System

Abstract

A system is provided.SOLUTION: When the user is successfully authenticated, a user profile is acquired, a session is started, a marketing target is received from the user, a trend is collected and analyzed, and a marketing measure is requested to the generated AI model based on the analyzed result and displayed to the user. A concrete action plan selection from a user is received, and a generation AI model is requested to generate contents and an advertisement sentence, which are displayed to the user. The user starts a real-time chat, communicates with the customer, receives a query from the customer, and requests the generated AI model to provide an appropriate answer. The generated answer is displayed to the user, the effect of the implemented measure is monitored, the collected information is analyzed, and a remarketing measure is requested to the generated AI model and displayed to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Traditional marketing work requires the analysis of a wide range of knowledge and data, consuming a great deal of time and effort. It is also difficult to quickly propose appropriate marketing measures, making it difficult to differentiate from competitors and improve ROI. Furthermore, it is difficult to respond to customers in real time, measure the effectiveness of measures, and optimize remarketing. The purpose of this invention is to solve these problems and enable marketing professionals to carry out their work efficiently and effectively and implement high-quality marketing strategies. [Means for solving the problem]

[0005] The present invention is a system comprising:

[0006] The system includes means for receiving and verifying authentication information from a user, means for acquiring user profile data upon successful verification, means for starting a session based on the acquired profile data, means for receiving marketing goal data from a user and collecting trend data from the Internet, means for analyzing the collected trend data and generating analysis results, means for requesting a marketing measure from a generation AI model based on the analysis results, means for displaying the generated marketing measure to the user, means for receiving a specific action plan selection from the user and requesting the generation AI model to generate content and advertising copy, means for displaying the generated content and advertising copy to the user, means for a user to start a real-time chat and communicate with customers, means for receiving inquiries from customers and requesting an appropriate response from the generation AI model, means for displaying the generated response to the user, means for monitoring the effectiveness of the implemented measures and analyzing collected data, means for requesting a remarketing measure from the generation AI model based on the analysis results, and means for displaying the generated remarketing measure to the user. This enables users to efficiently perform marketing tasks and implement measures quickly and accurately.

[0007] "User authentication" is the process of verifying the identity of a user attempting to access a system, and is a procedure for verifying that the authentication information entered is valid.

[0008] "Profile data" is a collection of personal information and attribute information about an authenticated user, and is data that is uniquely associated with a particular user.

[0009] "Session initiation" is the process of establishing communication for a fixed period of time for an authenticated user and providing a connection for the user to perform operations within the system.

[0010] "Marketing goal data" is information about specific marketing objectives and metrics that a user wants to achieve, and is data that serves as a basis for evaluating the success or failure of a campaign.

[0011] "Trend data collection" is the process of using the internet and external APIs to gather data on the latest industry trends and consumer interests.

[0012] "Trend data analysis" is the process of analyzing collected trend data using data analysis techniques to gain the insights necessary to decide on marketing strategies.

[0013] A "generative AI model" is an algorithm that uses artificial intelligence technology to automatically generate marketing strategies and measures based on user input and trend data.

[0014] "Marketing initiatives" are specific activities and action plans implemented to achieve goals, and include advertising, content creation, social media campaigns, etc.

[0015] "Real-time chat" is a communication method that allows users to exchange messages with customers in real time, and is a tool for responding to customers quickly.

[0016] "Remarketing" is a marketing activity aimed at re-approaching customers who have already been in contact with you, and involves proposing new campaigns and advertisements based on past behavioral data. [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 is a digital assistant system for improving the efficiency and quality of work for marketing professionals. This system includes functions such as user authentication, profile acquisition, receiving marketing goal data, collecting and analyzing trend data, generating marketing initiatives, real-time chat, measuring effectiveness, and generating and displaying remarketing initiatives. The specific operation of each function is described below.

[0039] User authentication and profile acquisition

[0040] 1. User: Enter your email address and password to log in to this system.

[0041] 2. Terminal: The entered authentication information is encrypted and sent to the server.

[0042] 3. Server: Receives the authentication information and authenticates the user by checking it against a database.

[0043] If authentication is successful, the user's profile data is retrieved and a session is initiated.

[0044] If the authentication fails, an error message is generated and sent to the terminal.

[0045] 4. Terminal: Notifies the user that authentication was successful and displays their profile data.

[0046] Initial data acquisition and trend analysis

[0047] 1. User: Enter the goals of your marketing campaign or initiative.

[0048] 2. Terminal: Sends target data to the server.

[0049] 3. Server: Based on the provided goal data, collects related trend data via the Internet or external APIs.

[0050] 4. Server: Analyzes the collected trend data and generates analytical results.

[0051] 5. Server: Sends the generated analysis results to the device.

[0052] 6. Terminal: displays the analysis results to the user.

[0053] Customised marketing strategy proposals

[0054] 1. User: Enter the marketing measures they would like to propose or the problem they want to solve.

[0055] 2. Terminal: Sends the entered information to the server.

[0056] 3. Server: Analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[0057] 4. Server: Sends the generated measures to the terminal.

[0058] 5. Terminal: The proposed measures are displayed to the user.

[0059] Implementing and optimizing measures

[0060] 1. User: Selects the items to implement from the proposed measures and requests the creation of a specific action plan.

[0061] 2. Terminal: Sends the selected measures to the server.

[0062] 3. Server: Requests the generative AI model to come up with content ideas, create advertising copy, and analyze target customers in line with the specified measures.

[0063] 4. Server: Sends the generated content and advertising copy to the device.

[0064] 5. Device: Displays the generated content and advertising copy to the user.

[0065] Real-time chat and customer service

[0066] 1. User: Launches the real-time chat feature and starts communicating with the customer.

[0067] 2. Terminal: Requests a chat session from the server and displays the chat screen.

[0068] 3. Terminal: Receives messages from customers and sends them to the server.

[0069] 4. Server: Analyzes received messages in real time and generates appropriate answers based on generative AI models.

[0070] 5. Server: Sends the generated answer to the device.

[0071] 6. Terminal: The generated answer is displayed to the user, who then replies to the customer.

[0072] Measurement and remarketing

[0073] 1. Server: Monitors the results of the implemented campaigns and collects key metrics such as click-through rate, conversion rate, and number of impressions.

[0074] 2. Server: Analyzes the collected data and generates statistics and insights.

[0075] 3. Server: Requests the AI ​​model to generate remarketing measures based on the analysis results.

[0076] 4. Server: Sends new remarketing offers to the device.

[0077] 5. Terminal: New policy proposals are displayed to the user.

[0078] 6. User: Review the proposed measures and select the items that apply.

[0079] Specific examples

[0080] For example, if a user wants to promote a new product online, they can use it in the following ways:

[0081] 1. User: Enter your login details and verify your profile.

[0082] 2. Terminal: Sends the user's marketing objectives to the server.

[0083] 3. Server: Analyzes industry trends and consumer interests in real time based on goals and displays the results.

[0084] 4. User: Selects social media advertising from the proposed marketing strategies and requests specific advertising copy and visuals to be created.

[0085] 5. Server: Uses AI models to generate effective advertising copy and visuals for target customers and provide them to users.

[0086] 6. Users: Use the chat feature to respond to customer inquiries in real time.

[0087] This allows marketing professionals to efficiently implement promotions and continuously measure and optimize the effectiveness of their efforts.

[0088] The processing flow will be explained below.

[0089] User authentication and profile acquisition

[0090] Step 1:

[0091] The user launches the application and enters their email address and password on the login screen.

[0092] Step 2:

[0093] The terminal encrypts the entered authentication information and sends it to the server.

[0094] Step 3:

[0095] The server compares the received authentication information with a database and authenticates the user.

[0096] If authentication is successful, retrieve the user's profile data and start a session. If authentication fails, generate an error message and send it to the terminal.

[0097] Step 4:

[0098] The terminal displays a successful authentication message and user profile data to the user.

[0099] Initial data acquisition and trend analysis

[0100] Step 5:

[0101] Users input goals for their marketing campaigns and initiatives.

[0102] Step 6:

[0103] The terminal transmits the target data input by the user to the server.

[0104] Step 7:

[0105] The server collects trend data via the Internet or external API based on the provided target data.

[0106] Step 8:

[0107] The server analyzes the collected trend data and generates analysis results.

[0108] Step 9:

[0109] The server transmits the generated analysis results to the terminal.

[0110] Step 10:

[0111] The terminal displays the analysis results to the user.

[0112] Customised marketing strategy proposals

[0113] Step 11:

[0114] The user inputs the marketing measures they would like to propose and the issues they would like to solve.

[0115] Step 12:

[0116] The terminal sends the user's input to the server.

[0117] Step 13:

[0118] The server analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[0119] Step 14:

[0120] The server transmits the generated marketing measures to the terminal.

[0121] Step 15:

[0122] The terminal displays the proposed measures to the user.

[0123] Implementing and optimizing measures

[0124] Step 16:

[0125] The user selects the items to be implemented from the proposed measures and requests the creation of a specific action plan.

[0126] Step 17:

[0127] The terminal transmits the user's selected action to the server.

[0128] Step 18:

[0129] Based on the selected strategy, the server requests the generative AI model to generate content ideas, create advertising copy, and analyze target customers.

[0130] Step 19:

[0131] The server transmits the generated content and advertising copy to the terminal.

[0132] Step 20:

[0133] The terminal displays the generated content and advertising copy to the user.

[0134] Real-time chat and customer service

[0135] Step 21:

[0136] The user launches the real-time chat function and begins communicating with the customer.

[0137] Step 22:

[0138] The terminal requests a chat session from the server and displays the chat screen.

[0139] Step 23:

[0140] The terminal receives a message from the customer and sends it to the server.

[0141] Step 24:

[0142] The server analyzes the received messages in real time and generates appropriate answers based on generative AI models.

[0143] Step 25:

[0144] The server sends the generated response to the terminal.

[0145] Step 26:

[0146] The terminal displays the generated answer to the user, who then replies to the customer.

[0147] Measurement and remarketing

[0148] Step 27:

[0149] The server monitors the results of the implemented campaigns and collects key metrics such as click-through rates, conversion rates, and number of impressions.

[0150] Step 28:

[0151] The server analyzes the collected data and generates statistics and insights.

[0152] Step 29:

[0153] The server requests the AI ​​model to generate remarketing measures based on the analysis results.

[0154] Step 30:

[0155] The server sends new remarketing campaigns to the device.

[0156] Step 31:

[0157] The terminal displays new policy proposals to the user.

[0158] Step 32:

[0159] The user reviews the proposed measures and selects the items to apply.

[0160] The above are the specific processing steps of the present invention.

[0161] Example 1

[0162] 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."

[0163] In traditional marketing systems, data collection, analysis, and the generation and application of measures are carried out separately, often resulting in a lack of efficiency and real-time response. There is also the risk of security issues arising in the user authentication information and customer support processes. Furthermore, because effectiveness measurement and remarketing measures are not integrated, continuous optimization of measures is difficult.

[0164] 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.

[0165] In this invention, the server includes: means for receiving and verifying authentication information from a user; means for acquiring user profile data when the verification is successful; means for starting a session based on the acquired profile data; means for receiving marketing goal data from the user and collecting trend data from external information sources; means for analyzing the collected trend data using data analysis technology and generating analysis results; means for generating prompt text based on the analysis results and requesting a generative AI model to generate a marketing measure; means for displaying the generated marketing measure to the user; means for receiving a specific action plan selection from the user and requesting the generative AI model to generate content and advertising copy; means for displaying the generated content and advertising copy to the user; means for a user to start a real-time chat and communicate with customers; means for receiving inquiries from customers and requesting an appropriate response from the generative AI model; means for displaying the generated response to the user; means for monitoring the effectiveness of the implemented measures and analyzing collected data using data analysis technology; means for generating prompt text based on the analysis results and requesting the generative AI model to generate a remarketing measure; means for displaying the generated remarketing measure to the user; and means for protecting authentication information using encryption technology. This enables a consistent, efficient and secure marketing process, from data collection and analysis, to the creation and application of measures, effectiveness measurement, and the creation of remarketing measures.

[0166] "Authentication information" refers to information such as an email address and password that a user provides to log in to a system.

[0167] "Verification" is the process of comparing received authentication information with existing records in a database to determine if it matches.

[0168] "User profile data" refers to data such as personal information and past activity history obtained about a successfully authenticated user.

[0169] A "session" is a state of communication with a server that persists while a user is authenticated.

[0170] "Marketing goal data" refers to data that includes specific goals and objectives for marketing campaigns and initiatives set by users.

[0171] "Trend data" is data collected from the internet or other external sources that indicates consumer interests and behavior over a specific period of time.

[0172] "Data analysis techniques" are techniques used to process collected data and generate analytical results, including statistical methods and machine learning algorithms.

[0173] A "prompt" is a sentence used to give instructions to a generative AI model.

[0174] A "generative AI model" is an artificial intelligence model that generates marketing strategies, advertising copy, content, etc. based on prompt text.

[0175] "Content" is information in the form of text, images, videos, etc., generated for marketing purposes.

[0176] "Ad copy" is text generated to promote a particular product or service.

[0177] "Real-time chat" is a communication function that allows users and customers to exchange messages instantly.

[0178] "Effectiveness measurement" is the process of monitoring the results of implemented marketing initiatives and collecting metrics such as click rates, conversion rates, and number of impressions.

[0179] "Remarketing" is a marketing strategy created to re-engage with existing customers or past visitors.

[0180] "Encryption technology" refers to technology used to protect the security of data.

[0181] The present invention is a digital assistant system for improving the efficiency and quality of work for marketing professionals. The system includes functions such as user authentication, profile acquisition, receiving marketing target data, collecting and analyzing trend data, generating marketing campaigns, real-time chat, measuring effectiveness, and generating and displaying remarketing campaigns.

[0182] User Authentication

[0183] The user enters their email address and password on the login screen. The device encrypts the entered authentication information using AES encryption technology and sends it to the server via HTTPS. The server receives the encrypted authentication information and compares it with the user information in its database. If the comparison is successful, the server obtains the user's profile data and starts a session. The device notifies the user of the authentication result and displays the profile data.

[0184] Hardware and software used:

[0185] Server: Database management system (e.g. MySQL), encryption library (e.g. OpenSSL)

[0186] Device: User's PC or smartphone

[0187] Receiving marketing target data and collecting and analyzing trend data

[0188] Users enter the goals of their marketing campaigns and initiatives on a dedicated input screen. The device sends this goal data in JSON format to the server. The server collects trend data using an external API (e.g., Google Trends API) and analyzes it using data analysis technology (e.g., pandas, numpy). The analysis results are sent to the device in JSON format and displayed to the user.

[0189] Marketing strategy generation

[0190] When a user requests a marketing proposal, the device sends the request to the server. The server generates a prompt based on the analysis data and requests a generative AI model (e.g., GPT-4) to generate an appropriate marketing proposal. The generated proposal is sent to the device and displayed to the user.

[0191] Example prompt sentence:

[0192] "Generate the best social media ad ideas for promoting a new product online."

[0193] "Please propose a marketing strategy based on industry trends over the past three months."

[0194] "Create a tagline and visuals that will appeal to your target audience."

[0195] Implementing and optimizing measures

[0196] The user selects the items they wish to implement from the proposed measures and requests a specific action plan. This selection information is sent from the device to the server, and content ideas and advertising copy are generated based on the generative AI model. The generated content and advertising copy are sent to the device and displayed to the user.

[0197] Real-time chat and effectiveness measurement

[0198] Users can communicate with customers using the real-time chat function. The server analyzes customer inquiries, generates appropriate answers based on the generative AI model, and sends them to the terminal for display.

[0199] The results of the implemented marketing measures are monitored on the server, and the collected data (e.g., click rate, conversion rate, number of impressions) is analyzed using data analysis technology. The analysis results are used to generate remarketing measures, and new measures are generated by a generative AI model and displayed to the user.

[0200] Specific examples

[0201] For example, if a user wants to promote a new product online, the process could look like this:

[0202] 1. The user enters their login information and verifies their profile.

[0203] 2. The terminal transmits the marketing target data to the server.

[0204] 3. The server analyzes industry trends and consumer interests in real time based on the goals and displays the results.

[0205] 4. The user selects social media advertising from the proposed marketing strategies and requests the creation of specific advertising copy and visuals.

[0206] 5. The server uses the AI ​​model to generate effective advertising copy and visuals for the target customer and provides them to the user.

[0207] 6. Users can use the chat feature to respond to customer inquiries in real time.

[0208] This enables marketing professionals to efficiently and effectively implement promotions and continuously measure and optimize the results of their efforts.

[0209] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0210] Step 1: User authentication

[0211] 1. User: Enter your email address and password on the login screen.

[0212] Input: Email address, Password

[0213] Output: Encrypted credentials

[0214] 2. Terminal: The entered authentication information is encrypted using AES encryption technology and sent to the server via HTTPS.

[0215] Specific behavior: Encryption using the OpenSSL library

[0216] 3. Server: Receives the encrypted authentication information and checks it against the user information in its database.

[0217] Input: Encrypted credentials

[0218] Output: Authentication success or failure result

[0219] What it does: It performs a database query and compares the encrypted password with the hash value stored in the database.

[0220] 4. Server: If authentication is successful, retrieves the user's profile data and starts a session. If authentication is unsuccessful, generates an error message.

[0221] Input: Authentication success or failure result

[0222] Output: Profile data or error message

[0223] Specific behavior: If successful, generate a session ID and retrieve profile data from the database.

[0224] 5. Terminal: Notifies the user of the authentication result and displays the profile data if authentication is successful.

[0225] Input: Profile data or error message

[0226] Output: Display of authentication result

[0227] Step 2: Receive marketing goal data

[0228] 1. User: Enter the goals of your marketing campaign or initiative in a dedicated input screen.

[0229] Input: Goal data

[0230] Output: Target data

[0231] 2. Terminal: Sends the target data in JSON format to the server.

[0232] Specific operation: Converts data into JSON format and sends it to the server via HTTPS

[0233] Step 3: Collect and analyze trend data

[0234] 1. Server: Uses external APIs (e.g., Google Trends API) to collect trend data related to the provided goal data.

[0235] Input: Goal data

[0236] Output: Trend data

[0237] Specific operation: Acquire data from external API and parse the format

[0238] 2. Server: Analyze the collected trend data using data analysis techniques (e.g., pandas, numpy).

[0239] Input: Trend data

[0240] Output: Analysis results

[0241] Specific actions: Analyze rising trends and related keywords

[0242] 3. Server: Sends the analysis results to the terminal in JSON format.

[0243] Input: Analysis results

[0244] Output: Sending the analysis results

[0245] 4. Terminal: displays the analysis results to the user.

[0246] Input: Analysis results

[0247] Output: Display of analysis results

[0248] Step 4: Generate marketing initiatives

[0249] 1. User: Enter the marketing measures they would like to propose or the problem they want to solve.

[0250] Input: Policy hopes or challenges

[0251] Output: desired measures or issues

[0252] 2. Terminal: The input content is sent to the server in JSON format.

[0253] Specific operation: Converts data into JSON format and sends it to the server via HTTPS

[0254] 3. Server: Generates prompts based on the analyzed data and requests a generative AI model (e.g., GPT-4) to generate appropriate marketing strategies.

[0255] Input: desired measures or issues, analysis data

[0256] Output: Marketing Strategy

[0257] Specific operation: Enter a prompt into GPT-4 and obtain the generated measures.

[0258] 4. Server: Sends the generated measures to the terminal in JSON format.

[0259] Input: Marketing Strategy

[0260] Output: Sending the measure

[0261] 5. Terminal: The proposed measures are displayed to the user.

[0262] Input: Marketing Strategy

[0263] Output: Display of measures

[0264] Step 5: Implementing and optimizing measures

[0265] 1. User: Selects the items to implement from the proposed measures and requests a specific action plan.

[0266] Input: Select the action to be taken

[0267] Output: Selected measures

[0268] 2. Terminal: Sends the selected measures to the server in JSON format.

[0269] Specific operation: Converts data into JSON format and sends it to the server via HTTPS

[0270] 3. Server: Based on the generative AI model, it requests content ideas, ad copy creation, and target customer analysis in line with the selected measures.

[0271] Input: Selected Measure

[0272] Output: Content, ad copy, targeting analysis

[0273] Specific operation: Generate a prompt sentence and input it into the AI ​​model

[0274] 4. Server: Sends the generated content and ad copy to the device in JSON format.

[0275] Input: Content, ad copy, targeting analysis

[0276] Output: Sending content and ad copy

[0277] 5. Device: Displays the generated content and advertising copy to the user.

[0278] Input: Content, ad copy, targeting analysis

[0279] Output: Display of content and ad copy

[0280] Step 6: Real-time chat

[0281] 1. User: Launches the real-time chat feature and starts communicating with the customer.

[0282] Input: Start chat command

[0283] Output: Chat screen display

[0284] 2. Terminal: Requests a chat session from the server and displays the chat screen.

[0285] Specific behavior: Generate a session ID and load the chat screen

[0286] 3. Terminal: Receives messages from customers and sends them to the server.

[0287] Input: Customer message

[0288] Output: Sending a message

[0289] 4. Server: Analyzes received messages in real time and generates appropriate answers based on generative AI models.

[0290] Input: Customer message

[0291] Output: Reply message

[0292] Specific operation: Generate a prompt sentence and input it into the AI ​​model

[0293] 5. Server: Sends the generated answer in JSON format to the device.

[0294] Input: Reply message

[0295] Output: Sending a response message

[0296] 6. Terminal: The generated answer is displayed to the user, who then replies to the customer.

[0297] Input: Reply message

[0298] Output: Display of response message

[0299] Step 7: Measure your results

[0300] 1. Server: Monitors the results of the implemented campaigns and collects key metrics such as click-through rate, conversion rate, and number of impressions.

[0301] Input: Measure execution results

[0302] Output: Collected data

[0303] Specific actions: Analyzing log data, using measurement tools

[0304] 2. Server: Analyzes the collected data using data analysis techniques (e.g., pandas, numpy) to generate statistics and insights.

[0305] Input: Collected data

[0306] Output: Statistics, insights

[0307] Specific behavior: Applying statistical methods and machine learning algorithms

[0308] Step 8: Generate and view remarketing initiatives

[0309] 1. Server: Generates prompt text based on the analysis results and requests the generative AI model to generate remarketing measures.

[0310] Input: Statistics, Insights

[0311] Output: Remarketing Initiative

[0312] Specific operation: Generate a prompt sentence and input it into the AI ​​model

[0313] 2. Server: Generates a new remarketing campaign and sends it to the device in JSON format.

[0314] Enter: Remarketing Initiative

[0315] Output: Sending the measure

[0316] 3. Terminal: New policy proposals are displayed to the user.

[0317] Enter: Remarketing Initiative

[0318] Output: Display of measures

[0319] 4. User: Review the proposed measures and select the items that apply.

[0320] Input: Select a measure

[0321] Output: Execution of selected measures

[0322] (Application example 1)

[0323] 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."

[0324] In today's world, marketing professionals spend a great deal of time and effort processing massive amounts of data to create and optimize effective advertising campaigns. It is also difficult to respond to customers in real time or immediately adjust marketing strategies. To solve these challenges, a system is needed that can automatically collect and analyze data, generate effective marketing strategies, and efficiently communicate and analyze performance in real time.

[0325] 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.

[0326] In this invention, the server includes means for receiving and verifying authentication information from a user, means for acquiring user profile data upon successful verification, means for initiating a session based on the acquired profile data, means for collecting trend data from the Internet, means for analyzing the collected trend data and generating an analysis result, means for requesting an AI model to generate a marketing strategy based on the analysis result, means for supporting optimization of an advertising campaign, and means for analyzing the performance of the advertising campaign, thereby enabling effective design and optimization of advertising campaigns, real-time customer service, continuous performance analysis, and generation of remarketing strategies.

[0327] "User authentication" is the process required to verify the identity of a user accessing a system.

[0328] "Profile data" is data that compiles information about a user, including the settings and history of an individual user.

