output: The response data generated is "We take your feedback seriously and will use it to improve our products in the future."

[1558] Step 13:

[1559] Server: Sends the generated response to the terminal. Receives the generated response data as input and converts it into a format that can be sent to the terminal.

[1560] Specific operation: Converts response data into JSON format and sends it to the terminal.

[1561] Data processing: Converts response data into a format for transmission.

[1562] Output: The generated response data is sent to the terminal.

[1563] Step 14:

[1564] Terminal: displays the sent response to the user. It takes the response data received as input for display and renders it in a form that is visible to the user.

[1565] Specific behavior: Display a message on the feedback form UI saying, "We take your feedback seriously and will use it to improve our products in the future."

[1566] Data processing: Display the received data in a format suitable for the user interface.

[1567] Output: The response is displayed to the user.

[1568] (Application example 2)

[1569] 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."

[1570] Today's customers expect consistent communication with brands, so it is important to find ways to effectively communicate the brand's values. However, traditional methods have made it difficult to recognize customer emotions in real time and respond appropriately based on them. Furthermore, in physical stores, there is a lack of support for staff to respond quickly and appropriately to individual customers' emotions and requests. This can lead to a decline in brand consistency and customer satisfaction.

[1571] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1572] In this invention, the server includes means for calling the generative model and generating text content for expressing the brand's values, means for saving the generated content in a database, means for delivering the saved content to a terminal, means for the terminal to display the content to the user, means for recognizing customer emotions in real time on a smart device, means for analyzing the recognized emotion data with an emotion engine, means for the generative model to generate an optimal response based on the analysis results, and means for displaying the generated response to store staff. This enables staff in physical stores to respond appropriately to customers' emotions in real time, thereby realizing consistent brand messaging and improved customer satisfaction.

[1573] A "generative model" is an algorithm or system that automatically generates text content and answers to express a brand's values.

[1574] "Brand value" is a concept that expresses a brand's philosophy, culture, product features, etc., and is an important element for appealing to customers.

[1575] "Text content" refers to textual information generated using a generative model, including brand messaging, product descriptions, etc.

[1576] A "database" is a system for storing and managing generated content and customer feedback.

[1577] A "terminal" is a device used by a user to display content and answers, and includes a smartphone, tablet, etc.

[1578] "Smart devices" are wearable devices equipped with cameras and computing power that are used to recognize customer emotions.

[1579] An "emotion engine" is a software system for analyzing recognized emotion data and identifying a customer's emotional state.

[1580] "Analysis results" are the results of customer emotion data analyzed by the emotion engine and used as input for the generative model.

[1581] "Store staff" refers to employees who deal with customers in physical stores and who wear smart devices.

[1582] To implement the present invention, the following system is used.

[1583] The server at the heart of the system first invokes the generative model to generate text content to express the brand's values. The generated text content reflects the brand's philosophy, culture, product features, etc., and functions as an effective message to customers. The generated content is stored in a database and distributed to devices as needed. The devices then display the content to the user.

[1584] Next, smart devices are used in brick-and-mortar stores. Wearable devices such as smart glasses capture images of customers' faces with a camera and recognize their emotions in real time. An emotion engine analyzes the recognized emotion data and identifies the customer's emotional state. Based on the analysis results, the server uses a generative model to generate the optimal response or answer. The generated response is displayed to store staff wearing smart glasses.

[1585] For example, consider the process when a customer asks a question about a product. The customer's question and the emotion recognized in real time are sent to the server. The emotion engine analyzes whether the customer is showing "interest," and based on the analysis results, the generative model generates a detailed product description. The generated description is then displayed to store staff through smart glasses, who then provide the customer with the appropriate information.

[1586] Examples:

[1587] Smart Devices: Smart Glasses

[1588] Software libraries: OpenCV, DeepFace

[1589] Generative model: GPT-4

[1590] Emotion Engine: DeepFace's emotion recognition module

[1591] Example prompt sentence:

[1592] Customer Question: "What's special about this product?"

[1593] Customer sentiment: "Interested"

[1594] Based on this prompt, a generative model generates text that is delivered to the device, resulting in more relevant information for customers in real time, improving consistent brand messaging and customer engagement.

[1595] In this way, the present invention realizes a system that enables consistent and effective communication between brands and customers in physical stores, thereby enhancing brand value.

[1596] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1597] Step 1:

[1598] The server calls the generative model and generates text content to express the brand's values. The input is information such as the brand's philosophy, culture, and product features, and the output is the generated text content. The generative model uses a natural language processing algorithm to generate text based on this input data.

[1599] Step 2:

[1600] The server stores the generated text content in a database. The input is the text content generated in step 1, and the output is the stored database entry. The database system stores the text content in an appropriate format, making it ready for later distribution.

[1601] Step 3:

[1602] The server delivers the stored content to the device. The input is the text content stored in the database, and the output is the text content sent to the device. The data is sent to the required device using the HTTP protocol or a specific API.

[1603] Step 4:

[1604] The terminal displays the content to the user. The input is the text content delivered from the server, and the output is the display on which the user views the content. The user interface displays the text in an appropriate format.

[1605] Step 5:

[1606] The smart device captures a customer's face with a camera and recognizes their emotions in real time. The input is the image data captured by the camera, and the output is the recognized emotion data. OpenCV and DeepFace libraries are used for image analysis and emotion recognition.

[1607] Step 6:

[1608] The emotion engine analyzes the recognized emotion data and identifies the customer's emotional state. The input is the emotion data obtained in step 5, and the output is the analysis result. The analysis result includes the customer's specific emotional state (e.g., "interested" or "anger").

[1609] Step 7:

[1610] The server uses a generative model based on the analysis results to generate the optimal response or answer. The input is the analysis results and the customer's question, and the output is the generated answer or response. A generative model (e.g., GPT-4) is used to generate the optimal text based on the analysis results.

[1611] Step 8:

[1612] The generated answer is sent to the terminal. The input is the text content generated by the server, and the output is the text content sent to the terminal. The server sends the generated text data to the terminal.

[1613] Step 9:

[1614] The terminal displays the generated answer to the store staff. The input is text content sent from the server, and the output is a display that the staff can view. A device such as smart glasses can visually present the generated text to the staff, allowing them to respond immediately.

[1615] 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.

[1616] 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.

[1617] 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.

[1618] [Fourth embodiment]

[1619] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1620] 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.

[1621] 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).

[1622] 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.

[1623] 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.

[1624] 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).

[1625] 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. 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.

[1626] 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.

[1627] 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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."

[1632] The present invention relates to a system for generating text content that expresses brand values ​​using a generative model, enhancing communication with users, and providing a consistent brand image. Specific embodiments of the system are described below.