[0329] A "session" refers to a series of interactions while a user is accessing a system.

[0330] "Marketing goal data" is data that represents the specific marketing goals and objectives that a user wants to achieve.

[0331] "Trend data" is data about current market and consumer trends, collected from the internet and external APIs.

[0332] "Analysis results" refers to information generated as a result of analysis performed on collected data.

[0333] A "generative AI model" is a type of artificial intelligence that uses specific algorithms to analyze data and generate content.

[0334] A "marketing initiative" is a specific action or plan taken to achieve a specific marketing goal.

[0335] "Content" refers to materials such as text and visuals created for advertising and marketing purposes.

[0336] "Ad copy" means text content intended for use in an advertising campaign.

[0337] "Real-time chat" is a chat function that allows users and customers to communicate instantly.

[0338] "Customer service" is the process of responding to inquiries and questions from customers.

[0339] "Effectiveness measurement" is a means of evaluating the results of implemented marketing measures.

[0340] "Remarketing" is a marketing activity aimed at re-approaching customers who have previously shown interest in a product or service.

[0341] "Advertising campaign optimization" is the process of applying techniques and adjustments to maximize advertising performance.

[0342] "Advertising campaign performance analysis" is the analysis of data to evaluate how successful an advertising campaign is.

[0343] This invention is a digital assistant system that streamlines and improves the quality of work for marketing professionals. The system performs a comprehensive process, starting with user authentication, entering marketing goals, collecting and analyzing trend data, creating marketing strategies, chatting in real time, and measuring effectiveness.

[0344] User authentication and profile acquisition

[0345] To access the system, a user enters an email address and password. This authentication information is encrypted on the terminal and sent to the server. The server receives the authentication information and authenticates the user by comparing it with a database. If authentication is successful, the server obtains the user's profile data and starts a session. If authentication fails, it generates an error message and sends it to the terminal. The terminal notifies the user that authentication was successful and displays the profile data.

[0346] Initial data acquisition and trend analysis

[0347] The user inputs the goals of a marketing campaign or initiative. The device sends the goal data to the server. Based on the goal data provided, the server collects related trend data via the Internet or an external API. (As an example, the Google Trends API can be used.) The server analyzes the collected trend data and generates analysis results. The generated analysis results are sent to the device and displayed to the user.

[0348] Customizing marketing proposals

[0349] The user inputs the marketing measures they would like to see proposed and the problem they want to solve. The device sends the input information to the server. The server analyzes the received information and generates appropriate marketing measures based on a generative AI model (e.g., OpenAI GPT-3). The server sends the generated measures to the device, and the device displays the proposed measures to the user.

[0350] Implementing and optimizing measures

[0351] The user selects the items they wish to implement from the proposed measures and requests the creation of a specific action plan. The selected measures are sent from the device to the server. The server then requests the generative AI model to generate content (advertising copy and visuals) in line with the specified measures. The generated content and advertising copy are then displayed to the user.

[0352] Real-time chat and customer service

[0353] The user launches the real-time chat function and begins communication with the customer. A chat session is requested from the device to the server, and the chat screen is displayed. When the user receives a message from the customer, it is sent to the server. The server analyzes the message in real time and generates an appropriate response based on the generative AI model. The generated response is sent to the device, and the user replies to the customer.

[0354] Measurement and remarketing

[0355] The server monitors the results of the implemented measures and collects key indicators such as click rate, conversion rate, and number of impressions. The collected data is analyzed on the server to generate statistical data and insights. Based on the analysis results, a new remarketing measure is generated by the AI ​​model and sent from the server to the device. The new measure proposals are displayed to the user, who can review the proposed measures and select the items to apply.

[0356] Specific examples

[0357] For example, if a user wants to promote a new product online, they can use:

[0358] 1. The user enters their login information and verifies their profile.

[0359] 2. The terminal transmits the user's marketing objectives to the server.

[0360] 3. The server analyzes industry trends and consumer interests in real time based on the goals and displays the results.

[0361] 4. The user selects SNS advertising from the proposed marketing strategies and requests the creation of specific advertising copy and visuals.

[0362] 5. The server uses the generative AI model to generate effective advertising copy and visuals for the target customer and provides them to the user.

[0363] 6. Users use the chat feature to respond to customer inquiries in real time.

[0364] An example prompt is:

[0365] "Generate ad copy for the online promotion of a new product. The target audience is young people in their 20s. The trending keywords are 'sustainability,' 'organic,' and 'latest technology.'"

[0366] This concrete example will enable marketing professionals to efficiently implement promotions and continuously measure and optimize the effectiveness of their efforts.

[0367] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0368] Step 1:

[0369] A user accesses the system and enters their email address and password. The entered authentication information is encrypted on the device and sent to the server. The server compares the received authentication information with a database and authenticates the user. If authentication is successful, the server obtains the user's profile data and sends it to the device. The device notifies the user that authentication was successful and displays the profile data.

[0370] Input: Email address, password

[0371] Output: Profile data, authentication notification

[0372] Step 2:

[0373] The user inputs the goals of a marketing campaign or initiative. The device sends the goal data to the server. The server receives the goal data and collects related trend data using the Internet or external APIs (e.g., Google Trends API). The server analyzes the collected trend data and generates analysis results. The generated analysis results are sent to the device and displayed to the user.

[0374] Input: Marketing goal data

[0375] Output: Trend data, analysis results

[0376] Step 3:

[0377] The user inputs the marketing measures they would like to see proposed or the problem they want to solve. The device sends the input information to the server. The server analyzes the information and generates appropriate marketing measures based on a generative AI model (e.g., OpenAI GPT-3). The generated measures are sent to the device and displayed to the user.

[0378] Input: Marketing strategy desired data

[0379] Output: Marketing Strategy

[0380] Step 4:

[0381] The user selects which of the proposed measures to implement and requests the creation of a specific action plan. The selected measures are sent from the device to the server. The server then requests the generative AI model to generate content (advertising copy and visuals) in line with the selected measures. The generated content and advertising copy are sent to the device and displayed to the user.

[0382] Input: Selected Measure

[0383] Output: Generated content, ad copy

[0384] Step 5:

[0385] The user activates the real-time chat function and starts communication with the customer. A request for a chat session is sent from the device to the server. The device displays the chat screen, and when the user receives a message from the customer, it is sent to the server. The server analyzes the message in real time and generates an appropriate response based on the generative AI model. The generated response is sent to the device, and the user replies to the customer.

[0386] Input: Message from customer

[0387] Output: The generated answer

[0388] Step 6:

[0389] The server monitors the results of the implemented measures and collects key indicators such as click-through rate, conversion rate, and number of impressions. The collected data is analyzed on the server to generate statistical data and insights. Based on the analysis results, new remarketing measures are requested from the AI ​​model. New measure proposals are sent to the device and displayed to the user. The user reviews the proposed measures and selects the items to apply.

[0390] Input: Policy result data

[0391] Output: Analysis results, new remarketing measures

[0392] 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.

[0393] This invention is a digital assistant system for improving the efficiency and quality of the work of marketing professionals, and further enhances the accuracy and applicability of marketing strategies by combining it with an emotion engine that recognizes user emotions. In addition to basic functions such as user authentication, profile acquisition, receiving marketing goal data, collecting and analyzing trend data, generating marketing strategies, real-time chat, measuring effectiveness, and generating and displaying remarketing strategies, this system also includes an emotion engine function that recognizes user emotions. The specific operation of each function is described below.

[0394] User authentication and profile acquisition

[0395] 1. User: Enter your email address and password to log in to this system.

[0396] 2. Terminal: The entered authentication information is encrypted and sent to the server.

[0397] 3. Server: Receives the authentication information and authenticates the user by checking it against a database. If authentication is successful, retrieves the user's profile data and starts a session. If authentication fails, generates an error message and sends it to the device.

[0398] 4. Terminal: Notifies the user that authentication was successful and displays their profile data.

[0399] Initial data acquisition and trend analysis

[0400] 1. User: Enter the goals of your marketing campaign or initiative.

[0401] 2. Terminal: Sends target data to the server.

[0402] 3. Server: Based on the provided goal data, collects related trend data via the Internet or external APIs.

[0403] 4. Server: Analyzes the collected trend data and generates analytical results.

[0404] 5. Server: Sends the generated analysis results to the device.

[0405] 6. Terminal: displays the analysis results to the user.

[0406] Emotion recognition by emotion engine

[0407] 1. Terminal: Sends user input and actions to the emotion engine.

[0408] 2. Server: The emotion engine analyzes the user's emotions and sends the results to the server.

[0409] 3. Server: Customize marketing strategies based on emotion recognition results.

[0410] 4. Server: Sends customized measures to the device.

[0411] 5. Terminal: Display the customized campaign to the user.

[0412] Customised marketing strategy proposals

[0413] 1. User: Enter the marketing measures they would like to propose or the problem they want to solve.

[0414] 2. Terminal: Sends the entered information to the server.

[0415] 3. Server: Analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[0416] 4. Server: Sends the generated measures to the terminal.

[0417] 5. Terminal: The proposed measures are displayed to the user.

[0418] Implementing and optimizing measures

[0419] 1. User: Selects the items to implement from the proposed measures and requests the creation of a specific action plan.

[0420] 2. Terminal: Sends the selected measures to the server.

[0421] 3. Server: Requests the generative AI model to come up with content ideas, create advertising copy, and analyze target customers in line with the specified measures.

[0422] 4. Server: Sends the generated content and advertising copy to the device.

[0423] 5. Device: Displays the generated content and advertising copy to the user.

[0424] Real-time chat and customer service

[0425] 1. User: Launches the real-time chat feature and starts communicating with the customer.

[0426] 2. Terminal: Requests a chat session from the server and displays the chat screen.

[0427] 3. Terminal: Receives messages from customers and sends them to the server.

[0428] 4. Server: Analyzes received messages in real time and generates appropriate answers based on generative AI models.

[0429] 5. Server: Sends the generated answer to the device.

[0430] 6. Terminal: The generated answer is displayed to the user, who then replies to the customer.

[0431] 7. Terminal: The emotions of the user and customer during the chat are also sent to the emotion engine for analysis.

[0432] 8. Server: Based on the results of emotion analysis, generate an appropriate response and provide it to the user.

[0433] Measurement and remarketing

[0434] 1. Server: Monitors the results of the implemented campaigns and collects key metrics such as click-through rate, conversion rate, and number of impressions.

[0435] 2. Server: Analyzes the collected data and generates statistics and insights.

[0436] 3. Server: Requests the AI ​​model to generate remarketing measures based on the analysis results.

[0437] 4. Server: Sends new remarketing offers to the device.

[0438] 5. Terminal: New policy proposals are displayed to the user.

[0439] 6. User: Review the proposed measures and select the items that apply.

[0440] Specific examples

[0441] For example, if a user wants to run an online promotion for a new product:

[0442] 1. User: Enter your login details and verify your profile.

[0443] 2. Terminal: Sends the user's marketing objectives to the server.

[0444] 3. Server: Analyzes industry trends and consumer interests in real time based on goals and displays the results.

[0445] 4. User: Selects social media advertising from the proposed marketing strategies and requests specific advertising copy and visuals to be created.

[0446] 5. Terminal: Sends the user's emotions to the emotion engine and sends the analysis results to the server.

[0447] 6. Server: Based on the results of sentiment analysis, an AI model is used to generate effective advertising copy and visuals for the target customer, and these are provided to the user.

[0448] 7. Users: Use the chat feature to respond to customer inquiries in real time.

[0449] 8. Terminal: Analyzes the emotions of users and customers during chat and receives appropriate responses from the server.

[0450] This allows marketing professionals to efficiently implement promotions while taking user emotions into account, and continuously measure and optimize the effectiveness of their initiatives.

[0451] The processing flow will be explained below.

[0452] User authentication and profile acquisition

[0453] Step 1:

[0454] The user launches the application and enters their email address and password on the login screen.

[0455] Step 2:

[0456] The terminal encrypts the entered authentication information and sends it to the server.

[0457] Step 3:

[0458] The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the user's profile data is obtained and a session is started. If authentication fails, an error message is generated and sent to the terminal.

[0459] Step 4:

[0460] The terminal displays a successful authentication message and user profile data to the user.

[0461] Initial data acquisition and trend analysis

[0462] Step 5:

[0463] Users input goals for their marketing campaigns and initiatives.

[0464] Step 6:

[0465] The terminal transmits the target data input by the user to the server.

[0466] Step 7:

[0467] The server collects trend data via the Internet or external API based on the provided target data.

[0468] Step 8:

[0469] The server analyzes the collected trend data and generates analysis results.

[0470] Step 9:

[0471] The server transmits the generated analysis results to the terminal.

[0472] Step 10:

[0473] The terminal displays the analysis results to the user.

[0474] Emotion recognition by emotion engine

[0475] Step 11:

[0476] The device sends the user's input and actions to the emotion engine.

[0477] Step 12:

[0478] The server receives the user's emotion results analyzed by the emotion engine.

[0479] Step 13:

[0480] The server customizes marketing measures based on the emotion recognition results.

[0481] Step 14:

[0482] The server sends the customized measures to the terminal.

[0483] Step 15:

[0484] The terminal displays the customized measures to the user.

[0485] Customised marketing strategy proposals

[0486] Step 16:

[0487] The user inputs the marketing measures they would like to propose and the issues they would like to solve.

[0488] Step 17:

[0489] The terminal sends the user's input to the server.

[0490] Step 18:

[0491] The server analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[0492] Step 19:

[0493] The server transmits the generated measures to the terminal.

[0494] Step 20:

[0495] The terminal displays the proposed measures to the user.

[0496] Implementing and optimizing measures

[0497] Step 21:

[0498] The user selects the items to be implemented from the proposed measures and requests the creation of a specific action plan.

[0499] Step 22:

[0500] The terminal transmits the user's selected action to the server.

[0501] Step 23:

[0502] Based on the selected strategy, the server requests the generative AI model to generate content ideas, create advertising copy, and analyze target customers.

[0503] Step 24:

[0504] The server transmits the generated content and advertising copy to the terminal.

[0505] Step 25:

[0506] The terminal displays the generated content and advertising copy to the user.

[0507] Real-time chat and customer service

[0508] Step 26:

[0509] The user launches the real-time chat function and begins communicating with the customer.

[0510] Step 27:

[0511] The terminal requests a chat session from the server and displays the chat screen.

[0512] Step 28:

[0513] The terminal receives a message from the customer and sends it to the server.

[0514] Step 29:

[0515] The server analyzes the received messages in real time and generates appropriate answers based on generative AI models.

[0516] Step 30:

[0517] The server sends the generated response to the terminal.

[0518] Step 31:

[0519] The terminal displays the generated answer to the user, who then replies to the customer.

[0520] Step 32:

[0521] The terminal transmits the emotions of the user and customer during the chat to the emotion engine, which performs emotion analysis.

[0522] Step 33:

[0523] Based on the emotion analysis results, the server generates an appropriate response and provides it to the user.

[0524] Measurement and remarketing

[0525] Step 34:

[0526] The server monitors the results of the implemented campaigns and collects key metrics such as click-through rates, conversion rates, and number of impressions.

[0527] Step 35:

[0528] The server analyzes the collected data and generates statistics and insights.

[0529] Step 36:

[0530] The server requests the AI ​​model to generate remarketing measures based on the analysis results.

[0531] Step 37:

[0532] The server sends new remarketing campaigns to the device.

[0533] Step 38:

[0534] The terminal displays new policy proposals to the user.

[0535] Step 39:

[0536] The user reviews the proposed measures and selects the items to apply.

[0537] The above are the specific processing steps of the present invention, which combines emotion engines.

[0538] Example 2

[0539] 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."

[0540] Traditional marketing systems lack the ability to analyze user emotions and behavior in real time and generate and customize effective marketing measures based on that analysis. Furthermore, the process of generating marketing measures and measuring their effectiveness after implementation and remarketing is often done manually, requiring time and resources. There is a need not only to improve user experience, but also to operate marketing measures efficiently and accurately.

[0541] 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.

[0542] In this invention, the server includes: means for receiving and verifying authentication information from a user; means for acquiring user profile data when the verification is successful; means for starting a session based on the acquired profile data; means for receiving marketing goal data from a user and collecting trend data from an external database; means for analyzing the collected trend data using a machine learning algorithm and generating analysis results; means for requesting a marketing measure from a generation AI model based on the analysis results; means for displaying the generated marketing measure to the user; means for receiving a specific action plan selection from the user and requesting the generation AI model to generate content and advertising copy; means for displaying the generated content and advertising copy to the user; means for a user to activate a real-time communication function and interact with a third party; means for receiving an inquiry from a third party and requesting an appropriate response from the generation AI model; means for displaying the generated response to the user; means for monitoring the effectiveness of the implemented measures and analyzing collected data; means for requesting a remarketing measure from the generation AI model based on the analysis results; means for displaying the generated remarketing measure to the user; and means for analyzing user input and behavior using a sentiment analysis engine and customizing the marketing measure based on the analysis results. This makes it possible to generate and customize marketing initiatives in real time based on user emotions and behavior.

[0543] "Authentication information" refers to information required to log in to a system, such as a user's email address and password.

[0544] "User profile data" is information related to an authenticated user, including, for example, name, job title, goals, and the like.

[0545] A "session" is a function that tracks activities while a user is logged into the system, and is a temporary connection that maintains user-specific operations.

[0546] "Marketing goal data" is information that indicates specific goals that a user wants to achieve in a marketing campaign or initiative.

[0547] "Trend data" is data that shows current market and consumer trends, and is mainly collected from the Internet and external databases.

[0548] A "machine learning algorithm" is a computational method for analyzing collected data and finding patterns and relationships.

[0549] A "generative AI model" is a model that uses artificial intelligence to generate responses and measures based on user input and the environment.

[0550] "Content" refers to information materials such as text, images, and videos used in marketing initiatives.

[0551] "Ad copy" is text that promotes a particular product or service.

[0552] The "real-time communication function" is a function that allows a user to have a timely conversation with a third party.

[0553] An "inquiry" is a question or request made by a third party.

[0554] "Effectiveness monitoring" is the process of measuring and evaluating the results of implemented measures.

[0555] A "remarketing initiative" is a new marketing initiative that is generated based on the results of analyzing collected data.

[0556] An "emotion analysis engine" is a technology that analyzes a user's input and behavior and infers their emotions.

[0557] This invention is a digital assistant system designed to streamline and improve the quality of work for marketing professionals. Furthermore, by incorporating an emotion analysis engine that recognizes user emotions, it enhances the accuracy and applicability of marketing strategies. In addition to basic functions such as user authentication, profile acquisition, receiving marketing goal data, collecting and analyzing trend data, generating marketing strategies, real-time communication, measuring effectiveness, and generating and displaying remarketing strategies, this system also includes an emotion analysis engine function that recognizes user emotions. The specific operation of each function is described below.

[0558] User authentication and profile acquisition

[0559] A user enters an email address and password to log in to the system. The terminal encrypts the entered authentication information using the AES-256 algorithm and sends it to the server. The server receives the authentication information and authenticates the user by checking it against the PostgreSQL database. If authentication is successful, it retrieves the user's profile data and starts a session. If authentication fails, it generates an error message and sends it to the terminal. The terminal notifies the user of successful authentication and displays the profile data.

[0560] Initial data acquisition and trend analysis

[0561] The user inputs the goals of a marketing campaign or initiative. The device sends the goal data to the server. The server uses a Python script to collect relevant trend data from external databases or APIs (e.g., the Google Trends API). The collected trend data is analyzed using machine learning algorithms (e.g., the scikit-learn library) to generate analysis results. The server sends the generated analysis results to the device, which then displays the analysis results to the user.

[0562] Emotion recognition by emotion engine

[0563] The device sends the user's input and behavioral data to a sentiment analysis engine (for example, IBM Watson Natural Language Understanding API). The server receives the sentiment analysis results and customizes marketing campaigns based on them. The customized campaigns are sent from the server to the device, which displays them to the user.

[0564] Customised marketing strategy proposals

[0565] The user inputs the marketing measures they would like to see proposed and the problems they want to solve. The device sends the information to the server. The server analyzes the received information and generates appropriate marketing measures using a generative AI model (e.g., OpenAI GPT-3). The generated measures are sent to the device and displayed to the user.

[0566] Implementing and optimizing measures

[0567] The user selects the items to implement from the proposed measures and requests the creation of a specific action plan. The device sends the selected measures to the server. The server uses a generative AI model to generate content ideas, create advertising copy, and analyze target customers based on the specified measures. The generated content and advertising copy are sent to the device and displayed to the user.

[0568] Real-time communication and customer service

[0569] The user activates the real-time communication function and begins a conversation with a third party (such as a customer). The device requests a chat session from the server and displays the chat screen. The message from the customer is received by the device and sent to the server. The server analyzes the received message in real time and generates an appropriate response based on the generative AI model. The generated response is sent to the device and displayed to the user. The emotions of the user and the third party during the chat are also sent to the emotion analysis engine for analysis. The analysis results are provided to the user as an appropriate response from the server.

[0570] Measurement and remarketing

[0571] The server monitors the results of the implemented measures and collects key indicators such as click-through rate, conversion rate, and number of impressions. The collected data is analyzed to generate statistics and insights (for example, using Jupyter Notebook). Based on the analysis results, a remarketing measure generation AI model is requested, and new remarketing measures are generated. The generated measures are sent to the device and displayed to the user. The user reviews the proposed measures and selects the ones they want to apply.

[0572] Specific examples

[0573] Example prompt sentence:

[0574] "Identify target customers and generate effective social media copy to promote a new product online."

[0575] For example, consider a user running an online promotion for a new product. The user enters their login information and confirms their profile. Next, they send their marketing goals from their device to the server. The server analyzes industry trends and consumer interests in real time based on their goals and displays the results on the device. The user selects a social media ad from the proposed marketing initiatives and requests the creation of specific ad copy and visuals. The device sends the user's emotions to a sentiment analysis engine and sends the analysis results to the server. Based on the analysis results, the server uses a generative AI model to generate effective ad copy and visuals for the target customer and provides them to the user. The user uses the chat function to respond to customer inquiries in real time. At this time, the server analyzes the emotions of both the user and the customer during the chat and receives an appropriate response from the server. In this way, marketing professionals can efficiently run promotions while taking user emotions into consideration and continuously measure and optimize the effectiveness of their initiatives.

[0576] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0577] Specific explanation of processing steps

[0578] Step 1: Enter user credentials

[0579] User: Enter your email address and password on the login screen.

[0580] Enter your email address and password

[0581] Output: Enter your credentials

[0582] What happens: A user enters their login information into a browser or application login screen and clicks the submit button.

[0583] Step 2: Encrypt and transmit credentials

[0584] Terminal: The entered authentication information is encrypted using the AES-256 algorithm and sent to the server.

[0585] Input: User credentials

[0586] Output: Encrypted credentials

[0587] How it works: Uses JavaScript or native code to encrypt input data and send it to the server over HTTPS.

[0588] Step 3: Verification of authentication information and notification of authentication result

[0589] Server: Receives the authentication information and authenticates the user against the PostgreSQL database. If authentication is successful, retrieves the user's profile data and starts a session.

[0590] Input: Encrypted credentials

[0591] Output: Authentication result, user profile data

[0592] How it works: The server uses the Python Flask framework to accept requests, perform database checks, and upon successful authentication, generates a JWT token and starts a secure session.

[0593] Step 4: Retrieving and displaying profile data

[0594] Server: If authentication is successful, retrieve the user's profile data.

[0595] Input: Authentication success flag

[0596] Output: Profile data

[0597] What it does: Retrieves and returns user profile information from the database.

[0598] Step 5: View profile data

[0599] Terminal: Notifies the user of successful authentication and displays profile data.

[0600] Input: Profile data

[0601] Output: Authentication successful message, profile data displayed

[0602] Behavior: A success message is displayed and user profile information is displayed on the screen.

[0603] Step 6: Enter your marketing goal data

[0604] Users: Enter the goals of their marketing campaigns and initiatives.

[0605] Input: Marketing Goal Data

[0606] Output: Target data input

[0607] What happens: A user enters numeric or text data into a goal field on a marketing dashboard.

[0608] Step 7: Submit goal data

[0609] Terminal: Sends target data to the server.

[0610] Input: Marketing Goal Data

[0611] Output: Target data sent

[0612] How it works: Uses JavaScript Ajax or the Fetch API to send input data in JSON format to the server.

[0613] Step 8: Trend data collection

[0614] Server: Uses Python scripts to collect relevant trend data from external databases and APIs (e.g., Google Trends API).

[0615] Input: Marketing Goal Data

[0616] Output: Trend data

[0617] Operation: Calls the API, retrieves data for the specified period, and stores it in the database.

[0618] Step 9: Analyze trend data

[0619] Server: Analyzes the collected trend data using machine learning algorithms (e.g., the scikit-learn library) and generates analytical results.

[0620] Input: Trend data

[0621] Output: Analysis results

[0622] What it does: Analyzes data by applying k-means clustering and linear regression models.

[0623] Step 10: Submit and view analysis results

[0624] Server: Sends the generated analysis results to the device.

[0625] Input: Analysis results

[0626] Output: Submitted analysis results

[0627] Behavior: The parsed results are encoded in JSON format and sent to the client.

[0628] Terminal: Displays the analysis results to the user.

[0629] Input: Analysis results

[0630] Output: Displayed analysis results

[0631] How it works: The data is visualized using a charting library and displayed to the user.

[0632] Step 11: Emotion Recognition with the Emotion Engine

[0633] Device: Sends user input and behavioral data to the sentiment analysis engine.

[0634] Input: User input and behavioral data

[0635] Output: Sentiment analysis request

[0636] How it works: Sends user input data and behavior to the sentiment analysis engine API via a POST request.

[0637] Server: Calls a sentiment analysis engine (e.g., IBM Watson Natural Language Understanding API) and receives the sentiment analysis results.

[0638] Input: Sentiment analysis request

[0639] Output: Emotion analysis results

[0640] What it does: Gets and stores the emotion score.

[0641] Step 12: Customize measures based on sentiment analysis results

[0642] Server: Customize marketing strategies based on sentiment analysis results.

[0643] Input: Sentiment analysis results

[0644] Output: Customized measures

[0645] What it does: Runs a script that adjusts tactics based on the sentiment score.

[0646] Server: Sends customized measures to the device.

[0647] Input: Customized Measures

[0648] Output: Customization measures sent

[0649] Operation: Encodes the policy data in JSON format and sends it to the device.

[0650] Device: Display customized campaigns to users.

[0651] Input: Customized Measures

[0652] Output: Displayed customization measures

[0653] How it works: The customized action is displayed in the UI and visually presented to the user.

[0654] Generative AI model, prompt sentence

[0655] Example prompt sentence:

[0656] "Identify target customers and generate effective social media copy to promote a new product online."

[0657] (Application example 2)

[0658] 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."

[0659] Conventional marketing systems typically implement fixed marketing strategies without considering user emotions, resulting in problems that prevent them from adequately meeting user needs. Furthermore, real-time collection and analysis of trend data is insufficient, making it difficult to provide timely and effective marketing strategies. Furthermore, in communications between users and customers, appropriate responses are sometimes not provided, reducing the work efficiency of marketing professionals. It is necessary to solve these problems.

[0660] 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.

[0661] In this invention, the server includes means for recognizing user emotions using an emotion engine that analyzes user emotions, means for customizing marketing measures based on the user emotion recognition results, and means for generating optimal advertising copy and visuals using a generative AI model based on the user emotion recognition results and trend data analysis results, thereby making it possible to provide personalized marketing measures that take user emotions into consideration.

[0662] "User authentication" is the process of collecting authentication information provided by a user and verifying that the user is a legitimate user.

[0663] "Profile data" is data that includes a user's personal information and past activity history, and is used to customize marketing strategies.

[0664] "Marketing goal data" is data indicating the goals of campaigns and promotions set by users.

[0665] "Trend data" is data that collects the latest information about current markets and consumer behavior.

[0666] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically generate marketing strategies and advertising copy.

[0667] "Real-time chat" is a communication method that allows users and customers to exchange messages in real time.

[0668] "Remarketing" is the process of implementing new marketing strategies based on the effectiveness of past marketing strategies.

[0669] An "emotion engine" is a software mechanism that analyzes emotions from user input and behavior and provides the results.

[0670] MODE FOR CARRYING OUT THE INVENTION

[0671] The present invention is a system that starts with user authentication, acquires user profile data, receives marketing goal data, collects and analyzes trend data, recognizes emotions using an emotion engine, generates and customizes marketing initiatives using generative AI models, has real-time chat functionality, measures effectiveness, and generates remarketing initiatives.

[0672] User authentication and profile acquisition

[0673] A user logs into the system by entering their email address and password. The device encrypts this authentication information and sends it to the server. The server then authenticates the user by checking it against a database. If authentication is successful, the server retrieves the user profile data and starts the session.

[0674] Initial data acquisition and trend analysis

[0675] Users input the goals of their marketing campaigns and initiatives. The device sends this goal data to the server. Based on the goal data provided, the server collects and analyzes relevant trend data via the Internet or external APIs. The analysis results are sent to the device and displayed to the user.

[0676] Emotion recognition by emotion engine

[0677] The device sends the user's input and actions to the emotion engine. The server analyzes the user's emotions using the emotion engine and receives the results. Based on the emotion recognition results, the server customizes marketing measures and sends them to the device. The customized measures are then displayed to the user.

[0678] Customised marketing strategy proposals

[0679] The user inputs the marketing measures they wish to propose and the problems they wish to solve. The device sends the input information to the server. The server analyzes the received information and generates appropriate marketing measures based on the generative AI model. These generated measures are sent to the device and displayed to the user.

[0680] Implementing and optimizing measures

[0681] The user selects an action item from the proposed measures and requests the creation of a specific action plan. The device sends the selected measures to the server. The server then requests the generative AI model to come up with content ideas in line with the specified measures, create advertising copy, and analyze the target customers. The generated content and advertising copy are sent to the device and displayed to the user.

[0682] Real-time chat and customer service

[0683] The user activates the real-time chat function and begins communication with the customer. The device requests a chat session from the server and displays the chat screen. The device receives the message from the customer and sends it to the server. The server analyzes the received message in real time and generates an appropriate response based on the generative AI model. This generated response is sent to the device and displayed to the user. Furthermore, the emotions of the user and customer during the chat are also sent to the emotion engine, and an appropriate response is generated based on the analysis results.

[0684] Measurement and remarketing

[0685] The server monitors the results of the implemented campaigns and collects key metrics such as click-through rates, conversion rates, and number of impressions. The collected data is analyzed to generate statistics and insights. Based on the analysis results, remarketing campaigns are requested from the generative AI model. The new remarketing campaigns are sent to the device and displayed to the user.

[0686] Specific examples

[0687] For example, if a user wants to promote a new product online, they can do so effectively through the following process:

[0688] 1. The user enters their login information and verifies their profile.

[0689] 2. The terminal transmits the user's marketing objectives to the server.

[0690] 3. The server analyzes industry trends and consumer interests in real time based on the goals and displays the results.

[0691] 4. The user selects SNS advertising from the proposed marketing measures and requests the creation of specific advertising copy and visuals.

[0692] 5. The device sends the user's emotions to the emotion engine and sends the analysis results to the server.

[0693] 6. Based on the results of the sentiment analysis, the server uses a generative AI model to generate effective advertising copy and visuals for the target customer and provides them to the user.

[0694] 7. Users use the chat feature to respond to customer inquiries in real time.

[0695] 8. The terminal analyzes the emotions of the user and customer during the chat and receives an appropriate response from the server.

[0696] Prompt Sentence Examples

[0697] 1. Prompt to ask the generative AI model to implement marketing strategies based on emotion recognition:

[0698] "Generate optimal copy and visuals for a new product online advertising campaign based on emotion recognition results and provided target data."

[0699] 2. Prompt to collect trend data using an external API:

[0700] "Collect recent trend data from the internet related to the campaign goals for a new product and return the analysis results."

[0701] As a result, the system of the present invention takes into account the user's emotions and enables highly personalized marketing measures, thereby realizing efficient and effective marketing.

[0702] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0703] Step 1:

[0704] User Authentication

[0705] The user enters login information (email address and password) into the smartphone app.

[0706] The terminal encrypts this authentication information and sends it to the server.

[0707] The server receives the encrypted authentication information and authenticates it against a database.

[0708] Input: Email address, password

[0709] Output: Authentication result (success / failure)

[0710] If the authentication is successful, the server obtains the user profile data and sends a notification of successful authentication to the terminal.

[0711] The terminal notifies the user that the authentication was successful and displays the profile data.

[0712] Step 2:

[0713] Initial data acquisition and trend analysis

[0714] Users input goals for their marketing campaigns and initiatives.

[0715] The terminal transmits this target data to the server.

[0716] The server collects trend data based on the provided target data via the Internet or external API.

[0717] Input: Marketing goal data

[0718] Output: Trend data

[0719] The server analyzes the collected trend data and sends the analysis results to the terminal.

[0720] The terminal displays the analysis results to the user.

[0721] Step 3:

[0722] Emotion recognition by emotion engine

[0723] The terminal transmits the user's input and actions to the emotion engine.

[0724] The server analyzes the user's emotions using an emotion engine.

[0725] Input: User input and behavioral data

[0726] Output: Emotion recognition result

[0727] The server customizes marketing measures based on the emotion recognition results and sends them to the device.

[0728] The terminal displays the customized measures to the user.

[0729] Step 4:

[0730] Customised marketing strategy proposals

[0731] Users input the marketing measures they would like to propose and the issues they would like to solve.

[0732] The terminal transmits this input content to the server.

[0733] The server analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[0734] Input: User's desired measures and issues

[0735] Output: Customized marketing initiatives

[0736] The server transmits the generated marketing measures to the terminal and displays them to the user.

[0737] Step 5:

[0738] Implementing and optimizing measures

[0739] The user selects items to be implemented from the proposed measures and wishes to create a specific action plan.

[0740] The terminal transmits the selected measure to the server.

[0741] The server asks the generative AI model to come up with content ideas in line with the specified measures, create advertising copy, and analyze target customers.

[0742] Input: Select the action to be taken

[0743] Output: Content, ad copy, target customer analysis results

[0744] The server transmits the generated content and advertising copy to the terminal and displays them to the user.

[0745] Step 6:

[0746] Real-time chat and customer service

[0747] The user activates the real-time chat function and starts communicating with the customer.

[0748] The terminal requests a chat session from the server and displays a chat screen.

[0749] The terminal receives messages from the customer and transmits them to the server.

[0750] The server analyzes the received messages in real time and generates appropriate answers based on the generative AI model.

[0751] Input: Message from customer

[0752] Output: Correct answer

[0753] The server sends the generated answer to the terminal and displays it to the user.

[0754] The terminal transmits the emotions of the user and customer during the chat to the emotion engine and transmits the analysis results to the server.

[0755] The server generates an appropriate response based on the emotion analysis results and sends it to the terminal.

[0756] Step 7:

[0757] Measurement and remarketing

[0758] The server monitors the results of the implemented measures and collects key metrics such as click-through rate, conversion rate, and number of impressions.

[0759] Input: Data on implemented measures

[0760] Output: Key indicator data

[0761] The server analyzes the collected data and generates statistics and insights.

[0762] The server then requests the AI ​​model to generate remarketing strategies based on the analysis results.

[0763] Input: Key indicator data

[0764] Output: Remarketing campaign

[0765] The server sends the new remarketing campaign to the terminal and displays it to the user.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] [Second embodiment]

[0770] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0771] 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.

[0772] 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).

[0773] 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.

[0774] 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.

[0775] 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).

[0776] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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."

[0782] The present invention is a digital assistant system for improving the efficiency and quality of work for marketing professionals. This system includes functions such as user authentication, profile acquisition, receiving marketing goal data, collecting and analyzing trend data, generating marketing initiatives, real-time chat, measuring effectiveness, and generating and displaying remarketing initiatives. The specific operation of each function is described below.

[0783] User authentication and profile acquisition

[0784] 1. User: Enter your email address and password to log in to this system.

[0785] 2. Terminal: The entered authentication information is encrypted and sent to the server.

[0786] 3. Server: Receives the authentication information and authenticates the user by checking it against a database.

[0787] If authentication is successful, the user's profile data is retrieved and a session is initiated.

[0788] If the authentication fails, an error message is generated and sent to the terminal.

[0789] 4. Terminal: Notifies the user that authentication was successful and displays their profile data.

[0790] Initial data acquisition and trend analysis

[0791] 1. User: Enter the goals of your marketing campaign or initiative.

[0792] 2. Terminal: Sends target data to the server.

[0793] 3. Server: Based on the provided goal data, collects related trend data via the Internet or external APIs.

[0794] 4. Server: Analyzes the collected trend data and generates analytical results.

[0795] 5. Server: Sends the generated analysis results to the device.

[0796] 6. Terminal: displays the analysis results to the user.

[0797] Customised marketing strategy proposals

[0798] 1. User: Enter the marketing measures they would like to propose or the problem they want to solve.

[0799] 2. Terminal: Sends the entered information to the server.

[0800] 3. Server: Analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[0801] 4. Server: Sends the generated measures to the terminal.

[0802] 5. Terminal: The proposed measures are displayed to the user.

[0803] Implementing and optimizing measures

[0804] 1. User: Selects the items to implement from the proposed measures and requests the creation of a specific action plan.

[0805] 2. Terminal: Sends the selected measures to the server.

[0806] 3. Server: Requests the generative AI model to come up with content ideas, create advertising copy, and analyze target customers in line with the specified measures.

[0807] 4. Server: Sends the generated content and advertising copy to the device.

[0808] 5. Device: Displays the generated content and advertising copy to the user.

[0809] Real-time chat and customer service

[0810] 1. User: Launches the real-time chat feature and starts communicating with the customer.

[0811] 2. Terminal: Requests a chat session from the server and displays the chat screen.

[0812] 3. Terminal: Receives messages from customers and sends them to the server.

[0813] 4. Server: Analyzes received messages in real time and generates appropriate answers based on generative AI models.

[0814] 5. Server: Sends the generated answer to the device.

[0815] 6. Terminal: The generated answer is displayed to the user, who then replies to the customer.

[0816] Measurement and remarketing

[0817] 1. Server: Monitors the results of the implemented campaigns and collects key metrics such as click-through rate, conversion rate, and number of impressions.

[0818] 2. Server: Analyzes the collected data and generates statistics and insights.

[0819] 3. Server: Requests the AI ​​model to generate remarketing measures based on the analysis results.

[0820] 4. Server: Sends new remarketing offers to the device.

[0821] 5. Terminal: New policy proposals are displayed to the user.

[0822] 6. User: Review the proposed measures and select the items that apply.

[0823] Specific examples

[0824] For example, if a user wants to promote a new product online, they can use it in the following ways:

[0825] 1. User: Enter your login details and verify your profile.

[0826] 2. Terminal: Sends the user's marketing objectives to the server.

[0827] 3. Server: Analyzes industry trends and consumer interests in real time based on goals and displays the results.

[0828] 4. User: Selects social media advertising from the proposed marketing strategies and requests specific advertising copy and visuals to be created.

[0829] 5. Server: Uses AI models to generate effective advertising copy and visuals for target customers and provide them to users.

[0830] 6. Users: Use the chat feature to respond to customer inquiries in real time.

[0831] This allows marketing professionals to efficiently implement promotions and continuously measure and optimize the effectiveness of their efforts.

[0832] The processing flow will be explained below.

[0833] User authentication and profile acquisition

[0834] Step 1:

[0835] The user launches the application and enters their email address and password on the login screen.

[0836] Step 2:

[0837] The terminal encrypts the entered authentication information and sends it to the server.

[0838] Step 3:

[0839] The server compares the received authentication information with a database and authenticates the user.

[0840] If authentication is successful, retrieve the user's profile data and start a session. If authentication fails, generate an error message and send it to the terminal.

[0841] Step 4:

[0842] The terminal displays a successful authentication message and user profile data to the user.

[0843] Initial data acquisition and trend analysis

[0844] Step 5:

[0845] Users input goals for their marketing campaigns and initiatives.

[0846] Step 6:

[0847] The terminal transmits the target data input by the user to the server.

[0848] Step 7:

[0849] The server collects trend data via the Internet or external API based on the provided target data.

[0850] Step 8:

[0851] The server analyzes the collected trend data and generates analysis results.

[0852] Step 9:

[0853] The server transmits the generated analysis results to the terminal.

[0854] Step 10:

[0855] The terminal displays the analysis results to the user.

[0856] Customised marketing strategy proposals

[0857] Step 11:

[0858] The user inputs the marketing measures they would like to propose and the issues they would like to solve.

[0859] Step 12:

[0860] The terminal sends the user's input to the server.

[0861] Step 13:

[0862] The server analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[0863] Step 14:

[0864] The server transmits the generated marketing measures to the terminal.

[0865] Step 15:

[0866] The terminal displays the proposed measures to the user.

[0867] Implementing and optimizing measures

[0868] Step 16:

[0869] The user selects the items to be implemented from the proposed measures and requests the creation of a specific action plan.

[0870] Step 17:

[0871] The terminal transmits the user's selected action to the server.

[0872] Step 18:

[0873] Based on the selected strategy, the server requests the generative AI model to generate content ideas, create advertising copy, and analyze target customers.

[0874] Step 19:

[0875] The server transmits the generated content and advertising copy to the terminal.

[0876] Step 20:

[0877] The terminal displays the generated content and advertising copy to the user.

[0878] Real-time chat and customer service

[0879] Step 21:

[0880] The user launches the real-time chat function and begins communicating with the customer.

[0881] Step 22:

[0882] The terminal requests a chat session from the server and displays the chat screen.

[0883] Step 23:

[0884] The terminal receives a message from the customer and sends it to the server.

[0885] Step 24:

[0886] The server analyzes the received messages in real time and generates appropriate answers based on generative AI models.

[0887] Step 25:

[0888] The server sends the generated response to the terminal.

[0889] Step 26:

[0890] The terminal displays the generated answer to the user, who then replies to the customer.

[0891] Measurement and remarketing

[0892] Step 27:

[0893] The server monitors the results of the implemented campaigns and collects key metrics such as click-through rates, conversion rates, and number of impressions.

[0894] Step 28:

[0895] The server analyzes the collected data and generates statistics and insights.

[0896] Step 29:

[0897] The server requests the AI ​​model to generate remarketing measures based on the analysis results.

[0898] Step 30:

[0899] The server sends new remarketing campaigns to the device.

[0900] Step 31:

[0901] The terminal displays new policy proposals to the user.

[0902] Step 32:

[0903] The user reviews the proposed measures and selects the items to apply.

[0904] The above are the specific processing steps of the present invention.

[0905] Example 1

[0906] 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."

[0907] In traditional marketing systems, data collection, analysis, and the generation and application of measures are carried out separately, often resulting in a lack of efficiency and real-time response. There is also the risk of security issues arising in the user authentication information and customer support processes. Furthermore, because effectiveness measurement and remarketing measures are not integrated, continuous optimization of measures is difficult.

[0908] 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.

[0909] In this invention, the server includes: means for receiving and verifying authentication information from a user; means for acquiring user profile data when the verification is successful; means for starting a session based on the acquired profile data; means for receiving marketing goal data from the user and collecting trend data from external information sources; means for analyzing the collected trend data using data analysis technology and generating analysis results; means for generating prompt text based on the analysis results and requesting a generative AI model to generate a marketing measure; means for displaying the generated marketing measure to the user; means for receiving a specific action plan selection from the user and requesting the generative AI model to generate content and advertising copy; means for displaying the generated content and advertising copy to the user; means for a user to start a real-time chat and communicate with customers; means for receiving inquiries from customers and requesting an appropriate response from the generative AI model; means for displaying the generated response to the user; means for monitoring the effectiveness of the implemented measures and analyzing collected data using data analysis technology; means for generating prompt text based on the analysis results and requesting the generative AI model to generate a remarketing measure; means for displaying the generated remarketing measure to the user; and means for protecting authentication information using encryption technology. This enables a consistent, efficient and secure marketing process, from data collection and analysis, to the creation and application of measures, effectiveness measurement, and the creation of remarketing measures.

[0910] "Authentication information" refers to information such as an email address and password that a user provides to log in to a system.

[0911] "Verification" is the process of comparing received authentication information with existing records in a database to determine if it matches.

[0912] "User profile data" refers to data such as personal information and past activity history obtained about a successfully authenticated user.

[0913] A "session" is a state of communication with a server that persists while a user is authenticated.

[0914] "Marketing goal data" refers to data that includes specific goals and objectives for marketing campaigns and initiatives set by users.

[0915] "Trend data" is data collected from the internet or other external sources that indicates consumer interests and behavior over a specific period of time.

[0916] "Data analysis techniques" are techniques used to process collected data and generate analytical results, including statistical methods and machine learning algorithms.

[0917] A "prompt" is a sentence used to give instructions to a generative AI model.

[0918] A "generative AI model" is an artificial intelligence model that generates marketing strategies, advertising copy, content, etc. based on prompt text.

[0919] "Content" is information in the form of text, images, videos, etc., generated for marketing purposes.

[0920] "Ad copy" is text generated to promote a particular product or service.

[0921] "Real-time chat" is a communication function that allows users and customers to exchange messages instantly.

[0922] "Effectiveness measurement" is the process of monitoring the results of implemented marketing initiatives and collecting metrics such as click rates, conversion rates, and number of impressions.

[0923] "Remarketing" is a marketing strategy created to re-engage with existing customers or past visitors.

[0924] "Encryption technology" refers to technology used to protect the security of data.

[0925] The present invention is a digital assistant system for improving the efficiency and quality of work for marketing professionals. The system includes functions such as user authentication, profile acquisition, receiving marketing target data, collecting and analyzing trend data, generating marketing campaigns, real-time chat, measuring effectiveness, and generating and displaying remarketing campaigns.

[0926] User Authentication

[0927] The user enters their email address and password on the login screen. The device encrypts the entered authentication information using AES encryption technology and sends it to the server via HTTPS. The server receives the encrypted authentication information and compares it with the user information in its database. If the comparison is successful, the server obtains the user's profile data and starts a session. The device notifies the user of the authentication result and displays the profile data.

[0928] Hardware and software used:

[0929] Server: Database management system (e.g. MySQL), encryption library (e.g. OpenSSL)

[0930] Device: User's PC or smartphone

[0931] Receiving marketing target data and collecting and analyzing trend data

[0932] Users enter the goals of their marketing campaigns and initiatives on a dedicated input screen. The device sends this goal data in JSON format to the server. The server collects trend data using an external API (e.g., Google Trends API) and analyzes it using data analysis technology (e.g., pandas, numpy). The analysis results are sent to the device in JSON format and displayed to the user.

[0933] Marketing strategy generation

[0934] When a user requests a marketing proposal, the device sends the request to the server. The server generates a prompt based on the analysis data and requests a generative AI model (e.g., GPT-4) to generate an appropriate marketing proposal. The generated proposal is sent to the device and displayed to the user.

[0935] Example prompt sentence:

[0936] "Generate the best social media ad ideas for promoting a new product online."

[0937] "Please propose a marketing strategy based on industry trends over the past three months."

[0938] "Create a tagline and visuals that will appeal to your target audience."

[0939] Implementing and optimizing measures

[0940] The user selects the items they wish to implement from the proposed measures and requests a specific action plan. This selection information is sent from the device to the server, and content ideas and advertising copy are generated based on the generative AI model. The generated content and advertising copy are sent to the device and displayed to the user.

[0941] Real-time chat and effectiveness measurement

[0942] Users can communicate with customers using the real-time chat function. The server analyzes customer inquiries, generates appropriate answers based on the generative AI model, and sends them to the terminal for display.