[1633] Telling your brand story

[1634] 1. Content Generation

[1635] Server: Using a generative model, the server generates text content to express the brand's values. The generated text includes the brand's philosophy, culture, product features, etc.

[1636] Example: A generative model generates a message that reads, "Our brand offers environmentally friendly products for a sustainable future." This message is displayed on the brand's website and social media.

[1637] 2. Content storage and distribution

[1638] Server: Stores the generated content in a database and delivers it to the device, which displays the content to the user.

[1639] Example: A web server stores generated text in a database and displays it on a web page when accessed by a user.

[1640] Improved customer engagement

[1641] 1. Receiving and processing inquiries

[1642] User: Enters a question about the brand through a terminal, for example, can use the application's chatbot.

[1643] Terminal: Sends a question to the server.

[1644] Server: Uses the generative model to generate answers to user questions.

[1645] Example: When a user asks, "How do I use this product?", the server generates an answer such as, "To use this product, press the power button and then select the desired function from the settings menu," and displays it to the user.

[1646] Unifying the brand image

[1647] 1. Message Creation and Storage

[1648] Server: Uses the generative model to generate consistent brand messages and store them in a database.

[1649] Example: A generative model generates the message "Our brand puts quality first and provides peace of mind and satisfaction to all customers," and saves this as the brand's official statement.

[1650] 2. Processing Feedback

[1651] Users: Provide feedback to the brand through their devices.

[1652] Device: Sends feedback to the server.

[1653] Server: Uses a generative model to generate consistent responses based on user feedback.

[1654] Example: When a user submits feedback saying, "I'm not happy with the product recently," the server generates a response saying, "We take your feedback seriously and will use it to improve our products in the future," and displays it to the user.

[1655] Overall processing flow

[1656] Users access a brand's website or application through their device and view generated text content. When they ask a question or provide feedback, the device sends it to the server, which uses the generative model to generate an answer or response, which is then sent back to the device and displayed to the user. This process maintains a consistent brand message and customer engagement.

[1657] Through the above means, the present invention can achieve consistent and effective communication between the brand and its customers, thereby improving the brand image.

[1658] The processing flow will be explained below.

[1659] Telling your brand story

[1660] Step 1:

[1661] Server: Calls the generative model and generates text content to express the brand values.

[1662] How it works: You provide the generative model with your brand's values ​​and philosophy as input, and the model generates a textual brand message.

[1663] Step 2:

[1664] Server: Stores the generated content in a database.

[1665] What it does: Runs a query to store the generated text data into a specific table in a database.

[1666] Step 3:

[1667] Server: Delivers stored content to devices.

[1668] How it works: A web server receives an HTTP request and returns a response containing generated content.

[1669] Step 4:

[1670] Terminal: Displays the content received from the server to the user.

[1671] What it does: Displays the received text content in the specified location on a web page or application.

[1672] Improved customer engagement

[1673] Step 1:

[1674] User: Enters a brand question through the device.

[1675] Action: A user enters a question into a form on a web page or application and clicks the submit button.

[1676] Step 2:

[1677] Terminal: Sends the user's question to the server.

[1678] What it does: Sends the question to the server as a POST request.

[1679] Step 3:

[1680] Server: Uses the generative model to generate answers to user questions.

[1681] How it works: Pass the received question to a generative model to generate an appropriate answer text.

[1682] Step 4:

[1683] Server: Generates and sends the answer back to the device.

[1684] Behavior: A response containing the generated answer text is sent to the terminal via HTTP.

[1685] Step 5:

[1686] Terminal: Display the answer to the user.

[1687] Behavior: Displays the received response text in a designated area on the user interface.

[1688] Unifying the brand image

[1689] Step 1:

[1690] Server: Uses generative models to generate consistent brand messages.

[1691] How it works: Provide the generative model with inputs based on your brand's personality and values ​​to generate a consistent message.

[1692] Step 2:

[1693] Server: Store this message in a database.

[1694] What it does: Executes a query to store the generated messages in a specific table in the database.

[1695] Step 3:

[1696] Users: Provide feedback about the brand through their devices.

[1697] What it does: Use the feedback form or rating feature to provide your thoughts and opinions, then click the submit button.

[1698] Step 4:

[1699] Device: Sends user feedback to the server.

[1700] Behavior: Sends the feedback content to the server as a POST request.

[1701] Step 5:

[1702] Server: Generates a response using a generative model based on the feedback.

[1703] How it works: Passes received feedback data to a generative model to generate appropriate response text.

[1704] Step 6:

[1705] Server: Generates and sends the response to the device.

[1706] Behavior: A response containing the generated response text is sent to the terminal via HTTP.

[1707] Step 7:

[1708] Terminal: Displays the response to the user.

[1709] Behavior: Displays the received response text in a designated area on the user interface.

[1710] Example 1

[1711] 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."

[1712] The lack of a system to provide a consistent message and ensure effective communication between the brand and customers leads to a lack of consistency in the brand image and reduced customer engagement.

[1713] 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.

[1714] In this invention, the server includes means for calling a generative model and generating text to express the brand's values, means for saving the generated text in data storage, means for delivering the saved text to a user device, means for the user device to display the text to the user, means for receiving a query from the user, means for processing the received query using a generative model and generating a response, means for transmitting the generated response to the user device, means for the user device to display the response to the user, means for generating a consistent brand message using the generative model, means for saving the message in data storage, means for receiving opinions from the user, means for generating a response based on the received opinions using the generative model, means for transmitting the generated response to the user device, and means for the user device to display the response to the user, thereby enabling the provision of a consistent brand message and effective communication with customers.

[1715] A "generative model" is a system that uses artificial intelligence techniques to generate natural language text based on input prompts.

[1716] "Brand value" is a general term for the unique philosophy, culture, beliefs, quality, etc. of a company or product, and refers to the added value that it provides to customers.

[1717] "Text" refers to the string of characters output by the generative model, and refers to sentences or messages used to express the brand's values.

[1718] "Data storage" refers to electronic storage devices for storing generated text and other digital data.

[1719] A "user device" is an electronic device that can be directly operated by a user, and includes a personal computer, a smartphone, a tablet, and the like.

[1720] "Inquiry" means a question or request submitted by a User seeking specific information or support.

[1721] A "response" is an answer that a generative model generates to a query, and includes information that is provided to the user.

[1722] "Opinions" refers to feedback and comments provided by users, including satisfaction with products and services and requests.

[1723] A "message" is a coherent text generated by a generative model that expresses the brand's values, philosophy, etc.

[1724] This invention uses a generative AI model to generate text content that expresses brand values, strengthens communication with users, and provides a consistent brand image.

[1725] Hardware and software configuration

[1726] This system mainly uses the following hardware and software:

[1727] Server: Provides the computational resources to run generative AI models (e.g., GPT-4) and also uses database systems such as MySQL for data storage.