[0943] The results of the implemented marketing measures are monitored on the server, and the collected data (e.g., click rate, conversion rate, number of impressions) is analyzed using data analysis technology. The analysis results are used to generate remarketing measures, and new measures are generated by a generative AI model and displayed to the user.

[0944] Specific examples

[0945] For example, if a user wants to promote a new product online, the process could look like this:

[0946] 1. The user enters their login information and verifies their profile.

[0947] 2. The terminal transmits the marketing target data to the server.

[0948] 3. The server analyzes industry trends and consumer interests in real time based on the goals and displays the results.

[0949] 4. The user selects social media advertising from the proposed marketing strategies and requests the creation of specific advertising copy and visuals.

[0950] 5. The server uses the AI ​​model to generate effective advertising copy and visuals for the target customer and provides them to the user.

[0951] 6. Users can use the chat feature to respond to customer inquiries in real time.

[0952] This enables marketing professionals to efficiently and effectively implement promotions and continuously measure and optimize the results of their efforts.

[0953] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0954] Step 1: User authentication

[0955] 1. User: Enter your email address and password on the login screen.

[0956] Input: Email address, Password

[0957] Output: Encrypted credentials

[0958] 2. Terminal: The entered authentication information is encrypted using AES encryption technology and sent to the server via HTTPS.

[0959] Specific behavior: Encryption using the OpenSSL library

[0960] 3. Server: Receives the encrypted authentication information and checks it against the user information in its database.

[0961] Input: Encrypted credentials

[0962] Output: Authentication success or failure result

[0963] What it does: It performs a database query and compares the encrypted password with the hash value stored in the database.

[0964] 4. Server: If authentication is successful, retrieves the user's profile data and starts a session. If authentication is unsuccessful, generates an error message.

[0965] Input: Authentication success or failure result

[0966] Output: Profile data or error message

[0967] Specific behavior: If successful, generate a session ID and retrieve profile data from the database.

[0968] 5. Terminal: Notifies the user of the authentication result and displays the profile data if authentication is successful.

[0969] Input: Profile data or error message

[0970] Output: Display of authentication result

[0971] Step 2: Receive marketing goal data

[0972] 1. User: Enter the goals of your marketing campaign or initiative in a dedicated input screen.

[0973] Input: Goal data

[0974] Output: Target data

[0975] 2. Terminal: Sends the target data in JSON format to the server.

[0976] Specific operation: Converts data into JSON format and sends it to the server via HTTPS

[0977] Step 3: Collect and analyze trend data

[0978] 1. Server: Uses external APIs (e.g., Google Trends API) to collect trend data related to the provided goal data.

[0979] Input: Goal data

[0980] Output: Trend data

[0981] Specific operation: Acquire data from external API and parse the format

[0982] 2. Server: Analyze the collected trend data using data analysis techniques (e.g., pandas, numpy).

[0983] Input: Trend data

[0984] Output: Analysis results

[0985] Specific actions: Analyze rising trends and related keywords

[0986] 3. Server: Sends the analysis results to the terminal in JSON format.

[0987] Input: Analysis results

[0988] Output: Sending the analysis results

[0989] 4. Terminal: displays the analysis results to the user.

[0990] Input: Analysis results

[0991] Output: Display of analysis results

[0992] Step 4: Generate marketing initiatives

[0993] 1. User: Enter the marketing measures they would like to propose or the problem they want to solve.

[0994] Input: Policy hopes or challenges

[0995] Output: desired measures or issues

[0996] 2. Terminal: The input content is sent to the server in JSON format.

[0997] Specific operation: Converts data into JSON format and sends it to the server via HTTPS

[0998] 3. Server: Generates prompts based on the analyzed data and requests a generative AI model (e.g., GPT-4) to generate appropriate marketing strategies.

[0999] Input: desired measures or issues, analysis data

[1000] Output: Marketing Strategy

[1001] Specific operation: Enter a prompt into GPT-4 and obtain the generated measures.

[1002] 4. Server: Sends the generated measures to the terminal in JSON format.

[1003] Input: Marketing Strategy

[1004] Output: Sending the measure

[1005] 5. Terminal: The proposed measures are displayed to the user.

[1006] Input: Marketing Strategy

[1007] Output: Display of measures

[1008] Step 5: Implementing and optimizing measures

[1009] 1. User: Selects the items to implement from the proposed measures and requests a specific action plan.

[1010] Input: Select the action to be taken

[1011] Output: Selected measures

[1012] 2. Terminal: Sends the selected measures to the server in JSON format.

[1013] Specific operation: Converts data into JSON format and sends it to the server via HTTPS

[1014] 3. Server: Based on the generative AI model, it requests content ideas, ad copy creation, and target customer analysis in line with the selected measures.

[1015] Input: Selected Measure

[1016] Output: Content, ad copy, targeting analysis

[1017] Specific operation: Generate a prompt sentence and input it into the AI ​​model

[1018] 4. Server: Sends the generated content and ad copy to the device in JSON format.

[1019] Input: Content, ad copy, targeting analysis

[1020] Output: Sending content and ad copy

[1021] 5. Device: Displays the generated content and advertising copy to the user.

[1022] Input: Content, ad copy, targeting analysis

[1023] Output: Display of content and ad copy

[1024] Step 6: Real-time chat

[1025] 1. User: Launches the real-time chat feature and starts communicating with the customer.

[1026] Input: Start chat command

[1027] Output: Chat screen display

[1028] 2. Terminal: Requests a chat session from the server and displays the chat screen.

[1029] Specific behavior: Generate a session ID and load the chat screen

[1030] 3. Terminal: Receives messages from customers and sends them to the server.

[1031] Input: Customer message

[1032] Output: Sending a message

[1033] 4. Server: Analyzes received messages in real time and generates appropriate answers based on generative AI models.

[1034] Input: Customer message

[1035] Output: Reply message

[1036] Specific operation: Generate a prompt sentence and input it into the AI ​​model

[1037] 5. Server: Sends the generated answer in JSON format to the device.

[1038] Input: Reply message

[1039] Output: Sending a response message

[1040] 6. Terminal: The generated answer is displayed to the user, who then replies to the customer.

[1041] Input: Reply message

[1042] Output: Display of response message

[1043] Step 7: Measure your results

[1044] 1. Server: Monitors the results of the implemented campaigns and collects key metrics such as click-through rate, conversion rate, and number of impressions.

[1045] Input: Measure execution results

[1046] Output: Collected data

[1047] Specific actions: Analyzing log data, using measurement tools

[1048] 2. Server: Analyzes the collected data using data analysis techniques (e.g., pandas, numpy) to generate statistics and insights.

[1049] Input: Collected data

[1050] Output: Statistics, insights

[1051] Specific behavior: Applying statistical methods and machine learning algorithms

[1052] Step 8: Generate and view remarketing initiatives

[1053] 1. Server: Generates prompt text based on the analysis results and requests the generative AI model to generate remarketing measures.

[1054] Input: Statistics, Insights

[1055] Output: Remarketing Initiative

[1056] Specific operation: Generate a prompt sentence and input it into the AI ​​model

[1057] 2. Server: Generates a new remarketing campaign and sends it to the device in JSON format.

[1058] Enter: Remarketing Initiative

[1059] Output: Sending the measure

[1060] 3. Terminal: New policy proposals are displayed to the user.

[1061] Enter: Remarketing Initiative

[1062] Output: Display of measures

[1063] 4. User: Review the proposed measures and select the items that apply.

[1064] Input: Select a measure

[1065] Output: Execution of selected measures

[1066] (Application example 1)

[1067] 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."

[1068] In today's world, marketing professionals spend a great deal of time and effort processing massive amounts of data to create and optimize effective advertising campaigns. It is also difficult to respond to customers in real time or immediately adjust marketing strategies. To solve these challenges, a system is needed that can automatically collect and analyze data, generate effective marketing strategies, and efficiently communicate and analyze performance in real time.

[1069] 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.

[1070] In this invention, the server includes means for receiving and verifying authentication information from a user, means for acquiring user profile data upon successful verification, means for initiating a session based on the acquired profile data, means for collecting trend data from the Internet, means for analyzing the collected trend data and generating an analysis result, means for requesting an AI model to generate a marketing strategy based on the analysis result, means for supporting optimization of an advertising campaign, and means for analyzing the performance of the advertising campaign, thereby enabling effective design and optimization of advertising campaigns, real-time customer service, continuous performance analysis, and generation of remarketing strategies.

[1071] "User authentication" is the process required to verify the identity of a user accessing a system.

[1072] "Profile data" is data that compiles information about a user, including the settings and history of an individual user.

[1073] A "session" refers to a series of interactions while a user is accessing a system.

[1074] "Marketing goal data" is data that represents the specific marketing goals and objectives that a user wants to achieve.

[1075] "Trend data" is data about current market and consumer trends, collected from the internet and external APIs.

[1076] "Analysis results" refers to information generated as a result of analysis performed on collected data.

[1077] A "generative AI model" is a type of artificial intelligence that uses specific algorithms to analyze data and generate content.

[1078] A "marketing initiative" is a specific action or plan taken to achieve a specific marketing goal.

[1079] "Content" refers to materials such as text and visuals created for advertising and marketing purposes.

[1080] "Ad copy" means text content intended for use in an advertising campaign.

[1081] "Real-time chat" is a chat function that allows users and customers to communicate instantly.

[1082] "Customer service" is the process of responding to inquiries and questions from customers.

[1083] "Effectiveness measurement" is a means of evaluating the results of implemented marketing measures.

[1084] "Remarketing" is a marketing activity aimed at re-approaching customers who have previously shown interest in a product or service.

[1085] "Advertising campaign optimization" is the process of applying techniques and adjustments to maximize advertising performance.

[1086] "Advertising campaign performance analysis" is the analysis of data to evaluate how successful an advertising campaign is.

[1087] This invention is a digital assistant system that streamlines and improves the quality of work for marketing professionals. The system performs a comprehensive process, starting with user authentication, entering marketing goals, collecting and analyzing trend data, creating marketing strategies, chatting in real time, and measuring effectiveness.

[1088] User authentication and profile acquisition

[1089] To access the system, a user enters an email address and password. This authentication information is encrypted on the terminal and sent to the server. The server receives the authentication information and authenticates the user by comparing it with a database. If authentication is successful, the server obtains the user's profile data and starts a session. If authentication fails, it generates an error message and sends it to the terminal. The terminal notifies the user that authentication was successful and displays the profile data.

[1090] Initial data acquisition and trend analysis

[1091] The user inputs the goals of a marketing campaign or initiative. The device sends the goal data to the server. Based on the goal data provided, the server collects related trend data via the Internet or an external API. (As an example, the Google Trends API can be used.) The server analyzes the collected trend data and generates analysis results. The generated analysis results are sent to the device and displayed to the user.

[1092] Customizing marketing proposals

[1093] The user inputs the marketing measures they would like to see proposed and the problem they want to solve. The device sends the input information to the server. The server analyzes the received information and generates appropriate marketing measures based on a generative AI model (e.g., OpenAI GPT-3). The server sends the generated measures to the device, and the device displays the proposed measures to the user.

[1094] Implementing and optimizing measures

[1095] The user selects the items they wish to implement from the proposed measures and requests the creation of a specific action plan. The selected measures are sent from the device to the server. The server then requests the generative AI model to generate content (advertising copy and visuals) in line with the specified measures. The generated content and advertising copy are then displayed to the user.

[1096] Real-time chat and customer service

[1097] The user launches the real-time chat function and begins communication with the customer. A chat session is requested from the device to the server, and the chat screen is displayed. When the user receives a message from the customer, it is sent to the server. The server analyzes the message in real time and generates an appropriate response based on the generative AI model. The generated response is sent to the device, and the user replies to the customer.

[1098] Measurement and remarketing

[1099] The server monitors the results of the implemented measures and collects key indicators such as click rate, conversion rate, and number of impressions. The collected data is analyzed on the server to generate statistical data and insights. Based on the analysis results, a new remarketing measure is generated by the AI ​​model and sent from the server to the device. The new measure proposals are displayed to the user, who can review the proposed measures and select the items to apply.

[1100] Specific examples

[1101] For example, if a user wants to promote a new product online, they can use:

[1102] 1. The user enters their login information and verifies their profile.

[1103] 2. The terminal transmits the user's marketing objectives to the server.

[1104] 3. The server analyzes industry trends and consumer interests in real time based on the goals and displays the results.

[1105] 4. The user selects SNS advertising from the proposed marketing strategies and requests the creation of specific advertising copy and visuals.

[1106] 5. The server uses the generative AI model to generate effective advertising copy and visuals for the target customer and provides them to the user.

[1107] 6. Users use the chat feature to respond to customer inquiries in real time.

[1108] An example prompt is:

[1109] "Generate ad copy for the online promotion of a new product. The target audience is young people in their 20s. The trending keywords are 'sustainability,' 'organic,' and 'latest technology.'"

[1110] This concrete example will enable marketing professionals to efficiently implement promotions and continuously measure and optimize the effectiveness of their efforts.

[1111] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1112] Step 1:

[1113] A user accesses the system and enters their email address and password. The entered authentication information is encrypted on the device and sent to the server. The server compares the received authentication information with a database and authenticates the user. If authentication is successful, the server obtains the user's profile data and sends it to the device. The device notifies the user that authentication was successful and displays the profile data.

[1114] Input: Email address, password

[1115] Output: Profile data, authentication notification

[1116] Step 2:

[1117] The user inputs the goals of a marketing campaign or initiative. The device sends the goal data to the server. The server receives the goal data and collects related trend data using the Internet or external APIs (e.g., Google Trends API). The server analyzes the collected trend data and generates analysis results. The generated analysis results are sent to the device and displayed to the user.

[1118] Input: Marketing goal data

[1119] Output: Trend data, analysis results

[1120] Step 3:

[1121] The user inputs the marketing measures they would like to see proposed or the problem they want to solve. The device sends the input information to the server. The server analyzes the information and generates appropriate marketing measures based on a generative AI model (e.g., OpenAI GPT-3). The generated measures are sent to the device and displayed to the user.

[1122] Input: Marketing strategy desired data

[1123] Output: Marketing Strategy

[1124] Step 4:

[1125] The user selects which of the proposed measures to implement and requests the creation of a specific action plan. The selected measures are sent from the device to the server. The server then requests the generative AI model to generate content (advertising copy and visuals) in line with the selected measures. The generated content and advertising copy are sent to the device and displayed to the user.

[1126] Input: Selected Measure

[1127] Output: Generated content, ad copy

[1128] Step 5:

[1129] The user activates the real-time chat function and starts communication with the customer. A request for a chat session is sent from the device to the server. The device displays the chat screen, and when the user receives a message from the customer, it is sent to the server. The server analyzes the message in real time and generates an appropriate response based on the generative AI model. The generated response is sent to the device, and the user replies to the customer.

[1130] Input: Message from customer

[1131] Output: The generated answer

[1132] Step 6:

[1133] The server monitors the results of the implemented measures and collects key indicators such as click-through rate, conversion rate, and number of impressions. The collected data is analyzed on the server to generate statistical data and insights. Based on the analysis results, new remarketing measures are requested from the AI ​​model. New measure proposals are sent to the device and displayed to the user. The user reviews the proposed measures and selects the items to apply.

[1134] Input: Policy result data

[1135] Output: Analysis results, new remarketing measures

[1136] 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.

[1137] This invention is a digital assistant system for improving the efficiency and quality of the work of marketing professionals, and further enhances the accuracy and applicability of marketing strategies by combining it with an emotion engine that recognizes user emotions. In addition to basic functions such as user authentication, profile acquisition, receiving marketing goal data, collecting and analyzing trend data, generating marketing strategies, real-time chat, measuring effectiveness, and generating and displaying remarketing strategies, this system also includes an emotion engine function that recognizes user emotions. The specific operation of each function is described below.

[1138] User authentication and profile acquisition

[1139] 1. User: Enter your email address and password to log in to this system.

[1140] 2. Terminal: The entered authentication information is encrypted and sent to the server.

[1141] 3. Server: Receives the authentication information and authenticates the user by checking it against a database. If authentication is successful, retrieves the user's profile data and starts a session. If authentication fails, generates an error message and sends it to the device.

[1142] 4. Terminal: Notifies the user that authentication was successful and displays their profile data.

[1143] Initial data acquisition and trend analysis

[1144] 1. User: Enter the goals of your marketing campaign or initiative.

[1145] 2. Terminal: Sends target data to the server.

[1146] 3. Server: Based on the provided goal data, collects related trend data via the Internet or external APIs.

[1147] 4. Server: Analyzes the collected trend data and generates analytical results.

[1148] 5. Server: Sends the generated analysis results to the device.

[1149] 6. Terminal: displays the analysis results to the user.

[1150] Emotion recognition by emotion engine

[1151] 1. Terminal: Sends user input and actions to the emotion engine.

[1152] 2. Server: The emotion engine analyzes the user's emotions and sends the results to the server.

[1153] 3. Server: Customize marketing strategies based on emotion recognition results.

[1154] 4. Server: Sends customized measures to the device.

[1155] 5. Terminal: Display the customized campaign to the user.

[1156] Customised marketing strategy proposals

[1157] 1. User: Enter the marketing measures they would like to propose or the problem they want to solve.

[1158] 2. Terminal: Sends the entered information to the server.

[1159] 3. Server: Analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[1160] 4. Server: Sends the generated measures to the terminal.

[1161] 5. Terminal: The proposed measures are displayed to the user.

[1162] Implementing and optimizing measures

[1163] 1. User: Selects the items to implement from the proposed measures and requests the creation of a specific action plan.

[1164] 2. Terminal: Sends the selected measures to the server.

[1165] 3. Server: Requests the generative AI model to come up with content ideas, create advertising copy, and analyze target customers in line with the specified measures.

[1166] 4. Server: Sends the generated content and advertising copy to the device.

[1167] 5. Device: Displays the generated content and advertising copy to the user.

[1168] Real-time chat and customer service

[1169] 1. User: Launches the real-time chat feature and starts communicating with the customer.

[1170] 2. Terminal: Requests a chat session from the server and displays the chat screen.

[1171] 3. Terminal: Receives messages from customers and sends them to the server.

[1172] 4. Server: Analyzes received messages in real time and generates appropriate answers based on generative AI models.

[1173] 5. Server: Sends the generated answer to the device.

[1174] 6. Terminal: The generated answer is displayed to the user, who then replies to the customer.

[1175] 7. Terminal: The emotions of the user and customer during the chat are also sent to the emotion engine for analysis.

[1176] 8. Server: Based on the results of emotion analysis, generate an appropriate response and provide it to the user.

[1177] Measurement and remarketing

[1178] 1. Server: Monitors the results of the implemented campaigns and collects key metrics such as click-through rate, conversion rate, and number of impressions.

[1179] 2. Server: Analyzes the collected data and generates statistics and insights.

[1180] 3. Server: Requests the AI ​​model to generate remarketing measures based on the analysis results.

[1181] 4. Server: Sends new remarketing offers to the device.

[1182] 5. Terminal: New policy proposals are displayed to the user.

[1183] 6. User: Review the proposed measures and select the items that apply.

[1184] Specific examples

[1185] For example, if a user wants to run an online promotion for a new product:

[1186] 1. User: Enter your login details and verify your profile.

[1187] 2. Terminal: Sends the user's marketing objectives to the server.

[1188] 3. Server: Analyzes industry trends and consumer interests in real time based on goals and displays the results.

[1189] 4. User: Selects social media advertising from the proposed marketing strategies and requests specific advertising copy and visuals to be created.

[1190] 5. Terminal: Sends the user's emotions to the emotion engine and sends the analysis results to the server.

[1191] 6. Server: Based on the results of sentiment analysis, an AI model is used to generate effective advertising copy and visuals for the target customer, and these are provided to the user.

[1192] 7. Users: Use the chat feature to respond to customer inquiries in real time.

[1193] 8. Terminal: Analyzes the emotions of users and customers during chat and receives appropriate responses from the server.

[1194] This allows marketing professionals to efficiently implement promotions while taking user emotions into account, and continuously measure and optimize the effectiveness of their initiatives.

[1195] The processing flow will be explained below.

[1196] User authentication and profile acquisition

[1197] Step 1:

[1198] The user launches the application and enters their email address and password on the login screen.

[1199] Step 2:

[1200] The terminal encrypts the entered authentication information and sends it to the server.

[1201] Step 3:

[1202] The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the user's profile data is obtained and a session is started. If authentication fails, an error message is generated and sent to the terminal.

[1203] Step 4:

[1204] The terminal displays a successful authentication message and user profile data to the user.

[1205] Initial data acquisition and trend analysis

[1206] Step 5:

[1207] Users input goals for their marketing campaigns and initiatives.

[1208] Step 6:

[1209] The terminal transmits the target data input by the user to the server.

[1210] Step 7:

[1211] The server collects trend data via the Internet or external API based on the provided target data.

[1212] Step 8:

[1213] The server analyzes the collected trend data and generates analysis results.

[1214] Step 9:

[1215] The server transmits the generated analysis results to the terminal.

[1216] Step 10:

[1217] The terminal displays the analysis results to the user.

[1218] Emotion recognition by emotion engine

[1219] Step 11:

[1220] The device sends the user's input and actions to the emotion engine.

[1221] Step 12:

[1222] The server receives the user's emotion results analyzed by the emotion engine.

[1223] Step 13:

[1224] The server customizes marketing measures based on the emotion recognition results.

[1225] Step 14:

[1226] The server sends the customized measures to the terminal.

[1227] Step 15:

[1228] The terminal displays the customized measures to the user.

[1229] Customised marketing strategy proposals

[1230] Step 16:

[1231] The user inputs the marketing measures they would like to propose and the issues they would like to solve.

[1232] Step 17:

[1233] The terminal sends the user's input to the server.

[1234] Step 18:

[1235] The server analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[1236] Step 19:

[1237] The server transmits the generated measures to the terminal.

[1238] Step 20:

[1239] The terminal displays the proposed measures to the user.

[1240] Implementing and optimizing measures

[1241] Step 21:

[1242] The user selects the items to be implemented from the proposed measures and requests the creation of a specific action plan.

[1243] Step 22:

[1244] The terminal transmits the user's selected action to the server.

[1245] Step 23:

[1246] Based on the selected strategy, the server requests the generative AI model to generate content ideas, create advertising copy, and analyze target customers.

[1247] Step 24:

[1248] The server transmits the generated content and advertising copy to the terminal.

[1249] Step 25:

[1250] The terminal displays the generated content and advertising copy to the user.

[1251] Real-time chat and customer service

[1252] Step 26:

[1253] The user launches the real-time chat function and begins communicating with the customer.

[1254] Step 27:

[1255] The terminal requests a chat session from the server and displays the chat screen.

[1256] Step 28:

[1257] The terminal receives a message from the customer and sends it to the server.

[1258] Step 29:

[1259] The server analyzes the received messages in real time and generates appropriate answers based on generative AI models.

[1260] Step 30:

[1261] The server sends the generated response to the terminal.

[1262] Step 31:

[1263] The terminal displays the generated answer to the user, who then replies to the customer.

[1264] Step 32:

[1265] The terminal transmits the emotions of the user and customer during the chat to the emotion engine, which performs emotion analysis.

[1266] Step 33:

[1267] Based on the emotion analysis results, the server generates an appropriate response and provides it to the user.

[1268] Measurement and remarketing

[1269] Step 34:

[1270] The server monitors the results of the implemented campaigns and collects key metrics such as click-through rates, conversion rates, and number of impressions.

[1271] Step 35:

[1272] The server analyzes the collected data and generates statistics and insights.

[1273] Step 36:

[1274] The server requests the AI ​​model to generate remarketing measures based on the analysis results.

[1275] Step 37:

[1276] The server sends new remarketing campaigns to the device.

[1277] Step 38:

[1278] The terminal displays new policy proposals to the user.

[1279] Step 39:

[1280] The user reviews the proposed measures and selects the items to apply.

[1281] The above are the specific processing steps of the present invention, which combines emotion engines.

[1282] Example 2

[1283] 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."

[1284] Traditional marketing systems lack the ability to analyze user emotions and behavior in real time and generate and customize effective marketing measures based on that analysis. Furthermore, the process of generating marketing measures and measuring their effectiveness after implementation and remarketing is often done manually, requiring time and resources. There is a need not only to improve user experience, but also to operate marketing measures efficiently and accurately.

[1285] 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.

[1286] In this invention, the server includes: means for receiving and verifying authentication information from a user; means for acquiring user profile data when the verification is successful; means for starting a session based on the acquired profile data; means for receiving marketing goal data from a user and collecting trend data from an external database; means for analyzing the collected trend data using a machine learning algorithm and generating analysis results; means for requesting a marketing measure from a generation AI model based on the analysis results; means for displaying the generated marketing measure to the user; means for receiving a specific action plan selection from the user and requesting the generation AI model to generate content and advertising copy; means for displaying the generated content and advertising copy to the user; means for a user to activate a real-time communication function and interact with a third party; means for receiving an inquiry from a third party and requesting an appropriate response from the generation AI model; means for displaying the generated response to the user; means for monitoring the effectiveness of the implemented measures and analyzing collected data; means for requesting a remarketing measure from the generation AI model based on the analysis results; means for displaying the generated remarketing measure to the user; and means for analyzing user input and behavior using a sentiment analysis engine and customizing the marketing measure based on the analysis results. This makes it possible to generate and customize marketing initiatives in real time based on user emotions and behavior.

[1287] "Authentication information" refers to information required to log in to a system, such as a user's email address and password.

[1288] "User profile data" is information related to an authenticated user, including, for example, name, job title, goals, and the like.

[1289] A "session" is a function that tracks activities while a user is logged into the system, and is a temporary connection that maintains user-specific operations.

[1290] "Marketing goal data" is information that indicates specific goals that a user wants to achieve in a marketing campaign or initiative.

[1291] "Trend data" is data that shows current market and consumer trends, and is mainly collected from the Internet and external databases.

[1292] A "machine learning algorithm" is a computational method for analyzing collected data and finding patterns and relationships.

[1293] A "generative AI model" is a model that uses artificial intelligence to generate responses and measures based on user input and the environment.

[1294] "Content" refers to information materials such as text, images, and videos used in marketing initiatives.

[1295] "Ad copy" is text that promotes a particular product or service.

[1296] The "real-time communication function" is a function that allows a user to have a timely conversation with a third party.

[1297] An "inquiry" is a question or request made by a third party.

[1298] "Effectiveness monitoring" is the process of measuring and evaluating the results of implemented measures.

[1299] A "remarketing initiative" is a new marketing initiative that is generated based on the results of analyzing collected data.

[1300] An "emotion analysis engine" is a technology that analyzes a user's input and behavior and infers their emotions.

[1301] This invention is a digital assistant system designed to streamline and improve the quality of work for marketing professionals. Furthermore, by incorporating an emotion analysis engine that recognizes user emotions, it enhances the accuracy and applicability of marketing strategies. In addition to basic functions such as user authentication, profile acquisition, receiving marketing goal data, collecting and analyzing trend data, generating marketing strategies, real-time communication, measuring effectiveness, and generating and displaying remarketing strategies, this system also includes an emotion analysis engine function that recognizes user emotions. The specific operation of each function is described below.

[1302] User authentication and profile acquisition

[1303] A user enters an email address and password to log in to the system. The terminal encrypts the entered authentication information using the AES-256 algorithm and sends it to the server. The server receives the authentication information and authenticates the user by checking it against the PostgreSQL database. If authentication is successful, it retrieves the user's profile data and starts a session. If authentication fails, it generates an error message and sends it to the terminal. The terminal notifies the user of successful authentication and displays the profile data.

[1304] Initial data acquisition and trend analysis

[1305] The user inputs the goals of a marketing campaign or initiative. The device sends the goal data to the server. The server uses a Python script to collect relevant trend data from external databases or APIs (e.g., the Google Trends API). The collected trend data is analyzed using machine learning algorithms (e.g., the scikit-learn library) to generate analysis results. The server sends the generated analysis results to the device, which then displays the analysis results to the user.

[1306] Emotion recognition by emotion engine

[1307] The device sends the user's input and behavioral data to a sentiment analysis engine (for example, IBM Watson Natural Language Understanding API). The server receives the sentiment analysis results and customizes marketing campaigns based on them. The customized campaigns are sent from the server to the device, which displays them to the user.

[1308] Customised marketing strategy proposals

[1309] The user inputs the marketing measures they would like to see proposed and the problems they want to solve. The device sends the information to the server. The server analyzes the received information and generates appropriate marketing measures using a generative AI model (e.g., OpenAI GPT-3). The generated measures are sent to the device and displayed to the user.

[1310] Implementing and optimizing measures

[1311] The user selects the items to implement from the proposed measures and requests the creation of a specific action plan. The device sends the selected measures to the server. The server uses a generative AI model to generate content ideas, create advertising copy, and analyze target customers based on the specified measures. The generated content and advertising copy are sent to the device and displayed to the user.

[1312] Real-time communication and customer service

[1313] The user activates the real-time communication function and begins a conversation with a third party (such as a customer). The device requests a chat session from the server and displays the chat screen. The message from the customer is received by the device and sent to the server. The server analyzes the received message in real time and generates an appropriate response based on the generative AI model. The generated response is sent to the device and displayed to the user. The emotions of the user and the third party during the chat are also sent to the emotion analysis engine for analysis. The analysis results are provided to the user as an appropriate response from the server.

[1314] Measurement and remarketing

[1315] The server monitors the results of the implemented measures and collects key indicators such as click-through rate, conversion rate, and number of impressions. The collected data is analyzed to generate statistics and insights (for example, using Jupyter Notebook). Based on the analysis results, a remarketing measure generation AI model is requested, and new remarketing measures are generated. The generated measures are sent to the device and displayed to the user. The user reviews the proposed measures and selects the ones they want to apply.

[1316] Specific examples

[1317] Example prompt sentence:

[1318] "Identify target customers and generate effective social media copy to promote a new product online."

[1319] For example, consider a user running an online promotion for a new product. The user enters their login information and confirms their profile. Next, they send their marketing goals from their device to the server. The server analyzes industry trends and consumer interests in real time based on their goals and displays the results on the device. The user selects a social media ad from the proposed marketing initiatives and requests the creation of specific ad copy and visuals. The device sends the user's emotions to a sentiment analysis engine and sends the analysis results to the server. Based on the analysis results, the server uses a generative AI model to generate effective ad copy and visuals for the target customer and provides them to the user. The user uses the chat function to respond to customer inquiries in real time. At this time, the server analyzes the emotions of both the user and the customer during the chat and receives an appropriate response from the server. In this way, marketing professionals can efficiently run promotions while taking user emotions into consideration and continuously measure and optimize the effectiveness of their initiatives.

[1320] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1321] Specific explanation of processing steps

[1322] Step 1: Enter user credentials

[1323] User: Enter your email address and password on the login screen.

[1324] Enter your email address and password

[1325] Output: Enter your credentials

[1326] What happens: A user enters their login information into a browser or application login screen and clicks the submit button.

[1327] Step 2: Encrypt and transmit credentials

[1328] Terminal: The entered authentication information is encrypted using the AES-256 algorithm and sent to the server.

[1329] Input: User credentials

[1330] Output: Encrypted credentials

[1331] How it works: Uses JavaScript or native code to encrypt input data and send it to the server over HTTPS.

[1332] Step 3: Verification of authentication information and notification of authentication result

[1333] Server: Receives the authentication information and authenticates the user against the PostgreSQL database. If authentication is successful, retrieves the user's profile data and starts a session.

[1334] Input: Encrypted credentials

[1335] Output: Authentication result, user profile data

[1336] How it works: The server uses the Python Flask framework to accept requests, perform database checks, and upon successful authentication, generates a JWT token and starts a secure session.

[1337] Step 4: Retrieving and displaying profile data

[1338] Server: If authentication is successful, retrieve the user's profile data.

[1339] Input: Authentication success flag

[1340] Output: Profile data

[1341] What it does: Retrieves and returns user profile information from the database.

[1342] Step 5: View profile data

[1343] Terminal: Notifies the user of successful authentication and displays profile data.

[1344] Input: Profile data

[1345] Output: Authentication successful message, profile data displayed

[1346] Behavior: A success message is displayed and user profile information is displayed on the screen.

[1347] Step 6: Enter your marketing goal data

[1348] Users: Enter the goals of their marketing campaigns and initiatives.

[1349] Input: Marketing Goal Data

[1350] Output: Target data input

[1351] What happens: A user enters numeric or text data into a goal field on a marketing dashboard.

[1352] Step 7: Submit goal data

[1353] Terminal: Sends target data to the server.

[1354] Input: Marketing Goal Data

[1355] Output: Target data sent

[1356] How it works: Uses JavaScript Ajax or the Fetch API to send input data in JSON format to the server.

[1357] Step 8: Trend data collection

[1358] Server: Uses Python scripts to collect relevant trend data from external databases and APIs (e.g., Google Trends API).

[1359] Input: Marketing Goal Data

[1360] Output: Trend data

[1361] Operation: Calls the API, retrieves data for the specified period, and stores it in the database.

[1362] Step 9: Analyze trend data

[1363] Server: Analyzes the collected trend data using machine learning algorithms (e.g., the scikit-learn library) and generates analytical results.

[1364] Input: Trend data

[1365] Output: Analysis results

[1366] What it does: Analyzes data by applying k-means clustering and linear regression models.

[1367] Step 10: Submit and view analysis results

[1368] Server: Sends the generated analysis results to the device.

[1369] Input: Analysis results

[1370] Output: Submitted analysis results

[1371] Behavior: The parsed results are encoded in JSON format and sent to the client.

[1372] Terminal: Displays the analysis results to the user.

[1373] Input: Analysis results

[1374] Output: Displayed analysis results

[1375] How it works: The data is visualized using a charting library and displayed to the user.

[1376] Step 11: Emotion Recognition with the Emotion Engine

[1377] Device: Sends user input and behavioral data to the sentiment analysis engine.

[1378] Input: User input and behavioral data

[1379] Output: Sentiment analysis request

[1380] How it works: Sends user input data and behavior to the sentiment analysis engine API via a POST request.

[1381] Server: Calls a sentiment analysis engine (e.g., IBM Watson Natural Language Understanding API) and receives the sentiment analysis results.

[1382] Input: Sentiment analysis request

[1383] Output: Emotion analysis results

[1384] What it does: Gets and stores the emotion score.

[1385] Step 12: Customize measures based on sentiment analysis results

[1386] Server: Customize marketing strategies based on sentiment analysis results.

[1387] Input: Sentiment analysis results

[1388] Output: Customized measures

[1389] What it does: Runs a script that adjusts tactics based on the sentiment score.

[1390] Server: Sends customized measures to the device.

[1391] Input: Customized Measures

[1392] Output: Customization measures sent

[1393] Operation: Encodes the policy data in JSON format and sends it to the device.

[1394] Device: Display customized campaigns to users.

[1395] Input: Customized Measures

[1396] Output: Displayed customization measures

[1397] How it works: The customized action is displayed in the UI and visually presented to the user.

[1398] Generative AI model, prompt sentence

[1399] Example prompt sentence:

[1400] "Identify target customers and generate effective social media copy to promote a new product online."

[1401] (Application example 2)

[1402] 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."

[1403] Conventional marketing systems typically implement fixed marketing strategies without considering user emotions, resulting in problems that prevent them from adequately meeting user needs. Furthermore, real-time collection and analysis of trend data is insufficient, making it difficult to provide timely and effective marketing strategies. Furthermore, in communications between users and customers, appropriate responses are sometimes not provided, reducing the work efficiency of marketing professionals. It is necessary to solve these problems.

[1404] 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.

[1405] In this invention, the server includes means for recognizing user emotions using an emotion engine that analyzes user emotions, means for customizing marketing measures based on the user emotion recognition results, and means for generating optimal advertising copy and visuals using a generative AI model based on the user emotion recognition results and trend data analysis results, thereby making it possible to provide personalized marketing measures that take user emotions into consideration.

[1406] "User authentication" is the process of collecting authentication information provided by a user and verifying that the user is a legitimate user.

[1407] "Profile data" is data that includes a user's personal information and past activity history, and is used to customize marketing strategies.

[1408] "Marketing goal data" is data indicating the goals of campaigns and promotions set by users.

[1409] "Trend data" is data that collects the latest information about current markets and consumer behavior.

[1410] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically generate marketing strategies and advertising copy.

[1411] "Real-time chat" is a communication method that allows users and customers to exchange messages in real time.

[1412] "Remarketing" is the process of implementing new marketing strategies based on the effectiveness of past marketing strategies.

[1413] An "emotion engine" is a software mechanism that analyzes emotions from user input and behavior and provides the results.

[1414] MODE FOR CARRYING OUT THE INVENTION

[1415] The present invention is a system that starts with user authentication, acquires user profile data, receives marketing goal data, collects and analyzes trend data, recognizes emotions using an emotion engine, generates and customizes marketing initiatives using generative AI models, has real-time chat functionality, measures effectiveness, and generates remarketing initiatives.

[1416] User authentication and profile acquisition

[1417] A user logs into the system by entering their email address and password. The device encrypts this authentication information and sends it to the server. The server then authenticates the user by checking it against a database. If authentication is successful, the server retrieves the user profile data and starts the session.

[1418] Initial data acquisition and trend analysis

[1419] Users input the goals of their marketing campaigns and initiatives. The device sends this goal data to the server. Based on the goal data provided, the server collects and analyzes relevant trend data via the Internet or external APIs. The analysis results are sent to the device and displayed to the user.

[1420] Emotion recognition by emotion engine

[1421] The device sends the user's input and actions to the emotion engine. The server analyzes the user's emotions using the emotion engine and receives the results. Based on the emotion recognition results, the server customizes marketing measures and sends them to the device. The customized measures are then displayed to the user.

[1422] Customised marketing strategy proposals

[1423] The user inputs the marketing measures they wish to propose and the problems they wish to solve. The device sends the input information to the server. The server analyzes the received information and generates appropriate marketing measures based on the generative AI model. These generated measures are sent to the device and displayed to the user.

[1424] Implementing and optimizing measures

[1425] The user selects an action item from the proposed measures and requests the creation of a specific action plan. The device sends the selected measures to the server. The server then requests the generative AI model to come up with content ideas in line with the specified measures, create advertising copy, and analyze the target customers. The generated content and advertising copy are sent to the device and displayed to the user.

[1426] Real-time chat and customer service

[1427] The user activates the real-time chat function and begins communication with the customer. The device requests a chat session from the server and displays the chat screen. The device receives the message from the customer and sends it to the server. The server analyzes the received message in real time and generates an appropriate response based on the generative AI model. This generated response is sent to the device and displayed to the user. Furthermore, the emotions of the user and customer during the chat are also sent to the emotion engine, and an appropriate response is generated based on the analysis results.

[1428] Measurement and remarketing

[1429] The server monitors the results of the implemented campaigns and collects key metrics such as click-through rates, conversion rates, and number of impressions. The collected data is analyzed to generate statistics and insights. Based on the analysis results, remarketing campaigns are requested from the generative AI model. The new remarketing campaigns are sent to the device and displayed to the user.

[1430] Specific examples

[1431] For example, if a user wants to promote a new product online, they can do so effectively through the following process:

[1432] 1. The user enters their login information and verifies their profile.

[1433] 2. The terminal transmits the user's marketing objectives to the server.

[1434] 3. The server analyzes industry trends and consumer interests in real time based on the goals and displays the results.

[1435] 4. The user selects SNS advertising from the proposed marketing measures and requests the creation of specific advertising copy and visuals.

[1436] 5. The device sends the user's emotions to the emotion engine and sends the analysis results to the server.

[1437] 6. Based on the results of the sentiment analysis, the server uses a generative AI model to generate effective advertising copy and visuals for the target customer and provides them to the user.

[1438] 7. Users use the chat feature to respond to customer inquiries in real time.

[1439] 8. The terminal analyzes the emotions of the user and customer during the chat and receives an appropriate response from the server.

[1440] Prompt Sentence Examples

[1441] 1. Prompt to ask the generative AI model to implement marketing strategies based on emotion recognition:

[1442] "Generate optimal copy and visuals for a new product online advertising campaign based on emotion recognition results and provided target data."

[1443] 2. Prompt to collect trend data using an external API:

[1444] "Collect recent trend data from the internet related to the campaign goals for a new product and return the analysis results."

[1445] As a result, the system of the present invention takes into account the user's emotions and enables highly personalized marketing measures, thereby realizing efficient and effective marketing.

[1446] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1447] Step 1:

[1448] User Authentication

[1449] The user enters login information (email address and password) into the smartphone app.

[1450] The terminal encrypts this authentication information and sends it to the server.

[1451] The server receives the encrypted authentication information and authenticates it against a database.

[1452] Input: Email address, password

[1453] Output: Authentication result (success / failure)

[1454] If the authentication is successful, the server obtains the user profile data and sends a notification of successful authentication to the terminal.

[1455] The terminal notifies the user that the authentication was successful and displays the profile data.

[1456] Step 2:

[1457] Initial data acquisition and trend analysis

[1458] Users input goals for their marketing campaigns and initiatives.

[1459] The terminal transmits this target data to the server.

[1460] The server collects trend data based on the provided target data via the Internet or external API.

[1461] Input: Marketing goal data

[1462] Output: Trend data

[1463] The server analyzes the collected trend data and sends the analysis results to the terminal.

[1464] The terminal displays the analysis results to the user.

[1465] Step 3:

[1466] Emotion recognition by emotion engine

[1467] The terminal transmits the user's input and actions to the emotion engine.

[1468] The server analyzes the user's emotions using an emotion engine.

[1469] Input: User input and behavioral data

[1470] Output: Emotion recognition result

[1471] The server customizes marketing measures based on the emotion recognition results and sends them to the device.

[1472] The terminal displays the customized measures to the user.

[1473] Step 4:

[1474] Customised marketing strategy proposals

[1475] Users input the marketing measures they would like to propose and the issues they would like to solve.

[1476] The terminal transmits this input content to the server.

[1477] The server analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[1478] Input: User's desired measures and issues

[1479] Output: Customized marketing initiatives

[1480] The server transmits the generated marketing measures to the terminal and displays them to the user.

[1481] Step 5:

[1482] Implementing and optimizing measures

[1483] The user selects items to be implemented from the proposed measures and wishes to create a specific action plan.

[1484] The terminal transmits the selected measure to the server.

[1485] The server asks the generative AI model to come up with content ideas in line with the specified measures, create advertising copy, and analyze target customers.

[1486] Input: Select the action to be taken

[1487] Output: Content, ad copy, target customer analysis results

[1488] The server transmits the generated content and advertising copy to the terminal and displays them to the user.

[1489] Step 6:

[1490] Real-time chat and customer service

[1491] The user activates the real-time chat function and starts communicating with the customer.

[1492] The terminal requests a chat session from the server and displays a chat screen.

[1493] The terminal receives messages from the customer and transmits them to the server.

[1494] The server analyzes the received messages in real time and generates appropriate answers based on the generative AI model.

[1495] Input: Message from customer

[1496] Output: Correct answer

[1497] The server sends the generated answer to the terminal and displays it to the user.

[1498] The terminal transmits the emotions of the user and customer during the chat to the emotion engine and transmits the analysis results to the server.

[1499] The server generates an appropriate response based on the emotion analysis results and sends it to the terminal.

[1500] Step 7:

[1501] Measurement and remarketing

[1502] The server monitors the results of the implemented measures and collects key metrics such as click-through rate, conversion rate, and number of impressions.

[1503] Input: Data on implemented measures

[1504] Output: Key indicator data

[1505] The server analyzes the collected data and generates statistics and insights.

[1506] The server then requests the AI ​​model to generate remarketing strategies based on the analysis results.

[1507] Input: Key indicator data

[1508] Output: Remarketing campaign

[1509] The server sends the new remarketing campaign to the terminal and displays it to the user.

[1510] 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.

[1511] 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.

[1512] 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.

[1513] [Third embodiment]

[1514] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1515] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1516] 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).

[1517] 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.

[1518] 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.

[1519] 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).

[1520] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1521] 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.

[1522] 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.

[1523] 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.

[1524] 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.

[1525] 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."

[1526] The present invention is a digital assistant system for improving the efficiency and quality of work for marketing professionals. This system includes functions such as user authentication, profile acquisition, receiving marketing goal data, collecting and analyzing trend data, generating marketing initiatives, real-time chat, measuring effectiveness, and generating and displaying remarketing initiatives. The specific operation of each function is described below.

[1527] User authentication and profile acquisition

[1528] 1. User: Enter your email address and password to log in to this system.

[1529] 2. Terminal: The entered authentication information is encrypted and sent to the server.

[1530] 3. Server: Receives the authentication information and authenticates the user by checking it against a database.

[1531] If authentication is successful, the user's profile data is retrieved and a session is initiated.

[1532] If the authentication fails, an error message is generated and sent to the terminal.

[1533] 4. Terminal: Notifies the user that authentication was successful and displays their profile data.

[1534] Initial data acquisition and trend analysis

[1535] 1. User: Enter the goals of your marketing campaign or initiative.

[1536] 2. Terminal: Sends target data to the server.

[1537] 3. Server: Based on the provided goal data, collects related trend data via the Internet or external APIs.

[1538] 4. Server: Analyzes the collected trend data and generates analytical results.

[1539] 5. Server: Sends the generated analysis results to the device.

[1540] 6. Terminal: displays the analysis results to the user.

[1541] Customised marketing strategy proposals

[1542] 1. User: Enter the marketing measures they would like to propose or the problem they want to solve.

[1543] 2. Terminal: Sends the entered information to the server.

[1544] 3. Server: Analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[1545] 4. Server: Sends the generated measures to the terminal.

[1546] 5. Terminal: The proposed measures are displayed to the user.

[1547] Implementing and optimizing measures

[1548] 1. User: Selects the items to implement from the proposed measures and requests the creation of a specific action plan.

[1549] 2. Terminal: Sends the selected measures to the server.

[1550] 3. Server: Requests the generative AI model to come up with content ideas, create advertising copy, and analyze target customers in line with the specified measures.

[1551] 4. Server: Sends the generated content and advertising copy to the device.

[1552] 5. Device: Displays the generated content and advertising copy to the user.

[1553] Real-time chat and customer service

[1554] 1. User: Launches the real-time chat feature and starts communicating with the customer.

[1555] 2. Terminal: Requests a chat session from the server and displays the chat screen.

[1556] 3. Terminal: Receives messages from customers and sends them to the server.

[1557] 4. Server: Analyzes received messages in real time and generates appropriate answers based on generative AI models.

[1558] 5. Server: Sends the generated answer to the device.

[1559] 6. Terminal: The generated answer is displayed to the user, who then replies to the customer.

[1560] Measurement and remarketing

[1561] 1. Server: Monitors the results of the implemented campaigns and collects key metrics such as click-through rate, conversion rate, and number of impressions.

[1562] 2. Server: Analyzes the collected data and generates statistics and insights.

[1563] 3. Server: Requests the AI ​​model to generate remarketing measures based on the analysis results.

[1564] 4. Server: Sends new remarketing offers to the device.

[1565] 5. Terminal: New policy proposals are displayed to the user.

[1566] 6. User: Review the proposed measures and select the items that apply.

[1567] Specific examples

[1568] For example, if a user wants to promote a new product online, they can use it in the following ways:

[1569] 1. User: Enter your login details and verify your profile.

[1570] 2. Terminal: Sends the user's marketing objectives to the server.

[1571] 3. Server: Analyzes industry trends and consumer interests in real time based on goals and displays the results.

[1572] 4. User: Selects social media advertising from the proposed marketing strategies and requests specific advertising copy and visuals to be created.

[1573] 5. Server: Uses AI models to generate effective advertising copy and visuals for target customers and provide them to users.

[1574] 6. Users: Use the chat feature to respond to customer inquiries in real time.