[1728] Terminal: A device operated by a user (such as a computer, smartphone, or tablet) on which a web browser or application runs.

[1729] Generative AI models: Models that generate text based on prompts, such as OpenAI's APIs (e.g., GPT-4).

[1730] Network: An internet connection for data communication between the server and the device.

[1731] Data processing and calculation

[1732] 1. Server: The server prepares prompts that users input to the generative AI model. An example prompt is, "Generate text that expresses our brand philosophy. Our focus is on providing environmentally friendly products."

[1733] 2. Content generation: The server sends the prompt to the generative model and receives generated text, such as "Our brand offers environmentally friendly products for a sustainable future."

[1734] 3. Text storage: Store the generated text in a data storage, such as a database like MySQL, in the appropriate format (e.g., timestamp or content ID).

[1735] 4. Display: When a user accesses a website or application on their device, the server receives a request to display the stored text and provides the appropriate content, which is then displayed on the user's device.

[1736] Examples and prompts

[1737] 1. Content generation prompt example:

[1738] "Generate text content that expresses your brand's values. The focus is on environmentally friendly product offerings."

[1739] 2. Example prompts for generating answers to user questions:

[1740] "A user asks, 'How do I use this product?' Please generate an answer to this question."

[1741] 3. Example prompts for generating responses to feedback:

[1742] "A user has given us feedback saying, 'I'm not happy with the product lately.' Please generate a response to this feedback."

[1743] With this system, the server uses a generative model to generate various texts, saves the generated texts in data storage, and delivers them to user devices. This allows for the provision of consistent brand messages and improved customer engagement. It also generates and provides quick and consistent answers to questions and feedback entered by users. This series of processes allows brands to achieve effective and consistent communication and improve their brand image.

[1744] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1745] Step 1:

[1746] Content generation prompt preparation

[1747] Server: Prepares prompts to send to the generative AI model based on information about the brand and its philosophy.

[1748] Action: Set a prompt like, "Generate text that expresses our brand philosophy. Our focus is on providing environmentally friendly products."

[1749] Input: Basic information about your brand's values ​​and philosophy.

[1750] Output: The prompt to send to the generative AI model.

[1751] Step 2:

[1752] Text generation using generative AI models

[1753] Server: Sends the prepared prompt sentence to a generative AI model (e.g., GPT-4) and retrieves the generated text.

[1754] Specific operation: Call the GPT-4 API to generate "text that expresses the brand's values" and receive the results.

[1755] Input: The prepared prompt statement.

[1756] Output: Generated text such as "Our brand offers eco-conscious products for a sustainable future."

[1757] Step 3:

[1758] Saving the generated text

[1759] Server: Stores the generated text in a data storage (e.g., MySQL database).

[1760] Specific behavior: Executes an SQL query such as "INSERT INTO contents (content, created_at) VALUES ('generated text', NOW())".

[1761] Input: The generated text.

[1762] Output: Text records saved in data storage.

[1763] Step 4:

[1764] Processing user requests and delivering text

[1765] Device: A user accesses a website or application and sends a request for content to the server.

[1766] Server: Searches and retrieves the generated text from data storage and delivers it to the user device.

[1767] Specific operation: Executes an SQL query such as "SELECT content FROM contents WHERE content_id=1234" and returns the result as an HTTP response.

[1768] Input: The user request.

[1769] Output: Text delivered to the user device.

[1770] Step 5:

[1771] Receiving user questions and generating answers

[1772] User: Enters a brand-related question through the device (e.g., "How do I use this product?").

[1773] Terminal: Sends a question to the server.

[1774] Server: Generates prompts to send questions to the generative model (e.g., "The user asked me, 'How do I use this product?' Please generate an answer to this question."), and calls the generative AI model to obtain the answer.

[1775] What it does: Creates prompts for GPT-4 and calls the API to generate answers.

[1776] Input: A question from the user.

[1777] Output: The generated answer (e.g., "Press the power button, then select the desired function in the settings menu.").

[1778] Step 6:

[1779] Submitting and viewing answers

[1780] Server: Sends the generated answer to the user device.

[1781] Terminal: Receives the answer and displays it to the user.

[1782] Specific operation: The server sends the answer as an HTTP response, and the device displays it.

[1783] Input: The generated answer.

[1784] Output: The answer that is displayed to the user.

[1785] Step 7:

[1786] Receiving feedback and generating responses

[1787] User: Enters feedback about the brand through a device (e.g., "I'm not happy with your recent product").

[1788] Device: Sends feedback to the server.

[1789] Server: Generates prompts to send feedback to the generative model (e.g., "The user has given us feedback saying, 'I'm dissatisfied with our recent product.' Please generate a response to this feedback."), and calls the generative AI model to obtain the response.

[1790] What it does: Creates a prompt for GPT-4 and calls an API to generate a response.

[1791] Input: User feedback.

[1792] Output: The generated response (e.g., "We take your feedback seriously and will use it to improve our products in the future.").

[1793] Step 8:

[1794] Sending and Displaying Responses

[1795] Server: Sends the generated response to the user device.

[1796] Terminal: Receives the response and displays it to the user.

[1797] Specific behavior: The server sends the response as an HTTP response, and the device displays it.

[1798] Input: The generated response.

[1799] Output: The response that is displayed to the user.

[1800] (Application example 1)

[1801] 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."

[1802] Modern brands are required to consistently communicate their values ​​and philosophy to consumers through various channels, including online and virtual. However, there is a lack of an appropriate system for increasing consumer engagement while maintaining a consistent brand message. In particular, virtual stores present a challenge, as consumers have difficulty providing real-time questions and feedback and receiving consistent answers and responses. Therefore, there is a need for a system that can consistently communicate brand values ​​and enable effective communication with consumers.

[1803] 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.

[1804] In this invention, the server includes means for calling a generative model and generating text content for expressing the brand's values, means for saving the generated content in a database, means for delivering the saved content to a terminal, means for receiving a question from a user through the terminal and generating an answer to the question using the generative model, means for receiving feedback from the user and responding to the feedback using the generative model, means for the terminal to display the answer to the question and the feedback response to the user, and means for expressing the brand's values ​​in a virtual store that the user can access using a smartphone or a head-mounted display. This enables effective communication with consumers in real time while maintaining consistency in the brand message.

[1805] A "generative model" is a type of artificial intelligence that generates text content to express a brand's values ​​and philosophy.

[1806] "Brand value" refers to the brand's philosophy, values, culture, and characteristics of its products and services.

[1807] "Content" refers to information materials such as text, images, and videos generated by a generative model to express the brand's values.

[1808] A "database" is a system for systematically storing generated content and for accessing and retrieving it as needed.

[1809] A "terminal" is a device used by a user to view content that expresses the brand's values, and includes a smartphone, a head-mounted display, etc.