[1575] This allows marketing professionals to efficiently implement promotions and continuously measure and optimize the effectiveness of their efforts.

[1576] The processing flow will be explained below.

[1577] User authentication and profile acquisition

[1578] Step 1:

[1579] The user launches the application and enters their email address and password on the login screen.

[1580] Step 2:

[1581] The terminal encrypts the entered authentication information and sends it to the server.

[1582] Step 3:

[1583] The server compares the received authentication information with a database and authenticates the user.

[1584] If authentication is successful, retrieve the user's profile data and start a session. If authentication fails, generate an error message and send it to the terminal.

[1585] Step 4:

[1586] The terminal displays a successful authentication message and user profile data to the user.

[1587] Initial data acquisition and trend analysis

[1588] Step 5:

[1589] Users input goals for their marketing campaigns and initiatives.

[1590] Step 6:

[1591] The terminal transmits the target data input by the user to the server.

[1592] Step 7:

[1593] The server collects trend data via the Internet or external API based on the provided target data.

[1594] Step 8:

[1595] The server analyzes the collected trend data and generates analysis results.

[1596] Step 9:

[1597] The server transmits the generated analysis results to the terminal.

[1598] Step 10:

[1599] The terminal displays the analysis results to the user.

[1600] Customised marketing strategy proposals

[1601] Step 11:

[1602] The user inputs the marketing measures they would like to propose and the issues they would like to solve.

[1603] Step 12:

[1604] The terminal sends the user's input to the server.

[1605] Step 13:

[1606] The server analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[1607] Step 14:

[1608] The server transmits the generated marketing measures to the terminal.

[1609] Step 15:

[1610] The terminal displays the proposed measures to the user.

[1611] Implementing and optimizing measures

[1612] Step 16:

[1613] The user selects the items to be implemented from the proposed measures and requests the creation of a specific action plan.

[1614] Step 17:

[1615] The terminal transmits the user's selected action to the server.

[1616] Step 18:

[1617] Based on the selected strategy, the server requests the generative AI model to generate content ideas, create advertising copy, and analyze target customers.

[1618] Step 19:

[1619] The server transmits the generated content and advertising copy to the terminal.

[1620] Step 20:

[1621] The terminal displays the generated content and advertising copy to the user.

[1622] Real-time chat and customer service

[1623] Step 21:

[1624] The user launches the real-time chat function and begins communicating with the customer.

[1625] Step 22:

[1626] The terminal requests a chat session from the server and displays the chat screen.

[1627] Step 23:

[1628] The terminal receives a message from the customer and sends it to the server.

[1629] Step 24:

[1630] The server analyzes the received messages in real time and generates appropriate answers based on generative AI models.

[1631] Step 25:

[1632] The server sends the generated response to the terminal.

[1633] Step 26:

[1634] The terminal displays the generated answer to the user, who then replies to the customer.

[1635] Measurement and remarketing

[1636] Step 27:

[1637] The server monitors the results of the implemented campaigns and collects key metrics such as click-through rates, conversion rates, and number of impressions.

[1638] Step 28:

[1639] The server analyzes the collected data and generates statistics and insights.

[1640] Step 29:

[1641] The server requests the AI ​​model to generate remarketing measures based on the analysis results.

[1642] Step 30:

[1643] The server sends new remarketing campaigns to the device.

[1644] Step 31:

[1645] The terminal displays new policy proposals to the user.

[1646] Step 32:

[1647] The user reviews the proposed measures and selects the items to apply.

[1648] The above are the specific processing steps of the present invention.

[1649] Example 1

[1650] 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."

[1651] In traditional marketing systems, data collection, analysis, and the generation and application of measures are carried out separately, often resulting in a lack of efficiency and real-time response. There is also the risk of security issues arising in the user authentication information and customer support processes. Furthermore, because effectiveness measurement and remarketing measures are not integrated, continuous optimization of measures is difficult.

[1652] 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.

[1653] In this invention, the server includes: means for receiving and verifying authentication information from a user; means for acquiring user profile data when the verification is successful; means for starting a session based on the acquired profile data; means for receiving marketing goal data from the user and collecting trend data from external information sources; means for analyzing the collected trend data using data analysis technology and generating analysis results; means for generating prompt text based on the analysis results and requesting a generative AI model to generate a marketing measure; means for displaying the generated marketing measure to the user; means for receiving a specific action plan selection from the user and requesting the generative AI model to generate content and advertising copy; means for displaying the generated content and advertising copy to the user; means for a user to start a real-time chat and communicate with customers; means for receiving inquiries from customers and requesting an appropriate response from the generative AI model; means for displaying the generated response to the user; means for monitoring the effectiveness of the implemented measures and analyzing collected data using data analysis technology; means for generating prompt text based on the analysis results and requesting the generative AI model to generate a remarketing measure; means for displaying the generated remarketing measure to the user; and means for protecting authentication information using encryption technology. This enables a consistent, efficient and secure marketing process, from data collection and analysis, to the creation and application of measures, effectiveness measurement, and the creation of remarketing measures.

[1654] "Authentication information" refers to information such as an email address and password that a user provides to log in to a system.

[1655] "Verification" is the process of comparing received authentication information with existing records in a database to determine if it matches.

[1656] "User profile data" refers to data such as personal information and past activity history obtained about a successfully authenticated user.

[1657] A "session" is a state of communication with a server that persists while a user is authenticated.

[1658] "Marketing goal data" refers to data that includes specific goals and objectives for marketing campaigns and initiatives set by users.

[1659] "Trend data" is data collected from the internet or other external sources that indicates consumer interests and behavior over a specific period of time.

[1660] "Data analysis techniques" are techniques used to process collected data and generate analytical results, including statistical methods and machine learning algorithms.

[1661] A "prompt" is a sentence used to give instructions to a generative AI model.

[1662] A "generative AI model" is an artificial intelligence model that generates marketing strategies, advertising copy, content, etc. based on prompt text.

[1663] "Content" is information in the form of text, images, videos, etc., generated for marketing purposes.

[1664] "Ad copy" is text generated to promote a particular product or service.

[1665] "Real-time chat" is a communication function that allows users and customers to exchange messages instantly.

[1666] "Effectiveness measurement" is the process of monitoring the results of implemented marketing initiatives and collecting metrics such as click rates, conversion rates, and number of impressions.

[1667] "Remarketing" is a marketing strategy created to re-engage with existing customers or past visitors.

[1668] "Encryption technology" refers to technology used to protect the security of data.

[1669] The present invention is a digital assistant system for improving the efficiency and quality of work for marketing professionals. The system includes functions such as user authentication, profile acquisition, receiving marketing target data, collecting and analyzing trend data, generating marketing campaigns, real-time chat, measuring effectiveness, and generating and displaying remarketing campaigns.

[1670] User Authentication

[1671] The user enters their email address and password on the login screen. The device encrypts the entered authentication information using AES encryption technology and sends it to the server via HTTPS. The server receives the encrypted authentication information and compares it with the user information in its database. If the comparison is successful, the server obtains the user's profile data and starts a session. The device notifies the user of the authentication result and displays the profile data.

[1672] Hardware and software used:

[1673] Server: Database management system (e.g. MySQL), encryption library (e.g. OpenSSL)

[1674] Device: User's PC or smartphone

[1675] Receiving marketing target data and collecting and analyzing trend data

[1676] Users enter the goals of their marketing campaigns and initiatives on a dedicated input screen. The device sends this goal data in JSON format to the server. The server collects trend data using an external API (e.g., Google Trends API) and analyzes it using data analysis technology (e.g., pandas, numpy). The analysis results are sent to the device in JSON format and displayed to the user.

[1677] Marketing strategy generation

[1678] When a user requests a marketing proposal, the device sends the request to the server. The server generates a prompt based on the analysis data and requests a generative AI model (e.g., GPT-4) to generate an appropriate marketing proposal. The generated proposal is sent to the device and displayed to the user.

[1679] Example prompt sentence:

[1680] "Generate the best social media ad ideas for promoting a new product online."

[1681] "Please propose a marketing strategy based on industry trends over the past three months."

[1682] "Create a tagline and visuals that will appeal to your target audience."

[1683] Implementing and optimizing measures

[1684] The user selects the items they wish to implement from the proposed measures and requests a specific action plan. This selection information is sent from the device to the server, and content ideas and advertising copy are generated based on the generative AI model. The generated content and advertising copy are sent to the device and displayed to the user.

[1685] Real-time chat and effectiveness measurement

[1686] Users can communicate with customers using the real-time chat function. The server analyzes customer inquiries, generates appropriate answers based on the generative AI model, and sends them to the terminal for display.

[1687] The results of the implemented marketing measures are monitored on the server, and the collected data (e.g., click rate, conversion rate, number of impressions) is analyzed using data analysis technology. The analysis results are used to generate remarketing measures, and new measures are generated by a generative AI model and displayed to the user.

[1688] Specific examples

[1689] For example, if a user wants to promote a new product online, the process could look like this:

[1690] 1. The user enters their login information and verifies their profile.

[1691] 2. The terminal transmits the marketing target data to the server.

[1692] 3. The server analyzes industry trends and consumer interests in real time based on the goals and displays the results.

[1693] 4. The user selects social media advertising from the proposed marketing strategies and requests the creation of specific advertising copy and visuals.

[1694] 5. The server uses the AI ​​model to generate effective advertising copy and visuals for the target customer and provides them to the user.

[1695] 6. Users can use the chat feature to respond to customer inquiries in real time.

[1696] This enables marketing professionals to efficiently and effectively implement promotions and continuously measure and optimize the results of their efforts.

[1697] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1698] Step 1: User authentication

[1699] 1. User: Enter your email address and password on the login screen.

[1700] Input: Email address, Password

[1701] Output: Encrypted credentials

[1702] 2. Terminal: The entered authentication information is encrypted using AES encryption technology and sent to the server via HTTPS.

[1703] Specific behavior: Encryption using the OpenSSL library

[1704] 3. Server: Receives the encrypted authentication information and checks it against the user information in its database.

[1705] Input: Encrypted credentials

[1706] Output: Authentication success or failure result

[1707] What it does: It performs a database query and compares the encrypted password with the hash value stored in the database.

[1708] 4. Server: If authentication is successful, retrieves the user's profile data and starts a session. If authentication is unsuccessful, generates an error message.

[1709] Input: Authentication success or failure result

[1710] Output: Profile data or error message

[1711] Specific behavior: If successful, generate a session ID and retrieve profile data from the database.

[1712] 5. Terminal: Notifies the user of the authentication result and displays the profile data if authentication is successful.

[1713] Input: Profile data or error message

[1714] Output: Display of authentication result

[1715] Step 2: Receive marketing goal data

[1716] 1. User: Enter the goals of your marketing campaign or initiative in a dedicated input screen.

[1717] Input: Goal data

[1718] Output: Target data

[1719] 2. Terminal: Sends the target data in JSON format to the server.

[1720] Specific operation: Converts data into JSON format and sends it to the server via HTTPS

[1721] Step 3: Collect and analyze trend data

[1722] 1. Server: Uses external APIs (e.g., Google Trends API) to collect trend data related to the provided goal data.

[1723] Input: Goal data

[1724] Output: Trend data

[1725] Specific operation: Acquire data from external API and parse the format

[1726] 2. Server: Analyze the collected trend data using data analysis techniques (e.g., pandas, numpy).

[1727] Input: Trend data

[1728] Output: Analysis results

[1729] Specific actions: Analyze rising trends and related keywords

[1730] 3. Server: Sends the analysis results to the terminal in JSON format.

[1731] Input: Analysis results

[1732] Output: Sending the analysis results

[1733] 4. Terminal: displays the analysis results to the user.

[1734] Input: Analysis results

[1735] Output: Display of analysis results

[1736] Step 4: Generate marketing initiatives

[1737] 1. User: Enter the marketing measures they would like to propose or the problem they want to solve.

[1738] Input: Policy hopes or challenges

[1739] Output: desired measures or issues

[1740] 2. Terminal: The input content is sent to the server in JSON format.

[1741] Specific operation: Converts data into JSON format and sends it to the server via HTTPS

[1742] 3. Server: Generates prompts based on the analyzed data and requests a generative AI model (e.g., GPT-4) to generate appropriate marketing strategies.

[1743] Input: desired measures or issues, analysis data

[1744] Output: Marketing Strategy

[1745] Specific operation: Enter a prompt into GPT-4 and obtain the generated measures.

[1746] 4. Server: Sends the generated measures to the terminal in JSON format.

[1747] Input: Marketing Strategy

[1748] Output: Sending the measure

[1749] 5. Terminal: The proposed measures are displayed to the user.

[1750] Input: Marketing Strategy

[1751] Output: Display of measures

[1752] Step 5: Implementing and optimizing measures

[1753] 1. User: Selects the items to implement from the proposed measures and requests a specific action plan.

[1754] Input: Select the action to be taken

[1755] Output: Selected measures

[1756] 2. Terminal: Sends the selected measures to the server in JSON format.

[1757] Specific operation: Converts data into JSON format and sends it to the server via HTTPS

[1758] 3. Server: Based on the generative AI model, it requests content ideas, ad copy creation, and target customer analysis in line with the selected measures.

[1759] Input: Selected Measure

[1760] Output: Content, ad copy, targeting analysis

[1761] Specific operation: Generate a prompt sentence and input it into the AI ​​model

[1762] 4. Server: Sends the generated content and ad copy to the device in JSON format.

[1763] Input: Content, ad copy, targeting analysis

[1764] Output: Sending content and ad copy

[1765] 5. Device: Displays the generated content and advertising copy to the user.

[1766] Input: Content, ad copy, targeting analysis

[1767] Output: Display of content and ad copy

[1768] Step 6: Real-time chat

[1769] 1. User: Launches the real-time chat feature and starts communicating with the customer.

[1770] Input: Start chat command

[1771] Output: Chat screen display

[1772] 2. Terminal: Requests a chat session from the server and displays the chat screen.

[1773] Specific behavior: Generate a session ID and load the chat screen

[1774] 3. Terminal: Receives messages from customers and sends them to the server.

[1775] Input: Customer message

[1776] Output: Sending a message

[1777] 4. Server: Analyzes received messages in real time and generates appropriate answers based on generative AI models.

[1778] Input: Customer message

[1779] Output: Reply message

[1780] Specific operation: Generate a prompt sentence and input it into the AI ​​model

[1781] 5. Server: Sends the generated answer in JSON format to the device.

[1782] Input: Reply message

[1783] Output: Sending a response message

[1784] 6. Terminal: The generated answer is displayed to the user, who then replies to the customer.

[1785] Input: Reply message

[1786] Output: Display of response message

[1787] Step 7: Measure your results

[1788] 1. Server: Monitors the results of the implemented campaigns and collects key metrics such as click-through rate, conversion rate, and number of impressions.

[1789] Input: Measure execution results

[1790] Output: Collected data

[1791] Specific actions: Analyzing log data, using measurement tools

[1792] 2. Server: Analyzes the collected data using data analysis techniques (e.g., pandas, numpy) to generate statistics and insights.

[1793] Input: Collected data

[1794] Output: Statistics, insights

[1795] Specific behavior: Applying statistical methods and machine learning algorithms

[1796] Step 8: Generate and view remarketing initiatives

[1797] 1. Server: Generates prompt text based on the analysis results and requests the generative AI model to generate remarketing measures.

[1798] Input: Statistics, Insights

[1799] Output: Remarketing Initiative

[1800] Specific operation: Generate a prompt sentence and input it into the AI ​​model

[1801] 2. Server: Generates a new remarketing campaign and sends it to the device in JSON format.

[1802] Enter: Remarketing Initiative

[1803] Output: Sending the measure

[1804] 3. Terminal: New policy proposals are displayed to the user.

[1805] Enter: Remarketing Initiative

[1806] Output: Display of measures

[1807] 4. User: Review the proposed measures and select the items that apply.

[1808] Input: Select a measure

[1809] Output: Execution of selected measures

[1810] (Application example 1)

[1811] 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."

[1812] In today's world, marketing professionals spend a great deal of time and effort processing massive amounts of data to create and optimize effective advertising campaigns. It is also difficult to respond to customers in real time or immediately adjust marketing strategies. To solve these challenges, a system is needed that can automatically collect and analyze data, generate effective marketing strategies, and efficiently communicate and analyze performance in real time.

[1813] 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.

[1814] In this invention, the server includes means for receiving and verifying authentication information from a user, means for acquiring user profile data upon successful verification, means for initiating a session based on the acquired profile data, means for collecting trend data from the Internet, means for analyzing the collected trend data and generating an analysis result, means for requesting an AI model to generate a marketing strategy based on the analysis result, means for supporting optimization of an advertising campaign, and means for analyzing the performance of the advertising campaign, thereby enabling effective design and optimization of advertising campaigns, real-time customer service, continuous performance analysis, and generation of remarketing strategies.

[1815] "User authentication" is the process required to verify the identity of a user accessing a system.

[1816] "Profile data" is data that compiles information about a user, including the settings and history of an individual user.

[1817] A "session" refers to a series of interactions while a user is accessing a system.

[1818] "Marketing goal data" is data that represents the specific marketing goals and objectives that a user wants to achieve.

[1819] "Trend data" is data about current market and consumer trends, collected from the internet and external APIs.

[1820] "Analysis results" refers to information generated as a result of analysis performed on collected data.

[1821] A "generative AI model" is a type of artificial intelligence that uses specific algorithms to analyze data and generate content.

[1822] A "marketing initiative" is a specific action or plan taken to achieve a specific marketing goal.

[1823] "Content" refers to materials such as text and visuals created for advertising and marketing purposes.

[1824] "Ad copy" means text content intended for use in an advertising campaign.

[1825] "Real-time chat" is a chat function that allows users and customers to communicate instantly.

[1826] "Customer service" is the process of responding to inquiries and questions from customers.

[1827] "Effectiveness measurement" is a means of evaluating the results of implemented marketing measures.

[1828] "Remarketing" is a marketing activity aimed at re-approaching customers who have previously shown interest in a product or service.

[1829] "Advertising campaign optimization" is the process of applying techniques and adjustments to maximize advertising performance.

[1830] "Advertising campaign performance analysis" is the analysis of data to evaluate how successful an advertising campaign is.

[1831] This invention is a digital assistant system that streamlines and improves the quality of work for marketing professionals. The system performs a comprehensive process, starting with user authentication, entering marketing goals, collecting and analyzing trend data, creating marketing strategies, chatting in real time, and measuring effectiveness.

[1832] User authentication and profile acquisition

[1833] To access the system, a user enters an email address and password. This authentication information is encrypted on the terminal and sent to the server. The server receives the authentication information and authenticates the user by comparing it with a database. If authentication is successful, the server obtains the user's profile data and starts a session. If authentication fails, it generates an error message and sends it to the terminal. The terminal notifies the user that authentication was successful and displays the profile data.

[1834] Initial data acquisition and trend analysis

[1835] The user inputs the goals of a marketing campaign or initiative. The device sends the goal data to the server. Based on the goal data provided, the server collects related trend data via the Internet or an external API. (As an example, the Google Trends API can be used.) The server analyzes the collected trend data and generates analysis results. The generated analysis results are sent to the device and displayed to the user.

[1836] Customizing marketing proposals

[1837] The user inputs the marketing measures they would like to see proposed and the problem they want to solve. The device sends the input information to the server. The server analyzes the received information and generates appropriate marketing measures based on a generative AI model (e.g., OpenAI GPT-3). The server sends the generated measures to the device, and the device displays the proposed measures to the user.

[1838] Implementing and optimizing measures

[1839] The user selects the items they wish to implement from the proposed measures and requests the creation of a specific action plan. The selected measures are sent from the device to the server. The server then requests the generative AI model to generate content (advertising copy and visuals) in line with the specified measures. The generated content and advertising copy are then displayed to the user.

[1840] Real-time chat and customer service

[1841] The user launches the real-time chat function and begins communication with the customer. A chat session is requested from the device to the server, and the chat screen is displayed. When the user receives a message from the customer, it is sent to the server. The server analyzes the message in real time and generates an appropriate response based on the generative AI model. The generated response is sent to the device, and the user replies to the customer.

[1842] Measurement and remarketing

[1843] The server monitors the results of the implemented measures and collects key indicators such as click rate, conversion rate, and number of impressions. The collected data is analyzed on the server to generate statistical data and insights. Based on the analysis results, a new remarketing measure is generated by the AI ​​model and sent from the server to the device. The new measure proposals are displayed to the user, who can review the proposed measures and select the items to apply.

[1844] Specific examples

[1845] For example, if a user wants to promote a new product online, they can use:

[1846] 1. The user enters their login information and verifies their profile.

[1847] 2. The terminal transmits the user's marketing objectives to the server.

[1848] 3. The server analyzes industry trends and consumer interests in real time based on the goals and displays the results.

[1849] 4. The user selects SNS advertising from the proposed marketing strategies and requests the creation of specific advertising copy and visuals.

[1850] 5. The server uses the generative AI model to generate effective advertising copy and visuals for the target customer and provides them to the user.

[1851] 6. Users use the chat feature to respond to customer inquiries in real time.

[1852] An example prompt is:

[1853] "Generate ad copy for the online promotion of a new product. The target audience is young people in their 20s. The trending keywords are 'sustainability,' 'organic,' and 'latest technology.'"

[1854] This concrete example will enable marketing professionals to efficiently implement promotions and continuously measure and optimize the effectiveness of their efforts.

[1855] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1856] Step 1:

[1857] A user accesses the system and enters their email address and password. The entered authentication information is encrypted on the device and sent to the server. The server compares the received authentication information with a database and authenticates the user. If authentication is successful, the server obtains the user's profile data and sends it to the device. The device notifies the user that authentication was successful and displays the profile data.

[1858] Input: Email address, password

[1859] Output: Profile data, authentication notification

[1860] Step 2:

[1861] The user inputs the goals of a marketing campaign or initiative. The device sends the goal data to the server. The server receives the goal data and collects related trend data using the Internet or external APIs (e.g., Google Trends API). The server analyzes the collected trend data and generates analysis results. The generated analysis results are sent to the device and displayed to the user.

[1862] Input: Marketing goal data

[1863] Output: Trend data, analysis results

[1864] Step 3:

[1865] The user inputs the marketing measures they would like to see proposed or the problem they want to solve. The device sends the input information to the server. The server analyzes the information and generates appropriate marketing measures based on a generative AI model (e.g., OpenAI GPT-3). The generated measures are sent to the device and displayed to the user.

[1866] Input: Marketing strategy desired data

[1867] Output: Marketing Strategy

[1868] Step 4:

[1869] The user selects which of the proposed measures to implement and requests the creation of a specific action plan. The selected measures are sent from the device to the server. The server then requests the generative AI model to generate content (advertising copy and visuals) in line with the selected measures. The generated content and advertising copy are sent to the device and displayed to the user.

[1870] Input: Selected Measure

[1871] Output: Generated content, ad copy

[1872] Step 5:

[1873] The user activates the real-time chat function and starts communication with the customer. A request for a chat session is sent from the device to the server. The device displays the chat screen, and when the user receives a message from the customer, it is sent to the server. The server analyzes the message in real time and generates an appropriate response based on the generative AI model. The generated response is sent to the device, and the user replies to the customer.

[1874] Input: Message from customer

[1875] Output: The generated answer

[1876] Step 6:

[1877] The server monitors the results of the implemented measures and collects key indicators such as click-through rate, conversion rate, and number of impressions. The collected data is analyzed on the server to generate statistical data and insights. Based on the analysis results, new remarketing measures are requested from the AI ​​model. New measure proposals are sent to the device and displayed to the user. The user reviews the proposed measures and selects the items to apply.

[1878] Input: Policy result data

[1879] Output: Analysis results, new remarketing measures

[1880] 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.

[1881] This invention is a digital assistant system for improving the efficiency and quality of the work of marketing professionals, and further enhances the accuracy and applicability of marketing strategies by combining it with an emotion engine that recognizes user emotions. In addition to basic functions such as user authentication, profile acquisition, receiving marketing goal data, collecting and analyzing trend data, generating marketing strategies, real-time chat, measuring effectiveness, and generating and displaying remarketing strategies, this system also includes an emotion engine function that recognizes user emotions. The specific operation of each function is described below.

[1882] User authentication and profile acquisition

[1883] 1. User: Enter your email address and password to log in to this system.

[1884] 2. Terminal: The entered authentication information is encrypted and sent to the server.

[1885] 3. Server: Receives the authentication information and authenticates the user by checking it against a database. If authentication is successful, retrieves the user's profile data and starts a session. If authentication fails, generates an error message and sends it to the device.

[1886] 4. Terminal: Notifies the user that authentication was successful and displays their profile data.

[1887] Initial data acquisition and trend analysis

[1888] 1. User: Enter the goals of your marketing campaign or initiative.

[1889] 2. Terminal: Sends target data to the server.

[1890] 3. Server: Based on the provided goal data, collects related trend data via the Internet or external APIs.

[1891] 4. Server: Analyzes the collected trend data and generates analytical results.

[1892] 5. Server: Sends the generated analysis results to the device.

[1893] 6. Terminal: displays the analysis results to the user.

[1894] Emotion recognition by emotion engine

[1895] 1. Terminal: Sends user input and actions to the emotion engine.

[1896] 2. Server: The emotion engine analyzes the user's emotions and sends the results to the server.

[1897] 3. Server: Customize marketing strategies based on emotion recognition results.

[1898] 4. Server: Sends customized measures to the device.

[1899] 5. Terminal: Display the customized campaign to the user.

[1900] Customised marketing strategy proposals

[1901] 1. User: Enter the marketing measures they would like to propose or the problem they want to solve.

[1902] 2. Terminal: Sends the entered information to the server.

[1903] 3. Server: Analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[1904] 4. Server: Sends the generated measures to the terminal.

[1905] 5. Terminal: The proposed measures are displayed to the user.

[1906] Implementing and optimizing measures

[1907] 1. User: Selects the items to implement from the proposed measures and requests the creation of a specific action plan.

[1908] 2. Terminal: Sends the selected measures to the server.

[1909] 3. Server: Requests the generative AI model to come up with content ideas, create advertising copy, and analyze target customers in line with the specified measures.

[1910] 4. Server: Sends the generated content and advertising copy to the device.

[1911] 5. Device: Displays the generated content and advertising copy to the user.

[1912] Real-time chat and customer service

[1913] 1. User: Launches the real-time chat feature and starts communicating with the customer.

[1914] 2. Terminal: Requests a chat session from the server and displays the chat screen.

[1915] 3. Terminal: Receives messages from customers and sends them to the server.

[1916] 4. Server: Analyzes received messages in real time and generates appropriate answers based on generative AI models.

[1917] 5. Server: Sends the generated answer to the device.

[1918] 6. Terminal: The generated answer is displayed to the user, who then replies to the customer.

[1919] 7. Terminal: The emotions of the user and customer during the chat are also sent to the emotion engine for analysis.

[1920] 8. Server: Based on the results of emotion analysis, generate an appropriate response and provide it to the user.

[1921] Measurement and remarketing

[1922] 1. Server: Monitors the results of the implemented campaigns and collects key metrics such as click-through rate, conversion rate, and number of impressions.

[1923] 2. Server: Analyzes the collected data and generates statistics and insights.

[1924] 3. Server: Requests the AI ​​model to generate remarketing measures based on the analysis results.

[1925] 4. Server: Sends new remarketing offers to the device.

[1926] 5. Terminal: New policy proposals are displayed to the user.

[1927] 6. User: Review the proposed measures and select the items that apply.

[1928] Specific examples

[1929] For example, if a user wants to run an online promotion for a new product:

[1930] 1. User: Enter your login details and verify your profile.

[1931] 2. Terminal: Sends the user's marketing objectives to the server.

[1932] 3. Server: Analyzes industry trends and consumer interests in real time based on goals and displays the results.

[1933] 4. User: Selects social media advertising from the proposed marketing strategies and requests specific advertising copy and visuals to be created.

[1934] 5. Terminal: Sends the user's emotions to the emotion engine and sends the analysis results to the server.

[1935] 6. Server: Based on the results of sentiment analysis, an AI model is used to generate effective advertising copy and visuals for the target customer, and these are provided to the user.

[1936] 7. Users: Use the chat feature to respond to customer inquiries in real time.

[1937] 8. Terminal: Analyzes the emotions of users and customers during chat and receives appropriate responses from the server.

[1938] This allows marketing professionals to efficiently implement promotions while taking user emotions into account, and continuously measure and optimize the effectiveness of their initiatives.

[1939] The processing flow will be explained below.

[1940] User authentication and profile acquisition

[1941] Step 1:

[1942] The user launches the application and enters their email address and password on the login screen.

[1943] Step 2:

[1944] The terminal encrypts the entered authentication information and sends it to the server.

[1945] Step 3:

[1946] The server compares the received authentication information with the database and authenticates the user. If authentication is successful, the user's profile data is obtained and a session is started. If authentication fails, an error message is generated and sent to the terminal.

[1947] Step 4:

[1948] The terminal displays a successful authentication message and user profile data to the user.

[1949] Initial data acquisition and trend analysis

[1950] Step 5:

[1951] Users input goals for their marketing campaigns and initiatives.

[1952] Step 6:

[1953] The terminal transmits the target data input by the user to the server.

[1954] Step 7:

[1955] The server collects trend data via the Internet or external API based on the provided target data.

[1956] Step 8:

[1957] The server analyzes the collected trend data and generates analysis results.

[1958] Step 9:

[1959] The server transmits the generated analysis results to the terminal.

[1960] Step 10:

[1961] The terminal displays the analysis results to the user.

[1962] Emotion recognition by emotion engine

[1963] Step 11:

[1964] The device sends the user's input and actions to the emotion engine.

[1965] Step 12:

[1966] The server receives the user's emotion results analyzed by the emotion engine.

[1967] Step 13:

[1968] The server customizes marketing measures based on the emotion recognition results.

[1969] Step 14:

[1970] The server sends the customized measures to the terminal.

[1971] Step 15:

[1972] The terminal displays the customized measures to the user.

[1973] Customised marketing strategy proposals

[1974] Step 16:

[1975] The user inputs the marketing measures they would like to propose and the issues they would like to solve.

[1976] Step 17:

[1977] The terminal sends the user's input to the server.

[1978] Step 18:

[1979] The server analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[1980] Step 19:

[1981] The server transmits the generated measures to the terminal.

[1982] Step 20:

[1983] The terminal displays the proposed measures to the user.

[1984] Implementing and optimizing measures

[1985] Step 21:

[1986] The user selects the items to be implemented from the proposed measures and requests the creation of a specific action plan.

[1987] Step 22:

[1988] The terminal transmits the user's selected action to the server.

[1989] Step 23:

[1990] Based on the selected strategy, the server requests the generative AI model to generate content ideas, create advertising copy, and analyze target customers.

[1991] Step 24:

[1992] The server transmits the generated content and advertising copy to the terminal.

[1993] Step 25:

[1994] The terminal displays the generated content and advertising copy to the user.

[1995] Real-time chat and customer service

[1996] Step 26:

[1997] The user launches the real-time chat function and begins communicating with the customer.

[1998] Step 27:

[1999] The terminal requests a chat session from the server and displays the chat screen.

[2000] Step 28:

[2001] The terminal receives a message from the customer and sends it to the server.

[2002] Step 29:

[2003] The server analyzes the received messages in real time and generates appropriate answers based on generative AI models.

[2004] Step 30:

[2005] The server sends the generated response to the terminal.

[2006] Step 31:

[2007] The terminal displays the generated answer to the user, who then replies to the customer.

[2008] Step 32:

[2009] The terminal transmits the emotions of the user and customer during the chat to the emotion engine, which performs emotion analysis.

[2010] Step 33:

[2011] Based on the emotion analysis results, the server generates an appropriate response and provides it to the user.

[2012] Measurement and remarketing

[2013] Step 34:

[2014] The server monitors the results of the implemented campaigns and collects key metrics such as click-through rates, conversion rates, and number of impressions.

[2015] Step 35:

[2016] The server analyzes the collected data and generates statistics and insights.

[2017] Step 36:

[2018] The server requests the AI ​​model to generate remarketing measures based on the analysis results.

[2019] Step 37:

[2020] The server sends new remarketing campaigns to the device.

[2021] Step 38:

[2022] The terminal displays new policy proposals to the user.

[2023] Step 39:

[2024] The user reviews the proposed measures and selects the items to apply.

[2025] The above are the specific processing steps of the present invention, which combines emotion engines.

[2026] Example 2

[2027] 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."

[2028] Traditional marketing systems lack the ability to analyze user emotions and behavior in real time and generate and customize effective marketing measures based on that analysis. Furthermore, the process of generating marketing measures and measuring their effectiveness after implementation and remarketing is often done manually, requiring time and resources. There is a need not only to improve user experience, but also to operate marketing measures efficiently and accurately.

[2029] 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.

[2030] In this invention, the server includes: means for receiving and verifying authentication information from a user; means for acquiring user profile data when the verification is successful; means for starting a session based on the acquired profile data; means for receiving marketing goal data from a user and collecting trend data from an external database; means for analyzing the collected trend data using a machine learning algorithm and generating analysis results; means for requesting a marketing measure from a generation AI model based on the analysis results; means for displaying the generated marketing measure to the user; means for receiving a specific action plan selection from the user and requesting the generation AI model to generate content and advertising copy; means for displaying the generated content and advertising copy to the user; means for a user to activate a real-time communication function and interact with a third party; means for receiving an inquiry from a third party and requesting an appropriate response from the generation AI model; means for displaying the generated response to the user; means for monitoring the effectiveness of the implemented measures and analyzing collected data; means for requesting a remarketing measure from the generation AI model based on the analysis results; means for displaying the generated remarketing measure to the user; and means for analyzing user input and behavior using a sentiment analysis engine and customizing the marketing measure based on the analysis results. This makes it possible to generate and customize marketing initiatives in real time based on user emotions and behavior.

[2031] "Authentication information" refers to information required to log in to a system, such as a user's email address and password.

[2032] "User profile data" is information related to an authenticated user, including, for example, name, job title, goals, and the like.

[2033] A "session" is a function that tracks activities while a user is logged into the system, and is a temporary connection that maintains user-specific operations.

[2034] "Marketing goal data" is information that indicates specific goals that a user wants to achieve in a marketing campaign or initiative.

[2035] "Trend data" is data that shows current market and consumer trends, and is mainly collected from the Internet and external databases.

[2036] A "machine learning algorithm" is a computational method for analyzing collected data and finding patterns and relationships.

[2037] A "generative AI model" is a model that uses artificial intelligence to generate responses and measures based on user input and the environment.

[2038] "Content" refers to information materials such as text, images, and videos used in marketing initiatives.

[2039] "Ad copy" is text that promotes a particular product or service.

[2040] The "real-time communication function" is a function that allows a user to have a timely conversation with a third party.

[2041] An "inquiry" is a question or request made by a third party.

[2042] "Effectiveness monitoring" is the process of measuring and evaluating the results of implemented measures.

[2043] A "remarketing initiative" is a new marketing initiative that is generated based on the results of analyzing collected data.

[2044] An "emotion analysis engine" is a technology that analyzes a user's input and behavior and infers their emotions.

[2045] This invention is a digital assistant system designed to streamline and improve the quality of work for marketing professionals. Furthermore, by incorporating an emotion analysis engine that recognizes user emotions, it enhances the accuracy and applicability of marketing strategies. In addition to basic functions such as user authentication, profile acquisition, receiving marketing goal data, collecting and analyzing trend data, generating marketing strategies, real-time communication, measuring effectiveness, and generating and displaying remarketing strategies, this system also includes an emotion analysis engine function that recognizes user emotions. The specific operation of each function is described below.

[2046] User authentication and profile acquisition

[2047] A user enters an email address and password to log in to the system. The terminal encrypts the entered authentication information using the AES-256 algorithm and sends it to the server. The server receives the authentication information and authenticates the user by checking it against the PostgreSQL database. If authentication is successful, it retrieves the user's profile data and starts a session. If authentication fails, it generates an error message and sends it to the terminal. The terminal notifies the user of successful authentication and displays the profile data.

[2048] Initial data acquisition and trend analysis

[2049] The user inputs the goals of a marketing campaign or initiative. The device sends the goal data to the server. The server uses a Python script to collect relevant trend data from external databases or APIs (e.g., the Google Trends API). The collected trend data is analyzed using machine learning algorithms (e.g., the scikit-learn library) to generate analysis results. The server sends the generated analysis results to the device, which then displays the analysis results to the user.

[2050] Emotion recognition by emotion engine

[2051] The device sends the user's input and behavioral data to a sentiment analysis engine (for example, IBM Watson Natural Language Understanding API). The server receives the sentiment analysis results and customizes marketing campaigns based on them. The customized campaigns are sent from the server to the device, which displays them to the user.

[2052] Customised marketing strategy proposals

[2053] The user inputs the marketing measures they would like to see proposed and the problems they want to solve. The device sends the information to the server. The server analyzes the received information and generates appropriate marketing measures using a generative AI model (e.g., OpenAI GPT-3). The generated measures are sent to the device and displayed to the user.

[2054] Implementing and optimizing measures

[2055] The user selects the items to implement from the proposed measures and requests the creation of a specific action plan. The device sends the selected measures to the server. The server uses a generative AI model to generate content ideas, create advertising copy, and analyze target customers based on the specified measures. The generated content and advertising copy are sent to the device and displayed to the user.

[2056] Real-time communication and customer service

[2057] The user activates the real-time communication function and begins a conversation with a third party (such as a customer). The device requests a chat session from the server and displays the chat screen. The message from the customer is received by the device and sent to the server. The server analyzes the received message in real time and generates an appropriate response based on the generative AI model. The generated response is sent to the device and displayed to the user. The emotions of the user and the third party during the chat are also sent to the emotion analysis engine for analysis. The analysis results are provided to the user as an appropriate response from the server.

[2058] Measurement and remarketing

[2059] The server monitors the results of the implemented measures and collects key indicators such as click-through rate, conversion rate, and number of impressions. The collected data is analyzed to generate statistics and insights (for example, using Jupyter Notebook). Based on the analysis results, a remarketing measure generation AI model is requested, and new remarketing measures are generated. The generated measures are sent to the device and displayed to the user. The user reviews the proposed measures and selects the ones they want to apply.

[2060] Specific examples

[2061] Example prompt sentence:

[2062] "Identify target customers and generate effective social media copy to promote a new product online."

[2063] For example, consider a user running an online promotion for a new product. The user enters their login information and confirms their profile. Next, they send their marketing goals from their device to the server. The server analyzes industry trends and consumer interests in real time based on their goals and displays the results on the device. The user selects a social media ad from the proposed marketing initiatives and requests the creation of specific ad copy and visuals. The device sends the user's emotions to a sentiment analysis engine and sends the analysis results to the server. Based on the analysis results, the server uses a generative AI model to generate effective ad copy and visuals for the target customer and provides them to the user. The user uses the chat function to respond to customer inquiries in real time. At this time, the server analyzes the emotions of both the user and the customer during the chat and receives an appropriate response from the server. In this way, marketing professionals can efficiently run promotions while taking user emotions into consideration and continuously measure and optimize the effectiveness of their initiatives.

[2064] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2065] Specific explanation of processing steps

[2066] Step 1: Enter user credentials

[2067] User: Enter your email address and password on the login screen.

[2068] Enter your email address and password

[2069] Output: Enter your credentials

[2070] What happens: A user enters their login information into a browser or application login screen and clicks the submit button.

[2071] Step 2: Encrypt and transmit credentials

[2072] Terminal: The entered authentication information is encrypted using the AES-256 algorithm and sent to the server.

[2073] Input: User credentials

[2074] Output: Encrypted credentials

[2075] How it works: Uses JavaScript or native code to encrypt input data and send it to the server over HTTPS.

[2076] Step 3: Verification of authentication information and notification of authentication result

[2077] Server: Receives the authentication information and authenticates the user against the PostgreSQL database. If authentication is successful, retrieves the user's profile data and starts a session.

[2078] Input: Encrypted credentials

[2079] Output: Authentication result, user profile data

[2080] How it works: The server uses the Python Flask framework to accept requests, perform database checks, and upon successful authentication, generates a JWT token and starts a secure session.

[2081] Step 4: Retrieving and displaying profile data

[2082] Server: If authentication is successful, retrieve the user's profile data.

[2083] Input: Authentication success flag

[2084] Output: Profile data

[2085] What it does: Retrieves and returns user profile information from the database.

[2086] Step 5: View profile data

[2087] Terminal: Notifies the user of successful authentication and displays profile data.

[2088] Input: Profile data

[2089] Output: Authentication successful message, profile data displayed

[2090] Behavior: A success message is displayed and user profile information is displayed on the screen.

[2091] Step 6: Enter your marketing goal data

[2092] Users: Enter the goals of their marketing campaigns and initiatives.

[2093] Input: Marketing Goal Data

[2094] Output: Target data input

[2095] What happens: A user enters numeric or text data into a goal field on a marketing dashboard.

[2096] Step 7: Submit goal data

[2097] Terminal: Sends target data to the server.

[2098] Input: Marketing Goal Data

[2099] Output: Target data sent

[2100] How it works: Uses JavaScript Ajax or the Fetch API to send input data in JSON format to the server.

[2101] Step 8: Trend data collection

[2102] Server: Uses Python scripts to collect relevant trend data from external databases and APIs (e.g., Google Trends API).

[2103] Input: Marketing Goal Data

[2104] Output: Trend data

[2105] Operation: Calls the API, retrieves data for the specified period, and stores it in the database.

[2106] Step 9: Analyze trend data

[2107] Server: Analyzes the collected trend data using machine learning algorithms (e.g., the scikit-learn library) and generates analytical results.

[2108] Input: Trend data

[2109] Output: Analysis results

[2110] What it does: Analyzes data by applying k-means clustering and linear regression models.

[2111] Step 10: Submit and view analysis results

[2112] Server: Sends the generated analysis results to the device.

[2113] Input: Analysis results

[2114] Output: Submitted analysis results

[2115] Behavior: The parsed results are encoded in JSON format and sent to the client.

[2116] Terminal: Displays the analysis results to the user.

[2117] Input: Analysis results

[2118] Output: Displayed analysis results

[2119] How it works: The data is visualized using a charting library and displayed to the user.

[2120] Step 11: Emotion Recognition with the Emotion Engine

[2121] Device: Sends user input and behavioral data to the sentiment analysis engine.

[2122] Input: User input and behavioral data

[2123] Output: Sentiment analysis request

[2124] How it works: Sends user input data and behavior to the sentiment analysis engine API via a POST request.

[2125] Server: Calls a sentiment analysis engine (e.g., IBM Watson Natural Language Understanding API) and receives the sentiment analysis results.

[2126] Input: Sentiment analysis request

[2127] Output: Emotion analysis results

[2128] What it does: Gets and stores the emotion score.

[2129] Step 12: Customize measures based on sentiment analysis results

[2130] Server: Customize marketing strategies based on sentiment analysis results.

[2131] Input: Sentiment analysis results

[2132] Output: Customized measures

[2133] What it does: Runs a script that adjusts tactics based on the sentiment score.

[2134] Server: Sends customized measures to the device.

[2135] Input: Customized Measures

[2136] Output: Customization measures sent

[2137] Operation: Encodes the policy data in JSON format and sends it to the device.

[2138] Device: Display customized campaigns to users.

[2139] Input: Customized Measures

[2140] Output: Displayed customization measures

[2141] How it works: The customized action is displayed in the UI and visually presented to the user.

[2142] Generative AI model, prompt sentence

[2143] Example prompt sentence:

[2144] "Identify target customers and generate effective social media copy to promote a new product online."

[2145] (Application example 2)

[2146] 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."

[2147] Conventional marketing systems typically implement fixed marketing strategies without considering user emotions, resulting in problems that prevent them from adequately meeting user needs. Furthermore, real-time collection and analysis of trend data is insufficient, making it difficult to provide timely and effective marketing strategies. Furthermore, in communications between users and customers, appropriate responses are sometimes not provided, reducing the work efficiency of marketing professionals. It is necessary to solve these problems.

[2148] 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.

[2149] In this invention, the server includes means for recognizing user emotions using an emotion engine that analyzes user emotions, means for customizing marketing measures based on the user emotion recognition results, and means for generating optimal advertising copy and visuals using a generative AI model based on the user emotion recognition results and trend data analysis results, thereby making it possible to provide personalized marketing measures that take user emotions into consideration.

[2150] "User authentication" is the process of collecting authentication information provided by a user and verifying that the user is a legitimate user.

[2151] "Profile data" is data that includes a user's personal information and past activity history, and is used to customize marketing strategies.

[2152] "Marketing goal data" is data indicating the goals of campaigns and promotions set by users.

[2153] "Trend data" is data that collects the latest information about current markets and consumer behavior.

[2154] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to automatically generate marketing strategies and advertising copy.

[2155] "Real-time chat" is a communication method that allows users and customers to exchange messages in real time.

[2156] "Remarketing" is the process of implementing new marketing strategies based on the effectiveness of past marketing strategies.

[2157] An "emotion engine" is a software mechanism that analyzes emotions from user input and behavior and provides the results.

[2158] MODE FOR CARRYING OUT THE INVENTION

[2159] The present invention is a system that starts with user authentication, acquires user profile data, receives marketing goal data, collects and analyzes trend data, recognizes emotions using an emotion engine, generates and customizes marketing initiatives using generative AI models, has real-time chat functionality, measures effectiveness, and generates remarketing initiatives.

[2160] User authentication and profile acquisition

[2161] A user logs into the system by entering their email address and password. The device encrypts this authentication information and sends it to the server. The server then authenticates the user by checking it against a database. If authentication is successful, the server retrieves the user profile data and starts the session.

[2162] Initial data acquisition and trend analysis

[2163] Users input the goals of their marketing campaigns and initiatives. The device sends this goal data to the server. Based on the goal data provided, the server collects and analyzes relevant trend data via the Internet or external APIs. The analysis results are sent to the device and displayed to the user.

[2164] Emotion recognition by emotion engine

[2165] The device sends the user's input and actions to the emotion engine. The server analyzes the user's emotions using the emotion engine and receives the results. Based on the emotion recognition results, the server customizes marketing measures and sends them to the device. The customized measures are then displayed to the user.

[2166] Customised marketing strategy proposals

[2167] The user inputs the marketing measures they wish to propose and the problems they wish to solve. The device sends the input information to the server. The server analyzes the received information and generates appropriate marketing measures based on the generative AI model. These generated measures are sent to the device and displayed to the user.

[2168] Implementing and optimizing measures

[2169] The user selects an action item from the proposed measures and requests the creation of a specific action plan. The device sends the selected measures to the server. The server then requests the generative AI model to come up with content ideas in line with the specified measures, create advertising copy, and analyze the target customers. The generated content and advertising copy are sent to the device and displayed to the user.

[2170] Real-time chat and customer service

[2171] The user activates the real-time chat function and begins communication with the customer. The device requests a chat session from the server and displays the chat screen. The device receives the message from the customer and sends it to the server. The server analyzes the received message in real time and generates an appropriate response based on the generative AI model. This generated response is sent to the device and displayed to the user. Furthermore, the emotions of the user and customer during the chat are also sent to the emotion engine, and an appropriate response is generated based on the analysis results.

[2172] Measurement and remarketing

[2173] The server monitors the results of the implemented campaigns and collects key metrics such as click-through rates, conversion rates, and number of impressions. The collected data is analyzed to generate statistics and insights. Based on the analysis results, remarketing campaigns are requested from the generative AI model. The new remarketing campaigns are sent to the device and displayed to the user.

[2174] Specific examples

[2175] For example, if a user wants to promote a new product online, they can do so effectively through the following process:

[2176] 1. The user enters their login information and verifies their profile.

[2177] 2. The terminal transmits the user's marketing objectives to the server.