[1810] A "virtual store" is a store that is not a physical store but that users can access online or in a virtual reality environment.

[1811] A "question" is a question that a user inputs via a terminal about a brand or its products.

[1812] "Feedback" refers to the opinions and impressions users provide about a brand or its products.

[1813] An "answer" is a response generated by a generative model in response to a user's question.

[1814] A "response" is a reply generated by a generative model based on user feedback.

[1815] A "smartphone" refers to a mobile information terminal that has communication capabilities and can be used by installing a variety of applications.

[1816] A "head-mounted display" is a display device worn on the head, and is used to display virtual reality and augmented reality.

[1817] This invention relates to a system that uses generative AI models to generate text content to express brand values, enhance communication with users, and provide a consistent brand image. This system is particularly useful in virtual stores to effectively communicate brand values.

[1818] System Program

[1819] The system includes the following components:

[1820] A server that invokes the generative model and generates text content to express the brand's values.

[1821] A server that stores generated content in a database

[1822] A server that delivers stored content to devices

[1823] A server that receives questions from users via their devices and generates answers to those questions using a generative model.

[1824] A server that receives feedback from users and responds to the feedback using a generative model

[1825] The device displays the answer to the question and the feedback response to the user.

[1826] The brand's values ​​are expressed in a virtual store, which users can access using a smartphone or head-mounted display.

[1827] System Description

[1828] Hardware and Software

[1829] Server: Use AWS or Google Cloud to host the generative AI model (e.g., OpenAI's GPT-3).

[1830] Device: Smartphone or head-mounted display (HMD).

[1831] Software: Build a web application using Flask and set up API endpoints.

[1832] Data processing and calculation

[1833] The server calls the generative AI model to generate text that expresses the brand's values ​​and stores it in a database. The generated text is then delivered to the device when the user accesses the virtual store, and the device displays the content.

[1834] When a user asks a question through the device, the server receives the question and generates an answer using the generative AI model. The generated answer is sent to the device and displayed to the user. Similarly, when feedback is received from the user, the server uses the generative AI model to generate a response to the feedback, sends it to the device and displays it to the user.

[1835] Specific examples

[1836] If a user asks "How do I use this product?" in a virtual store, the generative model generates an answer such as "First, turn on this product, then set it up using the dedicated app," and displays it to the user.

[1837] If a user sends feedback such as "I'm dissatisfied with our recent product," the generative model generates a response saying, "We take your feedback seriously and will use it to improve our products in the future," and displays it to the user.

[1838] Prompt Sentence Examples

[1839] Generate text that expresses your brand values

[1840] "Our brand is committed to providing a high-quality product. Generate messaging that expresses that philosophy."

[1841] Generating answers to user questions

[1842] "User Question: How do I use this product?\nAnswer:"

[1843] Generate responses to user feedback

[1844] "User feedback: I'm not happy with the product lately.\nResponse:"

[1845] merit

[1846] This system allows for effective real-time communication with consumers while maintaining consistency in brand messaging.

[1847] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1848] Step 1:

[1849] A user accesses the virtual store using a smartphone or a head-mounted display.

[1850] Input: User's access request

[1851] Output: Virtual store interface screen display

[1852] Specific operation: The terminal receives an access request from the user and sends it to the server. The server sends the interface data of the virtual store to the terminal, which then renders and displays it.

[1853] Step 2:

[1854] The server invokes the generative model to generate text content that expresses the brand's values.

[1855] Input: Prompt sentence to express brand values

[1856] Output: The generated text content

[1857] Specific operation: The server sends a prompt to the API of the generative model, which then generates text content. The generated text is returned to the server.

[1858] Step 3:

[1859] Store the generated text content in a database.

[1860] Input: Generated text content

[1861] Output: Text content stored in the database

[1862] Specific operation: The server receives the generated text content and stores it in a database.

[1863] Step 4:

[1864] The stored text content is delivered to the terminal, which displays the content to the user.

[1865] Input: Text content read from the database

[1866] Output: Text content displayed on the terminal

[1867] Specific operation: The server retrieves the necessary text content from the database and sends it to the terminal, which then displays the received text content to the user.

[1868] Step 5:

[1869] A user inputs a question through a terminal, and the terminal transmits the question to a server.

[1870] Input: User question

[1871] Output: The question data sent to the server

[1872] Specific operation: The terminal receives the question entered by the user in the virtual store and sends it to the server.

[1873] Step 6:

[1874] The server uses the generative model to generate answers to questions.

[1875] Input: User question and prompt

[1876] Output: Generated answer text

[1877] Specific operation: Based on the question received from the user, the server sends a request to the API of the generative model along with a prompt text. The generative model generates an answer, and the text is returned to the server.

[1878] Step 7:

[1879] The generated answer is sent to the terminal, which displays the answer to the user.

[1880] Input: Generated answer text

[1881] Output: The answer text that is displayed to the user

[1882] Specific operation: The server receives the generated answer text and sends it to the terminal, which then displays the received answer text to the user.

[1883] Step 8:

[1884] The user inputs feedback through the terminal, and the terminal transmits the feedback to the server.

[1885] Input: User feedback

[1886] Output: Feedback data sent to the server

[1887] Specific operation: The terminal receives the feedback entered by the user in the virtual store and sends it to the server.

[1888] Step 9:

[1889] The server uses the generative model to generate a response to the feedback.

[1890] Input: User feedback and prompts

[1891] Output: The generated response text

[1892] Specific operation: Based on the feedback received from the user, the server sends a request to the API of the generative model along with a prompt text. The generative model generates a response, and the text is sent back to the server.

[1893] Step 10:

[1894] The generated response is sent to the terminal, which displays the response to the user.

[1895] Input: Generated response text

[1896] Output: The response text that is displayed to the user

[1897] Specific operation: The server receives the generated response text and sends it to the terminal, which then displays the received response text to the user.

[1898] 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.

[1899] The present invention relates to a system that combines a generative model and an emotion engine to achieve consistent and effective communication between a brand and its users, thereby enhancing the value of the brand. Specific embodiments of the system are described below.

[1900] Telling your brand story

[1901] 1. Content Generation

[1902] Server: Calls the generative model to generate text content to express the brand's values. The generated text includes the brand's philosophy, culture, product features, etc.

[1903] Example: A generative model generates a message that reads, "Our brand offers environmentally friendly products for a sustainable future." This message is displayed on the brand's website and social media.

[1904] 2. Content storage and distribution

[1905] Server: Stores the generated content in a database and delivers it to the device, which displays the content to the user.

[1906] Example: A web server stores generated text in a database and displays it on a web page when accessed by a user.

[1907] Improved customer engagement

[1908] 1. Receiving and processing inquiries

[1909] User: Enters a question about the brand through a terminal, for example, can use the application's chatbot.

[1910] Terminal: Sends a question to the server.