[2178] 3. The server analyzes industry trends and consumer interests in real time based on the goals and displays the results.

[2179] 4. The user selects SNS advertising from the proposed marketing measures and requests the creation of specific advertising copy and visuals.

[2180] 5. The device sends the user's emotions to the emotion engine and sends the analysis results to the server.

[2181] 6. Based on the results of the sentiment analysis, the server uses a generative AI model to generate effective advertising copy and visuals for the target customer and provides them to the user.

[2182] 7. Users use the chat feature to respond to customer inquiries in real time.

[2183] 8. The terminal analyzes the emotions of the user and customer during the chat and receives an appropriate response from the server.

[2184] Prompt Sentence Examples

[2185] 1. Prompt to ask the generative AI model to implement marketing strategies based on emotion recognition:

[2186] "Generate optimal copy and visuals for a new product online advertising campaign based on emotion recognition results and provided target data."

[2187] 2. Prompt to collect trend data using an external API:

[2188] "Collect recent trend data from the internet related to the campaign goals for a new product and return the analysis results."

[2189] As a result, the system of the present invention takes into account the user's emotions and enables highly personalized marketing measures, thereby realizing efficient and effective marketing.

[2190] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2191] Step 1:

[2192] User Authentication

[2193] The user enters login information (email address and password) into the smartphone app.

[2194] The terminal encrypts this authentication information and sends it to the server.

[2195] The server receives the encrypted authentication information and authenticates it against a database.

[2196] Input: Email address, password

[2197] Output: Authentication result (success / failure)

[2198] If the authentication is successful, the server obtains the user profile data and sends a notification of successful authentication to the terminal.

[2199] The terminal notifies the user that the authentication was successful and displays the profile data.

[2200] Step 2:

[2201] Initial data acquisition and trend analysis

[2202] Users input goals for their marketing campaigns and initiatives.

[2203] The terminal transmits this target data to the server.

[2204] The server collects trend data based on the provided target data via the Internet or external API.

[2205] Input: Marketing goal data

[2206] Output: Trend data

[2207] The server analyzes the collected trend data and sends the analysis results to the terminal.

[2208] The terminal displays the analysis results to the user.

[2209] Step 3:

[2210] Emotion recognition by emotion engine

[2211] The terminal transmits the user's input and actions to the emotion engine.

[2212] The server analyzes the user's emotions using an emotion engine.

[2213] Input: User input and behavioral data

[2214] Output: Emotion recognition result

[2215] The server customizes marketing measures based on the emotion recognition results and sends them to the device.

[2216] The terminal displays the customized measures to the user.

[2217] Step 4:

[2218] Customised marketing strategy proposals

[2219] Users input the marketing measures they would like to propose and the issues they would like to solve.

[2220] The terminal transmits this input content to the server.

[2221] The server analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[2222] Input: User's desired measures and issues

[2223] Output: Customized marketing initiatives

[2224] The server transmits the generated marketing measures to the terminal and displays them to the user.

[2225] Step 5:

[2226] Implementing and optimizing measures

[2227] The user selects items to be implemented from the proposed measures and wishes to create a specific action plan.

[2228] The terminal transmits the selected measure to the server.

[2229] The server asks the generative AI model to come up with content ideas in line with the specified measures, create advertising copy, and analyze target customers.

[2230] Input: Select the action to be taken

[2231] Output: Content, ad copy, target customer analysis results

[2232] The server transmits the generated content and advertising copy to the terminal and displays them to the user.

[2233] Step 6:

[2234] Real-time chat and customer service

[2235] The user activates the real-time chat function and starts communicating with the customer.

[2236] The terminal requests a chat session from the server and displays a chat screen.

[2237] The terminal receives messages from the customer and transmits them to the server.

[2238] The server analyzes the received messages in real time and generates appropriate answers based on the generative AI model.

[2239] Input: Message from customer

[2240] Output: Correct answer

[2241] The server sends the generated answer to the terminal and displays it to the user.

[2242] The terminal transmits the emotions of the user and customer during the chat to the emotion engine and transmits the analysis results to the server.

[2243] The server generates an appropriate response based on the emotion analysis results and sends it to the terminal.

[2244] Step 7:

[2245] Measurement and remarketing

[2246] The server monitors the results of the implemented measures and collects key metrics such as click-through rate, conversion rate, and number of impressions.

[2247] Input: Data on implemented measures

[2248] Output: Key indicator data

[2249] The server analyzes the collected data and generates statistics and insights.

[2250] The server then requests the AI ​​model to generate remarketing strategies based on the analysis results.

[2251] Input: Key indicator data

[2252] Output: Remarketing campaign

[2253] The server sends the new remarketing campaign to the terminal and displays it to the user.

[2254] 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.

[2255] 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.

[2256] 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.

[2257] [Fourth embodiment]

[2258] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2259] 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.

[2260] 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).

[2261] 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.

[2262] 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.

[2263] 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).

[2264] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2265] 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.

[2266] 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.

[2267] 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.

[2268] 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.

[2269] 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.

[2270] 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."

[2271] The present invention is a digital assistant system for improving the efficiency and quality of work for marketing professionals. This system includes functions such as user authentication, profile acquisition, receiving marketing goal data, collecting and analyzing trend data, generating marketing initiatives, real-time chat, measuring effectiveness, and generating and displaying remarketing initiatives. The specific operation of each function is described below.

[2272] User authentication and profile acquisition

[2273] 1. User: Enter your email address and password to log in to this system.

[2274] 2. Terminal: The entered authentication information is encrypted and sent to the server.

[2275] 3. Server: Receives the authentication information and authenticates the user by checking it against a database.

[2276] If authentication is successful, the user's profile data is retrieved and a session is initiated.

[2277] If the authentication fails, an error message is generated and sent to the terminal.

[2278] 4. Terminal: Notifies the user that authentication was successful and displays their profile data.

[2279] Initial data acquisition and trend analysis

[2280] 1. User: Enter the goals of your marketing campaign or initiative.

[2281] 2. Terminal: Sends target data to the server.

[2282] 3. Server: Based on the provided goal data, collects related trend data via the Internet or external APIs.

[2283] 4. Server: Analyzes the collected trend data and generates analytical results.

[2284] 5. Server: Sends the generated analysis results to the device.

[2285] 6. Terminal: displays the analysis results to the user.

[2286] Customised marketing strategy proposals

[2287] 1. User: Enter the marketing measures they would like to propose or the problem they want to solve.

[2288] 2. Terminal: Sends the entered information to the server.

[2289] 3. Server: Analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[2290] 4. Server: Sends the generated measures to the terminal.

[2291] 5. Terminal: The proposed measures are displayed to the user.

[2292] Implementing and optimizing measures

[2293] 1. User: Selects the items to implement from the proposed measures and requests the creation of a specific action plan.

[2294] 2. Terminal: Sends the selected measures to the server.

[2295] 3. Server: Requests the generative AI model to come up with content ideas, create advertising copy, and analyze target customers in line with the specified measures.

[2296] 4. Server: Sends the generated content and advertising copy to the device.

[2297] 5. Device: Displays the generated content and advertising copy to the user.

[2298] Real-time chat and customer service

[2299] 1. User: Launches the real-time chat feature and starts communicating with the customer.

[2300] 2. Terminal: Requests a chat session from the server and displays the chat screen.

[2301] 3. Terminal: Receives messages from customers and sends them to the server.

[2302] 4. Server: Analyzes received messages in real time and generates appropriate answers based on generative AI models.

[2303] 5. Server: Sends the generated answer to the device.

[2304] 6. Terminal: The generated answer is displayed to the user, who then replies to the customer.

[2305] Measurement and remarketing

[2306] 1. Server: Monitors the results of the implemented campaigns and collects key metrics such as click-through rate, conversion rate, and number of impressions.

[2307] 2. Server: Analyzes the collected data and generates statistics and insights.

[2308] 3. Server: Requests the AI ​​model to generate remarketing measures based on the analysis results.

[2309] 4. Server: Sends new remarketing offers to the device.

[2310] 5. Terminal: New policy proposals are displayed to the user.

[2311] 6. User: Review the proposed measures and select the items that apply.

[2312] Specific examples

[2313] For example, if a user wants to promote a new product online, they can use it in the following ways:

[2314] 1. User: Enter your login details and verify your profile.

[2315] 2. Terminal: Sends the user's marketing objectives to the server.

[2316] 3. Server: Analyzes industry trends and consumer interests in real time based on goals and displays the results.

[2317] 4. User: Selects social media advertising from the proposed marketing strategies and requests specific advertising copy and visuals to be created.

[2318] 5. Server: Uses AI models to generate effective advertising copy and visuals for target customers and provide them to users.

[2319] 6. Users: Use the chat feature to respond to customer inquiries in real time.

[2320] This allows marketing professionals to efficiently implement promotions and continuously measure and optimize the effectiveness of their efforts.

[2321] The processing flow will be explained below.

[2322] User authentication and profile acquisition

[2323] Step 1:

[2324] The user launches the application and enters their email address and password on the login screen.

[2325] Step 2:

[2326] The terminal encrypts the entered authentication information and sends it to the server.

[2327] Step 3:

[2328] The server compares the received authentication information with a database and authenticates the user.

[2329] If authentication is successful, retrieve the user's profile data and start a session. If authentication fails, generate an error message and send it to the terminal.

[2330] Step 4:

[2331] The terminal displays a successful authentication message and user profile data to the user.

[2332] Initial data acquisition and trend analysis

[2333] Step 5:

[2334] Users input goals for their marketing campaigns and initiatives.

[2335] Step 6:

[2336] The terminal transmits the target data input by the user to the server.

[2337] Step 7:

[2338] The server collects trend data via the Internet or external API based on the provided target data.

[2339] Step 8:

[2340] The server analyzes the collected trend data and generates analysis results.

[2341] Step 9:

[2342] The server transmits the generated analysis results to the terminal.

[2343] Step 10:

[2344] The terminal displays the analysis results to the user.

[2345] Customised marketing strategy proposals

[2346] Step 11:

[2347] The user inputs the marketing measures they would like to propose and the issues they would like to solve.

[2348] Step 12:

[2349] The terminal sends the user's input to the server.

[2350] Step 13:

[2351] The server analyzes the received content and generates appropriate marketing measures based on the generative AI model.

[2352] Step 14:

[2353] The server transmits the generated marketing measures to the terminal.

[2354] Step 15:

[2355] The terminal displays the proposed measures to the user.

[2356] Implementing and optimizing measures

[2357] Step 16:

[2358] The user selects the items to be implemented from the proposed measures and requests the creation of a specific action plan.

[2359] Step 17:

[2360] The terminal transmits the user's selected action to the server.

[2361] Step 18:

[2362] Based on the selected strategy, the server requests the generative AI model to generate content ideas, create advertising copy, and analyze target customers.

[2363] Step 19:

[2364] The server transmits the generated content and advertising copy to the terminal.

[2365] Step 20:

[2366] The terminal displays the generated content and advertising copy to the user.

[2367] Real-time chat and customer service

[2368] Step 21:

[2369] The user launches the real-time chat function and begins communicating with the customer.

[2370] Step 22:

[2371] The terminal requests a chat session from the server and displays the chat screen.

[2372] Step 23:

[2373] The terminal receives a message from the customer and sends it to the server.

[2374] Step 24:

[2375] The server analyzes the received messages in real time and generates appropriate answers based on generative AI models.

[2376] Step 25:

[2377] The server sends the generated response to the terminal.

[2378] Step 26:

[2379] The terminal displays the generated answer to the user, who then replies to the customer.

[2380] Measurement and remarketing

[2381] Step 27:

[2382] The server monitors the results of the implemented campaigns and collects key metrics such as click-through rates, conversion rates, and number of impressions.

[2383] Step 28:

[2384] The server analyzes the collected data and generates statistics and insights.

[2385] Step 29:

[2386] The server requests the AI ​​model to generate remarketing measures based on the analysis results.

[2387] Step 30:

[2388] The server sends new remarketing campaigns to the device.

[2389] Step 31:

[2390] The terminal displays new policy proposals to the user.

[2391] Step 32:

[2392] The user reviews the proposed measures and selects the items to apply.

[2393] The above are the specific processing steps of the present invention.

[2394] Example 1

[2395] 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."

[2396] In traditional marketing systems, data collection, analysis, and the generation and application of measures are carried out separately, often resulting in a lack of efficiency and real-time response. There is also the risk of security issues arising in the user authentication information and customer support processes. Furthermore, because effectiveness measurement and remarketing measures are not integrated, continuous optimization of measures is difficult.

[2397] 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.

[2398] In this invention, the server includes: means for receiving and verifying authentication information from a user; means for acquiring user profile data when the verification is successful; means for starting a session based on the acquired profile data; means for receiving marketing goal data from the user and collecting trend data from external information sources; means for analyzing the collected trend data using data analysis technology and generating analysis results; means for generating prompt text based on the analysis results and requesting a generative AI model to generate a marketing measure; means for displaying the generated marketing measure to the user; means for receiving a specific action plan selection from the user and requesting the generative AI model to generate content and advertising copy; means for displaying the generated content and advertising copy to the user; means for a user to start a real-time chat and communicate with customers; means for receiving inquiries from customers and requesting an appropriate response from the generative AI model; means for displaying the generated response to the user; means for monitoring the effectiveness of the implemented measures and analyzing collected data using data analysis technology; means for generating prompt text based on the analysis results and requesting the generative AI model to generate a remarketing measure; means for displaying the generated remarketing measure to the user; and means for protecting authentication information using encryption technology. This enables a consistent, efficient and secure marketing process, from data collection and analysis, to the creation and application of measures, effectiveness measurement, and the creation of remarketing measures.

[2399] "Authentication information" refers to information such as an email address and password that a user provides to log in to a system.

[2400] "Verification" is the process of comparing received authentication information with existing records in a database to determine if it matches.

[2401] "User profile data" refers to data such as personal information and past activity history obtained about a successfully authenticated user.

[2402] A "session" is a state of communication with a server that persists while a user is authenticated.

[2403] "Marketing goal data" refers to data that includes specific goals and objectives for marketing campaigns and initiatives set by users.

[2404] "Trend data" is data collected from the internet or other external sources that indicates consumer interests and behavior over a specific period of time.

[2405] "Data analysis techniques" are techniques used to process collected data and generate analytical results, including statistical methods and machine learning algorithms.

[2406] A "prompt" is a sentence used to give instructions to a generative AI model.

[2407] A "generative AI model" is an artificial intelligence model that generates marketing strategies, advertising copy, content, etc. based on prompt text.

[2408] "Content" is information in the form of text, images, videos, etc., generated for marketing purposes.

[2409] "Ad copy" is text generated to promote a particular product or service.

[2410] "Real-time chat" is a communication function that allows users and customers to exchange messages instantly.

[2411] "Effectiveness measurement" is the process of monitoring the results of implemented marketing initiatives and collecting metrics such as click rates, conversion rates, and number of impressions.

[2412] "Remarketing" is a marketing strategy created to re-engage with existing customers or past visitors.

[2413] "Encryption technology" refers to technology used to protect the security of data.

[2414] The present invention is a digital assistant system for improving the efficiency and quality of work for marketing professionals. The system includes functions such as user authentication, profile acquisition, receiving marketing target data, collecting and analyzing trend data, generating marketing campaigns, real-time chat, measuring effectiveness, and generating and displaying remarketing campaigns.

[2415] User Authentication

[2416] The user enters their email address and password on the login screen. The device encrypts the entered authentication information using AES encryption technology and sends it to the server via HTTPS. The server receives the encrypted authentication information and compares it with the user information in its database. If the comparison is successful, the server obtains the user's profile data and starts a session. The device notifies the user of the authentication result and displays the profile data.

[2417] Hardware and software used:

[2418] Server: Database management system (e.g. MySQL), encryption library (e.g. OpenSSL)

[2419] Device: User's PC or smartphone

[2420] Receiving marketing target data and collecting and analyzing trend data

[2421] Users enter the goals of their marketing campaigns and initiatives on a dedicated input screen. The device sends this goal data in JSON format to the server. The server collects trend data using an external API (e.g., Google Trends API) and analyzes it using data analysis technology (e.g., pandas, numpy). The analysis results are sent to the device in JSON format and displayed to the user.

[2422] Marketing strategy generation

[2423] When a user requests a marketing proposal, the device sends the request to the server. The server generates a prompt based on the analysis data and requests a generative AI model (e.g., GPT-4) to generate an appropriate marketing proposal. The generated proposal is sent to the device and displayed to the user.

[2424] Example prompt sentence:

[2425] "Generate the best social media ad ideas for promoting a new product online."

[2426] "Please propose a marketing strategy based on industry trends over the past three months."

[2427] "Create a tagline and visuals that will appeal to your target audience."

[2428] Implementing and optimizing measures

[2429] The user selects the items they wish to implement from the proposed measures and requests a specific action plan. This selection information is sent from the device to the server, and content ideas and advertising copy are generated based on the generative AI model. The generated content and advertising copy are sent to the device and displayed to the user.

[2430] Real-time chat and effectiveness measurement

[2431] Users can communicate with customers using the real-time chat function. The server analyzes customer inquiries, generates appropriate answers based on the generative AI model, and sends them to the terminal for display.

[2432] The results of the implemented marketing measures are monitored on the server, and the collected data (e.g., click rate, conversion rate, number of impressions) is analyzed using data analysis technology. The analysis results are used to generate remarketing measures, and new measures are generated by a generative AI model and displayed to the user.

[2433] Specific examples

[2434] For example, if a user wants to promote a new product online, the process could look like this:

[2435] 1. The user enters their login information and verifies their profile.

[2436] 2. The terminal transmits the marketing target data to the server.

[2437] 3. The server analyzes industry trends and consumer interests in real time based on the goals and displays the results.

[2438] 4. The user selects social media advertising from the proposed marketing strategies and requests the creation of specific advertising copy and visuals.

[2439] 5. The server uses the AI ​​model to generate effective advertising copy and visuals for the target customer and provides them to the user.

[2440] 6. Users can use the chat feature to respond to customer inquiries in real time.

[2441] This enables marketing professionals to efficiently and effectively implement promotions and continuously measure and optimize the results of their efforts.

[2442] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2443] Step 1: User authentication

[2444] 1. User: Enter your email address and password on the login screen.

[2445] Input: Email address, Password

[2446] Output: Encrypted credentials

[2447] 2. Terminal: The entered authentication information is encrypted using AES encryption technology and sent to the server via HTTPS.

[2448] Specific behavior: Encryption using the OpenSSL library

[2449] 3. Server: Receives the encrypted authentication information and checks it against the user information in its database.

[2450] Input: Encrypted credentials

[2451] Output: Authentication success or failure result

[2452] What it does: It performs a database query and compares the encrypted password with the hash value stored in the database.

[2453] 4. Server: If authentication is successful, retrieves the user's profile data and starts a session. If authentication is unsuccessful, generates an error message.

[2454] Input: Authentication success or failure result

[2455] Output: Profile data or error message

[2456] Specific behavior: If successful, generate a session ID and retrieve profile data from the database.

[2457] 5. Terminal: Notifies the user of the authentication result and displays the profile data if authentication is successful.

[2458] Input: Profile data or error message

[2459] Output: Display of authentication result

[2460] Step 2: Receive marketing goal data

[2461] 1. User: Enter the goals of your marketing campaign or initiative in a dedicated input screen.

[2462] Input: Goal data

[2463] Output: Target data

[2464] 2. Terminal: Sends the target data in JSON format to the server.

[2465] Specific operation: Converts data into JSON format and sends it to the server via HTTPS

[2466] Step 3: Collect and analyze trend data

[2467] 1. Server: Uses external APIs (e.g., Google Trends API) to collect trend data related to the provided goal data.

[2468] Input: Goal data

[2469] Output: Trend data

[2470] Specific operation: Acquire data from external API and parse the format

[2471] 2. Server: Analyze the collected trend data using data analysis techniques (e.g., pandas, numpy).

[2472] Input: Trend data

[2473] Output: Analysis results

[2474] Specific actions: Analyze rising trends and related keywords

[2475] 3. Server: Sends the analysis results to the terminal in JSON format.

[2476] Input: Analysis results

[2477] Output: Sending the analysis results

[2478] 4. Terminal: displays the analysis results to the user.

[2479] Input: Analysis results

[2480] Output: Display of analysis results

[2481] Step 4: Generate marketing initiatives

[2482] 1. User: Enter the marketing measures they would like to propose or the problem they want to solve.

[2483] Input: Policy hopes or challenges

[2484] Output: desired measures or issues

[2485] 2. Terminal: The input content is sent to the server in JSON format.

[2486] Specific operation: Converts data into JSON format and sends it to the server via HTTPS

[2487] 3. Server: Generates prompts based on the analyzed data and requests a generative AI model (e.g., GPT-4) to generate appropriate marketing strategies.

[2488] Input: desired measures or issues, analysis data

[2489] Output: Marketing Strategy

[2490] Specific operation: Enter a prompt into GPT-4 and obtain the generated measures.

[2491] 4. Server: Sends the generated measures to the terminal in JSON format.

[2492] Input: Marketing Strategy

[2493] Output: Sending the measure

[2494] 5. Terminal: The proposed measures are displayed to the user.

[2495] Input: Marketing Strategy

[2496] Output: Display of measures

[2497] Step 5: Implementing and optimizing measures

[2498] 1. User: Selects the items to implement from the proposed measures and requests a specific action plan.

[2499] Input: Select the action to be taken

[2500] Output: Selected measures

[2501] 2. Terminal: Sends the selected measures to the server in JSON format.

[2502] Specific operation: Converts data into JSON format and sends it to the server via HTTPS

[2503] 3. Server: Based on the generative AI model, it requests content ideas, ad copy creation, and target customer analysis in line with the selected measures.

[2504] Input: Selected Measure

[2505] Output: Content, ad copy, targeting analysis

[2506] Specific operation: Generate a prompt sentence and input it into the AI ​​model

[2507] 4. Server: Sends the generated content and ad copy to the device in JSON format.

[2508] Input: Content, ad copy, targeting analysis

[2509] Output: Sending content and ad copy

[2510] 5. Device: Displays the generated content and advertising copy to the user.

[2511] Input: Content, ad copy, targeting analysis

[2512] Output: Display of content and ad copy

[2513] Step 6: Real-time chat

[2514] 1. User: Launches the real-time chat feature and starts communicating with the customer.

[2515] Input: Start chat command

[2516] Output: Chat screen display

[2517] 2. Terminal: Requests a chat session from the server and displays the chat screen.

[2518] Specific behavior: Generate a session ID and load the chat screen

[2519] 3. Terminal: Receives messages from customers and sends them to the server.

[2520] Input: Customer message

[2521] Output: Sending a message

[2522] 4. Server: Analyzes received messages in real time and generates appropriate answers based on generative AI models.

[2523] Input: Customer message

[2524] Output: Reply message

[2525] Specific operation: Generate a prompt sentence and input it into the AI ​​model

[2526] 5. Server: Sends the generated answer in JSON format to the device.

[2527] ...

Claims

1. means for receiving and verifying authentication information from a user; means for obtaining user profile data upon successful match; A means for initiating a session based on the acquired profile data; means for receiving marketing objective data from users and collecting trend data from the Internet; means for analyzing the collected trend data and generating analytical results; A method to request an AI model to generate marketing measures based on the analysis results, and A means for displaying the generated marketing measures to a user; A means to receive specific action plan selections from users and request a generative AI model to generate content and ad copy; a means for displaying the generated content and advertising copy to a user; A means for users to initiate real-time chat and communicate with customers; A means of receiving customer inquiries and requesting appropriate answers from a generative AI model; means for displaying the generated answers to the user; A means of monitoring the effectiveness of the implemented measures and analysing the collected data; A method to request an AI model to generate remarketing measures based on the analysis results, and a means for displaying the generated remarketing measures to the user; A system including:

2. 10. The system of claim 1, wherein the means for receiving marketing target data from the user utilizes an external API.

3. 2. The system of claim 1, wherein the generative AI model used to generate marketing strategies is based on AISAS or AIDMA.

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