[1911] Server: An emotion engine is used to recognize the user's emotions, and a generative model generates answers based on those emotions.

[1912] Example: When a user gently asks, "I'd like to know more about that product," the emotion engine recognizes the user's emotion as "interest," and the generative model generates an answer, "This product uses the latest technology and combines ease of use and performance," and displays it to the user.

[1913] Unifying the brand image

[1914] 1. Message Creation and Storage

[1915] Server: Uses the generative model to generate consistent brand messages and store them in a database.

[1916] Example: A generative model generates the message "Our brand puts quality first and provides peace of mind and satisfaction to all customers," and saves this as the brand's official statement.

[1917] 2. Processing Feedback

[1918] Users: Provide feedback to the brand through their devices.

[1919] Device: Sends feedback to the server.

[1920] Server: An emotion engine is used to recognize the user's emotions, and a generative model generates a response based on those emotions.

[1921] Example: If a user expresses anger by saying, "I'm dissatisfied with our latest product," the emotion engine recognizes the user's emotion as "dissatisfied," and the generative model generates a response saying, "We take your feedback seriously and will use it to improve our products in the future," and displays it to the user.

[1922] Overall processing flow

[1923] Users access a brand's website or application through their device and view the generated text content. When they ask a question or provide feedback, the device sends it to the server, which uses an emotion engine to recognize the user's emotion and a generative model to generate an answer or response, which is then sent back to the device and displayed to the user. This process ensures a consistent brand message and customer engagement.

[1924] Through the above means, the present invention can achieve consistent and effective communication between the brand and its customers, thereby improving the brand image.

[1925] The processing flow will be explained below.

[1926] Telling your brand story

[1927] Step 1:

[1928] Server: Calls the generative model and generates text content to express the brand values.

[1929] How it works: You provide the generative model with your brand's values ​​and philosophy as input, and the model generates a textual brand message.

[1930] Step 2:

[1931] Server: Stores the generated content in a database.

[1932] What it does: Runs a query to store the generated text data in a specific table in a database.

[1933] Step 3:

[1934] Server: Delivers stored content to devices.

[1935] How it works: A web server receives an HTTP request and returns a response containing generated content.

[1936] Step 4:

[1937] Terminal: Displays the content received from the server to the user.

[1938] What it does: Displays the received text content in the specified location on a web page or application.

[1939] Improved customer engagement

[1940] Step 1:

[1941] User: Enters a brand question through the device.

[1942] Action: A user enters a question into a form on a web page or application and clicks the submit button.

[1943] Step 2:

[1944] Terminal: Sends the user's question to the server.

[1945] What it does: Sends the question to the server as a POST request.

[1946] Step 3:

[1947] Server: Recognizes user emotions using an emotion engine.

[1948] How it works: Passes the received question data to the emotion engine to recognize the user's emotional state (e.g., joy, interest, anxiety).

[1949] Step 4:

[1950] Server: Uses a generative model to generate answers based on the user's emotions.

[1951] How it works: The generative model generates appropriate answer text, taking into account the perceived emotional state of the user.

[1952] Step 5:

[1953] Server: Generates and sends the answer back to the device.

[1954] Behavior: A response containing the generated answer text is sent to the terminal via HTTP.

[1955] Step 6:

[1956] Terminal: Display the answer to the user.

[1957] Behavior: Displays the received response text in a designated area on the user interface.

[1958] Unifying the brand image

[1959] Step 1:

[1960] Server: Uses generative models to generate consistent brand messages.

[1961] How it works: Provide the generative model with inputs based on your brand's personality and values ​​to generate a consistent message.

[1962] Step 2:

[1963] Server: Store this message in a database.

[1964] What it does: Executes a query to store the generated messages in a specific table in the database.

[1965] Step 3:

[1966] Users: Provide feedback about the brand through their devices.

[1967] What it does: Use the feedback form or rating feature to provide your thoughts and opinions, then click the submit button.

[1968] Step 4:

[1969] Device: Sends user feedback to the server.

[1970] Behavior: Sends the feedback content to the server as a POST request.

[1971] Step 5:

[1972] Server: Recognizes user emotions using an emotion engine.

[1973] How it works: Passes received feedback data to the emotion engine to recognize the user's emotional state (e.g., frustration, joy, excitement).

[1974] Step 6:

[1975] Server: Generates a response using a generative model based on the feedback.

[1976] How it works: The generative model generates an appropriate response text, taking into account the perceived emotional state of the user.

[1977] Step 7:

[1978] Server: Generates and sends the response to the device.

[1979] Behavior: A response containing the generated response text is sent to the terminal via HTTP.

[1980] Step 8:

[1981] Terminal: Displays the response to the user.

[1982] Behavior: Displays the received response text in a designated area on the user interface.

[1983] Example 2

[1984] 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."

[1985] With conventional brand communication systems, it was difficult to consistently convey brand values ​​and messages to users, and it was difficult to properly recognize customer sentiment and respond in real time. This made it difficult to unify brand image and improve customer engagement.

[1986] 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.

[1987] In this invention, the server includes means for calling a generative model to generate text data for expressing the brand's value, means for saving the generated text data in a data storage device, means for distributing the saved text data to an information processing device, means for receiving inquiries from users, means for recognizing user emotions from the received inquiries using a sentiment analysis device and generating a response using the generative model, means for transmitting the generated response to the information processing device, means for generating a consistent brand message using the generative model and saving it in the data storage device, means for receiving evaluations from users, means for recognizing user emotions using the sentiment analysis device based on the received evaluations and generating a response using the generative model, means for transmitting the generated response to the information processing device, and means for the terminal to display the text data, the response, and the response to the user, thereby enabling consistent brand messaging and recognizing customer emotions to generate and display appropriate responses in real time.

[1988] A "generative model" is a model that uses machine learning techniques to generate text or data based on specific prompts.

[1989] "Text data" refers to text content generated by a generative model to express brand values ​​and messages.

[1990] "Data storage device" refers to any storage device for storing generated text data and brand messages.

[1991] The term "information processing device" refers to a terminal in general that displays data and responses delivered from a server to a user.

[1992] An "inquiry" is a question or request sent by a user via a terminal about a brand.

[1993] An "emotion analyzer" is an analytical engine that analyzes the content of user inquiries and feedback and recognizes their emotions.

[1994] An "answer" is a response message generated in response to a user inquiry using a sentiment analyzer and a generative model.

[1995] A "rating" is feedback or opinion provided by a user to a brand via a device.

[1996] A "response" is a reply message from a brand that is generated using a generative model based on a user's evaluation.

[1997] A "brand message" is text content generated using a generative model to consistently express brand values ​​and ideals.

[1998] The present invention is a brand value maximization system that combines a generative model and an emotion engine. This system is implemented with the following specific hardware and software configuration.

[1999] System Configuration

[2000] 1. Server

[2001] Generative models: Includes generative models that use machine learning technology (e.g., GPT-4), which generate text data to express brand values.

[2002] Data storage device: A database for storing generated text data and brand messages.

[2003] Sentiment analyzer: An analytical engine that analyzes the content of user inquiries and feedback and recognizes their emotions (e.g., Azure Sentiment Analysis).

[2004] 2. Terminal

[2005] Information processing device: A device (e.g., smartphone, PC, tablet) that displays data and responses delivered from the server to the user.

[2006] 3. Users

[2007] Access: Access the system through a brand's website or application to view content, submit inquiries, and provide feedback.

[2008] Operation explanation

[2009] Content Generation and Delivery

[2010] Server: Calls the generative model and generates sentence data that expresses the brand's values. For example, by inputting the prompt "What is your brand's mission?" into the generative model, the generated sentence data is "Our brand values ​​sustainability and provides environmentally friendly products."

[2011] Server: The generated text data is stored in a data storage device, and the stored data can be accessed later.

[2012] Server: The stored text data is delivered to the user's device. For example, this message is displayed when the user visits the brand's website.

[2013] Device: The delivered text data is displayed to the user. For example, a message such as "Our brand values ​​sustainability and offers environmentally friendly products" is displayed on a web page or app.

[2014] User queries and response generation

[2015] User: Enter a brand-related inquiry through the terminal. For example, enter "What are the features of this product?"

[2016] Terminal: Sends the inquiry to the server.

[2017] Server: Recognizes the user's emotion using a sentiment analyzer. For example, recognizes the user's emotion of "interest" from the inquiry.

[2018] Server: Uses a generative model to generate an answer based on the recognized sentiment. For example, it might generate "This product uses the latest technology, combining ease of use with high performance."

[2019] Server: Sends the generated answer to the device.

[2020] Terminal: Displays submitted answers to the user.

[2021] User feedback and response generation

[2022] User: Enters their feedback about the brand through a device. For example, they might enter, "I'm not happy with your recent product."

[2023] Device: Sends feedback to the server.

[2024] Server: Uses a sentiment analyzer to recognize the sentiment of the feedback. For example, recognize the user's sentiment of "dissatisfied" from the feedback.

[2025] Server: Uses a generative model to generate a response based on the recognized sentiment. For example, it might generate "We take your feedback seriously and will use it to improve our products in the future."

[2026] Server: Generates and sends the response to the device.

[2027] Terminal: Displays the sent response to the user.

[2028] The system of the present invention allows brands to achieve high customer engagement by maintaining consistent messaging and responding to user emotions.

[2029] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2030] Step 1:

[2031] Server: Calls the generative model to express the brand values. By providing the prompt sentence as input to the generative model, it generates text data related to the brand’s philosophy and culture.

[2032] What it does: Sends an API request to a generative AI model (e.g., GPT-4) and enters the prompt, "What is your brand's mission statement?"

[2033] Data processing: The generative model generates sentence data based on the prompt sentence.

[2034] Output: The sentence "Our brand values ​​sustainability and offers environmentally friendly products" is generated.

[2035] Step 2:

[2036] Server: Stores the generated text data in a data storage device. Receives the generated text data as input and inserts it into the database.

[2037] Specific operation: Uses an SQL query to save text data to the database table "content".

[2038] Data processing: The generated text data is converted into a database format and saved.

[2039] Output: The sentence data is saved in the database.

[2040] Step 3:

[2041] Server: Delivers the saved text data to the user's device. Receives the user's request as input, retrieves the saved text data, and sends it to the device.

[2042] Specific behavior: Processes HTTP requests and retrieves stored text data from a database.

[2043] Data processing: Select the appropriate text data based on the request content and convert it into a format that can be sent to the terminal.

[2044] Output: The message "Our brand is committed to sustainability and offers environmentally friendly products" is sent to the device.

[2045] Step 4:

[2046] Terminal: Displays the delivered text data to the user. It receives text data received from the server as input for display and renders it in a form that is visible to the user.

[2047] Specific behavior: Renders submitted HTML and text data in web pages and applications.

[2048] Data processing: Display the received data in a format suitable for the user interface.

[2049] Output: A webpage will display the following statement: "Our brand is committed to sustainability and offers environmentally friendly products."

[2050] Step 5:

[2051] User: Enters a brand-related inquiry through a terminal. The user enters the question as input into an interface such as a chatbot and sends it.

[2052] Specific action: Enter "Please tell me the features of this product" into a chatbot or inquiry form and press the send button.

[2053] Data processing: Taking user input and converting it into a format that can be sent to the server.

[2054] Output: The question data "What are the features of this product?" is sent to the server.

[2055] Step 6:

[2056] Server: Recognizes the user's sentiment using a sentiment analyzer. The question data received as input is sent to the sentiment analyzer, and the sentiment is recognized.

[2057] Specific operation: The question content is sent to the sentiment analysis API and emotions such as "interest" are analyzed.

[2058] Data processing: The question data is analyzed by a sentiment analyzer to generate user sentiment data.

[2059] Output: Emotion data recognized as "interest" is generated.

[2060] Step 7:

[2061] Server: Uses a generative model to generate answers based on the recognized emotions. The generative model receives emotion data and question data as input and generates an appropriate answer.

[2062] Specific behavior: The prompt sentence "The user is interested in the features of this product" is input into the generative model, and an answer is generated.

[2063] Data processing: The generative model generates answer data based on the prompt sentence.

[2064] Output: The answer "This product uses the latest technology and is both easy to use and highly functional."

[2065] Step 8:

[2066] Server: Sends the generated answer to the terminal. Receives the generated answer data as input and converts it into a format that can be sent to the terminal.

[2067] Specific operation: Convert the response data into JSON format and send it to the device.

[2068] Data processing: Converting response data into a format for transmission.

[2069] Output: The generated answer data is sent to the device.

[2070] Step 9:

[2071] Terminal: displays submitted answers to the user. It takes the answer data received as input for display and renders it in a form that is visible to the user.

[2072] Specific behavior: Display the message "This product uses the latest technology, combining ease of use and high performance" on the chatbot UI.

[2073] Data processing: Display the received data in a format suitable for the user interface.

[2074] Output: The answer is displayed to the user.

[2075] Step 10:

[2076] User: Enters feedback about the brand through a terminal. The feedback is entered as input into the interface and sent.

[2077] Specific action: Enter "I'm dissatisfied with the recent product" into the feedback form and press the submit button.

[2078] Data processing: Taking user input and converting it into a format that can be sent to the server.

[2079] Output: The feedback data is sent to the server.

[2080] Step 11:

[2081] Server: Recognizes the feedback using a sentiment analyzer. The received feedback data is sent to the sentiment analyzer to recognize the user's emotions.

[2082] What it does: Sends feedback data to a sentiment analysis API and analyzes sentiment, such as "dissatisfied."

[2083] Data processing: Analyze the feedback data and generate user emotion data.

[2084] Output: Emotion data recognized as "dissatisfied" is generated.

[2085] Step 12:

[2086] Server: Uses a generative model to generate responses based on the recognized emotions. The generative model receives emotion data and feedback data as input and generates an appropriate response.

[2087] Specific behavior: The prompt sentence "The user is dissatisfied" is input into the generative model, and a response is generated.

[2088] Data processing: The generative model generates response data based on the prompt sentence.

[2089] Output: The response data generated is "We take your feedback seriously and will use it to improve our products in the future."

[2090] Step 13:

[2091] Server: Sends the generated response to the terminal. Receives the generated response data as input and converts it into a format that can be sent to the terminal.

[2092] Specific operation: Converts response data into JSON format and sends it to the terminal.

[2093] Data processing: Converts response data into a format for transmission.

[2094] Output: The generated response data is sent to the terminal.

[2095] Step 14:

[2096] Terminal: displays the sent response to the user. It takes the response data received as input for display and renders it in a form that is visible to the user.

[2097] Specific behavior: Display a message on the feedback form UI saying, "We take your feedback seriously and will use it to improve our products in the future."

[2098] Data processing: Display the received data in a format suitable for the user interface.

[2099] Output: The response is displayed to the user.

[2100] (Application example 2)

[2101] 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."

[2102] Today's customers expect consistent communication with brands, so it is important to find ways to effectively communicate the brand's values. However, traditional methods have made it difficult to recognize customer emotions in real time and respond appropriately based on them. Furthermore, in physical stores, there is a lack of support for staff to respond quickly and appropriately to individual customers' emotions and requests. This can lead to a decline in brand consistency and customer satisfaction.

[2103] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2104] In this invention, the server includes means for calling the generative model and generating text content for expressing the brand's values, means for saving the generated content in a database, means for delivering the saved content to a terminal, means for the terminal to display the content to the user, means for recognizing customer emotions in real time on a smart device, means for analyzing the recognized emotion data with an emotion engine, means for the generative model to generate an optimal response based on the analysis results, and means for displaying the generated response to store staff. This enables staff in physical stores to respond appropriately to customers' emotions in real time, thereby realizing consistent brand messaging and improved customer satisfaction.

[2105] A "generative model" is an algorithm or system that automatically generates text content and answers to express a brand's values.

[2106] "Brand value" is a concept that expresses a brand's philosophy, culture, product features, etc., and is an important element for appealing to customers.

[2107] "Text content" refers to textual information generated using a generative model, including brand messaging, product descriptions, etc.

[2108] A "database" is a system for storing and managing generated content and customer feedback.

[2109] A "terminal" is a device used by a user to display content and answers, and includes a smartphone, tablet, etc.

[2110] "Smart devices" are wearable devices equipped with cameras and computing power that are used to recognize customer emotions.

[2111] An "emotion engine" is a software system for analyzing recognized emotion data and identifying a customer's emotional state.

[2112] "Analysis results" are the results of customer emotion data analyzed by the emotion engine and used as input for the generative model.

[2113] "Store staff" refers to employees who deal with customers in physical stores and who wear smart devices.

[2114] To implement the present invention, the following system is used.

[2115] The server at the heart of the system first invokes the generative model to generate text content to express the brand's values. The generated text content reflects the brand's philosophy, culture, product features, etc., and functions as an effective message to customers. The generated content is stored in a database and distributed to devices as needed. The devices then display the content to the user.

[2116] Next, smart devices are used in brick-and-mortar stores. Wearable devices such as smart glasses capture images of customers' faces with a camera and recognize their emotions in real time. An emotion engine analyzes the recognized emotion data and identifies the customer's emotional state. Based on the analysis results, the server uses a generative model to generate the optimal response or answer. The generated response is displayed to store staff wearing smart glasses.

[2117] For example, consider the process when a customer asks a question about a product. The customer's question and the emotion recognized in real time are sent to the server. The emotion engine analyzes whether the customer is showing "interest," and based on the analysis results, the generative model generates a detailed product description. The generated description is then displayed to store staff through smart glasses, who then provide the customer with the appropriate information.

[2118] Examples:

[2119] Smart Devices: Smart Glasses

[2120] Software libraries: OpenCV, DeepFace

[2121] Generative model: GPT-4

[2122] Emotion Engine: DeepFace's emotion recognition module

[2123] Example prompt sentence:

[2124] Customer Question: "What's special about this product?"

[2125] Customer sentiment: "Interested"

[2126] Based on this prompt, a generative model generates text that is delivered to the device, resulting in more relevant information for customers in real time, improving consistent brand messaging and customer engagement.

[2127] In this way, the present invention realizes a system that enables consistent and effective communication between brands and customers in physical stores, thereby enhancing brand value.

[2128] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2129] Step 1:

[2130] The server calls the generative model and generates text content to express the brand's values. The input is information such as the brand's philosophy, culture, and product features, and the output is the generated text content. The generative model uses a natural language processing algorithm to generate text based on this input data.

[2131] Step 2:

[2132] The server stores the generated text content in a database. The input is the text content generated in step 1, and the output is the stored database entry. The database system stores the text content in an appropriate format, making it ready for later distribution.

[2133] Step 3:

[2134] The server delivers the stored content to the device. The input is the text content stored in the database, and the output is the text content sent to the device. The data is sent to the required device using the HTTP protocol or a specific API.

[2135] Step 4:

[2136] The terminal displays the content to the user. The input is the text content delivered from the server, and the output is the display on which the user views the content. The user interface displays the text in an appropriate format.

[2137] Step 5:

[2138] The smart device captures a customer's face with a camera and recognizes their emotions in real time. The input is the image data captured by the camera, and the output is the recognized emotion data. OpenCV and DeepFace libraries are used for image analysis and emotion recognition.

[2139] Step 6:

[2140] The emotion engine analyzes the recognized emotion data and identifies the customer's emotional state. The input is the emotion data obtained in step 5, and the output is the analysis result. The analysis result includes the customer's specific emotional state (e.g., "interested" or "anger").

[2141] Step 7:

[2142] The server uses a generative model based on the analysis results to generate the optimal response or answer. The input is the analysis results and the customer's question, and the output is the generated answer or response. A generative model (e.g., GPT-4) is used to generate the optimal text based on the analysis results.

[2143] Step 8:

[2144] The generated answer is sent to the terminal. The input is the text content generated by the server, and the output is the text content sent to the terminal. The server sends the generated text data to the terminal.

[2145] Step 9:

[2146] The terminal displays the generated answer to the store staff. The input is text content sent from the server, and the output is a display that the staff can view. A device such as smart glasses can visually present the generated text to the staff, allowing them to respond immediately.

[2147] 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.

[2148] 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.

[2149] 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.

[2150] 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.

[2151] 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.

[2152] 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.

[2153] 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).

[2154] 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.

[2155] 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."

[2156] 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.

[2157] 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).

[2158] 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.

[2159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2160] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2162] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2163] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2168] The following is further disclosed regarding the above embodiment.

[2169] (Claim 1)

[2170] A means for invoking the generative model to generate text content to express the brand values;

[2171] a means for storing the generated content in a database;

[2172] means for delivering the stored content to the device;

[2173] means for the terminal to display content to the user;

[2174] A system including:

[2175] (Claim 2)

[2176] means for receiving a query from a user;

[2177] means for processing the received question using a generative model to generate an answer;

[2178] means for transmitting the generated answer to the terminal;

[2179] means for the terminal to display the answer to the user;

[2180] 10. The system of claim 1, comprising:

[2181] (Claim 3)

[2182] A means of generating consistent brand messaging using generative models;

[2183] A means of storing this message in a database;

[2184] means for receiving feedback from a user;

[2185] means for generating a response using the generative model based on the received feedback;

[2186] means for transmitting the generated response to the terminal;

[2187] means for the terminal to display a response to the user;

[2188] 10. The system of claim 1, comprising:

[2189] "Example 1"

[2190] (Claim 1)

[2191] A means for invoking the generative model to generate text to express the brand values;

[2192] means for storing the generated text in a data storage;

[2193] means for delivering the stored text to a user device;

[2194] means for the user device to display text to the user;

[2195] A system including:

[2196] (Claim 2)

[2197] means for receiving a query from a user;

[2198] means for processing the received query using the generative model to generate a response;

[2199] means for transmitting the generated response to the user device;

[2200] means for the user device to display the response to the user;

[2201] 10. The system of claim 1, comprising:

[2202] (Claim 3)

[2203] A means for generating consistent brand messaging using a generative model;

[2204] a means for storing said message in data storage;

[2205] means for receiving feedback from users;

[2206] means for generating a response based on the received opinions using a generative model;

[2207] means for transmitting the generated response to the user device;

[2208] means for the user device to display the response to the user;

[2209] 10. The system of claim 1, comprising:

[2210] "Application Example 1"

[2211] (Claim 1)

[2212] A means for invoking the generative model to generate text content to express the brand values;

[2213] a means for storing the generated content in a database;

[2214] means for delivering the stored content to the device;

[2215] means for the terminal to display content to the user;

[2216] means for receiving a question from a user via a terminal and generating an answer to the question using a generative model;

[2217] means for receiving feedback from a user and responding to the feedback using the generative model;

[2218] means for the terminal to display the answer to the question and the feedback response to the user;

[2219] A means for expressing brand values ​​within a virtual store and for users to access it using a smartphone or head-mounted display;

[2220] A system including:

[2221] (Claim 2)

[2222] means for receiving a query from a user;

[2223] means for processing the received question using a generative model to generate an answer;

[2224] means for transmitting the generated answer to the terminal;

[2225] means for the terminal to display the answer to the user;

[2226] A means for displaying text content generated to express the brand's values ​​within the virtual store and promoting interaction with users;

[2227] 10. The system of claim 1, comprising:

[2228] (Claim 3)

[2229] A means of generating consistent brand messaging using generative models;

[2230] A means of storing this message in a database;

[2231] means for receiving feedback from a user;

[2232] means for generating a response using the generative model based on the received feedback;

[2233] means for transmitting the generated response to the terminal;

[2234] means for the terminal to display a response to the user;

[2235] Users can access the virtual store via smartphones or head-mounted displays, and communicate the brand's value.

[2236] 10. The system of claim 1, comprising:

[2237] "Example 2: Combining Emotion Engines"

[2238] (Claim 1)

[2239] A means for calling a generative model to generate sentence data for expressing brand values;

[2240] means for storing the generated text data in a data storage device;

[2241] means for delivering the stored text data to an information processing device;

[2242] means for displaying text data to a user by an information processing device;

[2243] A system including:

[2244] (Claim 2)

[2245] means for receiving an inquiry from a user;

[2246] means for recognizing user sentiment from received queries using a sentiment analyzer and generating answers using a generative model;

[2247] means for transmitting the generated answer to the information processing device;

[2248] means for displaying an answer to a user by an information processing device;

[2249] 10. The system of claim 1, comprising:

[2250] (Claim 3)

[2251] a means for generating a consistent brand message using the generative model and storing the message in a data storage device;

[2252] means for receiving ratings from users;

[2253] means for recognizing user emotions using a sentiment analyzer based on the received ratings and generating a response using a generative model;

[2254] means for transmitting the generated response to the information processing device;

[2255] means for displaying a response to a user by the information processing device;

[2256] 10. The system of claim 1, comprising:

[2257] "Application example 2 when combining emotion engines"

[2258] (Claim 1)

[2259] A means for invoking the generative model to generate text content to express the brand values;

[2260] a means for storing the generated content in a database;

[2261] means for delivering the stored content to the device;

[2262] means for the terminal to display content to the user;

[2263] A means to recognize customer emotions in real time using smart devices,

[2264] means for analyzing the recognized emotion data with an emotion engine;

[2265] A means for the generative model to generate an optimal response based on the analysis results;

[2266] a means for displaying the generated response to store staff;

[2267] A system including:

[2268] (Claim 2)

[2269] means for receiving a query from a user;

[2270] means for processing the received question using a generative model to generate an answer;

[2271] means for transmitting the generated answer to the terminal;

[2272] means for the terminal to display the answer to the user;

[2273] 10. The system of claim 1, comprising:

[2274] (Claim 3)

[2275] A means of generating consistent brand messaging using generative models;

[2276] A means of storing this message in a database;

[2277] means for receiving feedback from a user;

[2278] means for generating a response using the generative model based on the received feedback;

[2279] means for transmitting the generated response to the terminal;

[2280] means for the terminal to display a response to the user;

[2281] 10. The system of claim 1, comprising: [Explanation of symbols]

[2282] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for invoking the generative model to generate text content to express the brand values; a means for storing the generated content in a database; means for delivering the stored content to the device; means for the terminal to display content to the user; A system including:

2. means for receiving a query from a user; means for processing the received question using a generative model to generate an answer; means for transmitting the generated answer to the terminal; means for the terminal to display the answer to the user; The system of claim 1 , comprising:

3. A means of generating consistent brand messaging using generative models; A means of storing this message in a database; means for receiving feedback from a user; means for generating a response using the generative model based on the received feedback; means for transmitting the generated response to the terminal; means for the terminal to display a response to the user; The system of claim 1 , comprising:

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